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Top 10 Junk Science and Bogus Health Claims ACSH Debunked in 2020

Alex Berezow, PhD

Former Vice President of Scientific Communications

Dr. Alex Berezow is a PhD microbiologist, science writer, and public speaker who specializes in the debunking of junk science for the American Council on Science and Health. He is also a member of the USA Today Board of Contributors and a featured speaker for The Insight Bureau . Formerly, he was the founding editor of RealClearScience.

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Behind a lot of flashy headlines may lie questionable scientific claims - what should people be aware of when reading the news?

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Disclosure statement

Saeed Pahlevansharif has received several research grants for unrelated projects. There is no conflict of interests regarding the publication of this article.

Hassam Waheed has received several research grants for unrelated projects. There is no conflict of interests regarding the publication of this article.

Kelly-Ann Allen has received several research grants for unrelated projects. There is no conflict of interests regarding the publication of this article.

Navaz Naghavi has received several research grants for unrelated projects. There is no conflict of interests regarding the publication of this article.

Nicholas Gamble receives funding from the Australian Research Council.

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Traditional and social media play an important role in disseminating scientific breakthroughs to the public. However, we as an audience, must be cautious in how we consume information from these publicly available sources.

From claims of harmful effects of vaccines to studies on the extent of climate change , we have learned that behind some news headlines or articles lie either questionable, oversold, or misinterpreted research findings.

So what should readers be aware of when reading news that contain scientific claims?

A lot of studies don’t hold up to replication

The first thing that readers should understand before coming to a conclusion when reading research findings in the news, is acknowledging that there is a well-known ‘ replication crisis ’ in academic research.

This means that a lot of studies that you read in the news fail to produce similar outcomes when other scientists try to confirm them.

For instance, Nature revealed that more than 70% of researchers have failed to reproduce another scientist’s findings, and more than 40% have even failed to reproduce their own findings.

Similarly, a 2012 study reported that only 11% of the 53 new cancer treatments they identified in the previous decade could be replicated, while another that examined 159 empirical economics studies showed that 80% of these papers had exaggerated their findings.

Factors that may lead to these non-reproducible results include honest human-error mistakes, poor sampling, “cherrypicking” scientific findings, and in rare cases data manipulation.

A survey from the University of Melbourne , Australia, that involved 800 ecologists and biologists, found that 64% of them had at least once failed to report results from their study because they were not “statistically significant” - meaning they did not show results that the scientists hoped for.

The media often feeds on our need for hope

Although the vast majority of scientific research are reputable and reliable, there is the potential for error, fraud, or overstatement of findings.

However, at times, the media can overlooks these flaws - intentionally or otherwise - particularly when it comes to medical research that offer hopes of curing diseases and illnesses.

Let’s recall a breaking news story in 2009 about an Italian researcher, Paolo Zamboni , who claimed to cure his wife’s Multiple Sclerosis (MS) by “unblocking” the veins in her neck. He challenged the mainstream belief about MS as a disorder of the immune system, and instead, theorised it as a vascular disease - one that could be cured by clearing blood vessels.

For the media, however, the most appealing part of this research may have been a man’s quest to save his beloved wife. This romance-fuelled medical triumph - which is a popular story for health reports - appeared to restore the hope of many patients around the world.

Sadly, however, Zamboni’s research had a very small sample size and the design of the experiment had some defects. What attracted much attention was the hype of his romantic story rather than what was supposed to be a medical breakthrough.

Since then, other researchers’ attempt to replicate his findings were not successful and many incidents of patients’ complications and relapses of the disorder were reported.

Zamboni’s case, however, was just a small story in the bigger picture of how the media can misinterpret or overstate research. It is common for promising health interventions, initially promoted in the media, to not be replicated and failing to result in actual clinical practice.

A 2003 study published in the American Journal of Medicine looked at 101 articles published in six major science journals that offered novel therapeutic promises. However, among them only five were licensed for clinical use 20 years later and only one had been proven to have a significant health impact.

There are potential incentives to misreport findings

Around the world, researchers’ job targets, income, bonus, and promotion can be tied to their publications .

On the other hand, many high-impact scientific journals - and consequently the media - can seem more attracted to ‘significant’ or positive results , even though non-‘significant’ results and unsuccessful replications can make substantial contributions to scientific knowledge.

Researchers from the University of California Davis in the US reviewed 359 studies published in leading medical journals in the 1990s, and stated that most of the studies were “reported in a potentially misleading way, with statistics designed to make the results more positive than if other statistical tests were used”.

Many faculty staff have also heard anecdotal accounts of researchers and PhD students re-framing their data or findings to support their initial hypotheses or vice versa. They may even delete, add alter their data to make their work more publishable and appealing for media coverage.

Every now and then the scientific community catches manipulated studies and journals would then retract them from publication.

We should read the news with a critical eye

Every research study has the potential to improve our understanding of the world we live in.

However, we should be careful of overstated findings, studies that have yet to be replicated, or research that has not been published in credible peer-reviewed sources.

It will take more effort, but readers should be cautious of single studies, and instead seek to look at what the broader scientific community says about the topic.

The COVID-19 pandemic highlighted the dangers of misinformation and how it can spread faster than any natural airborne virus. If the findings we read seem too good to be true, they probably are!

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Introduction
Conclusions
Article Information

Illustrated are the ratios of the actual likelihood of having a given number of future-period paid claims (based on Table 1 ) to the average likelihood for a physician to have the indicated numbers of future-period claims if claims arrived at the average state-specific rates observed for the prior period, but otherwise at random (independent of physician skill) and all physicians active during the prior period remained active in the future period. A, The ratio of actual/predicted future-period claims varied with number of prior claims for physicians with 0, 1, or 2 future-period claims, using a y-axis scale running from 0 to 80. B, The ratio of actual/predicted future-period claims for physicians having 3 or more future-period claims using a y-axis scale running from 0 to 8000. The shaded areas above and below each main line represent the 95% CIs for estimates. See eTable 4 (Panel D) in Supplement 1 for numerical values underlying this Figure.

Illustrated is the ratio of the actual likelihood that an Illinois physician will have a paid claim in the future period (2010-2014) to likelihood if claims arrived randomly at the average rate for all high-risk (lower-risk) specialties. The data set was paid medical malpractice claims for Illinois physicians over the period from 1990-2016. Predicted numbers are mean values from 10 000 simulations. Ratios are normalized to likelihood for physicians with 0 paid claims in the prior period (2005-2009). High-risk specialties include obstetrics and gynaecology, surgery (including surgery subspecialties), urology, and otolaryngology. Lower-risk specialties are all other specialties. Dashed or dotted lines above and below each main line represent the 95% CIs for estimates. See eTables 8 and 9 (Panel C) in Supplement 1 for numerical values underlying this Figure.

The ratio of (1) the probability that a physician with the indicated number of prior-period paid claims will have 1 or more future-period paid claims to (2) this probability for physicians with 0 prior-period paid claims. The lines with squares show ratios for states where paid medical malpractice claims are publicly reported; the lines with circles show ratios for states where paid claims are not publicly reported. The shaded areas above and below each main line represent the 95% CIs for estimates. See eTable 10 in Supplement 1 for numerical values.

eAppendix. Simple Model: Medical Malpractice Claims as an Imperfect Proxy for Negligent Care

Simulation Methodology

eTable 1. Risk of a Paid Claim by State

eTable 2. Specialty-Specific Risk of Paid Claims: Illinois Data

eTable 3. Panel A. NPDB Summary statistics by claim year

eTable 3. Panel B. Percentage of Physicians with Paid Claims Over Different Time Periods

eTable 4. Summary Statistics, and Risk for Future-Period Claims, Given Prior-Period Claim History

eTable 5. Ratio of Actual to Predicted Future Claims

eTable 6. Risk Ratios for Future Claims, for Varying Periods

eTable 7. Paid Claim Risk by Specialty (Risk Level from Jena et al, 2011)

eTable 8. Risk for Future Claims for Illinois high-risk specialties

eTable 9. Risk for Future Claims for Illinois Lower-Risk Specialties

eTable 10. Effect of Public Disclosure of Paid Claims on Future Claim Risk

eFigure 1. Total Paid Claims per 1000 Active Physicians from 1992-2016

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Hyman DA , Lerner J , Magid DJ , Black B. Association of Past and Future Paid Medical Malpractice Claims. JAMA Health Forum. 2023;4(2):e225436. doi:10.1001/jamahealthforum.2022.5436

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Association of Past and Future Paid Medical Malpractice Claims

1 Georgetown University Law Center, Washington, DC
2 NORC at the University of Chicago, Chicago, Illinois
3 School of Public Health, University of Colorado, Lafayette
4 Kellogg School of Management and Pritzker School of Law, Northwestern University, Chicago, Illinois

Question Do prior paid medical malpractice claims predict future paid claims?

Findings In this retrospective, case-control study including all 881 876 physicians licensed to practice in the US at the time of the study, physicians with 1 paid claim (regardless of specialty) were almost 4 times more likely to have 1 or more paid claims in the next 5 years compared with physicians with no prior paid claims. The likelihood of future claims rose monotonically with the number of prior claims and was unaffected by whether paid claims were publicly disclosed.

Meaning The findings of this study suggest that paid medical malpractice claims are not random events; timely noncoercive intervention has the potential to reduce future claims.

Importance Many physicians believe that most medical malpractice claims are random events. This study assessed the association of prior paid claims (including a single prior claim) with future paid claims; whether public disclosure of prior paid claims affects future paid claims; and whether the association of prior and future paid claims decayed over time.

Objective To examine the association of 1 or more prior paid medical malpractice claims with future paid claims.

Design, Setting, and Participants This study assessed the association between prior paid claims (including a single prior claim) with future claims; whether public disclosure of prior claims affects future paid claims; and whether the association of prior and future paid claims decayed over time. This retrospective case-control study included all 881 876 licensed physicians in the US. All data analysis took place between July, 2018 and January, 2023.

Exposure Paid medical malpractice claims.

Main Outcome and Measures Association between a prior paid medical malpractice claim and likelihood of a paid claim in a future period, compared with simulated results expected if paid claims are random events. Using the same outcomes, we also assessed whether public disclosure of paid claims affects future paid claim rates.

Results This study included all 881 876 physicians licensed to practice in the US at the time of the study. Overall, 3.3% of the 841 961 physicians with 0 paid claims in the prior period had 1 or more claims in the future period vs 12.4% of the 34 512 physicians with 1 paid claim in the prior period; 22.4% of the 4189 physicians with 2 paid claims in the prior period; and 37% of the 1214 physicians with 3 paid claims in the prior period. The association between prior claims and future claims was similar for high-medical-malpractice-risk and lower-risk specialties; 1 prior-period claim was associated with a 3.1 times higher likelihood of a future-period claim for high-risk specialties (95% CI, 2.8-3.4) vs a 4.2 times higher likelihood for lower-risk specialties (95% CI, 3.8-4.6). The predictive power of a prior paid claim for future claims declined gradually as the time since the prior claim increased, for prior or future periods up to 10 years. Public disclosure did not affect the association between prior and future paid claims.

Conclusions and Relevance In this study of paid medical malpractice claims for all US physicians, a single prior paid claim was associated with substantial, long-lived higher future claim risk, independent of whether a physician was practicing in a high- or low-risk specialty, or whether a state publicly disclosed paid claims. Timely, noncoercive intervention, including education, has the potential to reduce future claims.

It has long been known that some physicians are prone to multiple medical malpractice claims. These claim-prone physicians have attracted considerable public concern. 1 , 2 One concern is whether hospitals are too liberal in granting privileges, and state medical boards are too willing to grant or renew licenses for physicians with multiple paid claims.

Physicians typically argue that a medical malpractice claim, especially a single claim, is often a random event, reflecting bad luck rather than lack of skill. In this view, past claim history would not be associated with future paid claims, except to the extent that plaintiffs’ lawyers are more likely to bring claims against physicians with known paid claims (ie, a “blood in the water” effect). Prior research shows that, controlling for specialty, physicians with multiple prior paid claims face higher future claim risk than physicians with a single prior paid claim. 3 A single-state study using 1980s data assessed whether physicians with 1 or more prior claims faced higher future claim risk than physicians with no prior claims. 4 Our recent study using national data from 2006 to 2016 examined the same issue. 5 But, to our knowledge, no prior study has assessed whether public disclosure of paid claims affects future claim rates. Similarly, we know of no prior study that has evaluated the relative association of future claims with recent vs more distant prior paid claims.

This study used data on paid medical malpractice claims for all physicians in the US to assess the association between experiencing 1 or more paid claims in a prior 5-year period (2009-2013) and the risk of 1 or more future paid claims in the next 5 years (2014-2018). This study also compared observed claim rates with those that would be expected if paid claims were random events, that arrived at rates that depend on state, but are independent of other physician characteristics. The study also assessed whether public disclosure of past paid claims was associated with future paid claim rates, and how the strength of the association between prior and future paid claims varied with the time since a prior paid claim occurred. Finally, the study used data from Illinois to assess whether the association of prior paid claims and future paid claims was different for physicians in high– vs low–malpractice-risk specialties.

The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines. The principal data source for this study is the National Practitioner Data Bank (NPDB), a national repository of all paid medical malpractice claims since 1992 involving individual health care professionals; this study focused on physicians with an MD. Each physician received an anonymized, time-consistent identifier.

Counts of active practicing nonfederal physicians (below, “active physicians”) in the 51 states (50 states plus DC) were obtained from the Area Health Resource File. Data on when physicians enter or leave active practice is not available, so the study assumes no entry or exit when evaluating the association of prior with future paid claims.

This study compared the actual and simulated probabilities that a physician in a given state, with a specified number of prior paid claims in a prior period (for example, 1 claim in the past 5 years) will have 1 or more paid claims in a subsequent future period (for example, 1 or more claims in the next 5 years). The principal results used a 5-year prior period from 2009 to 2013, and a 5-year future period from 2014 to 2018. The study grouped physicians based on the number of prior-period paid claims (0, 1, 2, or ≥3 claims), and measured the number of members of each group who experience 0, 1, 2, or 3 or more future-period paid claims.

The simulation of the paid claim counts that would be expected with random claim arrival at state-specific rates was derived by computing observed state-specific annual risk of a paid claim, defined as:

f state = (average No. of claims in the state over 2009-2018)/(average number of physicians in the state over 2014 to 2016 [first 3 years of future period]).

State-specific annual claim risk was used for each physician because of wide variation in state-level claim frequency (eTable 1 in Supplement 1 ). The state-specific risks were used to simulate how many claims each physician in each state would receive in the future period if claims arrived randomly at state-specific rates. In each simulation, claims were randomly assigned to physicians at the appropriate state-specific rate. For example, if there were Y annual claims in state X , which had n practicing physicians, claims were assigned at random to the physicians in that state, so that the expected number of paid claims for all physicians was Y . The number of physicians nationally who received 0, 1, 2, or 3 or more pseudo claims over the future period was determined and then rounded up to the nearest whole number. This simulation was run 10 000 times, and the counts were averaged and again rounded up to the nearest whole number. The simulation runs were also used to measure 95% CIs around the average counts. See eMethods in Supplement 2 for additional simulation details.

To evaluate the relative association of more distant vs more recent prior claims with future claims, the duration of both the prior and future periods was varied from 1 to 10 years. To assess whether public disclosure of prior paid claims was associated with future paid claims, future paid claim rates for the 19 states that publicly disclose which physicians have paid malpractice claims were compared with future paid claim rates in the remaining states, which do not disclose this information.

Because the NPDB data set does not include physician specialty, the study used data from Illinois from 1990 to 2016 (see Hyman et al, 2021, for data set details 6 ). Illinois physicians were divided into specialties with high medical malpractice risk (obstetrics and gynecology, general surgery, and all surgical specialties) vs lower-risk specialties (all others) based on specialty-specific risks of paid claims (eTable 2 in Supplement 1 ). The prior period was 2005 to 2009; the future period was 2010 to 2014. The analysis was otherwise similar to the national analysis and was run separately for each group.

Table 1 shows the actual number of paid claims during the study period, stratified by number of paid claims (0, 1, 2, or ≥3) during the prior period (the 5 years from 2009-2013). During the prior period approximately 96% of physicians had 0 claims, 3% had 1 claim, and less than 1% had 2 or more claims. There is a similar pattern during the 5-year future period (2014-2018): 97% of physicians had 0 claims, 3% had 1 claim, and less than 1% had 2 or more claims. eTable 3 and the eFigure in Supplement 1 provide additional summary statistics on paid claim rates.

Table 1 shows that the risk of a future-period claim increased monotonically with the number of prior-period paid claims. For example, the proportion of physicians with 1 or more future-period claims was 3.3% for the 841 961 physicians with 0 prior-period claims, 12.4% for the 34 512 physicians with 1 prior claim, 22.4% for the 4189 physicians with 2 prior claims, and 37% for the 1214 physicians with 3 or more prior claims. Relative to physicians with no prior-period claims, the risk of a future-period claim was 3.7 times higher for physicians with 1 prior-period claim (95% CI, 3.3-4.4); 6.7 times higher for physicians with 2 prior-period claims (95% CI, 5.9-7.9); and 11.2 times higher for physicians with 3 or more prior-period claims (95% CI, 9.8-13.1). Similarly, the proportion of physicians with 2 or more future-period claims was 0.3% for the physicians with 0 prior-period claims, 2.4% for physicians with 1 prior paid claim, 6.5% for physicians with 2 prior paid claims, and 14.4% for physicians with 3 or more prior paid claims. Consistent patterns are found if one separates the 3 or more group into 3 vs 4 or more claims (eTable 4 in Supplement 1 ).

Figure 1 illustrates, in a different way, how the likelihood of a future paid claim, or multiple future paid claims, varies with the number of prior-period claims. Figure 1 shows the ratio of actual future-period claims to the number expected from our simulation of random claim arrival. The likelihood of future claims rose with the number of prior-period claims, the actual-to-predicted ratios became large for physicians with multiple prior-period claims, multiple future- period claims, or both (see eTable 5 in Supplement 1 for the underlying data). Figure 1 A shows these ratios for physicians with 0, 1, or 2 prior-period claims, using a y-axis scale running from 0 to 80. Physicians with 1 paid claim in the prior period were 16 times more likely to have 2 future-period claims (95% CI, 12.5-19.3), and physicians with 3 or more prior-period claims were 63 times more likely to have 2 future-period claims than would be the case with random claim arrival (95% CI, 50-76). Figure 1 B shows ratios for physicians with 3 or more future-period claims, using a y-axis scale running from 0 to 8000. Physicians with 1 prior-period claim were 338 times more likely to have 3 future-period paid claims (95% CI, 267-409), and physicians with 3 prior-period claims were 6506 times more likely to have 3 future-period claims, relative to random claim arrival (95% CI, 5140-7872). These high ratios reflect the meaningful numbers of physicians who had these patterns, shown in Table 1 , vs the low probabilities that these patterns would be observed if claims arrived at random.

In Table 2 , the study uses a 10-year prior period, divided into a pre-preperiod (years −10 to −6) and a preperiod (years −5 to −1). Table 2 reflects the likelihood of a future-period paid claim for physicians with (1) 1 or more claims in both the pre-preperiod and the preperiod; (2) 1 or more claims in 1 period but not the other, and (3) physicians with no claims in either prior period. Physicians with paid claims in both prior periods had the highest risk (23.0%); physicians with 0 claims in both prior periods had the lowest risk (2.8%). For physicians with a paid claim in 1 of the prior periods, those with a more recent paid claim had a 12.2% risk of a future period claim, vs 8.4% for those with an older prior claim.

In eTable 6 in Supplement 1 , we varied the length of the future period from 1 to 9 years. Consistent with the difference in Table 2 between future claim rates for physicians with pre-preperiod vs preperiod paid claims, the association between prior-period and future-period paid claims gradually weakened as the length of the future period increased.

What about variation by specialty? Figure 2 uses Illinois data and shows the ratio of actual-to-predicted physicians with 1 or more future-period paid claims, based on the number of prior-period claims, separately for high-malpractice-risk and lower-risk specialties. eTables 2 and 7 in Supplement 1 provide Illinois and national data on specialty-specific risks. Physicians with 1 prior-period claim in lower-risk specialties had a 4.2 times higher risk of a future-period claim (95% CI, 3.8-4.6), compared with physicians in high-risk specialties who have a 3.1 times higher risk of a future-period claim (95% CI, 2.8-3.4). Physicians with 2 or more prior-period claims in lower-risk specialties had a 5.2 times higher risk of a future-period claim (95% CI, 4.7-5.7), compared with physicians in high-risk specialties who had a 3.7 times higher risk of a future-period claim (95% CI, 3.3-4.0). Thus, although absolute future-period claim risk was higher for high-malpractice-risk specialties, the relative increase in future claim risk for physicians with vs without prior-period claims was similar for high- and low-risk specialties.

The study also assessed whether physicians were more likely to face future claims because of public disclosure of prior paid claims. The likelihood of future paid claims, given similar prior-period history, was compared for the 19 states in which paid claims are publicly disclosed vs the remaining states, where prior-claim records are generally not available. Figure 3 shows that public disclosure of prior paid claims had no significant effect on the likelihood that physicians would experience future-period paid claims. The bottom lines (lines with squares for states in which paid claims are publicly reported; lines with circles for the other states) show the ratio of the likelihood that a physician with 1 prior-period paid claim would have 1 or more future-period paid claims vs this probability for physicians with no prior-period claims. The top lines are similar, but the numerator of each ratio is the number of physicians with 2 or more prior-period paid claims. If public reporting of paid claims made physicians more likely to experience additional paid claims, the dashed lines would be above the solid lines. This is not observed.

Physicians with even a single paid medical malpractice claim in a prior period were shown to have a greatly elevated risk of having additional paid claims during a future period. With 5-year prior and future periods, a single paid claim in the prior period was associated with a roughly 4 times higher likelihood of a future-period paid claim, relative to the likelihood for physicians with no prior-period claims. The elevation of risk was similar for both high-risk and lower-risk specialties. The greater the number of prior-period paid claims, the greater the likelihood of having a paid claim over any given future period, as well as the expected number of future-period claims. This pattern was not affected by whether plaintiffs’ lawyers had access to information about physicians’ past paid claims.

The increased future-period risk, relative to the risk physicians would face if claims were random events, was much higher for physicians who had multiple paid claims in both the prior and future periods. For the 1214 physicians with 3 or more prior-period claims, 83 had 2 future-period claims, and 92 had 3 or more future-period claims. Yet if claims arrived at random, there should be no physicians in either group (the simulated number was less than 0.1 for 2 future-period claims, and less than 0.02 for 3 or more future-period claims).

Physicians with 1 or more claims in both a pre-preperiod (years −10 to −6) and a preperiod (years −5 to −1) had higher risk of a future-period claim than those with a prior claim in 1 but not both periods, and much higher risk than those with no prior claim in either period. The degree of association between past and future claims fell gradually as the time since the prior-period claim increased.

Some factors that are associated with future claim risk are ones that hospitals and medical boards cannot act on (ie, age, gender, and specialty). 3 - 5 , 7 - 15 In addition, prior research suggests 10 that some physicians are claim-prone because of issues with their communication skills/bedside manner, as opposed to technical skill. We could not distinguish between these 2 sources of claim risk with our data, but note that our data are limited to claims that are paid, not merely initiated. Future work will be needed to disaggregate the contribution of each of these elements to claim-proneness.

Paid claims are an imperfect signal of low-quality care. Still, we offer evidence that even 1 claim provides an important signal, and that multiple claims provide a strong signal. This signal can likely be strengthened by combining information on paid claims with data on unpaid medical malpractice claims, specialty, and disciplinary sanctions imposed by state medical boards, loss of hospital privileges, and other adverse events.

Can our findings of a strong association between a single prior-period paid claim and future claim risk be reconciled with the common physician view that many malpractice claims are random events and that someone with a single paid claim was probably just unlucky? Prior work indicates that only a small percentage of medical errors lead to claims, but a substantial majority of paid claims reflect probable negligence. 15 , 16 We provide a simple formal model in the eAppendix in Supplement 1 , which builds on these background facts, in which the positive predictive value (PPV) of a paid claim (the likelihood that a paid claim reflects actual negligence) depends on physician skill, and could be low for high-skill physicians. Our results for the association between a prior and a future paid claim reflect the average signal conveyed by prior claim history, across both high-skill and low-skill physicians.

If future claim risk is reliably associated with past claims, one should consider interventions designed to reduce future claim risk. Even physicians with a single paid claim could benefit from efforts aimed at reducing future claim risk, despite the potential for the paid claim to be a false positive. Intervention following a single paid claim should be voluntary, and could be as simple as offering continuing medical education opportunities focusing on error avoidance and posterror communication with patients.

For physicians with 2 or more paid claims, especially within a limited time period, stronger interventions should be considered. Possible steps for physicians with 3 or more paid claims (or perhaps 2 or more in a low-risk specialty) could include closer supervision; counseling to improve their communication skills; refresher training; and perhaps encouragement to move to nonclinical practice. Outlier paid claim records could justify investigation of practice patterns, to assess whether license suspension or revocation is appropriate. The extent of interventions, and whether they are voluntary or mandatory, should reflect the number and recency of prior paid claims. Implemented properly, such graduated strategies have the potential to reduce future paid claims and patient harm.

This study has some limitations. Data on how many patients each physician sees were not available. But in our analysis, each physician served, in effect, as his or her own control. This approach relies on physician practice patterns being reasonably stable over time, including volume of patients seen and changes in patient mix. We are not aware of any data indicating that patient volume is likely to fluctuate greatly during the period when a physician is in active practice. Patient mix could change if a physician moves from one practice setting to another.

We cannot observe whether some physicians are willing to treat riskier patients and thus face a higher risk of adverse outcomes and thus medical malpractice claims. We did not observe unpaid claims. Some claims may not be reported to the NPDB, although in prior work 6 we found close correspondence between claims reported to the NPDB and claims reported to the Illinois Department of Insurance and to the Illinois Department of Professional and Financial Responsibility.

We did not observe physician entry and exit, and our analysis assumed no entry or exit of physicians between the prior and future periods. There is evidence that physicians with prior paid claims leave medical practice at somewhat higher rates than other physicians. 14 , 17 These higher exit rates would bias downward the tendency we found for physicians with prior paid claims to have higher future claim risk than physicians without prior paid claims.

For physician specialty, we only had data from Illinois, but Studdert et al 3 found similar results for physicians with 1 or more paid claims using national data.

In this case-control study of the association between prior-period paid malpractice claims and future-period claim risk, having even 1 prior-period paid claim was associated with a roughly 4-fold higher risk of a future-period paid claim, and even more elevated risk of having multiple future-period claims. This study also found that having multiple prior claims predicted a very large increase in the likelihood of multiple future-period claims, relative to either the null hypothesis of random claim arrival, or the actual claims experienced by physicians with no prior-period claims. The association between prior-period claims and future-period claim risk is similar for high-malpractice-risk and lower-risk specialties. We also find no evidence of a blood in the water effect, in which public disclosure of prior paid claims increases future-claim risk. Some paid claims are false-positive claims, but taken as a whole, paid claims convey a strong signal of future risk. The policy challenge is how to use this information to reduce future medical malpractice claims and patient harm without overreacting to the signal conveyed by a single paid claim.

Accepted for Publication: December 13, 2022.

Published: February 10, 2023. doi:10.1001/jamahealthforum.2022.5436

Corresponding Author: David A. Hyman, JD, MD, Georgetown University Law Center, 600 New Jersey Ave, Washington, DC 20001 ( [email protected] ).

Author Contributions: Dr Hyman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Hyman, Lerner, Black.

Drafting of the manuscript: Hyman, Lerner, Black.

Critical revision of the manuscript for important intellectual content: Hyman, Magid, Black.

Statistical analysis: Lerner, Black.

Administrative, technical, or material support: Hyman.

Supervision: Hyman.

Conflict of Interest Disclosures: None reported.

Data Sharing Statement: See Supplement 2 .

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Published: 20 November 2013

Policy: Twenty tips for interpreting scientific claims

William J. Sutherland 1 ,
David Spiegelhalter 2 &
Mark Burgman 3

Nature volume 503 , pages 335–337 ( 2013 ) Cite this article

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This list will help non-scientists to interrogate advisers and to grasp the limitations of evidence, say William J. Sutherland, David Spiegelhalter and Mark A. Burgman.

Calls for the closer integration of science in political decision-making have been commonplace for decades. However, there are serious problems in the application of science to policy — from energy to health and environment to education.

One suggestion to improve matters is to encourage more scientists to get involved in politics. Although laudable, it is unrealistic to expect substantially increased political involvement from scientists. Another proposal is to expand the role of chief scientific advisers 1 , increasing their number, availability and participation in political processes. Neither approach deals with the core problem of scientific ignorance among many who vote in parliaments.

Perhaps we could teach science to politicians? It is an attractive idea, but which busy politician has sufficient time? In practice, policy-makers almost never read scientific papers or books. The research relevant to the topic of the day — for example, mitochondrial replacement, bovine tuberculosis or nuclear-waste disposal — is interpreted for them by advisers or external advocates. And there is rarely, if ever, a beautifully designed double-blind, randomized, replicated, controlled experiment with a large sample size and unambiguous conclusion that tackles the exact policy issue.

In this context, we suggest that the immediate priority is to improve policy-makers' understanding of the imperfect nature of science. The essential skills are to be able to intelligently interrogate experts and advisers, and to understand the quality, limitations and biases of evidence. We term these interpretive scientific skills. These skills are more accessible than those required to understand the fundamental science itself, and can form part of the broad skill set of most politicians.

To this end, we suggest 20 concepts that should be part of the education of civil servants, politicians, policy advisers and journalists — and anyone else who may have to interact with science or scientists. Politicians with a healthy scepticism of scientific advocates might simply prefer to arm themselves with this critical set of knowledge.

We are not so naive as to believe that improved policy decisions will automatically follow. We are fully aware that scientific judgement itself is value-laden, and that bias and context are integral to how data are collected and interpreted. What we offer is a simple list of ideas that could help decision-makers to parse how evidence can contribute to a decision, and potentially to avoid undue influence by those with vested interests. The harder part — the social acceptability of different policies — remains in the hands of politicians and the broader political process.

Of course, others will have slightly different lists. Our point is that a wider understanding of these 20 concepts by society would be a marked step forward.

Differences and chance cause variation. The real world varies unpredictably. Science is mostly about discovering what causes the patterns we see. Why is it hotter this decade than last? Why are there more birds in some areas than others? There are many explanations for such trends, so the main challenge of research is teasing apart the importance of the process of interest (for example, the effect of climate change on bird populations) from the innumerable other sources of variation (from widespread changes, such as agricultural intensification and spread of invasive species, to local-scale processes, such as the chance events that determine births and deaths).

No measurement is exact. Practically all measurements have some error. If the measurement process were repeated, one might record a different result. In some cases, the measurement error might be large compared with real differences. Thus, if you are told that the economy grew by 0.13% last month, there is a moderate chance that it may actually have shrunk. Results should be presented with a precision that is appropriate for the associated error, to avoid implying an unjustified degree of accuracy.

Bias is rife. Experimental design or measuring devices may produce atypical results in a given direction. For example, determining voting behaviour by asking people on the street, at home or through the Internet will sample different proportions of the population, and all may give different results. Because studies that report 'statistically significant' results are more likely to be written up and published, the scientific literature tends to give an exaggerated picture of the magnitude of problems or the effectiveness of solutions. An experiment might be biased by expectations: participants provided with a treatment might assume that they will experience a difference and so might behave differently or report an effect. Researchers collecting the results can be influenced by knowing who received treatment. The ideal experiment is double-blind: neither the participants nor those collecting the data know who received what. This might be straightforward in drug trials, but it is impossible for many social studies. Confirmation bias arises when scientists find evidence for a favoured theory and then become insufficiently critical of their own results, or cease searching for contrary evidence.

Bigger is usually better for sample size. The average taken from a large number of observations will usually be more informative than the average taken from a smaller number of observations. That is, as we accumulate evidence, our knowledge improves. This is especially important when studies are clouded by substantial amounts of natural variation and measurement error. Thus, the effectiveness of a drug treatment will vary naturally between subjects. Its average efficacy can be more reliably and accurately estimated from a trial with tens of thousands of participants than from one with hundreds.

Correlation does not imply causation. It is tempting to assume that one pattern causes another. However, the correlation might be coincidental, or it might be a result of both patterns being caused by a third factor — a 'confounding' or 'lurking' variable. For example, ecologists at one time believed that poisonous algae were killing fish in estuaries; it turned out that the algae grew where fish died. The algae did not cause the deaths 2 .

Regression to the mean can mislead. Extreme patterns in data are likely to be, at least in part, anomalies attributable to chance or error. The next count is likely to be less extreme. For example, if speed cameras are placed where there has been a spate of accidents, any reduction in the accident rate cannot be attributed to the camera; a reduction would probably have happened anyway.

Extrapolating beyond the data is risky. Patterns found within a given range do not necessarily apply outside that range. Thus, it is very difficult to predict the response of ecological systems to climate change, when the rate of change is faster than has been experienced in the evolutionary history of existing species, and when the weather extremes may be entirely new.

Beware the base-rate fallacy. The ability of an imperfect test to identify a condition depends upon the likelihood of that condition occurring (the base rate). For example, a person might have a blood test that is '99% accurate' for a rare disease and test positive, yet they might be unlikely to have the disease. If 10,001 people have the test, of whom just one has the disease, that person will almost certainly have a positive test, but so too will a further 100 people (1%) even though they do not have the disease. This type of calculation is valuable when considering any screening procedure, say for terrorists at airports.

Controls are important. A control group is dealt with in exactly the same way as the experimental group, except that the treatment is not applied. Without a control, it is difficult to determine whether a given treatment really had an effect. The control helps researchers to be reasonably sure that there are no confounding variables affecting the results. Sometimes people in trials report positive outcomes because of the context or the person providing the treatment, or even the colour of a tablet 3 . This underlies the importance of comparing outcomes with a control, such as a tablet without the active ingredient (a placebo).

Randomization avoids bias. Experiments should, wherever possible, allocate individuals or groups to interventions randomly. Comparing the educational achievement of children whose parents adopt a health programme with that of children of parents who do not is likely to suffer from bias (for example, better-educated families might be more likely to join the programme). A well-designed experiment would randomly select some parents to receive the programme while others do not.

Seek replication, not pseudoreplication. Results consistent across many studies, replicated on independent populations, are more likely to be solid. The results of several such experiments may be combined in a systematic review or a meta-analysis to provide an overarching view of the topic with potentially much greater statistical power than any of the individual studies. Applying an intervention to several individuals in a group, say to a class of children, might be misleading because the children will have many features in common other than the intervention. The researchers might make the mistake of 'pseudoreplication' if they generalize from these children to a wider population that does not share the same commonalities. Pseudoreplication leads to unwarranted faith in the results. Pseudoreplication of studies on the abundance of cod in the Grand Banks in Newfoundland, Canada, for example, contributed to the collapse of what was once the largest cod fishery in the world 4 .

Scientists are human. Scientists have a vested interest in promoting their work, often for status and further research funding, although sometimes for direct financial gain. This can lead to selective reporting of results and occasionally, exaggeration. Peer review is not infallible: journal editors might favour positive findings and newsworthiness. Multiple, independent sources of evidence and replication are much more convincing.

Significance is significant. Expressed as P , statistical significance is a measure of how likely a result is to occur by chance. Thus P = 0.01 means there is a 1-in-100 probability that what looks like an effect of the treatment could have occurred randomly, and in truth there was no effect at all. Typically, scientists report results as significant when the P -value of the test is less than 0.05 (1 in 20).

Separate no effect from non-significance. The lack of a statistically significant result (say a P -value > 0.05) does not mean that there was no underlying effect: it means that no effect was detected. A small study may not have the power to detect a real difference. For example, tests of cotton and potato crops that were genetically modified to produce a toxin to protect them from damaging insects suggested that there were no adverse effects on beneficial insects such as pollinators. Yet none of the experiments had large enough sample sizes to detect impacts on beneficial species had there been any 5 .

Effect size matters. Small responses are less likely to be detected. A study with many replicates might result in a statistically significant result but have a small effect size (and so, perhaps, be unimportant). The importance of an effect size is a biological, physical or social question, and not a statistical one. In the 1990s, the editor of the US journal Epidemiology asked authors to stop using statistical significance in submitted manuscripts because authors were routinely misinterpreting the meaning of significance tests, resulting in ineffective or misguided recommendations for public-health policy 6 .

Study relevance limits generalizations. The relevance of a study depends on how much the conditions under which it is done resemble the conditions of the issue under consideration. For example, there are limits to the generalizations that one can make from animal or laboratory experiments to humans.

Feelings influence risk perception. Broadly, risk can be thought of as the likelihood of an event occurring in some time frame, multiplied by the consequences should the event occur. People's risk perception is influenced disproportionately by many things, including the rarity of the event, how much control they believe they have, the adverseness of the outcomes, and whether the risk is voluntarily or not. For example, people in the United States underestimate the risks associated with having a handgun at home by 100-fold, and overestimate the risks of living close to a nuclear reactor by 10-fold 7 .

Dependencies change the risks. It is possible to calculate the consequences of individual events, such as an extreme tide, heavy rainfall and key workers being absent. However, if the events are interrelated, (for example a storm causes a high tide, or heavy rain prevents workers from accessing the site) then the probability of their co-occurrence is much higher than might be expected 8 . The assurance by credit-rating agencies that groups of subprime mortgages had an exceedingly low risk of defaulting together was a major element in the 2008 collapse of the credit markets.

Data can be dredged or cherry picked. Evidence can be arranged to support one point of view. To interpret an apparent association between consumption of yoghurt during pregnancy and subsequent asthma in offspring 9 , one would need to know whether the authors set out to test this sole hypothesis, or happened across this finding in a huge data set. By contrast, the evidence for the Higgs boson specifically accounted for how hard researchers had to look for it — the 'look-elsewhere effect'. The question to ask is: 'What am I not being told?'

Extreme measurements may mislead. Any collation of measures (the effectiveness of a given school, say) will show variability owing to differences in innate ability (teacher competence), plus sampling (children might by chance be an atypical sample with complications), plus bias (the school might be in an area where people are unusually unhealthy), plus measurement error (outcomes might be measured in different ways for different schools). However, the resulting variation is typically interpreted only as differences in innate ability, ignoring the other sources. This becomes problematic with statements describing an extreme outcome ('the pass rate doubled') or comparing the magnitude of the extreme with the mean ('the pass rate in school x is three times the national average') or the range ('there is an x -fold difference between the highest- and lowest-performing schools'). League tables, in particular, are rarely reliable summaries of performance.

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William J. Sutherland is professor of conservation biology in the Department of Zoology, University of Cambridge, UK.,

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David Spiegelhalter is at the Centre for Mathematical Sciences, University of Cambridge.,

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Mark Burgman is at the Centre of Excellence for Biosecurity Risk Analysis, School of Botany, University of Melbourne, Parkville, Australia.,

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Sutherland, W., Spiegelhalter, D. & Burgman, M. Policy: Twenty tips for interpreting scientific claims. Nature 503 , 335–337 (2013). https://doi.org/10.1038/503335a

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Published: 16 May 2019

Claims of causality in health news: a randomised trial

Rachel C. Adams 1 ,
Aimée Challenger 2 ,
Luke Bratton 2 ,
Jacky Boivin 2 ,
Lewis Bott 2 ,
Georgina Powell 2 ,
Andy Williams 3 ,
Christopher D. Chambers 1 &
Petroc Sumner 2

BMC Medicine volume 17 , Article number: 91 ( 2019 ) Cite this article

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Misleading news claims can be detrimental to public health. We aimed to improve the alignment between causal claims and evidence, without losing news interest (counter to assumptions that news is not interested in communicating caution).

We tested two interventions in press releases, which are the main sources for science and health news: (a) aligning the headlines and main causal claims with the underlying evidence (strong for experimental, cautious for correlational) and (b) inserting explicit statements/caveats about inferring causality. The ‘participants’ were press releases on health-related topics ( N = 312; control = 89, claim alignment = 64, causality statement = 79, both = 80) from nine press offices (journals, universities, funders). Outcomes were news content (headlines, causal claims, caveats) in English-language international and national media (newspapers, websites, broadcast; N = 2257), news uptake (% press releases gaining news coverage) and feasibility (% press releases implementing cautious statements).

News headlines showed better alignment to evidence when press releases were aligned (intention-to-treat analysis (ITT) 56% vs 52%, OR = 1.2 to 1.9; as-treated analysis (AT) 60% vs 32%, OR = 1.3 to 4.4). News claims also followed press releases, significant only for AT (ITT 62% vs 60%, OR = 0.7 to 1.6; AT, 67% vs 39%, OR = 1.4 to 5.7). The same was true for causality statements/caveats (ITT 15% vs 10%, OR = 0.9 to 2.6; AT 20% vs 0%, OR 16 to 156). There was no evidence of lost news uptake for press releases with aligned headlines and claims (ITT 55% vs 55%, OR = 0.7 to 1.3, AT 58% vs 60%, OR = 0.7 to 1.7), or causality statements/caveats (ITT 53% vs 56%, OR = 0.8 to 1.0, AT 66% vs 52%, OR = 1.3 to 2.7). Feasibility was demonstrated by a spontaneous increase in cautious headlines, claims and caveats in press releases compared to the pre-trial period (OR = 1.01 to 2.6, 1.3 to 3.4, 1.1 to 26, respectively).

Conclusions

News claims—even headlines—can become better aligned with evidence. Cautious claims and explicit caveats about correlational findings may penetrate into news without harming news interest. Findings from AT analysis are correlational and may not imply cause, although here the linking mechanism between press releases and news is known. ITT analysis was insensitive due to spontaneous adoption of interventions across conditions.

Trial registration

ISRCTN10492618 (20 August 2015)

Peer Review reports

Each year, thousands of news stories make claims about health and millions of readers use them as their main source for up-to-date information [ 1 , 2 , 3 ]. Established news media are the most widespread means to disseminate beneficial information [ 4 ], but misleading claims are common [ 5 , 6 ] and may damage public health and create confusion and mistrust [ 7 , 8 , 9 , 10 , 11 ]. The Academy of Medical Sciences recently reported that only 37% of British adults trust scientific evidence [ 12 ], potentially undermining the timely seeking of, and engagement with, medical or healthcare advice [ 13 ]. Trust entails that strong claims are backed by strong evidence and that caution and caveats are expressed where appropriate. But in a competitive media market, it is common to assume that news has no place for cautiousness and caveats. Here, we test this assumption.

Most biomedical and health news stories make a prominent causal claim in either the headline or first two sentences (e.g. ‘statins raise diabetes risk’; ‘statins slash breast cancer death rates’). It is these headlines and main claims that are most eye-catching, most shared and that also frame the rest of a story [ 14 , 15 ]. However, many are based on correlational evidence [ 16 , 17 ], where causal conclusions often prove incorrect [ 18 ]. For example, in a sample of 130 prominent health stories, 49% had causal claims based on non-randomised designs [ 6 ] (see also Additional file 1 : Figure S1). Thus, our first intervention (described below) attempted to improve the alignment between the strength of prominent news claims and the nature of the underlying evidence.

Later in a news story, caveats occasionally appear, adding a qualification about the work. These are rare and normally nonspecific, such as suggesting more research is needed [ 19 , 20 , 21 ]. News almost never explicitly comments on whether the evidence can support a strong causal claim, such as mentioning the limitations of correlational data. Our second intervention attempted to change this.

Changes to science and health news are most likely to be achieved via press releases from journals, universities and funders, which stimulate and provide content for news. Previous observational research has found that health news content is strongly associated with press release content [ 5 , 22 , 23 , 24 , 25 ]. Thus, we undertook a randomised trial intervening in press release content, moderating causal claims and inserting caveats in press releases as means to improve health news. The critical questions were whether news would change, whether the ability to attract news would drop and whether the suggested improvements would be feasible at scale.

In collaboration with nine UK press offices, we ran a randomised controlled trial in which the ‘participants’ were press releases ( N = 312) distributed to international media outlets over a 20-month period from September 2016 to May 2017. To operationalise evidence strength, we concentrated on the basic distinction between correlational and experimental types of evidence, a keystone for assessing the ability to support causal conclusions [ 26 ].

The collaborating press offices sent their biomedical and health-related press releases to us just prior to release. We randomly allocated each press release to receive one, both or neither of two interventions. The first intervention was causal claim alignment . We made suggestions to align the headline and prominent claims with the evidence, such that direct causal claims were only made for experimental evidence, while correlational data carried cautious claims, using words such as might and may . The second intervention was a causality statement/caveat . We inserted an explicit statement about whether the evidence could support a causal conclusion (e.g. this was an observational study, which does not allow us to conclude that drinking wine caused the increased cancer risk ).

The press office was then free to accept, edit or reject the proposals (sometimes in consultation with academics according to their normal procedures) and issued the release as normal. We searched for arising news (print, online and broadcast; total N = 2257), and its content was double-coded by two researchers blind to condition and press release content. The protocol was pre-registered ( https://doi.org/10.1186/ISRCTN10492618 , 20/08/2015) and approved by the Research Ethics Committee at the School of Psychology, Cardiff University. We do not name press offices to avoid identifying individuals. All data are available online at https://osf.io/apc6d/

Participants: press releases

The ‘participants’ in the trial were press releases. For inclusion criteria, see Fig. 1 .

CONSORT diagram for the press releases (participants) in the trial. Inclusion criteria: participating press offices were asked to send each press release based on peer-reviewed research that was relevant to human health, broadly defined (all biomedical, psychological or lifestyle topics), where the press office was leading the press release (rather than collaborating on a release by another office outside the trial) and the academic authors consented (we used opt-out consent). Our focus was on observational and experimental studies. Observational studies included cross-sectional and longitudinal designs as well as meta-analyses and systematic reviews based solely on observational research. Experimental research included randomised controlled trials, other experiments and meta-analyses or systematic reviews based solely on experimental designs. Press releases on studies that could not be classified as experimental or observational (e.g. simulations and mixed methods reviews) were excluded

Sample size

We estimated we would achieve 300–500 press releases based on 100% coverage of eligible press releases from participating offices. In practice, some offices released fewer relevant press releases than expected and some eligible press releases were not sent to us for a variety of reasons (Fig. 1 ; 261 of 499 eligible press releases were sent; see reasons beyond the exclusion criteria of joint release and author consent). We therefore extended the trial duration and introduced a stopping rule of 75 press releases per bin (prior to exclusion of study designs not classifiable as experimental or correlational). Since we used pure randomisation, some bins were larger than others (Additional file 1 : Table S2) and the total was 312 following study-design exclusion. Note that the power calculations in the protocol are only indications, since actual power depended on the clustering structure in the GEE analyses.

Randomisation and blinding

Randomisation was by independent random number generation for each press release received (and therefore allowed unequal cell sizes by chance) and occurred prior to any assessment of content (and therefore before exclusion of simulations and mixed-methods reviews which reduced some cells below 75; Table 1 ). We did not communicate the condition to the press office. There were three researchers coordinating the trial (RCA, AC and LB). For each batch of press releases, RCA or AC coordinated randomisation and interventions, while the other two would remain blind for double-coding the outcomes.

Interventions

Causal claim alignment

The main causal claims in the headline and body of the press release were altered to align with the evidence underlying those claims. If claims were already aligned with the evidence, these were not modified. Based on previous results [ 27 ] showing which causal phrases readers distinguish or treat equivalently, all claims for observational evidence were modified to use hedged/cautious or associative language ( may , could , might ; e.g. ‘drinking wine may increase cancer risk’; associated, linked ; e.g. ‘drinking wine is associated with increased cancer risk’) unless such language was already used. Claims for experimental evidence were modified to (or left as) direct causal statements (e.g. ‘drinking wine increases cancer risk’) or can cause statements (‘drinking wine can increase cancer risk’). In the registered protocol, we referred to alignment as accuracy (see Additional file 1 : Figure S2).

Causality statement/caveat

Unless it already existed, a statement was inserted into the press release body to convey the design of the study and the strength of causal conclusions that could be justified from this design. For example, ‘this was an observational study, which does not allow us to conclude that drinking wine caused the increased cancer risk’ or ‘this study was a randomised controlled trial, which is one of the best ways for determining whether an intervention has a causal effect’ (in the registered protocol, we labelled this intervention study design statement ; see Additional file 1 : Figure S2). These statements were inserted at the earliest point where they fitted with the press release content. The majority were inserted into text, not into quotes, because feedback from press officers indicated that it was normally not pragmatic to get author approval for new quotes before release.

Causal claim alignment + causality statement

In this condition we suggested changes according to both A and B above, unless they were already present.

The control condition was a suggested synonym change for a word that was not relevant to the main causal claims or study design (e.g. ‘beverage’ changed to ‘drink’).

Primary outcomes

News content.

From each pre-intervention press release, a list of search terms was generated to search for print, online and broadcast news coverage from a pre-defined list of top-tier national and international news outlets (see Additional file 1 : Figure S3). Searches were conducted using Nexis, Google and TV Eyes. News coverage was sourced for 1 week prior to the press release date (to cover date differences due to time zones and any breaches of embargo) and for 28 days following the release. Two researchers blind to condition and final press release content coded the news using a standard protocol abbreviated from Sumner et al. [ 25 ] to extract the content outcomes listed below. All discrepancies in coding were resolved so that the final concordance was 100%. See open data for the full coding sheet.

Causal headline and claim alignment : We coded whether the news headline and news main claims were direct causal , can cause or hedged causal/associative. Alignment was defined relative to the study design of the peer-reviewed journal article. Following Adams et al. (2017), we grouped direct cause and can cause together as strong claims appropriate for experimental evidence, and we refer to hedged cause/associative statements as cautious claims appropriate for correlational evidence [ 27 ]. We coded and analysed headlines and main claims separately as they are normally written by different people (sub-editor and journalist); headlines are most prominent but the writers are one step further removed from the press release. We operationalised main claims as those made in the first two sentences beyond the headline (excluding context sentences not about the new study). We excluded news headlines or claims that were not causal/associative or made a claim of no cause (‘wine does not raise cancer risk’). We also excluded news claims that were about entirely different variables than the press release.

Causality statement/caveat : We coded whether a statement relating study design to cause-and-effect was present in news stories. We did not require that the news used scientific terms such as correlation or randomised controlled trial, but rather that the news contained a relevant statement about the possibility or difficulty of causal inference. For correlational evidence this had to be a caveat (e.g. ‘we don’t know if wine is directly responsible for cancer risk’ or ‘we cannot draw conclusions about cause and effect’).

News uptake

It is the proportion of press releases that attract news. Following Sumner et al. [ 20 , 25 ], we simply scored news as present or absent, rather than discriminating between types of news and the differing media targets that some press releases may have. We also counted number of news stories (though this is an imperfect measure due to non-independence where some stories are copied across outlets; we present the results in Additional file 1 : Figure S4). Although for news content we separated headlines and main claims, the outcome measure of uptake does not separate them. Therefore, we operationalised aligned press releases as follows: press releases for observational studies were aligned only if both headline and main claim used cautious language (and conversely, press releases for experimental studies were aligned if either the headline or the main claim used direct or can cause phrases).

Secondary outcomes

We also coded whether news contained exaggerated advice or exaggerated inference from non-human research. These outcomes do not correspond to our main interest here, but were included for comparison with previous research [ 20 , 25 ]. Analysis and results are in Additional file 1 : Figure S5.

Feasibility and acceptability

As a pre-requisite for interpreting the main news outcomes, and to assess whether alignment, caution and caveats are generally feasible and acceptable to integrate in press releases, we assessed the number of pre-intervention press releases that already contained them spontaneously, the number of suggestions made, accepted (including those edited while maintaining the distinction between cautious and strong), or rejected, and hence the numbers of our intended interventions present in the released versions of the press releases in each condition. Note that for our interest, spontaneous presence of appropriately cautious claims or caveats is more valuable than accepting our interventions, since intervention is not a feature of normal press release process. For this reason, we also assessed change between the trial and a baseline period of 2 years prior to the trial. To do this, we randomly sampled up to 20 press releases for each collaborating centre from 2014 and 2015 (10 from each year, or all eligible press releases from a centre if less than 10 were available), using the same eligibility criteria (except consent, as these press releases are in the public domain). We double-coded them in the same way as the press releases in the trial.

Analysis and statistical methods

We focus the analysis on the main effects of causal claim alignment and causality statements/caveats separately, as recommended by [ 28 ], because the 2 × 2 design was not powered for the interaction (we report interactions as secondary analyses [ 28 ]). Causal phrasing could be coded and analysed where the headline or main statement made a causal or associative claim (excluding those that made no claim about a health outcome, or made a claim of no cause, e.g. wine does not cause …). Presence or absence of causality statements/caveats could be assessed for all. For causal claim alignment, we also separated news headlines and main claims, as explained above.

For the primary outcome measures of news content and uptake, we used both intention-to-treat (ITT) and as-treated (AT) analytic approaches. ITT analysis maintained the randomisation, comparing news content and uptake in conditions that attempted to make interventions against those that did not regardless of whether a suggestion was possible or accepted, and what the final press releases actually contained. AT analysis, on the other hand, depended on the content of the finally released press releases. This corresponds directly to what the journalists actually saw, but it disregards the randomisation and is therefore an associative analysis subject to selection bias, for which causal inference is not directly possible. However, it becomes useful when there are high levels of treatment mixing within groups due to spontaneous presence in the control group or non-acceptance in the intervention group—both of which we anticipated here and which can render ITT difficult to interpret (and would also severely reduce N for a per-protocol analysis, which we did not perform).

To account for the clustering of news to press releases or press releases to press office, we used generalised estimating equations (GEE, using a binary logistic model with exchangeable correlation matrix) as in our previous work [ 20 , 25 ]. Since our intervention suggestions depended on study design (observational vs experimental), we also tested interactions with study design (data plotted in Additional file 1 : Figure S6).

To assess feasibility, we estimated usage rates of caution and caveats in both pre-intervention and final press releases and compared them to the baseline period, using GEE as above to compensate for the clustering of press releases to press office.

Causal headlines and main claims

We coded whether the news headline and news main claims were strongly causal or cautious , following the distinctions readers make between causal phrases [ 27 ] . Alignment was defined as strong claims for experimental evidence and cautious claims for correlational evidence. We coded and analysed headlines separately from main claims in the body of the story as they are normally written by different people (sub-editor and journalist); headlines are most prominent but the writers are one step further removed from the press release. We used both intention-to-treat (ITT) and as-treated (AT) analytic approaches. For the ITT analysis, we compared news for the intervention groups where we intended to make suggestions for causal claim alignment (whether in isolation or combined with causality statements/caveats, and regardless of whether suggestions were accepted), with news from the press releases without alignment suggestions (regardless of whether alignment already existed in these control press releases). ITT analysis revealed a small significant rise the proportion of aligned news headlines for the groups with press release headline intervention compared to those without (Fig. 2 a left; 56% [53 to 58] vs 52% [50 to 54], 95%CI of the OR = 1.2 to 1.9). The equivalent comparison for main claims was not significant (Fig. 2 a right; 62% [55 to 69] vs 60% [54 to 66], 95% CI of the OR = 0.7 to 1.6). ITT analysis was relatively insensitive because the majority of control press releases also contained alignment through spontaneous adoption (see below). The interaction with causality statements (for which the study was not powered) was significant for headlines (OR = 1.3 to 2.2), such that the main effect was driven by the condition with both interventions (estimates for the conditions control, claim alignment, causality statement and both were 52%, 49%, 52% and 62%, respectively). The interaction was not significant for main claims (OR = 0.5 to 2.4; estimates for the conditions: 59%, 59%, 62% and 65%).

a News follows the phrasing of the press release: In ITT and AT analysis, news headlines were more likely to align to evidence if the press release phrasing did so; and in the AT analysis, claims in the news text were also more likely to do so if the press release did so. The discrepancy between ITT and AT analyses was due to a high level of condition mixing (see text). b ITT and AT analyses both show no evidence of reduced news uptake for press releases whose headlines and main claims aligned to evidence (see also Additional file 1 : Figure S4 for the average number of news per press release). Error bars are 95% CIs. For each bar, n reports total number of news ( a ) or press releases ( b ) in that analysis group (i.e. the denominator of the proportion that the bar displays; total n is lower for AT than ITT analysis, because AT was possible only for press releases with causal claims present in headlines or main claims)

For AT analysis, we compared news for press releases that did or did not have aligned headlines and main claims at the point of release. This corresponds directly to what the journalists actually saw, but it disregards the randomisation and is therefore an associative analysis. This was possible for 247 press releases that contained a causal claim present in the headline or main claim. The proportion of news headlines using aligned language was 60% (CI 53 to 67%) when the press release headline did so, compared to 32% (CI 23 to 42%) when the press release did not (OR = 2.4, CI 1.3 to 4.4; N news = 1251). The proportion of news main claims using aligned language was 67% (CI 61 to 72%) when the press release did so, compared to 39% (CI 29 to 50%) when it did not (OR = 2.8, CI 1.4 to 5.7; N news = 1768). Note that the majority of the press releases were based on observational studies (72%; N = 179/247), where aligned claims meant cautious wording. These effects were still strong when analysing observational studies alone (headlines: 56 vs 23%, CI of the OR = 1.9 to 9.4; claims 64% vs 34%, CI of the OR = 1.8 to 6.4). The interactions with the study design were not significant (Additional file 1 : Figure S6). Neither were the interactions with causality statements (headlines: estimates for the 4 cells were 31%, 65%, 33% and 54%, OR = 0.6 to 4.8; main claims: 41%, 71%, 37% and 62%, OR = 0.5 to 3.6).

Importantly, there was no detectable cost to news uptake. The proportion of press releases that attracted news did not significantly differ in either ITT analysis (Fig. 2 b; 55% vs 55%, OR = 0.7 to 1.3) or AT analysis (Fig. 2 b; 60% vs 58%, OR = 0.7 to 1.7). The pattern was similar for observational and experimental studies with no significant interaction (see Additional file 1 : Figure S7). The interaction with causality statements was underpowered and inconsistent across analyses: uptake for each cell (control, claim alignment, causality statement and both) was estimated as 54%, 60%, 57% and 50%, respectively in ITT (OR = 1.0 to 2.7), and 69%, 51%, 62% and 73%, respectively, in AT (OR = 1.5 to 8.2).

Feasibility/acceptability and group mixing

Since we already know that strong claims are common in press releases and news, the key interest was the feasibility of cautious claims for observational studies, employing words like may , might or using associative language. The majority of the press releases were based on observational research (73%; N = 229/312); among these, we could analyse 151 headlines and 177 main claims (excluding those that made no claim relating an IV and DV, or made a claim of no cause, e.g. wine does not cause ….). Figure 3 shows the estimated proportions of headlines and main claims that were already cautious (i.e. aligned to their observational study design) in the pre-intervention text and in the final press releases, compared to the baseline period prior to the trial. The most salient point is the spontaneous increase in alignment in both headlines and main claims in pre-intervention press releases (mid-grey) since the baseline period (light grey; headlines OR = 1.6, 95% CI 1.01 to 2.6; main claims OR = 2.1, 95% CI 1.3 to 3.4). The further increase from pre-intervention to final press release followed suggestions in the relevant conditions of the trial. For headlines, in the subset where suggestions could be made, 41% were accepted (including those edited, but maintaining the distinction between cautious and strong); for the main claim, 60% were accepted.

Feasibility and growing use of cautious headlines and main claims in observational research (error bars are 95% CIs). Feasibility is indicated by the increase in spontaneous use in pre-intervention (draft) press releases since the baseline period (2014/15). Final press releases showed small further increases in cautious wording following suggestions in the trial. For each bar, n reports the total number of press releases in that analysis group (i.e. the denominator of the proportion that the bar displays)

Overall, cautious headlines and main claims occurred frequently in press releases of observational studies, demonstrating caution is feasible and acceptable to the authors. In most cases, this was already implemented in the draft press releases before any trial suggestions were made. This spontaneous presence of caution strongly indicates feasibility, but when added to incomplete intervention acceptance, it meant that the proportions of aligned claims in final press releases hardly differed across conditions (GEE estimates: with intervention 76% (56 to 88) of headlines and 91% (82 to 96) of main claims; without intervention 70% (61 to 78) of headlines and 82% (77 to 86) of main claims). This made ITT analysis much less sensitive than AT analysis.

Causality statements/caveats

We coded whether a statement relating study design to cause-and-effect was present in news stories. We did not require that the news used scientific terms such as correlation or randomised controlled trial, but rather that the news contained a relevant statement about the possibility or difficulty of causal inference. For correlational evidence, this had to be a caveat (e.g. ‘we don’t know if wine is directly responsible for cancer risk’ or ‘we cannot draw conclusions about cause and effect’). ITT analysis found 15% (11% to 19%) of news contained causality statements for the conditions with statement/caveat suggestions, compared with 10% (7 to 14%) for the conditions without such suggestions (Fig. 4 a, right, OR = 0.91 to 2.6). There was no interaction with claim alignment interventions (OR = 0.6 to 5.0, estimates for the four conditions were 8%, 12%, 16% and 14%).

Use of causality statements/caveats (error bars are 95%CIs). a ITT was insensitive to differences in news content; AT showed that 20% of news contained causality statements or caveats if the press release did, and almost never otherwise. b ITT shows no reduction of news uptake and AT shows an increase in news for press releases containing causality statements/caveats (see also Additional file 1 : Figure S4 for average number of news per press release). c Feasibility is indicated by the increase in spontaneous caveats for observational research since the baseline period (2014/2015). Final press releases showed a further increase following suggestions in relevant trial conditions. For each bar, n reports total number of news ( a ) or press releases ( b , c ) in that analysis group (i.e. the denominator of the proportion that the bar displays)

AT analysis compared news for press releases with and without such statements/caveats regardless of the randomised condition, and found that the proportion of news containing a causality statement or caveat was 20% (CI: 16% to 24%) when the press release contained one compared to under 1% (CI: 0% to 1%) when it did not (OR = 50, CI: 16 to 156; N news = 2257). As noted above, the majority of these press releases were about observational studies where the causality statement was an explicit caveat. The effect was similarly strong in the observational studies alone (20% vs 1%, CI of the OR = 12 to 180) and did not interact significantly with study design (Additional file 1 : Figure S8). There was an interaction with claim alignment (OR = 1.2 to 154, estimates for the four cells were 3%, 0%, 18% and 22%).

ITT analysis showed no significant difference in news uptake between conditions with and without intervention (Fig. 4 b left; 53% vs 56%, OR = 0.8 to 1.03). AT analysis showed higher news uptake for press releases containing causality statements/caveats (Fig. 4 b right; AT 66% vs 52%, OR = 1.3 to 2.7). This effect was present in the observational studies alone for which these statements are explicit caveats (OR = 1.4 to 5.3) and did not interact significantly with study design (Additional file 1 : Figure S8). Interaction with claim alignment is given in section A (the outcome measure of news uptake is identical here).

Feasibility/acceptability and condition mixing

The critical feasibility question concerns explicit caveats about causality for observational studies. Figure 4 c shows that spontaneous usage of such caveats in press releases rose from under 10% in the baseline period (light grey, 2014/2015) to over 30% in the draft press releases in the trial (mid-grey, OR = 1.1 to 26). Following intervention in relevant trial conditions, 59% of suggestions were accepted, so that approximately half the press releases about observational studies contained explicit caveats about cause and effect when they were released (dark grey).

Spontaneous presence demonstrates feasibility, but meant there were causality statements/caveats in press releases in control conditions as well as intervention conditions (GEE estimates: with intervention 40% (30 to 51); without intervention 17% (7 to 36); OR = 1.7 to 6.6; these estimates differ in exact value from Fig. 4 because they include experimental and observational studies and because GEE adjusts estimates to different amounts given different clustering within press offices).

Prominent claims in news headlines and stories showed better alignment with the underlying evidence when press releases paid attention to this alignment. Additionally, 20% of news explicitly stated whether causality can be inferred when prompted to do so by press release text. Explicit causality statements have almost never been seen in news previously and almost never occurred in our large sample unless the press release contained it. Most of these statements were caveats and were not within quotes, making it more remarkable that they carried through to news (it is likely that carry-through for quotes would be higher). We found no evidence that news uptake is lower for press releases with aligned claims or caveats. The spontaneous use of explicit caution has risen since the baseline period before the trial, demonstrating that press officers find cautious headlines and explicit caveats feasible.

This trial was the first to intervene systematically in press release content and test the outcomes for health news. The main limitation was reliance on the AT (associative) analyses for most of the inferred effects of press release content on news content. The ITT analyses were insensitive and only significant for the effect on news headlines. The likely reason is that the trial saw a spontaneously increased rate of alignment in draft press releases before allocation to the condition—similar to a classic Hawthorne effect, but possibly because press offices that joined the trial were already changing their practices. For the narrow purpose of running a trial, this meant insufficient difference between conditions for sensitive ITT analysis. From a broader perspective, it is a strength that press officers have already demonstrated spontaneous willingness to apply the alignment and cautious language our interventions were suggesting. The pitfall for previous advice and guidelines for responsible science reporting has always been whether press officers and journalists find such guidance feasible within the constraints of writing pithy newsworthy text.

There are weaknesses and strengths for basing conclusions on AT analysis. It is correlational observation (although in this case, we know the linking mechanism between variables: journalists read the press releases). However, while ITT focusses on whether the intervention protocol itself causes a difference (with non-adherence to the protocol being an important part of the assessment), here, an intervention is not a normal part of the press release process. AT focuses on the content of the issued press releases. For this reason, it is more sensitive when assessing potential harms (in this case, the possibility of lower news uptake).

That news claims and causality statements/caveats correlated strongly with press release content (Figs. 2 a and 4 a) confirms previously observed associations for other content [ 5 , 20 , 24 , 25 , 29 ]. We built on this research in three main ways: previous findings have been based on naturally arising content, while we ran an intervention trial; we emphasised the key role of the headline (the most prominent and most difficult-to-influence part of a news story); our suggested in-text caveats were considerably more explicit than normally contained in news or press releases [ 20 ].

Readers are not expected to understand the technical distinctions between study designs that underlie stronger or weaker evidence. Indeed studies show that even college students who have taken research design courses find this difficult to discern [ 30 , 31 , 32 ]. What readers do perceive are systematic differences between levels of caution or strength in causal claims [ 27 ] and additional phrasing that implies caution [ 21 ] (‘One limitation…’). We focussed on these phrases that readers understand and differentiate.

Unanswered questions

For our study, the outcomes were focussed on aligning prominent claims in news with underlying evidence. The extent to which this would influence public health is difficult to determine. Previous research has shown an association between health behaviour and specific topics in health news (e.g. vaccines, statins) [ 10 , 11 ], and ‘spin’ in news has been experimentally shown to influence clinicians’ interpretation [ 33 ]. The effects of ubiquitously boosting the alignment between news and evidence remain to be tested. We would predict that better alignment could help achieve goals promoted by health academies: for example, reducing perceived conflict in health news and improving trust in evidence-based medicine (e.g. [ 12 ]). It could help readers make more informed health decisions and ultimately improve public health.

We limited our focus to only one facet of evidence strength, the distinction between experimental and correlational evidence, because of their fundamentally different relationship with causal inference [ 26 ]. Distinctions within these classes of design are just as important—such as between small-scale simple correlations and large epidemiological studies. Since our data showed similar patterns across study designs (Additional file 1 : Figure S6, S7 and S8), we infer that the salient dimension for journalists is the confidence or caution in the claims, rather than the study design itself. Thus, our conclusions should apply to using cautious claims and caveats wherever relevant, transferring to other facets of evidence strength. This remains to be confirmed.

One unexpected result was the higher news uptake we found for press releases with caveats (Fig. 4 b and Additional file 1 : Figure S8B). Future research could test whether these explicit caveats increased perceived credibility [ 34 ]. Parallel research has found that caveats lead readers to rate researchers as less confident, without lowering interest [ 21 ].

Our results imply that small changes in press release headline and claim wording, followed by explicit caveats or statements in the text, are a realistic means to improve coherence between the linguistic forcefulness of news claims and the evidence underlying those claims. Clinicians, scientists and press officers can take encouragement that deft caution and clear caveats are unlikely to harm news interest and can penetrate through to news and even to news headlines. If writers of abstracts, press releases and news were to systematically align cautious language (e.g. may cause ) to most correlational evidence (unless the weight of evidence is unusually large), and strong language (direct constructions or can cause) to most experimental evidence (unless the weight of evidence is low), this would not only supply information to those who know how to interpret the convention, it would also cement a relevant and meaningful distinction for non-experts reading health and science news. Critically, this convention is pragmatic, as shown by the rates of spontaneous adoption (Fig. 3 ), making use of the phrases already used by writers and understood by readers. Equally importantly, this information can be carried by the headlines and prominent claims themselves, which most widely circulate via social media.

Abbreviations

as-treated analysis

Confidence interval

Dependent variable

Generalised estimating equations

Intention-to-treat analysis

Independent variable

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Acknowledgements

We thank all the participating press officers whose collaboration made the trial possible. We thank Louise White for research assistance.

This work was supported by ESRC grant ES/M000664/1 (PS) and H2020 ERC Consolidator grant 647893-CCT (CDC). The funders had no role in design, data collection, analysis, interpretation or reporting.

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Data are available online at https://osf.io/apc6d/

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Contributions

RCA, AC and L Bratton coordinated and carried out the trial. CDC and PS led the project. JB, L Bott and AW collaborated on the design, management and analysis approach. RCA and PS analysed the results, with supporting data analysis by GP. All authors contributed to writing the paper. All authors read and approved the final manuscript.

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Correspondence to Christopher D. Chambers or Petroc Sumner .

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The protocol was approved by the Research Ethics Committee at the School of Psychology, Cardiff University. We obtained explicit opt-in consent from the press release authors (the press officers) and informed opt-out consent from the academics whose work each press release was based on.

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Figures and Tables S1 to S8: supplementary data, methodological detail and figures separating the data by study design. (PDF 219 kb)

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Adams, R.C., Challenger, A., Bratton, L. et al. Claims of causality in health news: a randomised trial. BMC Med 17 , 91 (2019). https://doi.org/10.1186/s12916-019-1324-7

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What is a claim?

A claim is a statement that presents an idea or series of ideas as arguments. Arguments therefore consist of claims, or another way to put it is, to say that claims are the building blocks of a good argument.

In research writing, claims will be the backbone that form a thesis or a hypothesis (here the term ‘hypothesis’ refers to the argument that is evidenced within the scope of the work).

According to Heady (2013) “Claims are the points you want to prove, interpretations you want to offer, and assertions you want to make” (p. 74). Importantly, in academia claims are statements that can be supported by evidence.

‘Traditional classroom teaching is boring’

For example, claiming that traditional classroom teaching is boring is not a good claim because it lacks definition (what does ‘traditional classroom teaching’ actually mean? and how do we measure ‘boring’)? It may also be a ‘sweeping statement’ (meaning it’s far too general in scope). However, claiming that “traditional teaching methods, like didactic instruction, do not provide sufficient interaction with students and lead to poor learning outcomes” is a good argumentative claim, because it can be investigated and measured.

Characteristics of a good claim

In order to make effective claims it is important to understand the difference between statements and sentences. While a statement is also a sentence (in that it is a grammatical unit with subject, verb, object clause), not all sentences are statements (in other words, not all sentences consist of a stance or a position).

The following provides examples of the difference between sentences and statements. The statements present a stance or position about the topic under discussion. This is important to understand as all claims must consist of a stance towards the topic.

sentences	statements
Bulldogs are a common breed of dog. They originated in the British isles.	Bulldogs are a dangerous breed and should be regulated.
Fat is one of three macronutrients. The others being carbohydrate and protein.	Fat has been misrepresented as a leading cause of heart disease. New research challenges this finding.

Function of claims

The function of claims in academic writing is to provoke, analyse, or interpret rather than merely describe or present facts. They can do this by affirming, acknowledging, confirming, or refuting the proposition being made. In this way, claims do the job of building an overall argument or thesis in a piece of work (i.e. each claim progresses the key argument). It is for this reason that claims will appear in topic sentences, thesis statements, introductory and concluding sentences/paragraphs.

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Nutrition and Health Claims: Consumer Use and Evolving Regulation

Elizabeth p. neale.

1 School of Medical, Indigenous and Health Sciences, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, NSW 2522 Australia

2 Illawarra Health and Medical Research Institute, Wollongong, NSW Australia

Linda C. Tapsell

Purpose of review.

The value of nutrition and health claims (N&HC) depends on how consumers use them and the regulatory framework that enables them. This paper aims to explore the impact of claims on consumer behaviour and identify evolving regulatory challenges, using the Australian experience as a reference point.

Recent Findings

N&HC can influence consumer food purchasing and consumption, but how consumers interpret and act on specific claims is less well understood, and regulatory frameworks are evolving. In the last 10 years, changes to the Australian regulatory framework have exposed greater opportunities for promoting foods, albeit with challenges regarding self-substantiation of claims.

N&HC can play a significant role in driving consumer choices towards a healthier food supply. The Australian experience demonstrates how N&HC can continue to evolve, reflecting developments in methodologies and a fundamental appreciation of the relationship between food and health.

Introduction

Understanding the impact of food consumption on health is not just the domain of scientists, it has significant implications for consumers, food producers and regulators alike. There is a common ground in communicating the benefits of foods to assist in food choices, but this is a complex area of varying cultures and priorities. From a nutritional health perspective, there are two main areas in which food consumption can provide benefit: delivering key nutrients to meet requirements, and supporting health (assisting normal growth and development, and protecting against chronic disease) [ 1 ]. The science that underpins these perspectives draws on research on nutrient action, nutrient requirements in health and disease and the relationships between dietary intakes and growth, development and disease [ 2 ]. These are complex areas that nevertheless require adequate translation and communication for all stakeholders in the health claims arena.

Where food packaging and advertising is concerned, nutrition and health claims (N&HC) cover a range of statements established by regulatory bodies within jurisdictions around the globe. These include the US Food and Drug Administration (FDA) [ 3 ], Health Canada [ 4 ], the European Food Safety Authority (EFSA) [ 5 ] and Food Standards Australia New Zealand (FSANZ) [ 6 ]. While there is some variation between the specific definitions and types of claims, as well as regulatory processes and requirements, a consistent characteristic is the presence of nutrition content claims, and one or more level of health claims. Reflecting the key benefits of food consumption, these statements are primarily gauged in terms of nutrient action and diet-disease relationships and are underpinned by scientific evidence derived from current nutrition knowledge. Thus, while precise definitions may differ between regulatory bodies, the purposes of assuring delivery of key nutrients, and addressing the relationship between food consumption and health/disease remain common. Claims typically relate to ‘nutrient content’, referring to the amount of a nutrient contained in a food product; and ‘health’ referring to either the effects of a nutrient on functions of the body or the dietary association with risk of disease [ 3 – 6 ]. It follows that Health Claims are typically further sub-categorised into general level claims on nutrient functions and processes (e.g. calcium is necessary for normal teeth and bone structure [ 7 ]) or high level claims based on reduction in disease risk (e.g. a diet high in calcium with adequate vitamin D status reduces risk of osteoporosis in persons 65 years and older [ 7 ]).

In Australia, the Food Standards Code outlines the nutrition content and health claims which can be made on food labels and in advertising, and the conditions by which they can be made [ 6 ]. While FSANZ is responsible for the Food Standards Code, health authorities at the state and territory levels are responsible for enforcement or monitoring of compliance with the Code, requiring co-ordination across state jurisdictions. Furthermore, aspects of the framework differ between the different types of claims. For nutrient content claims to be made, the foods must adhere to pre-specified conditions, e.g. meeting a defined level of the referent nutrient. For instance, to make claims on vitamin or mineral content, a serving of food must contain at least 10% of the recommended dietary intake [ 6 ]. Health claims address the more complex issue of the relationship between diet and health (in the case of general level health claims), and diet and disease (in the case of high level health claims), which goes beyond the simple delivery of nutrients. This relationship has been studied on a number of levels. One of the major challenges in this area is the recognised interdependence between nutrients, foods and dietary patterns [ 1 ]. Nutrients are delivered by foods, and the combination of foods (dietary patterns) ultimately influences health outcomes [ 1 ]. The evidence to date is that, chronic disease is linked to dietary patterns that exceed energy requirements, and contain high levels of saturated fats, sodium and added sugars. On the other hand, dietary patterns rich in vegetables, fruit, nuts and with adequate levels of fibre and protein appear protective [ 8 , 9 ]. A practical translation of this knowledge is the Nutrient Profiling Scoring Criterion (NPSC), used to evaluate foods based on a range of components judged as ‘negative for health’ (energy, saturated fat, total sugars and sodium) and ‘positive for health’ (fruit, vegetables, nuts, legumes, fibre and protein). Thus, general or high level health claims can only be applied for foods with a score below a set cut-off [ 6 ]. While there are obvious limitations in applying dietary pattern–based evidence to single foods, it is a reasonable assumption that if ‘negative’ nutrients are discouraged, the total diet will benefit.

In continuing to refine the science and its translation, nutrition scientists and food regulators must also stay aware of developments in the food supply and importantly consider the impact on consumers. The end focus for all stakeholders is food purchase, but with varying interests, be it health profiles, product sales or food consumption. Whether and how health claims influence consumer decisions is highly relevant to regulators as they work to protect public health and safety. To do this, however, there must also be uptake from food manufacturers, and an agreed process for managing claims. This paper examines the issue of the translation of science within N&HC by reviewing the impact of health claims on consumer behaviour and identifying issues that may arise from claims regulation in Australia.

How Do Consumers Interact with Nutrition and Health Claims on Foods?

Increasing numbers of studies have explored the influence of nutrition and health claims on consumer perceptions of products and the likelihood of purchasing or consuming products. For example, in one meta-analysis [ 10 ], products carrying a claim were significantly more likely to be purchased or consumed than an identical product without a claim (odds ratio: 1.75, 95% confidence intervals: 1.60–1.91). Interestingly, both nutrition content (odds ratio: 1.74, 95% confidence intervals: 1.29–2.35) and health claims (odds ratio: 1.73, 95% confidence intervals: 1.57–1.91) seemed to have similar effects on the likelihood of product purchasing or consumption. This suggests that, overall, N&HC may influence the way consumers discriminate between products, but the nutrition concepts underpinning different types of claims may not be appreciated at point of purchase. On the other hand, holistic health reasons rather than simple nutrient delivery may influence intention to purchase. In an analysis of some 24 studies [ 11 ], consumers expressed positive views towards products that contained all types of claims, but lent more toward high level health claims than general level health claims or nutrition content claims. Further research may be informative of how these distinctions play out, but either way, it appears that N&HC represents an opportunity to influence public health and the quality of food supply.

More specifically, claims relating to energy and fat content may be of particular public health interest, given the context of global obesity [ 12 ]. This is an example where total diet is paramount, but individual foods can differ widely in their contributions to excessive intakes. In addition, while the concept of total energy intake could be seen as relatively straightforward, the science behind dietary fat is quite complicated and presents with real challenges for translation [ 13 ]. One review [ 14 ] found varying degrees of influence on food choice from energy and fat claims. While there was some evidence that these claims resulted in decreased consumption of energy-dense, nutrient-poor foods, or increased consumption of nutrient-dense foods, other studies in the review found either no or undesirable impacts on consumption. Similarly, Oostenbach et al. [ 15 ••] examined the evidence on the effect of nutrition content claims (specifically those related to fat, sugar and energy) on food choices and energy intake. Overall, products containing nutrient content claims were considered to be healthier and have a lower energy content than products without claims. Nutrient content claims could influence purchase intentions and increase consumption, although these effects did vary based on product and claim type.

At this stage, it appears that N&HC can influence consumer purchasing and consumption of food but more research is required on how specific claims may be interpreted and actioned by consumers. Addressing the concepts behind the claims and their relevance to dietary patterns may be a good start.

How Do Regulators and Manufacturers Work with N&HC?

The regulatory system is a major interface for addressing the impact of food consumption on health. From a public health perspective, it provides a vehicle for communicating the benefits of foods to assist in food choices, but this action is multifaceted and involves many key stakeholders in the food system. The ongoing evolution of N&HC in Australia attests to the processes and challenges which need to be addressed. In the first instance, while the lack of mandatory regulations on N&HC was recognised as a potential risk to public health and safety [ 16 ], a substantial amount of work was required over many years to develop a standard. Following a discussion and concept paper developed by the Australia New Zealand Food Authority (ANZFA, the predecessor of FSANZ) in 1993 and 1996, respectively [ 17 ], and a 2004 proposal (Proposal P293) [ 18 ], the Food Standards Code was amended. The updated Standard 1.2.7 (Nutrition, Health, and Related Claims) was gazetted in 2013, becoming mandatory in 2016 [ 6 ], some 20 years after the original concept paper.

Ongoing research continues to inform the development of the system. For example, a number of studies have explored the use of N&HC by manufacturers for selected food categories. In the Illawarra region, south of Sydney, Sussman et al. [ 19 ] conducted a cross-sectional audit of N&HCs on breakfast cereal products in supermarkets and found that of the 329 products audited, 315 (95.7%) carried at least one claim, and the majority of claims were nutrition content claims. Later, Wadhwa et al. [ 20 ] conducted a similar audit of dairy yoghurts, with 97.9% of products carrying at least one claim, predominantly nutrition content claims. Despite some differences in methodology, this was an increase in the use of claims found in a national study conducted by Pulker et al. prior to Standard 1.2.7 [ 21 ]. In this earlier study, Pulker et al. gathered data from both websites and physical stores and found only 59% of products (ultra-processed foods including breakfast cereals, confectionary, and snacks) carried nutrition or health claims. A Sydney-based supermarkets study by Wellard-Cole et al. [ 22 ], auditing claims on non-alcoholic beverages, cereal bars and breakfast cereals in 2011 and again in 2016, confirmed that change was happening (increasing from 67% of audited products in 2011, to 76% in 2016, p < 0.001). Of the 1737 products audited in 2016, 76% carried at least one claim, the majority (82%) being content claims [ 22 ]. Thus, there appeared to be a high prevalence of claims on foods in Australian supermarkets, with increasing use over time, suggesting a high acceptance of the system by manufacturers. Nevertheless, concerns have continued to be expressed on the potential to mislead consumers [ 22 ], who may attribute broader health benefits to products on the basis of their content claims [ 23 ], especially with the high use of content claims reported in the literature [ 19 – 22 ].

Potential limitations have also been highlighted in the avenues by which claims are approved. Within Standard 1.2.7, there are a number of pre-approved general and high level health claims which can be made, dependent on a food meeting the NPSC and specific conditions such as minimum nutrient levels [ 6 ]. In the case of general level health claims however, food businesses may also self-substantiate a food-health relationship via a process involving a systematic review [ 6 ]. Following completion of the systematic review, the business notifies FSANZ of their substantiated food-health relationship [ 6 , 24 ], and if they meet the requirements of Standard 1.2.7, may then make the claim. The regulatory frameworks of a range of countries include processes allowing for consideration of food-health relationships which are not currently pre-approved [ 3 – 5 ]. It should be noted, however, that under the current Australian and New Zealand system, FSANZ performs the function of the standard setting body [ 24 ]. Compliance with the Food Standards Code is the responsibility of the State and Territory authorities and the New Zealand government, so in a case where a complaint is made, these authorities evaluate the systematic review supporting a self-substantiated general level health claim. This process also places the onus for accurately substantiating the review on the food business, a potential limitation of the current process [ 25 ••, 26 ]. However, in New Zealand, claims which have been notified to FSANZ are formally reviewed by the New Zealand Ministry for Primary Industries. This involves a process of evaluating the systematic review underpinning the food-health relationship [ 27 ]. If the New Zealand Ministry for Primary Industries finds that the food-health relationship cannot be substantiated, food businesses must request FSANZ remove their notified relationship. This process ensures a level of regulatory oversight, which has been criticized as lacking in the Australian process [ 25 ••].

Recently, Wellard-Cole et al. [ 25 ••] explored the robustness of the self-substantiation process by monitoring food-health relationships notified to FSANZ between 2013 and 2017. Of the 63 food-health relationships notified by Australian companies during this time, only 52% were considered by the study authors to have adequate published evidence to substantiate the relationship. A total of 27 food-health relationships were determined to have equivocal or insufficient evidence and were subsequently referred to authorities, resulting in several relationships being removed from the FSANZ website. These findings expose significant problems with the current framework for self-substantiation of general level health claims. While the situation may vary across global jurisdictions, pre-approval of food-health relationships may be required before claims can be made recommended.

Future Directions

It appears both nutrition content and health claims may generally influence consumer’s purchasing, consumption and overall perceptions of a product, but it is unclear how these work more specifically in terms of targeted changes to dietary patterns. Likewise, the Australian experience suggests N&HC are being taken up by manufacturers, but there are issues with the management of evidence review. From a nutrition perspective, both these problems are related to the translation of science, how it is applied, communicated, understood and acted upon [ 26 ]. The ongoing development of regulatory frameworks need to address these issues to assure a robust system that supports consumers to make informed and healthy choices.

After almost 30 years, the Australian FSANZ Act is currently under major review [ 28 ]. The options under consideration include maintaining the status quo; modernising the Act to make it agile, resilient and fit for purpose; and extending the role of FSANZ. Modernising includes a component of risk-based approaches to developing and amending food regulatory measures. There is recognition of evolving new technologies, global supply chains, food innovation and shifts in dietary patterns and consumer expectations. Importantly the value of leveraging food regulation to influence dietary patterns toward better health remains. Addressing the issues raised in this review will need to occur in this revised context.

New ways of thinking around consumer involvement have also emerged in the literature. Rather than simply test consumer opinions, there is strong argument for engaging consumers more directly through their ‘lived experiences’ [ 29 ]. There may be a need to rethink the way N&HC are constructed, not just in wording (which strongly privileges a scientific discourse), but also in terms of the contexts in which foods are purchased and consumed. In addition genuine efforts to improve the nutrition science literacy of the population may assist in establishing greater connections with the type of information that is currently available on labels. The findings that consumers appear to value N&HC is promising. Even so, current N&HC and associated tools, such as the Nutrient Profiling Scoring Criterion, need to keep up to date with emerging evidence of the diet-disease relationship. Ways need to be found of evaluating individual foods in terms of contributions to dietary patterns, to better reflect the interdependence of foods and total diets and communicate that to consumers.

The science of evidence evaluation in nutrition which underpins N&HC is also constantly evolving [ 26 , 30 •]. Associated new methodologies and technologies need to be taken up in a fit for purpose food regulation system. Up to date food composition data and dietary intake surveys are fundamental components of the risk assessment associated with food regulation. Even food categorisation is under review, with a closer eye now aimed toward degree of processing [ 31 , 32 ]. Models for assessment of N&HC could be developed that focus on health priorities and are more pro-active rather than reactive in relation to food innovation and associated claims. Efficiencies in evidence review could be achieved by integrating efforts with other areas associated with food policy, such as national dietary guidelines. Finally building nutrition science and communications capacity in food regulation seems essential to manage and assist all levels of operations, including businesses keen to use the N&HC opportunities. This may also help to ensure compliance and reduce the burden on enforcement agencies which are required to investigate complaints.

There are risks associated with change that should also be considered. For example, presently in Australia, high level health claims may not be self-substantiated; only pre-approved claims are allowed. However, food companies may apply to change the Food Standards Code, through a formal process evaluated by FSANZ, which includes a systematic review of the proposed food-health relationship. As part of the FSANZ Act review, it is has been proposed that this pathway be abolished due to limited uptake, but opportunities may be lost, for example, if dietary guidelines go another way [ 28 ]. These issues may also be relevant to other jurisdictions across the globe.

Conclusions

Nutrition and health claims can play a significant role in driving consumer choices towards a healthier food supply. As part of the food regulation system, however, they fit within a complex interplay between multiple groups of stakeholders. Nutrition science underpins the public health agenda and informs the development of N&HCs, but a broader sensitivity to consumer understandings of nutrition and their lived experiences with food may be required. As an example, the Australian experience with N&HC continues to evolve, reflecting developments in methodologies and a fundamental appreciation of the relationship between food and health, positions which are universal to all jurisdictions.

Open Access funding enabled and organized by CAUL and its Member Institutions.

Compliance with Ethical Standards

Elizabeth P Neale has previously prepared dossiers to examine food-health relationships to inform the substantiation of general level health claims for Horticulture Australia Limited and Nuts for Life and Australian Health and Nutrition Association Limited, Linda C Tapsell is a member of the FSANZ Consumer and Public Health Dialogue and has previously prepared dossiers to examine food-health relationships to inform the substantiation of general level health claims for Horticulture Australia Limited and Nuts for Life.

This article does not contain any studies with human or animal subjects performed by any of the authors.

This article is part of the Topical Collection on Public Health Nutrition

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Epidemiological Principles in Claims of Causality: An Enquiry into Repetitive Head Impacts (RHI) and Chronic Traumatic Encephalopathy (CTE)

Current Opinion
Open access
Published: 15 September 2024

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Lauren V. Fortington ORCID: orcid.org/0000-0003-2760-9249 1 ,
J. David Cassidy 2 ,
Rudolph J. Castellani 3 ,
Andrew J. Gardner 4 ,
Andrew S. McIntosh 5 ,
Michael Austen 6 , 7 , 8 ,
Zachary Yukio Kerr 9 &
Kenneth L. Quarrie 10 , 11 , 12

Determining whether repetitive head impacts (RHI) cause the development of chronic traumatic encephalopathy (CTE)-neuropathological change (NC) and whether pathological changes cause clinical syndromes are topics of considerable interest to the global sports medicine community. In 2022, an article was published that used the Bradford Hill criteria to evaluate the claim that RHI cause CTE. The publication garnered international media attention and has since been promoted as definitive proof that causality has been established. Our counterpoint presents an appraisal of the published article in terms of the claims made and the scientific literature used in developing those claims. We conclude that the evidence provided does not justify the causal claims. We discuss how causes are conceptualised in modern epidemiology and highlight shortcomings in the current definitions and measurement of exposures (RHI) and outcomes (CTE). We address the Bradford Hill arguments that are used as evidence in the original review and conclude that assertions of causality having been established are premature. Members of the scientific community must be cautious of making causal claims until the proposed exposures and outcomes are well defined and consistently measured, and findings from appropriately designed studies have been published. Evaluating and reflecting on the quality of research is a crucial step in providing accurate evidence-based information to the public.

Graphical abstract

Explore related subjects

Medical Ethics

Avoid common mistakes on your manuscript.

Exploring the causal links between repetitive head impacts (RHI) and chronic traumatic encephalopathy (CTE) is a topic of considerable interest globally. In July 2022, a paper claimed definitive evidence that RHI cause CTE.

It is part of the scientific process to examine all claims made by researchers and issue advocates and our counterpoint was designed to do this. We conclude that the evidence presented in the paper does not justify causal claims.

Members of the scientific community must be cautious of making or accepting causal claims until the proposed causes and health outcomes are well-defined, properly designed epidemiological studies have been undertaken, and the relationships between exposure to the proposed causes and the outcome(s) in which they are hypothesised to result have been accurately quantified.

1 Introduction

In July 2022, a paper published in Frontiers in Neurology [ 1 ] claimed ‘conclusive evidence’ [ 2 ] that repetitive head impacts (RHI) were a ‘definitive cause’ [ 3 ] of chronic traumatic encephalopathy (CTE). Exploring the causal links between RHI and CTE is a topic of considerable interest globally, and unsurprisingly there was substantial media attention given to the publication. In their paper, the authors had applied the nine Bradford Hill criteria to the evidence they curated from the scientific literature to support their claim. They declared that certain microscopic phenomena [referred to hereafter as chronic traumatic encephalopathy neuropathologic change (CTE-NC)] found within autopsied brains could be directly attributed to exposure to RHI sustained earlier in life. In the conclusions section of their article, having made the caveat that all evidence is forever imperfect, they stated ‘After reviewing the medical literature on RHI and CTE through the Bradford Hill criteria, we have the highest confidence in the conclusion that RHI causes CTE’ [ 1 , p 14].

It is part of the scientific process to critically examine all claims made by researchers and advocates; evaluating whether assertions are justified by the facts forms the basis of the peer-review system. It is with this spirit of critical enquiry that we have evaluated the causal claims made in the narrative review [ 1 ]. We aim to provide a counterpoint, through additional scrutiny of the supporting literature that was presented, and to consider how the authors’ claims hold against modern causal thinking in epidemiology.

This counterpoint is important because the assertion made in the original manuscript [ 1 ] has been accepted as fact in subsequent publications [ 4 , 5 ] and in several influential settings, including the National Institute of Health in the USA [ 6 ] and an Australian Senate (national parliamentary) inquiry [ 7 ]. It is crucial that researchers strive to present objective, evidence-based information not only for the individuals who have experienced RHI, and/or fear the potential consequences of exposure to them [ 8 ], but also for those who rely on such evidence to inform policies and regulations, including practitioners in clinical medicine and/or public health, sports administrators, insurance actuaries, educators, lawmakers and the judiciary. To help readers navigate the concepts of causality presented in this manuscript, a visual overview is presented in the Graphical Abstract, with section numbers corresponding to the relevant text.

2 Understanding Causation

If, say, more than one factor is responsible for some effect, it is important that we do not pre-empt the scientific judgement: there is always the danger that we might refuse to admit any other ideas than the ones we happen to have at hand… Sir Karl Popper [ 9 ].

The term ‘cause’ (along with ‘causal’, ‘causation’ and ‘causality’) has different interpretations in different professions. As an example, when considering a ‘cause of death’, the acceptance of evidence as causal differs in a court of law [ 10 ] to that required for completion of a death certificate [ 11 ]. In a topical scientific issue requiring consideration and investigation by biomechanists, psychologists, neuropathologists, sports medicine physicians, epidemiologists, sociologists and others, establishing and reaching consensus upon terms is a fundamental requirement.

Within epidemiology, the causal agent of injury is generally accepted to be ‘energy transfer’ [ 12 ]. For certain sports injuries, the cause and outcome can be obvious when there is an acute onset with instantaneous tissue failure, such as with a fractured lower leg, which results in immediate pain, functional impairment and visible bone damage. However, other injuries result not from a single identifiable event but from exposure to repetitive loads, and are considered to be ‘gradual onset’ in nature [ 13 ], (e.g. conditions such as ‘jumper’s knee’ or a bone stress injury).

Multiple contributory factors can play a role in the aetiology of both acute and gradual onset injuries and contribute to undesirable long-term outcomes. These factors include biomechanical load, age, genetics and environmental conditions, among others. A major goal of analytical epidemiology is to understand which factors are, and are not, on the causal pathway for a given health outcome. [ 14 ].

The complexity of the causal proposition at hand, that RHI causes a neurodegenerative disease, should not be underestimated. Notably, in the narrative review, the authors refer to CTE as a ‘neurodegenerative disease’, and thus the issue of an environmental cause (RHI) for a neurodegenerative disease is raised. Considerable uncertainty among experts remains about possible environmental causes of canonical neurodegenerative diseases (e.g. Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, amyotrophic lateral sclerosis) and in fact the causes of most neurodegenerative diseases are yet to be established despite significant research efforts [ 15 , 16 ]. Dementia, which is an umbrella term describing the progressive cognitive impairments that accompany many neurodegenerative diseases, has been linked with a wide range of possible causes [ 17 ], and 12 risk factors identified through systematic reviews and meta-analyses have been suggested to account for approximately 40% of dementia cases world-wide [ 17 ]. The factors are ‘less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, infrequent social contact, excessive alcohol consumption, head injury, and air pollution’ [ 17 ]. Risk factors are not necessarily ‘causes’—for example, hearing loss may be a cause, an early symptom or both of dementia [ 18 , 19 ]. A small subset of neurodegenerative diseases are strongly hereditary, and known to be driven by genetic mutations (e.g. Huntington’s disease).

Does exposure to A, either in isolation or in concert with other agents, cause B?

Although this simple question captures the essence of what we want to know regarding causal relationships in epidemiology, philosophers of science from Hume [ 20 ] onwards have pointed out that drawing inferences from the specific to the general rests on inductive reasoning, and can thus only ever provide probabilistic evidence (in contrast to the logical certainty inherent in deductive reasoning based on Aristotelean syllogisms). Even so, causal pragmatists [ 21 ] following the ideas of Mill [ 22 ] hold that evidence of causation can be sufficiently well established to allow a basis for action via careful application of scientific methods to knowledge acquisition founded on systematic observation and experimentation.

In 1965, Bradford Hill set forth nine ‘viewpoints’ for evaluating whether evidence from associations via observational studies could be construed to be causal. These viewpoints were an expansion of causal criteria from a landmark report published by the U.S. Surgeon General on smoking and health. That report documented the results of 29 case–control and cohort studies from the UK and the USA that showed a very strong relationship between smoking and lung cancer (i.e. risk estimates over ten) [ 23 ]. Since that time there have been considerable further developments in epidemiology with respect to appraising whether causal claims are well supported by the evidence [ 24 ].

Modern epidemiology is based on testing competing theories (e.g. hypothesis testing) by conjecture (i.e. stating a hypothesis) and refutation (i.e. testing the hypothesis). Case reports, case series and cross-sectional study designs have an important role in generating hypotheses to be tested in more rigorous designs. For testing causal hypotheses, injury epidemiology relies mostly on observational designs such as case–control and cohort studies. [In some cases, causation can be inferred by testing injury prevention strategies in a randomised controlled trial (RCT). If mitigation of a risk factor in a RCT results in control or prevention of an outcome, it follows that the risk factor plays some role in the causal chain of events.]

The publication of the biopsychosocial model of the determinants of disease by Engel [ 25 ], and its subsequent evolution alongside contemporaneous work in the ‘new public health’ [ 26 ] and modern epidemiology by Rothman [ 27 ] (amongst others), highlighted that in real-world settings, multi-causality in the development of health and disease outcomes is the norm, rather than the exception. Recognition that complex interactions among multiple factors that may vary over time [ 28 ] was a feature of the aetiology of many diseases and health conditions (e.g. cancers, cardiovascular diseases and Alzheimer’s disease), resulted in new thinking about how to consider causation and drove developments in analytical methods in epidemiology that were able to deal with multiple time-dependent contributing and confounding factors [ 29 , 30 ]. With this evolution, causal questions have extended beyond ‘does exposure to agent A cause outcome B?’ to ‘what conditions hold under which the causal relationship exists in the specified population/setting in the first place?’ The answer to multi-causal questions may help guide the best point of intervention for effective preventative efforts.

In practice, acceptance of a causal relationship (as opposed to the existence of a causal relationship) is a social phenomenon, resting upon the accrual, systematic synthesis and evaluation of factual evidence (and the absence of counter-evidence) from appropriately designed and conducted studies sufficient to satisfy subject-matter experts [ 31 ] in the scientific community [ 32 ] and society at large that the relationship under consideration is causal [ 33 ].

There are no criteria available against which epidemiological evidence can be set that allows researchers to state unequivocally that exposure to agent A causes outcome B. Bradford Hill explicitly recognised this fact:

What I do not believe—and this has been suggested—is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect. None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non. What they can do, with greater or less strength, is to help us to make up our minds on the fundamental question—is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect? [ 34 ]

Several theories and models for evaluating causal relationships in epidemiology exist, with most textbooks providing detailed descriptions of the required concepts [ 35 chapter 5, and other sections, 36 chapter 2, among other sections, 37 chapter 3, among other sections], summarised as:

a clearly defined causal agent with a valid method of identifying/quantifying exposure to the agent;

that precedes

a clearly defined health outcome with a valid method of identifying/quantifying cases of the outcome.

The causal agent and health outcome can then be considered through appropriate research designs that are capable of testing causal hypotheses in an unbiased manner.

Absence of these conditions means that the relationship between the potential causal agent and the outcome cannot be accurately determined and renders further consideration of a given causal claim otiose. As noted by Nieuwenhuijsen [ 38 , p 5] in their text on exposure assessment in environmental epidemiology, ‘quantification of the relation between exposure and adverse human health effects requires the use of exposure estimates that are accurate, precise, and biologically relevant for the critical exposure period, and show a range of exposure levels in the population under study…’ It is for these reasons that epidemiologists take great care to create accurate definitions of the proposed agents and outcomes and consider how they will be measured as well as what hypothesis-testing research designs are best suited to study causal relationships.

3 Gaps in the Presented Evidence

3.1 repetitive head impacts are not a clearly defined or reliably measured causal agent.

In the review [ 1 ], the agent postulated to be the cause of CTE-NC is ‘repetitive head impacts’, defined as ‘the cumulative exposure to recurrent concussive and subconcussive events’ [ 1 ]. Please refer to Additional file 1: Introduction to head impact forces for background to this section.

3.1.1 Differentiating Concussive and Subconcussive Events

It is unclear exactly what the authors of the review [ 1 ] mean by ‘subconcussive’ or ‘concussive’ events, and whether the ‘event’ should be considered in relation to injury or independently. If ‘subconcussive events’ are referring to impacts that do not result in signs or symptoms of concussion, then presumably ‘concussive events’ refers to those impacts that do result in signs and symptoms of concussion.

One of the problems with the definition of RHI provided in the review [ 1 ] is that impact events are described in terms of the outcomes from them, rather than in terms of the biomechanical characteristics of the impacts themselves (i.e. considering the causal agent to be energy transfer). This means that possible interpretations of a ‘concussive event’ for the purposes of researchers trying to quantify exposure to them could include any of the following that an individual sustained:

an impact event that resulted in having and/or reporting symptoms and/or displaying signs of a concussion injury; or

an impact event that resulted in a clinical diagnosis of a concussion by a medical professional qualified to provide such a diagnosis; or

an impact event that resulted in signs or symptoms of brain injury, regardless of the type or severity of brain injury sustained, and regardless of whether medical attention was received or a clinical diagnosis given (noting that not all brain injuries resulting from impact are ‘concussions’—for example, diffuse cerebral swelling, subdural haematomas and other injuries that can result in long-term or permanent disablement or death).

The application of each of the interpretations above would yield different measurements of cumulative exposure to RHI (assuming ‘subconcussive events’ were able to be operationally defined and information about them consistently obtained) and in turn, different relationships with any given health outcome would be apparent, including, in the current case, CTE-NC.

The exposure of RHI is referred to inconsistently in the review article with respect to whether ‘subconcussions’ are included or not. The authors state ‘…these questions also remain for RHI and subconcussive impacts…’ [ 1 ]. The terminology is further confused in the discussion of criteria for traumatic encephalopathy syndrome (TES) where it is mentioned that ‘all criteria for TES proposed to date, across multiple research groups, require a history of exposure to head injuries, either characterized as RHI, TBI, concussion, or subconcussive injuries’ [ 1 ].

In a systematic review of 56 studies looking at subconcussive head impacts in sport, Mainwaring et al. identified that there was no defined minimum threshold for exposure to either ‘subconcussive’ or ‘concussive’ events, concluding that subconcussion was ‘inconsistently used, poorly defined, and misleading’ [ 39 ]. They further stated that the terms ‘… “subconcussion” and “subconcussive injuries” are vague and have not been operationalized’ [ 39 ].

Nowinski et al. (2024) have reflected on the limitations of the term ‘subconcussive’, calling it a ‘dangerous misnomer’ and noting that a ‘subconcussive’ event does not necessarily involve less force than an event that results in concussion [ 40 ]. Their editorial recommends replacement of ‘subconcussive’ with the term ‘non-concussive’ to describe ‘an impact that may be of greater or less force than a concussive impact but is not associated with a diagnosed concussion’ [ 40 ]. It remains unclear how the term ‘non-concussive’ would be operationalised and whether a minimum threshold would be applied for a head acceleration event to be deemed a ‘non-concussive impact’. When used in conjunction with ‘concussive impacts’, the term ‘non-concussive’ is still defined by an outcome resulting from the application of forces, rather than in terms of the nature of the forces applied to the head. We believe the term ‘non-concussive’ will suffer from the same drawbacks as ‘subconcussive’ until biomechanical thresholds for such events are developed. Thresholds would need to incorporate the body orientation and posture of an individual at the time the impact occurs in conjunction with the direction and magnitude of force applied.

3.1.2 Limitations in Measuring Exposures

Table 1 of the review [ 1 ] presents six studies that are heavily relied on to support the argument for a causal relationship between RHI and CTE. The definitions of RHI vary across the six studies and are not comparable to each other. Under the definition of RHIs provided by the authors, exposure to RHIs cannot be quantified, and measurements of the relationship between RHI and CTE-NC—whether causal, correlative or spurious cannot be accurately ascertained, that is, there is no clearly defined causal agent.

The use of proxy measures to estimate exposure to a postulated cause is commonplace in epidemiology because it can often be difficult or impossible to obtain actual measures of exposure. Therefore, to develop hypotheses of what relationships ‘might’ hold between exposure to an agent or agents and outcomes, researchers will often use the best estimate of exposure that is available to them.

With respect to RHI in collision sports, proxy estimates have included information from interviews or surveys of participants (or next of kin of decedents) regarding recollections of exposure to brain injuries and time involved in sport participation. Interview and survey data often rely on recollection of events that may have occurred many years previously and are thus subject to information biases including, among others, recall bias, availability bias and unacceptability bias [ 41 , 42 , 43 , 44 ] (in addition to the direct measurement issues identified earlier).

The impact of information biases is illustrated in the work of Mez et al., who in their Table 1 A present data on the number of concussions reported by participants acting on behalf of a decedent examined for CTE-NC [ 45 ]. The difference in median concussion count when informants were provided a definition of concussion was remarkable: from a median count of 5 (interquartile range: 1–10) without a definition to 47.5 (IQR: 12–150) when a definition is provided [ 45 ]. Further challenging accurate measurement, it is known that participants may choose not to disclose sensitive or personal information, especially if they fear that such information could damage their reputation or have other negative impacts on them if it came to public notice [ 46 ]. Researchers have also used participation in contact/collision sports (yes/no), the duration of participation in contact/collision sports (i.e. years played), counts of matches or trainings in which the player was involved during their career and/or the level of play at which the athletes participated as providing proxy measures of exposure to RHI. This information can provide useful insights to understanding potential associations between RHI and health outcomes, but the accuracy of the observed relationship still depends on the degree to which the proxy provides a valid estimate of RHI. The use of participation in collision sports as a proxy for RHIs in studies that have combined a number of sports without controlling for the type of sport played is problematic because the actual exposure to RHI (i.e. the frequency and nature of head impacts) varies widely across activities. Mez et al. acknowledge that ‘years played serves as an imperfect proxy for RHI exposure from American football… an athlete who played for 1 year as a starter on offense and defense may have had more exposure than an athlete who played only sparingly for 1 year’ [ 45 , p 129].

Another method has been to directly measure head accelerations sustained by a sample over a period, and then apply the mean number of head impacts per period from the sample group to the periods of exposure of other groups. This method can yield useful information but it also has well-recognised limitations, such as the fact that the nature and frequency of impacts sustained by participants varies by level of play [ 47 ].

The potential influence of confounding variables on observed associations also needs to be considered. Confounding bias is a common problem in epidemiological research and confounding variables need to be accurately measured and accounted for in analyses. Put simply, a confounding variable is an unmeasured, or unaccounted for, factor that is related and has influence on, or from, both the exposure and outcome [ 48 ]. Because the unit of epidemiological research is groups of people, rather than individuals [ 49 ], measures applied to groups of athletes such as match involvement and years played in collision sports capture exposure not just to head impacts but many other factors as well. In other words, sports participants, and especially elite/professional athletes, are differentially exposed to a range of factors in comparison to their non-participating counterparts. If those other factors also contribute to later life health outcomes, and they are not explicitly dealt with in the design and analysis of studies, there is a real risk of confounding impacting any observed relationship. Examples of confounders are presented by Iverson et al. in their review of health risks associated with sport-related concussion [ 50 ]. The six studies cited in Table 1 of the review [ 1 ] either ignore confounding factors or control for only a few common features, such as age and sex.

Information about the validity of proxy measures for estimating exposure to RHIs (however defined) is yet to be published. The limitations in measurement of RHI are important in understanding why many of the arguments presented in the narrative review [ 1 ] misrepresent the strength of evidence that currently exists for a potential causal relationship between RHI and CTE-NC.

3.2 Chronic Traumatic Encephalopathy Neuropathologic Change is not a Clearly Defined and Measurable Health Outcome

3.2.1 defining and measuring cte-nc.

Attempts have been made to define the ‘pathognomonic’ lesion of CTE-NC, along with quantification of the extent and distribution of the pathology, so that neuropathologists are reliably able to identify CTE-NC and distinguish it from other pathologies. The concept of CTE-NC is evolving; several descriptions of the defining characteristics of the pathology have been published, with significant differences amongst them [ 53 ]. At consensus meetings under the auspices of the National Institute of Neurological Disorders and Stroke (NINDS) and National Institute of Biomedical Imaging and Bioengineering (NIBIB), a required feature of the proposed 2016 article definition [phosphorylated tau (p-tau) aggregates within astrocytes] [ 52 ] was dropped in the updated definition of 2021, while an additional nuance—the depth of p-tau aggregates relative to the pial surface—was added [ 51 ]. Essentially, what was counted as a case in a study published a decade ago would not necessarily be counted as a case were the study to be done today. Such changes to the definition of CTE-NC can result in substantial differences to estimates of prevalence and observed associations with putative causal factors over time. For example, 98% (117 of 119) of professional football players were reported to have CTE-NC in a study from the Boston University CTE Center’s brain bank in 2017 [ 54 ], whereas a 2023 study from the same brain bank that included an additional 165 professional players, and which reported that the 2021 consensus definition had been used, found CTE-NC in 251 of 284 cases (88%). It is unclear from the paper whether the retroactive application of the 2021 criteria resulted in cases being reclassified from ‘CTE-NC’ to ‘no CTE-NC’ (assuming that the criteria were applied across the entire case-series). If the new criteria were not applied retroactively then there would be different thresholds for cases depending on the time at which the case was evaluated. The application of the new criteria appears to have coincided with a decrease in the percentage of professional football players diagnosed with CTE-NC.

The NINDS/NIBIB consensus group in 2021 ‘endorsed a single pathognomonic lesion in the cortex as the minimum threshold for CTE’ and further suggested additional bilateral sampling under certain circumstances including ‘clinical concern’ [ 51 ]. The low threshold, together with extensive tissue interrogation, results in maximum sensitivity towards a (statistically) positive case outcome. Maximising sensitivity comes at a cost to specificity: in this case, the plausibility that such a minimal neuropathological outcome has any clinical relevance. In other words, even if a causal relationship between RHI and CTE-NC using such definitions were demonstrated, any biological or clinical significance would remain an open question. This vital issue is not addressed in the article of focus [ 1 ].

Once defined, a health outcome in epidemiology (in this case CTE-NC) also requires that a valid and reliable measure of the outcome can be used by researchers. Throughout the review [ 1 ] is the assertion that the cited evidence examines neuropathologically ‘confirmed’ cases, largely from the US Department of Veterans Affairs—Boston University—Concussion Legacy Foundation (VA-BU-CLF) brain bank. This is problematic because pathologists can and do disagree on the presence and extent of CTE neuropathology [ 52 ]. Distinguishing CTE neuropathology from concomitant neurodegenerative and ageing-related pathologies is currently a significant challenge for diagnosticians. More information is provided in Additional File 2—Threshold and measurement of CTE-NC.

3.2.2 Clinical syndromes linked with CTE-NC

Traumatic encephalopathy syndrome (TES) is a provisional research construct that was initially proposed in 2014 as an attempt to identify clinical correlates with CTE-NC at autopsy [ 55 ]. The original criteria were heavily focussed on mental health problems, and concerns were raised about whether they could be used to reliably distinguish between individuals with CTE-NC and those with other conditions [ 56 ]. For example, Mez et al. [ 57 ] reported on 309 donors to the VA-BU-CLF brain bank, of which 244 had CTE. With the pathological diagnosis of CTE-NC as the gold standard, the clinical diagnosis of TES demonstrated a sensitivity of 0.97 and a specificity of 0.21. Interpretation of these statistics indicates that the 2014 clinical criteria for a diagnosis of TES are very sensitive (i.e. few cases would be missed), but the specificity is very poor (i.e. the 2014 criteria for TES did not provide clinicians with a decision support process from which to distinguish CTE from other conditions that affect mood, behaviour and cognition).

In 2021, TES was redefined by a group of clinicians and researchers using a modified Delphi process [ 58 ]. Psychiatric features, such as ‘anxiety, depression, apathy, and paranoia’, which were considered core clinical features in the research criteria proposed for TES in 2014, were moved from ‘core’ to ‘supporting’ features. Despite the change, the question of specificity persists with the new criteria. In a sample of 507 older adults evaluated by Terry et al., approximately 1 in 4 met the symptom criteria for TES, many of whom had no history of repetitive neurotrauma [ 59 ]. Terry et al. also surveyed 1100 participants from a national health volunteer registry using the refined TES criteria, again finding that it was not possible to distinguish symptoms related to repeated head trauma or concussion from other conditions, particularly mental health related conditions, in the general population [ 60 ]. Similarly, Iverson et al. compared brain donors within the VA–BU–CLF brain bank, and found no statistically significant differences in any of 11 mental health outcomes in those with CTE-NC at autopsy compared with those without CTE-NC [ 61 ].

Given the lack of specificity, the current TES research criteria do not appear to provide a basis for clinical appraisal, and the authors of the criteria state: ‘These NINDS Consensus Diagnostic Criteria for TES are meant primarily for research purposes and should be used cautiously in clinical and medicolegal settings, avoiding equivalence with a diagnosis of CTE, and using appropriate care when communicating a diagnosis of TES’ [ 58 , p 860].

4 Testing Causal Hypotheses is Premature

Bradford Hill’s considerations were developed under the assumption that there are results available from hypothesis-testing studies, such as case–control and cohort studies. To the best of our knowledge, findings of studies using such designs to examine whether RHIs cause CTE-NC, and whether CTE-NC represents a progressive neurodegenerative disease, have not yet been published.

Beyond the fact that the conditions for causal claims are yet to be met, we believe the statements by the authors [ 1 ] implying that the application of the Bradford Hill considerations means that RHI has been established as the cause of CTE contains several significant inaccuracies. In the sections below we provide our reasoning for our belief, and highlight research methods and approaches to public health and risk management that we think can better address the concerns raised by the authors [ 1 ].

4.1 Strength of Association

The strength of association among variables is often presented via statistics such as correlations, risk and rate differences and hazard, risk, rate and odds ratios. While there is a general premise that a stronger (positive) association between an exposure and outcome is more likely to be a relationship that is causal in nature, this does not always hold:

…a strong association is neither necessary nor sufficient for causality… [ 62 ]

The review authors describe ‘six well-conducted case–control studies where the researchers made a reasonable attempt to identify RHI history and had more than 50 subjects to be sufficiently powered for statistical significance…’ [ 1 , p 4]. The data available in the six studies provide either weak evidence, due to a mix of ascertainment bias and unclear validity of proxy measures used to estimate exposure to RHI, or are unsuitable to report odds ratios reflective of the likelihood of developing CTE-NC on the basis of exposure to RHI due to the way cases and controls were selected. We agree with the authors of a systematic review that these six studies are not case–control or cohort designs [ 50 ].

Because these studies do not meet the requirements of a case–control design, the odds ratios presented are invalid. As an example, one of the key sources is the VA–BU–CLF brain bank, which sought inclusion of cases on the basis of their exposure to RHI in the first place.

All 269 brains from the VA–BU–CLF Brain Bank had contact-sports history. [ 1 ]

If cases are selected into a case–control study because they were exposed, the odds of exposure in the case group are meaningless. In turn, the odds ratio comparing exposure in the case group with that of the controls does not represent the relationship between development of the outcome and exposure to the agent. The selection of cases on the basis of exposure is why some of the calculations reported required imputed values, and why the odds ratios appear unusually large. In addition, because the prevalence of CTE-NC is so high in the VA–BU–CLF brain bank, applying the rare disease assumption to the use of odds ratios to approximate relative risks is inappropriate, and yields a gross overestimate of the risks [ 63 ]. Refer to Additional files 4: Case control studies and odds ratios.

4.2 Consistency

Consistency refers to repeated findings within and between studies, settings, timepoints and populations, and similar to strength of association, consistency does not necessarily imply a causal relationship, nor does lack of consistency rule out a causal association [ 62 ]. In their Table 3, the review authors present ‘CTE cases diagnosed globally’ as justification for causality through consistency, with a summary of case series published by different brain banks/groups [ 1 ]. The consistency of brain bank data demonstrating both CTE pathology together with a retrospective history of RHI, regardless of whether co-morbid pathology is present, is difficult to accept as evidence for causation, given the sources of error with outcomes and exposures already described.

‘McKee et al. (reference number 18 [ 64 ],) noted that prior to 2009, there were only 48 cases of CTE in the literature, in contrast to the hundreds of cases of CTE since (reference numbers 19–30)’ [ 1 , p 2]. Further, in their Table 3, the authors present ‘the largest CTE case series’ published at various brain banks that are ‘understood to be using NINDS/NIBIB consensus criteria for diagnosis’ [ 1 , p 5–6].

These data (from references 19–30 and Table 3) on the number of CTE cases diagnosed globally are presented as justification for causality through consistency. There are overlapping cases from the VA–BU–CLF brain bank (reference numbers 19, 20, 21, 22) and we are unable to differentiate the exact number of unique cases from these series. Table 1 summarises the sources cited, noting critically that not all cases in this table are confirmed with histopathological CTE-NC. Some findings were based on the first NINDS/NIBIB consensus meeting (published in 2016), some cases have multiple diagnoses, and two studies were published in 2015, before the first NINDS/NIBIB criteria were reported.

We emphasise that generalisations and exaggeration are not helpful for understanding the natural history or pathology of CTE-NC and any potential relationship with RHI. The information further empasises the importance of defining and measureing exposures and outcomes accurately.

4.3 Specificity

RHI is the only factor common to reported CTE cases, and there is almost no evidence of CTE in those examined who have not sustained RHI [ 1 , p 7].

We believe the claims made by the review authors [ 1 ] in support of this section to be misleading of the current literature, particularly as they chose to focus on RHI as the lead risk factor for CTE-NC, as opposed to exploring the question ‘what are the potential contributing risk factors?’.

There are several alternative, and reasonable, factors explored in the literature that may cause CTE-NC, either independently of, or in conjunction with, RHI, including genetics, inflammatory responses, ergogenic aids and substance use to name a few [ 79 , 80 ]. There is also more than ‘almost no evidence’ of CTE-NC without RHI, with various cases from literature having been described [ 81 , 82 ]. Finally, irrespective of the issues identified, and sufficient alone, is that the absence of evidence is not evidence for its absence. Rather, we need to continue asking the right questions and addressing them with suitable study designs.

4.4 Temporality

Temporality is perhaps one of the more intuitive concepts to understand in establishing cause: the risk factor (or exposure) must occur before the disease (or outcome). While temporality seems straightforward, until there are clear parameters to define and measure RHI and better information regarding any clinical manifestation of CTE-NC is available, this viewpoint also remains uncertain.

Establishing temporal associations of RHI and CTE-NC is challenged by not knowing the evolution of CTE-NC before, during and after RHI exposure, whether any pathology becomes stationary, whether it is reversible, or whether it is progressive (and why that might be the case in the absence of further exposure to RHI) [ 83 ]. The authors write:

As outlined in the above section on specificity, the exposure to RHI is associated with CTE pathology and, especially with the introduction of the aforementioned revised NINDS/NIBIB neuropathologic criteria requiring neuronal involvement in the perivascular deposition of tau, this pathology occurs nearly exclusively in the presence of clearly identified RHI exposure [ 1 , p 7].

Here, the authors have exemplified several of our concerns with the claim of ‘RHI’ being a ‘clearly identified exposure’, as presented earlier.

4.5 Biologic Gradient (dose–response)

This section of the causal claims rests partly on a description of historical cases. The condition termed ‘CTE’ in reports of ‘dementia pugilistica’ among boxers is qualitatively different from that which is currently termed CTE-NC. This is incorrect and misleading of what is currently known about CTE-NC.

The following two quotes exemplify how the authors of the review equate ‘dementia puglistica’, or ‘punch drunk’ syndrome, with modern conceptions of CTE:

Dr. Harrison Martland is credited with first identifying the syndrome that was later called CTE in his article Punch Drunk, published in the Journal of the American Medical Association in 1928 [ 1 , p 7]. While CTE has been known in the literature for nearly a century, most of the research on CTE has occurred only in the last decade [ 1 , p 2].

These statements leave the reader with an impression that historical punch-drunk syndrome and modern day CTE are exchangeable when, in fact, they are markedly different. Punch-drunk syndrome, characterised by clinical signs such as dysarthria, shuffling gait and Parkinson’s-type symptoms, was identified and conceptualised on the basis of clinical neurological examination (multiple and variable neurological deficits from extreme neurotrauma exposure). Modern CTE-NC is a purely neuropathological finding (or more specifically, an immunohistochemical finding) that, thus far, lacks a specific clinical presentation. Neither Martland nor Critchley reported pathological changes as is implied. Further, the work of Goldfinger et al., who re-examined the Corsellis series using the 2016 NINDS/NIBIB criteria and modern immunohistochemical techniques, refuting several of the original Corsellis findings, has been overlooked in the narrative review [ 84 ].

Focal deficits attributed to boxing such as slurring dysarthria, tremor and gait disturbances at or before retirement, were common in early twentieth-century boxers with prolonged neurotrauma exposure. These visible signs are not commonly seen in the case of modern athletes; for example, one is hard pressed to find even a single case of an American football player with a focal neurological deficit at retirement. Further information is presented in Additional files 3—Misrepresentations of historical research .

The issue of selection bias is also raised in this section on biological gradient. Selection bias describes a systematic difference in the relationship of exposure and disease between those who participate in a study and those who in theory could be eligible for the study but did not partake [ 85 ]. In the case of brain bank cohorts, participants are not randomly assigned to be investigated, rather they or their next of kin choose to donate their brain, often because of specific health concerns. Because the donation of brains into many brain banks is based on symptoms during life, as well as contact sport or RHI exposure, apparent relationships found in the data may not generalise to the wider populations.

To account for selection bias, the authors [ 1 ] refer to the findings of LeClair et al. [ 86 ], who explored the influence of selection bias through simulated analyses. While the use of quantitative methods of bias analysis is endorsed by experts in epidemiological statistics, such as Greenland [ 87 ] and Lash et al. [ 88 ], both highlight that the methods often require the use of unverifiable assumptions about probabilities of selection and non-selection across groups. To the extent that modelling does incorporate such assumptions, the results of sensitivity analyses reflect plausible conjectures about the effects that would have been found had selection bias not been a feature of the study, rather than direct evidence of the size and direction of the true effect. As Lash notes, ‘…bias analyses do not establish the existence or absence of causal effects any more than do conventional analyses’ and ‘…when examining a bias analysis, a reader must bear in mind that other reasonable inputs might produce quite different results’ [ 88 , p 714].

In relation to the LeClair study, Nowsinki et al. write ‘the researchers found that highest level of football play was associated with CTE diagnosis in a dose–response manner’ [ 1 , p 9]. However, as described in the study limitations by LeClair et al., ‘exposure’ was treated as a categorical variable in which the ‘…highest level of American football playing served as a proxy measure for RHI…’ and ‘we were unable to consider other measures of exposure, such as frequency of RHI, or even duration of play…’ [ 86 , p 1441]. Ultimately, the ‘dose’ argument of RHI has shifted well away from being ‘the cumulative exposure to recurrent concussive or subconcussive events’ [ 1 , p 2].

4.6 Plausibility

Findings from animal and simulation studies are provided in the narrative review as examples that ‘provide evidence of a credible mechanistic hypothesis for the location of the pathognomonic lesion and the association between RHI and CTE alongside the paucity of CTE cases in individuals not exposed to RHI all support that RHI exposure is a plausible cause of CTE’ [ 1 , p 9]. We accept that the evidence presented in this section is consistent with the hypothesis that RHIs may be a causal factor in CTE-NC. Further, we recognise the value that animal studies and simulations have in understanding the aetiology of human disease processes, challenges in translating findings from animals to humans notwithstanding. As Shimonovich et al. [ 24 ] write, however: ‘the plausibility of the causal relationship is both dependent on and limited by knowledge available at the time. It may be further limited by assumptions based on investigators’ beliefs rather than empirical evidence’ [ 24 , p 882].

4.7 Coherence

Coherence requires that what is known about the cause and effect proposed does not conflict with what is known about the natural history of disease. The review authors state ‘we must demonstrate that the association between RHI and CTE pathology does not conflict with what we know about the development of CTE pathology or RHI’ [ 1 , p 9]. As pointed out elsewhere, however, coherence, ‘provides at best only weak support for causality, because many theories will exhibit such coherence, including most theories that are proposed and eventually refuted’ [ 89 , p 21].

In their section on coherence, the authors [ 1 ] briefly consider the potential of other causal variables. The authors focus on opiate misuse as the only variable that ‘has been proposed as a potential alternative cause of CTE’, subsequently dismissing it on the basis of one study that reported ‘tau deposition from opiate use is easily distinguished from the pathognomonic CTE lesion’. They conclude the section with the following statement: ‘With what is known in the literature about computer modeling of brain trauma, post-mortem confirmed cases without a history of RHI exposure, sex differences, opiate use, and CTE genetics, RHI remains the only candidate risk factor for CTE causation’ [ 1 , p 10].

We recommend further scrutiny of existing evidence before drawing conclusions from these arguments of coherence, not least because studies that would permit proper evaluation of a range of possible contributing factors to the development of CTE have not yet been conducted. We note that other authors have raised multiple candidate risk factors for both CTE-NC and clinical and functional outcomes, including pre-existing psychiatric conditions, sleep disorders, substance use, chronic pain, genetic factors and exposure to anaesthesia [ 79 , 90 ]. We do not believe that the statement that RHI is the only candidate risk factor for CTE causation is well supported by the evidence at this point.

4.8 Experimental Evidence

Although all study designs used in epidemiology have their limitations [ 87 ], findings from well-designed cohort studies and case–control studies are generally accepted among epidemiologists as capable of testing causal hypotheses. Randomised controlled trials are best suited to testing causal hypotheses but are limited in their application for many public health issues, including injury. Clinical case-series and cross-sectional studies have important roles in epidemiology, especially with respect to identifying novel health outcomes and developing hypotheses to be tested in more rigorous designs. Case-series and cross-sectional studies also have significant, and well-recognised, limitations with respect to generalising results from the individuals and groups studied to the wider population.

Establishing causation (or not, as the case may be) between RHI and CTE-NC can in principle be ascertained via the application of observational research designs [ 91 ]. Such studies need to be designed to properly account for the effects of random error, confounding, information bias and selection bias. Prospective and retrospective cohort studies, as well as case–control studies, could be developed that would provide answers to many important questions including:

whether, and to what extent, RHIs are a causal factor of CTE-NC;

whether, and to what extent, factors other than RHIs cause CTE-NC;

whether CTE-NC represents a progressive neurological disease; and

whether, and to what extent, CTE-NC causes the range of clinical outcomes to which it has been linked via cross-sectional analyses.

The same or similar studies [ 92 ] could simultaneously address the effects of RHI on other health outcomes of interest, such as depression, neurodegenerative diseases and dementia, and whether they were related to CTE-NC.

Once definitions of RHIs and CTE-NC are developed, agreed on and validated, cohort (retrospective and prospective) and case–control studies are likely to provide much stronger evidence of the relationship between RHIs and CTE-NC than has yet been presented, at which point cautious judgements about the likelihood of observed relationships being causal can, and should, be made. In their discussion section, the authors have implied such studies are impossible to conduct, with the claim that they would require unfeasible studies of identical twins and unethical assignment to groups which are, and are not, subjected to head injuries from early in life. We disagree with that view of study design and reiterate that much research in public health is conducted using observational designs, with true experimental designs unsuitable in many public health scenarios [ 93 ].

4.9 Analogy

In this section, analogies are drawn between the level of evidence that was obtained regarding the causal relationship between cigarette smoking and lung cancer being ‘well established’ with that between RHI and CTE-NC, and further between issues regarding how exposure to cigarette smoke has been quantified in observational studies and how exposure to RHIs have been quantified.

In their review [ 1 , p 11], the authors claim that with respect to exposure to cigarette smoking ‘key questions remain unanswered or incompletely answered, including what precisely constitutes a smoked cigarette (the dose), why some smokers develop cancer and others do not, how many cigarettes are too many, or which specific cigarette or carcinogen sparked the lung cancer’ [ 1 , p 11]. No cited evidence is provided in support of their claim that the lack of a precise measurement of a smoked cigarette is actually a feature of the epidemiological evidence, but they do use it to set up the following argument: ‘The fact that these questions also remain for RHI and subconcussive impacts is often raised as a reason that conclusions on RHI/CTE causation cannot be drawn’ [ 1 , p 11]. They conclude that: ‘These knowledge gaps have not limited the ability to assert a causal link between smoking and lung cancer, and similarly should not limit the ability to determine the likelihood of a causal link between RHI and CTE’ [ 1 , p 12].

The argument is an example of the ‘straw man’ fallacy. Focussing on the first claim, regarding exposure to smoked cigarettes, a systematic review and meta-analysis examining survey-based assessments of exposure to cigarette smoking published in 1994 found that self-reported smoking status had generally high levels of sensitivity (87%) and specificity (89%) when validated against biochemical measures of exposure across the 26 studies [ 94 ]. Although it is acknowledged that survey methods provide less accurate information in some situations (for example, when assaying cigarette use among pregnant women), [ 94 ] there is no doubt in the epidemiological community that measures of ‘dose’ captured through surveys asking about cigarettes smoked per day or pack years of exposure have yielded valid information regarding the link between smoking and lung cancer.

The qualitative and quantitative differences in the amount of evidence regarding smoking causing lung cancer and RHIs causing CTE-NC are currently so large that claims of the two issues being comparable are misleading. As noted above, the studies identified in the review [ 1 ] regarding the relationship between RHI and CTE-NC have employed a range of approaches that have yet to be validated in assessing exposure to RHIs, along with definitions of CTE-NC that have varied over time. To date, studies have primarily used case-series and cross-sectional designs with papers from overlapping subsets of the VA–BU–CLF brain bank case series providing the data for the great majority of the existing publications, as well as the case material for consensus efforts. The autopsy case-series data have been supplemented by interviews and surveys of ‘informants’, who are predominantly next of kin of the deceased.

By contrast, the use of case–control and cohort designs using consistent methods of appraising exposure are a feature of the studies examining the relationship between smoking and lung cancer in humans. The amount of supporting evidence for the contention that cigarette smoking causes lung cancer differs from that regarding RHIs and CTE-NC by an order of magnitude. A systematic review and meta-analysis published in 2012 of the results of studies published up till the year 2000 identified 267 ‘principal’ (and 20 subsidiary) studies, of which 209 used case–control and 52 used prospective cohort designs [ 95 ].

Questions such as ‘which specific cigarette sparked the lung cancer’ or ‘which specific head impact sparked the development of CTE-NC’ might form the basis of legal arguments or judgements regarding insurance claims but are not questions that epidemiological studies would address because the questions are directed at individuals, not at population groups. The question of why some smokers develop lung cancer while others do not is relevant to considerations of how cause is conceptualised, but as discussed by Brand and Finkel [ 96 ] and reiterated in the review [ 1 ], the fact that a cause of a health outcome is a cause does not necessarily imply that all those exposed to it will develop the outcome, nor that people not exposed to it will not. It does, however, imply that factors other than the specified cause contribute to the outcome.

Cohort study designs, which permit the evaluation of multiple potential mediators and confounding variables are valuable in enabling understanding of how strongly associated with an outcome a given cause is, and how that cause interacts with other potential causes and confounding factors. The claim that RHIs are ‘the only candidate risk factor for CTE causation’ [ 1 , p 10] reflects a lack of information about other candidate risk factors derived from studies that would provide good evidence about them, rather than reflecting supporting evidence for the contention that RHIs are, in fact, the only candidate risk factor.

4.10 Comments on Children’s Participation in Sport

In the discussion section of the review, the authors emphasise the question: what impact do head injuries sustained in youth sport have on participants in the long term? writing :

Perhaps most consequential would be the positive health impact for children. As it stands today, tens of millions of children as young as 5 years old are exposed to RHI in sports because they are playing by rules that were originally designed for adults. Armed with confidence in the causal connection between RHI and CTE, parents and youth coaches may reject exposing their children to a preventable degenerative brain disease simply because the current rules (tackling, heading) make RHI inevitable, especially when non-RHI versions of those contact sports exist, as well as alternative sports without RHI. Considering that both CTE onset and severity have been associated with a dose-response, strict reforms that lower the dose could effectively prevent new cases of the disease [ 1 , p 13–14].

This addition to the paper doesn’t follow from the prior content, because evidence about the effects of children’s sport on health outcomes was not presented. Rather, it serves as an appeal to inherent emotional concerns. The statement was made upfront that ‘any reference to CTE in this review refers to cases that have been confirmed by autopsy’ [ 1 , p 3] so declarations that ‘parents and youth coaches may reject exposing their children to a preventable degenerative brain disease’ [ 1 , p 14] is overreaching.

In a 2019 narrative review into the age of first exposure to tackle football and later life outcomes Alosco and Stern stated ‘…it is our opinion that more methodologically rigorous research on the long-term neurologic consequences of youth tackle football is needed before policy and safety guidelines can be accurately informed’ [ 97 , p 113]. Further, in a 2021 narrative review, Iverson et al. [ 98 , p 1] concluded ‘The accumulated research to date suggests that earlier AFE (age of first exposure) to contact/collision sports is not associated with worse cognitive functioning or mental health in (i) current high school athletes, (ii) current collegiate athletes, or (iii) middle-aged men who played high school football. The literature on former NFL players is mixed and does not, at present, clearly support the theory that exposure to tackle football before age 12 is associated with later in life cognitive impairment or mental health problems’. More recently, in 2023, the Concussion in Sport Group did not find evidence to support the notion that participants in youth, high school and collegiate sports are at risk for long-term consequences [ 50 ].

Of course, encouraging individuals and sports organisations to reduce exposure to brain injuries is sensible, and sports organisations have made relevant changes that are being closely monitored. For example, the age for body-checking in youth ice hockey was raised [ 99 , 100 , 101 , 102 ]. There has also been a general cultural shift to understand reasoning for, and discourage, poor compliance from players, parents or others with changes that are implemented to reduce exposure to, and consequences from, brain injuries sustained in sports [ 103 , 104 ]. The review authors [ 1 ] call out the ‘tens of millions’ of children who have participated in contact or collision sports during their youth and suggest they have higher risks of developing neurodegenerative diseases later in life than those who have not played such sports. There is no basis for this claim presented in their review. Such sentiment needs to be avoided to do justice to the importance of this issue and to respect those whose health has been impacted by brain injuries sustained during their participation in sport.

5 Discussion

We raise the counterpoints to the authors’ [ 1 ] interpretation of the Bradford Hill considerations because the paper has had significant influence and the issue is important. The paper has been repeatedly promoted as having definitively established causality between exposure to RHI and CTE [ 3 ]. This claim is not supported by the arguments presented in the paper, nor by systematic appraisals of the wider evidence [ 50 ].

There are sound reasons why systematic reviews and meta-analyses are preferred over narrative reviews when researchers seek to evaluate questions of causal relationships. Published guidance on how to conduct systematic reviews and meta-analyses of observational studies (e.g. COSMOS-EA) [ 105 ] and how to evaluate a given corpus of evidence (e.g. GRADE) stress the importance of having a pre-defined and well-documented search strategy, clear criteria upon which studies are included or excluded and explicit evaluation of study design and biases [ 106 ]. The aim of the systematic process is to ensure that the entirety of the evidence is properly, and without bias, evaluated, synthesised and summarised to produce the best evidence base to inform clinical and health policy.

As creators and consumers of research, it is up to all of us to question findings and approach their interpretation with caution and critique. The question of whether RHI causes CTE-NC remains open for two reasons. The first is that the absence of clear operational definitions of postulated causal agents and health outcomes means that exposure to the agent cannot be accurately quantified, and thus health outcomes cannot be accurately related to exposure to the potential causes. This fact currently represents an undercutting defeater of any causal claims between RHIs and CTE-NC, because those minimum requirements have yet to be met. Meeting them requires the development of operational definitions of RHIs and CTE-NC that become generally accepted by the research community, and consistently applied in future studies. Secondly, even if scientific consensus on what constitutes RHI and CTE-NC had already been established, the application of those operational definitions in the studies from which the review authors draw their conclusions would still not be sufficient to make any causal conclusions on this matter due to the inherent limitations in case-series and cross-sectional study designs. Rather than overstating the implications of hypothesis-generating studies, we could better move this debate forward by undertaking hypothesis-testing research designs such as cohort and case–control studies.

Although the review authors [ 1 ] do not ‘substantially explore the separate question of a causal relationship between CTE neuropathology and clinical symptoms’, that question is arguably of greater public health relevance than the question of a causal relationship between RHI and CTE-NC. Understanding whether findings of CTE-NC at autopsy represents an important public health issue depends on both how prevalent CTE-NC pathology is in various populations, and how strongly related CTE-NC is to clinical syndromes, neither of which are yet established.

In a systematic review of the long-term consequences of sports concussions, Iverson et al. did not find any case–control or cohort studies addressing the risk of CTE-NC after head injury or participation in collision sports [ 50 ]. Other authors have also highlighted a lack of hypothesis-testing study designs compounded by high-volume publication of hypothesis-generating studies in the TBI literature [ 107 , 108 , 109 ]. Iverson et al. [ 50 ] also reported on several studies comparing professional athletes with the general population that found associations between collision sports participation and dementia and amyotrophic lateral sclerosis (ALS) as a cause of death. In the case of American football, for example, a small fraction of participants compete professionally. For the remaining majority of amateur American football players, there is no literature to indicate any adverse long-term neurological or psychiatric problems from contact sport participation [ 50 ]. Moreover, studies suggesting ‘neurodegenerative’ associations in professional athletes are dominated by ecological designs (e.g. studies of death certificates spanning several or more decades with data mining of health records) that lack individual exposure data and adequate control for confounding (e.g. genetics, demographic, health related or environmental) [ 110 , 111 ] make it difficult to infer risk, let alone cause. Other similarly designed studies showed no associations with neurological, including neurodegenerative, problems [ 112 , 113 ].

We strongly encourage researchers to undertake studies to address the gap in knowledge for contributing factors of CTE-NC and note that several are now in progress (selection of examples cited [ 114 , 115 , 116 , 117 , 118 ]), along with further investigation of the relationships among CTE-NC, co-existing pathologies and clinical outcomes.

There is an assumption in the review [ 1 ], as well in as other research on CTE [ 119 ], that CTE-NC represents a canonical neurodegenerative disease, and that it causes clinical outcomes, for which the evidence available is lacking. The question as to whether CTE-NC or some other pathological outcome yet to be elucidated is related to clinical signs and symptoms among individuals exposed to brain trauma is a high-priority question still to be answered. With that being said, a responsible approach to managing risks to participants in collision sports is to utilise the precautionary principle, and for sports administrators, regulators and other interested parties it is to act to eliminate and minimise the frequency and magnitude head impacts as far as is reasonably practicable, and to educate participants about prevention and avoiding injuries in sports. In our view, this needs to happen even though considerable uncertainty about the presence and magnitude of the risk across different sports remain. Importantly, managing risks in sport does not imply eliminating all injury risks [ 120 ], whether injury to the brain or otherwise.

6 Conclusions

This evaluation of evidence presented in a review article [ 1 ] identified several inaccuracies and misrepresentations that refute claims of a “definitively established” causal relationship between RHI and CTE-NC. The fundamental criteria for establishing causality are not fulfilled. We have identified that the quantity and quality of the evidence in the review does not support the conclusions the paper draws and that the discussion and conclusions sections are a series of arguments advocating for acceptance of their claims, rather than offering a rigorous scientific evaluation of the evidence presented to substantiate those claims. Alongside methodological work to establish clearly defined and quantifiable variables, the conduct of well-designed cohort studies, with attention on a wide range of candidate risk and protective factors, are in progress. Until the findings of several such studies are published, the scientific community, and all those who distribute research findings, must be cautious of making or accepting causal claims in this field.

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Lauren V. Fortington

Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

J. David Cassidy

Division of Neuropathology, Northwestern University Feinberg School of Medicine and Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Chicago, IL, USA

Rudolph J. Castellani

Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia

Andrew J. Gardner

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Andrew S. McIntosh

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Michael Austen

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High Court of New Zealand, Auckland, New Zealand

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Zachary Yukio Kerr

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Lauren V Fortington, Ph.D, is supported through research grant funding at Edith Cowan University. She has received project funding from several sports, health and government agencies including: Australian Football League, Australian Institute of Health and Welfare, Australian Institute of Sport, Combat Sport Commission Western Australia, Cricket Australia, Defence Science WA, Exercise & Sports Science Australia, Injury Matters, International Olympic Committee, KidSafe WA, Rugby Australia, State Government of Victoria and VicSport. Lauren Fortington is an editorial board member of Sports Medicine. She was not involved in any of the editorial process or decisions for this manuscript. J. David Cassidy, PhD, Dr.Med.Sc., has received funds for expert testimony on long-term consequences of injuries, including TBI and sports concussions. He has received funding from various organizations in Canada, the USA, Sweden and Denmark to study traffic injuries and to undertake and publish systematic reviews on head and neck injuries. Rudolph J. Castellani, MD, is a collaborator on a grant funded by the National Football League to study the spectrum of concussion, including possible long-term effects. He has a consulting practice in forensic neuropathology, including expert testimony, which has involved former athletes at amateur and professional levels as well as sport organizations. He was reimbursed for hotel costs at the World Rugby Medical Commission Conference in 2022. Andrew J Gardner, PhD, has a clinical practice in neuropsychology involving individuals who have sustained sport-related concussion. He is a contracted concussion consultant to Rugby Australia. He has received travel funding or been reimbursed by professional sporting bodies, and commercial organisations for discussing or presenting sport-related concussion research at meetings, scientific conferences, workshops and symposiums. He is a member of the World Rugby Concussion Working Group, and a member of the Australian Football League Concussion Scientific Advisory Committee. He is currently supported by a National Health and Medical Research Council (NHMRC) investigator grant. He acknowledges unrestricted philanthropic support from the Tooth Foundation for concussion research and the National Rugby League for research in former professional rugby league players. Andrew McIntosh, PhD, is a self-employed consultant in biomechanics and ergonomics. His 1995 PhD was on the topic of head injury biomechanics. He commenced research on concussion in sport in the late 1990s. He has worked in the area of head injury biomechanics and prevention for 30 years. He holds two adjunct (honorary) academic appointments. As a university academic (employee and honorary), he has been awarded research grants paid to the university to fund research on sports injuries. Funding has been received from a range of national and international sporting organisations. As a consultant, he has been paid to assist a range of state, national and international organisations on injury biomechanics and safety, including concussion in sport and its prevention. His paid employment includes expert testimony on injury causation. Michael Austen has no conflicts of interest to declare. Zachary Yukio Kerr, PhD, acknowledges previous support from the Center for Disease Control and Prevention, National Institutes of Health, Department of Defense, National Football League, National Collegiate Athletic Association and National Operating Committee on Standards for Athletic Equipment. Kenneth L Quarrie, PhD, has been employed by New Zealand Rugby since 2000 and currently occupies the role of Chief Scientist, New Zealand Rugby. He also sits on World Rugby’s Scientific Committee and has contributed to various World Rugby working groups focussed on player welfare issues from 2011 to the time of publication. He has received funding for travel and accommodation from World Rugby to present at their medical conferences and meetings. He is the co-principal investigator on the Kumanu Tāngata project, a retrospective cohort study funded by World Rugby and the New Zealand Rugby Foundation. The purpose of the Kumanu Tāngata project is to investigate long-term health outcomes associated with participation in first-class rugby (provincial level and above) in New Zealand.

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Fortington, L.V., Cassidy, J.D., Castellani, R.J. et al. Epidemiological Principles in Claims of Causality: An Enquiry into Repetitive Head Impacts (RHI) and Chronic Traumatic Encephalopathy (CTE). Sports Med (2024). https://doi.org/10.1007/s40279-024-02102-4

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ICAHN ENTERPRISES VINDICATED BY DISMISSAL OF MERITLESS LAWSUIT THAT PARROTED FALSE AND MISLEADING CLAIMS PUBLISHED BY HINDENBURG "RESEARCH"

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Sep 16, 2024, 08:00 ET

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SUNNY ISLES BEACH, Fla. , Sept. 16, 2024 /PRNewswire/ -- Icahn Enterprises (IEP) is pleased to announce that the proposed class action lawsuit against IEP and certain directors and officers has been dismissed without prejudice.

On September 13, 2024 , U.S. District Court Judge K. Michael Moore found that the lawsuit — comprised of meritless claims cut and pasted from false and misleading reports published by Hindenburg "Research" — failed to show that IEP had made material misrepresentations or omissions or did so with an intent to defraud. A copy of the U.S. District Court's decision is available at Carlicahn.com .

Carl C. Icahn , Chairman of IEP, stated: "We are pleased that the spurious claims of various unscrupulous characters, working together in a coordinated and clandestine network, have been debunked. Those making these claims include short sellers who peddle false and misleading information styled as "research", their sketchy and anonymous financial backers who short securities in the shadows, thereby amplifying the disinformation campaign and profiting at the expense of long-term investors, those in the press who are used in the scheme as tools to repeat the fallacious claims, and the bottom-feeding "strike suit" lawyers who travel in their wake and attempt to extort quick settlements from victimized companies. We are also encouraged that the SEC is now beginning to take long needed action against this "short and distort" practice, which disproportionately impacts small investors."

"We are also happy to have recently settled the investigation initiated by the SEC following the publication of Hindenburg's false and misleading "research" report. We cooperated fully with the SEC and the SEC found no fraud , inflation of our net asset value or impropriety in our distributions, nor did it validate any of Hindenburg's other spurious claims. Instead, we settled a technical disclosure violation."

IEP would also like to correct the record regarding the prospectus supplement that was filed recently with the SEC. Several media outlets repeated the false claim that this filing indicated an intent for Chairman Carl Icahn to sell large amounts of IEP units. To the contrary, as Mr. Icahn stated publicly, he is "absolutely not selling" IEP units. Neither is there a plan for IEP to conduct a massive sale of units, as was also reported erroneously. The filing was merely a routine refreshment of the registration statement related to our at-the-market (ATM) offering program. Since the date of that filing, IEP has sold under $3.5 million worth of units under the ATM program. There are no plans to conduct any offerings outside of the ordinary course and consistent with our past practices.

Icahn Enterprises L.P., a master limited partnership, is a diversified holding company owning subsidiaries currently engaged in the following continuing operating businesses: Investment, Energy, Automotive, Food Packaging, Real Estate, Home Fashion and Pharma.

Caution Concerning Forward-Looking Statements

This release may contain certain "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995, many of which are beyond our ability to control or predict. Forward-looking statements may be identified by words such as "expects," "anticipates," "intends," "plans," "believes," "seeks," "estimates," "will" or words of similar meaning and include, but are not limited to, statements about current, pending or future lawsuits or government investigations, securities transactions, and the expected future business and financial performance of Icahn Enterprises and its subsidiaries. The outcome of any current, pending or future litigation or government investigations involving Icahn Enterprises and its subsidiaries or their respective officers, employees or directors may differ materially from our current expectations. The class action lawsuits discussed above were dismissed without prejudice, and may be amended and refiled, and the court's decision may be appealed, and the ultimate outcome of these lawsuits may not be in our favor. As described in our Annual Report on Form 10-K and Quarterly Reports on Form 10-Q filed with the SEC, from time to time we and our subsidiaries are involved in various lawsuits and investigations arising in the normal course of business. Actual events, results and outcomes with respect to these matters and the other statements made in this press release may differ materially from our current expectations due to a variety of known and unknown risks, uncertainties and other factors, including risks related to current, pending or future litigation or investigations, judicial and regulatory process, and risks related to economic downturns, substantial competition and rising operating costs; the impacts from the ongoing Russia / Ukraine conflict and conflict in the Middle East , including economic volatility and the impacts of export controls and other economic sanctions; risks related to our investment activities, including the nature of the investments made by the private funds in which we invest, including the impact of the use of leverage through options, short sales, swaps, forwards and other derivative instruments; declines in the fair value of our investments, losses in the private funds and loss of key employees; risks related to our ability to continue to conduct our activities in a manner so as to not be deemed an investment company under the Investment Company Act of 1940, as amended, or to be taxed as a corporation; risks related to short sellers and associated litigation and regulatory inquiries; risks relating to our general partner and controlling unitholder; pledges of our units by our controlling unitholder; risks related to our energy business, including the volatility and availability of crude oil, other feed stocks and refined products, declines in global demand for crude oil, refined products and liquid transportation fuels, unfavorable refining margin (crack spread), interrupted access to pipelines, significant fluctuations in nitrogen fertilizer demand in the agricultural industry and seasonality of results; risks related to potential strategic transactions involving our Energy segment; risks related to our automotive activities and exposure to adverse conditions in the automotive industry, including as a result of the Chapter 11 filing of our automotive parts subsidiary; risks related to our food packaging activities, including competition from better capitalized competitors, inability of our suppliers to timely deliver raw materials, and the failure to effectively respond to industry changes in casings technology; supply chain issues; inflation, including increased costs of raw materials and shipping, labor shortages and workforce availability; risks related to our real estate activities, including the extent of any tenant bankruptcies and insolvencies; risks related to our home fashion operations, including changes in the availability and price of raw materials, manufacturing disruptions, and changes in transportation costs and delivery times; and other risks and uncertainties detailed from time to time in our filings with the Securities and Exchange Commission including out Annual Report on Form 10-K and our quarterly reports on Form 10-Q under the caption "Risk Factors". Additionally, there may be other factors not presently known to us or which we currently consider to be immaterial that may cause our actual results to differ materially from the forward-looking statements. Past performance in our Investment segment is not indicative of future performance. We undertake no obligation to publicly update or review any forward-looking information, whether as a result of new information, future developments or otherwise.

This release and the information contained herein do not constitute an offer of any securities for sale or the solicitation of an offer to purchase securities.

Investor Contact: Ted Papapostolou , Chief Financial Officer [email protected] (800) 255-2737

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Also from this source, icahn enterprises l.p. (nasdaq: iep) today announced its second quarter 2024 financial results.

Second quarter net loss attributable to IEP of $331 million, a decline of $62 million over prior year quarter Second quarter Adjusted EBITDA...

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article 26 cpc
Amount of claims and articles in four newspapers
How to Write a Research Article
Frances' claims and evidence Types of claim Claim Type(s) of evidence
(PDF) Improving Trust in Research: Supporting Claims with Evidence
Claims Research: Who, What, When, Where & Why

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COMMENTS

Top 10 Junk Science and Bogus Health Claims ACSH Debunked in 2020
Here are the top 10 junk science and bogus health claims we debunked in 2020. ... or 5G wireless technology caused it. Yet, one of the proponents of the former is an MIT senior research scientist. A different scientist claims that our cells are being poisoned by 5G, and in response, they produce viruses. Our fact checkers rated these claims as: ...
Scientific Evaluation and Review of Claims in Health Care (SEaRCH): A
It does this by accurately describing the practice, precisely defining what it claims to do, and determining readiness, capacity, and resources involved in further research or evaluation. 8 The second method is the REAL, which is a streamlined, efficient systematic review process conducted to determine the quantity, quality, and strength of ...
Key considerations when using health insurance claims data in advanced
Claims data research can provide important recommendations to improve healthcare systems. Claims data are increasingly and effectively used to conduct epidemiological studies, international comparisons and evaluate healthcare utilisation by providing relatively rapid access to collected information (Tuppin et al., 2017). Findings from such ...
Recommendations to protect patients and health care practices from
This article also describes the 3 primary laws that prohibit fraud and gives fraud case examples relevant to each law, including the False Claims Act, Anti-Kickback Statute, and the Stark Law. We also discuss currently trending and emerging areas, including opioid and pharmacogenetic testing; both have experienced heavier and higher-profile ...
Appraising Evidence Claims
For research evidence to inform decision making, an appraisal needs to be made of whether the claims are justified and whether they are useful to the decisions being made. This chapter provides a high level framework of core issues relevant to appraising the "fitness for purpose" of evidence claims. The framework includes (I) the variation ...
Full article: Claims management: a review of challenges faced
Lack of established procedure for claims management. Research evaluating the challenges in claims management identify lack of procedures and poor adherence to the same as a critical concern (Bakhary et al. Citation 2018; Hai Citation 2019; Hansen and Rostiyanti Citation 2019; Asuquo et al. Citation 2020; Seo and Kang Citation 2020).
Analysis of Sampling Bias in Large Health Care Claims Databases
Health care claims databases that aggregate claims from multiple commercial insurers are increasingly being used to generate clinical evidence. 1-3 These databases represent a nonrandom sample of the underlying population, but often little attention is paid to the inherent sampling bias within the data, and how it might affect results. As an illustrative example, we characterize variation in ...
(PDF) The Use of Claims Data in Healthcare Research
In addition, we identified several other review studies on the secondary use of RWD data [30][31][32]. The paper by Ferver et al [30] provided a narrative review of 1956 claims-based studies in 5 ...
Coronavirus misinformation, and how scientists can help to fight it
Here's how some scientists are fighting back. As the coronavirus has spread around the world, so has misinformation. Credit: Nguyen Huy Kham/Reuters. Eating sea lettuce or injecting disinfectant ...
Claims, Errors, and Compensation Payments in Medical Malpractice
Previous research has established that the great majority of patients who sustain a medical injury as a result of negligence do not sue. 10,11 However, the merit of claims that are brought, and ...
Behind a lot of flashy headlines may lie questionable scientific claims
From claims of harmful effects of vaccines to studies on the extent of climate change, we have learned that behind some news headlines or articles lie either questionable, oversold, or ...
Key considerations when using health insurance claims data in advanced
We describe the experiences of a group of academic researchers in using an extensive seven-year repository of US medical and pharmaceutical claims data in a research study, and provide an overview of the challenges encountered with handling claims records for data analysis while sharing suggestions on how to address these challenges.
Association of Past and Future Paid Medical Malpractice Claims
The association between prior claims and future claims was similar for high-medical-malpractice-risk and lower-risk specialties; 1 prior-period claim was associated with a 3.1 times higher likelihood of a future-period claim for high-risk specialties (95% CI, 2.8-3.4) vs a 4.2 times higher likelihood for lower-risk specialties (95% CI, 3.8-4.6).
Policy: Twenty tips for interpreting scientific claims
Policy: Twenty tips for interpreting scientific claims. Nature 503, 335-337 (2013) Cite this article. This list will help non-scientists to interrogate advisers and to grasp the limitations of ...
The importance of health insurance claims data in creating learning
Research reported in this article was partially funded through a Patient-Centered Outcomes Research Institute (PCORI) Award (HSD-1603-35039; Principal Investigator: MAS). The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology ...
Claims of causality in health news: a randomised trial
Most biomedical and health news stories make a prominent causal claim in either the headline or first two sentences (e.g. 'statins raise diabetes risk'; 'statins slash breast cancer death rates'). It is these headlines and main claims that are most eye-catching, most shared and that also frame the rest of a story [14, 15].
What is a claim?
Definition. A claim is a statement that presents an idea or series of ideas as arguments. Arguments therefore consist of claims, or another way to put it is, to say that claims are the building blocks of a good argument. In research writing, claims will be the backbone that form a thesis or a hypothesis (here the term 'hypothesis' refers to ...
Emerging trends in claims transformation
The case for the exponential claims professional. Claims is by far a property and casualty insurer's biggest cost component, as paid losses combined with investigative and settlement expenses accounted for around 70% of US premiums collected in 2020. 1 The pressure is always on to augment claims processing with new technologies and data ...
Claims management: a review of challenges faced
The adopted research methodology included (1) reviewing the literature regarding the main principles underlying global claims, (2) deducing the success and failure criteria for such claims through ...
Identifying Claims
Suggested Answers. a) The two frequency claims are "95 percent of teenagers had access to a smartphone, and 45 percent said they were online 'almost constantly.'". b) "the longer adolescents were engaged with screens, the greater their likelihood of having symptoms of depression or of attempting suicide."
PDF Summary and Analysis of Scientific Research Articles
The analysis shows that you can evaluate the evidence presented in the research and explain why the research could be important. Summary. The summary portion of the paper should be written with enough detail so that a reader would not have to look at the original research to understand all the main points. At the same time, the summary section ...
Nutrition and Health Claims: Consumer Use and Evolving Regulation
Nutrient content claims could influence purchase intentions and increase consumption, although these effects did vary based on product and claim type. At this stage, it appears that N&HC can influence consumer purchasing and consumption of food but more research is required on how specific claims may be interpreted and actioned by consumers.
Epidemiological Principles in Claims of Causality: An Enquiry into
Determining whether repetitive head impacts (RHI) cause the development of chronic traumatic encephalopathy (CTE)-neuropathological change (NC) and whether pathological changes cause clinical syndromes are topics of considerable interest to the global sports medicine community. In 2022, an article was published that used the Bradford Hill criteria to evaluate the claim that RHI cause CTE. The ...
Chapter 03; Three Claims, Four Validities
The verb is "prevent", which is a causal claim verb. The empirical article title is "Plant-based and vegetarian diets are associated with reduced obstructive sleep apnoea risk". ... ..the research team asked 159 volunteers aged 8 to 13 to watch short movie clips. Some of these clips were neutral, others cheerful, and others awe-inspiring. .
Icahn Enterprises Vindicated by Dismissal of Meritless Lawsuit That
On September 13, 2024, U.S. District Court Judge K. Michael Moore found that the lawsuit — comprised of meritless claims cut and pasted from false and misleading reports published by Hindenburg ...

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