Assessing the Population Impact of Published Intervention Studies
Background: Despite greater spending on health care and biomedical research, the United States has poorer health outcomes than competitive nations. Information is needed on the potential impact of interventions to better guide resources allocation.
Objective: To assess whether research on interventions is concentrated in areas with the greatest potential population health benefit.
Design: Secondary data analysis to perform a best-case study of the potential population impact of published intervention studies.
Study selection: A random sample of 20 intervention studies published in the New England Journal of Medicine in 2011.
Data extraction: One reviewer extracted data using a standardized form, and another reviewer verified the data.
Measurements: The incremental gain of applying the intervention versus the control estimated in quality-adjusted life years (QALY) at the population level.
Results: Of the 20 studies, 13 had a statistically significant effect size, and 3 studies accounted for 80 percent of the total population health impact. Studies of less common conditions had smaller population health impact, though greater individual level impact. Studies generally did not report the information required to estimate the anticipated population health impact.
Limitations: The heterogeneity of outcome measures and the use of multiple data sources result in a large degree of uncertainty in the estimates. The use of an intervention effect measured in a study setting is likely to overestimate its real-world impact. Although random, the sample of studies selected here may not be representative of intervention studies in general.
Conclusions: Research priorities should be heavily informed by the potential population health impact. Researchers, proposal reviewers, and funders should understand those impacts before intervention studies are initiated. We recommend that this information be uniformly included in research proposals and reports.
Not only does the United States spend more per capita on medical care than any other nation, and more than twice as much as the average for all other countries in the Organisation for Economic Co-operation and Development, it spends more on medical research as well. [1, 2] Yet despite the high level of spending, our health outcomes are mediocre at best; the United States ranks 26th in life expectancy and 31st in infant mortality among developed nations.  This discrepancy raises the question about the value derived from the governmental and nongovernmental investments in medical research. In contradistinction to basic science research, for which the goal is arguably to advance science for the development of knowledge, translational science seeks to improve health through the development of bench-to-bedside interventions and to assure their use in patients and populations that would benefit from them. To improve health measurably, translational research needs to focus on identifying interventions that are likely to provide the greatest population health benefit as well as interventions that are widely used but are ineffective or harmful. In a prior paper we proposed a set of criteria for researchers, funders, journal editors, and consumers of research to assess the importance and value of studies on health interventions—to answer the “so what” question.  Those criteria include the burden of disease (quality-adjusted life years [QALYs] lost due to the condition), the preventable burden (how much health burden could be alleviated if the intervention were successful), the economic value (cost and cost-effectiveness), and the additional information gained from the study (e.g., sufficient information to change an evidence-based recommendation).
Interventions may be traditional clinical interventions; changes to health care systems; or population-health interventions, such as changes in policy, interventions to address behaviors or underlying social and environmental determinants, or public health programs. Despite their potentially large impacts, population-health interventions are inadequately studied.
There are, of course, many reasons for conducting studies other than their potential aggregate health impact, including the need to address specific rare diseases or improve understanding of disease processes, though we would argue a primary goal of publicly supported research should be to conduct studies of interventions with the clear potential to improve population health and intended to be implemented rapidly. We therefore turned to a leading medical journal known for publishing cutting-edge research to estimate the likely impact of published intervention studies.
We identified original articles describing an intervention study published in The New England Journal of Medicine between January and December 2011 (Figure 1). In every other issue, the title and abstract of all original articles were examined to classify articles by study design (descriptive, observational, or experimental [e.g., randomized clinical trial]) and to identify articles describing the evaluation of an intervention. Exclusion criteria included topics of mental health, cost or cost-effectiveness analysis (if there was no assessment of effectiveness), safety assessment, and system-level intervention (e.g., studies of hospital quality improvement systems). A sample of 20 articles was then randomly selected from the studies meeting the criteria. Articles excluded and not selected are listed in Tables A-1 and A-2 in the appendix.
The abstraction protocol was adapted from the methods developed for the Guide to Community Preventive Services. [6, 7] We used a standardized form to record information on the nature of the intervention, study population, time horizon, outcome measures, study rationale, and sources of funding (see appendix). In addition, we evaluated articles for the inclusion of information required for calculating the expected population impact of the intervention: burden of disease (incidence, prevalence, and mortality) and expected changes in quality of life. Catherine Chanfreau-Coffinier performed the literature abstraction, and Steven Teutsch reviewed it. We resolved disagreements through consensus.
We performed a best-case study for the expected population impact of the interventions, assuming that all patients with the targeted condition(s) were eligible to receive the treatment and would initiate the treatment. We estimated the total burden of disease and the expected population impact of the interventions using multiple sources of information. Where available we used data presented in the evaluated article. Where data were not included we used (1) statistics from the Centers for Disease Control and Prevention (www.cdc.gov), (2) Cochrane reviews, (The Cochrane Collaboration conducts independent, systematic reviews of evidence to inform health decisions, and their work is available at http://www.cochrane.org/.) and (3) high-quality literature, in that order. All values and sources are listed in Table A-3 in the appendix.
Net Health Benefits
Net health benefits were calculated as lives saved and as gains in QALYs. QALYs are a measure of life expectancy in years of life adjusted for the quality of life [QOL], with an adjustment factor ranging from 1 for a perfect health state to 0 for the worst possible health state.  Using QALYs allows for the comparison of interventions across diverse health conditions. In our framework, QALYs gained are the differences in QALYs resulting from applying the intervention versus the control procedure in each study. QALYs were calculated using the QOL factors documented in the evaluated article whenever available; otherwise, QOL factors were collected from a comprehensive review by Tengs and Wallace  or high-quality QOL or cost-effectiveness literature. All values and sources are listed in Table A-3 in the appendix. The formulas used to estimate population impacts of the intervention are shown in the appendix. QALYs may be gained by saving additional years of life with the intervention and/or by increasing the quality of life over a period of time. In brief, we subtracted the gain in QALYs achieved with the intervention from the gain achieved with the control procedure. In addition, we evaluated average QALYs gained per case and average QALYs gained per life saved, if applicable.
Evaluation of the Intervention Impact at the Patient Level
The expected effect of the intervention per patient was based on the effect size for the primary outcome reported in the study. We assume that the intervention would have the same effect in real-world practice as found in the study and that the effect size would remain constant over time. The studies assessed were all efficacy studies. Thus the expected impact in practice is likely overestimated. Because many studies had small or null effect sizes, we anticipate they would have an even smaller impact in practice.
The average impact of the intervention (or control procedure) was estimated for a “typical” patient. The life expectancy of the “typical” patient at the time of treatment was based on the average age of the sample in the study, the life expectancy for people of that age reported in the 2009 United States Life Tables,  and the average loss of life years expected for a patient with that condition.
Evaluation of the Intervention Impact at the Population Level
The impact of the intervention (based on QALYs gained per patient) was then applied to the whole patient population. To ensure the comparability among studies, we performed calculations on the basis of the current burden of disease. When available, we included information on the treatment uptake and adherence as reported in the studies and incorporated those factors to estimate treatment effectiveness. We calculated the average QALY gain per patient as the total number of QALYs gained for an intervention divided by the number of cases expected for the condition.
To account for the inherent uncertainty, we calculated all point estimates for the population impact of the intervention using the lower and upper bound values for the variables following the methods of the Guide to Community Preventive Services. [6, 7]
We examined 106 original articles describing an intervention study in 26 issues of The New England Journal of Medicine published from January to December 2011 (Figure 1). On the basis of our selection criteria, we identified 64 eligible articles and excluded five articles: three safety studies, one mental health intervention, and one system intervention (Table A-1). Random selection of 20 articles among the 59 included resulted in 20 randomized controlled trial (RCT) studies (the articles not selected are listed in Table A-2).
Most studies were U.S.-based; only six were non-U.S. or international studies. Funding sources were diverse, with nine studies publicly funded, six studies funded by industry, and five with both public and industry funding. Three studies are secondary analyses of large clinical trials. [11,12,13] Although the rationale for each study was clearly documented by the authors, information necessary to evaluate the burden of disease was present in only 50 percent of the articles, and only one study documented quality of life, which was the primary outcome of the intervention. 
We used the information summarized in Table A-3 to calculate the expected population impact of the intervention compared to the control procedures. The minimum and maximum expected values were calculated by applying the intervention to the entire eligible patient population (Table 1). Calculations were performed only for the 13 interventions that found a significant difference in effect between the intervention and the control procedure. The estimated effect sizes vary greatly both in magnitude and in uncertainty across studies (Figure 2). Some of the variations reflect differences in the patient population that may benefit from the intervention: if the eligible patient population is large, even a modest intervention effect may result in a large gain in total QALYs. Consequently, we find that a small number of interventions account for the majority of the population impact, with three of the interventions accounting for more than 80 percent of all expected gains in QALYs. In contrast, the six interventions with the lowest impact produced an expected total QALY gain of less than 1 percent, or 100,000 QALYs.
When viewed on a per patient basis, many of the interventions were found to have a small effect. The exceptions were the cases of less common conditions (type 1 diabetes,  aplastic anemia,  and Turner syndrome ) for which interventions were found to have a large impact at the individual level.
We estimated the likely population impact of intervention studies published in a preeminent journal. A significant difference in effect between the intervention and the control procedure was found in only 13 of the 20 evaluated studies. We observed a wide variation in effect sizes across the studies, both in magnitude and in uncertainty. In particular, we found that most interventions with large population-level impact had a modest intervention effect at the individual level, whereas several interventions for less common or rare conditions had small population effects, but relatively large patient-level impact. Therefore, both population-based and patient-based measures are important to consider, as they may result in very different ranking of the impacts. 
The National Commission on Prevention Priorities has assessed the population impact of clinical preventive services recommended by the U.S. Preventive Services Task Force,  the Agency for Healthcare Research and Quality has assessed the effectiveness of care, and the Patient Centered Outcomes Research Institute has prioritized important clinical care questions. A recent National Heart, Lung, and Blood Institute study showed large differences in the potential impacts of trials it sponsored.  We believe that translational research should focus on those interventions with the greatest potential population health impact.
By systematically sampling 20 intervention studies from a single leading medical journal, all of which were randomized trials, we have demonstrated the feasibility of assessing population health impacts if the study interventions were to be adopted in practice. Remarkably, few of the studies actually report the information needed to assess the population impact, though we were able to obtain the data needed from other sources. Even fewer calculate the overall population impact, and they rarely provide cost or comparisons of costs to health or economic benefits. The lack of this information means reviewers and readers must infer the relevant population and basic information such as the baseline life expectancy and quality of life. It is reasonable to expect researchers to determine or at least estimate the impacts they anticipated prior to embarking on intervention studies. Their ability to do so would be significantly enhanced by more standardized and accessible values for quality of life.
We assessed the incremental value of the interventions compared to the control procedures used in the trials. Choice of trial comparator is of singular importance, and selection of placebo or suboptimal therapy as comparators will overstate the potential impact of interventions. To understand the importance and incremental value of the intervention, trial comparators should include the best available, most reasonable alternative. This was not always the case.
This study has a number of important limitations. The studies included represent a small sample of intervention studies, all of which were published in the same journal in the same year. Thus they may reflect the selection criteria of the journal or may not be representative of intervention studies more generally. Several sources were used to obtain parameter estimates for calculating the population impact, and these sources do not use consistent methods. All studies in our sample were randomized trials, and selection criteria used in each study limit their generalizability to the population as a whole and likely overestimate adherence in both the intervention and control groups. In addition, studies are of limited duration, so estimating longterm effects adds uncertainty. In each case, however, we erred on the side of choosing parameters that would provide the greatest potential impact. Hence, these results are “best case” scenarios that provide an upper bound on population health impacts; real-world impacts are likely to be smaller. Despite these limitations, there are very large differences in population health impact among the studies, differences not plausibly due to methodological decisions or uncertainty. What is apparent is that most studies have at best small or no population health effects and that a small number have substantial population effects.
Three studies were secondary analyses of already-published RCTs. In those cases, we did not assess the impact of the trials as a whole, but rather assessed the effect of the secondary analysis. Although we believe our estimates of population health impacts are reasonable, there is variability in the underlying parameters (e.g., variations by ethnic or racial groups, by geography, or in the definition of certain conditions) and data sources (e.g., estimates can come from cross-sectional studies or clinical environments, be self-reported, be clinician diagnosed, or have definitive diagnoses). These variations introduce uncertainty into the assessment process, and thus the findings should be considered estimates rather than precise determinations. In addition, there are differences in timing of benefits and harms and differential impacts among population groups. Consequently, small differences among studies we examined should be interpreted with caution. In prior work  the population health impact of recommended clinical preventive services differed by several orders of magnitude and services were grouped into five impact categories to reduce attention to small differences among them and to emphasize the enormous, often poorly understood differences in effectiveness and cost-effectiveness. As the number of studies assessing the population health accumulates, a similar approach could minimize overinterpretation of small differences.
We studied the results of published studies, not the original research proposals themselves. Nonetheless, the likelihood of finding little population effect could often have reasonably been anticipated beforehand. The secondary analyses of previously published trials might have been done to expand indications, potentially to identify a subgroup for which use of a technology found ineffective in the primary analysis could be justified.
Because of the limited resources for research, we must always make choices about which studies to conduct. The paucity of studies of population health interventions (policies and programs) reflects both the research priorities of decision makers as well as the complexity of conducting such studies. We believe that research priorities should be heavily informed by the potential population health impact and that researchers, proposal reviewers, and funders need to understand those impacts before intervention studies are initiated. This approach was recently used to estimate the expected value of a proposed study  and to justify the undertaking of a new controlled trial.  To that end, we recommend that the information necessary to estimate the impact of the proposed intervention be uniformly included in research proposals and reports. All requests for proposals of interventions should explicitly require that study proposals project the population health impact of the study using standard procedures such as those we used, and funders should assess the projections and use them in funding decisions. Journals should require that the information be included in reports of intervention studies, and reviewers should be tasked with evaluating the adequacy of the assessments. The addition of this new component in the peer-review process will likely require additional training of the reviewers. By the same token, journals should insist that this information be conveyed effectively to readers so the latter can use the results more effectively. Constant attention to the “so what” question should help direct our public translational research investments in ways that provide the greatest good for the investors and primary beneficiaries, the American public.
Appendix – The Importance of Published Intervention Studies
Table A-1: Articles Reporting an Intervention Excluded from the Sample and Reason for the Exclusion
Table A-2: Articles Reporting an Intervention Not Selected by the Random Draw
Table A-3: Information and Sources on Interventions, Disease Burden, and Expected Impact of the
Intervention (Lives Saved, Cases Averted, and QALY Gains)
References for the Appendix
Data from each of the 20 studies included in the sample were abstracted using the following grid:
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