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In response to the Institute on Minority Health and Health Disparities’ (NIMHD) new health disparities research framework, we call on the National Institutes of Health (NIH) to acknowledge Arabs in the United States as a health disparity population. Arab classification as White leads to their cultural invisibility and perpetuates a cycle of undocumented health disparities. We provide examples of how this contested identity reinforces challenges associated with identifying this population and contributes to enactments of structural violence and undocumented health disparities. Decades of research with Arabs in the United States provides consistent evidence that their health does not fit the health profile of White Americans and that Arabs do not benefit from Whiteness and White privilege associated with their White racial categorization. On the contrary, Arabs in the United States experience discrimination and health disparities that require urgent attention; this can be achieved only by identifying the population with a racial category other than White. We conclude with recommendations to NIH and NIMHD to revise their definition of health disparity populations to include Arabs in the United States.

Public health programs may be seriously affected in periods of federal retrenchment. During these times, state-based strategies provide an alternate pathway for advancing public health. A 12-year campaign to secure state support for a network of Centers of Excellence in Children’s Environmental Health (CEH) promoting health of children across New York State is described. It was driven by rising rates of asthma, birth defects, developmental disorders, and other noncommunicable diseases in children; growing evidence associating hazardous environmental exposures with these conditions; and recognition that federal resources in CEH are insufficient. Critical campaign elements were (1) formation of a statewide coalition of academic health centers, health care providers, public health officials, community advocates, and other stakeholders; (2) bipartisan collaborations with legislative champions and government leaders; (3) assessment of the burden of developmental disorders and noncommunicable diseases associated with environmental exposures among children; (4) maps documenting the presence of environmental hazards in every county statewide; (5) iterative charting of a changing political landscape; and (6) persistence. The 2017 award of a 5-year, $10 million contract to establish Centers of Excellence in CEH demonstrates the value of this statewide strategy.

Evaluamos el uso en la salud pública del número reproductivo básico (R0), por el cual se estima la velocidad con que una enfermedad puede propagarse en una población. Estas estimaciones son de gran interés en el campo de la salud pública como quedó de manifiesto en ocasión de la pandemia del 2009 por el virus gripal A (H1N1). Revisamos los métodos usados comúnmente para estimar el R0, examinamos su utilidad práctica y determinamos la forma en que las estimaciones de este parámetro epidemiológico pueden servir de fundamento para tomar decisiones relativas a las estrategias de mitigación. Por sí solo, el R0 es una medida insuficiente de la dinámica de las enfermedades infecciosas en las poblaciones; hay otros parámetros que pueden aportar información más útil. No obstante, la estimación del R0 en una población determinada es útil para entender la transmisión de una enfermedad en ella. Si se considera el R0 en el contexto de otros parámetros epidemiológicos importantes, su utilidad puede consistir en que permite conocer mejor un brote epidémico y preparar la respuesta de salud pública correspondiente.

Although the number of people incarcerated in the United States has grown dramatically, research on how incarceration affects individuals and the communities they return to has lagged behind. This may be because of the unique challenges of doing research within carceral systems and the relatively small number of investigators who are competent to undertake these efforts. We provide a primer for investigators with limited experience conducting research in carceral settings and highlight considerations and recommendations that may aid those conducting health research with incarcerated persons. We follow this with an illustrative case example exemplifying how the considerations apply to recent health research that our team conducted on mental illness prevalence in a large regional jail. Understanding how to effectively conduct research with criminal justice populations and systems is the first step in beginning to understand the effects of mass incarceration as a driver of health disparities and health inequity.

Prompt treatment of ill persons with influenza antivirals will be an important part of a future pandemic influenza response. This essay reviews key lessons learned from the 2009 H1N1 pandemic and the changing landscape of antiviral drug availability, and identifies and describes the multiple components needed to ensure the timely administration of antiviral drugs during a future pandemic. Fortunately, many of these planning efforts can take place before a pandemic strikes to improve outcomes during a future public health emergency.

We provide an overview of classical and newer methods for the control of confounding of time-invariant interventions to permit causal inference in public health evaluations. We estimated the causal effect of gender on all-cause mortality in a large HIV care and treatment program supported by the President’s Emergency Program for AIDS Relief in Dar es Salaam, Tanzania, between 2004 and 2012. We compared results from multivariable modeling, three propensity score methods, inverse-probability weighting, doubly robust methods, and targeted maximum likelihood estimation. Considerable confounding was evident, and, as expected by theory, all methods considered gave the same result, a statistically significant approximately 20% increased mortality rate in men. In general, there is no clear advantage of any of these methods for causal inference over classical multivariable modeling, from the point of view of either bias reduction or efficiency. Rather, given sufficient data to adequately fit the multivariable model to the data, multivariable modeling will yield causal estimates with the greatest statistical efficiency. All methods can adjust only for well-measured confounders—if there are unmeasured or poorly measured confounders, none of these methods will yield causal estimates.

Objectives. To address shortcomings of previous research exploring trends in racial, educational, and race by educational disparities in infant mortality rates (IMRs) by using nonlinear methods to compare improvement within and between disparity domains. Methods. We used joinpoint regression modeling to perform a cross-sectional analysis of IMR trends from linked birth and death certificates in Wisconsin between 1999 and 2016. Results. In the race and education domains, IMR decreased by 1.9% per year for infants of White mothers and 1.1% per year for infants of less-educated mothers. Further analysis showed these IMR reductions to be among infants of White mothers with more education (–0.6%/year) and Black mothers with less education (–2.0%/year). Conclusions. As previously reported, gaps in IMR by race and education in Wisconsin appear to be closing; however, only the change by education is statistically significant. Evidence suggests the racial divide in IMR might soon widen after years of progress in reducing IMR among infants of Black mothers. Public Health Implications. Those advancing strategies to address IMR disparities should pursue data and methods that provide the most accurate and refined information about the challenges that persist and progress that has been realized.

Objectives. To characterize the conflict of sex and gender identity variables in the 2014 Behavioral Risk Factor Surveillance System (BRFSS) sample and examine how this may affect the administration of sex-related health behavior items to transgender participants. Methods. We conducted a secondary analysis of the 2014 BRFSS gender identity, sex, and sex-related health behavior variables. Twenty states administered the gender-identity variables (n = 154 062), and 691 respondents identified as transgender in the survey (0.4%). We examined conflict among sex, gender identity, and gender-related variables, and compared conflicting and nonconflicting groups across 4 sociodemographic characteristics. Results. Nearly one third of respondents (27.8%; n = 171) who identified as transgender received sex-specific items that conflicted with their natal sex, thereby reducing the already small subsample of valid responses. There were no significant differences between conflicting and nonconflicting groups on the basis of region, age, race/ethnicity, or type of interview. Conclusions. Public health surveys should ask respondents to self-identify their sex and gender identity. Interviewer assumptions of respondents’ sex may lead to erroneous collection of sex- and gender-based items, inhibit survey administration, and create problems in data quality.

Objectives. To assess the use of local measures of segregation for monitoring health inequities by local health departments. Methods. We analyzed preterm birth and premature mortality (death before the age of 65 years) rates for Boston, Massachusetts, for 2010 to 2012, using the Index of Concentration at the Extremes (ICE) and the poverty rate at both the census tract and neighborhood level. Results. For premature mortality at the census tract level, the rate ratios comparing the worst-off and best-off terciles were 1.58 (95% confidence interval [CI] = 1.36, 1.83) for the ICE for income, 1.66 (95% CI = 1.43, 1.93) for the ICE for race/ethnicity, and 1.63 (95% CI = 1.40, 1.90) for the ICE combining income and race/ethnicity, as compared with 1.47 (95% CI = 1.27, 1.71) for the poverty measure. Results for the ICE and poverty measures were more similar for preterm births than for premature mortality. Conclusions. The ICE, a measure of social spatial polarization, may be useful for analyzing health inequities at the local level. Public Health Implications. Local health departments in US cities can meaningfully use the ICE to monitor health inequities associated with racialized economic segregation.

Counterfactual frameworks and statistical methods for supporting causal inference are powerful tools to clarify scientific questions and guide analyses in public health research. Counterfactual accounts of causation contrast what would happen to a population’s health under alternative exposure scenarios. A long-standing debate in counterfactual theory relates to whether sex, race, and biological characteristics, including obesity, should be evaluated as causes, given that these variables do not directly correspond to clearly defined interventions. We argue that sex, race, and biological characteristics are important health determinants. Quantifying the overall health effects of these variables is often a natural starting point for disparities research. Subsequent assessments of biological or social pathways mediating those effects can facilitate the development of interventions designed to reduce disparities.

Objectives. To describe results of points of dispensing (POD) medical countermeasure drill performance among local jurisdictions. Methods. To compare POD setup times for each year, we calculated descriptive statistics of annual jurisdictional POD setup data submitted by over 400 local jurisdictions across 50 states and 8 US territories to a Centers for Disease Control and Prevention (CDC) program monitoring database from July 2012 to June 2016. Results. In data collected from July 2012 to June 2015, fewer than 5% of PODs required more than 240 minutes to set up, although the proportion increased from July 2015 to June 2016 to almost 12%. From July 2012 to June 2016, more than 60% of PODs were set up in less than 90 minutes, with 60 minutes as the median setup time during the period. Conclusions. Our results yield evidence of national progress for response to a mass medical emergency. Technical assistance may be required to aid certain jurisdictions for improvement. Public Health Implications. The results of this study may inform future target times for performance on POD setup activities and highlight jurisdictions in need of technical assistance.

Objectives. To demonstrate how we applied the capture–recapture method for population estimation directly in a rural Appalachian county (Cabell County, WV) to estimate the number of people who inject drugs (PWID). Methods. We conducted 2 separate 2-week periods of data collection in June (“capture”) and July (“recapture”) 2018. We recruited PWID from a syringe services program and in community locations where PWID were known to congregate. Participants completed a survey that included measures related to sociodemographics, substance use, and HIV and hepatitis C virus prevention. Results. In total, 797 surveys were completed; of these surveys, 49.6% (n = 395) reflected PWID who reported injection drug use in the past 6 months and Cabell County residence. We estimated that there were 1857 (95% confidence interval = 1147, 2567) PWID in Cabell County. Among these individuals, most reported being White (83.4%), younger than 40 years (70.9%), and male (59.5%). The majority reported injecting heroin (82.0%), methamphetamine (71.0%), and fentanyl (56.3%) in the past 6 months. Conclusions. Capture–recapture methods can be applied in rural settings to estimate the size of PWID populations.

Research about relationships between place characteristics and racial/ethnic inequities in health has largely ignored conceptual advances about race and place within the discipline of geography.Research has also almost exclusively quantified these relationships using effect estimates (e.g., odds ratios), statistics that fail to adequately capture the full impact of place characteristics on inequities and thus undermine our ability to translate research into action.We draw on geography to further develop the concept of “racialized risk environments,” and we argue for the routine calculation of race/ethnicity-specific population-attributable risk percentages.

We have summarized the evolution of the Nurses’ Health Study (NHS), a prospective cohort study of 121 700 married registered nurses launched in 1976; NHS II, which began in 1989 and enrolled 116 430 nurses; and NHS3, which began in 2010 and has ongoing enrollment. Over 40 years, these studies have generated long-term, multidimensional data, including lifestyle- and health-related information across the life course and an extensive repository of various biological specimens. We have described the questionnaire data collection, disease follow-up methods, biorepository resources, and data management and statistical procedures. Through integrative analyses, these studies have sustained a high level of scientific productivity and substantially influenced public health recommendations. We have highlighted recent interdisciplinary research projects and discussed future directions for collaboration and innovation.

In public health evaluations, confounding bias in the estimate of the intervention effect will typically threaten the validity of the findings. It is a common misperception that the only way to avoid this bias is to measure detailed, high-quality data on potential confounders for every intervention participant, but this strategy for adjusting for confounding bias is often infeasible. Rather than ignoring confounding altogether, the two-phase design and analysis—in which detailed high-quality confounding data are obtained among a small subsample—can be considered. We describe the two-stage design and analysis approach, and illustrate its use in the evaluation of an intervention conducted in Dar es Salaam, Tanzania, of an enhanced community health worker program to improve antenatal care uptake.

Nonrandomized studies are essential in the postmarket activities of the US Food and Drug Administration, which, however, must often act on the basis of imperfect data. Systematic errors can lead to inaccurate inferences, so it is critical to develop analytic methods that quantify uncertainty and bias and ensure that these methods are implemented when needed. “Quantitative bias analysis” is an overarching term for methods that estimate quantitatively the direction, magnitude, and uncertainty associated with systematic errors influencing measures of associations. The Food and Drug Administration sponsored a collaborative project to develop tools to better quantify the uncertainties associated with postmarket surveillance studies used in regulatory decision making. We have described the rationale, progress, and future directions of this project.

In a stepped wedge design (SWD), an intervention is rolled out in a staggered manner over time, in groups of experimental units, so that by the end, all units experience the intervention. For example, in the MaxART study, the date at which to offer universal antiretroviral therapy to otherwise ineligible clients is being randomly assigned in nine “steps” of four months duration so that after three years, all 14 facilities in northern and central Swaziland will be offering early treatment. In the common alternative, the cluster randomized trial (CRT), experimental units are randomly allocated on a single common start date to the interventions to be compared. Often, the SWD is more feasible than the CRT, both for practical and ethical reasons, but takes longer to complete. The SWD permits both within- and between- unit comparisons, while the CRT only allows between-unit comparisons. Thus, confounding bias with respect to time-invariant factors tends to be lower in an SWD than a CRT, but the SWD cannot as readily control for confounding by time-varying factors. SWDs have generally more statistical power than CRTs, especially as the intraunit correlation and the number of participants within unit increases. Software for both designs are available, although for a more limited set of SWD scenarios.

Objectives. To demonstrate an information technology–based approach to assess characteristics of streets and intersections associated with injuries that is less costly and time-consuming than location-based studies of pedestrian injury. Methods. We used imagery captured by Google Street View from 2007 to 2011 to assess 9 characteristics of 532 intersections within New York City. We controlled for estimated pedestrian count and estimated the relation between intersections’ characteristics and frequency of injurious collisions. Results. The count of pedestrian injuries at intersections was associated with the presence of marked crosswalks (80% increase; 95% confidence interval [CI] = 2%, 218%), pedestrian signals (156% increase; 95% CI = 69%, 259%), nearby billboards (42% increase; 95% CI = 7%, 90%), and bus stops (120% increase; 95% CI = 51%, 220%). Injury incidence per pedestrian was lower at intersections with higher estimated pedestrian volumes. Conclusions. Consistent with in-person study observations, the information-technology approach found traffic islands, visual advertising, bus stops, and crosswalk infrastructures to be associated with elevated counts of pedestrian injury in New York City. Virtual site visits for pedestrian injury control studies are a viable and informative methodology.

In the first contribution to a new section in AJPH that will address critical methodological issues in evaluations of public health interventions, I will discuss topics in study design and analysis, covering the most innovative emerging methodologies and providing an overview of best practices. The methods considered are motivated by public health evaluations, both domestic and global. In this first contribution, I also define implementation science, program evaluation, impact evaluation, and cost-effectiveness research, disciplines that have tremendous methodological and substantive overlap with evaluation of public health interventions—the focus of this section.

Objectives. To describe disparities in HIV infection and syphilis among gay, bisexual, and other men who have sex with men (MSM) in US states through ratio-based measures and graphical depictions of disparities. Methods. We used state-level surveillance data of reported HIV and syphilis cases in 2015 and 2016, and estimates of MSM population sizes to estimate HIV and syphilis prevalence by race/ethnicity and rate ratios (RRs) and to visually display patterns of disparity and prevalence among US states. Results. State-specific rates of new HIV diagnoses were higher for Black than for White MSM (RR range = 2.35 [Rhode Island] to 10.12 [Wisconsin]) and for Hispanic than for White MSM (RR range = 1.50 [Tennessee] to 5.78 [Pennsylvania]). Rates of syphilis diagnoses were higher for Black than for White MSM in 42 of 44 states (state RR range = 0.89 [Hawaii] to 17.11 [Alaska]). Scatterplots of HIV diagnosis rates by race showed heterogeneity in epidemic scenarios, even in states with similar ratio-based disparity measures. Conclusions. There is a widely disparate impact of HIV and syphilis among Black and Hispanic MSM compared with White MSM. Between-state variation suggests that states should tailor and focus their prevention responses to best address state data.

Rapidly training numerous staff and volunteers to distribute and dispense medical countermeasures is challenging because of limited resources and evolving information during public health emergencies. The Applied Learning and Development Team within the Division of State and Local Readiness at the Centers for Disease Control and Prevention (CDC) proposes just-in-time training (JITT) templates that can be rapidly customized and implemented early in any public health emergency. The proposed template model aligns with modular training design research to increase relevance and rapid deployment of training. Two case studies are described to demonstrate the potential for training templates to support medical countermeasure responses: (1) customization and implementation of a JITT to prepare staff to work on a CDC task force during the 2016–2017 Zika virus response and (2) a new modular, customizable course to teach the basics about working at a point-of-dispensing site. Flexible JITT templates in these cases reduce the burden on emergency planners and trainers, allowing for rapidly developed, customized training viable for all emergency responses.

The choice of the standard population is important when calculating adjusted rates for a military population: results can influence policies and funding allocations for programs and initiatives for suicide prevention. We describe the methodological considerations and decision-making process used in choosing a standard population for adjusting rates to compare suicide among US Army soldiers and the general US population. We examined 5 different standard populations, using the direct method to adjust annual suicide rates for the Army and the US population, 2004 to 2015, for age and for age and sex. The pattern of the Army and US population age- and sex-adjusted rates remained consistent with crude rates when adjusted to any of the Army standard population distributions. Using an Army distribution as the standard population produces suicide rates consistent with routine messaging about suicide trends among Army soldiers.

Life expectancy (LE) based on a period life table (PLT) traditionally serves as a general population summary metric. It is, however, becoming more frequently reported for chronically afflicted subpopulations. In general populations, there is always an obvious real cohort sharing the hypothetical PLT cohort characteristics, and the LE estimate is intuitively understood as that real cohort mean survival time, assuming constancy of death risks. In diseased subpopulations, the correspondence between the hypothetical cohort and a real cohort is not straightforward. Furthermore, the excess mortality of chronic diseases usually changes according to age at onset and time since onset. The standard PLT method does not allow for proper control of these issues, so the LE estimate can only be deemed valid under specific assumptions. Without clear statements about the real cohort to whom the estimate is intended and the assumptions allowing disregard of the effect of age at onset and time since onset, LEs of afflicted subpopulations computed with the PLT are only abstract numbers summarizing mortality rates. If called “life expectancy,” they can be seriously misleading. The same applies to health-adjusted LE.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

Objectives. To determine the generalizability of crowdsourced, electronic health data from self-selected individuals using a national survey as a reference. Methods. Using the world’s largest crowdsourcing platform in 2015, we collected data on characteristics known to influence cardiovascular disease risk and identified comparable data from the 2013 Behavioral Risk Factor Surveillance System. We used age-stratified logistic regression models to identify differences among groups. Results. Crowdsourced respondents were younger, more likely to be non-Hispanic and White, and had higher educational attainment. Those aged 40 to 59 years were similar to US adults in the rates of smoking, diabetes, hypertension, and hyperlipidemia. Those aged 18 to 39 years were less similar, whereas those aged 60 to 75 years were underrepresented among crowdsourced respondents. Conclusions. Crowdsourced health data might be most generalizable to adults aged 40 to 59 years, but studies of younger or older populations, racial and ethnic minorities, or those with lower educational attainment should approach crowdsourced data with caution. Public Health Implications. Policymakers, the national Precision Medicine Initiative, and others planning to use crowdsourced data should take explicit steps to define and address anticipated underrepresentation by important population subgroups.

Objectives. To evaluate whether the quality of implementation of a water, sanitation, and hygiene program called SHEWA-B and delivered by UNICEF to 20 million people in rural Bangladesh was associated with health behaviors and sanitation infrastructure access. Methods. We surveyed 33 027 households targeted by SHEWA-B and 1110 SHEWA-B hygiene promoters in 2011 and 2012. We developed an implementation quality index and compared the probability of health behaviors and sanitation infrastructure access in counterfactual scenarios over the range of implementation quality. Results. Forty-seven percent of households (n = 14 622) had met a SHEWA-B hygiene promoter, and 47% of hygiene promoters (n = 527) could recall all key program messages. The frequency of hygiene promoter visits was not associated with improved outcomes. Higher implementation quality was not associated with better health behaviors or infrastructure access. Outcomes differed by only 1% to 3% in scenarios in which all clusters received low versus high implementation quality. Conclusions. SHEWA-B did not meet UNICEF’s ideal implementation quality in any area. Improved implementation quality would have resulted in marginal changes in health behaviors or infrastructure access. This suggests that SHEWA-B’s design was suboptimal for improving these outcomes.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have highlighted the developments of the past 13 years in design with a companion article to focus on developments in analysis. As a pair, these articles update the 2004 review. We have discussed developments in the topics of the earlier review (e.g., clustering, matching, and individually randomized group-treatment trials) and in new topics, including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped-wedge GRT, the pseudocluster randomized trial, and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

Objectives. To compare the predictive power of synthetic absolute income measures with that of asset-based wealth quintiles in low- and middle-income countries (LMICs) using child stunting as an outcome. Methods. We pooled data from 239 nationally representative household surveys from LMICs and computed absolute incomes in US dollars based on households’ asset rank as well as data on national consumption and inequality levels. We used multivariable regression models to compare the predictive power of the created income measure with the predictive power of existing asset indicator measures. Results. In cross-country analysis, log absolute income predicted 54.5% of stunting variation observed, compared with 20% of variation explained by wealth quintiles. For within-survey analysis, we also found absolute income gaps to be predictive of the gaps between stunting in the wealthiest and poorest households (P Conclusions. Our results suggest that absolute income levels can greatly improve the prediction of stunting levels across and within countries over time, compared with models that rely solely on relative wealth quintiles.

To systematically improve population health in the United States, community health records (CHRs) must be defined, developed, and implemented. Like electronic and personal health records, CHRs have both unique and overlapping information. CHRs contain data about communities, including the social, physical, and lifestyle determinants of health. These records will serve to complement electronic and personal health records to provide a more complete view of population health, allowing stakeholders to target community health and quality-of-life interventions in a data-driven and evidence-based manner, establishing the basis from which organizations can develop a systematic approach to improving community health. This commentary calls on the United States to conduct a set of consensus activities to define and implement CHRs.

The production of systematic reviews is increasing, but their credibility is under threat. Although systematic reviews are an important tool for policymaking, their influence can be weakened by methodological problems and poor policy relevance. Using Cochrane as an example, I address standards for systematic reviews, the influence of special interests on these reviews, and ways to increase their relevance for policymakers.

Systematic reviews have, increasingly, informed policy for almost 3 decades. In many countries, systematic reviews have informed policy for public and population health, paying for health care, increasing the quality and efficiency of interventions, and improving the effectiveness of health sector professionals and the organizations in which they work. Systematic reviews also inform other policy areas: criminal justice, education, social welfare, and the regulation of toxins in the environment. Although the production and use of systematic reviews has steadily increased, many clinicians, public health officials, representatives of commercial organizations, and, consequently, policymakers who are responsive to them, have been reluctant to use these reviews to inform policy; others have actively opposed using them. Systematic reviews could inform policy more effectively with changes to current practices and the assumptions that sustain these practices—assumptions made by researchers and the organizations that employ them, by public and private funders of systematic reviews, and by organizations that finance, set priorities and standards for, and publish them.

Surveying governmental public health practitioners is a critical means of collecting data about public health organizations, their staff, and their partners. A greater focus on evidence-based practices, practice-based systems research, and evaluation has resulted in practitioners consistently receiving requests to participate in myriad surveys. This can result in a substantial survey burden for practitioners and declining response rates for researchers. This is potentially damaging to practitioners and researchers as well as the field of public health more broadly. We have examined recent developments in survey research, especially issues highly relevant for public health practice. We have also proposed a process by which researchers can engage with practitioners and practitioner groups on research questions of mutual interest.

The Nurses’ Health Study and many other large longitudinal cohorts around the world use the food frequency questionnaire to assess dietary intake over time, and to relate diet to health. Controversies concerning this questionnaire’s ability to adequately measure diet have led to a flurry of methods for evaluating the magnitude of measurement error and misclassification in exposure assessment, and for correcting the point and interval estimates of effect on the basis of these assessment methods for this error. Nurses’ Health Study investigators have been in the forefront of these developments and their applications, although hundreds of other investigators have also used them. This commentary provides an overview of the methods and their uses, and concludes with remarks on their potential applications in the evaluation of public health interventions.

Objectives. To review the contribution of the Nurses’ Health Studies (NHSs) to diet assessment methods and evidence-based nutritional policies and guidelines. Methods. We performed a narrative review of the publications of the NHS and NHS II between 1976 and 2016. Results. Through periodic assessment of diet by validated dietary questionnaires over 40 years, the NHSs have identified dietary determinants of diseases such as breast and other cancers; obesity; type 2 diabetes; cardiovascular, respiratory, and eye diseases; and neurodegenerative and mental health disorders. Nutritional biomarkers were assessed using blood, urine, and toenail samples. Robust findings, from the NHSs, together with evidence from other large cohorts and randomized dietary intervention trials, have contributed to the evidence base for developing dietary guidelines and nutritional policies to reduce intakes of trans fat, saturated fat, sugar-sweetened beverages, red and processed meats, and refined carbohydrates while promoting higher intake of healthy fats and carbohydrates and overall healthful dietary patterns. Conclusions. The long-term, periodically collected dietary data in the NHSs, with documented reliability and validity, have contributed extensively to our understanding of the dietary determinants of various diseases, informing dietary guidelines and shaping nutritional policy.

Objectives. To study the effects of several survey features on response rates in a general population health survey. Methods. In 2012 and 2013, 8000 households in British Columbia, Canada, were randomly allocated to 1 of 7 survey variants, each containing a different combination of survey features. Features compared included administration modes (paper vs online), prepaid incentive ($2 coin vs none), lottery incentive (instant vs end-of-study), questionnaire length (10 minutes vs 30 minutes), and sampling frame (InfoCanada vs Canada Post). Results. The overall response rate across the 7 groups was 27.9% (range = 17.1–43.4). All survey features except the sampling frame were associated with statistically significant differences in response rates. The survey mode elicited the largest effect on the odds of response (odds ratio [OR] = 2.04; 95% confidence interval [CI] = 1.61, 2.59), whereas the sampling frame showed the least effect (OR = 1.14; 95% CI = 0.98, 1.34). The highest response was achieved by mailing a short paper survey with a prepaid incentive. Conclusions. In a mailed general population health survey in Canada, a 40% to 50% response rate can be expected. Questionnaire administration mode, survey length, and type of incentive affect response rates.

We discuss 3 popular misconceptions about Cronbach α or coefficient α, traditionally used in public health and the behavioral sciences as an index of test score reliability. We also review several other indices of test score reliability. We encourage researchers to thoughtfully consider the nature of their data and the options when choosing an index of reliability, and to clearly communicate this choice and its implications to their audiences.

Objectives. To investigate accuracy of distance measures computed from Global Positioning System (GPS) points in New York City. Methods. We performed structured walks along urban streets carrying Globalsat DG-100 GPS Data Logger devices in highest and lowest quartiles of building height and tree canopy cover. We used ArcGIS version 10.1 to select walks and compute the straight-line distance (Geographic Information System–measured) and sum of distances between consecutive GPS waypoints (GPS-measured) for each walk. Results. GPS distance overestimates were associated with building height (median overestimate = 97% for high vs 14% for low building height) and to a lesser extent tree canopy (43% for high vs 28% for low tree canopy). Conclusions. Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially by context. Researchers studying urban spaces may prefer alternative mode identification techniques.

Objectives. We evaluated use of the Index of Concentration at the Extremes (ICE) for public health monitoring. Methods. We used New York City data centered around 2010 to assess cross-sectional associations at the census tract and community district levels, for (1) diverse ICE measures plus the US poverty rate, with (2) infant mortality, premature mortality (before age 65 years), and diabetes mortality. Results. Point estimates for rate ratios were consistently greatest for the novel ICE that jointly measured extreme concentrations of income and race/ethnicity. For example, the census tract–level rate ratio for infant mortality comparing the bottom versus top quintile for an ICE contrasting low-income Black versus high-income White equaled 2.93 (95% confidence interval [CI] = 2.11, 4.09), but was 2.19 (95% CI = 1.59, 3.02) for low versus high income, 2.77 (95% CI = 2.02, 3.81) for Black versus White, and 1.56 (95% CI = 1.19, 2.04) for census tracts with greater than or equal to 30% versus less than 10% below poverty. Conclusions. The ICE may be a useful metric for public health monitoring, as it simultaneously captures extremes of privilege and deprivation and can jointly measure economic and racial/ethnic segregation.

Objectives. We sought to outline the framework and methods used by the World Health Organization (WHO) STEPwise approach to noncommunicable disease (NCD) surveillance (STEPS), describe the development and current status, and discuss strengths, limitations, and future directions of STEPS surveillance. Methods. STEPS is a WHO-developed, standardized but flexible framework for countries to monitor the main NCD risk factors through questionnaire assessment and physical and biochemical measurements. It is coordinated by national authorities of the implementing country. The STEPS surveys are generally household-based and interviewer-administered, with scientifically selected samples of around 5000 participants. Results. To date, 122 countries across all 6 WHO regions have completed data collection for STEPS or STEPS-aligned surveys. Conclusions. STEPS data are being used to inform NCD policies and track risk-factor trends. Future priorities include strengthening these linkages from data to action on NCDs at the country level, and continuing to develop STEPS’ capacities to enable a regular and continuous cycle of risk-factor surveillance worldwide.

The article focuses on personalized medicine in disease and injury prevention. It states recommendations in regards to prevention shouldn't change among subgroups unless the physician believes the expected benefit from the recommendation would differ to a large degree and that emotional, physical, and economic costs for patient adherence would be too great to justify the cost. It talks about considering potential years of life saved when gauging a difference in impact of preventive medicine.

Objectives. To evaluate publicly available data sets to determine their utility for studying refugee health. Methods. We searched for keywords describing refugees in data sets within the Society of General Internal Medicine Dataset Compendium and the Inter-University Consortium for Political and Social Research database. We included in our analysis US-based data sets with publicly available documentation and a self-defined, health-related focus that allowed for an examination of patient-level factors. Results. Of the 68 data sets that met the study criteria, 37 (54%) registered keyword matches related to refugees, but only 2 uniquely identified refugees. Conclusions. Few health data sets identify refugee status among participants, presenting barriers to understanding refugees’ health and health care needs. Public Health Implications. Information about refugee status in national health surveys should include expanded demographic questions and focus on mental health and chronic disease.

We assessed public health use of R0, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic. We reviewed methods commonly used to estimate R0, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions. In isolation, R0 is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R0 for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R0 may lie in better understanding an outbreak and in preparing a public health response.