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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.

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.

Evaluamos el uso en la salud pública del número reproductivo básico (R 0 ), 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 R 0 , 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 R 0 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 R 0 en una población determinada es útil para entender la transmisión de una enfermedad en ella. Si se considera el R 0 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.

Despite the ubiquity of health-related communications via social media, no consensus has emerged on whether this medium, on balance, jeopardizes or promotes public health. During the COVID-19 pandemic, social media has been described as the source of a toxic “infodemic” or a valuable tool for public health. No conceptual model exists for examining the roles that social media can play with respect to population health. We present a novel framework to guide the investigation and assessment of the effects of social media on public health: the SPHERE (Social media and Public Health Epidemic and REsponse) continuum. This model illustrates the functions of social media across the epidemic–response continuum, ranging across contagion, vector, surveillance, inoculant, disease control, and treatment. We also describe attributes of the communications, diseases and pathogens, and hosts that influence whether certain functions dominate over others. Finally, we describe a comprehensive set of outcomes relevant to the evaluation of the effects of social media on the public’s health.

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.

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 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. 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.

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 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.