OBJECTIVES: This study assessed mortality associated with the mid-July 1995 heat wave in Chicago. METHODS: Analyses focused on heat-related deaths, as designated by the medical examiner, and on the number of excess deaths. RESULTS: In July 1995, there were 514 heat-related deaths and 696 excess deaths. People 65 years of age or older were overrepresented and Hispanic people underrepresented. During the most intense heat (July 14 through 20), there were 485 heat-related deaths and 739 excess deaths. CONCLUSIONS: The methods used here provide insight into the great impact of the Chicago heat wave on selected populations, but the lack of methodological standards makes comparisons across geographical areas problematic.

BackgroundImplementing treatments and interventions with demonstrated effectiveness is critical for improving patient health outcomes at a reduced cost. When an evidence-based intervention (EBI) is implemented with fidelity in a setting that is very similar to the setting wherein it was previously found to be effective, it is reasonable to anticipate similar benefits of that EBI. However, one goal of implementation science is to expand the use of EBIs as broadly as is feasible and appropriate in order to foster the greatest public health impact. When implementing an EBI in a novel setting, or targeting novel populations, one must consider whether there is sufficient justification that the EBI would have similar benefits to those found in earlier trials.DiscussionIn this paper, we introduce a new concept for implementation called “scaling-out” when EBIs are adapted either to new populations or new delivery systems, or both. Using existing external validity theories and multilevel mediation modeling, we provide a logical framework for determining what new empirical evidence is required for an intervention to retain its evidence-based standard in this new context. The motivating questions are whether scale-out can reasonably be expected to produce population-level effectiveness as found in previous studies, and what additional empirical evaluations would be necessary to test for this short of an entirely new effectiveness trial. We present evaluation options for assessing whether scaling-out results in the ultimate health outcome of interest.ConclusionIn scaling to health or service delivery systems or population/community contexts that are different from the setting where the EBI was originally tested, there are situations where a shorter timeframe of translation is possible. We argue that implementation of an EBI in a moderately different setting or with a different population can sometimes “borrow strength” from evidence of impact in a prior effectiveness trial. The collection of additional empirical data is deemed necessary by the nature and degree of adaptations to the EBI and the context. Our argument in this paper is conceptual, and we propose formal empirical tests of mediational equivalence in a follow-up paper.

Implementation science has great potential to improve the health of communities and individuals who are not achieving health equity. However, implementation science can exacerbate health disparities if its use is biased toward entities that already have the highest capacities for delivering evidence-based interventions. In this article, we ex­amine several methodologic approaches for conducting implementation research to ad­vance equity both in our understanding of what historically disadvantaged populations would need—what we call scientific equi­ty—and how this  knowledge can be applied to produce health equity. We focus on rapid ways to gain knowledge on how to engage, design research, act, share, and sustain successes in partnership with communities. We begin by describing a principle-driven partnership process between community members and implementation researchers to overcome disparities. We then review three innovative implementation method paradigms to improve scientific and health equity and provide examples of each. The first paradigm involves making efficient use of existing data by applying epidemiologic and simulation modeling to understand what drives disparities and how they can be overcome. The second paradigm involves designing new research studies that include, but do not focus exclusively on, popula­tions experiencing disparities in health domains such as cardiovascular disease and co-occurring mental health conditions. The third paradigm involves implementation research that focuses exclusively on popula­tions who have experienced high levels of disparities. To date, our scientific enterprise has invested disproportionately in research that fails to eliminate health disparities. The implementation research methods discussed here hold promise for overcoming barri­ers and achieving health equity.Ethn Dis. 2019;29(Suppl 1):83-92; doi:10.18865/ed.29.S1.83.

Background AIDSVu is a public resource for visualizing HIV surveillance data and other population-based information relevant to HIV prevention, care, policy, and impact assessment. Objective The site, AIDSVu.org, aims to make data about the US HIV epidemic widely available, easily accessible, and locally relevant to inform public health decision making. Methods AIDSVu develops visualizations, maps, and downloadable datasets using results from HIV surveillance systems, other population-based sources of information (eg, US Census and national probability surveys), and other data developed specifically for display and dissemination through the website (eg, pre-exposure prophylaxis [PrEP] prescriptions). Other types of content are developed to translate surveillance data into summarized content for diverse audiences using infographic panels, interactive maps, local and state fact sheets, and narrative blog posts. Results Over 10 years, AIDSVu.org has used an expanded number of data sources and has progressively provided HIV surveillance and related data at finer geographic levels, with current data resources providing HIV prevalence data down to the census tract level in many of the largest US cities. Data are available at the county level in 48 US states and at the ZIP Code level in more than 50 US cities. In 2019, over 500,000 unique users consumed AIDSVu data and resources, and HIV-related data and insights were disseminated through nearly 4,000,000 social media posts. Since AIDSVu’s inception, at least 249 peer-reviewed publications have used AIDSVu data for analyses or referenced AIDSVu resources. Data uses have included targeting of HIV testing programs, identifying areas with inequitable PrEP uptake, including maps and data in academic and community grant applications, and strategically selecting locations for new HIV treatment and care facilities to serve high-need areas. Conclusions Surveillance data should be actively used to guide and evaluate public health programs; AIDSVu translates high-quality, population-based data about the US HIV epidemic and makes that information available in formats that are not consistently available in surveillance reports. Bringing public health surveillance data to an online resource is a democratization of data, and presenting information about the HIV epidemic in more visual formats allows diverse stakeholders to engage with, understand, and use these important public health data to inform public health decision making.

BackgroundWe evaluated willingness to participate in CVCT and associated factors among MSM in the United States.Methods5,980 MSM in the US, recruited through MySpace.com, completed an online survey March-April, 2009. A multivariable logistic regression model was built using being “willing” or “unwilling” to participate in CVCT in the next 12 months as the outcome.ResultsOverall, 81.5% of respondents expressed willingness to participate in CVCT in the next year. Factors positively associated with willingness were: being of non-Hispanic Black (adjusted odds ratio [aOR]: 1.5, 95% confidence interval [CI]: 1.2–1.8), Hispanic (aOR: 1.3, CI: 1.1–1.6), or other (aOR: 1.4, CI: 1.1–1.8) race/ethnicity compared to non-Hispanic White; being aged 18–24 (aOR: 2.5, CI: 1.7–3.8), 25–29 (aOR: 2.3, CI: 1.5–3.6), 30–34 (aOR: 1.9, CI: 1.2–3.1), and 35–45 (aOR: 2.3, CI: 1.4–3.7) years, all compared to those over 45 years of age; and having had a main male sex partner in the last 12 months (aOR: 1.9, CI: 1.6–2.2). Factors negatively associated with willingness were: not knowing most recent male sex partner’s HIV status (aOR: 0.81, CI: 0.69–0.95) compared to knowing that the partner was HIV-negative; having had 4–7 (aOR: 0.75, CI: 0.61–0.92) or >7 male sex partners in the last 12 months (aOR: 0.62, CI: 0.50–0.78) compared to 1 partner; and never testing for HIV (aOR: 0.38, CI: 0.31–0.46), having been tested over 12 months ago (aOR: 0.63, CI: 0.50–0.79), or not knowing when last HIV tested (aOR: 0.67, CI: 0.51–0.89), all compared to having tested 0–6 months previously.ConclusionsYoung MSM, men of color, and those with main sex partners expressed a high level of willingness to participate in couples HIV counseling and testing with a male partner in the next year. Given this willingness, it is likely feasible to scale up and evaluate CVCT interventions for US MSM.