Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Estimating the prevalence and incidence of treated type 2 diabetes using prescription data as a proxy: A stepwise approach on Iranian data

Aims: Type 2 diabetes is a serious health challenge, and large-scale studies on its prevalence in Iran are lacking. In pharmacoepidemiology, case-finding can be done by reviewing the prescription databases for specific drug(s) prescribed for a disease. We aimed to determine the prevalence and incidence of type 2 diabetes in Fars province, Iran, using prescription data and a stepwise approach to ascertain the results. Methods: A dataset of 3,113 insured individuals aged ≥35 years were selected. Their Prescription Data Centre records were reviewed for all drugs frequently used in controlling type 2 diabetes available in the Iranian pharmacopeia. Then we used a stepwise method for case-finding. In step one, each individual with a positive drug history for type 2 diabetes was labeled as an individual with diabetes. The next two steps were implemented for ascertainment of step one estimations. Results: Prevalence of type 2 diabetes based on prescription, internist opinion, and phone call verification in 2015 and 2016 was 9.3% and 10.3%, 8.5% and 9.8%, and 7.2% and 8.7%, respectively. An incidence of 1.9% was determined for 2016. Conclusions: We obtained a realistic estimation of prevalence and incidence of treated type 2 diabetes, using prescription data which are large-scale, low cost, and real-time

Characteristics and outcomes of patients presenting with acute myocardial infarction and cardiogenic shock during COVID-19.

ObjectivesTo evaluate characteristics and outcomes of patients presenting with acute myocardial infarction and cardiogenic shock (AMICS) during the coronavirus disease 2019 (COVID-19) pandemic.BackgroundThe COVID-19 pandemic has created challenges in delivering acute cardiovascular care. Quality measures and outcomes of patients presenting with AMICS during COVID-19 in the United States have not been well described.MethodsWe identified 406 patients from the National Cardiogenic Shock Initiative (NCSI) with AMICS and divided them into those presenting before (N = 346, 5/9/2016-2/29/2020) and those presenting during the COVID-19 pandemic (N = 60, 3/1/2020-11/10/2020). We compared baseline clinical data, admission characteristics, and outcomes.ResultsThe median age of the cohort was 64 years, and 23.7% of the group was female. There were no significant differences in age, sex, and medical comorbidities between the two groups. Patients presenting during the pandemic were less likely to be Black compared to those presenting prior. Median door to balloon (90 vs. 88 min, p = 0.38), door to support (88 vs. 78 min, p = 0.13), and the onset of shock to support (74 vs. 62 min, p = 0.15) times were not significantly different between the two groups. Patients presented with ST-elevation myocardial infarction more often during the COVID-19 period (95.0% vs. 80.0%, p = 0.005). In adjusted logistic regression models, COVID-19 period did not significantly associate with survival to discharge (odds ratio [OR] 1.09, 95% confidence interval [CI] 0.54-2.19, p = 0.81) or with 1-month survival (OR 0.82, 95% CI 0.42-1.61, p = 0.56).ConclusionsCare of patients presenting with AMICS has remained robust among hospitals participating in the NCSI during the COVID-19 pandemic