Pursuing population health gains through better implementation.
The CUNY Institute for Implementation Science in Population Health (ISPH) was founded on the notion that substantial improvements in population health can be efficiently achieved through better implementation of existing strategies, policies, and interventions across multiple sectors. With that in mind, we study how to translate and scale-up evidence-based interventions and policies within clinical and community settings in order to improve population health and reduce health disparities.
The Institute uses a range of digital communication channels to disseminate news and information among a global network of research colleagues and partners. This website serves as a platform for disseminating our scientific work and tools we have developed, as well as showcasing emergent topics in the field of implementation science.
Recent ISPH Publications
Screening and management of mental health and substance use disorders in HIV treatment settings in low- and middle-income countries within the global IeDEA consortium. Parcesepe AM, Mugglin C, Nalugoda F, Bernard C, Yunihastuti E, Althoff K, Jaquet A, Haas AD, Duda SN, Wester CW, and Nash D; International epidemiology to Evaluate AIDS (IeDEA) Consortium. Journal of the International AIDS Society.
The Key Role of Work in Population Health Inequities. Landsbergis PA, Choi B, Dobson M, Sembajwe G, Slatin C, Delp L, Siqueira CE, Schnall P, Baron S. American Journal of Public Health.
Data and Statistical Methods To Analyze the Human Microbiome. Waldron L. mSystems.
HIV treatment eligibility expansion and timely antiretroviral treatment initiation following enrollment in HIV care: A metaregression analysis of programmatic data from 22 countries. Tymejczyk O, Brazier E, Yiannoutsos C, Wools-Kaloustian K, Althoff K, Crabtree-Ramírez B, Van Nguyen K, Zaniewski E, Dabis F, Sinayobye JD, Anderegg N, Ford N, Wikramanayake R, Nash D; IeDEA Collaboration. PLoS Med.
Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections. Bailey SL, Bono RS, Nash D, Kimmel AD.PLoS ONE.