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.


Staff Spotlight>>

Lucas Schiffer photo

Lucas Schiffer

Investigator Spotlight>>

Abigail Baim-Lance photo

Abigail Baim-Lance



Recent ISPH Publications

HIV Care and Viral Load Suppression After Sexual Health Clinic Visits by Out-of-Care HIV-Positive Persons. Tymejczyk O, Jamison K, Pathela P, Braunstein S, Schillinger JA, Nash D. AIDS Patient Care and STDS.

Continuity of transcriptomes among colorectal cancer subtypes based on meta-analysis. Ma S, Ogino S, Parsana P, Nishihara R, Qian Z, Shen J, Mima K, Masugi Y, Cao Y, Nowak J, Shima K, Hoshida Y, Giovannucci E, Gala M, Chan A, Fuchs C, Parmigiani G, Huttenhower C, Waldron L. Genome Biology.

Using Registry Data to Construct a Comparison Group for Programmatic Effectiveness Evaluation: The New York City HIV Care Coordination Program. Robertson MM, Waldron L, Robbins RS, Chamberlin S, Penrose K, Levin B, Kulkarni S, Braunstein SL, Irvine MK, Nash D. American Journal of Epidemiology.

Food insecurity, HIV status and prior testing at South African primary healthcare clinics. Nyirenda M, Street R, Reddy T, Hoffman S, Dawad S, Blanchard K, Exner TM, Kelvin EA, Mantell JE, Ramjee G. South African Journal of Science.

The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models. Zhang Y, Bernau C, Parmigiani G, Waldron L. Biostatistics.