TY - JOUR
T1 - Proposing the observational–implementation hybrid approach
T2 - designing observational research for rapid translation
AU - Knox, Justin
AU - Schwartz, Sheree
AU - Duncan, Dustin T.
AU - Curran, Geoff
AU - Schneider, John
AU - Stephenson, Rob
AU - Wilson, Patrick
AU - Nash, Denis
AU - Sullivan, Patrick
AU - Geng, Elvin
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/9
Y1 - 2023/9
N2 - We propose the observational–implementation hybrid approach—the incorporation of implementation science methods and measures into observational studies to collect information that would allow researchers to anticipate, estimate, or infer the effects of interventions and implementation strategies. Essentially, we propose that researchers collect implementation data early in the research pipeline, in situations where they might not typically be thinking about implementation science. We describe three broad contextual scenarios through which the observational–implementation hybrid approach would most productively be applied. The first application is for observational cohorts that individually enroll participants—either for existing (to which implementation concepts could be added) or for newly planned studies. The second application is with routinely collected program data, at either the individual or aggregate levels. The third application is to the collection of data from study participants enrolled in an observational cohort study who are also involved in interventions linked to that study (e.g., collecting data about their experiences with those interventions). Examples of relevant implementation data that could be collected as part of observational studies include factors relevant to transportability, participant preferences, and participant/provider perspectives regarding interventions and implementation strategies. The observational–implementation hybrid model provides a practical approach to make the research pipeline more efficient and to decrease the time from observational research to health impact. If this approach is widely adopted, observational and implementation science studies will become more integrated; this will likely lead to new collaborations, will encourage the expansion of epidemiological training, and, we hope, will push both epidemiologists and implementation scientists to increase the public health impact of their work.
AB - We propose the observational–implementation hybrid approach—the incorporation of implementation science methods and measures into observational studies to collect information that would allow researchers to anticipate, estimate, or infer the effects of interventions and implementation strategies. Essentially, we propose that researchers collect implementation data early in the research pipeline, in situations where they might not typically be thinking about implementation science. We describe three broad contextual scenarios through which the observational–implementation hybrid approach would most productively be applied. The first application is for observational cohorts that individually enroll participants—either for existing (to which implementation concepts could be added) or for newly planned studies. The second application is with routinely collected program data, at either the individual or aggregate levels. The third application is to the collection of data from study participants enrolled in an observational cohort study who are also involved in interventions linked to that study (e.g., collecting data about their experiences with those interventions). Examples of relevant implementation data that could be collected as part of observational studies include factors relevant to transportability, participant preferences, and participant/provider perspectives regarding interventions and implementation strategies. The observational–implementation hybrid model provides a practical approach to make the research pipeline more efficient and to decrease the time from observational research to health impact. If this approach is widely adopted, observational and implementation science studies will become more integrated; this will likely lead to new collaborations, will encourage the expansion of epidemiological training, and, we hope, will push both epidemiologists and implementation scientists to increase the public health impact of their work.
KW - Implementation science
KW - Observational study
KW - Outcome studies
UR - http://www.scopus.com/inward/record.url?scp=85153796456&partnerID=8YFLogxK
U2 - 10.1016/j.annepidem.2023.03.008
DO - 10.1016/j.annepidem.2023.03.008
M3 - Comment/debate
C2 - 37015306
AN - SCOPUS:85153796456
SN - 1047-2797
VL - 85
SP - 45
EP - 50
JO - Annals of Epidemiology
JF - Annals of Epidemiology
ER -