Inge Petersen, Christopher G. Kemp, Deepa Rao, Bradley H. Wagenaar, Kenneth Sherr, Merridy Grant, Max Bachmann, Ruanne V. Barnabas, Ntokozo Mntambo, Sithabisile Gigaba, André Van Rensburg, Zamasomi Luvuno, Ishmael Amarreh, Lara Fairall, Nikiwe N. Hongo, & Arvin Bhana
University of Washington affiliated authors are displayed in bold.
Published: March 2021
John A.Naslund, Jasmine Kalha, Juliana L. Restivo, Ishmael Amarreh, Tamora Callands, Hongtu Chen, Carlos Gomez-Restrepo, Hesham M. Hamoda, Arjun Kapoor, Sue Levkoff, Jones Masiye, Maria A. Oquendo, Vikram Patel, Inge Petersen, Ozge Sensoy Bahar, Laura Shields-Zeeman, Fred M. Ssewamala, Deepak Tugnawat, José Miguel Uribe-Restrepo, Lakshmi Vijayakumar, Bradley H. Wagenaar, Milton L.Wainberg, Larry Wissow, Haja Ramatulai Wurie, Chifundo Zimba, & Soumitra Pathare
University of Washington affiliated authors are displayed in bold.
Published: January 2021
Katrin E. Fabian, Alberto Muanido, Vasco F. J. Cumbe, Nelia Manaca, Leecreesha Hicks, Bryan J. Weiner, Kenneth Sherr, Bradley H. Wagenaar
University of Washington affiliated authors are displayed in bold.
Published: November 2020
Arianna Rubin Means, Christopher G. Kemp, Marie-Claire Gwayi-Chore, Sarah Gimbel, Caroline Soi, Kenneth Sherr, Bradley H. Wagenaar, Judith N. Wasserheit, & Bryan J. Weiner
University of Washington affiliated authors are displayed in bold.
Published: March 2020
The Consolidated Framework for Implementation Research (CFIR) is a determinants framework that may require adaptation or contextualization to fit the needs of implementation scientists in low- and middle-income countries (LMICs). The purpose of this review is to characterize how the CFIR has been applied in LMIC contexts, to evaluate the utility of specific constructs to global implementation science research, and to identify opportunities to refine the CFIR to optimize utility in LMIC settings.
A systematic literature review was performed to evaluate the use of the CFIR in LMICs. Citation searches were conducted in Medline, CINAHL, PsycINFO, CINAHL, SCOPUS, and Web of Science. Data abstraction included study location, study design, phase of implementation, manner of implementation (ex., data analysis), domains and constructs used, and justifications for use, among other variables. A standardized questionnaire was sent to the corresponding authors of included studies to determine which CFIR domains and constructs authors found to be compatible with use in LMICs and to solicit feedback regarding ways in which CFIR performance could be improved for use in LMICs.
Our database search yielded 504 articles, of which 34 met final inclusion criteria. The studies took place across 21 countries and focused on 18 different health topics. The studies primarily used qualitative study designs (68%). Over half (59%) of the studies applied the CFIR at study endline, primarily to guide data analysis or to contextualize study findings. Nineteen (59%) of the contacted authors participated in the survey. Authors unanimously identified culture and engaging as compatible with use in global implementation research. Only two constructs, patient needs and resources and individual stages of change were commonly identified as incompatible with use. Author feedback centered on team level influences on implementation, as well as systems characteristics, such as health system architecture. We propose a “Characteristics of Systems” domain and eleven novel constructs be added to the CFIR to increase its compatibility for use in LMICs.
These additions provide global implementation science practitioners opportunities to account for systems-level determinants operating independently of the implementing organization. Newly proposed constructs require further reliability and validity assessments.
PROSPERO, CRD42018095762
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Bradley H. Wagenaar, Wilson H. Hammett, Courtney Jackson, Dana L. Atkins, Jennifer M. Belus, and Christopher G. Kemp
University of Washington affiliated authors are displayed in bold.
Published: March 2020
We systematically reviewed implementation research targeting depression interventions in low- and middle-income countries (LMICs) to assess gaps in methodological coverage.
PubMed, CINAHL, PsycINFO, and EMBASE were searched for evaluations of depression interventions in LMICs reporting at least one implementation outcome published through March 2019.
A total of 8714 studies were screened, 759 were assessed for eligibility, and 79 studies met inclusion criteria. Common implementation outcomes reported were acceptability (n = 50; 63.3%), feasibility (n = 28; 35.4%), and fidelity (n = 18; 22.8%). Only four studies (5.1%) reported adoption or penetration, and three (3.8%) reported sustainability. The Sub-Saharan Africa region (n = 29; 36.7%) had the most studies. The majority of studies (n = 59; 74.7%) reported outcomes for a depression intervention implemented in pilot researcher-controlled settings. Studies commonly focused on Hybrid Type-1 effectiveness-implementation designs (n = 53; 67.1), followed by Hybrid Type-3 (n = 16; 20.3%). Only 21 studies (26.6%) tested an implementation strategy, with the most common being revising professional roles (n = 10; 47.6%). The most common intervention modality was individual psychotherapy (n = 30; 38.0%). Common study designs were mixed methods (n = 27; 34.2%), quasi-experimental uncontrolled pre-post (n = 17; 21.5%), and individual randomized trials (n = 16; 20.3).
Existing research has focused on early-stage implementation outcomes. Most studies have utilized Hybrid Type-1 designs, with the primary aim to test intervention effectiveness delivered in researcher-controlled settings. Future research should focus on testing and optimizing implementation strategies to promote scale-up of evidence-based depression interventions in routine care. These studies should use high-quality pragmatic designs and focus on later-stage implementation outcomes such as cost, penetration, and sustainability.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Anjuli D. Wagner, Sarah Gimbel, Kristjana H. Ásbjörnsdóttir, Peter Cherutich, Joana Coutinho, Jonny Crocker, Emiliae Cruz, Fatima Cuembelo, Vasco Cumbe, McKenna Eastment, Jennifer Einberg, Florencia Floriano, Douglas Gaitho, Brandon L. Guthrie, Grace John-Stewart, Alex H. Kral, Barrot H. Lambdin, Shan Liu, Martin Bcom Maina, Nelia Manaca, Mika Matsuzaki, Loris Mattox, Nancy Mburu, R. Scott McClelland, Mark A. Micek, Ana Olga Mocumbi, Albertoe Muanido, Ruth Nduati, Irene N. Njuguna, Geoffrey Oluoch, Laura B. Oyiengo, Keshet Ronen, Caroline Soi, Bradley H. Wagenaar, George Wanje, Lynn D. Wenger, and Kenneth Sherr
University of Washington affiliated authors are displayed in bold.
Published: December 2019
Christopher G. Kemp, Bradley H. Wagenaar, and Emily E. Haroz
University of Washington affiliated authors are displayed in bold.
Published: November 2019
Successful implementation reflects the interplay between intervention, implementation strategy, and context. Hybrid effectiveness-implementation studies allow investigators to assess the effects of both intervention and implementation strategy, though the role of context as a third independent variable (IV) is incompletely specified. Our objective is to expand the hybrid typology to include mixtures of all three types of IVs: intervention, implementation strategy, and context.
We propose to use I to represent the IV of intervention, IS to represent implementation strategy, and C to represent context. Primary IVs are written first and in upper case. Secondary IVs are written after a forward slash and in lower case; co-primary IVs are written after a dash and in upper case. The expanded framework specifies nine two-variable hybrid types: I/is, I-IS, IS/i, IS/c, IS-C, C/is, C/i, I-C, and I/c. We describe four in detail: I/is, IS/c, IS-C, and C/is. We also specify seven three-variable hybrid types. We argue that many studies already meet our definitions of two- or three-variable hybrids.
Our proposal builds from the typology proposed by Curran et al. (1), but offers a more complete specification of hybrid study types. We need studies that measure the implementation-related effects of variations in contextual determinants, both to advance the science and to optimize intervention delivery in the real world. Prototypical implementation studies that evaluate the effectiveness of an implementation strategy, in isolation from its context, risk perpetuating the gap between evidence and practice, as they will not generate context-specific knowledge around implementation, scale-up, and de-implementation.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Bradley H. Wagenaar, Lisa R. Hirschhorn, Catherine Henley, Artur Gremu, Ntazana Sindano, Roma Chilengi and the AHI PHIT Partnership Collaborative
University of Washington affiliated authors are displayed in bold.
Published: December 2017
Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation’s African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.
Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.
Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.
Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external “audit.”
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Hema Magge, Roma Chilengi, Elizabeth F. Jackson, Bradley H. Wagenaar, Almamy Malick Kante and the AHI PHIT Partnership Collaborative
University of Washington affiliated authors are displayed in bold.
Published: December 2017
The Doris Duke Charitable Foundation’s African Health Initiative supported the implementation of Population Health Implementation and Training (PHIT) Partnership health system strengthening interventions in designated areas of five countries: Ghana, Mozambique, Rwanda, Tanzania, and Zambia. All PHIT programs included health system strengthening interventions with child health outcomes from the outset, but all increasingly recognized the need to increase focus to improve health and outcomes in the first month of life. This paper uses a case study approach to describe interventions implemented in newborn health, compare approaches, and identify lessons learned across the programs’ collective implementation experience.
Case studies were built using quantitative and qualitative methods, applying the World Health Organization Health Systems Strengthening Framework, and maternal, newborn and child health continuum of care framework. We identified the following five primary themes in health systems strengthening intervention strategies used to target improvement in newborn health, which were incorporated by all PHIT projects with varying results: health service delivery at the community level (Tanzania), combining community and health facility level interventions (Zambia), participatory information feedback and clinical training (Ghana), performance review and enhancement (Mozambique), and integrated clinical and system-level improvement (Rwanda), and used individual case studies to illustrate each of these themes.
Tanzania and Zambia included significant community-based components, including mobilization and sensitization for increased uptake of essential services, while Ghana, Mozambique, and Rwanda focused more efforts on improving the quality of services delivered once a patient enters a health facility. All countries included aspects that improved communication across levels of the health system, whether through district-wide data sharing and peer learning networks in Mozambique and Rwanda, or improved referral processes and systems in Tanzania, Zambia, and Ghana.
Key lessons learned include the importance of focusing intervention components on addressing drivers of neonatal mortality across the maternal and newborn care continuum at all levels of the health system, matching efforts to improve service utilization with provision of high quality facility-based services, and the critical role of leadership to catalyze improvements in newborn health.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
James T. Pfeiffer; Manuel Napúa; Bradley H. Wagenaar; Falume Chale; Roxanne Hoek; Mark Micek; João Manuel; Cathy Michel; Jessica Greenberg Cowan; James F. Cowan; Sarah Gimbel; Kenneth Sherr; Stephen Gloyd; Rachel R. Chapman
University of Washington affiliated authors are displayed in bold.
Published: November 2017