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Why do strategies to strengthen primary health care succeed in some places and fail in others? Exploring local variation in the effectiveness of a community health worker managed digital health intervention in rural India
  1. Gill Schierhout1,2,
  2. Devarsetty Praveen3,4,
  3. Bindu Patel1,2,
  4. Qiang Li1,2,
  5. Kishor Mogulluru5,
  6. Mohammed Abdul Ameer6,
  7. Anushka Patel1,2,
  8. Gari D Clifford7,8,
  9. Rohina Joshi1,5,
  10. Stephane Heritier9,
  11. Pallab Maulik2,10,
  12. David Peiris1,2
  1. 1The George Institute for Global Health, Newtown, New South Wales, Australia
  2. 2UNSW, Sydney, New South Wales, Australia
  3. 3The George Institute for Global Health India, Hyderabad, India
  4. 4UNSW Sydney, Sydney, New South Wales, Australia
  5. 5The George Institute for Global Health India, New Delhi, Delhi, India
  6. 6The George Institute for Global Health India, Hyderberad, India
  7. 7Department of Biomedical Informatics and Biomedical Engineering, Emory University, Atlanta, Georgia, USA
  8. 8Department of Biomedical Informatics and Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
  9. 9Monash University, Clayton, Victoria, Australia
  10. 10George Institute for Global Health India, New Delhi, India
  1. Correspondence to Dr Devarsetty Praveen; dpraveen{at}


Introduction Digital health interventions (DHIs) have huge potential as support modalities to identify and manage cardiovascular disease (CVD) risk in resource-constrained settings, but studies assessing them show modest effects. This study aims to identify variation in outcomes and implementation of SMARTHealth India, a cluster randomised trial of an ASHA-managed digitally enabled primary healthcare (PHC) service strengthening strategy for CVD risk management, and to explain how and in what contexts the intervention was effective.

Methods We analysed trial outcome and implementation data for 18 PHC centres and collected qualitative data via focus groups with ASHAs (n=14) and interviews with ASHAs, PHC facility doctors and fieldteam mangers (n=12) Drawing on principles of realist evaluation and an explanatory mixed-methods design we developed mechanism-based explanations for observed outcomes.

Results There was substantial between-cluster variation in the primary outcome (overall: I2=62.4%, p<=0.001). The observed heterogeneity in trial outcomes was not attributable to any single factor. Key mechanisms for intervention effectiveness were community trust and acceptability of doctors’ and ASHAs’ new roles, and risk awareness. Enabling local contexts were seen to evolve over time and in response to the intervention. These included obtaining legitimacy for ASHAs’ new roles from trusted providers of curative care; ASHAs’ connections to community and to qualified providers; their responsiveness to community needs; and the accessibility, quality and appropriateness of care provided by higher level medical providers, including those outside of the implementing (public) subsystem.

Conclusion Local contextual factors were significant influences on the effectiveness of this DHI-enabled PHC service strategy intervention. Local adaptions need to be planned for, monitored and responded to over time. By identifying plausible explanations for variation in outcomes between clusters, we identify potential strategies to strengthen such interventions.

  • health systems evaluation
  • health services research
  • cluster randomized trial
  • other study design
  • prevention strategies

Data availability statement

Data are available in a public, open access repository. Data are available on the Harvard Dataverse platform and can be accessed here:

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Data availability statement

Data are available in a public, open access repository. Data are available on the Harvard Dataverse platform and can be accessed here:

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  • Handling editor Stephanie M Topp

  • Twitter @davidpeiris

  • Contributors GS, DPr and DPe conceptualised this mixed-methods study. KM, MAM and DPe designed and led qualitative data collection in the field. DPr, GS and BP led the analysis of the qualitative data and conducted the mixed-methods analysis. QL provided statistical analysis and quantitative data management. GS drafted the paper and incorporated feedback. All authors contributed to interpretation of the findings.

  • Funding This study was funded by an Australian National Health and Medical Research Council (NHMRC) Global Alliances for Chronic Disease Grant (ID1040147). RJ is funded through a Future Leader Fellowship by the National Heart Foundation (Grant number 102059) and the UNSW Scientia Fellowship.

  • Disclaimer The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests The authors have declared that no competing interests exist. The George Institute for Global Health has a wholly owned social enterprise that is conducting commercial projects that include aspects of the intervention tested in this study.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.