Objective This study aims to evaluate the effectiveness of a digital health intervention model by observing people’s health conditions in a rural area of Bangladesh. Through a repeated cross-sectional design, health outcomes were assessed at six-month intervals over 18 months. Methods The data presented in this paper are obtained from the model’s pilot implementation in one catchment area in Bangladesh. The model involved community health workers (CHWs) using a smart health kit and Artificial Intelligence(AI)-based mobile application to provide monthly doorstep health education, screenings, risk assessments, and digital referrals. Socio-demographic and health measurement data are presented as proportions with 95% confidence intervals (CIs). Multivariate logistic regression was used to analyze the association between diseases and their respective risk factors. We compared health vitals across three consecutive periods to determine the effectiveness of the model in improving health outcomes over time. Results The model served 32,581 people from 7,090 households during this operation. We found that 21.76% of the served population were overweight, 8.18% had prehypertension, 16.45% had high blood glucose, and 11% children were malnourished. From the analysis of risk factors, we found that people aged <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_22770_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">$>\text {40}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>></mo> <mtext>40</mtext> </mrow> </math></EquationSource> </InlineEquation> were associated with developing hypertension, diabetes, and cardiovascular diseases(CVD). CVD was associated with hypertension and stroke. A comparative analysis of different periods showed an improvement in BMI, BP, and MUAC. Blood glucose measurements did not show significant improvement. Conclusion This study highlights the feasibility and effectiveness of digital health interventions in enhancing primary healthcare in rural settings. Integration of AI-driven decision support with CHW-led health education and screenings can improve early detection, intervention and management of non-communicable diseases (NCDs). Scaling up this model through public-private partnerships offers a cost-effective approach to bridging healthcare gaps and promoting universal health coverage.

Efficacy of using a digital health intervention model using community health workers for primary health services in Bangladesh: a repeated cross-sectional observational study

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2025年5月19日

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Marzia Zaman,
Rubaiyat Alim Hridhee,
Refat Ahmed Bhuiyan,
Charles Aunkan Gomes,
Md. Mashiar Rahman,
Sheikh Mohammed Shariful Islam,
Farhana Sarker,
Khondaker A. Mamun

摘要

Objective

This study aims to evaluate the effectiveness of a digital health intervention model by observing people’s health conditions in a rural area of Bangladesh. Through a repeated cross-sectional design, health outcomes were assessed at six-month intervals over 18 months.

Methods

The data presented in this paper are obtained from the model’s pilot implementation in one catchment area in Bangladesh. The model involved community health workers (CHWs) using a smart health kit and Artificial Intelligence(AI)-based mobile application to provide monthly doorstep health education, screenings, risk assessments, and digital referrals. Socio-demographic and health measurement data are presented as proportions with 95% confidence intervals (CIs). Multivariate logistic regression was used to analyze the association between diseases and their respective risk factors. We compared health vitals across three consecutive periods to determine the effectiveness of the model in improving health outcomes over time.

Results

The model served 32,581 people from 7,090 households during this operation. We found that 21.76% of the served population were overweight, 8.18% had prehypertension, 16.45% had high blood glucose, and 11% children were malnourished. From the analysis of risk factors, we found that people aged $>\text {40}$ $> 40$ were associated with developing hypertension, diabetes, and cardiovascular diseases(CVD). CVD was associated with hypertension and stroke. A comparative analysis of different periods showed an improvement in BMI, BP, and MUAC. Blood glucose measurements did not show significant improvement.

Conclusion

This study highlights the feasibility and effectiveness of digital health interventions in enhancing primary healthcare in rural settings. Integration of AI-driven decision support with CHW-led health education and screenings can improve early detection, intervention and management of non-communicable diseases (NCDs). Scaling up this model through public-private partnerships offers a cost-effective approach to bridging healthcare gaps and promoting universal health coverage.