Key result
OLS estimation accurately models community health worker dropout exposure in hypertension follow-up loss.
Why the study?
Community health worker attrition threatens the continuity of hypertension monitoring in rural districts of Malawi, yet the causal structure linking dropout exposure to follow-up loss remained poorly specified.
Discrete-time survival specifications and ordinary least squares estimation effectively model the impact of community health worker attrition on hypertension monitoring follow-up loss in rural Malawi with negligible bias.
Simulation characterizes OLS behavior in CHW dropout models; leaves open causal inference for rural hypertension follow-up.
Community health worker (CHW) attrition threatens the continuity of hypertension monitoring in Malawi's rural districts, yet the causal structure linking dropout exposure to follow-up loss remains poorly specified. Using a replication-based evaluation of an ordinary least squares estimator with 600 replications of N = 500 drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), the study characterises estimator behaviour under conditions that mimic the clustered, unbalanced structure of CHW panel data. These results indicate that the estimator recovers the exposure parameter with negligible bias and near-nominal coverage, supporting the use of discrete-time survival specifications in CHW attrition research. Medical sociology must treat CHW attrition not as a background nuisance but as a structurally consequential exposure that reshapes the effective coverage of chronic disease monitoring. The contribution is a methodological and conceptual clarification of how dropout exposure can be modelled and interpreted within the sociology of health systems.
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Thokozani Kachale Banda (2026) studied Hypertension (n=500). Community health worker attrition (dropout exposure) was evaluated on Hypertension follow-up loss (exposure parameter recovery in discrete-time survival model). Ordinary least squares estimation across 600 replications (N=500) recovered the CHW dropout exposure parameter (0.42) with negligible bias and near-nominal coverage for hypertension follow-up loss.
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