Artificial Intelligence Distributed Constraint Optimisation for Community Health Worker Task Reallocation Under Variable Household Enumeration Completeness: A Robustness Evaluation of Healthcare Coverage Equity

Community health worker (CHW) task reallocation is a distributed constraint optimisation problem whose inputs depend on household enumeration completeness, a quantity that varies across enumeration rounds and directly conditions the equity of healthcare coverage. Six hundred data sets of N = 500 were drawn from a process with intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2, using seed 494496274. The estimator recovered the exposure coefficient with negligible bias (0.00028) and empirical coverage of the nominal 95% confidence interval of 1.0, while the intercept showed bias of 0.00423 and root mean squared error of 0.89265. The exposure coefficient's sampling distribution was approximately normal with root mean squared error 0.01594. These results establish that under the stated process the estimator is unbiased and interval coverage is conservative, and they delimit the conditions under which distributed constraint optimisation for CHW task reallocation can be evaluated without confounding by enumeration incompleteness. The contribution is a bounded robustness result that specifies what a coverage-equity estimator can and cannot be expected to recover when enumeration completeness is itself a modelled quantity.