Browsing by Author "Palermo, T"

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    Geostatistical linkage of national demographic and health survey data: a case study of Tanzania
    (Population Health Metrics, 2021) Yoo, E; Palermo, T; Maluka, Stephen
    Background: When Service Provision Assessment (SPA) surveys on primary health service delivery are combined with the nationally representative household survey—Demographic and Health Survey (DHS), they can provide key information on the access, utilization, and equity of health service availability in low- and middle-income countries. However, existing linkage methods have been established only at aggregate levels due to known limitations of the survey datasets. Methods: For the linkage of two data sets at a disaggregated level, we developed a geostatistical approach where SPA limitations are explicitly accounted for by identifying the sites where health facilities might be present but not included in SPA surveys. Using the knowledge gained from SPA surveys related to the contextual information around facilities and their spatial structure, we made an inference on the service environment of unsampled health facilities. The geostatistical linkage results on the availability of health service were validated using two criteria—prediction accuracy and classification error. We also assessed the effect of displacement of DHS clusters on the linkage results using simulation. Results: The performance evaluation of the geostatistical linkage method, demonstrated using information on the general service readiness of sampled health facilities in Tanzania, showed that the proposed methods exceeded the performance of the existing methods in terms of both prediction accuracy and classification error. We also found that the geostatistical linkage methods are more robust than existing methods with respect to the displacement of DHS clusters. Conclusions: The proposed geospatial approach minimizes the methodological issues and has potential to be used in various public health research applications where facility and population-based data need to be combined at fine spatial scale.