Monitoring routine Health indicators from District Health Information System (Dhis2): a Statistical Subsampling approach

Abstract

Routine health facility data is collected using health information systems. In Kenya, it’s collected using the District Health Information System (DHIS2). This data is continuously collected and cheaper to obtain compared to surveys. Currently, there has been increased advocacy for using this data by governments and development organizations such as the World Health Organization (WHO). Currently, it is unclear about how much DHIS2 data one needs to estimate indicators. All the studies that have used routine data use all the available reports to obtain estimates. This study proposes a novel sub-sampling approach to the estimation of indicators from routine data. The null-hypothesis the study set out was that smaller subsamples of routine data provide credible estimates. Data from 1,808 health facilities in Western Kenya is obtained from DHIS2. Information of 5 data elements, the number of DPT1 doses, the number of DPT3 doses, the number of LLITNs distributed to pregnant mothers attending ANC, the number of pregnant women completing at least 4 ANC visits, and the number of pregnant women completing the rst ANC visits are used to compute three indicators. The three indicators that were calculated from the 5 data elements are; the coverage of the third dose of pentavalent vaccine (DPT3), the proportion of pregnant women who receive LLINs, and the proportion of pregnant women who completed at least 4 ANC visits. The study then uses both spatial and non-spatial sampling to obtain proportions of data from the entire dataset and compute estimates. The proportions were 90%,80%,70%,60%,50%,40%,30%, and 20%. Spatial sampling was used because of the indicators of interest exhibit some spatial variability. The study then used a z-test to determine whether a signi cant di erence exists between the subsample estimates and the population estimates. We also used power calculations to determine the statistical power each subsample had. The results from the study indicate that there was no signi cant di erence between the population estimate and sub-sample estimates after using both spatial and non spatial sampling (all p-values > 0.05). This implies that one doesn’t need the whole data set to obtain estimates from DHIS2, and the sampling design doesn’t matter unless the indicator of interest exhibits some spatial variation. However, based on the con dence intervals, we found that larger samples had narrower con dence intervals, so we recommend sampling above 60%. The power calculation also supported this conclusion. We found that although the power of the subsamples to obtain estimates was generally high (> 70%), it reduced as the sample size reduced.