980Quantifying inadvertent data-duplication- findings from validation of antenatal-registration and HIV-testing data-sets from an Indian state

Abstract

Background India plans elimination of HIV-Mother-to-Child-Transmission in 2020. Targets include >95% coverage of Antenatal-care (ANC) and HIV-testing. In 2015-16, while 43% of the estimated Indian pregnant-women (PW) received HIV-tests, one state reported >95% testing. Indian public-health-care is a three-tiered system from primary-level sub-centres (population-5000) to tertiary-level hospitals. ANC involves multiple-visits per pregnancy at different care-levels and data are aggregated in the Health-Management-Information-System (HMIS) at all levels. We validated (public and private-sector data from this state, for duplication in ANC registration and HIV-testing using mixed methods. Methods In the absence of guidelines for assessing aggregate-data duplication, we used mixed-methods, including surveys among 9845 PW and providers from 240 facilities in 10/36 representative districts; in-depth-interviews; case-studies and analysis of HMIS and HIV-program data (April 2015-Mar 2017). Interviews and case-studies highlighted inadvertent duplicate data-capture. Surveys quantified levels of duplication and adjustment factors (public and private-sector) were developed. Results Twenty-four% PW, visited multiple facilities for ANC, while 81% providers reported all the PW coming to their facilities as new ANC registrations (irrespective of lower-tier registration); identifying a minimum duplication of 19% (24%*81%) in ANC coverage. Twenty-nine% and 28% PW from public and private-facilities reported >1 HIV-test; while 75% and 36% reported visiting another public-facility where HIV test was likely to be reported again. Minimum duplication of 22% and 10% in HIV testing was noted in public and private-sectors respectively. Conclusions We report methods to quantify repeat HIV-testing and duplicate-reporting, due to inherent processes in ANC in public-healthcare in India. Modification of data-capture was recommended and adopted across India. Key messages Assessing duplication in aggregate health data is key to developing robust datasets for disease elimination