Lessons From Rapid Field Implementation of an HIV Population-Based Survey in Nigeria, 2018

Abstract

Background:The need for accurate HIV annual program planning data motivated the compressed timeline for the 2018 Nigerian HIV/ AIDS Indicator and Impact Survey (NAIIS). The survey team used stakeholder cooperation and responsive design, using survey process and paradata to refine survey implementation, to quickly collect high-quality data. We describe processes that led to generation of data for program and funding decisions, ensuring HIV services were funded in 2019.Setting: Nigeria is the most populous country in Africa, with approximately 195 million people in 36 states and the Federal Capital Territory. Challenges include multiple security threats, poor infrastructure, seasonal rains, and varied health system capacity.Methods: Stakeholders worked together to plan and implement NAIIS. Methods from other population-based HIV impact assessments were modified to meet challenges and the compressed timeline. Data collection was conducted in 6 webs. Responsive design included reviewing survey monitoring paradata and laboratory performance. Costs required to correct data errors, for example, staff time and transportation, were tracked.Results: NAIIS data collection was completed in 23 weeks, ahead of the originally scheduled 24 weeks. Responsive design identified and resolved approximately 68,000 interview errors, affecting approximately 62,000 households, saving about US$4.4 million in costs. Biweekly field laboratory test quality control improved from 50% to 100% throughout NAIIS.Conclusions: Cooperation across stakeholders and responsive design ensured timely release of NAIIS results and informed planning for HIV epidemic control in Nigeria. Based on NAIIS results, funds were provided to place an additional 500,000 HIVpositive Nigerians on antiretroviral therapy by the end of 2020, pushing Nigeria toward epidemic control.

Prevalence of HIV drug resistance in Nigeria: results from a cross-sectional, population-based survey of Nigerian adults with unsuppressed viral load

“…Statistically significant odds ratios are bolded. ART, antiretroviral therapy; HIVDR, HIV drug resistance; PI, protease inhibitor.aCombining self-report and ARV biomarker.bAssessed via Limiting Antigen-Avidity enzyme immunoassays [19].cOnly majority HIV subtypes G and CRF02_AG were compared in regression analyses.dDetection of efavirenz, lopinavir, atazanavir, and nevirapine in blood specimens at the time of sample collection [21].eSelf-reported.fART-related statistics were calculated among participants with serological evidence of antiretroviral drugs at the time of sample collection.gNon-nucleoside reverse transcriptase inhibitor.…”

“…Survey sampling and population NAIIS was a cross-sectional survey of households throughout Nigeria conducted in 2018. The methodology of NAIIS has been previously described in detail [2,19]. Briefly, NAIIS was designed to provide estimates of HIV prevalence, HIV incidence, viral load suppression, and related health indicators from a nationally representative sample of the Nigerian population.…”

Individual and household factors associated with non-disclosure of positive HIV status in a population-based HIV serosurvey

“…The primary objective of NAIIS was to obtain nationally representative estimates of HIV prevalence, incidence, and viral load suppression [5]. The methodology of NAIIS has been described in detail elsewhere [5,15]. For this analysis, we selected adults (aged 15–64 years) who tested positive for HIV in NAIIS and had ARV analytes in their blood at the time of sampling.…”

Positioning household surveys for the next decade

“…The results showed that the design reduced data collection costs through significantly fewer call attempts [76]. Another example comes from Nigeria, where a population-based HIV survey adopted a responsive survey design that used paradata to expedite the survey data collection and release [77]. The overall data collection duration was reduced by 1 week from the original plan and saved about $4.4 million in costs.…”