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.
Background: Against a background of security challenges, Nigeria conducted recently the largest population-based HIV survey in the world to ascertain the burden of the HIV disease in the country. Objective: We evaluated the main outcomes of the survey and the level of success using participation/response indicators. Methods: The survey was conducted from July–December 2018 by over 6,000 field staff across Nigeria in six consecutive webs, using two-stage cluster sampling. We estimated the prevalence of HIV, hepatitis B and hepatitis C in the entire country and by conflict zone status. Adjusted odds ratios (OR) and 95% confidence intervals (CI) from survey logistic regression models were used to compare the likelihood of test positivity for the three infections between zones. Findings: A total of 186,405 adults were interviewed from 97,250 households in 3,848 census enumeration areas. The overall HIV, hepatitis B and hepatitis C positivity rates were 1.55%, 7.63% and 1.73%, respectively. The prevalence of HIV, hepatitis B and C infection was significantly greater in conflict than non-conflict zones (HIV: 1.75% versus 1.0%; hepatitis B: 9.9% versus 7.3%; and hepatitis C: 3.2% versus 0.3%; p < 0.01 in all cases). Individuals living in conflict zones were about three times as likely to test positive for HIV (OR = 2.80, 95% CI = 2.08, 3.60) and nearly six times as likely to test positive for hepatitis C (OR = 5.90, 95% CI = 2.17, 16.67). Conclusion: Large population-based surveys are feasible, even in armed conflict settings. The burden of HIV, hepatitis B and hepatitis C was significantly higher in areas of conflict in Nigeria, highlighting the need for reinforced public health control measures in these settings in order to attain UNAIDS’ 95-95-95 targets of controlling the HIV epidemic in sub-Saharan Africa by 2030.
Background Ineffective linkage to care (LTC) is a known challenge for community HIV testing. To overcome this challenge, a robust linkage to care strategy was adopted by the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). The NAIIS linkage to care strategy was further adapted to improve Nigeria’s programmatic efforts to achieve the 1st 90 as part of the Nigeria Antiretroviral Therapy (ART) Surge initiative, which also included targeted community testing. In this paper we provide an overview of the NAIIS LTC strategy and describe the impact of this strategy on both the NAIIS and the Surge initiatives. Methods The NAIIS collaborated with community-based organizations (CBOs) and deployed mobile health (mHealth) technology with real-time dashboards to manage and optimize community LTC for people living with HIV (PLHIV) diagnosed during the survey. In NAIIS, CBOs’ role was to facilitate linkage of identified PLHIV in community to facility of their choice. For the ART Surge, we modified the NAIIS LTC strategy by empowering both CBOs and mobile community teams as responsible for not only active LTC but also for community testing, ART initiation, and retention in care. Results Of the 2,739 PLHIV 15 years and above identified in NAIIS, 1,975 (72.1%) were either unaware of their HIV-positive status (N = 1890) or were aware of their HIV-positive status but not receiving treatment (N = 85). Of these, 1,342 (67.9%) were linked to care, of which 952 (70.9%) were initiated on ART. Among 1,890 newly diagnosed PLHIV, 1,278 (67.6%) were linked to care, 33.7% self-linked and 66.3% were linked by CBOs. Among 85 known PLHIV not on treatment, 64 (75.3%) were linked; 32.8% self-linked and 67.2% were linked by a CBO. In the ART Surge, LTC and treatment initiation rates were 98% and 100%, respectively. Three-month retention for monthly treatment initiation cohorts improved from 76% to 90% over 6 months. Conclusions Active LTC strategies by local CBOs and mobile community teams improved LTC and ART initiation in the ART Surge initiative. The use of mHealth technology resulted in timely and accurate documentation of results in NAIIS. By deploying mHealth in addition to active LTC, CBOs and mobile community teams could effectively scale up ART with real-time documentation of client-level outcomes.
Background Data on awareness of HIV status among people living with HIV (PLHIV) are critical to estimating progress toward epidemic control. To ascertain the accuracy of self-reported HIV status and antiretroviral drug (ARV) use in the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS), we compared self-reported HIV status with HIV rapid diagnostic test (RDT) results and self-reported ARV use with detectable blood ARV levels. Methods On the basis of responses and test results, participants were categorized by HIV status and ARV use. Self-reported HIV status and ARV use performance characteristics were determined by estimating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Proportions and other analyses were weighted to account for complex survey design. Results During NAIIS, 186,405 participants consented for interview out of which 58,646 reported knowing their HIV status. Of the 959 (weighted, 1.5%) who self-reported being HIV-positive, 849 (92.1%) tested HIV positive and 64 (7.9%) tested HIV negative via RDT and polymerase chain reaction test for discordant positive results. Of the 849 who tested HIV positive, 743 (89.8%) reported using ARV and 72 (10.2%) reported not using ARV. Of 57,687 who self-reported being HIV negative, 686 (1.2%) tested HIV positive via RDT, with ARV biomarkers detected among 195 (25.1%). ARV was detected among 94.5% of those who self-reported using ARV and among 42.0% of those who self-reported not using ARV. Overall, self-reported HIV status had sensitivity of 52.7% (95% confidence interval [CI]: 49.4%–56.0%) with specificity of 99.9% (95% CI: 99.8%–99.9%). Self-reported ARV use had sensitivity of 95.2% (95% CI: 93.6%–96.7%) and specificity of 54.5% (95% CI: 48.8%–70.7%). Conclusions Self-reported HIV status and ARV use screening tests were found to be low-validity measures during NAIIS. Laboratory tests to confirm self-reported information may be necessary to determine accurate HIV and clinical status for HIV studies in Nigeria.
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