DATA.FI INFORMATION PRODUCTS

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in

August 2020

bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g Background The Government of Nigeria — through the National Agency for the Control of In addition to conducting the NAIIS, the Nigerian government and donors have AIDS (NACA), Ministries of Health (MOHs) at both the state and national made significant investments in capturing, digitizing, and managing health levels, and other related Ministries, Departments, and Agencies (MDAs) — data, building platforms such as the National Data Repository (NDR) and the has undertaken concerted efforts to reduce the spread of HIV with the support Data for Accountability Transparency and Impact Monitoring (DATIM), which of bi- and multi-lateral donors and partners. captures HIV/AIDS program data of implementing partners. Such efforts have led to aggregated facility-level data within the continuum of HIV care and In 2018, as part of these efforts, the country conducted the Nigeria HIV/AIDS treatment. Indicator and Impact Survey (NAIIS), a cross-sectional survey that assessed the prevalence of key HIV-related health indicators. This was a two-stage While great strides have been made on data collection, challenges remain in cluster survey of 88,775 randomly selected households sampled from among utilizing the data to inform targeted interventions in programmatic planning 3,551 nationally representative sample clusters. toward epidemic control. The goals of the NAIIS were to: The Data.FI project works with USAID Missions and country governments to ▪ Examine the distribution of HIV disease in Nigeria. overcome these types of challenges by using innovative analytical approaches that provide implementing partners with insight into performance against ▪ Assess the coverage and impact of HIV services at the population level. targets for HIV/AIDS indicators — aligned with the Joint United Nations ▪ Measure HIV-related risk behaviors using a nationally representative Programme on HIV and AIDS (UNAIDS) 95-95-95 strategy — and sample of persons aged 0–64 years. identification of gaps in HIV/AIDS service provision. The NAIIS's primary objectives were to estimate the following in a household- based, nationally representative sample of adults aged 15–64 years: ▪ National HIV incidence (the number of recent HIV infections in the survey period). ▪ National and sub-national (state) prevalence of suppressed HIV viral load (less than 1,000 copies/ml). ▪ National and sub-national (state) HIV prevalence.

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 1 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g Introduction Methodology Data.FI worked with the Akwa Ibom State MOH and implementing partners to The geographical analysis to determine unmet treatment need among PLHIV conduct a study to identify HIV service delivery gaps in Akwa Ibom State, in across the 31 LGAs in Akwa Ibom State was conducted using secondary data part by leveraging existing data from the NAIIS and DATIM, described above. from various database platforms, including DATIM, national estimates (the The sections below detail this study and its results. 2018 NAIIS), estimated projected number of PLHIV by small area (LGA) provided by USAID, and partner-based reporting platforms. The most recent Akwa Ibom State is located in the southeastern part of Nigeria, bordering the data reported for the program indicators were extracted from DATIM for Atlantic Ocean on its southern side. is the state capital. Other major towns Quarter 1 of Fiscal Year 2020 (FY20Q1 — October 1 through December 31, include , , , , Ikot Abasi, , Itu, and Oron. The 2019). Geographic datasets — including the state-level and LGA-level Oron–Ikot-Ekpene highway and the are major arteries of boundaries and population data — were provided by USAID. transportation. With the annual growth rate of the population projected at 3.4%, the 2016 projected population was estimated at about 5.4 million (Nigerian Bureau of Statistics). Table 1. Data sources and indicators The 2018 NAIIS revealed that Akwa Ibom State has one of the top three Data sources Recency of data Indicators highest HIV prevalence rates among Nigerian states, at 5.5% with an HTS_TST; HTS_TST_POS; TX_CURR; estimated burden of 178,000 PLHIV. Akwa Ibom is classified as one of the DATIM FY20Q1 TX_PVLS (N); TX_PVLS (D) surge states in the "red" category by USAID. NAIIS 2018 HIV prevalence estimate

Objectives USAID 2018 spectrum data Population estimates, geopolitical borders The study to identify service delivery gaps had three objectives: ▪ To describe the local government areas (LGAs) in Akwa Ibom State that have the greatest numbers of unmet treatment need among people living with HIV (PLHIV). ▪ To compare the 95-95-95 clinical cascade indicators to the estimated unmet need among PLHIV for the 31 LGAs in Akwa Ibom State.

▪ To describe under-performance in HIV testing, treatment, and viral load (VL) suppression across the 31 LGAs.

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 2 To estimate the PLHIV cohort in each LGA, the LGA-specific prevalence (from the NAIIS) was multiplied by the LGA population. Then, the unmet treatment need Key PEPFAR indicators of interest among PLHIV was derived from the difference between the PLHIV estimate and HTS_TST: Number of individuals who received HIV testing PLHIV receiving antiretroviral therapy (ART) as measured by the reported TX_CURR indicator at the end of FY20Q1. (See box on this page for details of the services (HTS) and received their test results. formulas and calculations used.) HTS_TST_POS: Number of individuals who received HIV testing Through an automated process in the programming language Python, data sets services (HTS) and received a positive result. were joined using the unique LGA names in DATIM and the national shapefiles TX_CURR: Number of adults and children currently receiving ART. (geographical information system (GIS) mapping of national, state, and LGA boundaries). Key PEPFAR indicators reviewed included PLHIV, HTS_TST, TX_PVLS(N): Number of adult and pediatric patients on ART with HTS_TST_POS, TX_CURR, TX_PVLS(D), and TX_PVLS(N). (See box on this suppressed viral load results (<1,000 copies/ml) documented in the page for the definition of these indicators.) medical records and /or supporting laboratory results within the past 12 months. (N) means numerator. GIS and statistical techniques were used to categorize LGAs into quartiles based on each relevant indicator and to rank order LGAs across Akwa Ibom State. The TX_PLVS(D): Number of adult and pediatric ART patients with a results were then mapped to illustrate the four LGA quartiles, with color gradients viral load result documented in the patient medical record and/or indicating the level of unmet treatment need among PLHIV in Akwa Ibom. laboratory records in the past 12 months. (D) means denominator. In addition to the four quartiles for unmet need among PLHIV, a fifth group of LGAs was added — those that had more patients on treatment than estimated PLHIVs. Two LGAs fell into this category — Oron and Uyo — and they were shaded in Formulas used white on the map and further examined. A deep-dive analysis of these two LGAs PLHIV = Population x HIV prevalence was done to determine where their clients live, including an examination of facility- based patient line lists. Variables related to the patient residence were used to Number unreached = PLHIV 2018 - TX_CURR.Week current further illustrate the broader catchment area for patients seeking care in these two HTS_ TST = ∑ HTS_TST all weeks LGAs. HTS_(TST_POS ) = ∑ HTS_TST_POS all weeks Positivity yield = HTS_TST_POS / HTS_TST The draft analysis was shared with stakeholders (USAID, implementing partners, and the MOH) as part of an iterative process to check assumptions and ∑ observations with the subject matter experts in Akwa Ibom. TX_PVLS_D = TX_PVLS_D all weeks VL coverage = TX_PVLS_D / TX_CURR.Week current TX_PVLS_N = ∑ TX_PVLS_N all weeks VL suppression = TX_PVLS_N / TX_PVLS_D

bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g FINDINGS The analysis produced five maps illustrating the LGA quartile categorization for each of the priority indicators—unmet treatment need, HIV case finding, HIV testing, and VL testing coverage and suppression. Further, the LGA treatment gap (or PLHIV not on treatment) was compared to the indicator performance. Stakeholder feedback confirmed that the analysis corresponds to their observations and what is obtainable in the state. The following findings were made:

Figure 1. Distribution of unmet need among PLHIV in Akwa Ibom State, 1. Mapping of LGA quartiles highlights the variation by quartile in levels of unmet need for treatment of PLHIV throughout the state

Most of the treatment need among PLHIV in Akwa Ibom State (i.e., the first and second quartile) was estimated to be around the southwestern and central part of the state — specifically, in Abak, Etinan, , Ikot Abasi, Nsit Atai, Nsit Ibom, Mkpat Enin, Nsit Ubium, , , and LGAs. The treatment gap was notably high in three LGAs in the northeastern part of the state — Ibiono Ibom, , and Itu — which fall in the second quartile of unmet need. The southeastern part of Akwa Ibom showed the smallest treatment gaps, with four LGAs in the bottom quartile of unmet need — Eket, Ibeno, Mbo, and Udung Uko.

As mentioned above, two LGAs — Oron and Uyo — had more patients on treatment than estimated PLHIV (see next section).

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 4 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g 2. Deep-dive shows how client migration is inflating patients on treatment (TX_CURR) for Oron and Uyo LGAs From the initial analysis, Oron and Uyo LGAs had more patients on treatment Stakeholder feedback hypothesized that this may be due to a high density of (TX_CURR) than estimated PLHIV, which indicated that there was potential commercial activities in these LGAs, resulting in higher levels of migration. treatment saturation in these LGAs. A deep-dive analysis of facility patient line The map below depicts the location and density distribution of each client in lists and mapping of LGA of residence showed that out of 22,138 clients in care at the facilities in Oron and Uyo LGAs. Oron, 9,396 (42%) emanated from other LGAs, with 1,672 (8%) null values (no LGAs indicated). In Uyo, out of 34,196 clients, 12,644 (37%) emanated from other LGAs, with 2,203 (6%) null values.

Figure 2. Mapping of listed residences of clients accessing care in Oron and Uyo LGAs

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 5 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g 3. Varying yields in numbers of individuals testing positive for HIV (HTS_TST_POS) demonstrates need for increased investments in testing to better assess and address unmet need From a testing perspective, the highest numbers of HIV cases were identified in Correlating the two maps below, we can deduce that the LGAs with high unmet the northern part of the state (LGAs that fell in the first and second quartile of need have conducted fewer HIV tests, and likewise have fewer confirmed HIV positivity). There were also pockets of positive cases in the southern part of positive cases. Intensifying more effort in testing in the LGAs with high unmet the state. need could help to identify the unmet need, thereby closing the treatment gaps (see section on takeaways and recommendations below for further discussion Overall, most of the LGAs within the top 50% of unmet treatment need showed of recommendations). low HIV positivity yields. Only five LGAs that fell within the first and second quartile of unmet treatment need (Etinan, Ibesikpo Asuntan, Ibiono-Ibom, Itu, and Oruk Anam) were among the LGAs that had high HIV positivity rates.

Figure 3. Comparing patients tested (HTS_TST) and patients testing positive (HTS_TST_POS) in Akwa Ibom State, by quartile

HTS_TST HTS_TST_POS Number of Number of individuals individuals tested testing positive for for HIV in Akwa HIV in Akwa Ibom Ibom State as of Q1 State as of Q1

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 6 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g 4. Gaps in the number of ART patients with viral load documented (TX_PVLS(N)) and number of ART patients with viral load suppression (TX_PVLS(D)) emphasize need for more investment in care We compared VL testing coverage and VL suppression distribution with unmet Generally, it can be inferred from the two maps in Figure 4 below that LGAs treatment need among PLHIV. The LGAs with the top 50% of unmet treatment with high VL coverage have high VL suppression rates and lower treatment need showed poor VL coverage and low VL suppression. Only three LGAs gaps. Likewise, LGAs with poor VL coverage tend to have poor VL suppression (Abak, Etinan, and Ibiono Ibom) within the top 50% of unmet need had VL rates and high treatment gaps. Poor VL suppression rates — by increasing the coverage and VL suppression rates in the top quartiles. However, Mpat Enin, chances of new infections within LGAs — widen the treatment gap. Nsit Atai, and Oruk Anam had a high estimate of unmet treatment need among With the above in mind, a concerted investment should be made to increase PLHIV but low VL coverage, as well as a poor VL suppression rate. LGAs in VL coverage and VL suppression in these vulnerable LGAs in an attempt to the northern and central parts of Akwa Ibom, where there is considerably lower curtail new infections and reduce treatment gaps (see section on takeaways unmet need, showed good VL suppression (at least 82% in the region). and recommendations below for further discussion of recommendations).

Figure 4. Comparing VL coverage and VL suppression in Akwa Ibom State

%VL %VL Suppression Coverage Percentage of Percentage of documented viral viral load load suppression coverage in in Akwa Ibom Akwa Ibom State as of Q1 State as of Q1

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 7 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g Validation/feedback from state stakeholders programming in these LGAs are insufficient to control the epidemic as noted above. The viral transmission will continue with low levels of ART coverage The draft information product was presented on April 8, 2020, to Akwa Ibom and poor treatment outcomes measured by viral load suppression. stakeholders (USAID, implementing partners, and the MOH) for their contributions and perspective on the analysis. Stakeholders' feedback Potential programmatic interventions should be differentiated based on the confirmed that the analysis represents an accurate picture of what is need for HIV programming. Immediate attention should be given to the LGAs obtainable in the state, particularly concerning Oron and Uyo LGAs, which had with high levels of unmet need, to increase targeted testing to identify high- patients on treatment (TX_CURR) estimates greater than the estimated PLHIV yield settings and sub-populations. All identified cases should have their due to the increased commercial activities in these LGAs, which draws a contacts traced and tested for HIV, and be linked to treatment or prevention higher migration of people to the LGAs. interventions, as appropriate. Interventions to improve viral load coverage and suppression should also be prioritized in districts with high unmet need, to Potential takeaways and recommendations based on this control the epidemic through treatment success. analysis Stakeholders should leverage regular data review meetings to continuously This analysis aimed to estimate unmet treatment need among PLHIVs within identify inconsistencies with treatment retention, further refine programmatic the 31 LGAs in Akwa Ibom State. These estimates, in conjunction with the interventions, and measure the impact. Further analysis should also be comparison to the 95-95-95 clinical cascade, will aid implementing partners conducted to better estimate ART coverage by comparing the recorded LGA of and the state MOH to re-strategize and improve data-driven efforts for residence in the clinical data to the LGA PLHIV estimates. This may control for attaining epidemic control. intra-state migration to urban LGAs with more commercial activity.

The majority of the LGAs with high numbers of PLHIV not on treatment (i.e., Data protection and data use agreements high unmet treatment need) also had lower levels of testing, leading to fewer people testing HIV positive being identified and put on treatment, driving lower All data used for this analysis was provided by USAID and stored in various levels of VL suppression. This suggests that the current resources and secure databases.

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 8 bdfgdfLorem Ipsum Word Template Lorem [Type here] [Type here]g

HIV testing, treatment and viral load monitoring in Akwa Ibom State, by LGA

LGA PLHIV Prevalence HTS_TST HTS_TST_POS TX_CURR TX_PVLS_D TX_PVLS_N VL_Suppression % VL_Coverage & Unmet need Abak LGA 5578 5 1933 67 2058 1382 1244 90 67 3520 LGA 2123 5 303 7 410 37 32 87 9 1713 Eket LGA 8040 6 1927 31 6790 4151 3480 84 61 1250 LGA 3352 6 2140 415 1171 405 331 82 35 2181 LGA 5840 4 1409 63 3556 2062 1752 85 58 2284 LGA 3460 5 1013 65 1735 1068 907 85 62 1725 Etinan LGA 7223 5 1485 40 2740 2025 1731 86 74 4483 Ibeno LGA 3209 6 826 43 1917 648 516 80 34 1292 Ibesikpo Asutan LGA 6021 5 1404 38 1800 970 800 83 54 4221 Ibiono Ibom LGA 5986 4 1788 50 2183 1405 1136 81 64 3803 Ika LGA 1960 4 720 25 440 132 121 92 30 1520 Ikono LGA 4164 4 1056 34 969 481 410 85 50 3195 Ikot Abasi LGA 6460 5 908 15 2269 1303 1121 86 57 4191 Ikot Ekpene LGA 5232 4 2205 88 4940 3826 3420 89 77 292 Ini LGA 2106 2 685 20 683 212 175 83 31 1423 Itu LGA 4223 4 1046 37 1314 572 484 85 44 2909 Mbo LGA 4835 6 1707 34 4787 2213 1762 80 46 48 Mkpat Enin LGA 7894 5 504 16 1133 421 328 78 37 6761 Nsit Atai LGA 3282 5 351 4 712 344 252 73 48 2570 Nsit Ibom LGA 4939 5 249 9 998 528 401 76 53 3941 Nsit Ubium LGA 5467 6 523 26 1076 490 388 79 46 4391 Obot Akara LGA 3374 3 492 31 1152 579 475 82 50 2222 Okobo LGA 5475 6 1037 18 3435 1891 1474 78 55 2040 Onna LGA 5521 6 746 27 1111 564 457 81 51 4410 Oron LGA 3786 5 2739 71 5567 4603 3727 81 83 -1781 Oruk Anam LGA 8170 5 934 54 2115 635 484 76 30 6055 Udung Uko LGA 1744 5 174 4 1125 285 209 73 25 619 Ukanafun LGA 6055 6 247 11 794 400 334 84 50 5261 LGA 4936 5 2255 64 2472 1551 1297 84 63 2464 Urue-Offong/Oruko LGA 3094 6 752 21 1277 504 390 77 40 1817 Uyo LGA 10790 5 12321 293 15297 12088 10850 90 79 -4507

Mind the Gap: Leveraging the National HIV/AIDS Indicator and Impact Survey (NAIIS) Data to Identify Service Delivery Gaps in Akwa Ibom State 9

IP-20-01

FOR MORE INFORMATION

Emily Harris, Data.FI AOR [email protected] Jenifer Chapman, Data.FI Project Director [email protected] https://datafi.thepalladiumgroup.com/

Data for Implementation (Data.FI) is a five-year cooperative agreement funded by PEPFAR through USAID under Agreement No. 7200AA19CA0004, beginning April 15, 2019. It is implemented by Palladium, in partnership with JSI Research & Training Institute, Johns Hopkins University Department of Epidemiology, Right to Care, Cooper/Smith, IMC Worldwide, Jembi Health Systems, and macro-eyes, and supported by expert local resource partners.

This document was produced for review by the U.S. President’s Emergency Plan for AIDS Relief through the United States Agency for International Development. It was prepared by Data for Implementation. The information provided in this document is not official U.S. government information and does not necessarily reflect the views or positions of the U.S. President’s Emergency Plan for AIDS Relief, U.S. Agency for International Development or the United States Government.