Providing TB and HIV outreach services to internally displaced populations in Northeast Nigeria: Results of a controlled intervention study
Bibliographic record
Abstract
BACKGROUND: A decade of Boko Haram insurgency brought conflict, mass displacement, and the destruction of basic infrastructure to Northeast Nigeria. Over 2 million internally displaced persons (IDPs) suffering from lack of basic hygienic conditions, malnutrition, and disease live in camps or are hosted by communities in the region, where the conflict has contributed to a massive destruction of health facilities. Infectious diseases like tuberculosis (TB) and HIV are especially difficult to address under such conditions, and IDPs are vulnerable to both. Although international investment supports some health interventions among IDPs, locally sourced solutions are lacking. METHODS AND FINDINGS: We evaluated the impact of an active case finding (ACF) intervention for TB and testing for HIV in IDP communities and provided linkages to treatment in 3 states in Northeast Nigeria: Adamawa, Gombe, and Yobe. The ACF was a component of a multistakeholder collaboration between government, civil society, and IDP community partners, which also included mapping of IDP populations and health services, supporting existing health facilities, developing a sample transport network, and organizing community outreach to support ACF. Between July 1, 2017, and June 30, 2018, ACF was conducted in 26 IDP camps and 963 host communities in 12 local government areas (LGAs) with another 12 LGAs serving as a control population. Outreach efforts resulted in 283,556 screening encounters. We screened 13,316 children and 270,239 adults including 150,303 (55.6%) adult women and 119,936 (44.4%) men. We tested 17,134 people for TB and 58,976 for HIV. We detected 1,423 people with TB and 874 people living with HIV. We linked 1,419 people to anti-TB treatment and 874 people with HIV to antiretroviral treatment sites. We evaluated additional TB cases notified and conducted comparative interrupted time series (ITS) analyses to assess the impact of ACF on TB case notifications. Through our efforts, bacteriologically confirmed TB notifications increased by 847 (45.1%) during the intervention period, with IDPs accounting for 46% of these notifications. The ITS analyses detected significant positive postintervention trend differences in TB notification rates between the intervention and control areas in all forms TB (incidence rate ratio [IRR] = 1.136 [1.072, 1.204]; p ≤ 0.001) and bacteriologically positive TB (IRR = 1.141 [1.058, 1.229]; p = 0.001). The TB prevalence (502 cases per 100,000 screening encounters) was 10 times the national notification rates and 2.3 times the estimated national incidence. Rates of HIV infection (1.8%) were higher than HIV prevalence estimates in the 3 states. Our study was limited by the nonrandom selection of LGAs. Furthermore, we did not use sensitive screening tools like chest X-ray and likely missed people with TB. CONCLUSIONS: In this study, we observed a burden of TB in IDP populations of Northeast Nigeria many times higher than national rates and HIV rates higher than state level estimates. The impact of the intervention showed that ACF can greatly increase TB case notifications. Engaging IDP communities, local governments, and civil society organizations is essential to ensuring the success of interventions targeting TB and HIV, and such approaches can provide sustained solutions to these and other health crises among vulnerable populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".