PREVALENCE, TRENDS, AND PREDICTORS OF DOMESTIC VIOLENCE AMONG NIGERIAN WOMEN; AN ANALYSIS USING 2008, 2013 AND 2018 WAVES OF THE NIGERIA DEMOGRAPHIC HEALTH SURVEY (NDHS)

Domestic violence (abbreviated DV for this study) among women in African settings is often considered a ‘family matter’ and requires limited external interference. This study aims to identify the national and regional prevalence, trend in prevalence rates, influencing factors in predicting domestic violence among Nigerian women, victims’ response in seeking help, and potential victim-centered digital solutions to addressing domestic violence. In Nigeria, little has been done to assess; a nationally representative prevalence of DV among Nigerian women in the last decade to see if there are changing trends in prevalence; extensively investigate the influence of socio-demographic factors in predicting DV to target specific risk groups strategically, and suggest potential strategic interventions, policies, and actions needed to reduce DV among Nigerian women significantly. Therefore, I examine and analyze Nigeria\u27s largest nationally representative data, specifically, the domestic violence module of the Nigeria Demographic Health Survey (NDHS), for three consecutive wave periods of 2008, 2013, and 2018. Socio-demographic and economic variables that are family-centered, ecological, and community-driven were analyzed. Two outcome variables were assessed; experiencing DV and not seeking help. A comparison of the individual association of each variable to the outcome variable using bivariate analysis was conducted and advanced into multivariate analysis at p-value \u3c0.05 (95% confidence interval). Tests of collinearity among similar constructs such as respondent educational level and partner’s educational level; ethnicity, residence, and region, were conducted to eliminate any colinear covariates and produce the best fit regression analysis. Akaike test was deployed to identify the best-fit model. Findings suggest that the prevalence has remained relatively high across the three wave periods, with the most recent 12-month prevalence of 29.5% and lifetime prevalence of 36.2% in 2018, most commonly among ever married women aged 20-29. Emotional violence is most common, and the predominant predictor in this study is the geographical region with the highest predominance of DV in the North-East region of Nigeria, especially in Gombe, Bauchi, and Adamawa states, where women are 5.6 times at greater risk of experiencing DV, and 3.6 times higher odds of experiencing DV in the North Central region (especially Kogi, Kaduna and Plateau states) compared to women in South-West Nigeria. The duration of cohabitation, polygynous family union type, and being employed were positively correlated with domestic violence. While being in the richer and richest quintiles, access to mobile a phone and practicing the Islamic religious faith conferred protective effects against domestic violence. Further analysis suggests that the same wealth quintile associated with DV, being married, and women located in the South-West region compared to other regions have higher odds of not seeking help after experiencing domestic violence. The majority of women that do seek help sought help from family members. In summary, domestic violence is an epidemic crisis and is not decreasing. A robust family support system, integrating information about DV into health literacy programs, targeted programs directed at women in high-risk regions, and adoption of routine screening among women seeking health care are crucial in addressing domestic violence in Nigeria

Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria.

Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges. Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Persons with high CAD4TB score (≥80), who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months. An experimental calibration method achieved a viable CAD threshold for testing. High risk groups and key stakeholders were engaged. Operations evolved in real time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%) were recorded. Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores and initially spot -sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up. Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term