Rural–urban variation in insecticide-treated net utilization among pregnant women: evidence from 2018 Nigeria Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: In 2018, Nigeria accounted for the highest prevalence of malaria worldwide. Pregnant women and children under five years bear the highest risk of malaria. Geographical factors affect utilization of insecticide-treated nets (ITN), yet existing literature have paid little attention to the rural-urban dimension of ITN utilization in Nigeria. This study aimed at investigating the rural-urban variation in ITN utilization among pregnant women in Nigeria using data from the 2018 Demographic and Health Survey. METHODS: A total of 2909 pregnant women were included in the study. The prevalence of ITN utilization for rural and urban pregnant women of Nigeria were presented with descriptive statistics. Chi-square test was employed to assess the association between residence, socio-demographic characteristics and ITN utilization at 95% level of significance. Subsequently, binary logistic regression was used to assess the influence of residence on ITN utilization. RESULTS: Eight out of ten of the rural residents utilized ITN (86.1%) compared with 74.1% among urban residents. Relative to urban pregnant women, those in rural Nigeria had higher odds of utilizing ITNs both in the crude [cOR = 2.17, CI = 1.66-2.84] and adjusted models [aOR = 1.18, CI = 1.05-1.24]. Pregnant women aged 40-44 had lower odds of ITN utilization compared to those aged 15-19 [aOR = 0.63, CI = 0.44-0.92]. Poorer pregnant women had higher odds of ITN utilization compared with poorest pregnant women [aOR = 1.09, CI = 1.04-1.32]. Across regions, those in the south [aOR = 0.26, CI = 0.14-0.49] and south-west [aOR = 0.29, CI = 0.16-0.54] had lower odds of ITN use compared to their counterparts in the north-west region. CONCLUSION: The high use of ITNs among pregnant women in Nigeria may be due to the prioritization of rural communities by previous interventions. This is a dimension worth considering to enhance the attainment of the national anti-malarial initiatives. Since possession of ITN is not a guarantee for utilization, women in urban locations need constant reminder of ITN use through messages delivered at ANC and radio advertisements. Moreover, subsequent mass ITN campaigns ought to take cognizance of variations ITN use across regions and pragmatic steps be taken to increase the availability of ITN in households since there is a moderately high use in households with at least one ITN in Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".