Morbidity and health seeking behavior among children and adolescents (0–19 years): a household survey assessment in Northwestern Tanzania
Bibliographic record
Abstract
BACKGROUND: Information on morbidity and health-seeking behavior beyond early childhood is crucial for planning evidence-based interventions. Currently, data is limited to children under five. This study introduces a method for estimating morbidity and health-seeking behavior in older children and adolescents (5-19 years) using women's birth histories from a household survey in northwestern Tanzania, comparing it with data on children under five. METHODS: We conducted a household survey among women 15-49 years as part of the Magu Health and Demographic Surveillance from October 2020 to November 2021, including 16,896 children aged 0-19 living with their mothers. The study outcomes were the prevalence of reported illness in the last four weeks and health-seeking behavior, defined as visiting a health facility for recent illness. Modified Poisson regression analysis was performed, accounting for mothers as clusters and adjusting for child and mother characteristics. We compared the prevalence of recent illness and health-seeking behavior among older children and adolescents (5-19 years) with children under five within the same population. RESULTS: Morbidity presented as the prevalence of any illness decreased with age, from 26.1% in children under-five to 10.4% among adolescents aged 15-19. Health seeking behavior also decreased with age, from 48.2% in children under-five to nearly 30% among adolescents aged 15-19. Types of illnesses reported were similar across age groups, with Fever/Malaria accounting more than two-thirds, followed by respiratory tract illnesses. Higher illness prevalence was noted in rural areas for both age groups. Health seeking behavior was higher among mothers with secondary education and above for both children under-fives (APR:1.22;95% CI: 1.02, 1.47) and 5-19-year-olds (APR: 1.31; 95% CI:1.01, 1.70). Additionally, those with health insurance also reported higher health seeking behavior (APR: 1.38; 95% CI:1.07, 1.78), while lower for children in rural households (APR: 0.72; 95% CI:0.61, 0.83), for 5-19-year-olds. CONCLUSIONS: Our findings on morbidity and health-seeking behavior demonstrate the importance of extending health monitoring beyond early childhood. The inequalities identified point to gaps in programming and health service delivery that require attention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".