Health-seeking behavior among non-communicable disease patients globally, systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Non-communicable disease contributes to over 42 million deaths worldwide and it is estimated that 86% of non-communicable disease-related mortalities happen in low and middle-income countries. Understanding health-seeking behaviors like initiating care at the right time, with the right provider and maintaining regularity of care seeking is a prelude for a successful management of non-communicable diseases. Therefore, the aim of this systematic review and meta-analysis was to assess the pooled prevalence of health-seeking behavior for non-communicable disease and associated factors worldwide. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analysis checklist guideline was followed for this review and meta-analysis. Electronic data base, PubMed, EMBASE, Medline, Web of science, Google scholar and Science direct were used to retrieve studies reported in English language with publication year since 2018 worldwide. Studies reporting proportion of health-seeking behavior for non-communicable disease were evaluated. The pooled prevalence, odds ratio and confidence interval were calculated using Stata version 17 software. The quality of studies included in this review was checked using modified Newcastle-Ottawa scale for observational study checklist. Result: Ten studies which involved 63,498 patients with non-communicable disease were included in this review. The pooled estimated proportion of health-seeking behavior among non-communicable diseases patients from health facilities were 56% (95% CI: 44-68). Older age > 60, urban residency, being of female gender, high educational status, getting support during treatment, knowledge on non-communicable disease, having more than one non-communicable disease, presences of health insurance and middle and upper economic class were factors positively associated with health-seeking behavior for non-communicable diseases. Conclusion: Despite the fact that more than half of patients with non-communicable diseases have health-seeking behavior in health facilities, still, there are a considerable number of individuals with non-communicable diseases having no health-seeking behavior worldwide. Therefore, organizations working for the welfare of human betterment would do well in implementing strategies that could improve health-seeking behavior that would help to reduce the burdens on health systems and prevent premature death from non-communicable diseases.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".