Socio-economic inequalities in all-cause mortality during the COVID-19 period in north-western Tanzania, 2018–2021
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that the COVID-19 pandemic has exacerbated social and demographic inequalities in the communities through pathways of unequal exposure, vulnerability, and susceptibility. In Tanzania, evidence on COVID-19-related mortality is limited to health facility data, with little to no information on the mortality patterns in the general population. This study assessed sociodemographic inequalities in all-cause mortality during the COVID-19 period in north-western Tanzania. METHODS: We utilized available longitudinal data from the Magu Health and Demographic Surveillance System (HDSS) from January 2018 to December 2021. We compared the crude death rates between subgroups of age, sex, area of residence, and wealth index for a period before (2018/2019) and during (2020/2021) the COVID-19 pandemic. To quantify how mortality risk varies across the subgroups we fitted a Cox proportional hazard model with an interaction of the COVID-19 period. RESULTS: Overall mortality declined from 5.9 in 2018/2019 to 5.4 and 5.5 deaths per 1000 person-years in 2020 and 2021, respectively. We observed an increase in differences in crude death rates by age groups, area of residence, and wealth quintiles during the COVID-19 period. In the Cox proportional hazards model, compared to adults aged 15-49, we observed greater mortality risk in children under five (AHR:2.9; 95%CI: 2.2-3.9), older individuals aged 50-64 years (AHR:3.02; 95%CI:2.11-4.33) and 65 + (AHR:18.65; 95%CI:14.28-24.35) during COVID-19 period. Males were also at greater risk of death compared to females (AHR:1.30; 95%CI:1.06-1.59). CONCLUSION: Despite the overall mortality decline during the pandemic, we observed an increased risk of mortality among vulnerable subgroups (aged < 5 years and > 60 years) in the population. This highlights the need to take into account vulnerable subpopulations when addressing major public health issues in communities.
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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 | 0.002 |
| 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 | 0.001 |
| 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".