ArticleBMC public health2026
Association between regional income inequality and infant mortality rates in Türkiye: an ecological panel analysis of NUTS-2 regions, 2014-2024.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncome inequality may affect infant survival through socioeconomic and health-care pathways, but regional evidence from Türkiye remains limited. This study examined the association between regional income inequality and infant mortality rates across the 26 NUTS-2 regions of Türkiye during 2014-2024.
methodsThis longitudinal ecological panel study used publicly available data from the Turkish Statistical Institute. Income inequality was measured using the Gini coefficient and P80/P20 ratio. Linear mixed-effects models included a regional random intercept and a first-order autoregressive covariance structure for repeated annual observations. Year was modelled categorically in the primary analyses, and between-region and within-region effects were examined separately. Live-birth-weighted sensitivity analyses were used to assess whether differences in the precision of regional IMR estimates affected the findings.
resultsThe study included 286 region-year observations; income inequality models included 285. From 2014 to 2024, the regional mean Gini coefficient increased from 0.364 to 0.390 and the mean P80/P20 ratio from 6.42 to 7.22, while mean IMR declined from 10.81 to 8.86 per 1,000 live births. After adjustment for categorical year effects, no statistically significant association was detected for either the Gini coefficient (B = 0.041; 95% CI: -0.025 to 0.106; p = 0.221) or the P80/P20 ratio (B = 0.054; 95% CI: -0.145 to 0.252; p = 0.594). No statistically significant between-region or within-region associations were detected. Year-adjusted regional means ranged from 6.82 to 15.03, and 83.1% of the remaining variance was between regions. Live-birth-weighted sensitivity analyses likewise detected no statistically significant association for the Gini coefficient (B = 0.013; 95% CI: -0.041 to 0.067; p = 0.640) or the P80/P20 ratio (B = - 0.021; 95% CI: -0.189 to 0.146; p = 0.803).
conclusionsNo statistically significant association was detected between regional income inequality and IMR after accounting for year-specific effects and repeated observations. However, the confidence interval for the primary Gini estimate did not exclude small-to-moderate effects, so the findings should not be interpreted as evidence of no association. Persistent regional heterogeneity suggests that other demographic, social, and health-system factors may contribute to regional differences.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.