Evidence map›Paper›PMID 40552234›Full record

ArticleFrontiers in public health2025

Pre-COVID era pediatric disease incidence in Kazakhstan: regional panel data analysis of multiple disease groups (2010-2019).

Nurlan Smagulov, Olzhas Zhamantayev, Aidar Aitkulov, Nurbek Yerdessov, Karina Nukeshtayeva, Zhanerke Bolatova, Zhyldyz Kurzhunbaeva

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Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Nurlan SmagulovResearch Park of Biotechnology and Eco-Monitoring, Karaganda Buketov University, Karaganda, Kazakhstan.
Olzhas ZhamantayevSchool of Public Health, Karaganda Medical University, Karaganda, Kazakhstan.
Aidar AitkulovFaculty of Biology and Geography, Karaganda Buketov University, Karaganda, Kazakhstan.
Nurbek YerdessovSchool of Public Health, Karaganda Medical University, Karaganda, Kazakhstan.
Karina NukeshtayevaSchool of Public Health, Karaganda Medical University, Karaganda, Kazakhstan.
Zhanerke BolatovaSchool of Public Health, Karaganda Medical University, Karaganda, Kazakhstan.
Zhyldyz KurzhunbaevaDepartment of Science and High Technology, University of Insubria, Varese, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While child morbidity in Kazakhstan is studied, existing research often prioritizes mortality or infectious diseases over non-communicable conditions. This study fills this gap by examining socioeconomic, demographic, and healthcare factors linked to respiratory diseases, asthma, and nervous system disorders among children aged 0-14 years across Kazakhstan from 2010 to 2019 highlighting regional context. Methods: Panel data from 14 regions were analyzed using linear mixed models with autoregressive covariance to address regional and temporal heterogeneity. Log-transformed incidence rates of respiratory diseases (J00-J99), asthma (J45), and nervous system diseases (G00-G99) were modeled against predictors including GRP per capita, unemployment, population density, Gini coefficient, pediatrician density, and hospital resources. Other variables with variance inflation factors ≥5 were excluded to mitigate multicollinearity. Results: Respiratory diseases showed the highest mean incidence (57,329.86 per 100,000), with significant regional variation. Aqtöbe, Atyrau, and South Kazakhstan had 12-25% lower incidence compared to Zhambyl (reference), while Pavlodar and North Kazakhstan had 35-61% higher rates. A 1% increase in population density correlated with a 1.05% decrease in respiratory disease incidence ( Conclusion: The study addresses the lack of localized socioeconomic and healthcare analyses for respiratory diseases, asthma, and nervous system disorders among children, providing evidence for region-specific policy interventions. Respiratory diseases and asthma among Kazakhstani children 0-14 years had associations with the regional economic conditions, healthcare utilization, and inequality. Population density and income inequality were consistent predictors, while nervous system disorders showed fewer clear associations. Our findings show distinct regional patterns in pediatric morbidity, linking health outcomes to localized socioeconomic and healthcare conditions.

Indexed as

AsthmaNervous System DiseasesRespiratory Tract DiseasesAdolescentChildChild, PreschoolCOVID-19FemaleHumansIncidenceInfantInfant, NewbornKazakhstanMaleSocioeconomic Factorsasthmadisparityhealthcare accessKazakhstannervous system disorderspediatric morbidityrespiratory diseasessocioeconomic determinants

Identifiers

PMID40552234
PMCPMC12183960

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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.