ArticleBMC public health2023
Clustering of the causes of death in Northeast Iran: a mixed growth modeling.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Trajectories of oral mucosal dryness and multidimensional influencing factors in patients undergoing spine surgery: a prospective longitudinal study.BMC oral health · 2026Article
- Latent class analysis of inflammation and drug-induced liver injury phenotypes in older tuberculosis patients: associations with anxiety, depression, and insomnia.Frontiers in psychiatry · 2025Article
- Developmental trajectories and heterogeneity of social engagement among Chinese older adults: a growth mixture model.BMC geriatrics · 2024Article
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Abstract
backgroundProcessing and analyzing data related to the causes of mortality can help to clarify and monitor the health status, determine priorities, needs, deficiencies, and developments in the health sector in research and implementation areas. In some cases, the statistical population consists of invisible sub-communities, each with a pattern of different trends over time. In such cases, Latent Growth Mixture Models (LGMM) can be used. This article clusters the causes of individual deaths between 2015 and 2019 in Northeast Iran based on LGMM.
methodThis ecological longitudinal study examined all five-year mortality in Northeast Iran from 2015 to 2019. Causes of mortality were extracted from the national death registration system based on the ICD-10 classification. Individuals' causes of death were categorized based on LGMM, and similar patterns were placed in one category.
resultsOut of the total 146,100 deaths, ischemic heart disease (21,328), malignant neoplasms (17,613), cerebrovascular diseases (11,924), and hypertension (10,671) were the four leading causes of death. According to statistical indicators, the model with three classes was the best-fit model, which also had an appropriate interpretation. In the first class, which was also the largest class, the pattern of changes in mortality due to diseases was constant (n = 98, 87.50%). Second-class diseases had a slightly upward trend (n = 10, 8.92%), and third-class diseases had a completely upward trend (n = 4, 3.57%).
conclusionsIdentifying the rising trends of diseases leading to death using LGMM can be a suitable tool for the prevention and management of diseases by managers and health policy. Some chronic diseases are increasing up to 2019, which can serve as a warning for health policymakers in society.
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