Evidence map›Paper›PMID 39815276›Full record

ArticleBMC health services research2025

Latent class analysis of the capacity of countries to manage diabetes and its relationship with diabetes-related deaths and healthcare costs.

Samuel Akyirem, Emmanuel Ekpor, Charles Boakye Kwanin

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Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

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3citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Samuel AkyiremSchool of Nursing, Yale University, Orange, CT, USA.
Emmanuel EkporSchool of Nursing and Midwifery, University of Ghana, Accra, Ghana. ekpor707@gmail.com.
Charles Boakye KwaninSchool of Nursing, University of Washington, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries' capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs.

methodsEight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector. We performed LCA of these indicators, testing 1-5 class solutions, and selecting the best model based on Bayesian Information Criteria (BIC), entropy, corrected Akaike Information Criteria (cAIC), as well as theoretical interpretability. Multivariable linear regression was used to assess the association between capacity to manage diabetes (based on the latent class a country belongs) and diabetes-related deaths and healthcare costs.

resultsWe included 194 countries in this secondary analysis. Countries were classified into "high capacity" (88.7%) and "limited capacity" (11.3%) countries based on the two-class solution of the LCA (entropy = 0.91, cAIC = 1895.93, BIC = 1862.93). Limited capacity countries were mostly in Africa. Limited capacity countries had significantly higher percentage of their deaths attributable to diabetes (adjusted beta = 1.34; 95% CI: 0.15, 2.53; p = 0.027) compared to high capacity countries even after adjusting for income status and diabetes prevalence.

conclusionsOur findings support the report by the Lancet commission on diabetes, which suggests that differences in diabetes outcomes among countries may be explained by variations in the capacity of and investments made in their health systems. Future studies should evaluate initiatives such as the WHO Global Diabetes Compact that are currently underway to improve the capacity of resource-limited countries.

Indexed as

Diabetes MellitusGlobal HealthHealth Care CostsDeveloping CountriesGlycated HemoglobinHumansLatent Class AnalysisGlycated HemoglobinDiabetesDiabetes-related deathDiabetes-related expenditureLatent class analysisNational capacity

Identifiers

PMID39815276
PMCPMC11734536

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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.