Evidence map›Paper›PMID 40770322›Full record

ArticleBMC public health2025

Model-based estimation of the generalized impact fraction (GIF) and population attributable fraction (PAF) for interventions targeting risk factors affecting the five-year risk of diabetic retinopathy.

Mahdi Haresabadi, Ensieh Nasli Esfahani, Mohammad Ali Mansournia, Nazli Namazi, Bijan Pourmostadam, Afshin Ostovar, Akbar Fotouhi

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Mahdi HaresabadiDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Ensieh Nasli EsfahaniDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad Ali MansourniaDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Nazli NamaziDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Bijan PourmostadamDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Afshin OstovarDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Akbar FotouhiDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. afotouhi@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic Retinopathy (DR) is a microvascular complication of diabetes mellitus. Health policymakers are interested in studying the impact of interventions on reducing new cases of DR. The study aimed to calculate the generalized impact fraction (GIF) and population attributable fraction (PAF) of interventions targeting three risk factors on the five-year risk of DR.

methodsA retrospective cohort study was conducted among 1,742 patients with diabetes who completed their baseline assessment between 2015 and 2017. Follow-up studies were carried out during visits from 2020 to 2022, aiming to determine the five-year risk of DR. To estimate the PAF and GIF in different scenarios, a parametric G. formula was utilized.

resultsIn this study, all patients had type 2 diabetes, and the five-year risk of developing diabetic retinopathy (DR) was 16.1% (95% CI: 14.4-18.0). Lowering HbA1c to below 7% would be expected to prevent 35.07% (95% CI: 34.7-36.2) of DR cases. A reduction of 30% in patients with HbA1c levels above 7% corresponds to a GIF of 10.3% (95% CI: 9.9-10.7). Similarly, normalizing blood pressure levels resulted in a PAF of 22.1% (95% CI: 21.8-22.6). A 25% decrease in prevalence above 130/80 mmHg was associated with a GIF for DR of 5.3% (95% CI: 5.1-5.4). Reducing LDL cholesterol levels to less than 100 mg/dl had a lower impact, with a PAF of 1.9% (95% CI: 1.3-2.3). A 20% reduction in prevalence above this level corresponded to a GIF of 0.3% (95% CI: 0.2-0.5).

conclusionReducing HbA1c and blood pressure by 20-30% would be expected to prevent the five-year risk of DR by 15.5% (95% CI: 15.3-15.9). These levels of decrease are achievable and conceivable to policymakers, which can encourage them to implement a program. This could be a Policy-making approach to prevent DR.

Indexed as

Diabetes Mellitus, Type 2Diabetic RetinopathyAdultAgedFemaleGlycated HemoglobinHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsGlycated HemoglobinDiabetic retinopathyGeneralized impact fraction (GIF)Parametric G-formulaPopulation attributable fraction (PAF)

Identifiers

PMID40770322
PMCPMC12326611

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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.