Skip to main content
NCBI home page
Environmental Health Insights logoLink to Environmental Health Insights
. 2026 Sep 2;20:11786302261486668. doi: 10.1177/11786302261486668

Premature Mortality from Type 2 Diabetes Mellitus Attributable to Household Air Pollution from Solid Fuels in Eastern Sub-Saharan Africa, 1990 to 2023

Mihretu Tagesse Sergindo 1,✉, Begetayinoral Kussia Lahole 2, Abraham Lomboro Dimore 3, Melese Chego Cheme 4, Sina Temesgen Tolera 5, Hirut Teame 6, Embay Amare Alemseged 6, Medhin Mehari 6, Yonas Angaw 6, Hailu Lemma 7, Lechisa Asefa 7, Degefa Dhengesu Hero 7, Gedeno Karbana 7, Sebsibe Tadesse 8,9
PMCID: PMC13547809  PMID: 42707353

Abstract

Background

Type 2 diabetes mellitus is a growing public health crisis in Sub-Saharan Africa, where household air pollution from solid fuel use is a common contributor. This study estimated the premature mortality burden from type 2 diabetes mellitus attributable to household air pollution in the Eastern Sub-Saharan Africa region from 1990 to 2023.

Methods

A systematic analysis was conducted using data from the Global Burden of Diseases 2023 study for the Eastern Sub-Saharan countries. The Comparative Risk Assessment framework was used to estimate years of life lost (YLLs) by sex, age, and country. The percentage change between 1990 and 2023 was quantified to estimate the net change.

Results

There were an estimated 276,327 YLLs (95% UI: 165093, 422324) from Type 2 diabetes attributable to household air pollution from solid fuels in the Eastern Sub-Saharan-Africa region in 2023. The equivalent age-standardized rate was estimated to be 134 YLLs per 100,000 population (95% UI: 80, 206). Among adults aged 20 to 54 years old, there were an estimated 57 YLLs 100,000 (95% UI: 35, 88), and above 55 years old it was estimated 562 YLLs per 100,000 (95% UI: 334,862). There was no significant variation in the rate by sex and location throughout the region. The rate showed no improvement between 1990 and 2023 and increased with age.

Conclusion

Household air pollution from solid fuels caused a significant premature mortality related to type 2 diabetes mellitus in the Eastern Sub-Saharan Africa region, with a stagnant impact from 1990 to 2023 and a disproportionate impact on older adults. Thus, there is a critical need to integrate clean energy access with type 2 diabetes mellitus prevention and control strategies to ensure health equity in the region.

Keywords: household air pollution, type 2 diabetes mellitus, solid fuels use, premature mortality, Eastern Sub-Saharan Africa

Introduction

Diabetes is a serious chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. 1 According to the Global Burden of Diseases (GBD) 2021 study, approximately 529 million people were living with diabetes globally, of which 96% were type 2 diabetes mellitus (T2DM). 2 T2DM cases are also projected to increase by 142% in Sub-Saharan Africa by 2050. 3 Household air pollution (HAP) from biomass fuels use, such as wood, charcoal, and animal dung is overlooked driver of T2DM in Sub-Saharan Africa, where about 80% of households use biomass fuels for cooking. 4 The region faces a dual challenge of rapidly aging population with a sharp susceptibility to metabolic disorders and a high number of younger adults increasingly exposed to HAP due to energy poverty and occupational roles.5,6

Prolonged exposure to HAP induces pancreatic beta-cell apoptosis, a key pathological driver in the progression from insulin resistance to overt T2DM by reducing the total number of functional beta-cells and impairing the pancreas’s ability to meet systemic metabolic demands.7-10 Once established, T2DM leads to premature death through multiple interrelated pathways. 11 Chronic hyperglycemia and insulin resistance trigger oxidative stress, advanced glycation end-product formation, and sustained low-grade inflammation, which together promote microvascular damage (nephropathy, neuropathy, and retinopathy) and macrovascular atherosclerosis.12-14 These vascular complications substantially increase the risk of coronary artery disease, myocardial infarction, stroke, and peripheral vascular disease—together accounting for approximately 50–60% of deaths in people with T2DM.15,16 Renal failure, resulting from diabetic nephropathy, is another major contributor to mortality, particularly in low-resource settings with limited access to dialysis or transplantation.17,18 Additionally, hyperglycemia impairs immune function, heightening susceptibility to severe infections, including sepsis and pneumonia. 15 Acute metabolic emergencies such as hyperosmolar hyperglycemic state, though less common than in type 1 diabetes, also contribute to premature death, especially in older adults or those with poor glycaemic control. 16 The relative contribution of each cause varies by age, disease duration, and access to quality healthcare. 18

The rising burden of T2DM in low- and middle-income countries (LMICs) underscores the urgent need for integrated policies that address both clean energy access and diabetes prevention. Current policies, such as the World Health Organization’s clean household energy solutions toolkit and national clean cooking initiatives, prioritize respiratory health but lack integration with non-communicable diseases (NCDs) prevention strategies. 19 Moreover, there is a gap in epidemiological evidence to explain the long-term impacts of exposure to HAP on the distribution of T2DM in Sub-Saharan Africa, where exposure to HAP is highest.20,21

Despite the growing recognition of HAP as a metabolic risk factor, several critical research gaps remain unaddressed. First, no study has systematically quantified the premature mortality burden from T2DM attributable to HAP specifically for the Eastern Sub-Saharan African (ESSA) region, where biomass fuel use is nearly universal and health systems are particularly fragile. Second, existing global or continental estimates obscure substantial subnational and demographic disparities, leaving policymakers without the sex-, age-, and country-specific evidence needed to design targeted interventions. Third, the temporal trajectory of this burden over the past three decades has not been characterized, limiting understanding of whether the problem is worsening or improving in the context of ongoing energy transitions and urbanization. Finally, most previous work has focused on respiratory outcomes of HAP, with metabolic consequences—particularly diabetes—receiving far less attention in both research and policy agendas. This study directly addresses these gaps by providing the first comprehensive, region-specific assessment of premature mortality from T2DM attributable to HAP across all ESSA countries from 1990 to 2023.

The GBD 2023 introduces significant methodological updates, including improved geospatial exposure estimations, enhanced mortality data, and a new integrated statistical model that corrects for previous overestimations of mortality among older adults while providing a more precise, nuanced assessment of NCDs. 22 This study aimed to estimate premature mortality from T2DM attributable to HAP by sex, age, and country in the ESSA region from 1990 to 2023, using GBD 2023 estimates and methods. Rather than generating new primary estimates, the study extracted, synthesized, and contextualized existing GBD data for the ESSA region, offering current, actionable evidence to guide local policy interventions.

Methods

Study Setting

This systematic analysis of the 2023 GBD study was conducted for 15 countries member countries in the Eastern Sub-Saharan African region. These are Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mozambique, Rwanda, Somalia, South Sudan, Uganda, Tanzania, and Zambia. The ESSA region has an estimated population of over 600 million, with a high reliance on solid fuels for cooking, contributing to household air pollution.23,24 Health systems are often fragmented and under-resourced, struggling with a dual burden of infectious and rising non-communicable diseases. 25 Type 2 diabetes prevalence is increasing rapidly, linked to urbanization and changing lifestyles, yet the specific role of HAP in this epidemic remains critically understudied in this high-exposure setting. 3

Data Sources

Input data sources for this study were obtained from the Global Burden of Diseases (GBD) 2023 study, which synthesizes multiple data types to generate mortality and disease burden estimates. Data on type 2 diabetes mellitus mortality were derived from vital registration, verbal autopsy, sample registration systems, and published literature, with causes mapped to International Classification of Diseases (ICD) codes. Exposure data for household air pollution from solid fuels were estimated from surveys and censuses, including Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Population and Housing Censuses, providing information on fuel types used for cooking and heating. Covariates such as urbanization, income, and education were incorporated to improve predictions in data-sparse settings. All input data were extracted, standardized, and processed through the GBD 2023 pipeline, with missing values addressed via imputation and smoothing. Detailed metadata are available at: https://ghdx.healthdata.org/gbd-2023/sources and https://vizhub.healthdata.org/gbd-results/.

Inclusion and Exclusion Criteria

The GBD study included all available data sources that met predefined quality standards, including vital registration, verbal autopsy, sample registration systems, surveys, and published literature. Data sources were included if they contained complete information on age, sex, year, and location, and if cause-of-death data were classifiable to ICD codes. Sources with unclassifiable or nonspecific codes that could not be redistributed were excluded.

Data Analyses

The GBD method applies routine approaches to estimate premature mortality through specific procedures and interactive data tools available at: https://vizhub.healthdata.org/gbd-compare/ and https://ghdx.healthdata.org/gbd-results-tool. Premature mortality was measured by years of life lost (YLLs), representing the difference between standard life expectancy at birth and age at death. The Comparative Risk Assessment (CRA) framework was used to estimate YLLs from T2DM attributable to HAP from solid fuels by sex and age across the ESSA region and its member countries. CRA framework is a systematic, counterfactual approach to estimate the burden attributable to modifiable risk factors like HAP across ages, sexes, and locations over time. 26 Exposure is defined by the concentration of fine particulate matter (PM2.5) in μg/m3 to which an individual is exposed, estimated from fuel type, location, and year. This continuous measure is then directly applied to a non-parametric exposure-response curve to calculate the relative risk for specific health outcomes, such as type 2 diabetes mellitus. The degree of health risk is determined by the absolute concentration of PM2.5, with the theoretical minimum risk exposure level (TMREL) set to a background concentration of 2.4–5.9 μg/m3.

Spatiotemporal Gaussian Process Regression (ST-GPR) was used to smooth the spatial and temporal variation in estimates. The ST-GPR is a Bayesian non-parametric modeling technique that extends Gaussian Processes to model data varying over both time and space, generating smoothed estimates together with 95% uncertainty intervals (95% UIs). 27 The 95% UIs were generated by 1,000 draws from posterior distributions. The net change in the rates between 1990 and 2023 was estimated by comparing values at the baseline (1990) and end of the study period, 20 using the following formula: Percentage change=Value2023−Value1990Value1990 x 100. The study followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure the replication of its methods. The entire GBD time series is updated annually with new data sources and improved methods, and thus GBD 2023 findings replace all previous GBD findings.28,29 The ICD-10 code E11 is used for T2DM and encompasses a wide code range to capture various subcategories of the disease, ensuring accurate documentation of complications and conditions.

Results

Years of Life Lost

There were an estimated 276,327 YLLs (95% UI: 165093, 422324) from T2DM attributable to HAP from solid fuels in the ESSA region in 2023, with 160,112 YLLs (95% UI: 89214, 263106) among males and 116,216 YLLs (95% UI: 66332, 184031) among females. The equivalent age-standardized rate was estimated to be 134 YLLs per 100,000 population (95% UI: 80, 206), with 162 YLLs per 100,000 males (95% UI: 89, 269) and 109 YLLs per 100,000 females (95% UI: 62, 173). Among adults aged 20 to 54 years old, there were an estimated 57 YLLs (95% UI: 35, 88), with 71 (95% UI: 40, 117) among males and 44 YLLs (95%: UI: 26, 69) among females. The YLLs rate among adults aged 55 years and above were estimated 562 YLLs (95% UI: 334, 862), with 663 YLLs (95% UI: 356, 1095) among males and 469 YLLs (95% UI: 265, 746) among females. There was no significant variation in the rate by sex and location throughout the region. Between 1990 and 2023, the trends of the rate remain nearly unchanged (Table 1, Figures 1 and 2).

Table 1.

YLLs From T2DM Attributable to HAP From Solid Fuels by Sex and Location in the ESSA Region, 1990 to 2023

Location Sex Number Age-standardized rate % change Rate for age 20-54 years % change Rate for age ≥55 years % change
2023 2023 1990 – 2023 2023 1990 - 2023 2023 1990-2023
ESSA region Male 160112(89214, 263106) 162.1(88.5,269.2) -0.03 (-0.48, 0.60) 70.8(39.6,116.9) -0.12 (-0.51, 0.43) 662.5(355.9,1094.7) 0.00 (-0.47, 0.63)
Female 116216 (66332,184030) 109.2(61.5,173.2) -0.17 (-0.47, 0.37) 44.1(25.6,69.1) -0.19 (-0.51, 0.25) 468.5(265.4,746.1 -0.15 (-0.46, 0.43)
Both 276327 (165093,422324) 134.4(80,205.5) -0.09 (-0.39, 0.30) 57.1(34.5,87.9) -0.15 (-0.41, 0.18) 561.5(333.9,862.1) -0.08 (-0.39, 0.34)
Ethiopia Male 41304(21441,70071) 158.7(81.7,270.7) -0.11 (-0.56, 0.58) 65.0(34.6,112.3) -0.31 (-0.64, 0.23) 673.1(339.7,1132.9) -0.02 (-0.54, 0.77)
Female 27353(14207,45519) 109.1(57.9,179.2) -0.29 (-0.60, 0.30) 45.1(24.1,76.3) -0.31 (-0.62, 0.10) 459.6(244.0,786.9) -0.27 (-0.59, 0.33)
Both 68657(40227,106726) 134.7(78.2,209.7) -0.19 (-0.50, 0.28) 54.8(33.6,87.4) -0.31 (-0.55, 0.05) 570.7(325.1,897.3) -0.14 (-0.47, 0.39)
Djibouti Male 320 (135,633) 71.8(30.8,142.2) -0.36 (-0.72, 0.31) 39.7(16.9,76.8) -0.29 (-0.70, 0.51) 272.6(114.1,562.7) -0.35 (-0.71, 0.33)
Female 258(134,441) 68.7(34.8,118.4) -0.37 (-0.62, 0.13) 36.5(18.2,62.8) -0.22 (-0.58, 0.39) 274.1(139.9,466.6) -0.38 (-0.63, 0.14)
Both 578(300,1010) 70.5(36.8,121.8) -0.37 (-0.62, 0.06) 38.2(19.5,67.8) -0.26 (-0.57, 0.26) 273.3(143.0,462.7) -0.36 (-0.62, 0.07)
Madagascar Male 12348(6212,22673) 187.4(95.5,347.4) -0.02 (-0.53, 0.80) 85.1(40.2,155.0) -0.12 (-0.60, 0.52) 698.3(355.0,1275.5) -0.03 (-0.52, 0.81)
Female 10717(5563,19049) 156.4(80.5,278.9) -0.05 (-0.45, 0.65) 63.5(32.8,116.5) -0.20 (-0.54, 0.30) 621.6(325.1,1121.7) -0.03 (-0.44, 0.74)
Both 23065(12523,36640) 171.2(94.3,275.7) -0.04 (-0.37, 0.47) 73.9(41.8,118.8) -0.16 (-0.42, 0.22) 659.4(357.6,1054.1) -0.03 (-0.40, 0.50)
Kenya Male 23860 (12070,40741) 181.9(90.5,312.5) 0.02 (-0.53, 0.88) 74.4(36.0,130.6) 0.24 (-0.42, 1.27) 771.8(373.6,1291.3) 0.00 (-0.55, 0.83)
Female 16100 (9054,27638) 111.5(61.9,193.8) -0.10 (-0.47, 0.59) 39.8(23.1,64.4) 0.05 (-0.44, 0.79) 503.5(276.3,894.7) -0.08 (-0.46, 0.61)
Both 39960(23735,63576) 144.7(86.0,232.3) -0.03 (-0.40, 0.46) 56.8(32.3,91.3) 0.17 (-0.31, 0.80) 629.3(368.6,1019.3) -0.04 (-0.41, 0.42)
Tanzania Male 18571(10653,30104) 121.6(70.1,193.8) -0.08 (-0.39, 0.35) 56.1(28.2,98.4) -0.15 (-0.54, 0.55) 502.5(282.2,797.1 -0.04 (-0.38, 0.46)
Female 15751(8966,26390) 95.9(53.9,161.2) -0.20 (-0.47, 0.16) 35.7(18.7,63.5) -0.20 (-0.55, 0.45) 429.2(240.4,710.1) -0.19 (-0.45, 0.13)
Both 34322 (19870,53234) 108.2(63.0,170.4) -0.14 (-0.35, 0.14) 45.5(25.2,71.7) -0.17 (-0.45, 0.24) 464.1(276.1,727.0) -0.12 (-0.31, 0.17)
Uganda Male 12996 (6932,22996) 159.0(85.7,282.0) 0.02 (-0.50, 0.89) 67.6(33.9,122.7) 0.16 (-0.48, 1.19) 628.7(336.9,1112.8) -0.07 (-0.54, 0.72)
Female 8156(4379,13665) 83.9(45.3,144.8) 0.00 (-0.43, 0.79) 31.1(17.8,52.1) 0.18 (-0.39, 1.00) 362.7(190.6,632.8) -0.05 (-0.48, 0.76)
Both 21152 (12120,33707) 117.1(66.2,184.4) -0.01 (-0.41, 0.57) 48.5(27.8,80.3) 0.16 (-0.34, 0.86) 481.2(270.0,753.6) -0.08 (-0.45, 0.47)
Somalia Male 6396 (3213,11459) 147.9(72.5,264.4) 0.09 (-0.53, 1.22) 63.7(30.3,114.6) 0.02 (-0.56, 0.97) 618.1(299.3,1089.7) 0.15 (-0.53, 1.32)
Female 5386(2865,9802) 107.4(56.8,195.8) 0.02 (-0.40, 0.97) 46.2(24.4,84.4) 0.05 (-0.42, 0.99) 471.6(250.2,847.7) 0.08 (-0.41, 1.09)
Both 11782(6499,17950) 125.9(70.6,191.3) 0.05 (-0.35, 0.73) 55.2(30.6,86.3) 0.03 (-0.37, 0.68) 537.0(302.8,833.4) 0.11 (-0.32, 0.84)
Eritrea Male 3120 (1271,5863) 166.6(67.7,317.5) 0.10 (-0.48, 1.06) 92.8(39.3,176.2) 0.16 (-0.44, 1.16) 639.6(263.9,1265.7) 0.12 (-0.50, 1.17)
Female 3025 (1714,5332) 149.4(86.0,263.2) 0.01 (-0.44, 0.90) 75.3(41.1,137.0) 0.14 (-0.34, 1.02) 616.6(357.0,1113.1) 0.01 (-0.44, 0.93)
Both 6145(3383,9880) 157.7(87.0,255.3) 0.05 (-0.30, 0.70) 84.5(46.8,142.5) 0.16 (-0.25, 0.87) 626.9(358.1,1035.7) 0.06 (-0.32, 0.68)
Zambia Male 8153 (4217,14602) 206.7(104.2,375.6) 0.09 (-0.47, 1.04) 116.1(59.6,202.7) 0.24 (-0.44, 1.21) 744.6(372.7,1314.2) 0.03 (-0.50, 0.98)
Female 5825(3186,9870) 142.5(77.5,245.4) -0.11 (-0.47, 0.59) 59.4(31.8,100.1) -0.12 (-0.51, 0.61) 574.9(305.5,988.9) -0.09 (-0.47, 0.73)
Both 13978 (8081,21780) 173.2(102.8,275.5) -0.01 (-0.35, 0.55) 86.1(49.0,136.8) 0.08 (-0.36, 0.75) 653.9(388.7,1003.5) -0.04 (-0.38, 0.50)
South Sudan Male 4449(2287,7890) 186.1(94.7,325.7) -0.02 (-0.55, 0.96) 99.5(48.6,181.7) -0.02 (-0.59, 0.81) 709.3(345.8,1256.8) -0.01 (-0.57, 1.05)
Female 3065 (1602,5402) 132.6(68.7,229.8) -0.12 (-0.46, 0.62) 68.2(36.3,128.0) -0.10 (-0.46, 0.57) 506.6(262.0,850.1) -0.12 (-0.48, 0.69)
Both 7515(4119,11887) 160.1(88.2,251.1) -0.06 (-0.41, 0.65) 83.0(46.5,136.8) -0.06 (-0.44, 0.52) 613.8(335.5,952.7) -0.06 (-0.42, 0.74)
Burundi Male 4891(2257,8878) 164.4(76.1,297.2) 0.07 (-0.51, 1.10) 76.4(35.5,136.3) 0.04 (-0.53, 1.19) 630.1(291.3,1130.1) 0.01 (-0.55, 0.97)
Female 3221(1765,6150) 116.4(63.0,225.0) -0.03 (-0.40, 0.79) 49.0(25.7,93.2) -0.07 (-0.46, 0.98) 471.1(254.6,903.8) -0.08 (-0.45, 0.83)
Both 8112 (4530,13209) 140.9(77.6,227.0) 0.04 (-0.36, 0.80) 62.8(35.2,104.7) 0.00 (-0.41, 1.02) 554.2(303.6,887.7) -0.02 (-0.39, 0.70)
Comoros Male 365 (176,636) 126.9(61.3,219.7) -0.04 (-0.58, 0.74) 51.0(24.6,92.3) -0.12 (-0.61, 0.63) 596.7(286.4,1044.9) 0.04 (-0.54, 0.91)
Female 289(149,494) 92.2(48.3,159.0) -0.24 (-0.55, 0.30) 35.8(17.7,65.3) -0.23 (-0.56, 0.36) 452.0(229.4,776.7) -0.19 (-0.50, 0.44)
Both 654(362,1022) 108.7(60.3,170.1) -0.14 (-0.45, 0.31) 43.4(24.4,69.2) -0.16 (-0.46, 0.29) 521.3(289.3,812.0) -0.08 (-0.41, 0.39)
Mozambique Male 7938(4072,13337) 134.4(69.0,230.6) 0.02 (-0.47, 0.84) 40.7(17.8,71.5) 0.04 (-0.51, 0.79) 608.7(305.0,1064.3) 0.02 (-0.53, 0.94)
Female 5172 (3056,8969) 70.0(41.3,118.0) -0.10 (-0.44, 0.43) 21.3(9.9,38.9) -0.11 (-0.52, 0.48) 332.3(191.0,556.0) -0.11 (-0.49, 0.45)
Both 13110(7474.4,21203) 98.5(56.4,157.7) -0.04 (-0.37, 0.49) 30.3(15.0,50.4) -0.03 (-0.41, 0.48) 456.2(263.0,728.6) -0.04 (-0.40, 0.53)
Malawi Male 9450 (4783,16701) 234.6(119.9,410.6) -0.08 (-0.54, 0.61) 103.2(51.6,186.2) 0.01 (-0.56, 0.77) 948.0(490.5,1716.7) -0.11 (-0.62, 0.64)
Female 6814(3599,10883) 136.5(72.4,224.4) -0.20 (-0.57, 0.30) 61.5(32.2,102.7) -0.12 (-0.54, 0.52) 574.6(295.0,976.8) -0.21 (-0.59, 0.47)
Both 16264 (9791,26183) 179.8(106.6,284.8) -0.14 (-0.43, 0.28) 81.6(49.3,134.8) -0.05 (-0.40, 0.46) 737.7(428.7,1189.0) -0.16 (-0.48, 0.35)
Rwanda Male 5818 (2973,10227) 178.6(90.9,316.1) -0.03 (-0.60, 0.77) 78.2(38.8,137.4) -0.11 (-0.64, 0.77) 707.5(363.4,1280.3) -0.01 (-0.63, 0.92)
Female 4988(2655,8756) 115.4(60.9,202.4) -0.24 (-0.62, 0.30) 52.4(27.4,88.7) -0.32 (-0.67, 0.27) 489.3(257.3,840.6) -0.21 (-0.60, 0.37)
Both 10807 (6259,16558) 141.5(81.7,217.9) -0.12 (-0.50, 0.40) 64.8(37.0,102.7) -0.20 (-0.54, 0.37) 581.4(327.2,901.8) -0.10 (-0.50, 0.51)
Open in a new tab

The rates of premature mortality increased with age. However, there was no variation in the rate by sex, location, and year.

Figure 1.

Figure 1.

Open in a new tab

Trends of age-standardized YLLs per 100,000 population from T2DM attributable to HAP from solid fuels in the ESSA region, 1990 to 2023. There was no variation in the rate by location and year

Figure 2.

Figure 2.

Open in a new tab

Age and sex distribution of YLLs per 100,000 population from T2DM attributable to HAP from solid fuels in the ESSA region, 2023. The rate increased with age

Discussion

This study demonstrated that there was a substantial burden of premature mortality from T2DM attributable to HAP across the ESSA region from 1990 to 2023. The age-standardized rate was estimated at 134 YLLs per 100,000 population. The YLLs rate increased as the age of the individuals increased across the region. The rate of change was statistically stable between 1990 and 2023, with no significant increase or decrease estimated across any country or sex group.

The YLLs for T2DM attributable to HAP in the ESSA region indicate a massive and distinct public health challenge compared to other developed countries. Unlike that of high-income countries, more than 80% of the population in the ESSA region is primarily dependent on solid fuel as energy, which leads to HAP.24,25 The YLLs rate is substantial because exposure to risk factors in the region is chronic and high intensity, causing lifelong cumulative exposure that accelerates T2DM development. This elevated rate is further compounded by the lack of timely diagnosis and poor management of diabetes in the region, meaning the disease often leads quickly to severe complications and premature death. 30 Consequently, this high YLLs rate underlines HAP’s role as a major, modifiable, and concentrated environmental factor fueling the T2DM disease epidemic in the ESSA region.

The increase of YLLs rate with the age increase of the population reflects both cumulative exposure to solid fuel use over the life course and the increased vulnerability of older populations to metabolic complications, which is supported by the study by Dehghani S et al. 31 Our findings highlight the intersection of exposures for HAP and aging-related health risks in shaping diabetes mortality in the region. These findings indicated that while T2DM remains a chronic disease of adulthood, its mortality burden is heavily concentrated in later life, suggesting by other studies that preventive interventions may be most impactful if initiated earlier to reduce cumulative exposure to HAP. 32 The lack of significant variations of YLLs rate across the region within different sex groups is indicating that the problem is consistent over the region for all sex group, implying that HAP has been an underlying risk factor for the T2DM disease in the ESSA region.

A critical finding from the long-term analysis is the statistical stability of the YLLs rate across the 34-year period. The trend in the T2DM burden attributable to HAP in the ESSA region showed no significant change from 1990 to 2023. In contrast, a study by Raina et al (2024) focusing on trend analysis in the United States demonstrated a decrease in the T2DM burden during the specific period of time. 33 This reduction in the U.S. was primarily attributed to improved access to cleaner cooking and heating technologies and the application of effective public health interventions. The stagnant burden in the ESSA region for 34 years highlights the urgent need to revise existing interventions and their implementation strategies. Such revisions are critical to accelerating the electrification for clean fuel use and replacement of traditional cooking methods, which continue to be a considerable contributor to reducing the T2DM burden epidemic in the region.

The findings have important implications for health and energy policy in the ESSA region. The high YLLs burden attributable to HAP underscores the need to integrate clean energy interventions with NCD prevention strategies. Current clean cooking initiatives often emphasize respiratory outcomes, but this study demonstrates the broader metabolic and cardiovascular consequences of continued reliance on solid fuels, which is supported by other studies.10,26 Scaling up access to affordable clean fuels and stoves, coupled with community-based education on diabetes risk factors, could help reduce premature mortality. At the same time, health systems must be strengthened to detect and manage diabetes earlier, particularly among older adults who are disproportionately affected. Integrating energy, environment, and health policies into a single framework could accelerate progress toward the Sustainable Development Goals (SDGs), while advancing equity in regions where energy poverty and NCDs intersect.

This analysis has several limitations that should be acknowledged. First, the estimates rely on GBD modeling, which is reserved by the availability and quality of local data. Diabetes surveillance and indoor air pollution monitoring remain limited in many ESSA countries, which may influence the internal and external validity of our findings. Second, the exposure-response functions linking HAP and T2DM are largely derived from studies in Asia and high-income countries, which may not fully capture ESSA regional differences in exposure levels, cooking practices, and population susceptibility. Third, our analysis did not incorporate the regional disease burden estimates by socio-demographic index. Fourth, the absence of patient-level socio-demographic information constrains our ability to assess within-country disparities, which will require future primary research. Fifth, YLLs estimates do not account for years lived with disability (YLDs), meaning the full health burden of HAP-related diabetes is likely underestimated. Finally, our study did not include trend decomposition, joinpoint regression, or age-period-cohort modeling. Future regional studies should incorporate these advanced frameworks to better isolate temporal and cohort-specific trends.

Conclusion

This study revealed a persistently high and age-concentrated premature mortality burden from T2DM attributable to HAP in the ESSA region over 34 years. To accelerate progress, we recommend integrating clean cooking interventions with diabetes prevention strategies, prioritizing older adults for early detection and management, and strengthening health systems for timely diagnosis. Policy-makers should also invest in surveillance to monitor exposure and evaluate intervention effectiveness. Multisectoral collaboration across energy, health, and environment sectors is essential to reduce this modifiable burden.

Acknowledgements

The authors would like to acknowledge the Bill & Melinda Gates Foundation for their support of the GBD study, which allowed the public resources necessary for this evidence synthesis.

Footnotes

Author Contributions: ST conceived and designed the study. MTS drafted the original manuscript. Analysis and interpretation of the data were conducted by MTS, BKL, AL, ALD, MCC, STT, HT, EAA, MM, YA, HL, LA, DDH, GK, and ST. All authors have read and approved the final version of the manuscript for publication.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article. We used public data from the Global Burden of Disease (GBD) 2023 study which has been supported by IHME and the Bill & Melinda Gates Foundation.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Mihretu Tagesse Sergindo https://orcid.org/0000-0003-1459-5490

Embay Amare Alemseged https://orcid.org/0000-0003-3567-1931

Gedeno Karbana https://orcid.org/0009-0008-4827-3020

Sebsibe Tadesse https://orcid.org/0000-0002-9153-5400

References

  • 1.World Health Organization . Diabetes [Internet]. Geneva: World Health Organization; 2023. Available from. https://www.who.int/news-room/fact-sheets/detail/diabetes [Google Scholar]
  • 2.Ong KL, Stafford LK, McLaughlin SA, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203-234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.International Diabetes Federation (IDF) . IDF Diabetes Atlas. 11th ed. Brussels: International Diabetes Federation; 2025. Available from. https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/ [Google Scholar]
  • 4.Health Effects Institute . State of Air Quality and Health Impacts in Africa – a Report from the State of Global Air Initiative; 2022. [Google Scholar]
  • 5.Idemyor V. Diabetes in Sub-Saharan Africa: Health Care Perspectives, Challenges, and the Economic Burden of Disease. J Natl Med Assoc. 2010;102(7):650-653. [DOI] [PubMed] [Google Scholar]
  • 6.Mercer T, Chang AC, Fischer L, et al. Mitigating the burden of diabetes in sub-saharan Africa through an integrated diagonal health systems approach. Diabetes, Metab Syndr Obes. 2019;12:2261-2272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Rajagopalan S, Brook RD. Air pollution and type 2 diabetes: mechanistic insights. Diabetes. 2012;61(12):3037-3045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Brook RD, Jerrett M, Burnett RT, Bard RL, Finkelstein MM. The relationship between diabetes mellitus and traffic-related air pollution. J Occup Environ Med. 2008;50(1):32-38. [DOI] [PubMed] [Google Scholar]
  • 9.Liu C, Bai L, Yin P, et al. Ambient air pollution and diabetes: a systematic review and meta-analysis. Environ Res. 2020;187:109641. [DOI] [PubMed] [Google Scholar]
  • 10.Yang BY, Fan S, Thiering E, et al. Ambient air pollution and diabetes: a systematic review and meta-analysis. Diabetes Care. 2020;43(5):1044-1052. [DOI] [PubMed] [Google Scholar]
  • 11.Rao KSS, Kaptoge S, Thompson A, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364(9):829-841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Emerging Risk Factors Collaboration. Sarwar N, Gao P, Seshasai SR, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215-2222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Afkarian M, Zelnick LR, Hall YN, et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988-2014. JAMA. 2016;316(6):602-610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Thomas MC, Cooper ME, Zimmet P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nat Rev Nephrol. 2016;12(2):73-81. [DOI] [PubMed] [Google Scholar]
  • 15.Koh GCKW, Peacock SJ, van der Poll T, Wiersinga WJ. The impact of diabetes on the pathogenesis of sepsis. Eur J Clin Microbiol Infect Dis. 2012;31(4):379-388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Carey IM, Critchley JA, DeWilde S, Harris T, Hosking FJ, Cook DG. Risk of infection in type 1 and type 2 diabetes compared with the general population: a matched cohort study. Diabetes Care. 2018;41(3):513-521. [DOI] [PubMed] [Google Scholar]
  • 17.Umpierrez GE, Murphy MB, Kitabchi AE. Diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome. Diabetes Spectrum. 2002;15(1):28-36. [Google Scholar]
  • 18.Gregg EW, Cheng YJ, Srinivasan M, et al. Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data. Lancet. 2018;391(10138):2430-2440. [DOI] [PubMed] [Google Scholar]
  • 19.Rosenthal J, Quinn A, Grieshop AP, Pillarisetti A, Glass RI. Clean cooking and the SDGs: Integrated analytical approaches to guide energy interventions for health and environment goals. Energy Sustain Dev. 2018;42:152-159. doi: 10.1016/j.esd.2017.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Misganaw A, Hailemariam A, Berheto TM, Lakew Y, Mengesha SD. Household air pollution impacts on mortality and disease burden in East Africa and Nile Basin African countries. Ethiop J Heal Dev. 2023;2023(7):152-159. [Google Scholar]
  • 21.Bennitt FB, Wozniak S, Causey Ss K, et al. Global, regional, and national burden of household air pollution, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2025;405:1167-1181. doi: 10.1016/S0140-6736(24)02840-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Basil B, Wang Q, Mensah E, et al. African regional and national burden of diabetes mellitus and its attributable risk factors from 1990 to 2021: results from the global burden of disease study 2021. Front Endocrinol (Lausanne). 2025;16:1-13. doi: 10.3389/fendo.2025.1643999 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.UN . World Population Prospects; 2024. https://population.un.org/wpp/ [Google Scholar]
  • 24.Gangiah S. Pollution from cooking in rural and poor urban households of Africa: A methodological review. HTS Teol Stud / Theol Stud. 2022;78(2):1-11. doi: 10.4102/hts.v78i2.7708. [DOI] [Google Scholar]
  • 25.Anand RR. Healthcare and Disease Burden in Africa: The Impact of Socioeconomic Factors on Public Health. Ilha Niohuru. 2024;16:343-348. doi: 10.1163/09744061-bja10153. [DOI] [Google Scholar]
  • 26.Forouzanfar MH, Afshin A, Alexander LT, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2017;388:1990-2015. doi: 10.1016/S0140-6736(16)31679-8. Published online. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Mao Q, Zhu X, Zhang X, Kong Y. Effect of air pollution on the global burden of cardiovascular diseases and forecasting future trends of the related metrics: a systematic analysis from the Global Burden of Disease Study 2021. Front Med. 2024;11:1472996. doi: 10.3389/fmed.2024.1472996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Berhe AK, Tesfau YB, Amare HH, et al. Magnitude of Premature Mortality From Sudden Infant Death Syndrome in Ethiopia, From 1990 to 2023. Sage Open Pediatr; 2026. 10.1177/30502225261435035. Published online. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Stevens GA, Alkema L, Black RE, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet. 2016;388:19-23. doi: 10.1016/S0140-6736(16)30388-9. Published online. [DOI] [PubMed] [Google Scholar]
  • 30.Motala AA, Mbanya JC, Ramaiya K, Pirie FJ, Ekoru K. Type 2 diabetes mellitus in sub-Saharan Africa: challenges and opportunities. Nat Rev Endocrinol. 2022;18(4):219-229. doi: 10.1038/s41574-021-00613-y. [DOI] [PubMed] [Google Scholar]
  • 31.Dehghani S, Yousefi S, Oskoei V, et al. Ecological study on household air pollution exposure and prevalent chronic disease in the elderly. Sci Rep. 2023;13(1):1-9. doi: 10.1038/s41598-023-39059-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Hystad P, Duong M, Brauer M, et al. Health effects of household solid fuel use: Findings from 11 countries within the prospective urban and rural epidemiology study. Environ Health Perspect. 2019;127(5):1-10. doi: 10.1289/EHP3915.33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Raina M, Salerno P, Doshi K, Hu J, Rajagopalan S. Global and national burden of type 2 diabetes mellitus attributable to PM2.5 air pollution: An analysis of the GBD study from 1990 to 2019. Physiol Rep. 2024;12(14):e70074. doi: 10.14814/phy2.70074. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Environmental Health Insights are provided here courtesy of SAGE Publications

RESOURCES