Burden of Deaths and Disability-adjusted Life Years Attributable to Suboptimal High Temperature in India: Analysis of the Global Burden of Disease Estimates (1990–2021)

Abstract

Background:

Rising ambient temperatures are an escalating health risk in India, yet long-term, disease-specific burdens are poorly quantified. The present study assessed national and subnational trends in mortality and disability-adjusted life years (DALYs) attributable to suboptimal high temperatures in India and quantify 1990–2021 trends in heat-attributable deaths and DALYs.

Methods:

We conducted a secondary descriptive analysis of Global Burden of Disease 2021 estimates for India and all states/union territories (UTs) from 1990 to 2021. Outcomes were absolute deaths/DALYs and age-standardized rates per 100,000, stratified by sex and cause groups. Estimates with 95% uncertainty intervals were extracted from Global burden of disease (GBD) Compare; no additional modeling beyond GBD methods was performed.

Results:

India’s heat-attributable burden more than tripled in absolute terms between 1990 and 2021, while age-standardized death rates increased modestly (7.78 to 9.89 per 100,000). Males consistently had higher mortality and DALY rates than females. A marked shift from communicable, maternal, neonatal, and nutritional (CMNN) diseases to non-communicable diseases (NCDs) was observed. Among selected NCDs, ischemic heart disease and stroke showed increasing age-standardized DALY rates, whereas chronic kidney disease and chronic respiratory diseases showed slight declines. State-level heterogeneity was substantial, with Kerala, Tamil Nadu, and Delhi showing stable/declining rates, whereas Maharashtra, Chhattisgarh, Bihar, West Bengal, and several northeastern states recorded pronounced increases.

Conclusions:

Heat-attributable deaths and DALYs in India have risen sharply with an epidemiological shift toward NCD-related vulnerability and subnational disparities. Findings underscore the urgent need to implement state-tailored heat action plans with health-system preparedness that integrate NCD risk management and prioritize high-risk populations and geographies.

INTRODUCTION

Climate change is an escalating global health challenge with major implications for population health.[1] Rising ambient temperatures, altered rainfall patterns, and extreme weather events disproportionately affect low- and middle-income countries (LMICs), where adaptive capacity remains limited.[2] Among these climate-related exposures, heat is a significant environmental stressor with direct and indirect impacts on morbidity and mortality.[3,4] Elevated ambient temperatures can exacerbate existing chronic diseases, heighten the risk of acute health events, and strain already overburdened health systems.[5] With a population of over 1.4 billion, India is especially vulnerable to the health consequences of high temperatures. Its tropical location, prolonged summers, and increasing frequency and intensity of heatwaves make heat exposure a major public health concern.[6] These exposures are compounded by rapid urbanization, the heat-island effect, high population density, and socioeconomic inequalities that limit access to cooling, healthcare, and adaptive infrastructure.[7,8] Evidence indicates that vulnerable groups such as the elderly, children, outdoor workers, and individuals with pre-existing chronic illnesses are disproportionately affected.[9]

Historically, research on the health impacts of temperature in India has been dominated by short-term epidemiological studies linking heatwaves with excess mortality or hospitalizations. While these provide valuable insights into acute impacts, they fail to capture the broader, long-term, and disease-specific burden attributable to high temperature. Much of the available evidence is also concentrated in selected cities, such as Delhi and Ahmedabad, leaving uncertainty about national trends and state-level heterogeneity in heat-attributable health outcomes.[10] Given India’s vast geographic and climatic diversity, it is critical to understand how the health impacts of high temperature vary across states and union territories. Such evidence is essential for developing targeted climate adaptation strategies sensitive to local vulnerabilities.

The global burden of disease (GBD) study provides standardized estimates of mortality, disability-adjusted life years (DALYs), attributable to diseases, injuries, and risk factors across countries and subnational units over time.[11] Within this framework, suboptimal temperature is assessed as an environmental risk factor, allowing estimation of deaths and DALYs attributable to high temperature at national and subnational levels. This is particularly relevant for India, where heat exposure intersects with demographic aging, urbanization, occupational exposure, and the ongoing epidemiological transition from communicable, maternal, neonatal, and nutritional (CMNN) diseases toward non-communicable diseases (NCDs).[12,13] However, the long-term national and subnational distribution of heat-attributable mortality and DALYs in India, including the major disease categories contributing to this burden, remains insufficiently described. Against this background, the present study uses GBD 1990–2021 estimates to comprehensively assess the mortality and DALY burden attributable to suboptimal high temperature in India and its states/union territories. Specifically, we aim to quantify national trends in heat-related deaths and DALYs over three decades, examine regional and sex-specific patterns, and identify the major disease categories contributing to this burden to inform state-specific heat-health preparedness and climate adaptation strategies in India.

METHODS

GBD overview

This study used GBD 2021 estimates to assess deaths and DALYs attributable to high temperature in India and its states/union territories (UTs) from 1990 to 2021.[14] GBD 2021 provides standardized estimates of mortality, years of life lost (YLLs), years lived with disability (YLDs), and DALYs by age, sex, cause, risk factor, location, and year. The GBD 2021 cycle produced estimates for 371 diseases and injuries and 88 risk factors across 204 countries and territories, including 811 subnational locations, from 1990 to 2021.[15] Although GBD is a global comparative health assessment, the present analysis was restricted to India and its states/UTs. Estimates were accessed through the GBD Compare tool.

Study design and data source

This was a secondary descriptive analysis of publicly available GBD 2021 modeled estimates. No primary data collection was undertaken. The analysis was designed to describe national burden, state/UT-level heterogeneity, sex-specific patterns, and cause-group distribution of deaths and DALYs attributable to high temperature in India. The GBD 2021 study comprehensively assesses time trends for a mutually exclusive and collectively exhaustive list of diseases and injuries. It estimates incidence, prevalence, mortality, YLLs, YLDs, and DALYs for 371 diseases and injuries among 204 countries and territories.[16] The GBD 2021 study draws on multiple global data sources; however, the present study did not collect primary data. We extracted the final GBD 2021 modeled estimates for deaths and DALYs attributable to high temperature for India and its states/UTs.[17] The study estimated the attributable high-temperature-related risk factors under the broad environmental risk factor heading to the burden of morbidity, using deaths and DALYs (sum of YLLs and YLDs) for all age categories in Indian states from 1990 to 2021. These estimates were obtained using the GBD 1990–2021 results following standard protocols.

Data extraction process

Under the GBD results window, risk factor estimates for high temperature were calculated for DALY and death indicators and presented as numbers, percentages, and rates for all ages and the age-standardized population of India. The attributable factors for the association between the probability of disease or injury and CMNNs and NCDs in India, for the age-standardized population between 1990 and 2021, were calculated. GBD 2021 estimates account for misclassification and underreporting in civil registration and vital statistics systems through standard cause-of-death redistribution procedures and ensemble modeling. Cause of Death Ensemble modeling (CODEm) combines multiple statistical models and covariates to generate internally consistent cause-specific mortality estimates across age, sex, location, and year. Similarly, DisMod-MR 2.1, a Bayesian meta-regression tool, synthesizes heterogeneous epidemiological data to produce internally consistent estimates of disease incidence, prevalence, remission, mortality, and excess mortality. These methods have been described in detail in previous GBD methodological publications.[18,19] In the present analysis, we used the final GBD 2021 estimates for deaths and DALYs attributable to high temperature; no additional modeling was performed by the authors. To evaluate trends from 1990 to 2021 across all NCD causes, the attributable risk of high temperature was examined in the age-standardized population of both sexes. It was expressed in numbers, percentages, and rates.

Statistical analysis

This was a secondary descriptive analysis of GBD 2021 estimates. We extracted absolute deaths and DALYs, age-standardized death rates, and age-standardized DALY rates attributable to high temperature for India and its states/UTs. Estimates were summarized for 1990 and 2021, and relative change was calculated as the proportional change in age-standardized rates between these two time points. Annual trends from 1990 to 2021 were presented descriptively using GBD Compare outputs. Formal trend modeling, including joinpoint regression or average annual percentage change (AAPC), was not applied because the objective was to summarize burden, relative change, and subnational heterogeneity rather than identify statistical inflection points in annual modeled estimates. All estimates are presented with 95% uncertainty intervals derived from GBD posterior draws.

RESULTS

Table 1 presents deaths and age-standardized death rates attributable to suboptimal high temperature in India and its states/UTs from 1990 to 2021. At the national level, absolute heat-attributable deaths increased more than threefold, from 30,769.5 deaths in 1990 (95% uncertainty interval [UI]: 14,895.7–46,355.8) to 104,887.8 deaths in 2021 (95% UI: 63,793.1–150,538.6). In contrast, the national age-standardized death rate increased only modestly, from 7.8 per 100,000 in 1990 (95% UI: 3.7–12.0) to 9.9 per 100,000 in 2021 (95% UI: 6.0–14.3). Males consistently had higher age-standardized death rates than females in both 1990 and 2021. Subnational patterns showed marked heterogeneity. Some states showed stable or declining age-standardized death rates, including Delhi (18.5 to 13.5 per 100,000), Tamil Nadu (14.9 to 13.9 per 100,000), and Punjab (16.9 to 16.2 per 100,000). In contrast, increases were observed in several states, including Gujarat (12.0 to 18.4 per 100,000), Maharashtra (3.8 to 6.3 per 100,000), Chhattisgarh (7.7 to 12.4 per 100,000), West Bengal (5.8 to 10.5 per 100,000), and Uttar Pradesh (9.7 to 12.5 per 100,000). Supplementary Material 1 ^{(1.9MB, tif)} further shows that the heat-attributable burden shifted by cause group over time. Age-standardized death and DALY rates attributable to high temperature increased for NCDs, whereas rates for CMNN diseases declined from 1990 to 2021.

Table 1.

Trends in age-standardized deaths attributable to suboptimal high temperature in India and its states/UTs, using subnational GBD estimates (1990–2021)[20]

		Deaths Absolute numbers (95% UI)				Age-Standardized Death Rate Per 100,000 (95% UI)
		1990		2021		1990		2021		Relative change
India
Overall		30,769.5 (14,895.7–46,355.8)		104,887.8 (63,793.1–150,538.6)		7.8 (3.7–12.0)		9.9 (6.0–14.3)		0.3 (0.1–0.7)
Male		18,090.5 (8,519.1–28,037.8)		59,377.6 (35,837.4–87,762.5)		8.9 (4.1–14.3)		11.8 (7.0–17.5)		0.3 (0.1–0.8)
Female		12,679.1 (6,429.6–19,391.3)		45,510.2 (28,056.6–65,479.8)		6.6 (3.2–10.3)		8.2 (5.0–11.7)		0.2 (0.0–0.7)
Regions of India
North
Jammu & Kashmir and Ladakh		75.4 (7.3–169.5)		254.5 (64.4–564.2)		2.4 (0.1–5.7)		2.4 (0.6–5.3)		0.0 (-0.3–2.4)
Himachal Pradesh		70.9 (8.4–158.7)		175.1 (32.6–405.0)		2.5 (0.2–5.7)		2.3 (0.4–5.2)		-0.1 (-0.4–0.6)
Punjab		1,950.1 (521.5–3,419.4)		5,051.0 (1,498.8–8,750.7)		16.9 (4.5–29.4)		16.2 (4.8–28.1)		-0.0 (-0.3–0.3)
Haryana		1,000.6 (355.8–1,668.2)		3,263.8 (1,271.7–5,381.1)		12.2 (4.2–20.7)		14.5 (5.7–24.0)		0.2 (-0.1–0.7)
Uttarakhand		105.0 (-11.2–266.3)		294.0 (-1.1–751.0)		3.8 (-0.6–10.3)		3.6 (-0.1–9.1)		-0.0 (-1.0–1.1)
Delhi		555.8 (238.9–869.9)		1,833.0 (802.1–2,967.1)		18.5 (7.7–29.3)		13.5 (5.8–21.8)		-0.3 (-0.4–-0.1)
Rajasthan		2,407.0 (949.8–4,238.4)		8,336.7 (3,988.1–13,900.0)		13.2 (5.0–24.5)		16.0 (7.4–27.2)		0.2 (-0.0–0.8)
West
Gujarat		2,259.1 (1,508.2–3,112.2)		10,432.5 (7,243.4–14,174.2)		12.0 (8.1–16.6)		18.4 (12.9–25.0)		0.5 (0.3–0.9)
Maharashtra		1,414.8 (789.3–2,009.3)		6,980.5 (4,837.1–9,615.1)		3.8 (2.1–5.5)		6.3 (4.4–8.7)		0.6 (0.3–1.3)
Goa		1.3 (-1.5–3.4)		31.8 (24.0–40.9)		0.2 (-0.3–0.5)		1.6 (1.2–2.0)		6.5 (-151.1–103.1)
South
Andhra Pradesh		1,649.3 (983.5–2,232.0)		5,956.4 (4,314.8–7,790.6)		8.2 (5.0–11.2)		10.4 (7.5–13.5)		0.3 (-0.1–1.0)
Kerala		332.8 (252.3–400.0)		1,073.3 (885.6–1,340.2)		2.0 (1.5–2.4)		2.5 (2.1–3.1)		0.2 (0.0–0.6)
Telangana		926.8 (594.9–1,293.0)		3,136.1 (2,264.1–4,148.8)		7.5 (4.9–10.4)		10.8 (7.8–14.2)		0.5 (0.1–1.0)
Tamil Nadu		4,347.3 (3,008.7–5,400.1)		10,132.5 (8,098.3–12,096.9)		14.9 (10.4–18.4)		13.9 (11.1–16.5)		-0.1 (-0.3–0.2)
Karnataka		399.9 (37.4–698.7)		1,274.5 (841.0–1,798.1)		1.8 (0.1–3.1)		2.2 (1.4–3.1)		0.2 (-0.2–4.0)
Central
Madhya Pradesh		1,889.0 (722.0–3,068.9)		6,283.4 (3,279.3–9,475.4)		8.9 (3.2–14.9)		10.8 (5.5–16.4)		0.2 (-0.1–1.0)
Uttar Pradesh		6,029.1 (2,015.6–11,158.6)		18,877.8 (7,029.4–33,196.3)		9.7 (3.0–18.7)		12.5 (4.4–22.3)		0.3 (0.0–0.7)
Chhattisgarh		539.3 (134.9–937.6)		2,237.7 (1,506.7–3,108.7)		7.7 (1.9–13.4)		12.4 (8.4–17.1)		0.6 (0.1–3.3)
North East
Assam		126.2 (-165.3–458.9)		1,239.1 (69.9–2,436.4)		1.3 (-2.2–5.3)		5.8 (0.3–11.6)		3.5 (-23.4–42.9)
Nagaland		2.0 (-3.1–9.0)		20.4 (-0.6–49.5)		0.4 (-0.8–2.1)		1.8 (-0.1–4.4)		3.1 (-21.9–18.6)
Arunachal Pradesh		1.1 (-2.5–5.4)		13.1 (-0.8–32.9)		0.3 (-0.9–2.0)		1.7 (-0.1–4.2)		3.8 (-20.5–19.0)
Sikkim		0.1 (-0.1–0.2)		3.5 (0.8–6.5)		0.0 (-0.1–0.2)		0.6 (0.1–1.2)		14.2 (-60.0–47.0)
Meghalaya		1.9 (-3.4–7.8)		38.0 (4.3–77.5)		0.3 (-0.7–1.3)		2.3 (0.2–4.8)		6.9 (-47.2–55.8)
Mizoram		-0.0 (-2.3–2.3)		15.0 (-1.3–38.6)		-0.1 (-1.1–1.1)		1.9 (-0.2–5.0)		-23.9 (-81.1–87.5)
Tripura		14.1 (-15.9–48.9)		183.5 (24.4–371.4)		1.1 (-1.5–4.2)		5.8 (0.6–11.6)		4.3 (-49.8–85.3)
Manipur		2.1 (-4.2–10.2)		23.3 (-9.7–79.5)		0.2 (-0.5–1.2)		0.9 (-0.4–3.3)		3.0 (-19.0–18.4)
East
Jharkhand		419.6 (49.4–825.2)		925.3 (300.6–1,660.3)		4.3 (-0.5–8.5)		4.0 (1.2–7.1)		-0.1 (-0.4–1.6)
Bihar		2,251.8 (1,059.4–3,646.7)		6,091.9 (3,337.2–8,934.4)		7.4 (3.3–12.3)		8.2 (4.3–12.1)		0.1 (-0.1–0.5)
Odisha		308.3 (-140.4–643.1)		2,261.5 (1,591.8–3,124.3)		NA*		5.3 (3.8–7.3)		1.7 (-15.5–34.8)
West Bengal		1,649.4 (921.0–2,414.5)		8,264.3 (5,488.1–11,341.0)		5.8 (3.1–8.6)		10.5 (7.0–14.4)		0.8 (0.4–1.4)
Other Union Territories		39.7 (15.9–64.9)		184.2 (106.9–292.5)		4.5 (1.8–7.3)		6.2 (3.6–9.8)		0.4 (-0.1–1.2)

UI: Uncertainty interval. Relative change refers to the change in age-standardized death rates between 1990 and 2021. Negative lower uncertainty limits in some low-burden states/UTs reflect model uncertainty around near-zero estimates and should be interpreted cautiously. * Values not available in the extracted GBD Compare output used for this analysis despite repeated extractions. GBD=Global burden of disease, UI=Uncertainty interval, UT=Union territory, NA=Not available, ASDR=Age-standardized death rate

Table 2 presents DALYs and age-standardized DALY rates attributable to suboptimal high temperature in India and its states/UTs from 1990 to 2021. At the national level, absolute heat-attributable DALYs nearly tripled, from 847,779.9 in 1990 (95% UI: 411,565.7–1,252,447.4) to 2,477,660.1 in 2021 (95% UI: 1,530,638.3–3,521,542.2). The national age-standardized DALY rate increased from 174.1 per 100,000 in 1990 (95% UI: 84.5–262.1) to 208.1 per 100,000 in 2021 (95% UI: 127.5–296.7). Males had consistently higher age-standardized DALY rates than females in both 1990 and 2021. Subnational patterns showed considerable heterogeneity [Table 2, Figure 1]. Age-standardized DALY rates declined or remained relatively stable in some states/UTs, including Delhi (389.3 to 263.6 per 100,000), Tamil Nadu (336.2 to 288.2 per 100,000), Punjab (397.1 to 364.4 per 100,000), Himachal Pradesh (55.9 to 46.4 per 100,000), and Bihar (168.9 to 166.6 per 100,000). In contrast, notable increases were observed in Gujarat (267.4 to 397.2 per 100,000), Maharashtra (82.7 to 127.9 per 100,000), Chhattisgarh (168.6 to 256.5 per 100,000), West Bengal (126.6 to 211.4 per 100,000), Odisha (45.3 to 114.2 per 100,000), and Uttar Pradesh (218.9 to 267.5 per 100,000). Several northeastern states showed large relative increases, but these estimates had wide uncertainty intervals and should be interpreted cautiously. Supplementary Material 2 ^{(2.7MB, tif)} presents NCD-specific age-standardized death and DALY rates attributable to high temperature in India from 1990 to 2021. Among the selected NCDs, ischemic heart disease showed the highest DALY burden, increasing from 64.3 to 75.8 per 100,000, followed by stroke, which increased from 51.0 to 61.0 per 100,000. In contrast, chronic kidney disease showed a slight decline from 7.7 to 7.1 per 100,000, and chronic respiratory diseases declined from 32.3 to 27.2 per 100,000. Similar patterns were observed for age-standardized death rates.

Table 2.

Trends in age-standardized disability-adjusted life years (DALYs) attributable to suboptimal high temperature in India and its states/UTs, using GBD estimates (1990–2021)[20]

		Disability-adjusted life years n (95% UI)				Age-standardized rate (per 100,000 (95% UI))
		1990		2021 DALYs,		1990		2021		Relative change
India
Overall		847,779.9 (411,565.7–1,252,447.4)		2,477,660.1 (1,530,638.3–3,521,542.2)		174.1 (84.5–262.1)		208.1 (127.5–296.7)		0.2 (0.0–0.6)
Male		505,431.3 (243,303.9–761,397.8)		1,457,612.6 (897,718.6–2,161,930.8)		200.4 (94.7–310.5)		251.3 (152.5–372.7)		0.3 (0.0–0.7)
Female		342,348.6 (173,499.8–505,646.8)		1,020,047.5 (640,065.6–1,438,891.8)		146.3 (74.3–223.2)		167.9 (104.2–238.7)		0.2 (-0.1–0.5)
Regions of India
North
Jammu & Kashmir and Ladakh		2,019.0 (213.7–4,519.1)		5,719.0 (1,438.7–12,524.8)		51.9 (5.0–116.7)		51.1 (12.8–111.3)		0.0 (-0.3–1.5)
Himachal Pradesh		1,857.8 (285.1–3,988.4)		3,797.3 (760.9–8,703.3)		55.9 (7.4–123.7)		46.4 (9.2–106.0)		-0.2 (-0.4–0.3)
Punjab		53,822.1 (15,066.6–96,026.4)		120,348.5 (35,944.5–212,060.6)		397.1 (108.1–702.1)		364.4 (109.3–640.3)		-0.1 (-0.3–0.2)
Haryana		27,204.1 (10,230.6–44,588.2)		79,721.4 (30,632.6–130,546.0)		280.1 (101.8–467.9)		315.9 (124.2–523.5)		0.1 (-0.1–0.5)
Uttarakhand		2,966.8 (-168.9–7,035.0)		7,369.8 (-14.5–18,841.8)		85.2 (-9.1–215.1)		77.3 (-1.5–200.4)		-0.1 (-1.2–0.7)
Delhi		16,345.8 (7,014.0–25,979.1)		42,394.1 (17,914.4–67,457.5)		389.3 (165.4–608.9)		263.6 (115.5–424.2)		-0.3 (-0.5–-0.1)
Rajasthan		64,733.2 (27,253.0–110,570.8)		196,288.8 (100,569.2–314,059.1)		284.8 (113.0–500.6)		332.7 (165.1–539.0)		0.2 (-0.1–0.7)
West
Gujarat		62,371.4 (41,211.7–85,859.7)		249,438.0 (172,700.4–341,303.8)		267.4 (178.1–368.8)		397.2 (275.6–539.5)		0.5 (0.2–0.9)
Maharashtra		37,206.2 (20,819.4–52,541.4)		153,768.8 (105,636.9–210,910.7)		82.7 (46.4–117.3)		127.9 (88.1–175.9)		0.6 (0.2–1.2)
Goa		31.9 (-41.8–90.1)		656.7 (489.8–853.2)		4.4 (-5.4–11.8)		32.4 (24.2–42.1)		6.4 (-113.1–68.9)
South
Andhra Pradesh		45,116.2 (26,599.2–60,979.5)		130,129.0 (93,009.3–172,642.8)		184.3 (109.7–249.1)		221.5 (158.5–293.8)		0.2 (-0.2–1.0)
Kerala		7,754.4 (5,849.6–9,401.5)		21,939.6 (18,079.9–27,772.2)		40.1 (30.5–48.5)		48.3 (39.7–61.3)		0.2 (0.0–0.6)
Telangana		25,708.3 (16,263.4–35,824.5)		72,944.1 (51,594.2–98,203.8)		168.0 (107.8–234.1)		214.8 (154.4–285.3)		0.3 (-0.1–0.9)
Tamil Nadu		122,987.2 (83,906.5–153,060.3)		241,571.3 (193,281.3–291,761.4)		336.2 (232.3–417.5)		288.2 (230.2–345.8)		-0.1 (-0.3–0.1)
Karnataka		11,044.9 (1,188.7–19,214.5)		29,729.5 (19,481.8–42,270.9)		41.0 (3.8–71.5)		46.4 (30.3–66.0)		0.1 (-0.3–3.6)
Central
Madhya Pradesh		52,851.3 (20,293.5–84,037.2)		153,073.1 (81,668.3–229,430.1)		197.0 (76.0–318.8)		228.0 (121.1–343.9)		0.2 (-0.1–0.9)
Uttar Pradesh		165,343.4 (61,352.8–286,998.9)		459,446.0 (190,778.2–771,166.6)		218.9 (75.1–397.7)		267.5 (105.8–458.1)		0.2 (0.0–0.6)
Chhattisgarh		15,182.8 (3,707.9–26,198.5)		57,481.8 (38,242.9–80,082.6)		168.6 (41.8–293.5)		256.5 (173.0–353.0)		0.5 (0.0–3.5)
North East
Assam		4,039.0 (-4,305.2–12,735.2)		31,399.4 (2,977.7–60,899.9)		33.1 (-42.8–119.9)		122.6 (8.6–241.3)		2.7 (-22.8–24.5)
Nagaland		59.4 (-82.5–255.4)		483.5 (3.4–1,129.7)		10.1 (-15.6–44.7)		38.4 (0.0–91.5)		2.8 (-24.6–23.5)
Arunachal Pradesh		36.6 (-61.4–157.5)		312.8 (-13.5–743.8)		8.4 (-18.0–39.9)		33.8 (-1.8–83.2)		3.0 (-14.3–18.3)
Sikkim		1.9 (-3.5–7.2)		96.8 (21.0–179.1)		1.0 (-2.2–3.9)		16.1 (3.6–29.9)		15.8 (-87.5–48.0)
Meghalaya		63.1 (-81.8–222.0)		925.1 (152.7–1,833.5)		7.1 (-12.6–28.9)		46.4 (6.1–93.0)		5.5 (-44.1–50.2)
Mizoram		2.8 (-57.0–61.3)		336.0 (5.2–805.8)		-0.5 (-21.4–21.6)		36.6 (-1.6–91.5)		-81.6 (-49.3–61.8)
Tripura		418.6 (-385.5–1,341.2)		4,336.1 (651.2–8,560.1)		27.3 (-29.2–92.9)		122.6 (18.7–246.1)		3.5 (-34.2–38.3)
Manipur		57.6 (-106.6–272.2)		576.8 (-244.8–1,985.8)		5.6 (-11.1–27.1)		20.5 (-8.8–70.2)		2.7 (-14.2–15.7)
East
Jharkhand		11,917.0 (1,815.7–22,880.5)		22,385.5 (7,944.2–39,305.2)		97.4 (12.5–191.2)		80.6 (27.8–143.3)		-0.2 (-0.4–1.1)
Bihar		61,591.9 (29,479.4–96,568.2)		141,584.2 (80,709.7–204,995.0)		168.9 (80.5–271.1)		166.6 (94.0–240.9)		0.0 (-0.2–0.4)
Odisha		8,678.6 (-4,241.6–18,135.8)		52,533.8 (36,762.2–72,573.9)		45.3 (-21.4–94.7)		114.2 (80.4–157.9)		1.5 (-17.6–28.8)
West Bengal		45,271.6 (25,756.9–66,471.3)		192,421.0 (129,560.2–262,879.9)		126.6 (70.7–185.4)		211.4 (141.5–289.2)		0.7 (0.3–1.3)
Other Union Territories		1,095.1 (432.9–1,803.7)		4,452.7 (2,532.1–7,159.6)		100.0 (40.0–163.2)		129.6 (74.7–206.7)		0.3 (-0.1–1.1)

UI: Uncertainty interval. Relative change was calculated for age-standardized DALY rates between 1990 and 2021. Negative lower uncertainty limits in some low-burden states/UTs reflect model uncertainty and should be interpreted cautiously. DALY=Disability-adjusted life year, GBD=Global burden of disease, UI=Uncertainty interval, UT=Union territory

Figure 1.

Subnational burden of DALY attributed to high temperature-associated Non-communicable diseases in India as per 2021 GBD estimates.[20] DALY = Disability-adjusted life year, GBD = Global burden of disease

DISCUSSION

Ambient heat has been systematically linked to increased mortality and morbidity in diverse epidemiological studies, establishing it as a growing determinant of population health. Against this backdrop, our study provides a national and subnational assessment of long-term trends in heat-attributable mortality and DALYs in India. Four key findings emerged. First, absolute heat-attributable deaths and DALYs increased substantially between 1990 and 2021, whereas age-standardized rates increased only modestly. Second, males consistently had higher heat-attributable mortality and DALY rates than females. Third, the burden shifted from CMNN diseases toward NCDs, with ischemic heart disease and stroke contributing prominently to the NCD burden. Fourth, marked state-level heterogeneity was observed, with some states showing stable or declining age-standardized rates and others showing notable increases.

The sharp rise in absolute heat-attributable deaths and DALYs in India mirrors global trends, where demographic expansion and aging have been the main drivers of the growing health burden from high temperatures.[21] This underlines the dual challenge faced by India and other LMICs in managing the epidemiological transition toward NCDs while simultaneously coping with climate stressors.[22] Indian males are more vulnerable to heat-attributable mortality and DALYs. The results align with available evidence from several other LMICs. Large-scale studies from China and Thailand have shown that men of working age exhibit higher heat-related mortality than women, attributed to greater occupational exposure in outdoor and manual labor sectors such as agriculture, construction, and transportation.[23,24] In India, the gender gap may reflect both structural factors (male predominance in outdoor work, migration for labor) and behavioral aspects (delayed care-seeking among men). These findings reinforce the need for occupational health protections and targeted interventions for male workers in high-exposure settings.

The shift of heat-attributable burden in India from CMNN conditions toward NCDs mirrors global epidemiological transitions under climate stress.[25] This shift is biologically plausible. Heat stress increases cardiovascular load and can worsen cardiovascular, renal, respiratory, and metabolic conditions.[26,27] By contrast, the declining share of CMNN conditions in the heat-attributable burden is consistent with improvements in infectious disease control, vaccination, and maternal-child health services in India.[28,29] India’s expanded vaccination coverage, maternal-child health programs, and reductions in undernutrition have substantially lowered the baseline prevalence and fatality of CMNN conditions. As a result, even though heat continues to biologically influence CMNN outcomes, their relative contribution to overall heat-attributable mortality and DALYs has diminished.

Our findings demonstrated marked state-level heterogeneity in heat-attributable mortality and DALYs across India. States such as Kerala, Tamil Nadu, and Delhi exhibited stable or declining age-standardized rates, while others, including Maharashtra, Chhattisgarh, Bihar, West Bengal, and several northeastern states, experienced sharp increases. This heterogeneity is unlikely to reflect temperature exposure alone. It probably reflects the combined influence of baseline NCD prevalence, occupational exposure, urbanization, socioeconomic conditions, health-system capacity, heat-preparedness measures, and data completeness. Agricultural and informal-sector-dominant states such as Bihar, Chhattisgarh, Uttar Pradesh, Jharkhand, and West Bengal may be particularly vulnerable because large sections of the workforce are exposed to prolonged outdoor heat with limited access to cooling, hydration, and rest. Urbanizing states such as Gujarat and Maharashtra face the dual challenge of urban heat-island effects and high NCD burden, which may partly explain their elevated rates. Conversely, some states showed relatively stable or declining age-standardized rates, but the present descriptive analysis cannot determine the specific drivers of these patterns. Smaller and northeastern states, including Nagaland, Arunachal Pradesh, and Mizoram, depicted wider uncertainty intervals, suggesting greater model dependence and lower precision.

The strengths of this study lie in its comprehensive scope, methodological consistency, and granularity. By leveraging GBD subnational estimates, we were able to provide a uniform, comparable picture of the heat-attributable burden over 30 years, disaggregated by sex, cause group, and state. Few previous studies have combined mortality and DALY metrics, which together capture both fatal and non-fatal dimensions of disease burden.[30] The long time horizon also allows us to situate the findings within the context of India’s demographic and epidemiological transitions. However, several limitations must be acknowledged. First, GBD estimates rely on modeled outputs derived from multiple secondary data inputs rather than direct primary temperature–health exposure data from each Indian state/UT. The GBD Compare output used for this analysis does not provide state-wise counts of input studies or a classification of states with nil or sparse primary data for the specific high-temperature risk estimates. Therefore, we could not quantify how many studies contributed to each state-level estimate. This is an important limitation, particularly for smaller states and northeastern states, where wide uncertainty intervals suggest greater model dependence and lower precision. Because the study used aggregated GBD model estimates, we did not apply additional joinpoint regression or AAPC modeling to the annual series, which would require separate extraction and uncertainty propagation of annual posterior draws and was beyond the descriptive scope of this secondary analysis. Second, the metric “suboptimal high temperature” captures exposure relative to a theoretical minimum risk temperature but does not differentiate between acute extreme events (heatwaves) and chronic, sustained increases in baseline temperatures. In addition, the theoretical minimum risk temperature used in GBD comparative risk assessment may vary by location and over time. Therefore, direct comparison of high-temperature-attributable burden across Indian states with heterogeneous climatic profiles should be interpreted cautiously. Consequently, the health impacts of sudden, intense heat events may be underestimated. Third, potential confounders such as air pollution, occupational exposures, socioeconomic status, and urban heat-island effects could not be explicitly disentangled. These factors may amplify or mitigate the observed associations. Fourth, cause-specific attribution in GBD models relies on population-level associations rather than individual-level exposure–response data, limiting causal inference. Despite these limitations, the study provides robust macro-level insights that complement and extend previous event-based or localized studies. The findings are consistent with broader climate-health literature and underscore the urgency of integrating health dimensions into climate policy. Lastly, the present analysis focused on heat-attributable mortality and DALYs and did not estimate their proportional contribution to total all-cause mortality or DALYs. Such an analysis would require matched all-cause burden denominators by year, age, sex, and geography and could be addressed in future comparative risk assessment work.

The findings carry important implications for public health programs and climate-health policy in India. First, heat action plans should move beyond acute heat illness response and include NCD-sensitive preparedness, particularly for cardiovascular, renal, respiratory, and other heat-vulnerable conditions.[31,32] Primary care teams should be trained to identify heat-related worsening of chronic diseases, and high-risk patients should receive anticipatory counseling before heat seasons. Second, the marked state-level heterogeneity supports decentralized planning. This can draw on existing operational models: the WHO operational framework for climate-resilient health systems provides a structure for integrating climate risks into health governance, surveillance, service delivery, infrastructure, and workforce preparedness; India’s National Program on Climate Change and Human Health provides a national platform for heat-health preparedness; and the Ahmedabad Heat Action Plan offers an Indian city-level model combining early warning systems, intersectoral coordination, community outreach, and healthcare preparedness.[10,32,33] National guidance should provide a broad framework, but states should tailor interventions to their climatic exposure, NCD burden, occupational profile, urbanization, and health-system capacity. Agriculturally dominant states may prioritize occupational heat protection, hydration, and rest-shade practices, whereas urbanized states may prioritize heat-island mitigation, cooling spaces, and surveillance of vulnerable populations. Third, heat-attributable deaths and DALYs should be integrated into climate-health surveillance. Routine monitoring of heat-related morbidity, mortality, and NCD exacerbations would help evaluate heat action plans and guide resource allocation. These measures align with India’s commitments to SDG 3 and SDG 13.

Future research should address several gaps highlighted by this study. First, there is a need for high-resolution exposure–outcome data linking individual-level temperature exposure to cause-specific morbidity and mortality. Such studies would strengthen causal inference and reduce reliance on modeled estimates. Second, the role of modifying factors, including socioeconomic status, occupational exposures, housing quality, and urbanization, must be systematically investigated to better understand differential vulnerability. Third, evaluations of existing heat action plans should assess not only their capacity to reduce short-term mortality but also their long-term effectiveness in lowering NCD-related burden. Fourth, integrating air pollution data and co-exposures into climate-health models will provide a more holistic understanding of environmental determinants of health in India. Finally, qualitative research exploring community perceptions of heat risk and adaptation strategies would add an important human dimension to epidemiological evidence, informing more culturally sensitive interventions.

In conclusion, this study demonstrates that India’s heat-attributable health burden has grown substantially over the past three decades, primarily driven by demographic expansion and a shift toward NCDs, particularly ischemic heart disease and stroke. The striking subnational heterogeneity highlights the importance of decentralized, context-specific policies. As India grapples with the dual challenges of climate change and rising NCD prevalence, integrating health into climate adaptation policies, prioritizing vulnerable patient groups, and strengthening surveillance and health-system resilience will be essential steps forward.

Availability of data and materials

These estimates were obtained using the GBD 1990–2021 Results. Available from https://vizhub.healthdata.org/gbd-results/.[34] The data collected for the study is available and will be shared at request.

Ethics approval and consent to participate

Institutional Ethics Committee approval is not required for using data available on an open forum where no patient details are shared, as this information is in the public domain and readily accessible to anyone. There are no details of the patient in the study. Permission to use GBD results was obtained via email from the GBD Secretariat.

Conflicts of interest

There are no conflicts of interest.

Supplementary Material 1

Trends in age-standardized death rates and DALY rates attributable to high temperature in India, 1990–2021, stratified by cause group.

Supplementary Material 2

Trends in Deaths & DALYs by specific NCDs attributed to high temperature in India as per GBD estimates (1990–2021)

Funding Statement

Nil.