Abstract
Backgrounds
The global burden of non-communicable diseases (NCDs) among the elderly is rising, yet health inequalities persist across age, sex, and socio-demographic index (SDI) levels.
Objectives
This study aims to assess global, regional, and national disparities in disease burdens of NCDs among the elderly from 1990 to 2021, with projections up to 2050.
Methods
Using data from the Global Burden of Disease Study 2021, we analyzed four key metrics of incidence, prevalence, mortality, and disability-adjusted life years (DALY) for 10 NCDs across age, sex, and SDI levels. Temporal trends were quantified using annual percentage change (AAPC), and projections for NCDs burden up to 2050 were also performed under the assumption of stable socioeconomic conditions.
Results
In 2021, the global age-standardized rates for incidence (ASIR), prevalence (ASPR), mortality (ASMR), and DALY (ASDR) were 182,092.67, 99,905.95, 3,360.06, and 75,380.44 per 100,000 individuals among the elderly population, respectively. From 1990 to 2021, there was a slight increase in the ASPR of NCDs by 0.01%, whereas ASIR, ASMR, and ASDR showed declines with rates of -0.04%, -0.99%, and − 0.77%, respectively. Regions with lower SDI exhibited higher disease burden of NCDs, especially in terms of mortality and disability. Furthermore, the predictions for the NCDs burden among the elderly from 2022 to 2050 indicated an incremental trend in prevalence.
Conclusions
Persistent disparities in the NCDs burdens highlight the need for equitable healthcare strategies. Healthcare providers are encouraged to provide targeted interventions for specific age groups and address differences in healthcare resource allocation among the elderly across different SDI regions.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06344-3.
Keywords: Non-communicable diseases, Global burden, Health inequality, Elder population, Prediction
Introduction
Chronic non-communicable diseases (NCDs), including cardiovascular diseases (CVD), diabetes, and chronic respiratory diseases (CRD), etc., account for 74% of global deaths annually, imposing a heavy burden on health systems and quality of life worldwide [1, 2]. While high-income countries have made improvements in reducing premature NCDs mortality, low- and middle-income countries (LMICs) face a disproportionately higher risk of premature mortality from NCDs, largely due to limited healthcare access and resources [3–5]. Emerging evidence suggests that there is a higher prevalence of NCDs in women [6–8]. According to World Health Organization reports, NCDs are the number one cause of mortality in women worldwide, responsible for 75% of their deaths in 2019 [9]. This gender disparity is expected to widen, as the global population aged ≥ 60 years is projected to double by 2050, further straining healthcare systems and exacerbating health inequalities [10].
Previous studies have evaluated the NCDs burden from epidemiological metrics (prevalence or mortality rates) or socioeconomic determinants (such as income inequality), yet critical gaps persist. A prior study has revealed gender disparities in CVD mortality; it did not consider the intersectional effects of aging and socio-demographic index (SDI) variations [11]. Although the Global Burden of Disease (GBD) study provides regional data, the systematic assessments of sex-specific NCDs patterns among the elderly population, particularly across LMICs, remain insufficient [12]. To address these gaps, we employed a socio-epidemiological framework to systematically assess temporal trends in NCDs burden among the elderly population by analyzing age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life year (DALY or ASDR) rates across age, sex, and SDI levels from 1990 to 2021 and predicting future trends up to 2050. Our study aims to provide a more precise basis for addressing health inequities, optimizing resource allocation, and improving NCDs prevention and management for elderly populations.
Methods
Study population and data collection
We used the Global Health Data Exchange (GHDx) query tool (https://vizhub.healthdata.org/gbd-results/) to extract data related to the incidence, prevalence, mortality, and disease burden of NCDs. The GBD 2021 study provided the estimated values for 371 types of diseases and injury burdens across 204 countries and 811 regions from 1990 to 2021, using 95% uncertainty intervals (UIs) to reflect the range of certainty for an estimate [13].
In our study, the study population was defined as the elderly aged 60 to 95 plus. Ten types of NCDs were included: CVD, CRD, diabetes mellitus and chronic kidney diseases (DM and CKD), digestive diseases (DD), mental disorders (MD), musculoskeletal disorders (MSD), neoplasms, neurological disorders (ND), sense organ diseases (SOD), and skin and subcutaneous diseases (SD and SCD). The rates of incidence, prevalence, mortality, and DALY for NCDs were retrieved from the recently updated GBD 2021 data, which has achieved a comprehensive and detailed quantification of global health conditions, bringing vital registration systems, verbal autopsies, population censuses, household surveys, disease registries, health service utilization data, hospital records of outpatient and inpatient patients, health insurance records, and other information that provided by the GBD Collaborator Network [14]. Our study followed the standardized approach of the GBD 2021 to handle missing data. Specifically, we adopted the Bayesian imputation framework that combines fixed and random effects models [15]. This approach constructs a covariate matrix using three key variables: geographical proximity, time trends, and socio-economic factors, which has been validated for handling spatially and temporally correlated missing data in global health studies [16]. The imputation model incorporates Bayesian priors over location-specific and temporal trends, leveraging spatial and temporal smoothing techniques to address data sparsity, as formalized in the GBD methodological framework [16].
From the GBD 2021, SDI values were derived by calculating the geometric mean of total fertility rate under 25 (TFU25), average education level at 15 and above (EDU15), and income distribution index (LDI) to measure the correlation between development levels and health status [17]. The SDI values range from 0 to 1, where an SDI of 0 represents the lowest level of health-related development, and an SDI of 1 indicates that health outcomes are at the highest level of development. Based on the 2021 national-level estimates of SDI, 204 countries were classified into five SDI regions: low, middle-low, middle, middle-high, and high [13].
Statistical analysis
The age-specific number and rates of individuals with NCDs per 100,000 were obtained from the GBD 2021. These rates were assumed to be independent Poisson random variables, each rate was considered to have a probability distribution as a poisson random variable, which was used to estimate ASIR, ASPR, ASMR, and ASDR [18]. The direct standardized formula for calculating age-standardized rates is:
 |
Among the age groups, N represents the number of age groups; αi represents the prevalence of the i-age cycle; ωi represents the population proportion of each age group in the GBD standard population [19]. The 95% UI are the 25th and 95th values among the 1000 ordered estimates in the GBD model.
To evaluate the ASIR, ASPR, ASMR, ASDR, and the average change trend of disease burden among the elderly of NCDs, we employed the Jointpoint model to analyze the trend of health outcomes over time by calculating the AAPC along with its 95% confidence interval (CI) to infer the changing trend of NCDs from 1990 to 2021. First, we performed segmented regression analysis using a log-linear model:
 |
is equal to the ln (age-standardized rate), χ refers to the calendar year.
 |
After weighting the interval spans (w), we summed them up to obtain the annual percentage change (APC) in NCDs between each turning point. In general, both the AAPC and its 95% CI are greater than 0, which indicates that ASIR, ASPR, ASMR, and ASDR are trending upwards over time. Conversely, both the AAPC and its 95% CI are less than 0, which suggests a downward trend. An AAPC close to 0 implies that the disease burden remains relatively stable over time.
To predict the ASPR of NCDs among the elderly from 2022 to 2050, we employed a linear log-age-time regression model, which combines the capabilities of demography, temporal trends, and statistical modeling techniques to fit the recent trends. Using GBD 1990–2021 data, we first estimated annual prevalence growth rates through log-linear regression. These rates were then applied to generate country-specific projections to 2050. Final values were calculated as population-weighted averages across countries. The uncertainty intervals (95% UIs) were derived from 1,000 bootstrap iterations of the regression parameters. As the ensemble modeling framework of GBD already accounts for uncertainty through multisource integration, additional sensitivity analyses were not conducted [20]. This study utilized publicly available anonymized data and did not require further ethical approval. All data analysis and visualizations were performed using R software (Version 4.2.3).
Result
Global trends
In 2021, the global burden of NCDs was approximately 1.97 billion cases reported among the elderly. Mortality rates were notably higher, with 34.68 million deaths attributed to NCDs (ASMR: 3,360.06 per 100,000), and 805 million DALYs lost (ASDR: 75,380.44 per 100,000) (Fig. 1 & Table 1 & Table S1). While the global ASPR increased marginally (0.01%), the ASIR (−0.04%), ASMR (−0.99%), and ASDR (−0.77%) showed sustained declines from 1990 to 2021 (Fig. 1 & Table 2 & Table S2).
Fig. 1.
The dynamic patterns of global age-standardized incidence, prevalence, mortality, and DALY rates of NCDs among the elderly from 1990 to 2021. a The dynamic patterns of global age-standardized incidence rate of NCDs among the elderly from 1990 to 2021. b The dynamic patterns of global age-standardized prevalence rate of NCDs among the elderly from 1990 to 2021. c The dynamic patterns of global age-standardized mortality rate of NCDs among the elderly from 1990 to 2021. d The dynamic patterns of global age-standardized DALY rate of NCDs among the elderly from 1990 to 2021 DALY: disability-adjusted life years; ASIR: age-standardized incidence rate; ASPR: age-standardized prevalence rate; ASMR: age-standardized mortality rate; ASDR: age-standardized DALY rate; CVD: cardiovascular diseases; CRD: chronic respiratory diseases; DM and CKD: diabetes and kidney disease; DD: digestive diseases; MD: mental disorders; MSD: musculoskeletal disorders; ND: neurological disorders; SOD: sense organ diseases; SD and SCD: skin and subcutaneous diseases; NCDs: non-communicable diseases
Table 1.
Age-standardized incidence, prevalence, mortality and DALY rates of NCDs in the elderly aged 60 to 95 plus at the global and SDI regional levels in 2021
| Location | Cause | ASR (95%UI) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Incidence | Prevalence | Mortality | DALYs | ||||||||||
| Both | Male | Female | Both | Male | Female | Both | Male | Female | Both | Male | Female | ||
| NCDs | |||||||||||||
| Global | NCDs |
182092.67 (168988.85, 196266.21) |
172014.91 (159210.44, 185770.57) |
190937.74 (177289.7, 206170.85) |
99905.95 (99858.09, 99943.98) |
99872.96 (99807.53, 99923.73) |
99936.7 (99905.06, 99963.2) |
3360.06 (3215.19, 3520.06) |
4023.41 (3794.88, 4285.22) |
2845.53 (2701.47, 3001.99) |
75380.44 (69410.2, 81822.05) |
85639.79 (79093.83, 92840.47) |
66940.74 (60935.13, 73454.93) |
| High SDI | NCDs |
184334.56 (171729.98, 198925.03) |
174700.27 (162455.46, 188581.16) | 192859.48 (179464.22, 207867.83) | 99830.28 (99767.02, 99885.41) |
99791.63 (99715.66, 99859.26) |
99866.74 (99814.98, 99914.64) |
2394.57 (2356.56, 2440.71) |
2923.83 (2870.11, 2985.96) |
1981.24 (1946.04, 2025.54) |
58535.95 (53305.91, 64605.19) |
99791.63 (99715.66, 99859.26) |
51534.08 (46219.34, 57855.12) |
| High-middle SDI | NCDs |
175964.92 (162951.27, 190401.73) |
164125.83 (151381.51, 177591.6) | 185787.62 (172055.49, 201352.62) | 99912.8 (99859.13, 99952.89) |
99874.54 (99798.54, 99931.57) |
99947.15 (99914, 99972.84) |
3543.2 (3319.75, 3779.9) |
4386.93 (3969.21, 4843.47) |
2946.85 (2731.41, 3190.26) |
75967.76 (69606.03, 83002.64) |
99874.54 (99798.54, 99931.57) |
66043.3 (59195.17, 73166.99) |
| Middle SDI | NCDs |
173961.56 (161112.01, 188027.16) |
165276.25 (152447.62, 178562.66) |
181758.66 (168400.63, 196715.2) |
99935.11 (99890.6, 99967.96) |
99906.68 (99843.82, 99954.61) |
99961.86 (99934.24, 99981.56) |
3679.27 (3430.24, 3962.49) |
4500.75 (4082.18, 4983.26) |
3041.6 (2783.59, 3315.59) |
79816.46 (72724.32, 87051.06) |
99906.68 (99843.82, 99954.61) |
69924.14 (62837.13, 76973.19) |
| Low-middle SDI | NCDs | 193430.86 (179497.21, 208655.34) | 182931.54 (169336.16, 197621.65) |
202873.63 (188335.31, 218925.04) |
99939.17 (99902.9, 99967.72) |
99912.76 (99860.96, 99952.86) |
99963.91 (99940.5, 99982.42) |
4042.59 (3809.26, 4288.99) |
4519.29 (4190.05, 4842.47) |
3632.86 (3355.63, 3899.94) |
89974.59 (83330.64, 97244.18) |
99912.76 (99860.96, 99952.86) |
83078.43 (76185.51, 90502.58) |
| Low SDI | NCDs |
206081.99 (190325.02, 223596.85) |
192481.24 (177154.55, 209609.24) |
218817.36 (202380.1, 237467.82) |
99909.91 (99862.31, 99950.72) |
99874.43 (99807.87, 99929.28) |
99944.36 (99912.3, 99971.53) |
4109.13 (3744.24, 4496.71) |
4359.62 (3915.67, 4793.33) |
3879.27 (3451.9, 4290.02) |
90351.47 (81876.45, 99724.71) |
99874.43 (99807.87, 99929.28) |
86674.99 (78014.27, 96530.69) |
| CVD | |||||||||||||
| Global | CVD |
4098.97 (3388.13, 4977.54) |
4462.49 (3676.65, 5453.51) |
3777.58 (3124.47, 4573.96) |
35968.63 (32691.16, 39405.51) |
39121.5 (35487.03, 42926.2) |
33272.06 (30202.79, 36482.33) |
1580.85 (1413.56, 1704.08) |
1856.04 (1683.83, 2008.44) |
1364.45 (1180.2, 1493.81) |
26671.52 (24448.83, 28507.45) |
31780.28 (29295.55, 34279.16) |
22399.85 (19962.93, 24292.98) |
| High SDI | CVD |
3360.51 (2927.31, 3867.2) |
3783.67 (3304.83, 4356.07) |
2990.01 (2586.99, 3472.29) |
35850.04 (32994.65, 39059.17) |
40011.86 (37013.09, 43381.25) |
32253.65 (29438.31, 35473.51) |
829.16 (705.71, 893.31) |
1014.39 (907.9, 1071.67) |
680 (546.2, 750.46) |
14043.13 (12470.27, 15143.11) |
17752.16 (16251.36, 18893.2) |
10906.61 (9305.7, 11979.79) |
| High-middle SDI | CVD |
4529.71 (3708.34, 5540.75) |
4790.02 (3926.61, 5868.87) |
4287.56 (3497.38, 5252.14) |
37195.64 (33861.38, 40690.45) |
39485.17 (35932.99, 43268.44) |
35331.14 (31996.62, 38732.08) |
1862.63 (1637.15, 2036.65) |
2194.53 (1933.12, 2447.47) |
1622.67 (1380.97, 1808.92) |
29588.21 (26571.39, 32181.8) |
35721.74 (31831.29, 39679.36) |
24792.67 (21654.82, 27426.03) |
| Middle SDI | CVD |
4322.94 (3493.93, 5353.74) |
4661.99 (3747.4, 5821.45) |
4013.04 (3241.08, 4967.2) |
34803.58 (31324.52, 38542.31) |
37128.08 (33238.09, 41229.26) |
32776.58 (29459.86, 36224.48) |
1842.85 (1630.46, 2021.43) |
2239.82 (1994.73, 2501.29) | 1536.69 (1311.03, 1723.56) |
30357.8 (27419.61, 32958.55) |
36910.11 (33183.02, 40986.02) |
24,934 (21881.61, 27620.91) |
| Low-middle SDI | CVD |
4525.46 (3626.5, 5636.48) |
5016.19 (3989.42, 6310.21) |
4092.4 (3320.64, 5058.75) |
36808.72 (32719.2, 41053.17) |
41427.72 (36767.86, 46252.09) |
32741.44 (29106.72, 36518.68) |
1949.69 (1779.77, 2097) |
2167.66 (1967.28, 2345.02) |
1762.69 (1574.59, 1924.13) |
34281.02 (31666.12, 36746.07) |
38678.53 (35370.93, 41825.08) |
30372.07 (27521.62, 32992.15) |
| Low SDI | CVD |
4102.37 (3233.56, 5194.6) |
4416 (3435.24, 5662.95) |
3814.35 (3037.34, 4800.12) |
34412.77 (30422.85, 38779.13) |
37721.77 (33154.81, 42657.67) |
31359.17 (27816.51, 35304.91) |
1945.83 (1746.3, 2142.38) |
2035.34 (1809.07, 2260.47) | 1862.2 (1626.92, 2084.09) |
34294.61 (30909.12, 37726.6) |
36463.4 (32557.43, 40552.1) |
32235.67 (28448.03, 35885.69) |
| CRD | |||||||||||||
| Global | CRD |
1489.9 (1201.01, 1769.23) |
1586.82 (1296.07, 1868.89) |
1411.96 (1124.91, 1690.71) |
18041.61 (16128.96, 20037.32) |
18268.75 (16308.49, 20293.94) |
17886.33 (15997.41, 19852.71) |
386.2 (341.77, 427.75) |
507.55 (446.83, 558.85) |
298.56 (247.38, 349.34) |
7143.01 (6495.23, 7770.42) |
8908.34 (8005.89, 9735.35) |
5771.27 (4999.77, 6586.62) |
| High SDI | CRD |
1486.3 (1208.19, 1776.98) |
1535.97 (1267.14, 1806.23) |
1450.2 (1159.45, 1764.23) |
20880.17 (19113.47, 22729.31) |
20723.76 (18970.19, 22519.67) |
21162.95 (19295.93, 23091.88) |
176.22 (153.52, 188.29) |
231.09 (208.06, 244.75) |
138.53 (115.77, 151.17) |
3839.9 (3486.65, 4124.41) |
3271.33 (2908.07, 3576.58) |
4610.65 (4243.65, 4917.41) |
| High-middle SDI | CRD |
1301.03 (1030.16, 1570.67) |
1440.64 (1164.84, 1713.37) |
1196.65 (926.84, 1466.95) |
16220.47 (14289.51, 18273.37) |
16829.99 (14856.67, 18892.34) |
15773.88 (13858.28, 17819.31) |
291.78 (247.38, 334.32) |
437.8 (366.07, 506.61) |
201 (156.55, 250.84) |
5052.33 (4440.65, 5689.93) |
3748.73 (3098.1, 4482.87) |
6973.18 (5919.01, 8005.43) |
| Middle SDI | CRD |
1525.18 (1219.02, 1824.16) |
1648.02 (1326.79, 1960.63) |
1421.45 (1123.22, 1710.51) |
16660.14 (14476.55, 18834.09) |
17083.59 (14829.15, 19336.18) |
16258.1 (14119.14, 18405.58) |
477.79 (406.31, 545.94) |
641.84 (526.95, 737.66) |
356.99 (273.98, 443.12) |
8100.12 (7100.71, 9125.34) |
6300.44 (5115.92, 7585.9) |
10378.26 (8716.31, 11850.82) |
| Low-middle SDI | CRD |
1781.32 (1456.25, 2089.95) |
1825.74 (1494.63, 2144.5) |
1743.13 (1421.97, 2047.84) |
19266.06 (17114.29, 21415.73) |
18834.21 (16619.44, 21042.57) |
19625.19 (17478.97, 21695.38) |
757.2 (654.88, 858.56) |
873.48 (764.8, 990.1) |
656.7 (512.45, 803.52) |
13623.88 (12028.11, 15239.78) |
11770.08 (9535.16, 14062.57) |
15716.68 (13927.48, 17696.34) |
| Low SDI | CRD |
1510 (1226.57, 1789.14) |
1568.76 (1269.7, 1857.43) |
1459.33 (1186.2, 1738.14) |
16835.85 (14941.17, 18746.33) |
16625.1 (14626.14, 18662.12) |
17062.51 (15247.58, 18873.69) |
653.75 (555.31, 758.49) |
702.12 (592.57, 814.89) |
611.19 (468.03, 771.25) |
11949.5 (10400.1, 13647.24) |
11209.19 (8949.36, 13867.2) |
12760.03 (10939.95, 14632.69) |
| DM and CKD | |||||||||||||
| Global | DM and CKD |
1806.99 (1457.8, 2173.35) |
1761.62 (1424.56, 2117.71) |
1846.81 (1483.37, 2230.7) |
41678.06 (38907.78, 44554.32) |
41847.62 (39149.83, 44675.51) |
41490.23 (38677.25, 44437.31) |
239.78 (214.64, 257.17) |
269.44 (241.92, 291.02) |
218.7 (191.84, 237.87) |
6373.91 (5596.23, 7238.4) |
6951.85 (6111.95, 7897.04) |
5921.12 (5180.3, 6784.47) |
| High SDI | DM and CKD |
2344.6 (1901.09, 2834.79) |
2373.74 (1940.69, 2854.26) |
2313.07 (1860.68, 2812.67) |
44901.89 (42469, 47409.99) |
47133.49 (44651.09, 49616.1) |
42889.41 (40441.79, 45461) |
156.75 (134.61, 169.49) |
190.99 (170.77, 203.48) |
131.28 (107.61, 145.08) |
4915.8 (4090.72, 5858.09) |
5804.43 (4905.81, 6856.03) |
4187.74 (3437.36, 5035.46) |
| High-middle SDI | DM and CKD |
1541.21 (1221.89, 1877.13) |
1487.16 (1171.72, 1808.85) |
1595.58 (1267.26, 1949.5) |
37748.65 (35075.93, 40538.85) |
37157.77 (34506.42, 39899.27) |
38162.7 (35452.31, 41030.57) |
165.49 (144.77, 181.87) |
186.33 (163.38, 209.21) |
153.28 (130.79, 171.14) |
4610.19 (3942.25, 5344.58) |
4946.69 (4260.74, 5750.58) |
4385.44 (3702.73, 5105.9) |
| Middle SDI | DM and CKD |
1721.27 (1383.33, 2068.18) |
1604.94 (1282.83, 1934.77) |
1827.32 (1474.02, 2191.47) |
41238.8 (38287.13, 44282.13) |
40450.22 (37622.14, 43414.23) |
41922.37 (38910.1, 45023.41) |
282.7 (253.49, 304.8) |
306.03 (269.41, 337.43) |
266.6 (235.17, 291.34) |
7195.27 (6392.07, 8115.93) |
7526.04 (6643.57, 8522.24) |
6947.86 (6099.59, 7900.2) |
| Low-middle SDI | DM and CKD |
1568.25 (1248.35, 1902.6) |
1544.94 (1233.12, 1868.99) |
1587.84 (1259.26, 1939.32) |
43997.72 (40780.03, 47393.24) |
43557.81 (40395.26, 46864.01) |
44318.92 (40973.36, 47832.51) |
369.07 (332.25, 405) |
398.12 (350.81, 450.68) |
345.31 (304.16, 384.46) |
8926.95 (8014.87, 9970.71) |
9507.71 (8377.51, 10757.75) |
8425.09 (7498.74, 9551.02) |
| Low SDI | DM and CKD |
1360.85 (1105.12, 1636.08) |
1422.27 (1165.42, 1694.54) |
1300.31 (1040.19, 1582.7) |
38472.03 (35574.29, 41655.35) |
38978.23 (36108.6, 42141.7) |
37986.07 (35034.72, 41189.68) |
421.57 (374.34, 472.09) |
465.56 (405.76, 535.82) |
382.73 (329.41, 437.42) |
9371.89 (8313.42, 10569.29) |
10247.28 (8924.06, 11738.26) |
8571.07 (7542.67, 9758.44) |
| DD | |||||||||||||
| Global | DD |
11436.07 (9397.44, 13530.02) |
11097.27 (9112.97, 13181.33) |
11764.1 (9660.23, 13901.92) |
55564.03 (49541.49, 61701.26) |
55644.01 (49644.93, 61641.52) |
55561.86 (49533.91, 61777.75) |
147.71 (129.47, 163.42) |
178.47 (157.74, 197.52) |
122.07 (103.66, 138.72) |
3103.46 (2779.9, 3446.27) |
3720.27 (3360.26, 4128.18) |
2565.53 (2241.56, 2917.89) |
| High SDI | DD |
11776.73 (9731.31, 14145.76) |
11706.24 (9678.63, 14074.08) |
11892.58 (9812.96, 14278) |
47788.05 (42388.2, 53412.24) |
49885.89 (44220, 55559.26) |
46010.94 (40768.59, 51596.09) |
110.25 (95.95, 118.27) |
130.97 (118.98, 138.24) |
92.24 (75.94, 101.33) |
2369.53 (2108.2, 2605.41) |
2818.35 (2580.56, 3044.84) |
1970.75 (1697.39, 2219.62) |
| High-middle SDI | DD |
11311.87 (9402.19, 13416.14) |
10691.38 (8930.61, 12735.11) |
11869.57 (9827.44, 14039.99) |
56977.54 (50875.05, 63175.27) |
55570.53 (49746.84, 61549.7) |
58222.6 (51952.59, 64629.05) |
115.88 (102.45, 126.53) |
142.17 (126.85, 158.17) |
95.73 (81.42, 106.59) |
2512.49 (2257.97, 2782.52) |
3022.15 (2734.28, 3348.44) |
2096.96 (1825.58, 2363.9) |
| Middle SDI | DD |
10850.84 (8969.75, 12714.85) |
10396.03 (8567.7, 12198.92) |
11277.1 (9334.37, 13212.15) |
59040.43 (52691.88, 65594.26) |
57795.25 (51686.35, 64011.21) |
60215.64 (53693.78, 67091.11) |
146.9 (128.1, 165) |
182.64 (157.98, 207.35) |
117.54 (97.76, 136.95) |
3043.55 (2721.37, 3417.52) |
3707.18 (3280.45, 4181.94) |
2465.34 (2120.51, 2833.01) |
| Low-middle SDI | DD |
11964.11 (9499.86, 14296.1) |
11698.3 (9263.25, 14026.88) |
12204.47 (9689.59, 14567) |
58326.11 (51827.06, 65074.54) |
59410.83 (52753.91, 66252.71) |
57370.93 (50833.44, 64105.04) |
223.19 (180.33, 262.72) |
262.52 (209.44, 316.59) |
188.91 (149.74, 232.55) |
4499.25 (3722.59, 5268.58) |
5286.93 (4319.39, 6324.67) |
3791.38 (3083.97, 4577.24) |
| Low SDI | DD |
11899.04 (9326.54, 14416.57) |
11655.34 (9101.84, 14202.44) |
12128.41 (9471.37, 14631.9) |
56911.65 (50725.57, 63350.02) |
58427.32 (52114.63, 65000.36) |
55485.25 (49307.35, 61909.54) |
273.11 (237.79, 310.57) |
309.91 (269.59, 361.67) |
238.05 (198.64, 280.08) |
5408.86 (4753.3, 6130) |
6267.24 (5461.12, 7261.95) |
4584.05 (3891.59, 5349.77) |
| MD | |||||||||||||
| Global | MD |
6867.63 (5470.33, 8645.08) |
5557.22 (4432.15, 7007.93) |
8002.71 (6361.28, 10072.05) |
14801.62 (13203.04, 16645.75) |
13218.63 (11856.71, 14771.84) |
16169.32 (14344.47, 18366.6) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2095.43 (1537.49, 2718.99) |
1850.44 (1373.08, 2373.5) |
2307.69 (1680.61, 3016.21) |
| High SDI | MD |
4984.08 (3970.36, 6311.72) |
3885.56 (3091.24, 4892.22) |
5932.72 (4717.59, 7537.58) |
13713.45 (12222.02, 15593.9) |
12180.76 (10929.19, 13676.72) |
15049.76 (13307.25, 17312.72) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
1934.16 (1429.33, 2502.99) |
1687.88 (1268.04, 2138.89) |
2149.27 (1570.82, 2796.09) |
| High-middle SDI | MD |
6805.33 (5391.03, 8525.27) |
5130.94 (4092.78, 6429.15) |
8155.71 (6406.06, 10246.97) |
14739.2 (13111.25, 16599.82) |
12719.76 (11358.33, 14205.62) |
16366.35 (14487.78, 18628.29) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2092.87 (1535.05, 2717.79) |
1775.38 (1323.9, 2275.4) |
2349.85 (1704.72, 3083.75) |
| Middle SDI | MD |
6672.44 (5333.99, 8372.84) |
5479.27 (4379.55, 6893.04) |
7727.65 (6166.58, 9705.16) |
14904.71 (13324.04, 16722.7) |
13399.06 (12022.98, 14962.1) |
16230.47 (14427.34, 18342.07) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2073.44 (1517.53, 2697.07) |
1850.52 (1371.51, 2377.55) |
2270.52 (1652.24, 2979.75) |
| Low-middle SDI | MD |
9113.63 (7161.16, 11633.98) |
7777.04 (6120.53, 9911.53) |
10325.41 (8103.88, 13241.31) |
16011.83 (14218.03, 18088.51) |
14668.39 (13057.34, 16500.36) |
17221.02 (15237.49, 19534.24) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2304.31 (1685.66, 2996.81) |
2102.89 (1540.89, 2718.09) |
2486.17 (1798.81, 3242.32) |
| Low SDI | MD |
10748.45 (8232.15, 13994.39) |
9279.42 (7142.11, 12066.45) |
12129.21 (9253.01, 15871.66) |
16342.49 (14333.72, 18692.09) |
15082.59 (13313.4, 17118.29) |
17524.69 (15243.38, 20194.65) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2423.86 (1726.38, 3173.06) |
2218.5 (1589.18, 2883.82) |
2616.66 (1843.87, 3452.83) |
| MSD | |||||||||||||
| Global | MSD |
10698.9 (8216.36, 13547.58) |
8818.84 (6824.42, 11130) |
12334.34 (9446.09, 15649.06) |
56757.02 (52183.34, 61628.75) |
49453.64 (45153.64, 54117.14) |
63079.43 (58148.41, 68200.39) |
8.66 (7.16, 9.55) |
6.72 (4.7, 7.77) |
10.14 (8.4, 11.26) |
5063.87 (3552.08, 7128) |
4057.5 (2839.12, 5749.8) |
5938.52 (4180.09, 8345.98) |
| High SDI | MSD |
12032.88 (9425.99, 15006.04) |
10345.82 (8096.71, 12863.69) |
13504.77 (10557.98, 16839.79) |
62664.69 (58242.42, 67220.07) |
56035.32 (51659.74, 60724.19) |
68399.07 (63863.23, 72932.25) |
8.86 (7.52, 9.63) |
6.77 (6.03, 7.24) |
10.39 (8.58, 11.47) |
5792.61 (4101.42, 8104.3) |
4801.1 (3398.17, 6730.02) |
6654.01 (4715.96, 9311.15) |
| High-middle SDI | MSD |
11136.32 (8504.66, 14174.77) |
9159.18 (7059.42, 11614.73) |
12743.1 (9696.16, 16248.48) |
56052.77 (51189.79, 61057.93) |
48300.31 (43831.03, 53065.87) |
62335.84 (57165.65, 67671.62) |
6.48 (5.43, 7.35) |
4.75 (3.5, 5.82) |
7.7 (6.36, 8.77) |
4920.92 (3405.37, 6993.47) |
3861.27 (2656.37, 5552.81) |
5782.16 (4011.35, 8182.18) |
| Middle SDI | MSD |
9675.36 (7419.46, 12312.7) |
7770.32 (6013.14, 9837.02) |
11371.76 (8663.79, 14505.48) |
54457.97 (49855.96, 59348.53) |
47160.12 (42840.74, 51847.06) |
60928.17 (55916.69, 66109.11) |
7.29 (5.78, 8.35) |
6.42 (3.59, 7.88) |
8.07 (6.56, 9.31) |
4686.5 (3268.7, 6638.33) |
3735.19 (2601.99, 5318.06) |
5536.03 (3870.05, 7799.03) |
| Low-middle SDI | MSD |
10117.01 (7666.88, 12947.51) |
8120.61 (6209.94, 10372.64) |
11910.61 (8966.44, 15269.29) |
54428.14 (49639.51, 59618.39) |
46645.39 (42167.99, 51618.99) |
61429.33 (56223.06, 66987.83) |
14.3 (10.7, 16.65) |
10.59 (5.43, 13.56) |
17.37 (12.96, 20.68) |
4994.67 (3560.03, 6952.13) |
3917.79 (2776.86, 5492.9) |
5964.07 (4260.11, 8266.31) |
| Low SDI | MSD |
10363.23 (7845.66, 13282.48) |
8734.09 (6659.58, 11175.12) |
11903.63 (8964.93, 15301.85) |
51616.25 (46789.1, 56839.67) |
45480.72 (41074.3, 50360.84) |
57405.29 (52099.46, 63077.94) |
9.04 (6.71, 11.62) |
5.8 (3.23, 8.57) |
11.94 (8.62, 15.85) |
4606.87 (3246.88, 6467.96) |
3762.06 (2630.75, 5303.58) |
5404.68 (3835.63, 7551.08) |
| Neoplasms | |||||||||||||
| Global | Neoplasms |
2578.01 (2170.68, 3094.14) |
2974.79 (2590.74, 3473.61) |
2279.8 (1846.51, 2840.64) |
6392.33 (5796.9, 7153.68) |
6951.93 (6367.27, 7647.72) |
5970.36 (5307.36, 6811.87) |
690.74 (620.91, 744.29) |
908.14 (819.92, 992.43) |
523.12 (456.62, 570.75) |
12960.75 (11864.84, 13913.88) |
16721.54 (15204.95, 18264.15) |
9866.28 (8847.36, 10679.48) |
| High SDI | Neoplasms |
5586.03 (4687.11, 6755.22) |
6458.29 (5594.02, 7554.8) |
4919.85 (3958.81, 6156.63) |
13304.38 (12076.17, 14891.17) |
14731.97 (13558.26, 16255.68) |
12194.44 (10853.48, 13912.01) |
784.93 (699.5, 830.49) |
1024.35 (940.72, 1071.84) |
603.84 (515.46, 650.97) |
14602.02 (13354.3, 15331.42) |
18690.64 (17470.43, 19494.31) |
11277.47 (9977.57, 12010.51) |
| High-middle SDI | Neoplasms |
2138.76 (1812.06, 2548.49) |
2492.85 (2167.49, 2871.75) |
1893.45 (1535.86, 2345.95) |
6302.17 (5725.18, 6957.48) |
6852.7 (6191.81, 7561.54) |
5921.47 (5278.16, 6678.17) |
800.33 (712.55, 882.93) |
1128.49 (984.5, 1289.18) |
564.52 (484.54, 633.92) |
15285.27 (13718.39, 16866.21) |
21105.85 (18427.66, 24194.26) |
10770.68 (9389.48, 12033.24) |
| Middle SDI | Neoplasms |
1461.21 (1221.91, 1772.38) |
1696.93 (1436.47, 2018.28) |
1273.32 (1024.98, 1590.54) |
3485.26 (3103.55, 3948.46) |
3759.93 (3314.33, 4279.54) |
3269.86 (2859.91, 3785.92) |
631.59 (558.68, 703.44) |
845.91 (730.45, 974.37) |
459.54 (396.72, 519.64) |
11998.49 (10713.71, 13348.51) |
15738.51 (13604.84, 18176.33) |
8820.74 (7728.74, 9917.06) |
| Low-middle SDI | Neoplasms |
980.72 (789.39, 1234.24) |
1006.71 (831.64, 1231.18) |
963.24 (749.93, 1250.2) |
2154.24 (1846.28, 2576.97) |
2081.84 (1797.29, 2465.16) |
2229.46 (1881.8, 2708.35) |
452.7 (416.17, 487.35) |
533.83 (486.79, 581.95) |
384.27 (345.08, 420.14) |
8885.81 (8222.67, 9550.58) |
10309.47 (9433.44, 11218.49) |
7642.5 (6914.84, 8337.05) |
| Low SDI | Neoplasms |
902.79 (728.71, 1122.44) |
902.73 (730.1, 1102.66) |
907.88 (718.68, 1156.05) |
1807.4 (1523.97, 2167.23) |
1687.23 (1392.01, 2027.07) |
1926.13 (1621.96, 2331.14) |
494.64 (434.11, 557.21) |
546.83 (463.05, 629.43) |
449.96 (390.04, 508.07) |
9533.81 (8378.46, 10766.85) |
10264.15 (8714.95, 11870.77) |
8890.46 (7737.51, 10020.43) |
| ND | |||||||||||||
| Global | ND |
10396.94 (7088.48, 14551.48) |
9683.56 (6585.85, 13556.65) |
11008.27 (7534.52, 15410.19) |
39261.7 (31846.07, 48389.22) |
35318.62 (28194.7, 44372.11) |
42656.86 (34960.28, 51905.68) |
246.86 (100.82, 567.05) |
227.3 (105.96, 514.06) |
257.34 (96, 599.92) |
4936.9 (2921.26, 8988.82) |
4455.56 (2768.05, 8063.4) |
5270.6 (3006.9, 9669.44) |
| High SDI | ND |
11068.96 (7510.87, 15541.07) |
10260.28 (6944.06, 14394.27) |
11,762 (7971.01, 16520.39) |
42227.07 (34016.5, 52470.07) |
37794.39 (29808.3, 48001.82) |
46074.4 (37556.06, 56326.21) |
264.84 (115.48, 575.65) |
249.89 (124.02, 531.67) |
271.15 (108.08, 598.18) |
5210.77 (3181.38, 9058.51) |
4787.1 (3089.94, 8301.15) |
5495.84 (3233.33, 9604.71) |
| High-middle SDI | ND |
10269.43 (7064.65, 14376.9) |
9346.41 (6394.82, 13068.8) |
11021.53 (7566.84, 15453.9) |
39021.11 (31921.35, 47719) |
34316.54 (27634.23, 42794.72) |
42859.59 (35321.91, 51887.87) |
255.37 (100.77, 603.64) |
238.63 (107.47, 552.64) |
262.92 (95.16, 628.64) |
5182.65 (3038.99, 9571.79) |
4707.74 (2907.45, 8688.45) |
5483.74 (3111.57, 10166.52) |
| Middle SDI | ND |
10060.57 (6893.96, 14081.93) |
9287.37 (6343.87, 13003.59) |
10744.96 (7366.92, 15017.29) |
38052.14 (31051.12, 46636.44) |
33954.75 (27339.57, 42255.95) |
41658.84 (34293.77, 50429.38) |
234.71 (90.38, 559.45) |
214.29 (93.73, 503.02) |
247.21 (86.48, 596.27) |
4839.07 (2807.68, 8981.96) |
4326.27 (2623.92, 7997.82) |
5215.47 (2923.12, 9744.91) |
| Low-middle SDI | ND |
10422.71 (7059.43, 14678.06) |
10132.47 (6803.28, 14257.21) |
10675.73 (7247.23, 15084.51) |
38733.94 (31185.14, 48098.39) |
36380.43 (28857.3, 45712.17) |
40836.31 (33203.25, 50180.12) |
203.2 (85.06, 478.89) |
185.29 (86.16, 421.52) |
216.5 (83.14, 521.67) |
4141.91 (2456.89, 7726.36) |
3742.89 (2297.35, 6759.99) |
4467.81 (2561.42, 8511.97) |
| Low SDI | ND |
9725.49 (6548.07, 13742.66) |
9501.2 (6331.1, 13463.77) |
9927.8 (6718.18, 14037.91) |
35685.74 (28474.77, 44659.43) |
33642.77 (26423.27, 42779.27) |
37583.26 (30333.31, 46607.13) |
226.78 (94.4, 537.39) |
197.89 (94.18, 449.32) |
248.89 (92.64, 613.6) |
4411.25 (2577.02, 8406.6) |
3893.95 (2435.55, 7076.54) |
4839.91 (2671.12, 9555.9) |
| SOD | |||||||||||||
| Global | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
82220.71 (75674.04, 88678.53) |
82754.04 (76481.32, 88742.14) |
81729.97 (74856.99, 88677.35) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
3806.64 (2659.49, 5268.56) |
3814.04 (2662.79, 5280.01) |
3798.2 (2652.11, 5255.34) |
| High SDI | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
70227.62 (63702.4, 77588.08) |
72338.01 (65979.1, 79014.65) |
68262.52 (61410.65, 76799.24) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
2551.33 (1739.44, 3599.18) |
2635.75 (1791.27, 3706.93) |
2473.89 (1685.37, 3501.6) |
| High-middle SDI | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
83751.27 (77244.27, 90094.96) |
84057.58 (77775.12, 89922.24) |
83468.99 (76621.58, 90387.02) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
3682.84 (2536.23, 5140.25) |
3689.69 (2536.7, 5152.9) |
3670.25 (2529.46, 5131.27) |
| Middle SDI | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
88112.64 (81647.29, 93875.8) |
88200.52 (81843.12, 93731.97) |
88023.08 (81424.58, 94058.14) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
4351.11 (3042.09, 6011.93) |
4322.03 (3016.39, 5971.97) |
4370.7 (3059.06, 6042.7) |
| Low-middle SDI | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
86044.88 (79276.93, 92712.2) |
85685.71 (79286.39, 92118.35) |
86374.2 (79153.09, 93285.35) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
4838.18 (3435.8, 6601.42) |
4744.51 (3368.15, 6506.41) |
4920.37 (3499.81, 6691) |
| Low SDI | SOD |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
83780.82 (76690.21, 91065.26) |
83817.77 (77078.17, 90852.68) |
83748.9 (76199, 91429.58) |
0 (0, 0) |
0 (0, 0) |
0 (0, 0) |
4782.62 (3398.16, 6517) |
4711.79 (3343.07, 6454.08) |
4848.34 (3446.93, 6576.9) |
| SD and SCD | |||||||||||||
| Global | SD and SCD |
95687.33 (85037.56, 108449.37) |
95834.89 (85177.98, 108414.73) |
95602.66 (84818.91, 108469.79) |
39687.48 (37027.31, 42545.43) |
39959.42 (37323.42, 42850.63) |
39520.66 (36845.06, 42388.57) |
9.03 (7.87, 9.82) |
9.48 (8.48, 10.29) |
8.66 (7.3, 9.61) |
669.34 (475.21, 952.8) |
672.71 (479.67, 957.52) |
667.14 (471.52, 949.48) |
| High SDI | SD and SCD |
94689.74 (84642.71, 106759.84) |
94988.24 (84821.42, 107183.85) |
94489.66 (84431.13, 106565.63) |
44821.32 (42406.35, 47399.13) |
45613.04 (43193.82, 48209.99) |
44270.3 (41824.53, 46926.26) |
7.53 (6.34, 8.21) |
8.18 (7.19, 8.79) |
7.01 (5.71, 7.8) |
745.84 (527.29, 1056.79) |
749.11 (532.51, 1063.21) |
745.64 (524.51, 1055.43) |
| High-middle SDI | SD and SCD |
90501.19 (79867.95, 103145.97) |
90042.51 (79398.2, 102432.79) |
90925.34 (80200.18, 103671.39) |
38823.01 (36122.17, 41711.36) |
39198.68 (36525.28, 42095.2) |
38612.05 (35891.85, 41571.25) |
7.22 (6.17, 7.94) |
7.34 (6.46, 8.16) |
7.1 (5.89, 7.97) |
626.52 (436.33, 905.21) |
631.75 (439.31, 913.53) |
623 (432.43, 899.13) |
| Middle SDI | SD and SCD |
92006.15 (81121, 104810.85) |
92368.96 (81398.44, 104917.8) |
91749.77 (80751.09, 104804.84) |
37957.47 (35232.81, 40842.69) |
37995.51 (35220.25, 40951.6) |
37965.94 (35241.36, 40840.3) |
9.95 (8.5, 11.22) |
9.65 (8.33, 10.93) |
10.06 (8.31, 11.64) |
667.16 (473.35, 949.94) |
661.43 (470.99, 944.71) |
670.36 (474.08, 953.56) |
| Low-middle SDI | SD and SCD |
102840.83 (91410.13, 116213.72) |
103039.9 (91565.15, 116074.12) |
102674.03 (91150.61, 116141.91) |
35835.78 (33075.7, 38910.09) |
35469.18 (32682.1, 38540.96) |
36174.2 (33384.91, 39217.74) |
12.93 (10.97, 14.93) |
14.43 (11.68, 17.66) |
11.74 (9.62, 13.94) |
643.83 (471.34, 900.83) |
652.48 (476.23, 912.16) |
637.28 (462.7, 895.02) |
| Low SDI | SD and SCD |
119058.9 (105250.33, 135203.23) |
117502.14 (103591.44, 134000) |
120455.75 (106580.24, 136676.7) |
38952.34 (35473.6, 42839.33) |
39052.2 (35454.53, 43050.89) |
38854.11 (35484.91, 42611.87) |
10.4 (8.17, 13.24) |
10.82 (7.65, 15.06) |
9.93 (7.68, 13.02) |
601.68 (421.6, 885.49) |
601.86 (412.45, 886.16) |
601.01 (423.67, 881.21) |
NCDs Non-communicable Diseases, CVD Cardiovascular Diseases, CRD Chronic Respiratory Diseases, DM and CKD Diabetes and Kidney Diseases, DD Digestive Diseases, MD Mental Disorders, MSD Musculoskeletal Disorders, ND Neurological Disorders, SOD Sense Organ Diseases, SD and SCD Skin and Subcutaneous Diseases
Table 2.
Changing trends of age-standardized incidence, prevalence, mortality and DALY rates of NCDs in the elderly aged 60 to 95 plus at the global and SDI regional levels in 1990 to 2021
| Location | Sex | AAPC | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Incidence | Prevalence | Mortality | DALYs | ||||||
| 95%CI | Pval | 95%CI | Pval | 95%CI | Pval | 95%CI | Pval | ||
| NCDs | |||||||||
| Global | Both |
−0.04 (−0.05 to −0.04) |
< 0.001 |
0.01 (0.01 to 0.01) |
< 0.001 |
−0.99 (−1.11 to −0.87) |
< 0.001 |
−0.77 (−0.85 to −0.69) |
< 0.001 |
| High SDI | Both |
−0.03 (−0.05 to −0.01) |
0.001 |
0.00 (0.00 to 0.00) |
0.077 |
−1.51 (−1.62 to −1.41) |
< 0.001 |
−1.1 (−1.14 to −1.06) |
< 0.001 |
| High-middle SDI | Both |
−0.21 (−0.22 to −0.2) |
< 0.001 |
0.01 (0.01 to 0.01) |
< 0.001 |
−1.21 (−1.42 to −0.99) |
< 0.001 |
−0.99 (−1.18 to −0.79) |
< 0.001 |
| Middle SDI | Both |
0.07 (0.06 to 0.08) |
< 0.001 |
0.01 (0.01 to 0.01) |
< 0.001 |
−1.02 (−1.17 to −0.87) |
< 0.001 |
−0.83 (−1.01 to −0.64) |
< 0.001 |
| Low-middle SDI | Both |
0.06 (0.05 to 0.07) |
< 0.001 |
0.01 (0.01 to 0.01) |
0.002 |
−0.18 (−0.51 to 0.15) |
0.278 |
−0.20 (−0.35 to −0.05) |
0.011 |
| Low SDI | Both |
0.00 (−0.01 to 0.00) |
0.255 |
0.00 (0.00 to 0.00) |
0.992 |
−0.37 (−0.55 to −0.18) |
< 0.001 |
−0.39 (−0.48 to −0.30) |
< 0.001 |
| CVD | |||||||||
| Global | Both |
−0.49 (−0.56 to −0.43) |
< 0.001 |
−0.05 (−0.06 to −0.04) |
< 0.001 |
−1.37 (−1.53 to −1.20) |
< 0.001 |
−1.29 (−1.41 to −1.17) |
< 0.001 |
| High SDI | Both |
−1.13 (−1.22 to −1.03) |
< 0.001 |
−0.33 (−0.35 to −0.32) |
< 0.001 |
−2.76 (−2.89 to −2.62) |
< 0.001 |
−2.62 (−2.74 to −2.51) |
< 0.001 |
| High-middle SDI | Both |
−0.52 (−0.58 to −0.46) |
< 0.001 |
0.03 (0.01 to 0.05) |
0.045 |
−1.52 (−1.85 to −1.19) |
< 0.001 |
−1.51 (−1.85 to −1.16) |
< 0.001 |
| Middle SDI | Both |
−0.01 (−0.04 to 0.02) |
0.490 |
0.25 (0.24 to 0.26) |
< 0.001 |
−0.97 (−1.13 to −0.82) |
< 0.001 |
−1.04 (−1.24 to −0.85) |
< 0.001 |
| Low-middle SDI | Both |
−0.19 (−0.28 to −0.1) |
< 0.001 |
0.10 (0.09 to 0.10) |
< 0.001 |
−0.33 (−0.64 to −0.02) |
0.035 |
−0.38 (−0.56 to −0.19) |
< 0.001 |
| Low SDI | Both |
−0.30 (−0.38 to −0.21) |
< 0.001 |
−0.02 (−0.03 to −0.02) |
< 0.001 |
−0.47 (−0.65 to −0.29) |
< 0.001 |
−0.58 (−0.75 to −0.42) |
< 0.001 |
| CRD | |||||||||
| Global | Both |
−0.48 (−0.49 to −0.47) |
< 0.001 |
−0.73 (−0.75 to −0.70) |
< 0.001 |
−1.41 (−1.57 to −1.24) |
< 0.001 |
−1.41 (−1.5 to −1.33) |
< 0.001 |
| High SDI | Both |
−0.53 (−0.59 to −0.47) |
< 0.001 |
−1.06 (−1.09 to −1.02) |
< 0.001 |
−0.84 (−0.94 to −0.73) |
< 0.001 |
−0.95 (−1.03 to −0.86) |
< 0.001 |
| High-middle SDI | Both |
−0.70 (−0.74 to −0.66) |
< 0.001 |
−0.90 (−0.92 to −0.88) |
< 0.001 |
−2.46 (−2.67 to −2.26) |
< 0.001 |
−2.47 (−2.66 to −2.29) |
< 0.001 |
| Middle SDI | Both |
−0.59 (−0.6 to −0.57) |
< 0.001 |
−0.33 (−0.34 to −0.31) |
< 0.001 |
−2.39 (−2.6 to −2.19) |
< 0.001 |
−2.4 (−2.55 to −2.24) |
< 0.001 |
| Low-middle SDI | Both |
−0.29 (−0.31 to −0.27) |
< 0.001 |
−0.10 (−0.12 to −0.08) |
< 0.001 |
−0.28 (−0.83 to 0.28) |
0.328 |
−0.38 (−0.65 to −0.11) |
0.006 |
| Low SDI | Both |
−0.15 (−0.17 to −0.14) |
< 0.001 |
−0.07 (−0.08 to −0.05) |
< 0.001 |
−0.36 (−0.8 to 0.08) |
0.111 |
−0.44 (−0.73 to −0.15) |
0.003 |
| DM and CKD | |||||||||
| Global | Both |
0.76 (0.75 to 0.78) |
< 0.001 |
0.48 (0.47 to 0.50) |
< 0.001 |
0.56 (0.46 to 0.67) |
< 0.001 |
0.82 (0.75 to 0.89) |
< 0.001 |
| High SDI | Both |
0.74 (0.71 to 0.76) |
< 0.001 |
0.75 (0.72 to 0.79) |
< 0.001 |
0.16 (0.05 to 0.28) |
0.005 |
0.85 (0.7 to 0.99) |
< 0.001 |
| High-middle SDI | Both |
0.82 (0.77 to 0.86) |
< 0.001 |
0.29 (0.27 to 0.32) |
< 0.001 |
0.27 (0.08 to 0.46) |
0.006 |
0.51 (0.38 to 0.64) |
< 0.001 |
| Middle SDI | Both |
0.98 (0.96 to 1.00) |
< 0.001 |
0.31 (0.30 to 0.33) |
< 0.001 |
0.42 (0.24 to 0.61) |
< 0.001 |
0.62 (0.49 to 0.74) |
< 0.001 |
| Low-middle SDI | Both |
1.08 (1.02 to 1.14) |
< 0.001 |
0.56 (0.54 to 0.58) |
< 0.001 |
1.04 (0.99 to 1.09) |
< 0.001 |
1.13 (0.99 to 1.27) |
< 0.001 |
| Low SDI | Both |
0.7 (0.66 to 0.73) |
< 0.001 |
0.46 (0.43 to 0.48) |
< 0.001 |
0.21 (0.06 to 0.37) |
0.007 |
0.29 (0.16 to 0.42) |
< 0.001 |
| DD | |||||||||
| Global | Both |
−0.14 (−0.16 to −0.12) |
< 0.001 |
0.14 (0.13 to 0.16) |
< 0.001 |
−1.35 (−1.45 to −1.26) |
< 0.001 |
−1.28 (−1.36 to −1.21) |
< 0.001 |
| High SDI | Both |
−0.23 (−0.27 to −0.2) |
< 0.001 |
0.08 (0.06 to 0.09) |
< 0.001 |
−1.19 (−1.28 to −1.09) |
< 0.001 |
−1.07 (−1.18 to −0.96) |
< 0.001 |
| High-middle SDI | Both |
−0.23 (−0.25 to −0.2) |
< 0.001 |
0.13 (0.11 to 0.14) |
< 0.001 |
−1.32 (−1.55 to −1.09) |
< 0.001 |
−1.3 (−1.45 to −1.15) |
< 0.001 |
| Middle SDI | Both |
−0.01 (−0.02 to 0.00) |
0.151 |
0.13 (0.12 to 0.14) |
< 0.001 |
−1.74 (−1.94 to −1.55) |
< 0.001 |
−1.6 (−1.75 to −1.44) |
< 0.001 |
| Low-middle SDI | Both |
0.00 (0.00 to 0.00) |
0.173 |
0.09 (0.08 to 0.09) |
< 0.001 |
−1.59 (−1.85 to −1.32) |
< 0.001 |
−1.48 (−1.71 to −1.25) |
< 0.001 |
| Low SDI | Both |
0.02 (0.01 to 0.02) |
< 0.001 |
0.02 (0.02 to 0.03) |
< 0.001 |
−1.34 (−1.52 to −1.16) |
< 0.001 |
−1.38 (−1.48 to −1.27) |
< 0.001 |
| MD | |||||||||
| Global | Both |
0.22 (0.17 to 0.28) |
< 0.001 |
0.13 (0.09 to 0.17) |
< 0.001 | NA | NA |
0.15 (0.09 to 0.20) |
< 0.001 |
| High SDI | Both |
0.12 (0.07 to 0.17) |
< 0.001 |
0.13 (0.02 to 0.25) |
0.026 | NA | NA |
0.13 (0 to 0.26) |
0.051 |
| High-middle SDI | Both |
−0.03 (−0.08 to 0.02) |
0.235 |
0.06 (0.03 to 0.09) |
< 0.001 | NA | NA |
0.07 (0.04 to 0.09) |
< 0.001 |
| Middle SDI | Both |
0.42 (0.27 to 0.58) |
< 0.001 |
0.18 (0.13 to 0.23) |
< 0.001 | NA | NA |
0.27 (0.24 to 0.31) |
< 0.001 |
| Low-middle SDI | Both |
0.18 (0.15 to 0.21) |
< 0.001 |
0.09 (0.06 to 0.12) |
< 0.001 | NA | NA |
0.16 (0.1 to 0.21) |
< 0.001 |
| Low SDI | Both |
0.00 (−0.09 to 0.10) |
0.916 |
0.06 (0.03 to 0.09) |
< 0.001 | NA | NA |
0.07 (0.01 to 0.14) |
0.026 |
| MSD | |||||||||
| Global | Both |
−0.19 (−0.21 to −0.17) |
< 0.001 |
0.11 (0.1 to 0.12) |
< 0.001 |
−0.07 (−0.34 to 0.21) |
0.629 |
0.05 (0.03 to 0.07) |
< 0.001 |
| High SDI | Both |
−0.02 (−0.04 to 0) |
0.072 |
0.15 (0.13 to 0.16) |
< 0.001 |
−0.74 (−1.11 to −0.38) |
< 0.001 |
0.14 (0.1 to 0.17) |
< 0.001 |
| High-middle SDI | Both |
−0.33 (−0.34 to −0.31) |
< 0.001 |
0.04 (0.02 to 0.05) |
< 0.001 |
−0.18 (−0.68 to 0.32) |
0.483 |
−0.08 (−0.09 to −0.07) |
< 0.001 |
| Middle SDI | Both |
−0.17 (−0.18 to −0.16) |
< 0.001 |
0.18 (0.17 to 0.19) |
< 0.001 |
0.23 (0.01 to 0.45) |
0.042 |
0.11 (0.09 to 0.13) |
< 0.001 |
| Low-middle SDI | Both |
−0.08 (−0.1 to −0.05) |
< 0.001 |
0.22 (0.21 to 0.22) |
< 0.001 |
0.81 (0.27 to 1.35) |
0.003 |
0.23 (0.2 to 0.25) |
< 0.001 |
| Low SDI | Both |
−0.11 (−0.13 to −0.09) |
< 0.001 |
0.13 (0.13 to 0.14) |
< 0.001 |
0.44 (−0.04 to 0.92) |
0.072 |
0.12 (0.11 to 0.14) |
< 0.001 |
| Neoplasms | |||||||||
| Global | Both |
0.41 (0.33 to 0.5) |
< 0.001 |
0.47 (0.41 to 0.52) |
< 0.001 |
−0.59 (−0.72 to −0.46) |
< 0.001 |
−0.70 (−0.82 to −0.58) |
< 0.001 |
| High SDI | Both |
1.16 (1.08 to 1.24) |
< 0.001 |
0.75 (0.63 to 0.88) |
< 0.001 |
−0.84 (−0.93 to −0.75) |
< 0.001 |
−0.98 (−1 to −0.95) |
< 0.001 |
| High-middle SDI | Both |
0.02 (−0.03 to 0.07) |
0.446 |
0.62 (0.57 to 0.67) |
< 0.001 |
−0.51 (−0.73 to −0.29) |
< 0.001 |
−0.65 (−0.89 to −0.41) |
< 0.001 |
| Middle SDI | Both |
0.28 (0.21 to 0.36) |
< 0.001 |
1.1 (1.02 to 1.19) |
< 0.001 |
−0.45 (−0.57 to −0.34) |
< 0.001 |
−0.56 (−0.64 to −0.47) |
< 0.001 |
| Low-middle SDI | Both |
0.34 (0.27 to 0.41) |
< 0.001 |
0.92 (0.85 to 0.98) |
< 0.001 |
0.27 (0.12 to 0.42) |
< 0.001 |
0.2 (0.07 to 0.33) |
0.003 |
| Low SDI | Both |
0.11 (0.07 to 0.14) |
< 0.001 |
0.51 (0.46 to 0.56) |
< 0.001 |
−0.06 (−0.17 to 0.04) |
0.216 |
−0.21 (−0.27 to −0.15) |
< 0.001 |
| ND | |||||||||
| Global | Both |
−0.07 (−0.07 to −0.06) |
< 0.001 |
−0.03 (−0.04 to −0.03) |
< 0.001 |
0.05 (0.01 to 0.09) |
0.007 |
0.08 (0.06 to 0.11) |
< 0.001 |
| High SDI | Both |
−0.04 (−0.04 to −0.03) |
< 0.001 |
−0.03 (−0.03 to −0.02) |
< 0.001 |
0.05 (0.01 to 0.08) |
0.018 |
0.07 (0.04 to 0.09) |
< 0.001 |
| High-middle SDI | Both |
−0.13 (−0.16 to −0.11) |
< 0.001 |
−0.08 (−0.1 to −0.06) |
< 0.001 |
0.09 (0.05 to 0.12) |
< 0.001 |
0.14 (0.1 to 0.17) |
< 0.001 |
| Middle SDI | Both |
−0.01 (−0.02 to 0) |
0.097 |
0.07 (0.07 to 0.08) |
< 0.001 |
0.07 (−0.03 to 0.16) |
0.164 |
0.16 (0.11 to 0.21) |
< 0.001 |
| Low-middle SDI | Both |
−0.01 (−0.01 to 0.00) |
< 0.001 |
0.01 (0.01 to 0.01) |
< 0.001 |
0.41 (0.23 to 0.59) |
< 0.001 |
0.24 (0.13 to 0.35) |
< 0.001 |
| Low SDI | Both |
−0.01 (−0.02 to −0.01) |
< 0.001 |
0.00 (−0.01 to 0.00) |
0.354 |
0.30 (0.17 to 0.43) |
< 0.001 |
0.13 (0.05 to 0.21) |
0.002 |
| SOD | |||||||||
| Global | Both | NA | NA |
0.12 (0.09 to 0.15) |
< 0.001 | NA | NA |
−0.01 (−0.04 to 0.02) |
0.473 |
| High SDI | Both | NA | NA |
0.03 (0.02 to 0.04) |
< 0.001 | NA | NA |
−0.04 (−0.07 to 0) |
0.032 |
| High-middle SDI | Both | NA | NA |
0.17 (0.15 to 0.20) |
< 0.001 | NA | NA |
0.13 (0.09 to 0.17) |
< 0.001 |
| Middle SDI | Both | NA | NA |
0.08 (0.05 to 0.11) |
< 0.001 | NA | NA |
−0.14 (−0.19 to −0.09) |
< 0.001 |
| Low-middle SDI | Both | NA | NA |
0.03 (0.00 to 0.06) |
0.040 | NA | NA |
−0.46 (−0.49 to −0.42) |
< 0.001 |
| Low SDI | Both | NA | NA |
0.05 (0.02 to 0.08) |
0.001 | NA | NA |
−0.26 (−0.29 to −0.24) |
< 0.001 |
| SD and SCD | |||||||||
| Global | Both |
0.07 (0.07 to 0.07) |
< 0.001 |
0.14 (0.14 to 0.15) |
< 0.001 |
0.50 (0.31 to 0.70) |
< 0.001 |
0.20 (0.17 to 0.23) |
< 0.001 |
| High SDI | Both |
0.08 (0.07 to 0.09) |
< 0.001 |
0.18 (0.17 to 0.18) |
< 0.001 |
0.39 (0.10 to 0.68) |
0.009 |
0.17 (0.13 to 0.20) |
< 0.001 |
| High-middle SDI | Both |
−0.04 (−0.05 to −0.03) |
< 0.001 |
0.15 (0.14 to 0.15) |
< 0.001 |
1.25 (0.98 to 1.52) |
< 0.001 |
0.39 (0.35 to 0.43) |
< 0.001 |
| Middle SDI | Both |
0.15 (0.14 to 0.16) |
< 0.001 |
0.24 (0.24 to 0.25) |
< 0.001 |
0.25 (0.08 to 0.43) |
0.005 |
0.24 (0.20 to 0.28) |
< 0.001 |
| Low-middle SDI | Both |
0.11 (0.10 to 0.11) |
< 0.001 |
0.17 (0.16 to 0.17) |
< 0.001 |
0.39 (−0.09 to 0.88) |
0.112 |
0.19 (0.06 to 0.31) |
0.003 |
| Low SDI | Both |
0.02 (0.00 to 0.03) |
0.034 |
0.03 (0.02 to 0.04) |
< 0.001 |
−0.41 (−0.57 to −0.25) |
< 0.001 |
−0.12 (−0.18 to −0.06) |
< 0.001 |
NCDs Non-communicable Diseases, CVD Cardiovascular Diseases, CRD Chronic Respiratory Diseases, DM and CKD Diabetes and Kidney Diseases, DD Digestive Diseases, MD Mental Disorders, MSD Musculoskeletal Disorders, ND Neurological Disorders, SOD Sense Organ Diseases, SD and SCD Skin and Subcutaneous Diseases
The ranks of various NCDs demonstrated overall stability, SD/SCD, DD, and MSD consistently ranked as the top 3 in incidence and prevalence risk. CVD, neoplasms, and CRD remained the primary contributors to mortality and DALYs (Fig. 1).
SDI trends
In 2021, low SDI regions bore the highest ASIR (206,082/100,000) and ASDR (90,351/100,000), whereas low-middle SDI regions exhibited the highest ASPR (99,939/100,000) and ASMR (4,043/100,000) (Table 1). After excluding low SDI regions, a negative correlation was observed between SDI levels and ASIR of NCDs from 1990 to 2021 (Figure S1a). However, an upward trend in ASPR was observed in high-middle, middle, and low-middle SDI regions (Figure S1b). In addition, both the ASMR and ASDR demonstrated a declining trend (Figure S1c, d & Table 2).
In high SDI regions, SD/SCD and SOD were the leading causes of increased incidence and prevalence of NCDs, while neoplasms caused the most deaths and burdens (Tables 1 and 2).
Gender trends
In 2021, females experienced higher ASIR and ASPR of NCDs, particularly for MSD, DD, and ND. Conversely, males had higher ASMR and ASDR of NCDs, largely due to CVD, neoplasms, CRD, and DM/CKD (Fig. 2& Table 1). From 1990 to 2021, females experienced a more significant AAPC in ASIR (−0.05%) and ASMR (−1.06%) of NCDs than males. Temporal analyses showed a greater reduction of mortality in females, but a sharp decline of DALY in males (Fig. 2& Table S3).
Fig. 2.
The dynamic patterns of global sex-specific incidence, prevalence, mortality, and DALY rates of NCDs among the elderly from 1990 to 2021. a The dynamic patterns of global sex-specific incidence rate of NCDs among the elderly from 1990 to 2021. b The dynamic patterns of global sex-specific prevalence rate of NCDs among the elderly from 1990 to 2021. c The dynamic patterns of global sex-specific mortality rate of NCDs among the elderly from 1990 to 2021. d The dynamic patterns of global sex-specific DALY rate of NCDs among the elderly from 1990 to 2021 DALY: disability-adjusted life year; ASIR: age-standardized incidence rate; ASPR: age-standardized prevalence rate; ASMR: age-standardized mortality rate; ASDR: age-standardized DALY rate; CVD: cardiovascular diseases; CRD: chronic respiratory diseases; DM and CKD: diabetes and kidney disease; DD: digestive diseases; MD: mental disorders; MSD: musculoskeletal disorders; ND: neurological disorders; SOD: sense organ diseases; SD and SCD: skin and subcutaneous diseases; NCD: non-communicable diseases
Age trends
In 2021, NCDs burden increased with age, the peak was observed in the 95-plus age group for all metrics (ASIR, ASPR, ASMR, and ASDR) (Fig. 3 & Table S4). From 1990 to 2021, younger elderly (aged 60–64 years) showed a slight increase in ASIR (0.03%), while the 70 to 74 age group experienced a 0.05% decrease in ASIR (Fig. 3 & Table S5).
Fig. 3.
The dynamic patterns of global age-specific incidence, prevalence, mortality, and DALY rates of NCDs among the elderly in 2021. A The dynamic patterns of global age-specific incidence rate of NCDs among the elderly in 2021. B The dynamic patterns of global age-specific prevalence rate of NCDs among the elderly in 2021. C The dynamic patterns of global age-specific mortality rate of NCDs among the elderly in 2021. D The dynamic patterns of global age-specific DALY rate of NCDs among the elderly in 2021 DALY: disability-adjusted life year; CVD: cardiovascular diseases; CRD: chronic respiratory diseases; DM and CKD: diabetes and kidney disease; DD: digestive diseases; MD: mental disorders; MSD: musculoskeletal disorders; ND: neurological disorders; SOD: sense organ diseases; SD and SCD: skin and subcutaneous diseases; NCDs: non-communicable diseases
In the 60 to 64 age group, MSD, ND, and MD were the top 3 contributors to higher incidence, and SOD, DD, and MSD ranked as the top 3 NCDs associated with the elevated prevalence (Fig. 3 & Table S4).
Projections
The global ASPR for NCDs is projected to reach 100,026.33 per 100,000 by 2050, affecting more than 215 million elderly individuals. Notably, 151 countries and regions are expected to see a greater than 10% increase in prevalence, with 81 countries projected to exceed a 30% rise in prevalence (Fig. 4 & Table S6).
Fig. 4.
Projections of global sex-specific prevalence and their changing trend of NCDs among the elderly up to 2050. Axes labels in the line graph (specifying ASR per 100,000 individuals and years), and the shaded area representing forecast trends and its 95% UIs ASR: age-standardized rate; NCDs: non-communicable diseases; UIs: uncertainty intervals
Younger elderly groups (60 to 64 years) are projected to experience the highest NCDs prevalence. India, China, and Japan are anticipated to have prevalence rates above 30% in this age group. Over 80 countries, primarily in the developing countries, are forecasted to have NCDs prevalence rates exceeding 20% (Table S7).
Discussion
The global burden of NCDs, such as CVD, diabetes, and CRD, continues to rise and remains one of the most significant health challenges of the 21 st century [21]. This increasing burden arises from a complex interplay of social, cultural, economic, and environmental factors. Socioeconomic status plays a pivotal role in determining access to healthcare resources [22]. In LMICs, particularly those facing a dual burden of infectious disease and NCDs, the disparities in NCDs screening, diagnosis, and treatment services are exacerbated by an underdeveloped healthcare infrastructure [23]. Cultural factors significantly influence health perceptions and behaviors, leading to substantial variations in dietary habits, physical activity levels, tobacco use, and alcohol consumption across racial and socioeconomic groups [24]. Environmental factors, such as air pollution, further contribute to NCDs risk, with regional disparities in air quality intensifying health inequities [22]. These interconnected factors underscore the limitations of single-factor analyses in resolving NCDs disparities. For instance, economic disadvantages often intersect with cultural factors, as low-income communities face restricted access to healthy food, where affordability and availability dictate dietary choices. Likewise, traditional diets closely tied to local environmental conditions can influence NCDs risk profiles [25]. Therefore, comprehensive NCDs prevention requires an integrated framework addressing social, cultural, and ecological determinants. The WHO’s Global Action Plan for the Prevention and Control of NCDs (2013–2030) advocates a multisectoral approach to addressing the social determinants of health [26]. Our findings support this approach, especially the need for context-specific strategies in LMICs that strengthen primary healthcare and target elderly populations.
The rising burden of NCDs results from a complex interplay of population growth, environmental changes, and lifestyle factors, with older adults being particularly vulnerable [27]. The cumulative impact of risk factors, such as tobacco use, unhealthy diets, and excessive alcohol consumption, substantially increases the likelihood of developing NCDs in later life [28]. The COVID-19 pandemic further exacerbated these challenges by overwhelming healthcare systems and disrupting services for chronic disease management [29]. This disruption, particularly in LMICs, limited access to essential treatments, contributing to higher mortality rates [20]. Furthermore, evidence suggests that NCDs and their associated risk factors increased hospitalization and mortality rates from COVID-19, highlighting the inability of fragile healthcare systems to manage the dual burden of infectious diseases and NCDs [30]. Therefore, accelerating the implementation of the WHO NCDs Progress Monitor 2021 recommendations is critical, particularly the objective of sustaining essential NCDs services during emergencies through task-shifting initiatives and community-based care models, which is imperative to strengthen health system resilience.
Over the past two decades, NCDs have shifted from being primarily associated with affluent populations to becoming a global health crisis, affecting nearly 1.9 billion elderly people worldwide in 2021 [31]. Compared to high-SDI regions, middle- and low-SDI countries bear a disproportionately higher burden of NCDs [22, 32]. This disparity arises from multiple interconnected factors. Cross-SDI analyses reveal that the treatment gap for NCDs in conflict areas, such as Afghanistan, is several times greater than in stable LMICs [33]. Similarly, income-based disparities account for over one-third of the variation in treatment access in middle SDI countries like Brazil [34]. In addition, population aging and the increase of unhealthy lifestyles further exacerbate the burden of NCDs [35, 36]. This trend poses a major challenge to achieving the Sustainable Development Goals (SDGs), especially those related to poverty reduction, inequality, and healthcare access [37]. Addressing the NCDs crisis requires region-specific, multifaceted interventions. In LMICs, the effective strategies include population-based strategies targeting unhealthy behaviors, the expansion of telemedicine in NCDs clinics, and community-based initiatives promoting healthy lifestyles [38]. Notably, Thailand’s successful implementation of the WHO “Best Buy” strategy through its universal health coverage scheme led to an 18% reduction in CVD mortality in rural areas by leveraging community-led hypertension screening and subsidized generic medication [39]. Similarly, Rwanda’s national community health worker program reduced diabetes-related hospitalizations by 22% through monthly mobile clinic visits in low-resource settings [40]. These interventions directly support SDGs Target 3.4 (reducing premature mortality from NCDs by one-third) and SDGs Target 3.8 (achieving universal health coverage), while addressing equity benchmarks by improving access among vulnerable and underserved populations. We recommend two actionable strategies to reduce NCDs burden in LMICs. First, expansion of integrated community-based screening and referral systems: establish multi-disease screening packages delivered by trained community health workers, linked to referral hubs through mobile health (mHealth) platforms. These systems should include built-in triage protocols and real-time reporting dashboards to strengthen early detection, particularly in rural or underserved regions. Second, strengthening NCDs-specific primary healthcare infrastructure, upgrading frontline facilities with point-of-care diagnostics, a consistent supply of essential NCDs medicines, and training programs for task-shifting in chronic disease management. Implementation progress should be tracked via facility-level service coverage and treatment adherence indicators.
Our study highlighted notable gender disparities in NCDs burden, with women disproportionately affected. According to WHO reports, nearly two-thirds of women die from NCDs annually, resulting in nearly 19 million deaths worldwide [41]. Gender norms, inequalities, and intersecting determinants exacerbate women’s vulnerability to NCDs, especially in LMICs, where access to healthcare services and preventive interventions remains limited [42]. The aging population further intensifies this burden, as older women, especially those living in poverty, face the greatest challenges due to inadequate healthcare protections [43, 44]. Addressing these disparities requires implementing gender-sensitive health policies to ensure equitable access to NCDs prevention and treatment services [45, 46]. This aligns with the United Nations SDGs on NCDs reduction while highlighting gaps in current women’s health initiatives. Specifically, there is an urgent need for gender-responsive NCDs policies that account for caregiving responsibilities and socioeconomic barriers disproportionately affecting women in LMICs. To bridge these gaps, policymakers should implement gender-sensitive NCDs policies that ensure equitable prevention, early detection, and treatment. We propose two policy actions: (1) Develop gender-integrated NCDs surveillance systems: National health information systems should routinely disaggregate NCDs indicators by sex, age, and socioeconomic status to inform responsive policy design; (2) Introduce conditional cash transfers (CCTs) linked to women’s NCDs care: CCTs can reduce financial barriers for marginalized women by incentivizing clinic attendance, screening uptake, and treatment adherence.
Projections indicated that the prevalence of NCDs will continue to rise from 2021 to 2050, aligned with population growth and ongoing aging. Despite overall progress achieved by the Global Action Plan for the Prevention and Control of NCDs 2013 to 2020, the COVID-19 pandemic has disrupted health-related indicators and exacerbated health inequalities in areas such as access to healthcare services and routine immunization, reversing positive trends in NCDs control [47]. The proportion of mortality attributed to NCDs continues to rise, now accounting for approximately three-quarters of total annual deaths worldwide [48]. Given the significant impacts of the COVID-19 pandemic on various aspects of life, livelihoods, healthcare systems, communities, and society, there is an urgent need to allocate more resources to address the burden of NCDs, such as scaling telemedicine infrastructure to bridge rural-urban care gaps, funding community health worker programs for NCDs screening in low-resource settings, subsidizing essential medications for marginalized populations.
Our research contributes valuable insights into the burden of NCDs. First, it offers detailed projections and an understanding of epidemiological trends among elderly populations across different countries, providing a foundation for developing tailored health policies. Second, by comparing the burden of NCDs across various SDI levels, genders, and age groups, the study highlights health inequalities in specific regions and populations. Third, it emphasizes that NCDs are not solely health challenges but also issues of human rights and equity, particularly in low and low-middle SDI regions. This perspective contributes to global health equity and the resolution of health disparities.
Limitations and future research directions
Our study also has some limitations. First, the accuracy and comprehensiveness of GBD studies are more rely on the availability and quality of data across different countries. Variations in data collection methods can significantly affect the reliability of our results and the overall stability of the analysis. Second, compiling the GBD dataset is a time-intensive process, often spanning several years. As a result, the analysis may not reflect the health trends and patterns in a near-real-time manner. Third, limitations in data quality are particularly pronounced in LMICs, where reliance on interpolated data can introduce uncertainties. Therefore, it is essential to interpret our results with consideration of these methodological constraints, particularly regarding data availability and quality across different socioeconomic contexts. In addition, our study is based on aggregated data from the GBD database, which may introduce the risk of ecological fallacy (drawing inferences about individual-level relationships based on population-level trends). The observed associations between SDI levels and NCDs burden among elderly populations may not reflect the specific risk factors or health outcomes experienced by individuals within those regions.
Future research should prioritize three method-specific directions to strengthen the basis for NCDs prevention and management in elderly populations. First, implement subnational burden tracking through high-resolution spatiotemporal modeling to identify district-level NCDs hotspots and inform localized interventions, particularly in LMICs with heterogeneous health service coverage. Second, conduct longitudinal cohort studies that quantify individual-level exposure to ambient and household air pollution, enabling robust estimation of dose-response relationships for various NCDs such as ischemic heart disease and chronic obstructive pulmonary disease. Third, integrate health data (electronic medical records, medical insurance) with community-based surveys to assess the preparedness of healthcare systems for elderly populations, measuring gaps in geriatric workforce capacity, long-term care services, and infrastructure adequacy.
Conclusion
In conclusion, the growing burden of NCDs among the elderly poses a significant global health challenge that threatens sustainable development, the need for targeted interventions tailored to specific SDI regions and vulnerable populations is crucial for achieving health equity and mitigating the global NCDs burden. Future research on the long-term impacts of sociopolitical and environmental factors on NCD trends is essential to inform evidence-based policies and advance global health initiatives.
Supplementary Information
Acknowledgements
Dedicated to the memory of my grandfather, Tian-Xiang Wang, who has passed away peacefully on May 1, 2025 (1933-2025). He always believed in my ability to be successful in the academic arena. You are gone but your belief in me has made this journey possible. May he rest in peace.
Authors’ contributions
X.H. and S.Y. wrote the main manuscript text, Y.G. and Y.Z. prepared Figs. 1, 2, 3 and 4 and Y.H. and Y.L. analyzed the data, H.P. and P.W. revised the main manuscript text. All authors reviewed the manuscript.
Funding
This study was funded by grants from National Natural Science Foundation of China (82404354, 82273710), Anhui Provincial Natural Science Foundation (2108085Y26 and 2308085QH288), Research Fund of Anhui Institute of Translational Medicine (2021zhyx-B04), the Key Scientific Research Foundation of the Education Department of the Province Anhui (2022AH050653) and Natural Science Foundation of Anhui Medical University (2022xkj006).
Data availability
Data are available from the Global Health Data Exchange (GHDx) repository at http://ghdx.healthdata.org. We extracted GBD 2021 estimates using the query tool (https://vizhub.healthdata.org/gbd-results/).
Declarations
Ethical approval and consent to participate
No need for ethical approval as used of anonymous public data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiao Hu, Si-Jie Yu and Yue-Can Gao contributed equally to this work and should be considered co-first authors.
Contributor Information
Hai-Feng Pan, Email: panhaifeng@ahmu.edu.cn, Email: panhaifeng1982@sina.com.
Peng Wang, Email: wangpeng19910318@sina.com, Email: robertowang@ahmu.edu.cn.
References
- 1.Chikowore T, Kamiza AB, Oduaran OH, Machipisa T, Fatumo S. Non-communicable diseases pandemic and precision medicine: is Africa ready? EBioMedicine. 2021;65: 103260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jones CH, Dolsten M. Healthcare on the brink: navigating the challenges of an aging society in the united States. NPJ Aging. 2024;10:22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.collaborators NCDC2030. NCD countdown 2030: worldwide trends in non-communicable disease mortality and progress towards sustainable development goal target 3.4. Lancet. 2018;392:1072–88. [DOI] [PubMed] [Google Scholar]
- 4.Ezzati M, Pearson-Stuttard J, Bennett JE, Mathers CD. Acting on non-communicable diseases in low- and middle-income tropical countries. Nature. 2018;559:507–16. [DOI] [PubMed] [Google Scholar]
- 5.Halder P, Das S, Mamgai A, Sharma D, Behera A, Syal P, et al. Association of frailty with non-laboratory based cardiovascular disease risk score among older adults and elderly population: insights from longitudinal aging study in India (LASI-1st wave). BMC Public Health. 2025;25:269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.García-Olmos L, Salvador CH, Alberquilla Á, Lora D, Carmona M, García-Sagredo P, et al. Comorbidity patterns in patients with chronic diseases in general practice. PLoS One. 2012;7:e32141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.van Oostrom SH, Picavet HSJ, van Gelder BM, Lemmens LC, Hoeymans N, van Dijk CE, et al. Multimorbidity and comorbidity in the Dutch population - data from general practices. BMC Public Health. 2012;12:715. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Barrio-Cortes J, Castaño-Reguillo A, Beca-Martínez MT, Bandeira-de Oliveira M, López-Rodríguez C, Jaime-Sisó MÁ. Chronic diseases in the geriatric population: morbidity and use of primary care services according to risk level. BMC Geriatr. 2021;21:278. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.World Health Organization. Global health estimates: leading causes of death. https://go.nature.com/43nNG0w (2020).
- 10.Kanasi E, Ayilavarapu S, Jones J. The aging population: demographics and the biology of aging. Periodontol. 2000;2016(72):13–8. [DOI] [PubMed] [Google Scholar]
- 11.Rajendran A, Minhas AS, Kazzi B, Varma B, Choi E, Thakkar A, et al. Sex-specific differences in cardiovascular risk factors and implications for cardiovascular disease prevention in women. Atherosclerosis. 2023;384:117269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, et al. Population ageing and mortality during 1990–2017: a global decomposition analysis. PLoS Med. 2020;17:e1003138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403:2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403:2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Erler NS, Rizopoulos D, Jaddoe VW, Franco OH, Lesaffre EM. Bayesian imputation of time-varying covariates in linear mixed models. Stat Methods Med Res. 2019;28:555–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.GBD. 2021 Methods Appendices.https://www.healthdata.org/gbd/methods-appendices-2021.
- 17.Go D-S, Kim Y-E, Yoon S-J. Subnational burden of disease according to the sociodemographic index in South Korea. Int J Environ Res Public Health. 2020;17:5788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ortega-Del Vecchyo D, Marsden CD, Lohmueller KE. PReFerSim: fast simulation of demography and selection under the Poisson random field model. Bioinformatics. 2016;32:3516–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Butler WJ, Kalasinski LA. Statistical analysis of epidemiologic data of pregnancy outcomes. Environ Health Perspect. 1989;79:223–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Møller B, Fekjaer H, Hakulinen T, Sigvaldason H, Storm HH, Talbäck M, et al. Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches. Stat Med. 2003;22:2751–66. [DOI] [PubMed] [Google Scholar]
- 21.The Lancet null. Non-communicable diseases: what now? Lancet. 2022;399:1201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Front Public Health. 2020;8:574111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kassa MD, Grace JM. Noncommunicable diseases prevention policies and their implementation in africa: A systematic review. Public Health Rev. 2021;42:1604310. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Leon N, Xu H. Implementation considerations for non-communicable disease-related integration in primary health care: a rapid review of qualitative evidence. BMC Health Serv Res. 2023;23:169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Allen LN, Wigley S, Holmer H. Implementation of non-communicable disease policies from 2015 to 2020: a geopolitical analysis of 194 countries. Lancet Glob Health. 2021;9:e1528–38. [DOI] [PubMed] [Google Scholar]
- 26.The World Health Organization. The WHO Global Action Plan for the Prevention and Control of NCDs 2013–2030. Global action plan for the prevention and control of non communicable diseases 2013-2020. 2024. https://www.who.int/publications/i/item/9789241506236.
- 27.The Lancet Global Health null. Getting to the heart of non-communicable diseases. Lancet Glob Health. 2018;6:e933. [DOI] [PubMed] [Google Scholar]
- 28.Prüss-Ustün A, van Deventer E, Mudu P, Campbell-Lendrum D, Vickers C, Ivanov I, et al. Environmental risks and non-communicable diseases. BMJ. 2019;364:l265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gordon Patti K, Kohli P. COVID’s impact on non-communicable diseases: what we do not know may hurt us. Curr Cardiol Rep. 2022;24:829–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Luciani S, Caixeta R, Chavez C, Ondarsuhu D, Hennis A. What is the NCD service capacity and disruptions due to COVID-19? Results from the WHO non-communicable disease country capacity survey in the Americas region. BMJ Open. 2023;13:e070085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Jackson-Morris A, Nugent R. Tailored support for National NCD policy and programme implementation: an over-looked priority. BMJ Glob Health. 2020;5:e002598. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Halder P, Dixit J, Gupta N, Mehra N, Singh A, Malhotra P, et al. Access to timely cancer treatment initiation in India: extent, determinants and trends. Lancet Reg Health. 2025;32: 100514. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Neyazi N, Mosadeghrad AM, Afshari M, Isfahani P, Safi N. Strategies to tackle non-communicable diseases in Afghanistan: a scoping review. Front Public Health. 2023;11:982416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Laurentino JSL, Brito RCdaS, de Oliveira-Silva RT, Soares A, da Pereira T, de Lima C, et al. Association between food insecurity and chronic noncommunicable diseases in Brazil: a systematic review. Rev Bras Epidemiol. 2024;27:e240041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lessa I, Mendonça GA, Teixeira MT. Non-communicable chronic diseases in Brazil: from risk factors to social impact. Bol Oficina Sanit Panam. 1996;120:389–413. [PubMed] [Google Scholar]
- 36.Halder P, Saha S, Mamgai A, Kolachala ACS, Chattopadhyay A, Rathor S, et al. Distribution and association of road traffic accident with depression among Indian population aged 45 years and above: nested multilevel modelling analysis of nationally representative cross-sectional survey. Discov Ment Health. 2024;4:60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Chang AY, Cullen MR, Harrington RA, Barry M. The impact of novel coronavirus COVID-19 on noncommunicable disease patients and health systems: a review. J Intern Med. 2021;289:450–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Heine M, Lategan F, Erasmus M, Lombaard C-M, Mc Carthy N, Olivier J, et al. Health education interventions to promote health literacy in adults with selected non-communicable diseases living in low-to-middle income countries: a systematic review and meta-analysis. J Eval Clin Pract. 2021;27:1417–28. [DOI] [PubMed] [Google Scholar]
- 39.Aekplakorn W, Chariyalertsak S, Kessomboon P, Assanangkornchai S, Taneepanichskul S, Goldstein A, et al. Trends in hypertension prevalence, awareness, treatment, and control in the Thai population, 2004 to 2020. BMC Public Health. 2024;24:3149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Rutayisire R, Mutabazi F, Bayingana A, Miller AC, Gupta N, Ngoga G, et al. Integration of chronic oncology services in noncommunicable disease clinic in rural Rwanda. Ann Glob Health. 2020;86: 33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Women face many gender-based challenges in relation to chronic diseases, especially in low- and middle-income countries. 2023. https://ncdalliance.org/why-ncds/ncds-and-sustainable-development/women-and-ncds.
- 42.Woodward M. Cardiovascular disease and the female disadvantage. Int J Environ Res Public Health. 2019;16:1165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Marshall SJ. Developing countries face double burden of disease. Bull World Health Organ. 2004;82:556. [PMC free article] [PubMed] [Google Scholar]
- 44.Kibria GMA, Meghani A, Ssemagabo C, Wosu A, Nareeba T, Gyezaho C, et al. Geographical, sex, and socioeconomic differences in non-communicable disease indicators: a cross-sectional survey in Eastern Uganda. PLoS Glob Public Health. 2024;4:e0003308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Pedro MJC, Covane A, Emmanuel P. Engaging stakeholders in non-communicable diseases (NCDs) implementation research in low- and middle-income countries (LMICs): a scoping review protocol. BMJ Open. 2024;14:e089689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Manandhar M, Hawkes S, Buse K, Nosrati E, Magar V. Gender, health and the 2030 agenda for sustainable development. Bull World Health Organ. 2018;96:644–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Francis A, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, et al. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol. 2024;20:473–85. [DOI] [PubMed] [Google Scholar]
- 48. World Health Organization. World health statistics 2024: monitoring health for the SDGs, sustainable development goals. 2024. https://www.who.int/publications/i/item/9789240094703.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available from the Global Health Data Exchange (GHDx) repository at http://ghdx.healthdata.org. We extracted GBD 2021 estimates using the query tool (https://vizhub.healthdata.org/gbd-results/).