Global Epidemiology of Multidrug-Resistant Lower Respiratory Infections in Adults ≥55 years: Trends (1990–2021) and Projections to 2050

Global Epidemiology of Multidrug-Resistant Lower Respiratory Infections in Adults ≥55 years: Trends (1990–2021) and Projections to 2050 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Epidemiology of Multidrug-Resistant Lower Respiratory Infections in Adults ≥55 years: Trends (1990–2021) and Projections to 2050 Jiao Zhao, Guiyun Li, Yuyang Qiu, Yunqin Wang, Guanglin Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7950784/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The global aging population has amplified the public health burden of multidrug-resistant (MDR) lower respiratory infections (LRIs), particularly among adults aged ≥55 years. Acinetobacter baumannii (AB), Pseudomonas aeruginosa (PA), and Klebsiella pneumoniae (KP) (collectively KAP) are leading MDR Gram-negative pathogens causing LRIs in this demographic, yet comprehensive global epidemiological data on KAP-related LRIs in adults ≥55 years remain scarce. Methods : Using data from the Global Burden of Disease (GBD) 2021, we analyzed trends in disability-adjusted life years (DALYs) and age-standardized rates (ASR) of KAP-related LRIs in adults ≥55 years across 204 countries/territories from 1990 to 2021. Estimated annual percentage changes (EAPC) were calculated to assess temporal trends, with stratification by age, sex, and Socio-demographic Index (SDI). Future burdens (to 2050) were projected using dual models: Bayesian Age-Period-Cohort (BAPC) and Autoregressive Integrated Moving Average (ARIMA). Result： Globally, AB- and KP-related DALY ASRs declined significantly (EAPC: AB=-2.40, 95%CI:-2.48–-2.32; KP=-0.82, 95%CI:-0.89–-0.74) from 1990 to 2021, while PA-related ASR increased (EAPC=0.33, 95%CI:0.24–0.43) and was projected to stabilize post-2025. Burden was highest in adults ≥95 years (e.g., PA-related ASR 26-fold higher than 55–59 years) and males (2021: AB ASR males=72.97 vs. females=56.11 per 100,000). Low-middle SDI regions bore the heaviest burden, whereas high-SDI regions showed the steepest declines (AB EAPC=-3.81, 95%CI: -4.02 to -3.59). Dual models confirmed AB- and KP-related burdens would continue decreasing by 2050, but PA-related burden would persist. Conclusion: KAP-related LRIs in adults ≥55 years exhibit pathogen-specific, demographic, and geographic disparities, with PA emerging as a persistent threat. Targeted interventions are needed to mitigate burden and promote healthy aging globally. Antimicrobial resistance Adults ≥55 Years Epidemiology Global Burden of Disease 2021 Healthy policy KAP-related LRIs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The increasing aging of the global population has heightened the public health issue posed by multidrug-resistant(MDR) lower respiratory tract infections (LRIs), particularly among adults aged ≥ 55 years, which continue to be a major health concern[ 1 , 2 ]. In 2019, MDR bacteria led to an estimated 127 million fatalities globally, with a particularly severe impact on individuals aged 55 years and older[ 3 ].The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are currently the most common MDR pathogens causing LRIs worldwide[ 4 ]. Among these, three MDR Gram-negative bacteria—Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae (KAP)—are primary LRIs etiologies responsible for LRIs in adults ≥ 55 years, and they are associated with a very high mortality[ 5 , 6 ]. A study addressing the global impact of MDR diseases reports that In 2021, at least 100,000 fatalities were attributable to these three MDR pathogens[ 7 ]. Against this backdrop, assessing the disease burden of KAP-related LRTIs in adults ≥ 55 years and developing targeted monitoring and intervention strategies are urgently needed. Klebsiella pneumoniae (KP) represents the pathogen that poses the greatest disease burden among the three, making it a significant issue for the World Health Organization. A 2021 Global Burden of Disease(GBD) study reported that KP-related LRIs caused approximately 175,783.21(95%UI,158,749.05–193,923.83) deaths and 6,935,439.84(95%UI, 5,953,327.57–8,007,785.63) disability-adjusted life years (DALYs) worldwide, with adults ≥ 55 years accounting for 64% of these deaths and 26% of DALYs—consistent with its disproportionate impact on this age group[ 3 , 8 ]. And per the 2021 GBD report, Pseudomonas aeruginosa(PA) disease burden has ranked second only to that of KP over the past three decades; in recent years, this pathogen has resulted in more than 124,000 global annual deaths, of which adults ≥ 55 years accounted for 72.3%[ 9 ].At present, Acinetobacter baumannii (AB) causes more than 750,000 LRI-related fatalities globally in 2021, with adults ≥ 55 years constituting 69% of these deaths; notably, MDR strains of AB representing 20% of LRIs occurring in intensive care units (ICUs)[ 9 , 10 ]. For people aged over 60 years old and older, the impact of illness is expected to become more severe, with predictions indicating that by 2050, this impact will have increased twofold compared to 2012[ 11 ].Nevertheless, the widespread use of antibiotics around the world, coupled with the diversity of antibiotic-resistant bacterial genes, complicates the treatment and management of LRIs among older adults. Despite this substantial disease burden of KAP-related LRIs in aging populations, comprehensive global epidemiological studies targeting these pathogens specifically in adults ≥ 55 years remain lacking. This study, to the best of our understanding, represents the inaugural investigation that systematically examines the long-term trends (1990–2021) and future forecasts (up to 2050) of KAP-related LRI burden specifically among adults aged 55 years and older across 204 countries and territories, categorized by Sociodemographic Index (SDI) and GBD region. Our results will assist in pinpointing regions or countries with a high burden among adults ≥ 55 years, guide targeted interventions (e.g., age-specific antibiotic stewardship and KAP vaccines), and optimize the allocation of healthcare resources in aging populations. Methods Data Acquisition and Sources Data for this research were sourced from the 2021 Global Burden of Disease (GBD) dataset, offering extensive details about the worldwide and regional impact of 371 diseases, injuries, and 88 risk factors across 204 nations and territories spanning from1990 to 2021(13,14). In this investigation, we collected DALY data for KAP-related LRIs (ICD-10: J15.0, J15.1 and J15.16) among adults aged ≥ 55 years using ICD codes[ 12 ], along with their respective the age-standardized rate (ASR) per 100,000 at global, regional, and national levels. We calculated the average estimated annual percentage changes (EAPCs) using linear regression analysis. The datas were retrieved and downloaded from the Global Health Data Exchange (GHDx) platform ( http://ghdx.healthdata.org/gbd-results-tool).Additionall y, Sociodemographic Index (SDI) data were collected to evaluate how socioeconomic variables influence disease burden. Definition Sociodemographic Index (SDI) data were collected to evaluate the influence of socioeconomic variables on disease burden; the SDI, which serves as a measure of a nation’s socioeconomic status, is derived from the geometric mean of multiple indicators and ranges in value from 0 to 1[ 13 ]. These quintiles include low SDI (< 0.45), low-middle SDI (≥ 0.45 and < 0.60), middle SDI (≥ 0.60 and < 0.69), high-middle SDI (≥ 0.69 and < 0.80), and high SDI(≥ 0.80)[ 14 ]. Bayesian Age-Period-Cohort Model To project future disease burden, we implemented a Bayesian Age-Period-Cohort (BAPC) model[ 15 ], decomposing temporal trends into three dimensions: age (biological risk progression), period (time-specific external factors), and cohort (birth year-linked exposures). The number of cases is modeled through the use of the age-period-cohort (APC) framework, with age group A and period P: l og(Y ap ) = µ + α a + β p + γ c In this model, µ denotes the intercept term, while α a 、 β p and γ c represent the age, period, and cohort effects in the log scale, respectively. Bayesian estimation is performed using the Integrated Nested Laplace Approximation (INLA), which assumes that the second-order differences of age, period, and cohort effects follow independent zero-mean normal distributions, thereby ensuring parameter smoothness. The effect of period t , βp + tβp + t , is derived based on historical trends: βp + t ∣ βp , βp − 1∼N((1 + t ) βp − tβp − 1 , kβ − 1 i = 1∑ t i 2) Consequently, the introduction of a random effect terms, δap + t∼N(0,kδ − 1) δap + t ∼N(0, kδ − 1) was implemented to calibrate the model residuals.The BAPC demonstrated superior performance over non-Bayesian alternatives, evidenced by a 15.7% reduction in Deviance Information Criterion (DIC). Sensitivity analyses confirmed stability across prior distribution assumptions (coefficient variation < 5%). All analyses were conducted using the "BAPC" R package (v1.0.2). Autoregressive Integrated Moving Average Model Our findings further validated the accuracy of the BAPC prediction model using the Autoregressive Integrated Moving Average Model (ARIMA). The age-period-cohort model investigates how age, period, and birth cohort influence health outcomes. The age effect pertains to the likelihood of experiencing different outcomes at various ages. A period effect indicates how temporal changes impact outcomes across all age groups. Meanwhile, the cohort effect refers to shifts in outcomes among individuals born in the same time frame. The log-linear regression model can be represented by the equation: log(Yi) = µ + α * age i + β * period i + γ * cohort i + ε where Yi denotes the DALYs ASR of KAP-related LRIs, with α, β, and γ representing the coefficients associated with age, period, and cohort, respectively. µ serves as the intercept, and ε signifies the model’s residuals. To derive the net effects across these three dimensions, the intrinsic estimator (IE) method, incorporated within the age-period-cohort model, was employed[ 16 ]. Statistical Analysis All data presented in the figures and tables throughout this manuscript were sourced from the GBD 2021 database, ensuring authenticity and reliability. Following extraction, the data were re-analyzed and visualized through bar charts, line graphs, and map utilized software package (version 4.2.3) and JD_GBDR (V2.24, Jingding Medical Technology Co., Ltd.) categorized by age group, sex, and geographic region to characterize the epidemiological trend of KAP-related LRIs in Adults ≥ 55 Years. These maps utilized the `rnaturalearthdata` packages to display the distribution of the disease burden. All estimated values for the age-standardized rate, number of cases, and fluctuations in case numbers are presented with a 95% uncertainty interval (UI), which is defined as the range between the 2.5th and 97.5th percentiles among all 1000 simulations. The ASR can exclude the effects of imbalances in population size and age distribution, \(\:ASR=\frac{\sum\:_{i=1}^{A}\:{a}_{i}{w}_{i}}{\sum\:_{i=1}^{A}\:{a}_{i}}\times\:100000\) . In addition, the spatial and temporal trends in the disease burden of KAP-related LRIs were captured by using the EAPC corresponding to the DALY ASR per 100,000. Y = α + β X where Y is the lg (ASR) and X is the calendar year. The EAPC value was then calculated by the formula EAPC = 100 * (exp(β)-1). the EAPC is presented along with a 95% confidence interval (CI) to illustrate the magnitude and direction of temporal trends. its 95% CI are greater than zero, the corresponding age-specification rate tends to increase, and vice versa[ 17 ]. Gaussian curves were used to analyze associations between EAPC and rates and the Human Development Index of KAP-related LRIs in adults ≥ 55 years. To ensure the accuracy and robustness of the predictions, we utilize data on KAP-related LRIs in adults ≥ 55 years from 1990 to 2021. Results KAP-associated LRIs in Adults ≥ 55 Years: Global Trends Globally, AB-associated LRIs DALYs increased from 863,576.06 (95%UI:729,944.09–1,027,773.17) in 1990 to 951,777.08 (95%UI:817,985.69–1,102,615.61) in 2021, while its ASR halved from 128.62 (95%UI:108.72–153.07) to 64.05 (95%UI:55.05–74.20) per 100,000 population and declining trend (EAPC=-2.40,95%CI:-2.48–2.32) (Table 1 ). Conversely, PA-associated LRIs DALYs surged from 558,374.61 (508,216.59–603,950.56) to 1,406,482.17 (1,266,130.01–1,517,577.21), with ASR rising from 83.16 (75.69–89.95) to 94.65 (85.20–102.13) per 100,000 population (EAPC = 0.33, 95%CI:0.24–0.43) (Table 2). And KP caused 1,865,590.23 DALYs (95%UI:1,699,228.82–2,032,351.00) in 2021, with ASR at 125.55 (95%UI:114.35–136.77) per 100,000 population and declining trend (EAPC = − 0.82, 95%CI: −0.89 to − 0.74) (Table 3). In relation to sex and age, males aged ≥ 55 years exhibited elevated ASR and cases of DALYs in 2021(eg., AB males 72.97 vs females 56.11; PA burden; KP males 145.40 vs females 107.88 per 100,000),and peak burden at ≥ 95 years (eg., KP males 1064.85[95%UI:826.20–1192.52], females 775.37[95%UI:560.60–899.95] per 100,000) versus lowest in 55-59y (eg., KP males 62.45, females 39.45 per 100,000), positively correlated with age.(Fig. 1 A,B,C). Table 1 DALYs cases and ASR (per 100,000 population) of AB-associated LRIs among adults ≥ 55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021. 1990 2021 1990–2021 DALYs cases (95% UI) ASR of DALYs (per 100-000 population) DALYs cases (95% UI) ASR of DALYs (per 100-000 population) EAPCs of DALYs ASR (95% CI) Global 863576.06(729944.09-1027773.17) 128.62(108.72-153.07) 951777.08(817985.69-1102615.61) 64.05(55.05–74.20) -2.40(-2.48–2.32) Sex Male 476097.56(411017.03-563181.20) 152.85(131.95-180.81) 510454.59(450684.22-587851.22) 72.97(64.43–84.04) -2.56(-2.65–2.46) Female 387478.50(309290.29-476644.53) 107.65(85.93-132.42) 441322.49(355901.11-531364.77) 56.11(45.25–67.56) -2.22(-2.29–2.16) Socio-demographic index Low SDI 248785.61(201122.98-308139.65) 666.84(539.09-825.93) 252715.56(208096.11-306286.96) 307.97(253.60-373.26) -2.68(-2.83–2.54) Low-middle SDI 287201.63(239212.78-350299.42) 284.92(237.31-347.52) 354101.82(301381.64-417599.53) 146.88(125.01-173.22) -2.18(-2.27–2.10) Middle SDI 201945.69(171771.23-237451.55) 116.35(98.97-136.81) 244315.98(210218.73-281854.51) 52.00(44.74–59.99) -2.68(-2.73–2.63) High-middle SDI 70651.38(60748.63-81057.31) 40.95(35.21–46.98) 64339.89(55580.08-73840.09) 18.56(16.03–21.30) -2.77(-2.92–2.62) High SDI 54360.25(46813.37-61795.65) 29.15(25.11–33.14) 35531.43(29300.86-40699.31) 10.30(8.49–11.80) -3.81(-4.02–3.59) Region Andean Latin America 11906.25(9972.81-14145.55) 354.79(297.17-421.51) 10066.89(8031.74-12391.17) 101.62(81.08-125.08) -3.65(-3.89–3.41) Australasia 427.56(361.26-497.12) 10.85(9.17–12.62) 317.54(250.47-374.84) 3.59(2.84–4.24) -3.76(-4.15–3.36) Caribbean 5654.18(4429.12-7266.54) 131.20(102.77-168.61) 7955.36(6339.16-9902.43) 85.93(68.47-106.96) -1.43(-1.59–1.27) Central Asia 2837.06(2498.57-3201.55) 35.47(31.24–40.03) 3872.16(3371.63-4465.23) 26.61(23.17–30.69) -1.33(-1.75–0.92) Central Europe 8464.60(7474.84-9536.92) 31.92(28.19–35.96) 8677.37(7584.55-9872.95) 23.43(20.48–26.66) -1.29(-1.81–0.77) Central Latin America 16476.82(14421.81-18909.74) 121.42(106.28-139.35) 17862.78(15326.20-20953.77) 41.77(35.84-49.00) -3.41(-3.88–2.93) Central Sub-Saharan Africa 37236.75(26393.64-51421.49) 990.26(701.90-1367.48) 52713.80(36375.44-73487.31) 584.18(403.12–814.40) -1.76(-1.99–1.53) East Asia 116051.21(92871.94-138371.17) 77.91(62.35–92.90) 49962.32(40953.63-62605.06) 12.74(10.44–15.97) -6.90(-7.35–6.44) Eastern Europe 5508.84(4893.74-6194.47) 11.27(10.01–12.67) 7170.55(6162.65-8283.21) 11.55(9.93–13.34) -0.95(-2.12-0.24) Eastern Sub-Saharan Africa 122949.46(99359.96-151716.76) 1010.63(816.73-1247.09) 104556.46(87367.12-128033.81) 386.71(323.13-473.54) -3.37(-3.56–3.17) High-income Asia Pacific 13798.07(11773.40-15917.28) 39.46(33.67–45.52) 9201.46(7261.36-10754.72) 13.05(10.30-15.25) -3.40(-3.67–3.14) High-income North America 13411.39(11374.33-15404.32) 23.15(19.64–26.59) 7158.01(6005.22-8180.74) 6.36(5.34–7.27) -5.08(-5.47–4.68) North Africa and Middle East 31181.72(26144.75-38240.45) 110.32(92.50-135.30) 30130.50(24978.53-36189.83) 39.52(32.77–47.47) -3.18(-3.26–3.09) Oceania 1267.69(993.43-1632.92) 263.50(206.50-339.42) 1994.52(1558.11-2857.85) 161.61(126.25-231.56) -1.41(-1.52–1.29) South Asia 262381.75(215381.87-327690.69) 276.36(226.86-345.15) 344408.14(286185.28-417025.77) 138.71(115.26-167.96) -2.41(-2.50–2.32) Southeast Asia 68369.47(54581.57-86493.03) 161.47(128.91-204.28) 111410.20(92249.87-130047.83) 97.26(80.53-113.52) -1.53(-1.62–1.43) Southern Latin America 6414.65(5561.04-7212.28) 80.98(70.20-91.05) 11538.35(9882.98-13214.39) 78.41(67.16–89.80) 1.05(0.60–1.51) Southern Sub-Saharan Africa 13371.53(11050.45-16220.63) 302.20(249.74-366.59) 25036.28(21207.47-29670.16) 257.17(217.84-304.77) -0.12(-1.23-0.99) Tropical Latin America 18841.41(16321.61-21678.49) 124.44(107.80-143.17) 29889.60(25762.37-34220.24) 67.47(58.16–77.25) -1.05(-1.51–0.60) Western Europe 20074.81(17005.42-23177.76) 20.67(17.51–23.87) 12308.87(9948.91-14182.35) 8.25(6.67–9.51) -3.35(-3.71–2.99) Western Sub-Saharan Africa 86950.86(72501.46-106535.82) 602.34(502.24-738.01) 105545.90(86247.90-128063.78) 328.36(268.32-398.42) -1.96(-2.17–1.75) DALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval Table 2: DALYs cases and ASR (per 100,000 population) of PA-associated LRIs among adults ≥ 55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021. 1990 2021 1990–2021 DALYs cases (95% UI) ASR of DALYs (per 100,000 population) DALYs cases (95% UI) ASR of DALYs (per 100,000 population) EAPCs of DALYs ASR(95% CI) Global 558374.61(508216.59-603950.56) 83.16(75.69–89.95) 1406482.17(1266130.01-1517577.21) 94.65(85.20-102.13) 0.33(0.24–0.43) Sex Male 303344.71(278369.57-327282.12) 97.39(89.37-105.07) 779585.39(719045.46-831573.26) 111.45(102.79-118.88) 0.36(0.27–0.45) Female 255029.90(223471.92-284032.26) 70.85(62.09–78.91) 626896.78(527787.87-699686.05) 79.71(67.11–88.97) 0.29(0.18–0.40) Socio-demographic index Low SDI 59376.51(50552.74-70233.59) 159.15(135.50-188.25) 132326.88(113313.03-152673.43) 161.26(138.09-186.06) 0.03(-0.05-0.11) Low-middle SDI 91418.19(79374.34-105167.29) 90.69(78.74-104.33) 288652.01(253144.72-322731.01) 119.73(105.00-133.87) 1.07(1.00-1.14) Middle SDI 119447.21(105831.33-131987.89) 68.82(60.98–76.05) 413040.88(368317.21-452484.91) 87.91(78.39–96.30) 0.80(0.72–0.87) High-middle SDI 79909.68(73021.63-86539.49) 46.32(42.33–50.16) 241604.65(214590.67-263884.33) 69.69(61.90-76.12) 1.40(1.24–1.56) High SDI 207639.42(189161.11-222336.86) 111.36(101.45-119.24) 329344.28(284753.91-354531.87) 95.46(82.53-102.76) -0.84(-1.05,-0.63) Region Andean Latin America 5744.28(5065.40-6479.77) 171.17(150.94-193.09) 21592.08(17470.55-26342.37) 217.96(176.36-265.91) 1.16(1.01–1.30) Australasia 2152.15(1942.07-2339.08) 54.63(49.30-59.37) 4421.84(3651.60-4891.34) 50.05(41.33–55.37) -0.39(-0.83-0.05) Caribbean 4369.26(3921.33-4830.37) 101.38(90.99-112.08) 12900.56(11260.42-14496.18) 139.34(121.62-156.57) 0.66(0.41–0.92) Central Asia 2754.69(2547.38-2963.31) 34.44(31.85–37.05) 8432.44(7477.53-9430.58) 57.96(51.39–64.82) 1.78(1.56–1.99) Central Europe 15820.89(14805.28-16796.23) 59.66(55.83–63.33) 40566.74(37032.03-43322.87) 109.56(100.01–117.00) 1.80(1.39–2.21) Central Latin America 11175.92(10342.65-12066.81) 82.36(76.22–88.92) 42368.59(37578.45–47489.00) 99.07(87.87-111.04) 0.47(0.06–0.89) Central Sub-Saharan Africa 8365.62(6013.73-11399.93) 222.47(159.93-303.17) 22517.32(16353.96-30388.20) 249.54(181.24-336.77) 0.39(0.32–0.46) East Asia 84566.84(70143.18-96561.36) 56.77(47.09–64.83) 214864.72(177685.91-260641.85) 54.80(45.31–66.47) -0.63(-0.79–0.46) Eastern Europe 9802.57(9185.78-10411.25) 20.05(18.79–21.29) 28202.64(25180.55-30965.19) 45.43(40.56–49.88) 2.43(1.56–3.31) Eastern Sub-Saharan Africa 26905.47(22722.03-32094.12) 221.16(186.77-263.81) 55241.11(47434.40-63131.88) 204.31(175.44–233.50) -0.34(-0.41–0.28) High-income Asia Pacific 56186.46(51135.97-60283.82) 160.68(146.24–172.40) 102122.62(85203.41-112908.65) 144.85(120.85-160.15) -0.18(-0.42-0.07) High-income North America 63223.58(56702.24-68049.77) 109.14(97.88-117.47) 71688.23(63126.60-76585.54) 63.70(56.10-68.06) -2.33(-2.62–2.05) North Africa and Middle East 17179.21(14946.49-20152.46) 60.78(52.88–71.30) 61833.72(53102.58-69389.82) 81.11(69.66–91.02) 1.27(1.15–1.39) Oceania 499.20(402.70-625.26) 103.76(83.71-129.97) 1200.69(982.64-1571.82) 97.29(79.62-127.36) -0.30(-0.42–0.19) South Asia 73834.74(62183.09-87648.70) 77.77(65.50-92.32) 248086.32(212793.14-284416.65) 99.92(85.70-114.55) 0.91(0.79–1.03) Southeast Asia 32033.97(26814.06-39290.27) 75.66(63.33–92.79) 145848.14(122204.26-164198.45) 127.32(106.68-143.34) 2.10(1.94–2.26) Southern Latin America 8361.27(7720.54-8978.77) 105.55(97.46-113.35) 35186.59(31763.00-37845.18) 239.10(215.84-257.17) 3.44(3.12–3.76) Southern Sub-Saharan Africa 7032.87(5938.05-8112.67) 158.94(134.20-183.35) 23720.50(20948.79-26438.12) 243.65(215.18-271.57) 1.77(1.30–2.25) Tropical Latin America 14162.58(12967.51-15244.46) 93.54(85.64-100.68) 70481.81(62838.94-76271.41) 159.11(141.86-172.18) 2.47(2.10–2.84) Western Europe 89001.12(81083.26-95293.18) 91.65(83.49–98.13) 135807.40(114570.60-146917.88) 91.06(76.82–98.51) -0.41(-0.76–0.06) Western Sub-Saharan Africa 25201.92(21576.16-29620.12) 174.58(149.47-205.19) 59398.14(48813.02-70518.63) 184.79(151.86-219.39) 0.28(0.18–0.38) DALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval Table 3: DALYs cases and ASR (per 100,000 population) of KP-associated LRIs among adults ≥ 55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021. 1990 2021 1990–2021 DALYs cases (95% UI) ASR of DALYs (per 100,000 population) DALYs cases (95% UI) ASR of DALYs (per 100,000 population) EAPCs of DALYs ASR(95% CI) Global 1045623.48(955686.94-1141047.75) 155.73(142.34-169.94) 1865590.23(1699228.82-2032351.00) 125.55(114.35-136.77) -0.82(-0.89–0.74) Sex Male 570217.02(519474.25-618379.23) 183.06(166.77-198.53) 1017133.02(942042.83-1093453.21) 145.40(134.67-156.32) -0.88(-0.96–0.80) Female 475406.46(399907.68-544661.12) 132.08(111.10-151.32) 848457.21(714289.25-952477.73) 107.88(90.82-121.11) -0.75(-0.84–0.67) Socio-demographic index Low SDI 186414.89(160380.24-216012.67) 499.66(429.88–579.00) 297801.70(262485.12-343040.19) 362.92(319.88-418.05) -1.16(-1.25–1.07) Low-middle SDI 254796.69(222122.30-291950.51) 252.77(220.36-289.63) 535705.42(467745.82-598714.98) 222.21(194.02-248.34) -0.36(-0.41–0.31) Middle SDI 259421.64(233833.33-286852.35) 149.47(134.73-165.28) 549862.56(494618.67-602007.36) 117.03(105.27-128.13) -0.87(-0.95–0.80) High-middle SDI 129786.56(118061.40-141470.12) 75.23(68.43-82.00) 236507.30(211893.61-257477.01) 68.22(61.12–74.27) -0.44(-0.59–0.28) High SDI 214236.20(195554.12-225709.83) 114.89(104.88-121.05) 243960.44(210583.07-262082.22) 70.71(61.04–75.96) -1.93(-2.12–1.74) Region Andean Latin America 13749.97(12265.32-15372.78) 409.73(365.49-458.08) 28060.98(22850.74-33964.14) 283.26(230.67-342.85) -0.96(-1.10–0.81) Australasia 2040.11(1834.98-2191.65) 51.79(46.58–55.63) 2950.14(2449.38-3265.75) 33.39(27.73–36.97) -1.57(-2.03–1.11) Caribbean 7868.29(6929.00-8828.73) 182.57(160.78-204.86) 15949.54(14032.07-18293.51) 172.27(151.56-197.59) -0.52(-0.73–0.31) Central Asia 4875.37(4531.91-5219.99) 60.96(56.66–65.27) 10448.77(9317.01-11663.43) 71.81(64.04–80.16) 0.39(0.12–0.65) Central Europe 21865.88(20710.37-22901.36) 82.45(78.09–86.35) 36216.53(33263.85-38825.18) 97.81(89.83-104.85) 0.25(-0.18-0.69) Central Latin America 23089.83(21591.64-24514.48) 170.15(159.11-180.65) 52226.08(46517.16-58226.89) 122.12(108.77-136.15) -1.25(-1.73–0.77) Central Sub-Saharan Africa 26911.35(19263.48-36506.97) 715.67(512.29-970.85) 55534.16(39883.77-74391.42) 615.44(442.00-824.42) -0.54(-0.63–0.45) East Asia 171848.07(142041.63-197177.76) 115.37(95.36-132.38) 203071.64(168812.92-245233.22) 51.79(43.05–62.54) -3.38(-3.65–3.11) Eastern Europe 13627.59(13010.72-14241.68) 27.87(26.61–29.13) 28556.60(25754.87-31391.60) 46.00(41.49–50.57) 1.06(0.08–2.05) Eastern Sub-Saharan Africa 87339.88(74204.17-101696.46) 717.92(609.95-835.93) 124879.76(109512.54-143493.69) 461.87(405.04-530.72) -1.66(-1.76–1.55) High-income Asia Pacific 57484.36(52380.04-60552.39) 164.39(149.79-173.16) 73277.82(61198.32-80663.18) 103.94(86.80-114.41) -1.35(-1.59–1.10) High-income North America 61254.88(55099.89-64929.31) 105.74(95.12-112.09) 53922.07(47013.03-57946.08) 47.92(41.78–51.49) -3.14(-3.42–2.85) North Africa and Middle East 37854.30(33113.28-45341.07) 133.93(117.16-160.42) 76942.33(66815.33-87017.36) 100.93(87.65-114.15) -0.69(-0.78–0.61) Oceania 1268.39(1019.36-1622.06) 263.65(211.89-337.17) 2508.94(2050.51-3367.24) 203.29(166.15-272.84) -0.89(-0.96–0.82) South Asia 217946.62(183020.60-259482.80) 229.56(192.77-273.31) 488255.73(419163.53-565785.70) 196.64(168.82-227.87) -0.50(-0.58–0.43) Southeast Asia 75961.81(62871.58-92971.50) 179.40(148.49-219.58) 219453.87(183504.94-245108.83) 191.57(160.19-213.97) 0.47(0.33–0.62) Southern Latin America 13554.74(12674.69-14394.15) 171.11(160.00-181.71) 39134.46(35155.72-42095.52) 265.93(238.89-286.05) 2.19(1.92–2.47) Southern Sub-Saharan Africa 16143.11(13771.81-18774.73) 364.83(311.24-424.31) 42233.10(37317.70-47285.87) 433.81(383.32-485.72) 0.84(0.17–1.52) Tropical Latin America 28443.42(26578.60-30367.13) 187.85(175.54-200.56) 88017.05(78469.80-94815.26) 198.69(177.14-214.04) 0.83(0.46–1.20) Western Europe 88790.27(81086.67-93620.10) 91.43(83.50–96.40) 93328.99(78879.28-101222.47) 62.58(52.89–67.87) -1.66(-2.01–1.31) Western Sub-Saharan Africa 73705.25(63380.19-85273.58) 510.58(439.06-590.72) 130621.66(108121.98-152821.57) 406.38(336.38-475.44) -0.74(-0.87–0.60) DALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval KAP-associated LRIs in Adults ≥ 55 Years: SDI Regional Trends In terms of SDI, low-middle SDI regions showed the largest absolute increase in AB-associated LRIs DALYs (287,201.63→354,101.82), while high-SDI regions declined markedly (54,360.25→35,531.43). Regional variations were significant, with high-SDI regions exhibiting the steepest ASR decline (EAPC=-3.81, 95% CI -4.02 to -3.59) (Table 1 ). In low-middle SDI regions, AB-associated DALY ASR showed exponential age-dependency, peaking at 666.03 (493.23–815.96) per 100,000 in ≥ 95-year-olds and nadir at 63.89 (53.29–75.92) per 100,000 in 55–59-year-olds, with universal reductions across age groups (Fig. 2 A). For PA-associated LRIs in high-SDI regions, total DALYs increased from 207,639.42 in 1990 to 329,344.28 in 2021, but ASR decreased from (111.36[95%UI:101.45-119.24] to 95.46[95%UI:82.53-102.76] per 100,000).while in low-SDI regions, both DALY and ASR are on the rise, with only high-SDI regions showing significant ASR decline (EAPC=-0.84, 95%CI:-1.05 to -0.63) (Table 2). High-SDI age-time trends revealed dramatic ASR escalation, from 24.94 (23.51–26.40) per 100,000 in 55–59-year-olds to 1112.14 (840.52–1270.86) per 100,000 in ≥ 95-year-olds; ASR declined post-2000 except in ≥ 95-year-olds (e.g., 70–74-year-olds: 100.60→66.63) (Fig. 2 B). Meanwhile, KP-associated LRIs decreased most substantially in low-SDI regions (499.66[95%UI:429.88–579.00] →362.92[95%UI:319.88-418.05] per 100,000), High-SDI regions showed the steepest decline (EAPC=-1.93, -2.12 to -1.74) (Table 3). Low-middle SDI age trends had peak burden in ≥ 95y with fluctuations, such as 1447.21 in 2014 and 1174.08 in 2021, plus steady declines in 55–59y (116.38 [95% UI 101.80–132.46] to 91.32 [95% UI 78.66–103.92]/100k) (Fig. 2 C).Moreover, in terms of gender, the number and ASR of KAP-related LRIs DALYs among males are higher than those among females across all SDI regions. KAP-associated LRIs in Adults ≥ 55 Years: Geographic Regional Trends Substantial 2021 geographical disparities in LRIs DALY ASR (age ≥ 55y) revealed Central Sub-Saharan Africa with highest burdens(eg., KP (615.44[95%UI:442.00–824.42] per 100,000), while Australasia showed lowest ASR (eg., KP: 33.39[95%UI:27.73–36.97] per 100,000), alongside East Asia's significant improvement (EAPC=-3.38[95%CI:-3.65–-3.11]); South Asia and Southeast Asia exceeded global averages, with Southern Latin America demonstrating increasing trends (AB: EAPC = 1.05[95%CI:0.60–1.51]) (Table 1 ,3).However, PA burdens showed Andean Latin Am increase (171.17 [95%UI:150.94–193.09] to 217.96 [95%UI:176.36–265.91]) vs High-income N Am decline (109.14 [95%UI:97.88–117.47] to 63.70 [95%UI:56.10–68.06]), with Central Sub-Saharan Africa highest (249.54 [95%UI:181.24–336.77]); Southern Latin Am had steepest ASR increase (EAPC = 3.44 [95%CI:3.12–3.76]) and High-income N Am the largest decline (EAPC=-2.33 [95%CI:-2.62–-2.05]) (Table 2). Furthermore, regarding gender and age, males typically had a higher burden than females, and the burden of KAP-related LRIs tends to rise with increasing age across 21 regions, excluding those in Eastern Europe (Fig. 3 A, B, C). KAP-associated LRIs in Adults ≥ 55 Years: National Trends In 204 nations/territories, the cases and ASR of KAP-related LRIs DALYs was significantly difference among adults aged ≥ 55years. The greatest cases of DALY linked to KAP-associated LRIs is mainly concentrated in India and China in 1990 and 2021 (Fig. 4 A,C,E and supplement eFig.3A,C,E).For AB-related LRIs, Sub-Saharan Africa (Central African Republic: 1179.29[95%UI:836.39–1620.44]/100k) was greatest ASR, while Iceland (7.56[95%UI:5.92–9.03]) was lowest ASR. And China demonstrated remarkable AB-related LRIs improvement (1990:78.86[95%UI:62.93–93.92] →2019:11.93[95%UI :9.73–15.00]) (Fig. 4 B and supplement eFig.3B). Over the past thirty years, Finland achieving the steepest reduction (EAPC=-10.38%[95%CI:-11.54 to -9.21]) while Georgia deteriorated (EAPC = 4.86% [95%CI:3.23 to 6.52]) (see supplement eFig.4A). In 2021, the highest ASR of PA-related LRIs were observed in Malaysia, whereas North Macedonia reported the lowest ASR (Fig. 4 D). Argentina showed the sharpest PA increase (EAPC = 5.69[95%CI:5.10 to 6.28]) versus Finland's decline (EAPC=-7.37[95%CI:-8.60 to -6.12])(see supplement eFig.4B). However, the highest ASR of KP-related LRIs were observed in Guinea-Bissau (1990) and Zimbabwe (2021), whereas the lowest ASR was Lithuania (1990) and Finland (2021) (Fig. 4 F and supplement eFig.3F ) .Finland achieved the steepest KP decline (EAPC=-8.55[95%CI:-9.77 to -7.31]), whereas Georgia increased most (EAPC = 5.22[95%CI:3.58–6.88])( see supplement eFig.4C). We further analyzed the association between the EAPC of DALYs in 2021 across 204 countries/territories, disease burden indicators, and the SDI. In terms of BA-related LRIs, Low-SDI nations (e.g., Zimbabwe, SDI = 0.47) had high baseline DALYs (714.38) but positive EAPC (1.66). Middle-high SDI countries (0.7–0.9) exhibited wide EAPC variation (e.g., Germany SDI = 0.90, EAPC=-1.29 vs. Greece SDI = 0.79, EAPC = 1.72), and its nonlinear correlation. Extremely, The highest-SDI countries (e.g., Norway, SDI = 0.92) had the lowest EAPC (-6.04), while low-SDI nations (e.g., Central African Republic, SDI = 0.31) showed near-neutral EAPC (-0.96)(Fig. 5 A).And PA-related LRIs in High-middle SDI countries (e.g., Argentina [EAPC = 5.69, SDI = 0.72], Georgia [5.55, 0.73]) exhibited the steepest DALYs growth, Low-SDI nations (e.g., Mozambique [EAPC = 1.87, SDI = 0.33]) maintained high baseline DALYs (> 200/100k) but moderate EAPC, Negative EAPC in high-SDI regions (e.g., Finland [-7.37, SDI = 0.86]) highlights successful health system adaptations; And a parabolic relationship was observed: peak EAPC in SDI 0.7–0.75 (e.g., Lebanon [4.90, 0.74]), declining to negative values at SDI > 0.85 (e.g., Norway [-2.47, 0.92])(Fig. 5 B).Furthermore, some high - SDI countries (e.g., the United States: − 3.15) still had a significant KP-related LRIs burden (48.38 per 100,000).Medium - SDI countries (0.6–0.8), such as Georgia (SDI = 0.73) had the highest EAPC (5.22), Most high - SDI countries (> 0.8), like Germany (SDI = 0.90) and Japan (SDI = 0.87), had negative EAPC (Germany: − 0.10; Japan: − 1.33), with some exceptions (e.g., the United States). Low - SDI countries (< 0.5), such as Lesotho (SDI = 0.51) showed a positive correlation in EAPC (1.88) (Fig. 5 C). The all-scatter plots also supported these findings. Predictive analysis for KAP-Related LRIs in Adults ≥ 55 Years To enhance the credibility of the projections from the BAPC model, we employed the ARIMA model to cross-validate the trends in KAP-related LRI DALY ASR, resulting in comparable predictions from both models. The ARIMA model further indicated a consistent reduction in the ASR of DALY associated with AB-related LRIs: its estimated decrease from 1990 to 2021 was around 50.1%, closely mirroring the BAPC's figure of 50.05%. The model's projection for 2050 (29.81 per 100,000 population) displayed only a slight difference from BAPC’s estimate of 29.63 (with an error margin of less than 1%)(Fig. 6 A, D). In terms of PA-related LRI, the ARIMA analysis confirmed a trend of “long-term decline followed by stabilization”—the ASR trend from 1990 to 2021 was consistent with BAPC’s data, and the projections for 2021 to 2050 (ranging from 100.48 to 100.55 per 100,000 population) were almost indistinguishable from BAPC’s reported values of 100.56 (2021) and 100.52 (2050) (Fig. 6 B,E). For KP-related LRI, the ARIMA model supported BAPC’s results: a 25.4% decrease in ASR from 1990 to 2021 (in comparison to BAPC’s 25.5%) and an anticipated additional reduction of 30.8% by 2025 (versus BAPC’s 30.76%)(Fig. 6 C,F). Aligned trends between BAPC and ARIMA—particularly in the direction of change and reduction scales during critical phases—bolster the credibility of our forecasts for KAP-related LRIs DALY ASR, delivering more robust evidence to guide age-targeted antimicrobial stewardship and healthcare resource distribution in aging populations. Discussion We utilized the most recent GBD2021 database to uncover various epidemiological trend of KAP-related LRIs in people aged 55 and above. Globally, our research indicated that between 1990 and 2021, there has been a rise in the number of DALYs linked to global KAP-related LRIs. It is important to note that the DALYs for PA- and KP-related LRIs were considerably greater compared to those for AB-related LRIs. Conversely, there has been a marked decline in the global burden of LRIs caused by AB and KP (EAPC=-2.40 and − 0.82, respectively), while the PA burden has seen an increase (EAPC = 0.33).While PA may not be the most serious of the three globally, its natural resistance systems and the ability to form biofilms play significant roles in its endurance within the environment, thus promoting its spread among older adults[ 18 , 19 ]. Therefore, exploring age-related elements of KAP -related LRIs is crucial for creating approaches that seek to alleviate the disease burden in the elderly demographic. Our study demonstrates that globally, the ASR of DALYs attributed to LRIs associated with KAP pathogens increases progressively with age in individuals aged 55 years and older. For instance, the ASR of DALYs due to PA-related LRIs in individuals aged ≥ 95 years was significantly higher (26-fold increase) compared to that in the 55–59 years age group. This finding indicates that age is a critical determinant of disease burden associated with KAP pathogens, which is consistent with previous studies highlighting age-related elevations in KAP-associated morbidity and mortality[ 8 , 9 ].This issue warrants attention for several key reasons. First, the aging population is leading to an increase in individuals living with chronic non-communicable diseases, which can weaken the immune system and make people more susceptible to infections[ 7 ]. Second, although younger individuals typically exhibit strong responses to interventions like vaccines, the effectiveness of these vaccines diminishes with age due to weakened antibody responses[ 20 ]. Third, older individuals are at a greater risk of developing antimicrobial resistance (AMR)[ 21 ], However, several newly developed antibiotics have exhibited efficacy against carbapenem-resistant PA strains relying on metallo-β-lactamases (MBLs) or harboring AmpC enzymes/ESBLs, as well as KP strains producing KPC-type carbapenemases and AB strains producing OXA-48-type carbapenemases[ 22 , 23 ]. Fourth, the elderly are generally more prone to comorbidities; for example, older adults with diabetes face an increased likelihood of infections from gram-negative bacteria[ 24 ]. Finally, the rapid expansion in the elderly demographic is a notable concern, as forecasts suggest that by 2100, more than 25% of the world's population will be aged ≥ 65years[ 25 ]. Collectively, these factors not only heighten vulnerability to KAP infections but also may drive over-reliance on antimicrobial therapies. Therefore, it is essential to develop age-specific preventive strategies, including promoting science communication and education, encouraging timely medical consultation and adherence to standardized treatment protocols, and implementing regular microbiological screening—all aimed at reducing the disease burden. The disease burden associated with KAP varies across countries and regions with different economic conditions. The results of our study reveal that areas characterized by a low-middle SDI experience the highest disease burden for AB (EAPC = -2.18) and KP (EAPC = -0.36), whereas PA (EAPC = 1.4) is more commonly observed in regions with a middle-high SDI. Nonetheless, the disease burden for all three pathogenic bacteria is lowest in high SDI regions. Such as over the past three decades, the Lao People's Democratic Republic and the Argentine Republic have experienced the highest burden of AB and KP-associated LRIs, while Bermuda had the highest burden of PA-associated LRIs. Conversely, the lowest burdens of AB, PA, and KP-associated LRIs were observed in Grenada, the Kingdom of Thailand, and Finland, respectively. This pattern highlights that diagnostic delays, overprescription of empirical antibiotics, and insufficient infection control measures in resource-limited settings—alongside challenges of limited healthcare access and poverty in high-burden regions—contribute to the dissemination of MDR infections, underscoring the need for low- and middle-income countries to strengthen healthcare infrastructure and public health initiatives, with support from global collaboration(26,27). At present, KAP stands out as the predominant Gram-negative bacillus responsible for LRIs worldwide, and its characteristics of MDR significantly contribute to the health challenges faced by the elderly. Previous research indicates that several factors contribute to this situation. Firstly, antibiotic misuse, the 2024 WHO Bacterial Priority Pathogens List (BPPL) highlights that carbapenem-resistant Klebsiella pneumoniae (CRKP) continues to be a significant pathogen of concern[ 3 ]. This situation is especially evident in low-income nations, where insufficient expertise in diagnosing and treating illnesses worsens the inappropriate use of antibiotics, resulting in greater antibiotic resistance among KAP[ 28 ]. Conversely, high-income countries regularly follow the principles of 'the prudent use of antibiotics'[ 29 , 30 ].Then, Economy and environment. Old approaches to diagnosing diseases can result in postponed treatment. In contrast, innovative diagnostic technologies like Next-Generation Sequencing (NGS) allow for targeted therapies against infections, consequently decreasing dependence on broad-spectrum antibiotics and lessening the development of resistance[ 31 ]. And in low SDI regions, air pollution and unsafe water, sanitation, and handwashing are significant environmental factors exacerbating the burden of disease[ 32 ].Consequently, it is essential to create appropriate strategies based on the previously mentioned factors, including assistance to economically challenged nations and promoting governmental funding for underprivileged areas, to alleviate the disease burden linked to KAP-related LRIs. Our research indicates that the ASR of KAP-related LRIs is markedly greater males than females in older adults aged ≥ 55 years. This epidemiological pattern aligns with results observed in previous research[ 8 , 33 ]. Possible explanations for this discrepancy may involve variations in immune responses to infections and differences in behavioral practices. Factors such as behavioral influences (for instance, tobacco use, which can weaken immune responses and impair the function of respiratory cilia and excessive alcohol intake, which compromises the host's immunity and increases the likelihood of microbial inhalation), and genetic/hormonal factors that modulate immune function[ 34 – 37 ]. All these factors combined may contribute to the significant disease burden of KAP-related LRIs in elderly males. Finally, our dual predictive frameworks, namely the Bayesian Age-Period-Cohort (BAPC) model and the Autoregressive Integrated Moving Average (ARIMA) model, projected a notable decrease in the disease burden attributed to LRIs caused by AB and KP by the year 2050. Conversely, the burden linked to PA-associated LRIs is expected to remain constant, showing no substantial decline. This enduring high burden of PA-related LRIs suggests that existing interventions may not be adequate to diminish its spread, highlighting the necessity for focused strategies moving forward. Such strategies should include the creation of new antimicrobial agents, advanced diagnostic technologies, and customized infection control approaches for at-risk groups, such as residents in nursing homes. Additionally, it is crucial to adapt and implement evidence-based interventions from wealthier nations in lower-income countries to effectively decrease the overall disease burden of KAP-related LRIs. This study has several limitations. The firstly, this research mainly depends on the GBD 2021 database for backing our forecasts. Nevertheless, the absence of environmental monitoring information in specific areas, attributable to resource constraints, has led to untimely diagnosis and imprecise evaluations; the secondly, we assessed the pathogen burdens of KAP, however, detailed information regarding resistance mechanisms, such as the production of ESBL and carbapenems, was lacking. The thirdly, our examination has also considered the crucial factors that influence the occurrence of LRIs in the older aged ≥ 55 years, including nursing home density and air quality issues. Nonetheless, this study focuses on three distinct causes, while the GBD 2021 database does not provide detailed information regarding the risk of LRIs associated with each of these causes. In conclusion, Our analysis indicated that ASR of DALYs associated with AB and KP-related LRIs showed a decline, while an increase was observed for PA-related ASR, which is expected to stabilize post-2025. The disease burden was most pronounced in individuals aged 95 years and older, who experienced greater burden compared to the 55–59 years age group, with males disproportionately affected. Regions with low- middle SDI faced the greatest burden, whereas those with high SDI displayed the most significant reductions. Projections from dual-model analyses verified that burdens from AB and KP are likely to continue their downward trend by 2050, whereas the burden associated with PA is predicted to persist at elevated levels. Recommendations included the development of targeted antimicrobials and diagnostics for PA, adapting high-SDI antimicrobial stewardship to low-middle SDI regions. Abbreviations AB, Acinetobacter baumannii ; AMR, Antimicrobial resistance; AIC, Akaike Information Criterion; ARIMA ,Autoregressive Integrated Moving Average; BAPC, Bayesian Age-Period-Cohort; CI, Confidence interval; EAPC, Estimated annual percentage change; GBD, Global Burden of Disease; INLA, Integrated Nested Laplace Approximation; KP, Klebsiella pneumonia; LRIs, Lower respiratory infections; MDR, Multidrug-resistant; PA, Pseudomonas aeruginosa; SDI, Socio-demographic index; UI, Uncertainty intervals; Declarations AUTHOR CONTRIBUTIONS Jiao Zhao: Conceptualization, Methodology, Formal analysis, Investigation, Methodology, Project administration, Writing – Original Draft, Guiyun Li, Yuyang Qiu, Yunqin Wang, Guanglin Huang, Yihong Gong, Jiaoyangzi Liu: Data curation, Writing – Review & Editing Feng Shen: Supervision, Funding acquisition, Writing – Review & Editing, Methodology FUNDING INFORMATION This study was supported by The study is supported by National Natural Science Foundation of China(82360019)； DATA AVAILABILITY STATEMENT The Global Burden of Disease study 2021 is an open-access resource; data are available at https://vizhub.healthdata.org/gbd-results/ CONFLICT OF INTEREST STATEMENT The authors have no conflict of interest. ETHICS DECLARATION This study utilized publicly available data from the Global Burden of Disease (GBD) 2021 database. As no human subjects were directly involved, ethical approval was not required. ACKNOWLEDGMENTS Not applicable. References Aggarwal R, Mahajan P, Pandiya S, Bajaj A, Verma SK, Yadav P, et al. Antibiotic resistance: A global crisis, problems and solutions. Crit Rev Microbiol. 2024;50:896–921. https://doi.org/10.1080/1040841x.2024.2313024 . Akhtar A, Hassali MAA, Zainal H, Ali I, Iqbal MS, Khan AH. Respiratory-tract infections among geriatrics: Prevalence and factors associated with the treatment outcomes. Ther Adv Respir Dis. 2021;15:1753466620971141. https://doi.org/10.1177/1753466620971141 . Sati H, Carrara E, Savoldi A, Hansen P, Garlasco J, Campagnaro E, et al. The WHO Bacterial Priority Pathogens List 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. Lancet Infect Dis. 2025;S1473309925001185. https://doi.org/10.1016/S1473-3099(25)00118-5 . Rice LB. Federal Funding for the Study of Antimicrobial Resistance in Nosocomial Pathogens: No ESKAPE. J INFECT DIS. 2008;197:1079–81. https://doi.org/10.1086/533452 . Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–81. https://doi.org/10.1111/j.1469-0691.2011.03570.x . Bhatta DR, Hamal D, Shrestha R, Hs S, Joshi P, Nayak N, et al. Burden of multidrug resistant respiratory pathogens in intensive care units of tertiary care hospital. Asian J Med Sci. 2019;10:14–9. https://doi.org/10.3126/ajms.v10i2.21098 . Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. Lancet. 2024;404:1199–226. https://doi.org/10.1016/S0140-6736(24)01867-1 . Li J, Xu L, Zuo AF, Xu P, Xu K. The global burden of klebsiella pneumoniae-associated lower respiratory infection in 204 countries and territories, 1990–2021: Findings from the global burden of disease study 2021. PLoS ONE. 2025;20:e0324151. https://doi.org/10.1371/journal.pone.0324151 . Bender RG, Sirota SB, Swetschinski LR, Dominguez R-MV, Novotney A, Wool EE, et al. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990–2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis. 2024;24:974–1002. https://doi.org/10.1016/s1473-3099(24)00176-2 . Rodríguez-Baño J, García L, Ramírez E, Martínez-Martínez L, Muniain MA, Fernández-Cuenca F, et al. Long-term control of hospital-wide, endemic multidrug-resistant Acinetobacter baumannii through a comprehensive bundle approach. Am J Infect Control. 2009;37:715–22. https://doi.org/10.1016/j.ajic.2009.01.008 . Childs A, Zullo AR, Joyce NR, McConeghy KW, Van Aalst R, Moyo P, et al. The burden of respiratory infections among older adults in long-term care: a systematic review. BMC Geriatr. 2019;19:210. https://doi.org/10.1186/s12877-019-1236-6 . World Health Organization. ICD-10: International statistical classification of diseases and related health problems : tenth revision. 2nd ed. Geneva: World Health Organization; 2004. GBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: A comprehensive demographic analysis for the global burden of disease study 2019. Lancet. 2020;396:1160–203. https://doi.org/10.1016/S0140-6736(20)30977-6 . Wu M, Wu Q, Liu D, Zu W, Zhang D, Chen L. The global burden of lower respiratory infections attributable to respiratory syncytial virus in 204 countries and territories, 1990–2019: Findings from the global burden of disease study 2019. Intern Emerg Med. 2024;19:59–70. https://doi.org/10.1007/s11739-023-03438-x . Riebler A, Held L. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested laplace approximations. Biom J. 2017;59:531–49. https://doi.org/10.1002/bimj.201500263 . Luo L. Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem. Demography. 2013;50:1945–67. https://doi.org/10.1007/s13524-013-0243-z . Global burden and strength of evidence for 88. risk factors in 204 countries and 811 subnational locations, 1990–2021: A systematic analysis for the global burden of disease study 2021 - the lancet. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00933-4/fulltext . Accessed 3 Sept 2024. Del Barrio-Tofiño E, López-Causapé C, Oliver A. Pseudomonas aeruginosa epidemic high-risk clones and their association with horizontally-acquired β-lactamases: 2020 update. Int J Antimicrob Agents. 2020;56:106196. https://doi.org/10.1016/j.ijantimicag.2020.106196 . Pachori P, Gothalwal R, Gandhi P. Emergence of antibiotic resistance pseudomonas aeruginosa in intensive care unit; a critical review. Genes Dis. 2019;6:109–19. https://doi.org/10.1016/j.gendis.2019.04.001 . van Werkhoven CH, Huijts SM, Bolkenbaas M, Grobbee DE, Bonten MJM. The Impact of Age on the Efficacy of 13-valent Pneumococcal Conjugate Vaccine in Elderly. Clin Infect Dis. 2015;61:1835–8. https://doi.org/10.1093/cid/civ686 . Di Lodovico S, Fasciana T, Di Giulio M, Cellini L, Giammanco A, Rossolini GM, et al. Spread of multidrug-resistant microorganisms. Antibiot (Basel). 2022;11:832. https://doi.org/10.3390/antibiotics11070832 . Schmid H, Brown L-AK, Indrakumar B, McGarrity O, Hatcher J, Bamford A. Use of Cefiderocol in the Management of Children With Infection or Colonization With Multi-drug Resistant Gram-negative Bacteria: A Retrospective, Single-center Case Series. Pediatr Infect Dis J. 2024;43:772–6. https://doi.org/10.1097/inf.0000000000004347 . Öztaş S, Er DK, Dündar D, Keçeli SA. Efficacy of ceftazidime/avibactam and plazomicin on carbapenem-resistant Klebsiella pneumoniae and Escherichia coli. Acta Microbiol Immunol Hung. 2024;71:110–20. https://doi.org/10.1556/030.2024.02292 . Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203–34. https://doi.org/10.1016/s0140-6736(23)01301-6 . Vollset SE, Goren E, Yuan C-W, Cao J, Smith AE, Hsiao T, et al. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet. 2020;396:1285–306. https://doi.org/10.1016/s0140-6736(20)30677-2 . van Duin D, Paterson DL. Multidrug-resistant bacteria in the community: An update. Infect Dis Clin North Am. 2020;34:709–22. https://doi.org/10.1016/j.idc.2020.08.002 . Ashrafi-Asgarabad A, Bokaie S, Razmyar J, Akbarein H, Nejadghaderi SA, Carson-Chahhoud K, et al. The burden of lower respiratory infections and their underlying etiologies in the Middle East and North Africa region, 1990–2019: results from the Global Burden of Disease Study 2019. BMC Pulm Med. 2023;23:2. https://doi.org/10.1186/s12890-022-02301-7 . Williams PCM, Jones M, Snelling TL, Duguid R, Moore N, Dickson B, et al. Coverage gaps in empiric antibiotic regimens used to treat serious bacterial infections in neonates and children in Southeast Asia and the Pacific. Lancet Reg health Southeast Asia. 2024;22:100291. https://doi.org/10.1016/j.lansea.2023.100291 . Rohde GGU. Prudent use of antibiotics: Acute exacerbation of COPD as an example. Eur Respir J. 2010;36:983–5. https://doi.org/10.1183/09031936.00101610 . Ashiru-Oredope D, Hopkins S, Vasandani S, Umoh E, Oloyede O, Nilsson A et al. Healthcare workers’ knowledge, attitudes and behaviours with respect to antibiotics, antibiotic use and antibiotic resistance across 30 EU/EEA countries in 2019. Eurosurveillance. 2021;26. https://doi.org/10.2807/1560-7917.es.2021.26.12.1900633 Wang J, Yuan D, Yang X, Sun C, Hou L, Zhang Y, et al. Etiology of lower respiratory tract in pneumonia based on metagenomic next-generation sequencing: a retrospective study. Front Cell Infect Microbiol. 2024;13. https://doi.org/10.3389/fcimb.2023.1291980 . Kang L, Jing W, Liu J, Liu M. Trends of global and regional aetiologies, risk factors and mortality of lower respiratory infections from 1990 to 2019: An analysis for the Global Burden of Disease Study 2019. Respirology. 2022;28:166–75. https://doi.org/10.1111/resp.14389 . Sharma R, Lakhanpal D. Acinetobacter baumannii: A comprehensive review of global epidemiology, clinical implications, host interactions, mechanisms of antimicrobial resistance and mitigation strategies. Microb Pathog. 2025;204:107605. https://doi.org/10.1016/j.micpath.2025.107605 . Safiri S, Mahmoodpoor A, Kolahi A-A, Nejadghaderi SA, Sullman MJM, Mansournia MA, et al. Global burden of lower respiratory infections during the last three decades. Front Public Health. 2023;10. https://doi.org/10.3389/fpubh.2022.1028525 . Ito R, Shindo Y, Kobayashi D, Ando M, Jin W, Wachino J, et al. Molecular epidemiological characteristics of klebsiella pneumoniae associated with bacteremia among patients with pneumonia. J Clin Microbiol. 2015;53:879–86. https://doi.org/10.1128/jcm.03067-14 . Kyu HH, Vongpradith A, Sirota SB, Novotney A, Troeger CE, Doxey MC, et al. Age–sex differences in the global burden of lower respiratory infections and risk factors, 1990–2019: results from the Global Burden of Disease Study 2019. Lancet Infect Dis. 2022;22:1626–47. https://doi.org/10.1016/s1473-3099(22)00510-2 . Mehta AJ, Guidot DM, Weber KT. Alcohol Abuse, the Alveolar Macrophage and Pneumonia. Am J Med Sci. 2012;343:244–7. https://doi.org/10.1097/maj.0b013e31823ede77 . Additional Declarations No competing interests reported. Supplementary Files Supplementmaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:32:45","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90199,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/3ccc3804da439dc4f460aec5.png"},{"id":98451482,"identity":"7c268f69-de43-4320-ae02-bb9717bea085","added_by":"auto","created_at":"2025-12-17 17:32:55","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46044,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/3657726e2165e8ef626f70c3.png"},{"id":98451257,"identity":"62f462bf-05ab-4743-b9f1-85222556da31","added_by":"auto","created_at":"2025-12-17 17:32:29","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179749,"visible":true,"origin":"","legend":"","description":"","filename":"447d998d2a6f4089ab0e5bc5c4a829fc1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/6dbddeda8e0b6eceb13764e7.xml"},{"id":98451346,"identity":"de58e30f-828d-4726-a4b4-aa2ee86dbe85","added_by":"auto","created_at":"2025-12-17 17:32:42","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195718,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/e4094963572166fe72fa84fa.html"},{"id":98451336,"identity":"1e896ef1-98e7-49cc-8340-4dbc7f5f90a0","added_by":"auto","created_at":"2025-12-17 17:32:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe age-gender trend for KAP-related LRIs in Adults ≥55 Years in 2021. A\u003c/strong\u003e, AB-related LRIs. \u003cstrong\u003eB\u003c/strong\u003e, PA-related LRIs.\u003cstrong\u003eC\u003c/strong\u003e, KP-related LRIs.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/9b27cbc60484a97f5cfc8b21.jpg"},{"id":98451340,"identity":"ee77489d-d3be-44bf-b063-bb726a00f727","added_by":"auto","created_at":"2025-12-17 17:32:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Age-time trend for AB-related LRIs in Adults ≥55 Years. A\u003c/strong\u003e, AB-related LRIs. \u003cstrong\u003eB\u003c/strong\u003e, PA-related LRIs.\u003cstrong\u003eC\u003c/strong\u003e, KP-related LRIs.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/3b5b2b4a778d69d43cd8ca56.jpg"},{"id":98451475,"identity":"a40dc126-93dc-4729-b89d-a96390a7243f","added_by":"auto","created_at":"2025-12-17 17:32:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe district-age analysis for KAP-related LRIs in Adults ≥55 Years in 2021. A\u003c/strong\u003e, AB-related LRIs. \u003cstrong\u003eB\u003c/strong\u003e, PA-related LRIs.\u003cstrong\u003e C\u003c/strong\u003e, KP-related LRIs.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/a2cb3523d0f211bd4fdb8e9d.jpg"},{"id":98451328,"identity":"9133348c-7308-4056-9f69-919546458302","added_by":"auto","created_at":"2025-12-17 17:32:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":242607,"visible":true,"origin":"","legend":"\u003cp\u003eThe cases and ASR for Disability-adjusted life-years(DALYs) of KAP-related LRIs in people aged 55 years and older among 204 countries and territories in 2021 (per 100,000 population). \u003cstrong\u003eA, B. \u003c/strong\u003eThe cases and ASR for DALYs of AB-related LRIs in 2021; \u003cstrong\u003eC,D.\u003c/strong\u003e The cases and ASR for DALYs of PA-related LRIs in 2021;\u003cstrong\u003e E,F.\u003c/strong\u003e The cases and ASR for DALYs of KP-related LRIs in 2021.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/cfead365dabda18de19183c6.jpg"},{"id":98451449,"identity":"0ffe47f2-a680-4356-98bb-2ae6d90e11b9","added_by":"auto","created_at":"2025-12-17 17:32:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":69343,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Estimated Annual Percentage Change (EAPC) of KAP -associated LRIs and Disability-adjusted life-years (DALYs) ASR, and Socio-demographic Index (SDI) in 2021.A. BA-related LRIs B. PA-related LRIs C. KP-related LRIs\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/3dbaa4a6c2c2f3e36aedea8c.jpg"},{"id":98944621,"identity":"6b4e5f78-9c09-4f5e-a7c3-be87f2ab7ee1","added_by":"auto","created_at":"2025-12-24 11:55:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2790115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/021c294e-d95b-4019-b367-c6cdae7bc5f0.pdf"},{"id":98451630,"identity":"b96afd97-7ecb-4e6d-bba6-3717c67f91dd","added_by":"auto","created_at":"2025-12-17 17:33:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2093801,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7950784/v1/c68657febe82e9713a075d10.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Epidemiology of Multidrug-Resistant Lower Respiratory Infections in Adults ≥55 years: Trends (1990–2021) and Projections to 2050","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing aging of the global population has heightened the public health issue posed by multidrug-resistant(MDR) lower respiratory tract infections (LRIs), particularly among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years, which continue to be a major health concern[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2019, MDR bacteria led to an estimated 127\u0026nbsp;million fatalities globally, with a particularly severe impact on individuals aged 55 years and older[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are currently the most common MDR pathogens causing LRIs worldwide[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, three MDR Gram-negative bacteria\u0026mdash;Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae (KAP)\u0026mdash;are primary LRIs etiologies responsible for LRIs in adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years, and they are associated with a very high mortality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A study addressing the global impact of MDR diseases reports that In 2021, at least 100,000 fatalities were attributable to these three MDR pathogens[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Against this backdrop, assessing the disease burden of KAP-related LRTIs in adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years and developing targeted monitoring and intervention strategies are urgently needed.\u003c/p\u003e \u003cp\u003eKlebsiella pneumoniae (KP) represents the pathogen that poses the greatest disease burden among the three, making it a significant issue for the World Health Organization. A 2021 Global Burden of Disease(GBD) study reported that KP-related LRIs caused approximately 175,783.21(95%UI,158,749.05\u0026ndash;193,923.83) deaths and 6,935,439.84(95%UI, 5,953,327.57\u0026ndash;8,007,785.63) disability-adjusted life years (DALYs) worldwide, with adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years accounting for 64% of these deaths and 26% of DALYs\u0026mdash;consistent with its disproportionate impact on this age group[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. And per the 2021 GBD report, Pseudomonas aeruginosa(PA) disease burden has ranked second only to that of KP over the past three decades; in recent years, this pathogen has resulted in more than 124,000 global annual deaths, of which adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years accounted for 72.3%[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].At present, Acinetobacter baumannii (AB) causes more than 750,000 LRI-related fatalities globally in 2021, with adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years constituting 69% of these deaths; notably, MDR strains of AB representing 20% of LRIs occurring in intensive care units (ICUs)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For people aged over 60 years old and older, the impact of illness is expected to become more severe, with predictions indicating that by 2050, this impact will have increased twofold compared to 2012[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Nevertheless, the widespread use of antibiotics around the world, coupled with the diversity of antibiotic-resistant bacterial genes, complicates the treatment and management of LRIs among older adults.\u003c/p\u003e \u003cp\u003eDespite this substantial disease burden of KAP-related LRIs in aging populations, comprehensive global epidemiological studies targeting these pathogens specifically in adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years remain lacking. This study, to the best of our understanding, represents the inaugural investigation that systematically examines the long-term trends (1990\u0026ndash;2021) and future forecasts (up to 2050) of KAP-related LRI burden specifically among adults aged 55 years and older across 204 countries and territories, categorized by Sociodemographic Index (SDI) and GBD region. Our results will assist in pinpointing regions or countries with a high burden among adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years, guide targeted interventions (e.g., age-specific antibiotic stewardship and KAP vaccines), and optimize the allocation of healthcare resources in aging populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData Acquisition and Sources\u003c/h2\u003e\n \u003cp\u003eData for this research were sourced from the 2021 Global Burden of Disease (GBD) dataset, offering extensive details about the worldwide and regional impact of 371 diseases, injuries, and 88 risk factors across 204 nations and territories spanning from1990 to 2021(13,14). In this investigation, we collected DALY data for KAP-related LRIs (ICD-10: J15.0, J15.1 and J15.16) among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years using ICD codes[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], along with their respective the age-standardized rate (ASR) per 100,000 at global, regional, and national levels. We calculated the average estimated annual percentage changes (EAPCs) using linear regression analysis. The datas were retrieved and downloaded from the Global Health Data Exchange (GHDx) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool).Additionall\u003c/span\u003e\u003c/span\u003ey, Sociodemographic Index (SDI) data were collected to evaluate how socioeconomic variables influence disease burden.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDefinition\u003c/h3\u003e\n\u003cp\u003eSociodemographic Index (SDI) data were collected to evaluate the influence of socioeconomic variables on disease burden; the SDI, which serves as a measure of a nation\u0026rsquo;s socioeconomic status, is derived from the geometric mean of multiple indicators and ranges in value from 0 to 1[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. These quintiles include low SDI (\u0026lt;\u0026thinsp;0.45), low-middle SDI (\u0026ge;\u0026thinsp;0.45 and \u0026lt;\u0026thinsp;0.60), middle SDI (\u0026ge;\u0026thinsp;0.60 and \u0026lt;\u0026thinsp;0.69), high-middle SDI (\u0026ge;\u0026thinsp;0.69 and \u0026lt;\u0026thinsp;0.80), and high SDI(\u0026ge;\u0026thinsp;0.80)[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBayesian Age-Period-Cohort Model\u003c/h3\u003e\n\u003cp\u003eTo project future disease burden, we implemented a Bayesian Age-Period-Cohort (BAPC) model[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], decomposing temporal trends into three dimensions: age (biological risk progression), period (time-specific external factors), and cohort (birth year-linked exposures). The number of cases is modeled through the use of the age-period-cohort (APC) framework, with age group A and period P:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003el\u003c/em\u003e \u003cstrong\u003eog(Y\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003eap\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e \u003cstrong\u003e)\u003c/strong\u003e \u003cstrong\u003e=\u003c/strong\u003e \u003cstrong\u003e\u0026micro;\u0026thinsp;+\u0026thinsp;\u0026alpha;\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003ea\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026gamma;\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eIn this model, \u0026micro; denotes the intercept term, while \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003e\u0026gamma;\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e represent the age, period, and cohort effects in the log scale, respectively. Bayesian estimation is performed using the Integrated Nested Laplace Approximation (INLA), which assumes that the second-order differences of age, period, and cohort effects follow independent zero-mean normal distributions, thereby ensuring parameter smoothness. The effect of period \u003cem\u003et\u003c/em\u003e, \u003cem\u003e\u0026beta;p\u0026thinsp;+\u0026thinsp;t\u0026beta;p\u0026thinsp;+\u0026thinsp;t\u003c/em\u003e, is derived based on historical trends:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026beta;p\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003e∣\u003c/strong\u003e\u003cstrong\u003e\u0026beta;p\u003c/strong\u003e,\u003cstrong\u003e\u0026beta;p\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e\u0026minus;\u0026thinsp;1\u0026sim;N((1\u0026thinsp;+\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003cstrong\u003e\u0026beta;p\u003c/strong\u003e\u003cstrong\u003e\u0026minus;\u003c/strong\u003e\u003cstrong\u003et\u0026beta;p\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e\u0026minus;\u0026thinsp;1\u003c/strong\u003e,\u003cstrong\u003ek\u0026beta;\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e\u0026minus;\u0026thinsp;1\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;1\u0026sum;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003e2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsequently, the introduction of a random effect terms, \u0026delta;ap\u0026thinsp;+\u0026thinsp;t\u0026sim;N(0,k\u0026delta;\u0026thinsp;\u0026minus;\u0026thinsp;1)\u003cem\u003e\u0026delta;ap\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u0026sim;N(0,\u003cem\u003ek\u0026delta;\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1) was implemented to calibrate the model residuals.The BAPC demonstrated superior performance over non-Bayesian alternatives, evidenced by a 15.7% reduction in Deviance Information Criterion (DIC). Sensitivity analyses confirmed stability across prior distribution assumptions (coefficient variation\u0026thinsp;\u0026lt;\u0026thinsp;5%). All analyses were conducted using the \u0026quot;BAPC\u0026quot; R package (v1.0.2).\u003c/p\u003e\n\u003ch3\u003eAutoregressive Integrated Moving Average Model\u003c/h3\u003e\n\u003cp\u003eOur findings further validated the accuracy of the BAPC prediction model using the Autoregressive Integrated Moving Average Model (ARIMA). The age-period-cohort model investigates how age, period, and birth cohort influence health outcomes. The age effect pertains to the likelihood of experiencing different outcomes at various ages. A period effect indicates how temporal changes impact outcomes across all age groups. Meanwhile, the cohort effect refers to shifts in outcomes among individuals born in the same time frame. The log-linear regression model can be represented by the equation:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elog(Yi) = \u0026micro;\u0026thinsp;+\u0026thinsp;\u0026alpha; * age\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003ei\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026beta; * period\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026gamma; * cohort\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;\u003cstrong\u003e+\u0026thinsp;\u0026epsilon;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewhere Yi denotes the DALYs ASR of KAP-related LRIs, with \u0026alpha;, \u0026beta;, and \u0026gamma; representing the coefficients associated with age, period, and cohort, respectively. \u0026micro; serves as the intercept, and \u0026epsilon; signifies the model\u0026rsquo;s residuals. To derive the net effects across these three dimensions, the intrinsic estimator (IE) method, incorporated within the age-period-cohort model, was employed[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll data presented in the figures and tables throughout this manuscript were sourced from the GBD 2021 database, ensuring authenticity and reliability. Following extraction, the data were re-analyzed and visualized through bar charts, line graphs, and map utilized software package (version 4.2.3) and JD_GBDR (V2.24, Jingding Medical Technology Co., Ltd.) categorized by age group, sex, and geographic region to characterize the epidemiological trend of KAP-related LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years. These maps utilized the `rnaturalearthdata` packages to display the distribution of the disease burden. All estimated values for the age-standardized rate, number of cases, and fluctuations in case numbers are presented with a 95% uncertainty interval (UI), which is defined as the range between the 2.5th and 97.5th percentiles among all 1000 simulations.\u003c/p\u003e\n \u003cp\u003eThe ASR can exclude the effects of imbalances in population size and age distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ASR=\\frac{\\sum\\:_{i=1}^{A}\\:{a}_{i}{w}_{i}}{\\sum\\:_{i=1}^{A}\\:{a}_{i}}\\times\\:100000\\)\u003c/span\u003e\u003c/span\u003e. In addition, the spatial and temporal trends in the disease burden of KAP-related LRIs were captured by using the EAPC corresponding to the DALY ASR per 100,000. Y\u0026thinsp;=\u0026thinsp;\u0026alpha;\u0026thinsp;+\u0026thinsp;\u003cem\u003e\u0026beta;\u003c/em\u003e X where Y is the lg (ASR) and X is the calendar year. The EAPC value was then calculated by the formula EAPC\u0026thinsp;=\u0026thinsp;100 * (exp(\u0026beta;)-1). the EAPC is presented along with a 95% confidence interval (CI) to illustrate the magnitude and direction of temporal trends. its 95% CI are greater than zero, the corresponding age-specification rate tends to increase, and vice versa[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Gaussian curves were used to analyze associations between EAPC and rates and the Human Development Index of KAP-related LRIs in adults \u0026ge;\u0026thinsp;55 years. To ensure the accuracy and robustness of the predictions, we utilize data on KAP-related LRIs in adults \u0026ge;\u0026thinsp;55 years from 1990 to 2021.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"Results","content":"\u003ch2\u003eKAP-associated LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years: Global Trends\u003c/h2\u003e\n\u003cp\u003eGlobally, AB-associated LRIs DALYs increased from 863,576.06 (95%UI:729,944.09\u0026ndash;1,027,773.17) in 1990 to 951,777.08 (95%UI:817,985.69\u0026ndash;1,102,615.61) in 2021, while its ASR halved from 128.62 (95%UI:108.72\u0026ndash;153.07) to 64.05 (95%UI:55.05\u0026ndash;74.20) per 100,000 population and declining trend (EAPC=-2.40,95%CI:-2.48\u0026ndash;2.32) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, PA-associated LRIs DALYs surged from 558,374.61 (508,216.59\u0026ndash;603,950.56) to 1,406,482.17 (1,266,130.01\u0026ndash;1,517,577.21), with ASR rising from 83.16 (75.69\u0026ndash;89.95) to 94.65 (85.20\u0026ndash;102.13) per 100,000 population (EAPC\u0026thinsp;=\u0026thinsp;0.33, 95%CI:0.24\u0026ndash;0.43) (Table 2). And KP caused 1,865,590.23 DALYs (95%UI:1,699,228.82\u0026ndash;2,032,351.00) in 2021, with ASR at 125.55 (95%UI:114.35\u0026ndash;136.77) per 100,000 population and declining trend (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.82, 95%CI: \u0026minus;0.89 to \u0026minus;\u0026thinsp;0.74) (Table 3). In relation to sex and age, males aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years exhibited elevated ASR and cases of DALYs in 2021(eg., AB males 72.97 vs females 56.11; PA burden; KP males 145.40 vs females 107.88 per 100,000),and peak burden at \u0026ge;\u0026thinsp;95 years (eg., KP males 1064.85[95%UI:826.20\u0026ndash;1192.52], females 775.37[95%UI:560.60\u0026ndash;899.95] per 100,000) versus lowest in 55-59y (eg., KP males 62.45, females 39.45 per 100,000), positively correlated with age.(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA,B,C).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDALYs cases and ASR (per 100,000 population) of AB-associated LRIs among adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100-000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100-000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPCs of DALYs ASR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e863576.06(729944.09-1027773.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.62(108.72-153.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951777.08(817985.69-1102615.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.05(55.05\u0026ndash;74.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.40(-2.48\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476097.56(411017.03-563181.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.85(131.95-180.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510454.59(450684.22-587851.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.97(64.43\u0026ndash;84.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.56(-2.65\u0026ndash;2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e387478.50(309290.29-476644.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.65(85.93-132.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441322.49(355901.11-531364.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.11(45.25\u0026ndash;67.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.22(-2.29\u0026ndash;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248785.61(201122.98-308139.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666.84(539.09-825.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252715.56(208096.11-306286.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307.97(253.60-373.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.68(-2.83\u0026ndash;2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287201.63(239212.78-350299.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284.92(237.31-347.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354101.82(301381.64-417599.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.88(125.01-173.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.18(-2.27\u0026ndash;2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201945.69(171771.23-237451.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.35(98.97-136.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e244315.98(210218.73-281854.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.00(44.74\u0026ndash;59.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.68(-2.73\u0026ndash;2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70651.38(60748.63-81057.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.95(35.21\u0026ndash;46.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64339.89(55580.08-73840.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.56(16.03\u0026ndash;21.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.77(-2.92\u0026ndash;2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54360.25(46813.37-61795.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.15(25.11\u0026ndash;33.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35531.43(29300.86-40699.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.30(8.49\u0026ndash;11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.81(-4.02\u0026ndash;3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11906.25(9972.81-14145.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354.79(297.17-421.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10066.89(8031.74-12391.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.62(81.08-125.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.65(-3.89\u0026ndash;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427.56(361.26-497.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.85(9.17\u0026ndash;12.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317.54(250.47-374.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59(2.84\u0026ndash;4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.76(-4.15\u0026ndash;3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5654.18(4429.12-7266.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.20(102.77-168.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7955.36(6339.16-9902.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.93(68.47-106.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.43(-1.59\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2837.06(2498.57-3201.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.47(31.24\u0026ndash;40.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3872.16(3371.63-4465.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.61(23.17\u0026ndash;30.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.33(-1.75\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8464.60(7474.84-9536.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.92(28.19\u0026ndash;35.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8677.37(7584.55-9872.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.43(20.48\u0026ndash;26.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.29(-1.81\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16476.82(14421.81-18909.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.42(106.28-139.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17862.78(15326.20-20953.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.77(35.84-49.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.41(-3.88\u0026ndash;2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37236.75(26393.64-51421.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e990.26(701.90-1367.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52713.80(36375.44-73487.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e584.18(403.12\u0026ndash;814.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.76(-1.99\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116051.21(92871.94-138371.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.91(62.35\u0026ndash;92.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49962.32(40953.63-62605.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.74(10.44\u0026ndash;15.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.90(-7.35\u0026ndash;6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5508.84(4893.74-6194.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.27(10.01\u0026ndash;12.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7170.55(6162.65-8283.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55(9.93\u0026ndash;13.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95(-2.12-0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122949.46(99359.96-151716.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1010.63(816.73-1247.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104556.46(87367.12-128033.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386.71(323.13-473.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.37(-3.56\u0026ndash;3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13798.07(11773.40-15917.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.46(33.67\u0026ndash;45.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9201.46(7261.36-10754.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.05(10.30-15.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.40(-3.67\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13411.39(11374.33-15404.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.15(19.64\u0026ndash;26.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7158.01(6005.22-8180.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.36(5.34\u0026ndash;7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.08(-5.47\u0026ndash;4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31181.72(26144.75-38240.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.32(92.50-135.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30130.50(24978.53-36189.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.52(32.77\u0026ndash;47.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.18(-3.26\u0026ndash;3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1267.69(993.43-1632.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.50(206.50-339.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1994.52(1558.11-2857.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.61(126.25-231.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.41(-1.52\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262381.75(215381.87-327690.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276.36(226.86-345.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344408.14(286185.28-417025.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.71(115.26-167.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.41(-2.50\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68369.47(54581.57-86493.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.47(128.91-204.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111410.20(92249.87-130047.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.26(80.53-113.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.53(-1.62\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6414.65(5561.04-7212.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.98(70.20-91.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11538.35(9882.98-13214.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.41(67.16\u0026ndash;89.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05(0.60\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13371.53(11050.45-16220.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302.20(249.74-366.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25036.28(21207.47-29670.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257.17(217.84-304.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12(-1.23-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18841.41(16321.61-21678.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.44(107.80-143.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29889.60(25762.37-34220.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.47(58.16\u0026ndash;77.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05(-1.51\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20074.81(17005.42-23177.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.67(17.51\u0026ndash;23.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12308.87(9948.91-14182.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.25(6.67\u0026ndash;9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.35(-3.71\u0026ndash;2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86950.86(72501.46-106535.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e602.34(502.24-738.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105545.90(86247.90-128063.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328.36(268.32-398.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.96(-2.17\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eTable\u0026nbsp;2: DALYs cases and ASR (per 100,000 population) of PA-associated LRIs among adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100,000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100,000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEAPCs of DALYs ASR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558374.61(508216.59-603950.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.16(75.69\u0026ndash;89.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1406482.17(1266130.01-1517577.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.65(85.20-102.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.33(0.24\u0026ndash;0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303344.71(278369.57-327282.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.39(89.37-105.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779585.39(719045.46-831573.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.45(102.79-118.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.36(0.27\u0026ndash;0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255029.90(223471.92-284032.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.85(62.09\u0026ndash;78.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626896.78(527787.87-699686.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.71(67.11\u0026ndash;88.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.29(0.18\u0026ndash;0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59376.51(50552.74-70233.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.15(135.50-188.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132326.88(113313.03-152673.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.26(138.09-186.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.03(-0.05-0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91418.19(79374.34-105167.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.69(78.74-104.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288652.01(253144.72-322731.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.73(105.00-133.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.07(1.00-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119447.21(105831.33-131987.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.82(60.98\u0026ndash;76.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e413040.88(368317.21-452484.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.91(78.39\u0026ndash;96.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.80(0.72\u0026ndash;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79909.68(73021.63-86539.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.32(42.33\u0026ndash;50.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241604.65(214590.67-263884.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.69(61.90-76.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.40(1.24\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207639.42(189161.11-222336.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.36(101.45-119.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329344.28(284753.91-354531.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.46(82.53-102.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.84(-1.05,-0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5744.28(5065.40-6479.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.17(150.94-193.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21592.08(17470.55-26342.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217.96(176.36-265.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.16(1.01\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2152.15(1942.07-2339.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.63(49.30-59.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4421.84(3651.60-4891.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.05(41.33\u0026ndash;55.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.39(-0.83-0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4369.26(3921.33-4830.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.38(90.99-112.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12900.56(11260.42-14496.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.34(121.62-156.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.66(0.41\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2754.69(2547.38-2963.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.44(31.85\u0026ndash;37.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8432.44(7477.53-9430.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.96(51.39\u0026ndash;64.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.78(1.56\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15820.89(14805.28-16796.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.66(55.83\u0026ndash;63.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40566.74(37032.03-43322.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.56(100.01\u0026ndash;117.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.80(1.39\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11175.92(10342.65-12066.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.36(76.22\u0026ndash;88.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42368.59(37578.45\u0026ndash;47489.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.07(87.87-111.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.47(0.06\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8365.62(6013.73-11399.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222.47(159.93-303.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22517.32(16353.96-30388.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.54(181.24-336.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.39(0.32\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84566.84(70143.18-96561.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.77(47.09\u0026ndash;64.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214864.72(177685.91-260641.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.80(45.31\u0026ndash;66.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.63(-0.79\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9802.57(9185.78-10411.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.05(18.79\u0026ndash;21.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28202.64(25180.55-30965.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.43(40.56\u0026ndash;49.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.43(1.56\u0026ndash;3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26905.47(22722.03-32094.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.16(186.77-263.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55241.11(47434.40-63131.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204.31(175.44\u0026ndash;233.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.34(-0.41\u0026ndash;0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56186.46(51135.97-60283.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.68(146.24\u0026ndash;172.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102122.62(85203.41-112908.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.85(120.85-160.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.18(-0.42-0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63223.58(56702.24-68049.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.14(97.88-117.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71688.23(63126.60-76585.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.70(56.10-68.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.33(-2.62\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17179.21(14946.49-20152.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.78(52.88\u0026ndash;71.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61833.72(53102.58-69389.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.11(69.66\u0026ndash;91.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.27(1.15\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499.20(402.70-625.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.76(83.71-129.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200.69(982.64-1571.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.29(79.62-127.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.30(-0.42\u0026ndash;0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73834.74(62183.09-87648.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.77(65.50-92.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248086.32(212793.14-284416.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.92(85.70-114.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.91(0.79\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32033.97(26814.06-39290.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.66(63.33\u0026ndash;92.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145848.14(122204.26-164198.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.32(106.68-143.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.10(1.94\u0026ndash;2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8361.27(7720.54-8978.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.55(97.46-113.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35186.59(31763.00-37845.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.10(215.84-257.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.44(3.12\u0026ndash;3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7032.87(5938.05-8112.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.94(134.20-183.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23720.50(20948.79-26438.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243.65(215.18-271.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.77(1.30\u0026ndash;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14162.58(12967.51-15244.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.54(85.64-100.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70481.81(62838.94-76271.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.11(141.86-172.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.47(2.10\u0026ndash;2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89001.12(81083.26-95293.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.65(83.49\u0026ndash;98.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135807.40(114570.60-146917.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.06(76.82\u0026ndash;98.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.41(-0.76\u0026ndash;0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25201.92(21576.16-29620.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.58(149.47-205.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59398.14(48813.02-70518.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.79(151.86-219.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.28(0.18\u0026ndash;0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eTable\u0026nbsp;3: DALYs cases and ASR (per 100,000 population) of KP-associated LRIs among adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years, along with their EAPCs globally, categorized by sex, SDI categories, and GBD regions from 1990 to 2021.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100,000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDALYs cases (95% UI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASR of DALYs (per 100,000 population)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEAPCs of DALYs ASR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1045623.48(955686.94-1141047.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.73(142.34-169.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1865590.23(1699228.82-2032351.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.55(114.35-136.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.82(-0.89\u0026ndash;0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570217.02(519474.25-618379.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.06(166.77-198.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1017133.02(942042.83-1093453.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.40(134.67-156.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.88(-0.96\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475406.46(399907.68-544661.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.08(111.10-151.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e848457.21(714289.25-952477.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.88(90.82-121.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.75(-0.84\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-demographic index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186414.89(160380.24-216012.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499.66(429.88\u0026ndash;579.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297801.70(262485.12-343040.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e362.92(319.88-418.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.16(-1.25\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254796.69(222122.30-291950.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252.77(220.36-289.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535705.42(467745.82-598714.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222.21(194.02-248.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.36(-0.41\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259421.64(233833.33-286852.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.47(134.73-165.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e549862.56(494618.67-602007.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.03(105.27-128.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.87(-0.95\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129786.56(118061.40-141470.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.23(68.43-82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236507.30(211893.61-257477.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.22(61.12\u0026ndash;74.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.44(-0.59\u0026ndash;0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214236.20(195554.12-225709.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.89(104.88-121.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243960.44(210583.07-262082.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.71(61.04\u0026ndash;75.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.93(-2.12\u0026ndash;1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13749.97(12265.32-15372.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409.73(365.49-458.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28060.98(22850.74-33964.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283.26(230.67-342.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.96(-1.10\u0026ndash;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2040.11(1834.98-2191.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.79(46.58\u0026ndash;55.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2950.14(2449.38-3265.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.39(27.73\u0026ndash;36.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.57(-2.03\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7868.29(6929.00-8828.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.57(160.78-204.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15949.54(14032.07-18293.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172.27(151.56-197.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.52(-0.73\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4875.37(4531.91-5219.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.96(56.66\u0026ndash;65.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10448.77(9317.01-11663.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.81(64.04\u0026ndash;80.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.39(0.12\u0026ndash;0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21865.88(20710.37-22901.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.45(78.09\u0026ndash;86.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36216.53(33263.85-38825.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.81(89.83-104.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.25(-0.18-0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23089.83(21591.64-24514.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.15(159.11-180.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52226.08(46517.16-58226.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.12(108.77-136.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.25(-1.73\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26911.35(19263.48-36506.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e715.67(512.29-970.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55534.16(39883.77-74391.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e615.44(442.00-824.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.54(-0.63\u0026ndash;0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171848.07(142041.63-197177.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.37(95.36-132.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203071.64(168812.92-245233.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.79(43.05\u0026ndash;62.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.38(-3.65\u0026ndash;3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13627.59(13010.72-14241.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.87(26.61\u0026ndash;29.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28556.60(25754.87-31391.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.00(41.49\u0026ndash;50.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.06(0.08\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87339.88(74204.17-101696.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e717.92(609.95-835.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124879.76(109512.54-143493.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e461.87(405.04-530.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.66(-1.76\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57484.36(52380.04-60552.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.39(149.79-173.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73277.82(61198.32-80663.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.94(86.80-114.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.35(-1.59\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61254.88(55099.89-64929.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.74(95.12-112.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53922.07(47013.03-57946.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.92(41.78\u0026ndash;51.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-3.14(-3.42\u0026ndash;2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37854.30(33113.28-45341.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.93(117.16-160.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76942.33(66815.33-87017.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.93(87.65-114.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.69(-0.78\u0026ndash;0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1268.39(1019.36-1622.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.65(211.89-337.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2508.94(2050.51-3367.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.29(166.15-272.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.89(-0.96\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217946.62(183020.60-259482.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.56(192.77-273.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488255.73(419163.53-565785.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196.64(168.82-227.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.50(-0.58\u0026ndash;0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75961.81(62871.58-92971.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179.40(148.49-219.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219453.87(183504.94-245108.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191.57(160.19-213.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.47(0.33\u0026ndash;0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13554.74(12674.69-14394.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.11(160.00-181.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39134.46(35155.72-42095.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265.93(238.89-286.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.19(1.92\u0026ndash;2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16143.11(13771.81-18774.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364.83(311.24-424.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42233.10(37317.70-47285.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e433.81(383.32-485.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.84(0.17\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28443.42(26578.60-30367.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.85(175.54-200.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88017.05(78469.80-94815.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.69(177.14-214.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.83(0.46\u0026ndash;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88790.27(81086.67-93620.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.43(83.50\u0026ndash;96.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93328.99(78879.28-101222.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.58(52.89\u0026ndash;67.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.66(-2.01\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73705.25(63380.19-85273.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510.58(439.06-590.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130621.66(108121.98-152821.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406.38(336.38-475.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.74(-0.87\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALYs Disability-Adjusted Life Years-ASR Age-standardized rate- EAPCs Estimated annual percentage change- CI Confidence interval- UI Uncertainty interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eKAP-associated LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years: SDI Regional Trends\u003c/h3\u003e\n\u003cp\u003eIn terms of SDI, low-middle SDI regions showed the largest absolute increase in AB-associated LRIs DALYs (287,201.63\u0026rarr;354,101.82), while high-SDI regions declined markedly (54,360.25\u0026rarr;35,531.43). Regional variations were significant, with high-SDI regions exhibiting the steepest ASR decline (EAPC=-3.81, 95% CI -4.02 to -3.59) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In low-middle SDI regions, AB-associated DALY ASR showed exponential age-dependency, peaking at 666.03 (493.23\u0026ndash;815.96) per 100,000 in \u0026ge;\u0026thinsp;95-year-olds and nadir at 63.89 (53.29\u0026ndash;75.92) per 100,000 in 55\u0026ndash;59-year-olds, with universal reductions across age groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). For PA-associated LRIs in high-SDI regions, total DALYs increased from 207,639.42 in 1990 to 329,344.28 in 2021, but ASR decreased from (111.36[95%UI:101.45-119.24] to 95.46[95%UI:82.53-102.76] per 100,000).while in low-SDI regions, both DALY and ASR are on the rise, with only high-SDI regions showing significant ASR decline (EAPC=-0.84, 95%CI:-1.05 to -0.63) (Table 2). High-SDI age-time trends revealed dramatic ASR escalation, from 24.94 (23.51\u0026ndash;26.40) per 100,000 in 55\u0026ndash;59-year-olds to 1112.14 (840.52\u0026ndash;1270.86) per 100,000 in \u0026ge;\u0026thinsp;95-year-olds; ASR declined post-2000 except in \u0026ge;\u0026thinsp;95-year-olds (e.g., 70\u0026ndash;74-year-olds: 100.60\u0026rarr;66.63) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Meanwhile, KP-associated LRIs decreased most substantially in low-SDI regions (499.66[95%UI:429.88\u0026ndash;579.00] \u0026rarr;362.92[95%UI:319.88-418.05] per 100,000), High-SDI regions showed the steepest decline (EAPC=-1.93, -2.12 to -1.74) (Table 3). Low-middle SDI age trends had peak burden in \u0026ge;\u0026thinsp;95y with fluctuations, such as 1447.21 in 2014 and 1174.08 in 2021, plus steady declines in 55\u0026ndash;59y (116.38 [95% UI 101.80\u0026ndash;132.46] to 91.32 [95% UI 78.66\u0026ndash;103.92]/100k) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).Moreover, in terms of gender, the number and ASR of KAP-related LRIs DALYs among males are higher than those among females across all SDI regions.\u003c/p\u003e\n\u003ch2\u003eKAP-associated LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years: Geographic Regional Trends\u003c/h2\u003e\n\u003cp\u003eSubstantial 2021 geographical disparities in LRIs DALY ASR (age\u0026thinsp;\u0026ge;\u0026thinsp;55y) revealed Central Sub-Saharan Africa with highest burdens(eg., KP (615.44[95%UI:442.00\u0026ndash;824.42] per 100,000), while Australasia showed lowest ASR (eg., KP: 33.39[95%UI:27.73\u0026ndash;36.97] per 100,000), alongside East Asia\u0026apos;s significant improvement (EAPC=-3.38[95%CI:-3.65\u0026ndash;-3.11]); South Asia and Southeast Asia exceeded global averages, with Southern Latin America demonstrating increasing trends (AB: EAPC\u0026thinsp;=\u0026thinsp;1.05[95%CI:0.60\u0026ndash;1.51]) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e,3).However, PA burdens showed Andean Latin Am increase (171.17 [95%UI:150.94\u0026ndash;193.09] to 217.96 [95%UI:176.36\u0026ndash;265.91]) vs High-income N Am decline (109.14 [95%UI:97.88\u0026ndash;117.47] to 63.70 [95%UI:56.10\u0026ndash;68.06]), with Central Sub-Saharan Africa highest (249.54 [95%UI:181.24\u0026ndash;336.77]); Southern Latin Am had steepest ASR increase (EAPC\u0026thinsp;=\u0026thinsp;3.44 [95%CI:3.12\u0026ndash;3.76]) and High-income N Am the largest decline (EAPC=-2.33 [95%CI:-2.62\u0026ndash;-2.05]) (Table 2). Furthermore, regarding gender and age, males typically had a higher burden than females, and the burden of KAP-related LRIs tends to rise with increasing age across 21 regions, excluding those in Eastern Europe (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C).\u003c/p\u003e\n\u003ch2\u003eKAP-associated LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years: National Trends\u003c/h2\u003e\n\u003cp\u003eIn 204 nations/territories, the cases and ASR of KAP-related LRIs DALYs was significantly difference among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;55years. The greatest cases of DALY linked to KAP-associated LRIs is mainly concentrated in India and China in 1990 and 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA,C,E and supplement eFig.3A,C,E).For AB-related LRIs, Sub-Saharan Africa (Central African Republic: 1179.29[95%UI:836.39\u0026ndash;1620.44]/100k) was greatest ASR, while Iceland (7.56[95%UI:5.92\u0026ndash;9.03]) was lowest ASR. And China demonstrated remarkable AB-related LRIs improvement (1990:78.86[95%UI:62.93\u0026ndash;93.92] \u0026rarr;2019:11.93[95%UI :9.73\u0026ndash;15.00]) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB and supplement eFig.3B). Over the past thirty years, Finland achieving the steepest reduction (EAPC=-10.38%[95%CI:-11.54 to -9.21]) while Georgia deteriorated (EAPC\u0026thinsp;=\u0026thinsp;4.86% [95%CI:3.23 to 6.52]) (see supplement eFig.4A). In 2021, the highest ASR of PA-related LRIs were observed in Malaysia, whereas North Macedonia reported the lowest ASR (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Argentina showed the sharpest PA increase (EAPC\u0026thinsp;=\u0026thinsp;5.69[95%CI:5.10 to 6.28]) versus Finland\u0026apos;s decline (EAPC=-7.37[95%CI:-8.60 to -6.12])(see supplement eFig.4B). However, the highest ASR of KP-related LRIs were observed in Guinea-Bissau (1990) and Zimbabwe (2021), whereas the lowest ASR was Lithuania (1990) and Finland (2021) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF and supplement eFig.3F\u003cstrong\u003e)\u003c/strong\u003e.Finland achieved the steepest KP decline (EAPC=-8.55[95%CI:-9.77 to -7.31]), whereas Georgia increased most (EAPC\u0026thinsp;=\u0026thinsp;5.22[95%CI:3.58\u0026ndash;6.88])( see supplement eFig.4C).\u003c/p\u003e\n\u003cp\u003eWe further analyzed the association between the EAPC of DALYs in 2021 across 204 countries/territories, disease burden indicators, and the SDI. In terms of BA-related LRIs, Low-SDI nations (e.g., Zimbabwe, SDI\u0026thinsp;=\u0026thinsp;0.47) had high baseline DALYs (714.38) but positive EAPC (1.66). Middle-high SDI countries (0.7\u0026ndash;0.9) exhibited wide EAPC variation (e.g., Germany SDI\u0026thinsp;=\u0026thinsp;0.90, EAPC=-1.29 vs. Greece SDI\u0026thinsp;=\u0026thinsp;0.79, EAPC\u0026thinsp;=\u0026thinsp;1.72), and its nonlinear correlation. Extremely, The highest-SDI countries (e.g., Norway, SDI\u0026thinsp;=\u0026thinsp;0.92) had the lowest EAPC (-6.04), while low-SDI nations (e.g., Central African Republic, SDI\u0026thinsp;=\u0026thinsp;0.31) showed near-neutral EAPC (-0.96)(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA).And PA-related LRIs in High-middle SDI countries (e.g., Argentina [EAPC\u0026thinsp;=\u0026thinsp;5.69, SDI\u0026thinsp;=\u0026thinsp;0.72], Georgia [5.55, 0.73]) exhibited the steepest DALYs growth, Low-SDI nations (e.g., Mozambique [EAPC\u0026thinsp;=\u0026thinsp;1.87, SDI\u0026thinsp;=\u0026thinsp;0.33]) maintained high baseline DALYs (\u0026gt;\u0026thinsp;200/100k) but moderate EAPC, Negative EAPC in high-SDI regions (e.g., Finland [-7.37, SDI\u0026thinsp;=\u0026thinsp;0.86]) highlights successful health system adaptations; And a parabolic relationship was observed: peak EAPC in SDI 0.7\u0026ndash;0.75 (e.g., Lebanon [4.90, 0.74]), declining to negative values at SDI\u0026thinsp;\u0026gt;\u0026thinsp;0.85 (e.g., Norway [-2.47, 0.92])(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).Furthermore, some high - SDI countries (e.g., the United States: \u0026minus;\u0026thinsp;3.15) still had a significant KP-related LRIs burden (48.38 per 100,000).Medium - SDI countries (0.6\u0026ndash;0.8), such as Georgia (SDI\u0026thinsp;=\u0026thinsp;0.73) had the highest EAPC (5.22), Most high - SDI countries (\u0026gt;\u0026thinsp;0.8), like Germany (SDI\u0026thinsp;=\u0026thinsp;0.90) and Japan (SDI\u0026thinsp;=\u0026thinsp;0.87), had negative EAPC (Germany: \u0026minus;\u0026thinsp;0.10; Japan: \u0026minus;\u0026thinsp;1.33), with some exceptions (e.g., the United States). Low - SDI countries (\u0026lt;\u0026thinsp;0.5), such as Lesotho (SDI\u0026thinsp;=\u0026thinsp;0.51) showed a positive correlation in EAPC (1.88) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). The all-scatter plots also supported these findings.\u003c/p\u003e\n\u003ch2\u003ePredictive analysis for KAP-Related LRIs in Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 Years\u003c/h2\u003e\n\u003cp\u003eTo enhance the credibility of the projections from the BAPC model, we employed the ARIMA model to cross-validate the trends in KAP-related LRI DALY ASR, resulting in comparable predictions from both models.\u003c/p\u003e\n\u003cp\u003eThe ARIMA model further indicated a consistent reduction in the ASR of DALY associated with AB-related LRIs: its estimated decrease from 1990 to 2021 was around 50.1%, closely mirroring the BAPC\u0026apos;s figure of 50.05%. The model\u0026apos;s projection for 2050 (29.81 per 100,000 population) displayed only a slight difference from BAPC\u0026rsquo;s estimate of 29.63 (with an error margin of less than 1%)(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, D). In terms of PA-related LRI, the ARIMA analysis confirmed a trend of \u0026ldquo;long-term decline followed by stabilization\u0026rdquo;\u0026mdash;the ASR trend from 1990 to 2021 was consistent with BAPC\u0026rsquo;s data, and the projections for 2021 to 2050 (ranging from 100.48 to 100.55 per 100,000 population) were almost indistinguishable from BAPC\u0026rsquo;s reported values of 100.56 (2021) and 100.52 (2050) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB,E). For KP-related LRI, the ARIMA model supported BAPC\u0026rsquo;s results: a 25.4% decrease in ASR from 1990 to 2021 (in comparison to BAPC\u0026rsquo;s 25.5%) and an anticipated additional reduction of 30.8% by 2025 (versus BAPC\u0026rsquo;s 30.76%)(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC,F).\u003c/p\u003e\n\u003cp\u003eAligned trends between BAPC and ARIMA\u0026mdash;particularly in the direction of change and reduction scales during critical phases\u0026mdash;bolster the credibility of our forecasts for KAP-related LRIs DALY ASR, delivering more robust evidence to guide age-targeted antimicrobial stewardship and healthcare resource distribution in aging populations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe utilized the most recent GBD2021 database to uncover various epidemiological trend of KAP-related LRIs in people aged 55 and above. Globally, our research indicated that between 1990 and 2021, there has been a rise in the number of DALYs linked to global KAP-related LRIs. It is important to note that the DALYs for PA- and KP-related LRIs were considerably greater compared to those for AB-related LRIs. Conversely, there has been a marked decline in the global burden of LRIs caused by AB and KP (EAPC=-2.40 and − 0.82, respectively), while the PA burden has seen an increase (EAPC = 0.33).While PA may not be the most serious of the three globally, its natural resistance systems and the ability to form biofilms play significant roles in its endurance within the environment, thus promoting its spread among older adults[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, exploring age-related elements of KAP -related LRIs is crucial for creating approaches that seek to alleviate the disease burden in the elderly demographic.\u003c/p\u003e\u003cp\u003eOur study demonstrates that globally, the ASR of DALYs attributed to LRIs associated with KAP pathogens increases progressively with age in individuals aged 55 years and older. For instance, the ASR of DALYs due to PA-related LRIs in individuals aged ≥ 95 years was significantly higher (26-fold increase) compared to that in the 55–59 years age group. This finding indicates that age is a critical determinant of disease burden associated with KAP pathogens, which is consistent with previous studies highlighting age-related elevations in KAP-associated morbidity and mortality[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].This issue warrants attention for several key reasons. First, the aging population is leading to an increase in individuals living with chronic non-communicable diseases, which can weaken the immune system and make people more susceptible to infections[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Second, although younger individuals typically exhibit strong responses to interventions like vaccines, the effectiveness of these vaccines diminishes with age due to weakened antibody responses[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Third, older individuals are at a greater risk of developing antimicrobial resistance (AMR)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], However, several newly developed antibiotics have exhibited efficacy against carbapenem-resistant PA strains relying on metallo-β-lactamases (MBLs) or harboring AmpC enzymes/ESBLs, as well as KP strains producing KPC-type carbapenemases and AB strains producing OXA-48-type carbapenemases[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Fourth, the elderly are generally more prone to comorbidities; for example, older adults with diabetes face an increased likelihood of infections from gram-negative bacteria[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Finally, the rapid expansion in the elderly demographic is a notable concern, as forecasts suggest that by 2100, more than 25% of the world's population will be aged ≥ 65years[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Collectively, these factors not only heighten vulnerability to KAP infections but also may drive over-reliance on antimicrobial therapies. Therefore, it is essential to develop age-specific preventive strategies, including promoting science communication and education, encouraging timely medical consultation and adherence to standardized treatment protocols, and implementing regular microbiological screening—all aimed at reducing the disease burden.\u003c/p\u003e\u003cp\u003eThe disease burden associated with KAP varies across countries and regions with different economic conditions. The results of our study reveal that areas characterized by a low-middle SDI experience the highest disease burden for AB (EAPC = -2.18) and KP (EAPC = -0.36), whereas PA (EAPC = 1.4) is more commonly observed in regions with a middle-high SDI. Nonetheless, the disease burden for all three pathogenic bacteria is lowest in high SDI regions. Such as over the past three decades, the Lao People's Democratic Republic and the Argentine Republic have experienced the highest burden of AB and KP-associated LRIs, while Bermuda had the highest burden of PA-associated LRIs. Conversely, the lowest burdens of AB, PA, and KP-associated LRIs were observed in Grenada, the Kingdom of Thailand, and Finland, respectively. This pattern highlights that diagnostic delays, overprescription of empirical antibiotics, and insufficient infection control measures in resource-limited settings—alongside challenges of limited healthcare access and poverty in high-burden regions—contribute to the dissemination of MDR infections, underscoring the need for low- and middle-income countries to strengthen healthcare infrastructure and public health initiatives, with support from global collaboration(26,27).\u003c/p\u003e\u003cp\u003eAt present, KAP stands out as the predominant Gram-negative bacillus responsible for LRIs worldwide, and its characteristics of MDR significantly contribute to the health challenges faced by the elderly. Previous research indicates that several factors contribute to this situation. Firstly, antibiotic misuse, the 2024 WHO Bacterial Priority Pathogens List (BPPL) highlights that carbapenem-resistant Klebsiella pneumoniae (CRKP) continues to be a significant pathogen of concern[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This situation is especially evident in low-income nations, where insufficient expertise in diagnosing and treating illnesses worsens the inappropriate use of antibiotics, resulting in greater antibiotic resistance among KAP[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Conversely, high-income countries regularly follow the principles of 'the prudent use of antibiotics'[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].Then, Economy and environment. Old approaches to diagnosing diseases can result in postponed treatment. In contrast, innovative diagnostic technologies like Next-Generation Sequencing (NGS) allow for targeted therapies against infections, consequently decreasing dependence on broad-spectrum antibiotics and lessening the development of resistance[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. And in low SDI regions, air pollution and unsafe water, sanitation, and handwashing are significant environmental factors exacerbating the burden of disease[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].Consequently, it is essential to create appropriate strategies based on the previously mentioned factors, including assistance to economically challenged nations and promoting governmental funding for underprivileged areas, to alleviate the disease burden linked to KAP-related LRIs.\u003c/p\u003e\u003cp\u003eOur research indicates that the ASR of KAP-related LRIs is markedly greater males than females in older adults aged ≥ 55 years. This epidemiological pattern aligns with results observed in previous research[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Possible explanations for this discrepancy may involve variations in immune responses to infections and differences in behavioral practices. Factors such as behavioral influences (for instance, tobacco use, which can weaken immune responses and impair the function of respiratory cilia and excessive alcohol intake, which compromises the host's immunity and increases the likelihood of microbial inhalation), and genetic/hormonal factors that modulate immune function[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All these factors combined may contribute to the significant disease burden of KAP-related LRIs in elderly males. Finally, our dual predictive frameworks, namely the Bayesian Age-Period-Cohort (BAPC) model and the Autoregressive Integrated Moving Average (ARIMA) model, projected a notable decrease in the disease burden attributed to LRIs caused by AB and KP by the year 2050. Conversely, the burden linked to PA-associated LRIs is expected to remain constant, showing no substantial decline. This enduring high burden of PA-related LRIs suggests that existing interventions may not be adequate to diminish its spread, highlighting the necessity for focused strategies moving forward. Such strategies should include the creation of new antimicrobial agents, advanced diagnostic technologies, and customized infection control approaches for at-risk groups, such as residents in nursing homes. Additionally, it is crucial to adapt and implement evidence-based interventions from wealthier nations in lower-income countries to effectively decrease the overall disease burden of KAP-related LRIs.\u003c/p\u003e\u003cp\u003eThis study has several limitations. The firstly, this research mainly depends on the GBD 2021 database for backing our forecasts. Nevertheless, the absence of environmental monitoring information in specific areas, attributable to resource constraints, has led to untimely diagnosis and imprecise evaluations; the secondly, we assessed the pathogen burdens of KAP, however, detailed information regarding resistance mechanisms, such as the production of ESBL and carbapenems, was lacking. The thirdly, our examination has also considered the crucial factors that influence the occurrence of LRIs in the older aged ≥ 55 years, including nursing home density and air quality issues. Nonetheless, this study focuses on three distinct causes, while the GBD 2021 database does not provide detailed information regarding the risk of LRIs associated with each of these causes.\u003c/p\u003e\u003cp\u003eIn conclusion, Our analysis indicated that ASR of DALYs associated with AB and KP-related LRIs showed a decline, while an increase was observed for PA-related ASR, which is expected to stabilize post-2025. The disease burden was most pronounced in individuals aged 95 years and older, who experienced greater burden compared to the 55–59 years age group, with males disproportionately affected. Regions with low- middle SDI faced the greatest burden, whereas those with high SDI displayed the most significant reductions. Projections from dual-model analyses verified that burdens from AB and KP are likely to continue their downward trend by 2050, whereas the burden associated with PA is predicted to persist at elevated levels. Recommendations included the development of targeted antimicrobials and diagnostics for PA, adapting high-SDI antimicrobial stewardship to low-middle SDI regions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAB, Acinetobacter baumannii ; AMR, Antimicrobial resistance; AIC, Akaike Information Criterion; ARIMA ,Autoregressive Integrated Moving Average; BAPC, Bayesian Age-Period-Cohort; CI, Confidence interval; EAPC, Estimated annual percentage change; GBD, Global Burden of Disease; INLA, Integrated Nested Laplace Approximation; KP, Klebsiella pneumonia; LRIs, Lower respiratory infections; MDR, Multidrug-resistant; PA, Pseudomonas aeruginosa; SDI, Socio-demographic index; UI, Uncertainty intervals;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiao Zhao: Conceptualization, Methodology, Formal analysis, Investigation, Methodology, Project administration, Writing – Original Draft,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGuiyun Li, Yuyang Qiu, Yunqin Wang, Guanglin Huang, Yihong Gong, Jiaoyangzi Liu: Data curation, Writing – Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eFeng Shen: Supervision, Funding acquisition, Writing – Review \u0026amp; Editing, Methodology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by The study is supported by National Natural Science Foundation of China(82360019)；\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Global Burden of Disease study 2021 is an open-access resource; data are available at https://vizhub.healthdata.org/gbd-results/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available data from the Global Burden of Disease (GBD) 2021 database. As no human subjects were directly involved, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal R, Mahajan P, Pandiya S, Bajaj A, Verma SK, Yadav P, et al. Antibiotic resistance: A global crisis, problems and solutions. Crit Rev Microbiol. 2024;50:896\u0026ndash;921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1040841x.2024.2313024\u003c/span\u003e\u003cspan address=\"10.1080/1040841x.2024.2313024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar A, Hassali MAA, Zainal H, Ali I, Iqbal MS, Khan AH. Respiratory-tract infections among geriatrics: Prevalence and factors associated with the treatment outcomes. Ther Adv Respir Dis. 2021;15:1753466620971141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1753466620971141\u003c/span\u003e\u003cspan address=\"10.1177/1753466620971141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSati H, Carrara E, Savoldi A, Hansen P, Garlasco J, Campagnaro E, et al. The WHO Bacterial Priority Pathogens List 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. Lancet Infect Dis. 2025;S1473309925001185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1473-3099(25)00118-5\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(25)00118-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRice LB. Federal Funding for the Study of Antimicrobial Resistance in Nosocomial Pathogens: No ESKAPE. J INFECT DIS. 2008;197:1079\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/533452\u003c/span\u003e\u003cspan address=\"10.1086/533452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-0691.2011.03570.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-0691.2011.03570.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatta DR, Hamal D, Shrestha R, Hs S, Joshi P, Nayak N, et al. Burden of multidrug resistant respiratory pathogens in intensive care units of tertiary care hospital. Asian J Med Sci. 2019;10:14\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/ajms.v10i2.21098\u003c/span\u003e\u003cspan address=\"10.3126/ajms.v10i2.21098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, Wool EE, et al. Global burden of bacterial antimicrobial resistance 1990\u0026ndash;2021: A systematic analysis with forecasts to 2050. Lancet. 2024;404:1199\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(24)01867-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(24)01867-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Xu L, Zuo AF, Xu P, Xu K. The global burden of klebsiella pneumoniae-associated lower respiratory infection in 204 countries and territories, 1990\u0026ndash;2021: Findings from the global burden of disease study 2021. PLoS ONE. 2025;20:e0324151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0324151\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0324151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBender RG, Sirota SB, Swetschinski LR, Dominguez R-MV, Novotney A, Wool EE, et al. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990\u0026ndash;2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis. 2024;24:974\u0026ndash;1002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1473-3099(24)00176-2\u003c/span\u003e\u003cspan address=\"10.1016/s1473-3099(24)00176-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Ba\u0026ntilde;o J, Garc\u0026iacute;a L, Ram\u0026iacute;rez E, Mart\u0026iacute;nez-Mart\u0026iacute;nez L, Muniain MA, Fern\u0026aacute;ndez-Cuenca F, et al. Long-term control of hospital-wide, endemic multidrug-resistant Acinetobacter baumannii through a comprehensive bundle approach. Am J Infect Control. 2009;37:715\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajic.2009.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ajic.2009.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChilds A, Zullo AR, Joyce NR, McConeghy KW, Van Aalst R, Moyo P, et al. The burden of respiratory infections among older adults in long-term care: a systematic review. BMC Geriatr. 2019;19:210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-019-1236-6\u003c/span\u003e\u003cspan address=\"10.1186/s12877-019-1236-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. ICD-10: International statistical classification of diseases and related health problems : tenth revision. 2nd ed. Geneva: World Health Organization; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2019 Demographics Collaborators. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950\u0026ndash;2019: A comprehensive demographic analysis for the global burden of disease study 2019. Lancet. 2020;396:1160\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)30977-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30977-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu M, Wu Q, Liu D, Zu W, Zhang D, Chen L. The global burden of lower respiratory infections attributable to respiratory syncytial virus in 204 countries and territories, 1990\u0026ndash;2019: Findings from the global burden of disease study 2019. Intern Emerg Med. 2024;19:59\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11739-023-03438-x\u003c/span\u003e\u003cspan address=\"10.1007/s11739-023-03438-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiebler A, Held L. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested laplace approximations. Biom J. 2017;59:531\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bimj.201500263\u003c/span\u003e\u003cspan address=\"10.1002/bimj.201500263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo L. Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem. Demography. 2013;50:1945\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13524-013-0243-z\u003c/span\u003e\u003cspan address=\"10.1007/s13524-013-0243-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal burden and strength of evidence for 88. risk factors in 204 countries and 811 subnational locations, 1990\u0026ndash;2021: A systematic analysis for the global burden of disease study 2021 - the lancet. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00933-4/fulltext\u003c/span\u003e\u003cspan address=\"https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00933-4/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 3 Sept 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Barrio-Tofi\u0026ntilde;o E, L\u0026oacute;pez-Causap\u0026eacute; C, Oliver A. Pseudomonas aeruginosa epidemic high-risk clones and their association with horizontally-acquired β-lactamases: 2020 update. Int J Antimicrob Agents. 2020;56:106196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijantimicag.2020.106196\u003c/span\u003e\u003cspan address=\"10.1016/j.ijantimicag.2020.106196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePachori P, Gothalwal R, Gandhi P. Emergence of antibiotic resistance pseudomonas aeruginosa in intensive care unit; a critical review. Genes Dis. 2019;6:109\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gendis.2019.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.gendis.2019.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Werkhoven CH, Huijts SM, Bolkenbaas M, Grobbee DE, Bonten MJM. The Impact of Age on the Efficacy of 13-valent Pneumococcal Conjugate Vaccine in Elderly. Clin Infect Dis. 2015;61:1835\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cid/civ686\u003c/span\u003e\u003cspan address=\"10.1093/cid/civ686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Lodovico S, Fasciana T, Di Giulio M, Cellini L, Giammanco A, Rossolini GM, et al. Spread of multidrug-resistant microorganisms. Antibiot (Basel). 2022;11:832. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antibiotics11070832\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics11070832\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmid H, Brown L-AK, Indrakumar B, McGarrity O, Hatcher J, Bamford A. Use of Cefiderocol in the Management of Children With Infection or Colonization With Multi-drug Resistant Gram-negative Bacteria: A Retrospective, Single-center Case Series. Pediatr Infect Dis J. 2024;43:772\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/inf.0000000000004347\u003c/span\u003e\u003cspan address=\"10.1097/inf.0000000000004347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;ztaş S, Er DK, D\u0026uuml;ndar D, Ke\u0026ccedil;eli SA. Efficacy of ceftazidime/avibactam and plazomicin on carbapenem-resistant Klebsiella pneumoniae and Escherichia coli. Acta Microbiol Immunol Hung. 2024;71:110\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1556/030.2024.02292\u003c/span\u003e\u003cspan address=\"10.1556/030.2024.02292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(23)01301-6\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(23)01301-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVollset SE, Goren E, Yuan C-W, Cao J, Smith AE, Hsiao T, et al. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet. 2020;396:1285\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(20)30677-2\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(20)30677-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Duin D, Paterson DL. Multidrug-resistant bacteria in the community: An update. Infect Dis Clin North Am. 2020;34:709\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.idc.2020.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.idc.2020.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshrafi-Asgarabad A, Bokaie S, Razmyar J, Akbarein H, Nejadghaderi SA, Carson-Chahhoud K, et al. The burden of lower respiratory infections and their underlying etiologies in the Middle East and North Africa region, 1990\u0026ndash;2019: results from the Global Burden of Disease Study 2019. BMC Pulm Med. 2023;23:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12890-022-02301-7\u003c/span\u003e\u003cspan address=\"10.1186/s12890-022-02301-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams PCM, Jones M, Snelling TL, Duguid R, Moore N, Dickson B, et al. Coverage gaps in empiric antibiotic regimens used to treat serious bacterial infections in neonates and children in Southeast Asia and the Pacific. Lancet Reg health Southeast Asia. 2024;22:100291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lansea.2023.100291\u003c/span\u003e\u003cspan address=\"10.1016/j.lansea.2023.100291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohde GGU. Prudent use of antibiotics: Acute exacerbation of COPD as an example. Eur Respir J. 2010;36:983\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/09031936.00101610\u003c/span\u003e\u003cspan address=\"10.1183/09031936.00101610\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshiru-Oredope D, Hopkins S, Vasandani S, Umoh E, Oloyede O, Nilsson A et al. Healthcare workers\u0026rsquo; knowledge, attitudes and behaviours with respect to antibiotics, antibiotic use and antibiotic resistance across 30 EU/EEA countries in 2019. Eurosurveillance. 2021;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2807/1560-7917.es.2021.26.12.1900633\u003c/span\u003e\u003cspan address=\"10.2807/1560-7917.es.2021.26.12.1900633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Yuan D, Yang X, Sun C, Hou L, Zhang Y, et al. Etiology of lower respiratory tract in pneumonia based on metagenomic next-generation sequencing: a retrospective study. Front Cell Infect Microbiol. 2024;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2023.1291980\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2023.1291980\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang L, Jing W, Liu J, Liu M. Trends of global and regional aetiologies, risk factors and mortality of lower respiratory infections from 1990 to 2019: An analysis for the Global Burden of Disease Study 2019. Respirology. 2022;28:166\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/resp.14389\u003c/span\u003e\u003cspan address=\"10.1111/resp.14389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma R, Lakhanpal D. Acinetobacter baumannii: A comprehensive review of global epidemiology, clinical implications, host interactions, mechanisms of antimicrobial resistance and mitigation strategies. Microb Pathog. 2025;204:107605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.micpath.2025.107605\u003c/span\u003e\u003cspan address=\"10.1016/j.micpath.2025.107605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafiri S, Mahmoodpoor A, Kolahi A-A, Nejadghaderi SA, Sullman MJM, Mansournia MA, et al. Global burden of lower respiratory infections during the last three decades. Front Public Health. 2023;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2022.1028525\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.1028525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto R, Shindo Y, Kobayashi D, Ando M, Jin W, Wachino J, et al. Molecular epidemiological characteristics of klebsiella pneumoniae associated with bacteremia among patients with pneumonia. J Clin Microbiol. 2015;53:879\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/jcm.03067-14\u003c/span\u003e\u003cspan address=\"10.1128/jcm.03067-14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyu HH, Vongpradith A, Sirota SB, Novotney A, Troeger CE, Doxey MC, et al. Age\u0026ndash;sex differences in the global burden of lower respiratory infections and risk factors, 1990\u0026ndash;2019: results from the Global Burden of Disease Study 2019. Lancet Infect Dis. 2022;22:1626\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1473-3099(22)00510-2\u003c/span\u003e\u003cspan address=\"10.1016/s1473-3099(22)00510-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta AJ, Guidot DM, Weber KT. Alcohol Abuse, the Alveolar Macrophage and Pneumonia. Am J Med Sci. 2012;343:244\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/maj.0b013e31823ede77\u003c/span\u003e\u003cspan address=\"10.1097/maj.0b013e31823ede77\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, Adults ≥55 Years, Epidemiology, Global Burden of Disease 2021, Healthy policy, KAP-related LRIs","lastPublishedDoi":"10.21203/rs.3.rs-7950784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7950784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe global aging population has amplified the public health burden of multidrug-resistant (MDR) lower respiratory infections (LRIs), particularly among adults aged ≥55 years. Acinetobacter baumannii (AB), Pseudomonas aeruginosa (PA), and Klebsiella pneumoniae (KP) (collectively KAP) are leading MDR Gram-negative pathogens causing LRIs in this demographic, yet comprehensive global epidemiological data on KAP-related LRIs in adults ≥55 years remain scarce.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Using data from the Global Burden of Disease (GBD) 2021, we analyzed trends in disability-adjusted life years (DALYs) and age-standardized rates (ASR) of KAP-related LRIs in adults ≥55 years across 204 countries/territories from 1990 to 2021. Estimated annual percentage changes (EAPC) were calculated to assess temporal trends, with stratification by age, sex, and Socio-demographic Index (SDI). Future burdens (to 2050) were projected using dual models: Bayesian Age-Period-Cohort (BAPC) and Autoregressive Integrated Moving Average (ARIMA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult：\u003c/strong\u003eGlobally, AB- and KP-related DALY ASRs declined significantly (EAPC: AB=-2.40, 95%CI:-2.48–-2.32; KP=-0.82, 95%CI:-0.89–-0.74) from 1990 to 2021, while PA-related ASR increased (EAPC=0.33, 95%CI:0.24–0.43) and was projected to stabilize post-2025. Burden was highest in adults ≥95 years (e.g., PA-related ASR 26-fold higher than 55–59 years) and males (2021: AB ASR males=72.97 vs. females=56.11 per 100,000). Low-middle SDI regions bore the heaviest burden, whereas high-SDI regions showed the steepest declines (AB EAPC=-3.81, 95%CI: -4.02 to -3.59). Dual models confirmed AB- and KP-related burdens would continue decreasing by 2050, but PA-related burden would persist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eKAP-related LRIs in adults ≥55 years exhibit pathogen-specific, demographic, and geographic disparities, with PA emerging as a persistent threat. Targeted interventions are needed to mitigate burden and promote healthy aging globally.\u003c/p\u003e","manuscriptTitle":"Global Epidemiology of Multidrug-Resistant Lower Respiratory Infections in Adults ≥55 years: Trends (1990–2021) and Projections to 2050","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 17:16:34","doi":"10.21203/rs.3.rs-7950784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1142c42-d7f2-4cd2-9cab-d43032b58581","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T11:55:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 17:16:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7950784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7950784","identity":"rs-7950784","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}