Global burden of bacterial skin diseases from 1990 to 2045: An analysis based on Global Burden Disease data

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Objective We aimed to describe the burden and trend of bacterial skin diseases, to explore potential associated factors, and to predict the burden up to 2045. Methods Data on incidence and disability-adjusted life years (DALYs) of bacterial skin diseases were obtained from Global Burden of Disease 2021. We used average annual percent change (AAPC) by Joinpoint Regression to quantify the temporal trends. We conducted decomposition analysis to understand the contribution of aging, epidemiological changes, and population growth. Bayesian Age-Period-Cohort model was used to predict burden up to 2045. Results Global incidence rate of bacterial skin diseases increased from 8,988.74 per 100,000 in 1990 to 10,823.88 per 100,000, with AAPC of 0.62% (0.61 ~ 0.63%). The highest incidence rate was in low Socio-demographic Index (SDI) region and population aged 85. The major drivers of incident case rise were population growth, followed by epidemiological changes; the major drivers of DALY case rise were population growth, followed by aging. Increasing trends were seen in prediction of incidence rate, incident cases and DALY cases; decreasing trend of DALY rate prediction was seen. Conclusion The incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease. bacterial skin infections global disease burden forecast public health socio-demographic index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Skin disease is one of the most common diseases affecting humans of all ages, contributing to a heavy global disease burden. The incidence of disabling skin diseases is even higher in poor areas[ 1 – 4 ]. In a previous study, skin diseases ranked fourth among global disabling diseases[ 5 ]. The disease burden caused by bacterial skin diseases accounts for a significant proportion of skin diseases[ 6 , 7 ]. In International Classification of Diseases-11, published by the WHO, bacterial skin diseases refer to disorders of the skin and/or subcutaneous tissues caused by bacteria[ 8 ]. For example, impetigo is a prevalent superficial skin bacterial infection with a global disease burden of more than 140 million[ 9 ]. Leprosy, not only causes physical deterioration, but also increases the psychological burden[ 10 , 11 ]. In this study, we aimed to describe the burden and trend of bacterial skin diseases, to explore potential associated factors, and to predict the burden to 2045. Our findings could be helpful to develop targeted prevention and treatment measures. Methods Overview and data collection The Global Burden of Disease (GBD) study employs a robust methodology to quantify health challenges and their impact worldwide. The GBD utilizes a systematic approach to aggregate data across a wide spectrum of diseases, injuries, and risk factors[ 12 ]. A key metric derived from GBD is the Disability-adjusted life years (DALYs), which is the sum of Years of life lost (YLL) due to premature mortality and Years of healthy life lost due to disability (YLD)[ 13 ]. This composite measure offers a comprehensive assessment of the burden of disease on a population, serving as a critical tool for global health research, policy-making, and resource distribution. Bacterial skin diseases encompass a variety of conditions caused by bacterial infections that affect the skin's integrity and function. These conditions range from mild, localized infections such as impetigo to more severe and systemic infections like cellulitis[ 14 ]. The impact of bacterial skin diseases is not only limited to the physical discomfort and disfigurement they cause but also extends to the psychological and social well-being of affected individuals. In our research, we have extracted data from the GBD 2021 database, which offers a rich cross-sectional dataset from the Global Health Data Exchange. The data is categorized by age, presented in increments of five years, starting from under 5 to over 95 years. We have ensured that our study is representative of global demographics by stratifying the data by sex into male, female, and both categories. The geographical scope of our research is extensive, covering 204 countries and territories, providing a global perspective as well as insights into disparities across five distinct Socio-demographic Index (SDI) quintiles. Moreover, our findings are supported by the inclusion of 95% uncertainty intervals, which account for variability and enhance the reliability of our estimates. Statistical Analysis Joinpoint Regression Model The Joinpoint Regression Model is an essential tool for identifying pivotal shifts in disease trend trajectories and for calculating the annual percent change (APC) between these shifts, as well as the overall average annual percent change (AAPC)[ 15 ]. When the AAPC and the lower boundary of the 95% CI are positive, the age-standardized rate shows an upward trend; conversely, when the AAPC and the upper boundary of the 95% CI are negative, the age-standardized rate shows a downward trend. when 95%CI includes 0, it shows a stable trend. To compare the incidence and DALYs rates across different populations, we calculated the age-standardized incidence rate (ASIR) and the age-standardized DALYs rate (ASDR) by applying age-specific rates for each location, sex, and year to the GBD world standard population to adjust for potential confounding factors of age structure. The APC formula is (e^β − 1) x 100%, and the AAPC is the weighted average of the APCs for each segment. Decomposition analysis We conducted decomposition analysis to understand the contributions of demographic aging, epidemiological transitions, and population growth within various SDI strata to the burden of bacterial skin diseases as time progresses. For decomposition analysis, we used the HGQ method [ 16 ]. The basic calculation formula for the HGQ method can be expressed as: $$\:\varDelta\:Y={\sum\:}_{i=1}^{n}\varDelta\:{Y}_{i}+{\sum\:}_{i=1}^{n}{\sum\:}_{j=i+1}^{n}\varDelta\:{Y}_{ij}$$ ​ It is a multivariate decomposition analysis method that can consider the contributions of multiple factors to the overall change at the same time. Predict analysis In this research, we leverage the Bayesian Age-Period-Cohort (BAPC) model, coupled with a random-effects exponential link function, to perform predictive analysis within a Bayesian statistical framework[ 17 ]. The Integrated Nested Laplace Approximations method is utilized for its computational efficiency in approximate Bayesian inference, offering a valuable alternative to traditional Markov chain Monte Carlo techniques. The calculation method of the BAPC model as follows: $$\:{log}\left({Y}_{abc}\right)={\beta\:}_{0}+{\alpha\:}_{a}+{\lambda\:}_{p}+{\tau\:}_{C}+{ϵ}_{abc}$$ Our predictive model projects the age-standardized rates of incidence, and DALYs for bacterial skin diseases, extending from 2022 to 2045. Furthermore, we conduct a detailed stratified analysis for age groups ranging from under 5 to over 95 years in five-year intervals. All analyses were conducted using R version 4.3.3. A two-tailed P -value < 0.05 is considered statistically significant. Result Global burden Incidence Bacterial skin diseases, as a disease with a high global burden, have been increasing in recent years. Compared with 1990 (8,988.74 /100,000, 95%UI 8126.84 ~ 9885.03), the global overall incidence of bacterial skin diseases increases apparently in 2021 (10,823.88 /100,000, 95%UI 9,783.77 ~ 11,915.11), with an AAPC of 0.62% (0.61%~0.63%, P 50 million). The incident cases are higher in populations below 30 years old (rate > 10,000/100,000), the aged 30 ~ 55 is reduced, and then the incidence increases with age. The number and incidence of diseases in population before the age of 30 and those over 70 years old may tend to remain the same or even slightly decrease ( Fig. 1 a ) . Figure 2 a shows the findings of global Joinpoint analysis and trends. Five join-points are used to represent differential temporal trends. The general trend in incidence is on the increase from 1990 to 2021. Annual percentage is changing faster, which means the rate of global incidence is increasing (1990 ~ 1992 0.29, 1992 ~ 1995 0.41, 1995 ~ 2009 0.63, 2009 ~ 2019 0.71, 2019 ~ 2021 0.80, P < 0.05) (eTable 2) . DALY The global DALYs slightly increases from 20.82 years in 1990 to 25.45 years in 2021 ( Table 2 ) . DALYs in most age groups remained at a relatively low level (less than 100,000). The DALYs of infants and young children aged 0 ~ 4 years decreased with time, and people over 45 years increased. In terms of rate, in the past 30 years, the age of 0 ~ 4 has decreased, people older than 85 has increased distinctly, and the rest have basically stabilized ( Fig. 1 b ) . As to DALYs trend, it is used 3 joint point to reveal variable temporal trends. The three joint points are located in 2002, 2012, and 2019. From 1990 to 2002, it is a significant upward trend (APC = 0.82, P < 0.05), and 2002 to 2012 decline is even faster (APC =-1.47, P < 0.05). Although there is no statistical difference, 2012–2019 have risen again. After 2019, there is another significant decline (APC = -3,48, P < 0.05) ( Fig. 2 b ) (eTable 2) . Regional burden Incidence In 2021, the burden is still mainly concentrated on low SDI locations (22,475.21/100,000, 95%UI 20,152.69 ~ 24,880.69) and low-middle (15,646.61/100,000, 95%UI 14,133.01 ~ 17,227.75). For male, the incidence rate rise from 9,732.85/100,000 (95%UI 8,469.42 ~ 10,311.42) in 1990 to 11,192.10/100,000 (95%UI 10,108.57 ~ 12,315.34) in 2021, with an AAPC of 0.59%. Female showed a similar trend with an increase from 8,599.48/100,000 (95%UI 7,770.75 ~ 9,455.09) to 10,449.74/100,000 (95%UI 9,450.99 ~ 11,506.83), and an AAPC of 0.65%. The incidence of most locations has risen, with the highest AAPC being in the middle SDI (0.63%), followed by low-middle SDI (0.27%), low SDI (0.11%), and the high SDI is little change (0.02%). However, although there was no significant, there is a slight decrease in high-middle SDI locations (-0.08%) ( Table 1 ) . The age-standardized incidence related to bacterial skin diseases is different by countries and super-regions in 2021 but highest in sub-Saharan Africa, at 25,485.5 to 27,816.22 /100,000, and lowest in China, Caribbean and Central America and Southeast Asia super-region, at less than 3,697.4/100,000 ( Fig. 3 ) (eTable 1) . DALY DALYs of low and low-middle SDI locations is 28.5 and 34.09 in 2021. This is less than in 1990 but still higher than in other locations for the same time. There is a non-significant reduction of global AAPC (-0.11%, P > 0.05). The high SDI locations have been risen rapidly (2.06%, P < 0.05), with low-middle SDI (-0.83%, P < 0.05) and the middle (-0.14%, P < 0.05) decrease significantly ( Table 2 ) . In 2021, the DALYs of bacterial skin diseases is varied by countries and super regions in 2021, which is highest in South America, at 59.15 to 235.72, and lowest in China, Persian Gulf and Southeast Asia super-region, at less than 7.79. West Africa and Balkan Peninsula super-region are relatively lower ( Fig. 4 ) (eTable 1) . Decomposition analysis Incidence The main reason for the increasing global disease burden of bacterial skin diseases is population growth (246,053,254.36, 48.08%), followed by epidemiological changes (130,542,182.89, 25.51%). Among them, population is the main cause of all SDI locations. The most affected by the population is low (131,331,311.66, 113.83%) and low-middle SDI (106,290,492.56, 63.26%), followed by middle SDI location (43,476,784.73, 42.29%). The impact of epidemiological changes is predominantly in middle (26,536,783.67, 25.81%), low-middle (19,893,466.13, 11.65%) and low SDI (6,120,852.64,5.31%) locations. The high and high-middle SDI locations are very little affected by epidemiology changes. Aging has a certain effect on other locations, the difference is not apparent ( Fig. 5 a & b) DALY Regarding of DALYs, on a global scale, population emerges as the predominant factor driving the escalation of the disease burden for bacterial skin diseases (673,282.65, 45.85%). In regions with low (-171,983.34, -59.40%) and low-middle SDI (-243,680.88, -41.62%), epidemiological change leads to a decrease in the disease burden. Conversely, within high SDI (147,713.04, 132.20%) locations, epidemiological change is associated with an increase in the disease burden. For areas with high-middle and middle SDI, however, epidemiological change exerts no discernible influence on the disease burden ( Fig. 5 c & d) . Forecast Incidence Since 1990, the number of cases has continued to increase every year, reaching nearly 90 million in 2021 and is expected to reach nearly 1.2 billion by 2045. There is no sex difference in the trend of growth. The ASIR has also risen from less than 10,000/100,000 in 1990 to more than 11,000/100,000 in 2021, and is expected to reach more than 12,000/100,000 in 2045. The cases have increased from 1990 (number = 1,516,054), peaking in 2021 (number = 2,269,683), and decreased in 2020 and 2021. It is still predicted that it will rise again after it will be reduced to a minimum in 2035. The burden is higher for male than female ( Fig. 6 a ) (eTable 3) . DALY From 1990 to 2005, DALYs show an upward trend, starting at 30/100,000 and peaking at (32/100,000) in 2005, followed by a downward trend, and although there is a slight increase from 2012 to 2016, the overall trend is still decline. It is predicted that DALYs will decline continuously from 2022 to 2045 ( Fig. 6 b ) (eTable 3) . Discussion In our study, the incidence of bacterial skin diseases continues to rise around the world. Sub-Saharan Africa has the highest incidence, while the Caribbean and Southeast Asia have the lowest. The incidence is concentrated among infants and adolescents. Population and changes in epidemiology could be driving the increase. We predict that the global incidence of bacterial skin diseases will continue to rise until 2045. Overall, DALYs increase, though not significantly. In South America, DALYs are highest, while in the Persian Gulf and Southeast Asia, they are lowest. DALYs for infants notably decrease with age but begin to increase again after the age of 50. DALYs fluctuate at different times. The rate of DALYs will decline and the number of cases will be stabilized until 2045. Prior to this, two studies from the United States had found a gradual increase in the number of people hospitalized for bacterial infections[ 18 , 19 ]. Similarly, in the United Kingdom, the number of hospitalizations for bacterial skin diseases such as cellulitis has doubled in 22 years[ 20 ]. There are many reasons that can explain the increase. Improvements in diagnostic techniques, continuous updating of diagnostic criteria and clinical guidelines can improve the detection rate of diseases. For example, Mie scattering spectroscopy can be used to quickly diagnose bacterial infections in a noncontact way and to preliminarily distinguish infected strains[ 21 ]. Moreover, lack of reliable diagnostic evidence can lead to misdiagnosis[ 14 ]. A review of global guidelines on dermatology revealed that clinical practice guidelines for different skin diseases are not commensurate with their disease burden, and the number of clinical guidelines for bacterial skin diseases is insufficient[ 1 ]. Health-care policies can also lead to delays in diagnosis, such as the relaxation of leprosy prevention and control measures after it was declared eradicated in 2000[ 22 ]. Metagenomic DNA sequencing revealed that the skin had the highest abundance of bacteria at each spot[ 23 ]. Among them, Staphylococcus aureus and Pseudomonas aeruginosa are the most common pathogens in skin infections [ 24 ]. But the treatment of bacterial skin diseases seems to be increasingly tricky. First of all, whether it is oral medication or injection, there is a problem of low compliance[ 25 , 26 ]. Although transdermal microneedle injection can overcome this problem[ 27 ], it will take some time for this technique to be widely used in clinical practice. Second, antibiotic resistance is a growing problem. A comprehensive assessment of the burden of antimicrobial resistance found that nearly 500,000 deaths were linked to antibiotic resistance in 2019 alone[ 28 ]. In a retrospective study, nearly half of the cases of Staphylococcus aureus infection detected are MASA[ 29 ]. Finally, the use of antibiotics is confusing. The epidemiological characteristics vary from region to region. Escherichia coli and Staphylococcus aureus account for the largest burden of resistance in Europe [ 30 ], Acinetobacter baumannii resistance is mainly in Viet Nam, and E. coli is mainly in Indonesia and India[ 31 ]. Patients with cardiovascular disease, diabetes, immunosuppressed require special management. There are also some patients who receive inappropriate antibiotic treatment, which leads to longer hospital stays and increases their financial burden[ 32 , 33 ]. The concentration of the incidence in low and middle-low SDI is not only due to the large population base [ 34 ], but also the lack of access to adequate health funding and physician assistance[ 3 ]. Africa has a population of about 14.2 billion[ 35 ], but its government health expenditure of general government expenditure is 7.3%, well below the world average of 11.2%[ 36 ]. A significant proportion of sub-Saharan countries have a per capita daily income below the international poverty line[ 36 ]. Differences in patients' socioeconomic status, cultural notions of skincare and cosmetology, dietary patterns and lifestyles may have contributed to d discrepancy in SDI location[ 37 ]. Furthermore, the health threats to the world's children are predominantly in low- and middle-income countries[ 38 ].It seems that DALYs in infants and young children decrease as they age and their immunity increases[ 39 ]. Similarly, morbidity and disabling disease life expectancy in older people are likely to increase due to immunosenescence[ 40 ]. Both incidence and DALYs varied in 2019. This reminds us of the impact of COVID-19. Some studies have shown a decrease in respiratory virus transmission after some quarantine measures were imposed[ 41 – 45 ]. However, we have found that the incidence of bacterial skin diseases is still increasing, probably because most of the skin infections are due to dysbiosis, aging, and injury, rather than airborne transmission [ 46 , 47 ]. In previous studies of other viral pandemics, it has been found that infected people often have bacterial infections[ 48 ] [ 49 ]. A meta-analysis found that 6.9% of patients infected with COVID-19 also had bacterial infections, especially in critically ill patients[ 50 ]. In terms of funding and systematic reviews, the attention of bacterial skin diseases has always been low[ 51 , 52 ]. But our results show that the disease burden of bacterial skin diseases is increasing all the time. As previous studies have suggested, prevention should always come first. In order to improve the current situation, the leadership of the WHO and national health organizations are very important[ 53 ]. In addition, improving the training of specialist care and improving the literacy of dermatologists can shorten the treatment time of patients and improve their medical experience[ 54 ]. With the development of technology, the accessibility of telemedicine allows for more efficient treatment[ 55 ]. Our research sheds light on the global disease burden of bacterial skin diseases that cannot be ignored and provides a forecast for the next 20 years or so. These are some limitations. First, data for some regions are not well collected by GBD, resulting in their less representativeness. Second, the disability defined by GBD includes only symptoms such as physical and functional limitations, and does not include mental illness caused by secondary psychological burden, secondary infections and other complications. Conclusion The incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease. Declarations Funding information : Shenzhen Sanming Project (No.SZSM202311029), Shenzhen Key Medical Discipline Construction Fund (No. SZXK040) Conflicts of Interest : None declared. Ethical Approval: Not applicable. Ethics statement: Not applicable. Data availability statement : The data that support the findings of this study are available from the corresponding author upon reasonable request. Author contribution: J.X.: Data curation, Writing- Original draft preparation. J.W.: Writing- Original draft preparation. Y.L., Y.Z. and L.L. : Conceptualization, Methodology. Y.G. and B.Y.: Supervision. References Haw W. Y., Al-Janabi A., Arents B. W. M., Asfour L., Exton L. S., Grindlay D., et al.(2021) Global Guidelines in Dermatology Mapping Project (GUIDEMAP): a scoping review of dermatology clinical practice guidelines. Br J Dermatol. 2021;185(4):736-44. 10.1111/bjd.20428. Giesey R. L., Mehrmal S., Uppal P., Delost M. E., Delost G. R.(2020) Dermatoses of the Caribbean: Burden of skin disease and associated socioeconomic status in the Caribbean. JAAD Int. 2020;1(1):3-8. 10.1016/j.jdin.2020.03.002. Seth D., Cheldize K., Brown D., Freeman E. F.(2017) Global Burden of Skin Disease: Inequities and Innovations. 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Wang Y., Xiao S., Ren J., Zhang Y.(2022) Analysis of the epidemiological burden of acne vulgaris in China based on the data of global burden of disease 2019. Front Med (Lausanne). 2022;9:939584. 10.3389/fmed.2022.939584. World Health Organization. Child health [Available from: https://www.who.int/health-topics/child-health#tab=tab_2. Miles E. A., Childs C. E., Calder P. C.(2021) Long-Chain Polyunsaturated Fatty Acids (LCPUFAs) and the Developing Immune System: A Narrative Review. Nutrients. 2021;13(1). 10.3390/nu13010247. Barbé-Tuana F., Funchal G., Schmitz C. R. R., Maurmann R. M., Bauer M. E.(2020) The interplay between immunosenescence and age-related diseases. Semin Immunopathol. 2020;42(5):545-57. 10.1007/s00281-020-00806-z. Chow E. J., Uyeki T. M., Chu H. Y.(2023) The effects of the COVID-19 pandemic on community respiratory virus activity. Nat Rev Microbiol. 2023;21(3):195-210. 10.1038/s41579-022-00807-9. Collaborators GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance.(2024) 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. 10.1016/s1473-3099(24)00176-2. Ollila H. M., Partinen M., Koskela J., Borghi J., Savolainen R., Rotkirch A., et al.(2022) Face masks to prevent transmission of respiratory infections: Systematic review and meta-analysis of randomized controlled trials on face mask use. PLoS One. 2022;17(12):e0271517. 10.1371/journal.pone.0271517. Chen Y., Wang Y., Quan N., Yang J., Wu Y.(2022) Associations Between Wearing Masks and Respiratory Viral Infections: A Meta-Analysis and Systematic Review. Front Public Health. 2022;10:874693. 10.3389/fpubh.2022.874693. Nouvellet P., Bhatia S., Cori A., Ainslie K. E. C., Baguelin M., Bhatt S., et al.(2021) Reduction in mobility and COVID-19 transmission. Nat Commun. 2021;12(1):1090. 10.1038/s41467-021-21358-2. Smythe P., Wilkinson H. N.(2023) The Skin Microbiome: Current Landscape and Future Opportunities. Int J Mol Sci. 2023;24(4). 10.3390/ijms24043950. Lee H. J., Kim M.(2022) Skin Barrier Function and the Microbiome. Int J Mol Sci. 2022;23(21). 10.3390/ijms232113071. Rice T. W., Rubinson L., Uyeki T. M., Vaughn F. L., John B. B., Miller R. R., 3rd, et al.(2012) Critical illness from 2009 pandemic influenza A virus and bacterial coinfection in the United States. Crit Care Med. 2012;40(5):1487-98. 10.1097/CCM.0b013e3182416f23. Kumar A., Zarychanski R., Pinto R., Cook D. J., Marshall J., Lacroix J., et al.(2009) Critically ill patients with 2009 influenza A(H1N1) infection in Canada. Jama. 2009;302(17):1872-9. 10.1001/jama.2009.1496. Langford B. J., So M., Raybardhan S., Leung V., Westwood D., MacFadden D. R., et al.(2020) Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis. Clin Microbiol Infect. 2020;26(12):1622-9. 10.1016/j.cmi.2020.07.016. Karimkhani C., Boyers L. N., Margolis D. J., Naghavi M., Hay R. J., Williams H. C., et al.(2014) Comparing cutaneous research funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases with 2010 Global Burden of Disease results. PLoS One. 2014;9(7):e102122. 10.1371/journal.pone.0102122. Karimkhani C., Boyers L. N., Prescott L., Welch V., Delamere F. M., Nasser M., et al.(2014) Global burden of skin disease as reflected in Cochrane Database of Systematic Reviews. JAMA Dermatol. 2014;150(9):945-51. 10.1001/jamadermatol.2014.709. Faye O., Flohr C., Kabashima K., Ma L., Paller A. S., Rapelanoro F. R., et al.(2024) Atopic dermatitis: A global health perspective. J Eur Acad Dermatol Venereol. 2024;38(5):801-11. 10.1111/jdv.19723. Aneja S., Aneja S., Bordeaux J. S.(2012) Association of increased dermatologist density with lower melanoma mortality. Arch Dermatol. 2012;148(2):174-8. 10.1001/archdermatol.2011.345. Azfar R. S., Lee R. A., Castelo-Soccio L., Greenberg M. S., Bilker W. B., Gelfand J. M., et al.(2014) Reliability and validity of mobile teledermatology in human immunodeficiency virus-positive patients in Botswana: a pilot study. JAMA Dermatol. 2014;150(6):601-7. 10.1001/jamadermatol.2013.7321. Tables Table 1. Trends in incidence rate of bacterial skin diseases from 1990 to 2021 1990 2021 1990-2021 Incidence rate (per 100,000) 95%UI Incidence rate (per 100,000) 95%UI AAPC (%) Global Both 8988.74 8126.84~9885.03 10823.88 9783.77~11915.11 0.62 0.61~0.63* Male 9732.85 8469.42~10311.42 11192.10 10108.57~12315.34 0.59 0.58~0.60* Female 8599.48 7770.75~9455.09 10449.74 9450.99~11506.83 0.65 0.65~0.66* High SDI Both 6235.06 5678.68~6811.03 6265.22 5707.42~6838.84 0.02 0.00~0.04* Male 6263.16 5692.05~6860.63 6266.40 5699.56 ~6845.88 0.01 -0.01~0.02 Female 6169.11 5614.99~6740.43 6248.12 5698.73~6818.46 0.04 0.02~0.07* High-middle SDI Both 6053.88 5442.93~6697.69 5895.39 5295.52~6518.85 -0.08 -0.09~ -0.07* Male 5956.15 534268~6601.34 5892.68 5290.93~6525.83 -0.03 -0.04~-0.02* Female 6095.59 5483.23~6746.90 5872.43 5275.69~6493.48 -0.11 -0.12~-0.10* Middle SDI Both 5834.22 5265.99~6423.83 7101.17 6431.22~7814.08 0.63 0.62~0.65* Male 6320.76 5706.17~6959.82 7599.72 6875.44~8370.75 0.60 0.58~0.61* Female 5341.91 4826.15~5885.88 6601.24 5978.65~7267.62 0.68 0.66~0.69* Low-middle SDI Both 14430.52 13022.94~15895.56 15646.61 14133.01~17227.75 0.27 0.27~0.27* Male 15430.83 13916.98~17008.73 16477.00 14863.35~18148.11 0.22 0.21~0.23* Female 13405.82 12079.07~14793.46 14834.84 13387.66~16350.47 0.33 0.32~0.34* Low SDI Both 21788.22 19539.45~24135.24 22475.21 20152.69~24880.69 0.11 0.09~0.12* Male 22142.77 19817.58~24548.11 22831.59 20440.44~25287.67 0.10 0.09~0.11* Female 21450.07 19210.42~23374.86 22125.65 19824.58~24507.40 0.10 0.09~0.12* * P value<0.05, AAPC represents average annual percentage change, SDI represents Socio-demographic Index, UI represents uncertainty interval. Table 2. Trends in DALY rate of bacterial skin diseases from 1990 to 2021 1990 2021 1990-2021 DALY rate (per 100,000) 95%UI DALY rate (per 100,000) 95%UI AAPC (%) Global Both 20.82 15.61~25.49 25.45 21.46~30.36 -0.11 -0.34~0.13 Male 14.51 16.81~31.24 29.92 24.62~35.84 0.04 -0.10~0.19 Female 17.62 12.94~22.06 21.21 17.10~26.37 -0.34 -0.51~-0.17 High SDI Both 11.07 9.21~13.50 21.77 18.92~24.71 2.06 1.88~2.24 * Male 12.24 10.289~14.80 24.42 21.49~27.58 2.13 1.92~2.35 * Female 9.97 8.16~12.32 19.25 16.40~22.22 2.01 1.81~2.21 * High-middle SDI Both 14.96 12.40~17.81 17.88 15.58~20.89 -0.07 -0.47~ 0.33 Male 16.87 13.16~20.69 19.82 17.00~23.75 -0.05 -0.58~0.49 Female 13.46 11.13~16.28 15.87 13.40~19.08 -0.19 -0.65~0.27 Middle SDI Both 22.15 15.72~27.83 25.36 21.51~30.74 -0.14 -0.32~0.05 Male 24.70 15.64~32.23 29.19 24.12~35.61 0.02 -0.20~0.25 Female 19.69 13.47~26.13 21.59 17.14~27.92 -0.34 -0.52~-0.15 Low-middle SDI Both 36.22 23.74~47.94 34.09 25.82~43.91 -0.83 -1.00~-0.65 * Male 43.15 25.27~61.08 42.25 30.34~56.75 -0.55 -0.77~-0.33 * Female 29.25 16.39~41.64 26.59 17.70~37.13 -1.12 -1.41~-0.84 * Low SDI Both 35.30 19.72~52.91 28.50 16.17~40.94 -1.19 -1.29~-1.09 * Male 44.69 21.03~74.44 36.05 17.03~54.85 -1.02 -1.15~-0.88 * Female 25.93 12.75~41.39 21.28 10.44~34.08 -1.41 -1.50~-1.32 * * P value<0.05, AAPC represents average annual percentage change, DALY represents Disability-adjusted life years, SDI represents Socio-demographic Index, UI represents uncertainty interval. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 01 Nov, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers invited by journal 03 Sep, 2024 Editor assigned by journal 28 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 26 Aug, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4978734","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":359532060,"identity":"2d5be973-df62-485b-ba60-49e00463dfd3","order_by":0,"name":"Jiaxu Gu","email":"","orcid":"","institution":"Department of Dermatology, Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxu","middleName":"","lastName":"Gu","suffix":""},{"id":359532061,"identity":"783cb765-ed32-4260-880c-6db90c9af434","order_by":1,"name":"Jiaming Wang","email":"","orcid":"","institution":"Department of Dermatology, Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaming","middleName":"","lastName":"Wang","suffix":""},{"id":359532063,"identity":"e662a331-dd7b-4784-92d0-e2a3045e5ea2","order_by":2,"name":"Yannan Li","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Yannan","middleName":"","lastName":"Li","suffix":""},{"id":359532069,"identity":"b8a30cf1-2117-4181-b39c-e001e1adfbb6","order_by":3,"name":"Lianjie Li","email":"","orcid":"","institution":"Warren Skin Care Center","correspondingAuthor":false,"prefix":"","firstName":"Lianjie","middleName":"","lastName":"Li","suffix":""},{"id":359532071,"identity":"e77ea2cf-619e-4c01-80d8-24a4acd3c8cb","order_by":4,"name":"Yanfen Zou","email":"","orcid":"","institution":"Department of Dermatology, Peking University Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanfen","middleName":"","lastName":"Zou","suffix":""},{"id":359532072,"identity":"bac7e158-ce2a-4f9f-9870-61258c6f2017","order_by":5,"name":"Yang Guo","email":"","orcid":"","institution":"Department of Epidemiology and Statistics, School of Public Health, Hebei Key Laboratory of Environment and Human Health, Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Guo","suffix":""},{"id":359532073,"identity":"e040d096-310c-4c3a-a480-453a20e3d43d","order_by":6,"name":"Bo Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDCCAzxgirEBiB8kVNiQpoXZ4MGZNNK0sEk+bDtEWAff7bMHH/O2Mcj2S59Oq0hgO8DA396dgFeL5Lm8ZGOgFuOZfbnbbiTw3GGQOHN2A14tBmd4zKSBWhI3nOEFapF4xmAgkUuklv1ALQUJBodJ0LKBh3cbQ0ICEVokz/AlG845x2A84wzvZomEA2k8BP3Cd4b34IM3ZcAQ6+Hd+PHnPxs5/vZe/FpAgImH4T+cw0NQOQgw/iBK2SgYBaNgFIxYAABd9kni2C14GAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Dermatology, Peking University Shenzhen Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-08-26 14:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4978734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4978734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66887591,"identity":"1c7be8e5-0024-4c2e-81ce-67c8f799db15","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2164325,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Incidence Cases and Incidence(a), Number of Disability-Adjusted Life Years and Rates (b) by Age Groups.\u003c/p\u003e\n\u003cp\u003eThe line denotes 2022.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/1ce8b4207b14526058e349d0.png"},{"id":66887594,"identity":"28f79e43-0beb-4e8d-a4a6-1edb149196bb","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431751,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bacterial Skin Diseases Incidence(a) and the Change of DALYs(b) per 100,000 Population Analyzed by Joinpoint Analysis.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e* \u003c/sup\u003erepresents \u003cem\u003eP\u003c/em\u003e value\u0026lt;0.05. The points in the line where the gradient changes denote joinpoints. APC represents annual percent change, AAPC represents average annual percentage change, DALY represents Disability-Adjusted Life Year.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/fd6518fe9fc7738a59950cab.png"},{"id":66887593,"identity":"5d3523c1-dab0-43be-bf49-37e1b61dde2a","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232367,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Age-Standardized Incidence per 100,000 Population for Bacterial Skin Diseases of Super Regions, 2021\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/beb2af0748e9227f9b45eb2d.png"},{"id":66887595,"identity":"04c33a41-7bb1-4ff0-9d33-2b1b9c832779","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":234529,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal DALYs for Bacterial Skin Diseases of Super Regions, 2021\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/49ad2d1f14d202dca00581a0.png"},{"id":66887597,"identity":"61069a96-1558-4f2b-a33d-9272b9676096","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":761416,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition Analysis of Bacterial Skin Diseases on the Number(a) and Proportion(b) of Incidence Cases, Number(c) and Proportion(d) of DALYs.\u003c/p\u003e\n\u003cp\u003eDALY: Disability-Adjusted Life Year. SDI represents Socio-demographic Index\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/d94c2a052b5d0b45390c5062.png"},{"id":66888070,"identity":"8331eb47-0df3-452a-b5d9-4cead6115f8b","added_by":"auto","created_at":"2024-10-17 14:07:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83343,"visible":true,"origin":"","legend":"\u003cp\u003eAge- Standardized Rate of Incidence(a) and DALYs(b) of Bacterial Skin Diseases from 1990 to 2021 and Forecast to 2045.\u003c/p\u003e\n\u003cp\u003eASR: Age-Standardized Rate; DALY: Disability-Adjusted Life Year.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/e4f596144a94bc47a3f9ae2f.png"},{"id":66889006,"identity":"f60f0b9b-6921-4f29-a811-d135f3b80db2","added_by":"auto","created_at":"2024-10-17 14:15:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3977388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/753fe5f4-4f41-47f1-b340-c83497e86fce.pdf"},{"id":66887596,"identity":"12fb4d7b-6e2a-4655-8e60-59a14c693c41","added_by":"auto","created_at":"2024-10-17 13:59:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":430550,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4978734/v1/c4bf3e25dc8711bfe34f9c2c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global burden of bacterial skin diseases from 1990 to 2045: An analysis based on Global Burden Disease data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkin disease is one of the most common diseases affecting humans of all ages, contributing to a heavy global disease burden. The incidence of disabling skin diseases is even higher in poor areas[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In a previous study, skin diseases ranked fourth among global disabling diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe disease burden caused by bacterial skin diseases accounts for a significant proportion of skin diseases[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In International Classification of Diseases-11, published by the WHO, bacterial skin diseases refer to disorders of the skin and/or subcutaneous tissues caused by bacteria[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, impetigo is a prevalent superficial skin bacterial infection with a global disease burden of more than 140 million[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Leprosy, not only causes physical deterioration, but also increases the psychological burden[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aimed to describe the burden and trend of bacterial skin diseases, to explore potential associated factors, and to predict the burden to 2045. Our findings could be helpful to develop targeted prevention and treatment measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview and data collection\u003c/h2\u003e \u003cp\u003eThe Global Burden of Disease (GBD) study employs a robust methodology to quantify health challenges and their impact worldwide. The GBD utilizes a systematic approach to aggregate data across a wide spectrum of diseases, injuries, and risk factors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A key metric derived from GBD is the Disability-adjusted life years (DALYs), which is the sum of Years of life lost (YLL) due to premature mortality and Years of healthy life lost due to disability (YLD)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This composite measure offers a comprehensive assessment of the burden of disease on a population, serving as a critical tool for global health research, policy-making, and resource distribution.\u003c/p\u003e \u003cp\u003eBacterial skin diseases encompass a variety of conditions caused by bacterial infections that affect the skin's integrity and function. These conditions range from mild, localized infections such as impetigo to more severe and systemic infections like cellulitis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The impact of bacterial skin diseases is not only limited to the physical discomfort and disfigurement they cause but also extends to the psychological and social well-being of affected individuals.\u003c/p\u003e \u003cp\u003eIn our research, we have extracted data from the GBD 2021 database, which offers a rich cross-sectional dataset from the Global Health Data Exchange. The data is categorized by age, presented in increments of five years, starting from under 5 to over 95 years. We have ensured that our study is representative of global demographics by stratifying the data by sex into male, female, and both categories. The geographical scope of our research is extensive, covering 204 countries and territories, providing a global perspective as well as insights into disparities across five distinct Socio-demographic Index (SDI) quintiles. Moreover, our findings are supported by the inclusion of 95% uncertainty intervals, which account for variability and enhance the reliability of our estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eJoinpoint Regression Model\u003c/h2\u003e \u003cp\u003eThe Joinpoint Regression Model is an essential tool for identifying pivotal shifts in disease trend trajectories and for calculating the annual percent change (APC) between these shifts, as well as the overall average annual percent change (AAPC)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. When the AAPC and the lower boundary of the 95% CI are positive, the age-standardized rate shows an upward trend; conversely, when the AAPC and the upper boundary of the 95% CI are negative, the age-standardized rate shows a downward trend. when 95%CI includes 0, it shows a stable trend.\u003c/p\u003e \u003cp\u003eTo compare the incidence and DALYs rates across different populations, we calculated the age-standardized incidence rate (ASIR) and the age-standardized DALYs rate (ASDR) by applying age-specific rates for each location, sex, and year to the GBD world standard population to adjust for potential confounding factors of age structure. The APC formula is (e^β \u0026minus;\u0026thinsp;1) x 100%, and the AAPC is the weighted average of the APCs for each segment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition analysis\u003c/h2\u003e \u003cp\u003eWe conducted decomposition analysis to understand the contributions of demographic aging, epidemiological transitions, and population growth within various SDI strata to the burden of bacterial skin diseases as time progresses. For decomposition analysis, we used the HGQ method [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The basic calculation formula for the HGQ method can be expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:Y={\\sum\\:}_{i=1}^{n}\\varDelta\\:{Y}_{i}+{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{j=i+1}^{n}\\varDelta\\:{Y}_{ij}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e​ It is a multivariate decomposition analysis method that can consider the contributions of multiple factors to the overall change at the same time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePredict analysis\u003c/h2\u003e \u003cp\u003eIn this research, we leverage the Bayesian Age-Period-Cohort (BAPC) model, coupled with a random-effects exponential link function, to perform predictive analysis within a Bayesian statistical framework[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Integrated Nested Laplace Approximations method is utilized for its computational efficiency in approximate Bayesian inference, offering a valuable alternative to traditional Markov chain Monte Carlo techniques. The calculation method of the BAPC model as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{log}\\left({Y}_{abc}\\right)={\\beta\\:}_{0}+{\\alpha\\:}_{a}+{\\lambda\\:}_{p}+{\\tau\\:}_{C}+{ϵ}_{abc}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOur predictive model projects the age-standardized rates of incidence, and DALYs for bacterial skin diseases, extending from 2022 to 2045. Furthermore, we conduct a detailed stratified analysis for age groups ranging from under 5 to over 95 years in five-year intervals.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.3.3. A two-tailed \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGlobal burden\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eIncidence\u003c/h2\u003e \u003cp\u003eBacterial skin diseases, as a disease with a high global burden, have been increasing in recent years. Compared with 1990 (8,988.74 /100,000, 95%UI 8126.84\u0026thinsp;~\u0026thinsp;9885.03), the global overall incidence of bacterial skin diseases increases apparently in 2021 (10,823.88 /100,000, 95%UI 9,783.77\u0026thinsp;~\u0026thinsp;11,915.11), with an AAPC of 0.62% (0.61%~0.63%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall, the number of cases is on the rise across all age groups. Taking 2021 data as an example, the burden is concentrated on populations aged 0\u0026thinsp;~\u0026thinsp;35 (number\u0026thinsp;\u0026gt;\u0026thinsp;50\u0026nbsp;million). The incident cases are higher in populations below 30 years old (rate\u0026thinsp;\u0026gt;\u0026thinsp;10,000/100,000), the aged 30\u0026thinsp;~\u0026thinsp;55 is reduced, and then the incidence increases with age. The number and incidence of diseases in population before the age of 30 and those over 70 years old may tend to remain the same or even slightly decrease \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the findings of global Joinpoint analysis and trends. Five join-points are used to represent differential temporal trends. The general trend in incidence is on the increase from 1990 to 2021. Annual percentage is changing faster, which means the rate of global incidence is increasing (1990\u0026thinsp;~\u0026thinsp;1992 0.29, 1992\u0026thinsp;~\u0026thinsp;1995 0.41, 1995\u0026thinsp;~\u0026thinsp;2009 0.63, 2009\u0026thinsp;~\u0026thinsp;2019 0.71, 2019\u0026thinsp;~\u0026thinsp;2021 0.80, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(eTable 2)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDALY\u003c/h2\u003e \u003cp\u003eThe global DALYs slightly increases from 20.82 years in 1990 to 25.45 years in 2021 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. DALYs in most age groups remained at a relatively low level (less than 100,000). The DALYs of infants and young children aged 0\u0026thinsp;~\u0026thinsp;4 years decreased with time, and people over 45 years increased. In terms of rate, in the past 30 years, the age of 0\u0026thinsp;~\u0026thinsp;4 has decreased, people older than 85 has increased distinctly, and the rest have basically stabilized \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. As to DALYs trend, it is used 3 joint point to reveal variable temporal trends. The three joint points are located in 2002, 2012, and 2019. From 1990 to 2002, it is a significant upward trend (APC\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 2002 to 2012 decline is even faster (APC =-1.47, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although there is no statistical difference, 2012\u0026ndash;2019 have risen again. After 2019, there is another significant decline (APC = -3,48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e) (eTable 2)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRegional burden\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eIncidence\u003c/h2\u003e \u003cp\u003eIn 2021, the burden is still mainly concentrated on low SDI locations (22,475.21/100,000, 95%UI 20,152.69\u0026thinsp;~\u0026thinsp;24,880.69) and low-middle (15,646.61/100,000, 95%UI 14,133.01\u0026thinsp;~\u0026thinsp;17,227.75). For male, the incidence rate rise from 9,732.85/100,000 (95%UI 8,469.42\u0026thinsp;~\u0026thinsp;10,311.42) in 1990 to 11,192.10/100,000 (95%UI 10,108.57\u0026thinsp;~\u0026thinsp;12,315.34) in 2021, with an AAPC of 0.59%. Female showed a similar trend with an increase from 8,599.48/100,000 (95%UI 7,770.75\u0026thinsp;~\u0026thinsp;9,455.09) to 10,449.74/100,000 (95%UI 9,450.99\u0026thinsp;~\u0026thinsp;11,506.83), and an AAPC of 0.65%. The incidence of most locations has risen, with the highest AAPC being in the middle SDI (0.63%), followed by low-middle SDI (0.27%), low SDI (0.11%), and the high SDI is little change (0.02%). However, although there was no significant, there is a slight decrease in high-middle SDI locations (-0.08%) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The age-standardized incidence related to bacterial skin diseases is different by countries and super-regions in 2021 but highest in sub-Saharan Africa, at 25,485.5 to 27,816.22 /100,000, and lowest in China, Caribbean and Central America and Southeast Asia super-region, at less than 3,697.4/100,000 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e) (eTable 1)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDALY\u003c/h2\u003e \u003cp\u003eDALYs of low and low-middle SDI locations is 28.5 and 34.09 in 2021. This is less than in 1990 but still higher than in other locations for the same time. There is a non-significant reduction of global AAPC (-0.11%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The high SDI locations have been risen rapidly (2.06%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with low-middle SDI (-0.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the middle (-0.14%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decrease significantly \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In 2021, the DALYs of bacterial skin diseases is varied by countries and super regions in 2021, which is highest in South America, at 59.15 to 235.72, and lowest in China, Persian Gulf and Southeast Asia super-region, at less than 7.79. West Africa and Balkan Peninsula super-region are relatively lower \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e) (eTable 1)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDecomposition analysis\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eIncidence\u003c/h2\u003e \u003cp\u003eThe main reason for the increasing global disease burden of bacterial skin diseases is population growth (246,053,254.36, 48.08%), followed by epidemiological changes (130,542,182.89, 25.51%). Among them, population is the main cause of all SDI locations. The most affected by the population is low (131,331,311.66, 113.83%) and low-middle SDI (106,290,492.56, 63.26%), followed by middle SDI location (43,476,784.73, 42.29%). The impact of epidemiological changes is predominantly in middle (26,536,783.67, 25.81%), low-middle (19,893,466.13, 11.65%) and low SDI (6,120,852.64,5.31%) locations. The high and high-middle SDI locations are very little affected by epidemiology changes. Aging has a certain effect on other locations, the difference is not apparent \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea \u003cb\u003e\u0026amp; b)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDALY\u003c/h2\u003e \u003cp\u003eRegarding of DALYs, on a global scale, population emerges as the predominant factor driving the escalation of the disease burden for bacterial skin diseases (673,282.65, 45.85%). In regions with low (-171,983.34, -59.40%) and low-middle SDI (-243,680.88, -41.62%), epidemiological change leads to a decrease in the disease burden. Conversely, within high SDI (147,713.04, 132.20%) locations, epidemiological change is associated with an increase in the disease burden. For areas with high-middle and middle SDI, however, epidemiological change exerts no discernible influence on the disease burden \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec \u003cb\u003e\u0026amp; d)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eForecast\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eIncidence\u003c/h2\u003e \u003cp\u003eSince 1990, the number of cases has continued to increase every year, reaching nearly 90\u0026nbsp;million in 2021 and is expected to reach nearly 1.2\u0026nbsp;billion by 2045. There is no sex difference in the trend of growth. The ASIR has also risen from less than 10,000/100,000 in 1990 to more than 11,000/100,000 in 2021, and is expected to reach more than 12,000/100,000 in 2045. The cases have increased from 1990 (number\u0026thinsp;=\u0026thinsp;1,516,054), peaking in 2021 (number\u0026thinsp;=\u0026thinsp;2,269,683), and decreased in 2020 and 2021. It is still predicted that it will rise again after it will be reduced to a minimum in 2035. The burden is higher for male than female \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e) (eTable 3)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDALY\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2005, DALYs show an upward trend, starting at 30/100,000 and peaking at (32/100,000) in 2005, followed by a downward trend, and although there is a slight increase from 2012 to 2016, the overall trend is still decline. It is predicted that DALYs will decline continuously from 2022 to 2045 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb\u003cb\u003e) (eTable 3)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, the incidence of bacterial skin diseases continues to rise around the world. Sub-Saharan Africa has the highest incidence, while the Caribbean and Southeast Asia have the lowest. The incidence is concentrated among infants and adolescents. Population and changes in epidemiology could be driving the increase. We predict that the global incidence of bacterial skin diseases will continue to rise until 2045. Overall, DALYs increase, though not significantly. In South America, DALYs are highest, while in the Persian Gulf and Southeast Asia, they are lowest. DALYs for infants notably decrease with age but begin to increase again after the age of 50. DALYs fluctuate at different times. The rate of DALYs will decline and the number of cases will be stabilized until 2045.\u003c/p\u003e \u003cp\u003ePrior to this, two studies from the United States had found a gradual increase in the number of people hospitalized for bacterial infections[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, in the United Kingdom, the number of hospitalizations for bacterial skin diseases such as cellulitis has doubled in 22 years[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. There are many reasons that can explain the increase. Improvements in diagnostic techniques, continuous updating of diagnostic criteria and clinical guidelines can improve the detection rate of diseases. For example, Mie scattering spectroscopy can be used to quickly diagnose bacterial infections in a noncontact way and to preliminarily distinguish infected strains[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, lack of reliable diagnostic evidence can lead to misdiagnosis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A review of global guidelines on dermatology revealed that clinical practice guidelines for different skin diseases are not commensurate with their disease burden, and the number of clinical guidelines for bacterial skin diseases is insufficient[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Health-care policies can also lead to delays in diagnosis, such as the relaxation of leprosy prevention and control measures after it was declared eradicated in 2000[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetagenomic DNA sequencing revealed that the skin had the highest abundance of bacteria at each spot[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among them, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e are the most common pathogens in skin infections [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. But the treatment of bacterial skin diseases seems to be increasingly tricky. First of all, whether it is oral medication or injection, there is a problem of low compliance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although transdermal microneedle injection can overcome this problem[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], it will take some time for this technique to be widely used in clinical practice. Second, antibiotic resistance is a growing problem. A comprehensive assessment of the burden of antimicrobial resistance found that nearly 500,000 deaths were linked to antibiotic resistance in 2019 alone[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In a retrospective study, nearly half of the cases of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection detected are MASA[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Finally, the use of antibiotics is confusing. The epidemiological characteristics vary from region to region. \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e account for the largest burden of resistance in Europe [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e resistance is mainly in Viet Nam, and \u003cem\u003eE. coli\u003c/em\u003e is mainly in Indonesia and India[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Patients with cardiovascular disease, diabetes, immunosuppressed require special management. There are also some patients who receive inappropriate antibiotic treatment, which leads to longer hospital stays and increases their financial burden[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concentration of the incidence in low and middle-low SDI is not only due to the large population base [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], but also the lack of access to adequate health funding and physician assistance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Africa has a population of about 14.2 billion[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], but its government health expenditure of general government expenditure is 7.3%, well below the world average of 11.2%[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A significant proportion of sub-Saharan countries have a per capita daily income below the international poverty line[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Differences in patients' socioeconomic status, cultural notions of skincare and cosmetology, dietary patterns and lifestyles may have contributed to d discrepancy in SDI location[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, the health threats to the world's children are predominantly in low- and middle-income countries[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].It seems that DALYs in infants and young children decrease as they age and their immunity increases[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, morbidity and disabling disease life expectancy in older people are likely to increase due to immunosenescence[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth incidence and DALYs varied in 2019. This reminds us of the impact of COVID-19. Some studies have shown a decrease in respiratory virus transmission after some quarantine measures were imposed[\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, we have found that the incidence of bacterial skin diseases is still increasing, probably because most of the skin infections are due to dysbiosis, aging, and injury, rather than airborne transmission [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In previous studies of other viral pandemics, it has been found that infected people often have bacterial infections[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A meta-analysis found that 6.9% of patients infected with COVID-19 also had bacterial infections, especially in critically ill patients[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of funding and systematic reviews, the attention of bacterial skin diseases has always been low[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. But our results show that the disease burden of bacterial skin diseases is increasing all the time. As previous studies have suggested, prevention should always come first. In order to improve the current situation, the leadership of the WHO and national health organizations are very important[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In addition, improving the training of specialist care and improving the literacy of dermatologists can shorten the treatment time of patients and improve their medical experience[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. With the development of technology, the accessibility of telemedicine allows for more efficient treatment[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research sheds light on the global disease burden of bacterial skin diseases that cannot be ignored and provides a forecast for the next 20 years or so. These are some limitations. First, data for some regions are not well collected by GBD, resulting in their less representativeness. Second, the disability defined by GBD includes only symptoms such as physical and functional limitations, and does not include mental illness caused by secondary psychological burden, secondary infections and other complications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;information\u003c/strong\u003e:\u0026nbsp;Shenzhen Sanming Project (No.SZSM202311029),\u0026nbsp;Shenzhen Key Medical Discipline Construction Fund (No. SZXK040)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts\u0026nbsp;of\u0026nbsp;Interest\u003c/strong\u003e:\u0026nbsp;None\u0026nbsp;declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e \u003cstrong\u003eJ.X.:\u0026nbsp;\u003c/strong\u003eData curation, Writing- Original draft preparation.\u003cstrong\u003eJ.W.:\u003c/strong\u003eWriting- Original draft preparation.\u0026nbsp;\u003cstrong\u003eY.L., Y.Z.\u0026nbsp;\u003c/strong\u003eand\u0026nbsp;\u003cstrong\u003eL.L.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Methodology. \u003cstrong\u003eY.G.\u003c/strong\u003e and \u003cstrong\u003eB.Y.:\u003c/strong\u003eSupervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHaw W. 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S., Lee R. A., Castelo-Soccio L., Greenberg M. S., Bilker W. B., Gelfand J. M., et al.(2014) Reliability and validity of mobile teledermatology in human immunodeficiency virus-positive patients in Botswana: a pilot study. JAMA Dermatol. 2014;150(6):601-7. 10.1001/jamadermatol.2013.7321.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Trends in incidence rate of bacterial skin diseases from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990-2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 100,000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 100,000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAPC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e8988.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e8126.84~9885.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e10823.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e9783.77~11915.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.61~0.63*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e9732.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e8469.42~10311.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e11192.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e10108.57~12315.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.58~0.60*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e8599.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e7770.75~9455.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e10449.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e9450.99~11506.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.65~0.66*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6235.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5678.68~6811.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6265.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5707.42~6838.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.00~0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6263.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5692.05~6860.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6266.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5699.56 ~6845.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e-0.01~0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6169.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5614.99~6740.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6248.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5698.73~6818.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.02~0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6053.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5442.93~6697.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5895.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5295.52~6518.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.09~ -0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5956.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e534268~6601.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5892.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5290.93~6525.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e-0.04~-0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6095.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5483.23~6746.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5872.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5275.69~6493.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e-0.12~-0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5834.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e5265.99~6423.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7101.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e6431.22~7814.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.62~0.65*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6320.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5706.17~6959.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e7599.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e6875.44~8370.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.58~0.61*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e5341.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e4826.15~5885.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e6601.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e5978.65~7267.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.66~0.69*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e14430.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e13022.94~15895.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e15646.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e14133.01~17227.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.27~0.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e15430.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e13916.98~17008.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e16477.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e14863.35~18148.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.21~0.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e13405.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e12079.07~14793.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e14834.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e13387.66~16350.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.32~0.34*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" rowspan=\"3\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e21788.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e19539.45~24135.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e22475.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e20152.69~24880.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.09~0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e22142.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e19817.58~24548.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e22831.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e20440.44~25287.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.09~0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.641975308641975%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e21450.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e19210.42~23374.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e22125.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.45679012345679%\"\u003e\n \u003cp\u003e19824.58~24507.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.172839506172839%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.049382716049383%\"\u003e\n \u003cp\u003e0.09~0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003e*\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e value\u0026lt;0.05, AAPC represents average annual percentage change, SDI represents Socio-demographic Index, UI represents uncertainty interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Trends in DALY rate of bacterial skin diseases from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990-2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALY rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 100,000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDALY rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 100,000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%UI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAPC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e20.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e15.61~25.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e25.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e21.46~30.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-0.34~0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e14.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e16.81~31.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e24.62~35.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.10~0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e17.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e12.94~22.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e21.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e17.10~26.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.51~-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e9.21~13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e21.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e18.92~24.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1.88~2.24\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e12.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e10.289~14.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e24.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e21.49~27.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e1.92~2.35\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e8.16~12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e19.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e16.40~22.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e1.81~2.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e12.40~17.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e15.58~20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-0.47~ 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e16.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e13.16~20.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e19.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e17.00~23.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.58~0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e13.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e11.13~16.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e15.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e13.40~19.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.65~0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e15.72~27.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e25.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.20~0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e19.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e13.47~26.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e17.14~27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.52~-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e36.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e23.74~47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e34.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e25.82~43.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-1.00~-0.65\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e43.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e25.27~61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e42.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e30.34~56.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.77~-0.33\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e29.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e16.39~41.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e17.70~37.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.41~-0.84\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"3\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e35.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e19.72~52.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e28.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e16.17~40.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e-1.29~-1.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e44.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e21.03~74.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e36.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e17.03~54.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.15~-0.88\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e25.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.61904761904762%\"\u003e\n \u003cp\u003e12.75~41.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e21.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.238095238095237%\"\u003e\n \u003cp\u003e10.44~34.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.50~-1.32\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e value\u0026lt;0.05, AAPC represents average annual percentage change, DALY represents Disability-adjusted life years, SDI represents Socio-demographic Index, UI represents uncertainty interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-dermatological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Archives of Dermatological Research](https://www.springer.com/journal/403)","snPcode":"403","submissionUrl":"https://submission.nature.com/new-submission/403/3","title":"Archives of Dermatological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bacterial skin infections, global disease burden, forecast, public health, socio-demographic index","lastPublishedDoi":"10.21203/rs.3.rs-4978734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4978734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe global burden of bacterial skin diseases has not been well evaluated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe aimed to describe the burden and trend of bacterial skin diseases, to explore potential associated factors, and to predict the burden up to 2045.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eData on incidence and disability-adjusted life years (DALYs) of bacterial skin diseases were obtained from Global Burden of Disease 2021. We used average annual percent change (AAPC) by Joinpoint Regression to quantify the temporal trends. We conducted decomposition analysis to understand the contribution of aging, epidemiological changes, and population growth. Bayesian Age-Period-Cohort model was used to predict burden up to 2045.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGlobal incidence rate of bacterial skin diseases increased from 8,988.74 per 100,000 in 1990 to 10,823.88 per 100,000, with AAPC of 0.62% (0.61\u0026thinsp;~\u0026thinsp;0.63%). The highest incidence rate was in low Socio-demographic Index (SDI) region and population aged\u0026thinsp;\u0026lt;\u0026thinsp;35. The DALY rate increased from 20.82 per 100,000 in 1990 to 25.45 per 100,000 in 2021, with AAPC of -0.11% (-0.34\u0026thinsp;~\u0026thinsp;0.13%). The highest increase of DALY was in high SDI region and population aged\u0026thinsp;\u0026gt;\u0026thinsp;85. The major drivers of incident case rise were population growth, followed by epidemiological changes; the major drivers of DALY case rise were population growth, followed by aging. Increasing trends were seen in prediction of incidence rate, incident cases and DALY cases; decreasing trend of DALY rate prediction was seen.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease.\u003c/p\u003e","manuscriptTitle":"Global burden of bacterial skin diseases from 1990 to 2045: An analysis based on Global Burden Disease data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 13:59:26","doi":"10.21203/rs.3.rs-4978734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T19:36:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T23:43:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327110434909642994261675781473609151647","date":"2024-11-01T16:51:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84570917091360503849036262770198318625","date":"2024-09-26T22:59:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-03T21:36:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-28T12:51:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-28T12:47:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Dermatological Research","date":"2024-08-26T14:35:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-dermatological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Archives of Dermatological Research](https://www.springer.com/journal/403)","snPcode":"403","submissionUrl":"https://submission.nature.com/new-submission/403/3","title":"Archives of Dermatological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"736100fc-10e9-4485-957f-3f9acf700e1b","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-03T18:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 13:59:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4978734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4978734","identity":"rs-4978734","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}