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

doi:10.21203/rs.3.rs-4978734/v1

Background

The global burden of bacterial skin diseases has not been well evaluated.

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 < 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 ~ 0.13%). The highest increase of DALY was in high 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

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.

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.

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 < 0.05) (Table 1). 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 ~ 35 (number > 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. 1a).

Figure 2a 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. 1b). 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. 2b) (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. 5a & 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. 5c & 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. 6a) (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. 6b) (eTable 3).

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.

The incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease.

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.

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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.

No competing interests reported.

Supplement.docx

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

Status:

Version 1

Abstract

Figures

Introduction

Methods

Overview and data collection

Statistical Analysis

Joinpoint Regression Model

Decomposition analysis

Predict analysis

Result

Global burden

Incidence

DALY

Regional burden

Incidence

DALY

Decomposition analysis

Incidence

DALY

Forecast

Incidence

DALY

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1