Abstract
Background
Legionella infections are a major global health issue, yet there’s limited research on their impact and trends. We aimed to systematically analyzed the long-term trends in Legionella spp. infection-associated diseases (LSIADs) burden from 1990 to 2021.
Methods
Age-standardized disability-adjusted life years (ASR-DALYs) and age-standardized death rates (ASDRs) of LSIADs from 1990 to 2021 were accessed from the Global Burden of Disease (GBD) 2021, which utilized the Bayesian hierarchical meta-regression tool and the Cause of Death Ensemble model to estimate these metrics. Trends in disease burden across age, sex, region, and Socio-Demographic Index (SDI) levels were estimated using annual percentage changes (EAPCs) and annual percentage changes (APCs).
Results
Globally, the ASR-DALYs and ASDRs for LSIADs in 2021 were 24.74 and 0.86 per 100,000, respectively, with the highest rates observed in regions with low SDI. From 1990 to 2021, while the overall burden of LSIADs showed a downward trend, the highest ASR-DALYs (101.85 per 100,000) and ASDRs (8.15 per 100,000) were observed in individuals over 70 years of age, accompanied by a corresponding increase in deaths (EAPCs = 0.17%, 95% CI: 0.09–0.26%). Furthermore, increases in ASR-DALYs and ASDRs for LSIADs were also noted among those aged 15–49 years (EAPCs = 0.43% and 0.57%, respectively) and those aged 50–69 years (EAPCs = 0.14% and 0.09%, respectively).
Conclusion
Higher disease burdens and increasing trends have been observed in specific age groups and regions, which require the implementation of water quality management plans, enhanced readiness of health facilities, and improved sanitation infrastructure.
Supplementary Information
The online version contains supplementary material available at 10.1007/s44197-025-00342-9.
Keywords: Legionella spp. infection-associated diseases, Global burden of disease, Epidemiology, Trend, A observational study
Background
Legionella spp.is a gram-negative bacterium that are found primarily in the natural environment or in man-made systems of water [1, 2]. More than 60 Legionella species have been proposed, and at least 24 cause infection in humans [3]. The most common among them is Legionella pneumophila, which accounts for approximately 85% ~ 90% of infection cases. Human infection is transmitted mainly by inhaling aerosols containing microorganisms of the Legionellaceae and genus Legionella, but may also be transmitted from humans to humans [4, 5]. Since 1976, Legionella spp. infection has been recognized as a serious public health concern as the causative agent of Legionellosis [6, 7]. It is estimated that 25,000 to 100,000 cases in the United States are diagnosed legionellosis each year [8]. In North America and Western Europe, 1–13% of all types of pneumonia are associated with this pathogen [9], and surveillance systems to track the spread of the disease have revealed an increase in infection-related diseases caused by Legionella spp [10, 11]. Until now, the geographic variation of Legionnaires’ disease (LD), a prevalent cause of community-acquired pneumonia (CAP) necessitating hospitalization, remains uncertain [12].
Prior researches have indicated an elevated risk of Legionella infection attributable to the implementation of diverse mandatory quarantine measures and closures by various local and national governments worldwide in response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), initially identified in Wuhan, China, in December 2019 [13–15]. Moreover, Legionella combines with other microorganisms such as Streptococcus pneumoniae [16], or viral infections, such as SARS CoV-2 [17], to cause severe infection and sepsis. Therefore, there may be a change in the burden of Legionella spp. infection-associated diseases following the COVID-19 pandemic. However, the disease burden associated with Legionella spp. infection in humans is currently unknown, especially during the COVID-19 pandemic.
The Global Burden of Disease 2021 (GBD) study, which outlines the epidemiology and burden of disease caused by 371 diseases and injuries, reveals for the first time data such as incidence, prevalence, years lived with disability (YLDs), and disability-adjusted life-years (DALYs), associated with Legionella spp. infection, which have never been described previously [18, 19]. We used the GBD 2021 database to describe the DALYs and death burdens of Legionella spp. infection-associated diseases with disaggregation based on age, year and SDI region as well as to evaluate their temporal trends from 1990 to 2021. Additionally, we explored the differences in disease burden before and during the COVID-19 pandemic. The results of this study will contribute to the comprehensive understanding of the Legionella spp. infection burden, facilitating the development of a global response to mitigate its impact on public health.
Materials and Methods
Data Sources
We conducted an observational analysis of diseases associated with Legionella spp. infections utilizing data from the GBD 2021 study. The entirety of these data is freely accessible through the Global Health Data Exchange (http://ghdx.healthdata.org/gbd-results-tool), which provides comprehensive information on the data, methodologies, and statistical modeling from previous reports [18]. Seven super regions were classified by the GBD, i.e. Sub-Saharan Africa, North Africa and the Middle East, South Asia, Southeast Asia and East Asia and Oceania, Latin America and Caribbean, Central Europe and Eastern Europe and Central Asia, as well as high-income. GBD also categorizes countries into various regions and super regions. The sociodemographic (SDI), created by the Institute for Health Metrics and Evaluation in 2015, measures the development level of countries or regions by linking social development with population health outcomes. The geometric mean of three indices: total fertility rate for those under 25, mean education for those 15 and older, and lag-distributed income per capita are included. The 204 countries and territories were categorized into five SDI regions: low, low-middle, middle, high-middle, and high [18, 20]. To further refine the burden across different age groups, we divided the age into five groups (< 5 years, 5–14 years, 15–49 years, 50–69 years and > = 70 years).
Disease Definition
The Legionella spp. infection-associated diseases included Legionnaires disease and non-pneumonic Legionnaires disease (Pontiac fever) identified by the International Classification of Disease, Ninth Revision (ICD-9) codes (A48.1 and 48.2). Legionella spp. infection was considered positive if diagnostic tools including culture, antigen detection in urine, serological testing, direct fluorescent antibody staining or immunohistochemistry, or polymerase chain reaction (PCR) testing, yielded positive results.
Statistical Analysis
We used DALYs and death rates to measure the burdens of LSIADs. DALYs, a standard metric for measuring burden quantifies the total healthy years lost due to disease, combining years of life lost (YLLs) and years lived with disability (YLDs) [21], as expressed by the formula:
 |
The 95% uncertainty intervals (UIs) for each measure were provided, with the lower and upper bounds estimated as the 2.5th and 97.5th values, respectively, from the ordered set of 1000 samples drawn from the posterior distribution of each measure.
The estimated annual percentage change (EAPC) or annual percentage change (APCs) commonly used metric for tracking epidemiological trend over a predetermined time period [22, 23]. EAPC is calculated by fitting the natural logarithm of the rates to a linear regression model with time as a variable and determining the slope of this line, as expressed by the formula:
 |
 |
where x means calendar year, y means the natural logarithm of rates (such as prevalence and incidence rates), α means the intercept, β means the slope, and ε means the random error. The 95% confidence intervals (CIs) for the EAPC are derived from this model. A lower limit of the 95% CIs above 0 indicates an upward trend, an upper limit below 0 indicates a downward trend, and CIs including 0 indicate no significant trend change.
Furthermore, this study utilized the difference of DALYs and deaths in average rates between 2020 and 2021 and 2018–2019 to reflect the change in the burden of Legionella spp. infection-associated diseases before (defined as the period from January 2018 to December 2019) and during the COVID-19 pandemic (defined as the period from January 2020 to December 2021).
This study utilized R software (version 4.3.1) for data cleaning, computation, and graph plotting. The EAPC was calculated using a linear regression model in R, with visualizations created using the ggplot2 package. The APC was determined through a Joinpoint regression model [24].
Results
The Burdens of LSIADs
Globally, the ASR-DALYs and ASDRs of LSIADs in 2021 were 24.74 per 100,000 (95% UI: 20.88–29.35) and 0.86 per 100,000 (95% UI: 0.76–0.95), respectively (Table 1). In 7 super regions, the highest ASR-DALYs (1990: 69.47 per 100,000, 95% UI: 52.88–88.42; 2021: 51.46 per 100,000, 95% UI: 41.44–61.44) and ASDRs (1990: 1.90 per 100,000, 95% UI: 1.53–2.26; 2021: 1.75 per 100,000, 95% UI: 1.46–2.00) burden of LSIADs was in Sub-Saharan Africa both in 1990 and in 2021; the lowest ASR-DALYs (11.65 per 100,000, 95% UI: 10.59–12.37) and ASDRs (0.66 per 100,000, 95% UI: 0.57–0.72) was in high-income regions in 2021(Table 1and Fig. 1). Among all SDI stratifications, low SDI quintiles had the highest ASR-DALYs (46.54 per 100,000, 95% UI: 33.95–62.85) and ASDRs (1.37 per 100,000, 95% UI:1.15–1.6) in 2021 (Table 1).
Table 1.
The ASR-DALYs and ASDRs and their EAPCs of LSIADs worldwide in 1990 and 2021
| ASR-DALYs, per 100,00 | EAPCs%, 95%CI | ASDRs, per 100,000 | EAPCs%, 95%CI | |||
|---|---|---|---|---|---|---|
| 1990 | 2021 | 1990 | 2021 | |||
| Global | 37.85 (29.09–48.79) | 24.74 (20.88–29.35) | -1.42 (-1.48 - -1.36) * | 1.06 (0.92–1.22) | 0.86 (0.76–0.95) | -0.75 (-0.83 - -0.66) * |
| Age group | ||||||
| < 5 years | 210.75 (133.89–318.06) | 82.99 (54.75–123.17) | -2.97 (-3 - -2.94) * | 2.35 (1.49–3.54) | 0.92 (0.61–1.37) | -2.97 (-3.00 - -2.94) * |
| 5–14 years | 11.29 (9.15–13.38) | 6.61 (5.59–7.66) | -1.71 (-1.78 - -1.64) * | 0.14 (0.11–0.16) | 0.08 (0.07–0.09) | -1.70 (-1.77 - -1.63) * |
| 15–49 years | 5.70 (4.99–6.45) | 6.42 (5.75–7.17) | 0.43 (0.37–0.51) * | 0.10 (0.09–0.11) | 0.12 (0.11–0.13) | 0.57 (0.5 to 0.64) * |
| 50–69 years | 32.77 (28.83–36.91) | 33.2 (30–36.22) | 0.14 (0.09 to 0.2) * | 1.10 (0.97–1.24) | 1.12 (1.01–1.22) | 0.09 (0.02 to 0.18) * |
| 70 + years | 104.11 (90.7–115.97) | 101.85 (88.29–111.89) | -0.03 (-0.1 to 0.05) | 7.84 (6.82–8.69) | 8.15 (6.85–9.00) | 0.17 (0.09 to 0.26) * |
| SDI ranks | ||||||
| High SDI | 17.92 (16.29–19.53) | 12.19 (11.05–12.9) | -1.40 (-1.59 - -1.22) * | 1.01 (0.89–1.11) | 0.68 (0.58–0.74) | -1.48 (-1.66 - -1.3) * |
| High-middle SDI | 19.64 (16.42–23.5) | 13.25 (12.11–14.43) | -1.52 (-1.65 - -1.38) * | 0.59 (0.51–0.66) | 0.60 (0.53–0.66) | -0.03 (-0.19–0.14) |
| Middle SDI | 33.71 (26.58–41.37) | 20.45 (17.94–23.03) | -1.73 (-1.85 - -1.6) * | 0.92 (0.79–1.06) | 0.83 (0.73–0.92) | -0.42 (-0.49 - -0.35) * |
| Low-middle SDI | 46.54 (33.95–62.85) | 31.04 (25.31–37.76) | -1.13 (-1.18 - -1.08) * | 1.05 (0.86–1.27) | 1.01 (0.87–1.16) | 0.08 (0–0.15) * |
| Low SDI | 65.56 (48.32–86.86) | 42.7 (34.22–51.76) | -1.36 (-1.42 - -1.3) * | 1.63 (1.3–1.95) | 1.37 (1.15–1.6) | -0.53 (-0.61 - -0.46) * |
| GDB super regions | ||||||
| North Africa and Middle East | 34.39 (26.61–44.52) | 19.56 (16.8–22.38) | -1.57 (-1.66 - -1.48) * | 0.8 (0.67–0.97) | 0.77 (0.65–0.86) | 0.14 (0–0.27) * |
| Sub-Saharan Africa | 69.47 (52.88–88.42) | 51.46 (41.44–61.44) | -0.91 (-0.97 - -0.85) * | 1.9 (1.53–2.26) | 1.75 (1.46–2) | -0.25 (-0.32 - -0.18) * |
| Central Europe, Eastern Europe, and Central Asia | 27.27 (21.98–33.57) | 24.62 (21.9–28.11) | -0.87 (-1.19 - -0.55) * | 0.53 (0.46–0.61) | 0.66 (0.61–0.72) | 0.36 (0–0.71) * |
| Latin America and Caribbean | 29.76 (25.61–34.44) | 25.07 (22.33–27.85) | -0.45 (-0.7 - -0.19) * | 1.04 (0.91–1.17) | 1.05 (0.92–1.16) | 0.14 (-0.11–0.39) |
| Southeast Asia, East Asia, and Oceania | 33.34 (25.9–41.74) | 17.03 (14.82–19.39) | -2.59 (-2.73 - -2.45) * | 0.9 (0.75–1.04) | 0.75 (0.63–0.86) | -0.86 (-0.93 - -0.79) * |
| South Asia | 42.97 (30.04–60.09) | 27.51 (21.98–34.27) | -1.23 (-1.3 - -1.16) * | 0.91 (0.71–1.13) | 0.85 (0.72–1) | 0 (-0.12–0.11) |
| High-income | 16.85 (15.34–18.36) | 11.65 (10.59–12.37) | -1.3 (-1.49 - -1.1) * | 0.97 (0.86–1.07) | 0.66 (0.57–0.72) | -1.37 (-1.55 - -1.18) * |
* Asterisk (*) indicate P < 0.05
ASR-DALYs, age-standardized disability-adjusted life years; ASDR, age-standardized death rate; EAPCs, estimate annual percentage change; 95% CI, 95% Confidence Interval; SDI, sociodemographic index; LSIADs, Legionella spp. infection-associated diseases
Fig. 1.
Geographic distribution of ASR-DALYs and ASDRs of LSIADs and their EAPCs from 1990 to 2021. ASR-DALYs, age-standardized disability-adjusted life years; ASDR, age-standardized death rate; EAPCs, estimate annual percentage change; SDI, sociodemographic index; LSIADs, Legionella spp. infection-associated diseases
Further, we explored the ASR-DALYs and ASDRs burden in different age groups in globally and different SDI regions levels. We found the highest ASR-DALYs was in those under 5 years of age globally in 1990 (210.75 per 100,000, 95% UI: 133.89–318.06), but the highest ASR-DALYs was in individuals over 70 years of age in 2021 (101.85 per 100,000, 95% UI: 88.29–111.89), especially in low SDI regions (144.67 per 100,000, 95% UI: 121.90–170.56) (Table 1and Table 2). In addition, individuals over 70 years of age had the highest global ASDR in 2021 (1990: 7.84 per 100,000, 95% UI: 6.82–8.69; 2021: 8.15 per 100,000, 95% UI: 6.85–9.00), especially in low SDI regions (9.70 per 100,000, 95% UI: 8.12–11.42) (Table 1and Table 2).
Table 2.
The ASR-DALYs and ASDRs and their EAPCs of LSIADs among SDI quintiles in different age groups in 1990 and 2021
| Location | Age groups | ASR- DALYs, per 100,000 | EAPCs %, 95% CI | ASDRs, per 100,000 | EAPCs%, 95% CI | ||
|---|---|---|---|---|---|---|---|
| 1990 | 2021 | 1990 | 2021 | ||||
| High SDI | < 5 years | 10.84 (8.02–13.98) | 3.19 (2.48–3.92) | -3.87 (-4.04 - -3.73) * | 0.12 (0.09–0.16) | 0.04 (0.03–0.04) | -3.87 (-4.05 - -3.74) * |
| 5–14 years | 2.80 (2.52–3.11) | 1.36 (1.27–1.46) | -2.19 (-2.34 - -2.00) * | 0.03 (0.03–0.04) | 0.02 (0.02–0.02) | -2.2 (-2.37 - -2.01) * | |
| 15–49 years | 4.27 (3.94–4.65) | 4.13 (3.85–4.45) | 0.02 (-0.13–0.18) | 0.08 (0.07–0.09) | 0.08 (0.07–0.09) | 0.13 (-0.02–0.28) | |
| 50–69 years | 31.28 (29.11–33.73) | 25.07 (23.91–26.25) | -0.59 (-0.72 - -0.42) * | 1.09 (1.01–1.18) | 0.86 (0.82–0.91) | -0.69 (-0.85 - -0.53) * | |
| 70 + years | 152.62 (134.43–168.15) | 108.74 (90.35–119.64) | -1.13 (-1.28 - -1.00) * | 12.28 (10.63–13.59) | 9.7 (7.9–10.77) | -0.72 (-0.88 - -0.57) * | |
| High-middle SDI | < 5 years | 87.27 (61.63–121.2) | 12.73 (9.69–16.51) | -6.1 (-6.27 - -6.00) * | 0.97 (0.69–1.35) | 0.14 (0.11–0.18) | -6.10 (-6.27 - -6.00) * |
| 5–14 years | 6.05 (5.21–6.96) | 2.63 (2.38–3.00) | -2.72 (-2.88 - -2.57) * | 0.07 (0.06–0.08) | 0.03 (0.03–0.04) | -2.79 (-2.94 - -2.64) * | |
| 15–49 years | 4.19 (3.72–4.69) | 5.94 (5.5–6.46) | 1.18 (1.01–1.40) * | 0.08 (0.07–0.08) | 0.11 (0.11–0.12) | 1.40 (1.21–1.61) * | |
| 50–69 years | 20.51 (18.44–22.86) | 23.58 (21.84–25.59) | 0.63 (0.46–0.86) * | 0.68 (0.61–0.76) | 0.79 (0.73–0.86) | 0.65 (0.51–0.82) * | |
| 70 + years | 57.27 (49.61–64.29) | 77.7 (67.18–86.81) | 1.00 (0.92–1.09) * | 4.28 (3.71–4.80) | 6.4 (5.43–7.19) | 1.30 (1.21–1.40) * | |
| Middle SDI | < 5 years | 177.33 (121.6–250.74) | 44.33 (31.54–61.76) | -4.41 (-4.46 - -4.35) * | 1.98 (1.36–2.79) | 0.49 (0.35–0.69) | -4.41 (-4.47 - -4.35) * |
| 5–14 years | 5.82 (5.03–6.60) | 6.01 (5.42–6.64 ) | -2.61 (-2.67 - -2.55) * | 0.14 (0.12–0.16) | 0.06 (0.06–0.07) | -2.6 (-2.66 - -2.54) * | |
| 15–49 years | 4.19 (3.72–4.69) | 5.94 (5.5–6.46) | 0.11 (-0.02–0.22) | 0.1 (0.09–0.11) | 0.11 (0.1–0.12) | 0.32 (0.20–0.43) * | |
| 50–69 years | 20.51 (18.44–22.86) | 23.58 (21.84–25.59) | 0.14 (0.11–0.19) * | 0.97 (0.83–1.1) | 1.02 (0.91–1.11) | 0.18 (0.14–0.22) * | |
| 70 + years | 57.27 (49.61–64.29) | 77.7 (67.18–86.81) | 0.59 (0.56–0.62) * | 6.00 (5.07–6.94) | 7.83 (6.66–8.75) | 0.89 (0.85–0.92) * | |
| Low-middle SDI | < 5 years | 296.24 (180.4–464.25) | 110.19 (71.85–164.54) | -3.13 (-3.16 - -3.1) * | 3.3 (2.01–5.17) | 1.23 (0.8–1.83) | -3.13 (-3.16 - -3.10) * |
| 5–14 years | 13.73 (10.76–16.69) | 7.4 (6.13–8.72) | -1.94 (-2.18 - -1.69) * | 0.17 (0.13–0.2) | 0.09 (0.07–0.11) | -1.92 (-2.16 - -1.67) * | |
| 15–49 years | 6.25 (5.27–7.35) | 6.49 (5.56–7.51) | 0.14 (0.10–0.18) * | 0.11 (0.09–0.13) | 0.12 (0.1–0.14) | 0.25 (0.22–0.28) * | |
| 50–69 years | 42.88 (36.13–49.89) | 47.1 (40.03–53.6) | 0.34 (0.26–0.41) * | 1.43 (1.21–1.67) | 1.59 (1.35–1.81) | 0.37 (0.30–0.45) * | |
| 70 + years | 88.55 (73.08–106.9) | 116.4 (99–133.35) | 0.92 (0.84–1.01) * | 5.86 (4.81–7.1) | 8.06 (6.87–9.2) | 1.14 (1.05–1.22) * | |
| Low SDI | < 5 years | 383.8 (221.74–609.25) | 148.39 (94.4–225.96) | -3.02 (-3.05 - -2.99) * | 4.28 (2.48–6.79) | 1.65 (1.05–2.52) | -3.02 (-3.05 - -2.99) * |
| 5–14 years | 20.23 (15.02–26.08) | 11.89 (9.35–14.51) | -1.69 (-1.76 - -1.62) * | 0.25 (0.18–0.32) | 0.15 (0.11–0.18) | -1.67 (-1.74 - -1.61) * | |
| 15–49 years | 10.7 (8.49–12.86) | 9.92 (8.08–11.82) | -0.25 (-0.27 - -0.23) * | 0.19 (0.15–0.23) | 0.18 (0.14–0.21) | -0.24 (-0.26 - -0.21) * | |
| 50–69 years | 79.25 (64.53–95.84) | 68.56 (57.04–80.75) | -0.45 (-0.48 - -0.42) * | 2.64 (2.14–3.20) | 2.28 (1.89–2.69) | -0.46 (-0.48 - -0.43) * | |
| 70 + years | 145.01 (118.05–175.71) | 144.67 (121.9–170.56) | 0.02 (-0.02–0.05) | 9.2 (7.45–11.21) | 9.70 (8.12–11.42) | 0.20 (0.16–0.25) * | |
* Asterisk (*) indicate P < 0.05
ASR-DALYs, age-standardized disability-adjusted life years; ASDR, age-standardized death rate; EAPCs, estimate annual percentage change; 95% CI, 95% Confidence Interval; SDI, sociodemographic index; LSIADs, Legionella spp. infection-associated diseases
Trends of LSIADs
Although the global trends of the ASR-DALYs and ASDRs of LSIADs experienced signally decreasing from 1990 to 2021, with the EAPC of -1.42% (95% CI: -1.48 - -1.36) and − 0.75 (95% CI: -0.83 - -0.66), respectively, the global ASR-DALYs and ASDRs of LSIADs showed significant upward trends in 15-49-year age groups, with EAPC of 0.43% (95% CI: 0.37–0.51) and 0.57% (95% CI: 0.5–0.64) (Table 1). Furthermore, the global ASR-DALYs and ASDRs of LSIADs also showed slightly upward trends in 50-69-year age groups, with EAPC of 0.14% (95% CI: 0.09–0.2) and 0.09% (95% CI: 0.02–0.18) (Table 1).
Apart from the low-middle SDI regions, ASDRs of LSIADs exhibited a slight upward trend (EAPC = 0.08, 95%CI: 0.00–0.15), whereas ASR-DALYs and ASDR demonstrated a downward trend in other SDI regions (Tables 1 and Supplementary Fig. 1). But the change trends of the ASR-DALYs and ASDRs in different age groups were different. The ASR-DALYs and ASDRs in individuals over 70 years showed an increasing trend in high-middle, middle and middle-low SDI regions (Fig. 2). The trends of the ASR-DALYs and ASDRs in the 15-49-year age group in high SDI regions showed increasing trends from 2002 to 2021, with the APC of 0.80 (95%CI: 0.57–1.05) and 0.82 (95%CI: 0.6–1.06) (Fig. 2 and Supplementary Table 2). In the middle regions, the trends of the ASR-DALYs showed increasing trends in the 15-49-year age group from 2015 to 2021 (APC = 1.51, 95%CI: 0.6–3.79), the 50-69-year age group from 2006 to 2021 (APC = 0.62, 95%CI: 0.54–0.76), and individuals over 70 years of age from 2021 to 2021 (APC = 0.48, 95%CI: 0.38–0.55), respectively. The ASDRs also experienced similar increase trends in middle SDI regions in the 15-49-year age group, the 50-69-year age group and individuals over 70 years of age (Fig. 2 and Supplementary Table 2).
Fig. 2.
The EAPCs of ASR-DALYs and ASDRs of LSIADs in different age groups from 1990–2021. (A) Global. (B) High SDI areas. (C) High-middle SDI areas. (D) Middle SDI areas. (E) low-middle SDI areas. (F) low SDI areas. ASR-DALYs, age-standardized disability-adjusted life years; ASDR, age-standardized death rate; EAPCs, estimate annual percentage change; SDI, sociodemographic index; LSIADs, Legionella spp. infection-associated diseases
Rate Differences Before and During COVID-19 Pandemic
Compared to the pre-COVID-19 era, the global ASR-DALYs and ASDRs of LSIADs decreased slightly, with the value difference of -1.3420 per 100,000 and − 0.0313 per 100,000 during the COVID-19 pandemic. The ASR-DALYs and ASDRs in most SDI regions declined except in the middle SDI regions during the COVID-19 pandemic. However, in the middle SDI regions, the ASR-DALYs increased slightly in the 15-49-year age group and the 50-69-year age group, with the value difference of 0.0868 per 100,000 and 0.824 per 100,000, and the ASDRs also increased slightly in these two age groups, with the value difference of 0.0021 per 100,000 and 0.0055 per 100,000 (Supplementary Fig. 2).
Discussion
Legionella spp. infection is a worldwide problem. Our study is the first to assess ASR-DALYs and ASDRs in human Legionella spp. infection worldwide and compare changes in their burden during COVID-19 (2020–2021) and pre-COVID-19 (2018–2019). Although ASR-DALY and ASDR burdens have tended to decrease over the past 32 years, notable geographic and age-related disparities persist. A thorough understanding of the burden and trends of Legionella spp. infection is crucial for reducing the global population mortality and disease burden rates by 2030.
We explored the differences in disease burden by SDI region and age group. We found that the highest global ASR-DALY and ASDRs burdens of LSIADs were in low SDI regions, especially in sub-Saharan Africa and in individuals over 70 years of age. Infections with Legionella spp. cause a variety of clinical syndromes, including Pontiac fever, a self-limiting febrile illness, and Legionnaires disease (LD), a severe multisystem illness characterized by atypical pneumonia [6]. The clinical presentation of LD depends on the bacterial load present in the aerosol, virulence factors, and the individual immune status of the host [25]. The patients most susceptible to Legionella spp. infection are mainly the immunocompromised elderly and those with chronic diseases such as chronic obstructive pulmonary disease, cardiovascular disease or diabetes [26]. Additionally, Legionella spp. infection can be fatal for hemodialysis and kidney transplant patients [27, 28]. Individuals living in developing countries are at greater risk given their low socioeconomic status or poor hygiene conditions, especially elderly individuals. Despite the higher burden of Legionella spp. infection in low SDI areas such as Sub-Saharan Africa, where medical resources are scarce and relatively behind, the actual disease burden is likely to be vastly underestimated. Worldwide, health systems seek to achieve health equity by allocating health care resources in low- and middle-income countries [29]; although, the gap is reduced, it is still large. Measures like maintaining hot water systems, implementing water quality plans, enhancing health facility readiness, and improving sanitation can reduce Legionella spp. infection risk [30, 31]. Notably, ASR-DALYs and ASDRs in the 15-49-year age group and 50-69-year age group had increasing trends over the past decade, especially in the high and middle SDI regions. Moffa et al. [32] reported that the incidence of legionellosis has been steadily increasing in the United States over the past two decades. One possible explanation for this phenomenon is the presence of sufficient and advanced medical resources in regions with high SDI regions. Such resources significantly enhance the accuracy of disease diagnosis through the implementation of various diagnostic methodologies, including polymerase chain reaction (PCR), metagenomic next-generation sequencing [33], novel markers (e.g. peptidoglycan associated lipoprotein [34], ribosomal protein L7/L12 [35] and Interleukin-17 A [36]) and MALDI-TOF mass spectrometry [25, 37].In addition, the most common sources of outbreaks are contaminated cooling towers and hot water plumbing infrastructure, but decorative fountains, pools or home showers are also contributed to disease spread [38, 39]. Outbreak sources are more prevalent in regions with high SDI scores, and individuals aged 15–49 years, who engage in the most social activities, may consequently face heightened exposure risks. Epidemiological studies to pinpoint high-risk groups and settings for Legionella infection are crucial for understanding its influencing factors and forming prevention strategies. Additionally, increasing public awareness about its symptoms, transmission, and prevention is necessary.
In our study, during the COVID-19 pandemic, global ASR-DALYs and ASDRs for LSIADs slightly decreased compared to pre-pandemic levels. Most SDI regions experienced declines, except middle SDI regions, where ASR-DALYs and ASDRs slightly rose in the 15–49 and 50–69 age groups. Chao et al. [40] and Tang et al. [41] reported that the number of cases of legionellosis increased during the COVID-19 pandemic in Taiwan, China. Fischer et al. [15] reported that the annual rate of legionellosis cases increased from 1.1/100,000 individuals in 2000 to 5.6/100,000 in 2020 in Switzerland, but the highest rate was recorded in 2018, before the COVID-19 pandemic, with 6.7/100,000 individuals. However, The COVID-19 pandemic has caused a global spike in infections, straining healthcare systems and diverting resources primarily to COVID-19 care, potentially leading to a serious shortage of resources for the diagnosis and treatment of other diseases. Furthermore, the COVID-19 pandemic and its associated responses have exerted multifaceted influences on the epidemiology of numerous infectious diseases. During the COVID-19 pandemic, the government enforced the closure of numerous office buildings, as well as social, entertainment, and cultural centers, in an effort to mitigate virus transmission. This led to a subsequent decrease in water network usage and the formation of stagnant water within these facilities, thereby promoting biofilm development and significantly increasing the risk of colonization by Legionella strains [42–44]. Outbreaks of emerging infectious diseases continue to challenge human health, prompting reactive countermeasures, such as quarantine and lockdown, thus it is necessary to evaluate the likely risks of Legionella growth while communities are reopening.
This study has limitations. Firstly, GBD data may rely on extensive statistical models due to uneven data quality, especially in countries with limited raw data. Secondly, Due to the underdeveloped state of the economy and medical infrastructure, diagnosing infections caused by Legionella spp. in less developed regions remains challenging, resulting in an underestimated burden of disease. Then, although Legionella pneumophila is responsible for 85%~90% of known Legionnaires’ disease cases worldwide, this study did not assess the disease burden caused by Legionella species, which needs to be addressed in future studies. Finally, while we used the latest GBD data to evaluate Legionella spp. infection burden, our study only covers the pre-COVID-19 period and parts of the pandemic. The post-COVID-19 impact is still unknown, requiring further research and monitoring.
Conclusion
In conclusion, our study revealed that the global burden of LSIADs has declined over the past 32 years; however, low SDI regions, particularly sub-Saharan Africa, and the elderly continue to have a greater burden. Moreover, paying close attention to the burden trends of LSIADs among those aged 15–49 years and 50–69 years are also important, especially in high-middle and low-middle SDI regions. A better understanding of the burden and changes observed in legionellosis will help in identifying targets for improved prevention measures and help optimize public policy.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors express their gratitude to the Institute for Health Metrics and Evaluation for sharing valuable GBD data.
Abbreviations
- ASR- DALYs
Age-standardized disability-adjusted life years
- ASDRs
Age-standardized death rates
- DALYs
Disability-adjusted life years
- YLDs
Years lived with disability
- YLLs
Years of life lost
- EAPC
Estimated annual percentage change
- APC
Annual percentage change
- GBD
Global Burden of Disease
- SDI
Sociodemographic index
- 95% UI
95% Uncertainty intervals
- 95% CI
95% Confidence Interval
- COVID-19
Coronavirus disease 2019
- SARS-CoV-2
Severe acute respiratory syndrome Coronavirus type 2
- ICD-9
International Classification of Disease, Ninth Revision
- PCR
Polymerase chain reaction
- LD
Legionnaires disease
- LSIADs
Legionella spp. infection-associated diseases
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yonghong Zhong, Linfeng Shen, Yan Zhou, Yibo Sun, Xiaofang Fu, and Huaqiong Huang. The first draft of the manuscript was written by Yonghong Zhong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the National Natural Science Foundation of Zhejiang Province (LY22H010004) and the Zhejiang Medical Health Science and Technology Program (2024KY273).
Data Availability
Publicly available datasets were analyzed in this study. All data are open-access and are available from the Global Health Data Exchange query tool (http://ghdx.healthdata.org/gbd-results-tool.
Declarations
Ethics Approval
No applicable.
Competing Interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yonghong Zhong and Linfeng Shen contributed equally.
Contributor Information
Xiaofang Fu, Email: fxf20190227@163.com.
Huaqiong Huang, Email: zr_hhq@zju.edu.cn.
References
- 1.Chatziprodromidou IP, Savoglidou I, Stavrou V, Vantarakis G, Vantarakis A. Surveillance of Legionella spp. in Open Fountains: Does It Pose a Risk? Microorganisms 2022; 10(12). [DOI] [PMC free article] [PubMed]
- 2.Kowalczyk B, Chmiel E, Palusinska-Szysz M. The role of lipids in Legionella-host Interaction. Int J Mol Sci 2021; 22(3). [DOI] [PMC free article] [PubMed]
- 3.Parte AC, Sardà Carbasse J, Meier-Kolthoff JP, Reimer LC, Göker M. List of prokaryotic names with standing in nomenclature (LPSN) moves to the DSMZ. Int J Syst Evol Microbiol. 2020;70(11):5607–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Correia AM, Ferreira JS, Borges V, et al. Probable person-to-person transmission of Legionnaires’ Disease. N Engl J Med. 2016;374(5):497–8. [DOI] [PubMed] [Google Scholar]
- 5.Borges V, Nunes A, Sampaio DA, et al. Legionella pneumophila strain associated with the first evidence of person-to-person transmission of Legionnaires’ disease: a unique mosaic genetic backbone. Sci Rep. 2016;6:26261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Fields BS, Benson RF, Besser RE. Legionella and Legionnaires’ disease: 25 years of investigation. Clin Microbiol Rev. 2002;15(3):506–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Albert-Weissenberger C, Cazalet C, Buchrieser C. Legionella pneumophila - a human pathogen that co-evolved with fresh water protozoa. Cell Mol Life Sci. 2007;64(4):432–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Alarcon Falconi TM, Cruz MS, Naumova EN. The shift in seasonality of legionellosis in the USA. Epidemiol Infect. 2018;146(14):1824–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rota MC, Caporali MG, Bella A, Ricci ML, Napoli C. Legionnaires’ disease in Italy: results of the epidemiological surveillance from 2000 to 2011. Euro Surveill 2013; 18(23). [DOI] [PubMed]
- 10.Graham FF, White PS, Harte DJ, Kingham SP. Changing epidemiological trends of legionellosis in New Zealand, 1979–2009. Epidemiol Infect. 2012;140(8):1481–96. [DOI] [PubMed] [Google Scholar]
- 11.Group NARW. Australia’s notifiable disease status, 2012: Annual report of the National Notifiable diseases Surveillance System. Commun Dis Intell Q Rep. 2015;39(1):E46–136. [DOI] [PubMed] [Google Scholar]
- 12.Graham FF, Finn N, White P, Hales S, Baker MG. Global Perspective of Legionella Infection in Community-Acquired Pneumonia: a systematic review and Meta-analysis of Observational studies. Int J Environ Res Public Health 2022; 19(3). [DOI] [PMC free article] [PubMed]
- 13.Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lancaster EC, Lee J. Potential environmental and health risk when returning to normal amidst COVID-19 vaccination. Curr Opin Environ Sci Health. 2022;26:100328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Fischer FB, Mäusezahl D, Wymann MN. Temporal trends in legionellosis national notification data and the effect of COVID-19, Switzerland, 2000–2020. Int J Hyg Environ Health. 2023;247:113970. [DOI] [PubMed] [Google Scholar]
- 16.Orsini J, Frawley BJ, Gawlak H, Gooch R, Escovar J. Severe Sepsis with septic shock as a consequence of a severe community-acquired Pneumonia resulting from a combined Legionella pneumophila and Streptococcus pneumoniae infection. Cureus. 2020;12(10):e10966. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Shimizu M, Chihara Y, Satake S, et al. Co-infection with Legionella and SARS-CoV-2: a case report. JA Clin Rep. 2021;7(1):62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Collaborators GDI. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Collaborators GCoD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Collaborators GV. Five insights from the global burden of Disease Study 2019. Lancet. 2020;396(10258):1135–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Organization WH. WHO methods and data sources for global burden of disease estimates 2000–2019. 2020. chrome-extension://bnjoienjhhclcabnkbhhfndecoipmcdg/background/jgpdf/layout/index.html?file = https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_daly-methods.pdf?sfvrsn=31b25009_7 (accessed August 18 2024).
- 22.Liu Z, Jiang Y, Yuan H, et al. The trends in incidence of primary liver cancer caused by specific etiologies: results from the global burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019;70(4):674–83. [DOI] [PubMed] [Google Scholar]
- 23.Zhang L, Tong Z, Han R, Li K, Zhang X, Yuan R. Spatiotemporal trends in global burden of rheumatic heart disease and associated risk factors from 1990 to 2019. Int J Cardiol. 2023;384:100–6. [DOI] [PubMed] [Google Scholar]
- 24.Liu X, Jiang J, Yu C, et al. Secular trends in incidence and mortality of bladder cancer in China, 1990–2017: a joinpoint and age-period-cohort analysis. Cancer Epidemiol. 2019;61:95–103. [DOI] [PubMed] [Google Scholar]
- 25.Iliadi V, Staykova J, Iliadis S et al. Legionella pneumophila: the Journey from the Environment to the blood. J Clin Med 2022; 11(20). [DOI] [PMC free article] [PubMed]
- 26.Soda EA, Barskey AE, Shah PP, et al. Vital signs: Health Care-Associated Legionnaires’ Disease Surveillance Data from 20 States and a large Metropolitan Area - United States, 2015. MMWR Morb Mortal Wkly Rep. 2017;66(22):584–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Phin N, Parry-Ford F, Harrison T, et al. Epidemiology and clinical management of Legionnaires’ disease. Lancet Infect Dis. 2014;14(10):1011–21. [DOI] [PubMed] [Google Scholar]
- 28.Dominguez A, Alvarez J, Sabria M, et al. Factors influencing the case-fatality rate of Legionnaires’ disease. Int J Tuberc Lung Dis. 2009;13(3):407–12. [PubMed] [Google Scholar]
- 29.Datta NR, Rogers S, Bodis S. Challenges and opportunities to realize the 2030 agenda for Sustainable Development by the United Nations: implications for Radiation Therapy infrastructure in low- and Middle-Income Countries. Int J Radiat Oncol Biol Phys. 2019;105(5):918–33. [DOI] [PubMed] [Google Scholar]
- 30.National Academies of Sciences, Engineering, and Medicine. Management of Legionella in Water systems. Washington, DC: National Academies; 2020. 10.17226/25474. [Google Scholar]
- 31.Kanyangarara M, Allen S, Jiwani SS, Fuente D. Access to water, sanitation and hygiene services in health facilities in sub-saharan Africa 2013–2018: results of health facility surveys and implications for COVID-19 transmission. BMC Health Serv Res. 2021;21(1):601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Moffa MA, Rock C, Galiatsatos P, Gamage SD, Schwab KJ, Exum NG. Legionellosis on the rise: a scoping review of sporadic, community-acquired incidence in the United States. Epidemiol Infect. 2023;151:e133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Niu S, Zhao L. Metagenomic next-generation sequencing clinches the diagnosis of Legionella pneumonia in a patient with acute myeloid leukemia: a case report and literature review. Front Cell Infect Microbiol. 2022;12:924597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Kim SJ, Sin JI, Kim MJ. CD8(+) T cells Directed against a peptide Epitope Derived from Peptidoglycan-Associated Lipoprotein of Legionella pneumophila Confer Disease Protection. Front Immunol. 2020;11:604413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Nakamura A, Fukuda S, Kusuki M, et al. Evaluation of five Legionella urinary antigen detection kits including new Ribotest Legionella for simultaneous detection of ribosomal protein L7/L12. J Infect Chemother. 2021;27(10):1533–5. [DOI] [PubMed] [Google Scholar]
- 36.Higa F, Haroon A, Iha Y, et al. Interleukin-17A in Legionella pneumonia: a retrospective study. Jpn J Infect Dis. 2015;68(2):148–50. [DOI] [PubMed] [Google Scholar]
- 37.Graham FF, Harte D, Zhang J, Fyfe C, Baker MG. Increased incidence of Legionellosis after Improved Diagnostic methods, New Zealand, 2000–2020. Emerg Infect Dis. 2023;29(6):1173–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Holsinger H, Tucker N, Regli S, et al. Characterization of reported legionellosis outbreaks associated with buildings served by public drinking water systems: United States, 2001–2017. J Water Health. 2022;20(4):702–11. [DOI] [PubMed] [Google Scholar]
- 39.Cohn PD, Gleason JA, Rudowski E, Tsai SM, Genese CA, Fagliano JA. Community outbreak of legionellosis and an environmental investigation into a community water system. Epidemiol Infect. 2015;143(6):1322–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Chao CM, Lai CC. Increasing legionella in Taiwan during COVID-19 pandemic. Am J Infect Control. 2022;50(2):237–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Tang HJ, Lai CC, Chao CM. The collateral effect of COVID-19 on the epidemiology of Airborne/Droplet-Transmitted notifiable infectious diseases in Taiwan. Antibiot (Basel) 2022; 11(4). [DOI] [PMC free article] [PubMed]
- 42.Liang J, Swanson CS, Wang L, He Q. Impact of building closures during the COVID-19 pandemic on Legionella infection risks. Am J Infect Control. 2021;49(12):1564–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.De Giglio O, Diella G, Lopuzzo M, et al. Impact of lockdown on the microbiological status of the hospital water network during COVID-19 pandemic. Environ Res. 2020;191:110231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Palazzolo C, Maffongelli G, D’Abramo A et al. Legionella pneumonia: increased risk after COVID-19 lockdown? Italy, May to June 2020. Euro Surveill 2020; 25(30). [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Publicly available datasets were analyzed in this study. All data are open-access and are available from the Global Health Data Exchange query tool (http://ghdx.healthdata.org/gbd-results-tool.