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. 2025 Sep 14;7(5):295–305. doi: 10.1016/j.bsheal.2025.09.005

Global trends, age-period-cohort analysis, and future projections of diarrhea burden: Findings from the Global Burden of Disease Study 2021

Mengjiao Xie a,1, Yang Song b,1, Jing Tao a, Mengnan Jiang a, Yang Liu a, Io Hong Cheong c, Zisis Kozlakidis d, Zhaorui Chang b,⁎, Qiang Wei a,2,⁎
PMCID: PMC12624536  PMID: 41262457

Highlights

  • Scientific questions Diarrhea susceptibility varies with age, and its epidemiology evolves with period and cohort changes. Therefore, understanding age, period, and cohort effects on diarrhea incidence and death is crucial.

  • Evidence before this study Although the latest data from the Global Burden of Disease Study (GBD) 2021 showed that the number of deaths related to diarrhea worldwide decreased by 60.3% between 1990 and 2021, the problem of health inequality remains serious. The mortality rate of children with diarrhea in sub-Saharan Africa was more than 150 times that of high-income regions in 2021. Previous studies focused on trends or descriptions, lacking age-period-cohort analysis or burden driver decomposition.

  • New findings The epidemiology of diarrhea demonstrates distinct patterns across age groups, with incidence risk declining until the age of 20 before increasing again after 60. Similarly, the risk of death was highest in young children aged 0-4 and saw a resurgence in individuals over 60 years of age. Over time, population growth has been identified as the primary driver of changes in the overall disease burden. Despite this, the burden of diarrhea is forecast to continue its downward trend. A significant disparity remains, as countries with a lower sociodemographic Index (SDI) continue to bear a disproportionately high burden; however, it is noted that these inequality gaps have narrowed over time.

  • Significance of the study This study not only informs the development and allocation of health resources for diarrhea prevention, complementing previous GBD research, but also demonstrates the need for targeted public health strategies to address inequalities via improved healthcare, protection for vulnerable populations, and increased coverage and development of vaccines.

Keywords: Age-period-cohort modeling, Diarrhea, Global burden of disease, Incidence, Low- and middle-income countries (LMICs)

Abstract

Diarrhea is currently a prominent global public health issue. This study evaluated recent trends in the global burden of diarrhea and projected future changes over the next decade. Using the diarrhea data from the global burden of disease (GBD) 2021, this study assessed the temporal trends using Joinpoint regression and explored the impact of different factors using age-period-cohort modeling. Decomposition analysis identified drivers of disease burden changes, and the Bayesian age-period-cohort (BAPC) model predicted future trends. Additionally, health inequalities were measured by the inequality slope index and the concentration index. Results show a downward trend in the global burden of diarrhea since 1990. Age-period-cohort analysis suggests that the risk of incidence decreases with age until the age of 20, but increases with age after the age of 60. Risk of death from diarrhea was highest in children aged 0–4 and also increasesd after the age of 60. Decomposition identified population growth as the primary driver of burden changes, and BAPC projections indicated that the burden of diarrhea will continue declining. However, significant inequalities persist, with lower sociodemographic index (SDI) countries bearing a disproportionately high burden, although these gaps have decreased over time. The conclusion highlights that children under 5 and adults over 60 face the highest risks of diarrhea incidence and death. More attention should be paid to these populations, and effective public health policies should be implemented.

1. Introduction

Infectious diarrhea represents a cluster of gastrointestinal infectious diseases triggered by pathogenic microorganisms, their metabolic byproducts, or parasitic infections, with primary clinical manifestations including diarrhea and/or vomiting. It poses a long-term threat to human health. Diarrhea is a significant cause of morbidity and mortality across all age groups, especially in children under five [1]. According to World Health Organization (WHO) estimates, nearly 1.7 billion cases of childhood diarrheal diseases occur annually, with approximately 443,832 deaths in children under five [2]. Incidence and mortality rates of infectious diarrhea vary significantly between countries with different economic and health statuses [3]. Historically, the burden has been disproportionately high in developing countries, particularly in sub-Saharan Africa and South Asia, which accounted for the vast majority of global diarrheal deaths [4]. Though the latest data from the Global Burden of Disease Study (GBD) 2021 has observed global diarrhea-related deaths having decreased by 60.3 % between 1990 and 2021, mainly through humanitarian efforts globally to provide safe drinking water, sanitation facilities, rotavirus vaccination and oral rehydration therapy (ORT) [5,6]. However, the problem of health inequality remains severe, where the mortality rate of childhood diarrhea in sub-Saharan Africa was more than 150 times that in high-income regions in 2021 [6]. This striking inequality highlights the persistent gaps in resource allocation, health policy implementation, and access to preventive measures between high and low sociodemographic index (SDI) settings, thereby impeding equitable progress toward global health targets. Furthermore, the burden of adult diarrhea is on the rise in an aging society, which poses new challenges to public health policies and resource allocation.

Although existing studies have quantified the overall burden and pathogen distribution of diarrhea [7,8], most current research focuses on changes in age-standardized morbidity and mortality over time or only conducts traditional descriptive analyses of morbidity or mortality at specific ages at different times [[9], [10], [11]]. While these studies effectively illustrate broad trends in disease burden over time, they often fail to uncover the underlying biological and sociological mechanisms driving these patterns. In contrast, the age-period-cohort model offers a robust analytical framework that overcomes this limitation by statistically separating and quantitatively estimating the independent effects of age-period-cohort using population-level data and advanced estimation methods, such as the intrinsic estimator (IE) or constrained regression [12]. This approach enables a more nuanced dissection of the complex factors influencing disease trends, including how health outcomes evolve with age, how they are shaped by period-related changes (e.g., shifts in healthcare, environment, or policy), and how individuals within distinct birth cohorts—shaped by unique early-life experiences—may carry different risks [13]. By disentangling these interrelated dimensions, the age-period-cohort model supports the identification of high-risk birth cohorts, the assessment of the long-term impact of historical interventions, and the development of more accurate projections of future disease trends.

To gain a more comprehensive understanding of the changes in the burden of diarrheal diseases, this study utilizes the latest data from GBD 2021 to identify key turning points using the Joinpoint regression model and the age-period-cohort model. This approach enables the estimation of the independent effects of age-period-birth cohort on the long-term trend of the disease. Combined with decomposition analysis, the relative contributions of age structure, population growth, and epidemiological changes to the disease burden were quantified, and the potential burden of future diarrheal diseases was determined through the Bayesian age-period-cohort (BAPC) model. Additionally, the study also employed absolute and relative inequality indicators related to SDI to explore health disparities associated with diarrhea. These analyses not only supplement the descriptive results of existing studies but will also provide more in-depth evidence to support the precise implementation of public health interventions (such as vaccination, improvement of water sanitation facilities and nutritional support) and resource allocation. The study’s outcomes will contribute to the achievement of the Sustainable Development Goal of global diarrhea prevention and control.

2. Materials and methods

2.1. Data sources and disease definition

The cross-sectional data on diarrhea were obtained by using the query tool of Global Health Data Exchange (GHDx) (https://vizhub.healthdata.org/gbd-results), which includes national-level data of indicators such as incidence, death, and disability-adjusted life years rates (DALYs) classified by gender, age, region, and country. The SDI is estimated by a composite of income per capita, average years of education, and fertility rate in females under 25 years old. Ranging from 0 to 1, a higher SDI indicates greater socioeconomic development. The SDI divides regions into five levels with higher values indicating a higher economic level (Table S1). The characteristics of the GBD research version and the detailed steps for using this database have been described in previous studies [4,14]. Diarrheal disease is defined as having three or more loose stools within 24 hours. Since gastroenteritis is typically a syndrome that causes vomiting or diarrhea, it was excluded from GBD 2021. The International Classification of Diseases (ICD) provides specific identifiers for diarrhea [11] (Table S2).

2.2. Statistical analysis

To ensure comparability among statistical indicators, the age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALYs were employed to assess the burden of diarrheal disease, calculated according to the GBD world population age standard [15]. The Joinpoint regression model divides a long-term trend line into segments via time inflection points, with each segment described by a fitted continuous straight line [16]. It determines inflection points and optimizes the model using Monte Carlo permutation tests and Bayesian information criteria [17]. This study employs the log-linear model in Joinpoint to calculate annual percentage change (APC), average APC (AAPC) values, and 95 % CI of standardized incidence rates. APC > 0 indicates an upward trend, and APC < 0 indicates a downward trend. If APC = AAPC, it is a monotonic trend. Joinpoint (version 4.9.1.0) software analyzes and visualizes descriptive epidemiological data related to diarrhea of different genders, ages, and regions from 1990 to 2021.

Compared with descriptive methods such as age-standardization rates, the age-period-cohort model provides a holistic framework to disentangle the independent age, period, and cohort effects underlying the observed trends. The age-period-cohort model analysis was conducted using the age-period-cohort modeling network tool provided by the National Cancer Institute (https://analysistools.cancer.gov/apc/) with the median age, period, and cohort used as the reference group and the Wald chi-square test applied for parameter estimation [18]. The model segmented age, period, and cohort at 5-year intervals, dividing the age range between 0–95+ into 20 age groups; the period from 1992 to 2021 was divided into 6 periods; the birth cohort (1897–2017) was obtained by subtracting age from the period and was divided into 25 groups. The model evaluation indicators include net drift, which is the percentage change in the natural logarithm of the total population incidence rate after adjusting for the nonlinear period and birth cohort effects; local drift, which is the log-linear trend of the incidence rate in each age group after adjusting for period and birth cohort effects, i.e., the percentage change in the natural logarithm of the incidence rate in each age group; longitudinal age curve, which is the incidence rate curve for a specific age group after adjusting for period bias and can be considered as the age effect, to infer the influence of age effect on the trend of diarrhea; period (cohort) rate ratio, which is the age-specific relative risk (RR) value for the selected period (cohort) relative to the reference period (cohort), to infer the influence of period (cohort) effect on the trend of diarrhea. If the RR value is greater than 1, it indicates that the relative risk for the selected period or cohort is higher than that of the reference period or cohort, and if the RR value is less than 1, it indicates that the relative risk is lower.

Moreover, by employing Das Gupta's factor decomposition method, the changes in the incidence, death, and DALYs of diarrhea from 1990 to 2021 are decomposed into contributions from aging, population growth, and epidemiological changes. Unlike traditional methods such as linear regression, which primarily focus on establishing relationships between variables, decomposition analysis can meticulously assess the independent contribution of each factor to the overall change in disease burden. This provides a clearer understanding of the underlying drivers of the diarrhea burden [19]. And this study employs the BAPC model to forecast future disease burdens, utilizing the Bayesian model with integrated nested Laplace approximations (INLA) to predict age-specific and age-standardized incidence, death, and DALYs [20]. The strength of the BAPC model lies in its utilization of the INLA method to streamline the computation of posterior distributions, bypassing the complexities and convergence issues typical of traditional Markov Chain Monte Carlo methods while ensuring computational efficiency. This makes the BAPC model both flexible and robust for analyzing time series data and conducting long-term disease burden predictions [21].

To assess health inequalities related to socioeconomic development, the Lorenz curve was employed to calculate the slope index of inequality and the relative concentration index, representing absolute and relative health inequalities, respectively. The slope index of inequality is computed by regressing the DALYs rate of the global population across all ages on a scale of relative positions related to sociodemographic development, and deviations of the associated Lorenz curve from the line of equality indicate a concentration of the health outcome [22]. The concentration index is calculated through numerical integration of the area under the Lorenz curve, which is fitted using the cumulative proportion of health outcomes measured in DALYs and the relative distribution of the cumulative population ranked by SDI, thereby assessing how the diarrheal disease burden is distributed across the entire socioeconomic gradient [23]. These methods collectively allow us to move beyond mere geographical comparison and to precisely quantify the extent to which socioeconomic development, as captured by SDI, predicts and explains the global inequality in diarrheal disease burden.

R software (version 4.4.1), Joinpoint software (version 4.9.1.0), and the web tool for the APC model were utilized for data analysis. Following data cleaning and organization, the R software was employed to load and install essential software packages such as ggplot2 and maps for data analysis and visualization. A P-value of less than 0.05 (two-tailed) was considered statistically significant for all analyses.

3. Results

3.1. Temporal Joinpoint analysis

The ASIR of diarrhea showed a slight upward trend in general (AAPC = −0.41), but the local trend of different contacts was inconsistent. Specifically, the APC values for the ASIR trends showed five periods of decline: 1990–1993 (−0.79), 1993–1999 (−0.29), 2007–2011 (−0.39), 2011–2015 (−1.78), and 2015–2019 (−0.96), while increases were recorded in two periods: 1999–2007 (0.11) and 2019–2021 (1.49). The incidence turning points varied among SDI regions. However, they increased in most regions after 2019, especially in areas with low SDI (Fig. 1A). Age group analysis shows that the global incidence of diarrhea in children under 5 years and 5–9 years is declining, while other age groups are showing mixed performance and rising parts in different SDI regions. Furthermore, the ASDR of diarrhea has shown a decreasing trend (AAPC = −4.34), where the APC values for each timeframe’s ASDR declined at varying rates: 1990–1995 (−2.82), 1995–2002 (−4.08), 2002–2007 (−3.19), 2007–2013 (−5.13), 2013–2016 (−6.35), and 2016–2021 (−5.20). Comparatively in high SDI areas, the ASDR decreased in 1990–1998 (−1.61), increased sharply during 1998–2007 (10.37), and then decreased between 2007–2021 (−3.19) (Fig. 2B), It is worth noting that the ASDR for people over 65 years of age was on the rise, while the death rate for other age groups and regions continued to decline. The global trend of diarrhea DALYs also displayed a decreasing pattern (AAPC = −4.41), with accelerated declines observed by the APC values during 1990–1995 (−2.87), 1995–2007 (−3.75), 2007–2011 (−4.77), and 2011–2015 (−6.34), followed by a slower decrease from 2015 to 2021 (−5.46) (Fig. 2C). All regions have seen a decrease in diarrhea DALYs. However, in high SDI regions, DALYs for people over 45 have slightly increased, while other age groups have shown declines. In contrast, all age groups in other regions have consistently experienced a downward trend in DALYs (Table S3-S8).

Fig. 1.

Fig. 1

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Temporal trend changes of the global incidence (A), death (B), and DALYs (C) of diarrhea from 1990 to 2021 based on the Joinpoint regression model. Abbreviations: SDI, sociodemographic index; APC, annual percentage change (%); AAPC, average annual percentage change (%); DALYs, disability-adjusted life years; ∗P < 0.05.

Fig. 2.

Fig. 2

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Age-period-birth cohort effects on incidence of diarrhea by SDI quintiles. A) The age effect is depicted through the longitudinal rates specific to age, which are adjusted for variations across different birth cohorts, taking into account the period-specific deviations. B) Period effects are shown through the relative risk of incidence of diarrhea during different periods, calculated as the ratio of the age-specific rates from the period from 1992–1996 to 2017–2021, with the baseline period set as 2002–2006. C) Birth cohort effects are demonstrated by the cohort relative risk of incidence and calculated as the ratio of age-specific rates from 1897 to 1901 cohort to 2017–2021 cohort, with the reference cohort set at 1957–1961. Abbreviations: SDI, sociodemographic index.

3.2. Age-period-cohort analysis

Age-period-cohort analysis has revealed similar age effect patterns across different SDI regions, where the incidence risk decreased with age before reaching 20 years old but increased after 60 years old. High-middle SDI and middle SDI regions had the highest incidence risk in children aged 0–4, while the high SDI region had the highest incidence risk in the elderly over 95. The period effect indicated that the incidence risk decreased over time in high SDI and high-middle SDI regions, while it increased in other regions. The cohort effect showed a downward trend in incidence risk with the progression of birth years, with a more pronounced decline in regions with higher SDI (Fig. 3).

Fig. 3.

Fig. 3

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Changes in incidence (A), death (B), DALYs (C) of diarrhea according to population-level determinants of population growth, aging, and epidemiological change from 1990 to 2021 at the global level and by SDI quintile. The black dot represents the overall value of change contributed by all 3 components. For each component, the magnitude of a positive value indicates a corresponding increase in diarrheal diseases DALYs attributed to the component; the magnitude of a negative value indicates a corresponding decrease in diarrheal diseases DALYs attributed to the related component. Abbreviations: SDI, sociodemographic index; DALYs, disability-adjusted life years.

Globally, the risk of death from diarrhea was highest in children aged 0–4 and began to increase after the age of 60. Except for the high SDI region, other regions exhibited similar age effect patterns. In high SDI regions, the risk of death significantly increased after the age of 70, with the highest risk at age 95 and above. The period effect revealed a downward trend in the risk of death over time, but this effect was not evident in the high SDI region. Similar to the period effect, cohort effects demonstrated decreasing mortality risks for individuals born in later years, although this effect was not apparent in the high SDI region (Fig. S1). The age effect, period effect, and cohort effect of global diarrhea DALYs were similar to those of death (Fig. S2).

3.3. Decomposition analysis

Decomposition analysis revealed that the global incidence of diarrhea is increasing, however primarily due to population growth (225.09 %), while aging and epidemiological shifts have had adverse effects (−53.04 % and −72.05 %, respectively). Conversely, global diarrhea deaths have decreased (159.59 %) because of epidemiological changes, though aging and population growth have mitigated this reduction (−8.77 % and −50.82 %, respectively). In high SDI regions, deaths from diarrhea have risen, driven by aging (39.88 %), population growth (16.80 %), and epidemiological shifts (43.32 %). Globally, DALYs for diarrhea have decreased, driven by aging (16.97 %) and epidemiological shifts (123.18 %), though aging has also detracted from this decline (−40.15 %). Similar trends are observed elsewhere, but in high SDI regions, DALYs for diarrhea have increased, primarily due to aging (25.33 %), population growth (91.51 %), and epidemiological changes (−16.84 %) (Table S9 and Fig. 3).

3.4. Future forecasts of global burden by BAPC

Bayesian models forecast a rise in global diarrhea cases from 2022 to 2031, peaking at 13.2 billion cases. ASIR is expected to rise slightly to 55,449.36 per 100,000 people by 2031. Global diarrhea deaths and ASDR are projected to decline, with deaths reaching 836,725 and ASDR at 8.76 per 100,000 by 2031. Similarly, the DALYs for diarrhea are anticipated to drop to 34,523,620, with an age-standardized DALY rate of 422.81 per 100,000 by 2031 (Fig. 4).

Fig. 4.

Fig. 4

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Future forecasts of global burden of diarrhea by BAPC. Trends in age-standardized incidence rates (A), age-standardized death rates (B), and age-standardized DALYs (C) from 2022 to 2031, predicted using BAPC prediction models. The fan-shaped sections represent the 95 % confidence interval, where the color gradient from dark to light indicates increments of 1 % in the prediction confidence interval; the solid black line represents predicted values, and the black dots represent actual observed values; the vertical dashed line indicates the year in which predictions began. Abbreviations: DALYs, disability-adjusted life years; BAPC, Bayesian age-period-cohort.

3.5. Health inequality analysis

As demonstrated by the slope index of inequality, the incidence ratio among countries decreased from −134,848.00 (95 % CI: −146,454.90 to −123,241.10) in 1990 to −83,798.88 (95 % CI: −95,423.74 to −72,174.02) in 2021; the death ratio decreased from −158.23 (95 % CI: −169.20 to −147.26) in 1990 to −24.94 (95 % CI: −28.30 to −21.58) in 2021; the DALYs ratio decreased from −11,682.65 (95 % CI: −12,457.56 to −10,907.73) in 1990 to −1,623.19 (95 % CI: −1,818.10 to −1,428.28) in 2021 (Fig. 5), indicating that countries with lower SDI bear a disproportionately high burden. Over time, these inequalities have decreased significantly. However, the concentration indices for incidence, death, and DALYs in 2021 (0.38, 0.47, and 0.54, respectively) still show an uneven distribution of health burdens among countries with varying SDI levels.

Fig. 5.

Fig. 5

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Health inequality regression curves and concentration curves for diarrhea in terms of incidence, death, DALYs. Health inequality regression curves (A, C, E) and concentration curves (B, D, F) of incidence, death, DALYs for diarrhea. Each scattered point represents the population size of a country or region, and the larger the circle, the greater the population. The diagonal is called the “complete equality line”, representing that health outcomes are evenly distributed across all socioeconomic groups. Abbreviations: DALYs, disability-adjusted life years; SDI, sociodemographic index.

4. Discussion

Diarrhea remains a significant challenge to global public health. This study presents a comprehensive assessment based on trend, decomposition and predictive analysis, identifying key years when significant changes in disease indicators occurred. For the first time, the age-period-cohort model was used to analyze the global burden of diarrhea disease. Compared with previous GBD studies, this research provides a more nuanced understanding of disease trends and offers clearer insights into the effectiveness of health system responses, going beyond traditional epidemiological indicators.

The Joinpoint regression analysis reveals distinct trends in the incidence, death, and DALYs associated with diarrhea across different periods. Further stratification by SDI regions highlights nuanced variations, potentially reflecting differences in vaccination strategies, herd immunity levels, and health system resilience [[24], [25], [26]]. The results indicate a notable increase in diarrhea incidence after 2019, a trend that may be partly attributable to the emergence and global spread of the coronavirus disease 2019 (COVID-19) pandemic, which began at the end of 2019 and continued to disrupt health systems well into 2021. The pandemic overwhelmed healthcare facilities, leading to reduced capacity or temporary suspension of essential health services, including disease surveillance, diagnostics, and treatment [27]. The preventive measures adopted in response to the COVID-19 pandemic may have also influenced the trend of diarrhea disease burden. For instance, research has demonstrated that in Ghana, the implementation of COVID-19 hand hygiene protocols led to an 11 % reduction in diarrhea cases [28]. Comparatively, studies in Japan have indicated that following the COVID-19 outbreak, there was a surge in new cases of gastrointestinal infections [29,30]. Moreover, the incidence of gastrointestinal and enteric viral diseases among children and adolescents in China significantly decreased, but as COVID-19 restrictions were relaxed, the incidence gradually increased and eventually returned to average levels [31]. These cases showed that preventive measures by different countries against COVID-19 had influenced the local gastrointestinal disease incidence. Notably, age-group analysis revealed a rising trend in diarrhea among individuals over 10 years old globally, suggesting a shift from young children, who still bear the majority of the disease burden, to older children and adolescents. This shift may be attributed to significant reductions in the former group and slower progression in the latter [32]. To understand the impact of the COVID-19 pandemic on diarrhea and its pathogens, as well as the development trends before and after the pandemic, it is crucial to continue conducting relevant research.

The interaction of age, diagnosis period, and exposure experience may influence the epidemiological trends of diarrhea. The age-period-cohort model results found that the risk of onset decreases with age before the age of 20, which may be due to the protective effect of vaccination extending from early childhood to adolescence [33]. Conversely, beyond the age of 60, the risk escalates, likely due to the age-related decline in immune function, with concurrent medical conditions potentially exacerbating or precipitating intestinal infections, findings consistent with existing literature [34,35]. In addition, emerging evidence underscores the role of the gut microbiome, with age-related shifts in microbial composition potentially influencing susceptibility to enteric infections [[36], [37], [38]]. Probiotic interventions have also demonstrated protective benefits against diarrhea in both preventive and therapeutic contexts [39,40]. Furthermore, the birth cohort effect observed in this study indicates an overall pattern of initial increase followed by a decline in diarrhea risk across successive birth cohorts. This trend likely reflects the progressive introduction of vaccines in recent decades, along with broader improvements in WASH infrastructure, nutrition, and overall living conditions, which have reduced exposure to diarrheal pathogens [41,42]. By contrast, older cohorts born in earlier, less hygienic, or resource-limited settings may have experienced higher lifetime risks. These cohort-level differences underscore the long-term public health benefits of sustained investment in preventive strategies, particularly in low-resource settings. It is worth noting that the period effect in high SDI regions, where the period risk peaked around 2007–2011 before declining, is different from that in other regions.

Decomposition analysis indicates that population growth plays a significant role in the disease burden of diarrhea, particularly in low and low-middle-SDI regions. According to the World Population Prospects 2024, the global population is projected to reach nearly 8.2 billion by mid-2024, with an expected increase of 2 billion over the next 60 years, predominantly in low-income and lower-middle-income countries, especially in sub-Saharan Africa and South Asia, potentially exacerbating the diarrhea disease burden in these areas [43]. These demographic trends may further exacerbate the burden of diarrheal diseases in regions already facing structural challenges such as poor sanitation, limited healthcare access, and undernutrition. The elderly are more vulnerable to complications of diarrheal diseases. With the continuous increase in life expectancy, population aging poses new challenges to the control of diarrheal diseases, which extend not only to case management but also to preventive care and vaccination strategies for high-risk groups, placing further demands on the healthcare system [44,45].

Between 1990 and 2021, the declining trends in diarrhea incidence, mortality, and DALYs became less steep, reflecting persistent—and in some regions, widening—health inequalities. Addressing these disparities requires targeted, context-specific policy interventions and more equitable allocation of healthcare resources. High SDI countries should prioritize food safety, strengthen surveillance for emerging pathogens, and address the health needs of an aging population. In contrast, low SDI countries must tackle fundamental challenges related to population growth, WASH infrastructure deficits, and limited access to vaccines and medical care. International agencies such as the WHO and the United Nations should prioritize scaling up support for these regions through financial aid, technical assistance, and the dissemination of best practices. Key public health measures should include ensuring access to safe drinking water, promoting food hygiene, and improving sanitation and personal hygiene behaviors [46].

The continued efficacy of the rotavirus vaccine underscores the importance of maximizing immunization coverage, particularly in high-burden settings [47]. Future efforts should aim not only to scale up existing vaccines but also to invest in the development of next-generation vaccines targeting a broader spectrum of diarrheal pathogens. Equally important are systemic interventions that address the social determinants of health, including improving primary healthcare services, strengthening the resilience of health systems, and enhancing protections for vulnerable groups such as children, the elderly, and immunocompromised individuals. Looking ahead, achieving further reductions in the global burden of diarrhea will require coordinated global action and locally adapted strategies. This includes strengthening diarrhea surveillance systems, fostering cross-border collaboration on pathogen monitoring, and supporting research into the impacts of climate change, urbanization, and environmental degradation on disease transmission. Ensuring that no one is left behind will depend on aligning global sustainable development goals (e.g., Sustainable Development Goal 3: Good Health and Well-being) with national health strategies, mobilizing resources equitably, and fostering innovation in both prevention and care delivery.

There are several limitations from this study. Firstly, the comparability of diarrhea data is inevitably affected by differences in data collection methods, data sources, and reporting standards across different regions. Secondly, due to limited medical conditions and diagnostic capabilities, some countries lack high-quality epidemiological data on diarrhea, especially in low-income countries, which may lead to an underestimation of the results. Third, the GBD database does not include disaggregated incidence data by specific diarrheal pathogens, limiting the ability to assess the burden attributable to individual etiologies.

In conclusion, although the global burden of diarrhea has declined over the past few decades, specific populations remain disproportionately affected, notably children under 5 years of age and older individuals over 60, particularly in high SDI regions. More attention should be paid to these areas and populations to implement effective public health policies, including improving health care, strengthening protection for vulnerable groups, increasing coverage of existing vaccines, and developing more effective vaccines for the future.

Ethical statements

The institutional review board granted an exemption for this study, as it utilized publicly accessible data that contained no confidential or personally identifiable patient information.

Acknowledgements

This study was supported by the National Key Research and Development Program of China (2022YFC2602200 and 2022YFC2602301). We extend our gratitude to the contributors of the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) for their foundational work. We also acknowledge the Institute for Health Metrics and Evaluation (IHME) for providing open access to the GBD data. Additionally, we thank Jasper Luong for his professional translation and editorial refinement of the manuscript, which enhanced the linguistic quality and scientific clarity of the final version.

Conflcit of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Where authors are identified as personnel of the International Agency for Research on Cancer/WHO, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/WHO.

Author contributions

Mengjiao Xie: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Yang Song: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Jing Tao: Validation, Software, Investigation, Data curation. Mengnan Jiang: Validation, Methodology, Investigation. Yang Liu: Visualization, Validation. Io Hong Cheong: Validation, Methodology. Zisis Kozlakidis: Methodology. Zhaorui Chang: Writing – review & editing, Supervision, Methodology. Qiang Wei: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

Footnotes

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bsheal.2025.09.005. GBD study 2021 data resources were available online from the Global Health Data Exchange (GHDx) query tool (http://ghdx.healthdata.org/gbd-results-tool).

Contributor Information

Zhaorui Chang, Email: changzr@chinacdc.cn.

Qiang Wei, Email: weiqiang@chinacdc.cn.

Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (804.5KB, docx)

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