Appendicitis burden in Africa, 2010–2023: temporal trends, pandemic-era deviations, and projections to 2030

Abstract

Introduction

Appendicitis is a common abdominal surgical emergency, but continent-wide evidence on its burden and trends in Africa is limited. We assessed the burden, trends, geographic heterogeneity, demographic drivers, pandemic-era deviations, and future projections of appendicitis across Africa.

Methods

We conducted a population-level secondary analysis using Global Burden of Disease 2023 estimates for the African Union and 54 African countries from 2010 to 2023. We examined incidence, prevalence, deaths, and disability-adjusted life-years (DALYs) as absolute numbers and age-standardized rates (ASRs). Trends, drivers of change, pandemic-era deviations, and projections to 2030 were evaluated.

Results

From 2010 to 2023, appendicitis burden increased across Africa: incident cases rose from 2.45 to 3.73 million, prevalent cases from 92,805 to 141,711, deaths from 3,240 to 3,984, and DALYs from 182,086 to 226,592. Age-standardized incidence and prevalence increased modestly, whereas age-standardized death and DALY rates declined. Higher nonfatal burden was concentrated in parts of southern and western Africa, while fatal and disability burden remained highest in eastern Africa. Population growth was the main driver of increasing burden. During 2020–2023, incidence and prevalence exceeded expected levels, whereas deaths and DALYs were lower than expected. By 2030, incidence and prevalence are projected to increase in most countries, while death and DALY rates are projected to decline in most settings.

Conclusions

Appendicitis burden in Africa increased substantially between 2010 and 2023, largely driven by demographic expansion. Persistent geographic inequalities highlight the need to strengthen timely diagnosis, referral, and emergency surgical care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41182-026-00990-y.

Introduction

Appendicitis remains one of the most common abdominal surgical emergencies worldwide and continues to impose a substantial clinical and health-system burden. Although many patients recover uneventfully after timely diagnosis and appendectomy, delayed recognition or limited access to definitive care can result in perforation, abscess, sepsis, prolonged hospitalization, and death. Contemporary management has become increasingly nuanced, with advances in imaging, risk stratification, laparoscopic surgery, and selective nonoperative treatment; nevertheless, outcomes remain strongly influenced by the timeliness and quality of emergency surgical care [1–3].

Recent Global Burden of Disease analyses have shown that appendicitis remains a major public health problem across regions, with considerable variation by geography, age, sex, and level of development. The GBD 2021 appendicitis analysis reported approximately 17 million incident cases globally in 2021 and highlighted marked regional contrasts in age-standardized incidence and mortality. Earlier GBD-based studies likewise showed rising global incidence in absolute terms, alongside divergent trends in disability and fatal burden [4–6]. These findings suggest that appendicitis should not be viewed solely as an individual surgical diagnosis, but also as a population-level condition whose burden reflects broader demographic and health-system dynamics.

Africa is a particularly important setting in which to examine appendicitis burden. The continent is undergoing rapid population growth, urbanization, and epidemiologic transition, while access to timely, safe, and affordable surgical care remains uneven. The Lancet Commission on Global Surgery emphasized that delayed access to emergency and essential surgery remains a major barrier to improved outcomes in low-resource settings, and appendicitis has been used as a representative condition for quantifying unmet surgical need and its economic consequences [7, 8]. In addition, earlier clinical perspectives and contemporary African studies suggest that appendicitis in many African settings is shaped by late presentation, referral delays, limited diagnostic resources, and variable perioperative capacity, all of which may increase the risk of advanced disease and adverse outcomes [9–11].

Despite its clinical importance, contemporary continent-wide evidence on appendicitis in Africa remains limited. Much of the available literature is hospital-based, country-specific, or focused on management and postoperative outcomes rather than standardized comparisons of population burden across countries and over time [12, 13]. Moreover, although global studies have described appendicitis burden at broad regional levels, Africa-specific analyses integrating temporal trends, geographic heterogeneity, trajectory patterns, demographic drivers of change, pandemic-period deviations, and forward projections remain scarce [6, 14, 15]. This gap limits the ability of clinicians, public health planners, and surgical systems leaders to identify where the burden is concentrated, whether trends are worsening or improving, and to what extent observed changes are driven by epidemiologic shifts versus demographic expansion.

To address these gaps, we used data from the Global Burden of Disease Study 2023 to evaluate appendicitis burden across the African Union and 54 African countries from 2010 to 2023. We quantified incidence, prevalence, deaths, and disability-adjusted life-years (DALYs); assessed spatial distribution and temporal trends in age-standardized rates; identified country-level incidence trajectories; decomposed changes in burden into population growth, population aging, and epidemiologic change; examined age, period, and cohort effects; evaluated deviations during the COVID-19 period; and projected burden to 2030. By providing a continent-wide assessment of appendicitis burden and its drivers, this study aims to inform acute care prioritization, surgical system strengthening, and strategies to reduce inequities in emergency gastrointestinal care across Africa.

Methods

Study design and data source

We conducted a population-level secondary analysis of appendicitis burden in Africa using estimates from the Global Burden of Disease Study 2023 (GBD 2023). GBD 2023 provides standardized estimates of disease occurrence and health loss across locations, years, age groups, and sexes by integrating available epidemiological data, mortality records, surveys, registries, and statistical modeling within a unified analytical framework. We extracted appendicitis-specific estimates for the African Union and 54 African countries from 2010 to 2023.

Although GBD estimates are available for earlier years, we restricted the primary analysis to 2010–2023 to focus on contemporary appendicitis burden in Africa and to provide a recent analytical window relevant to current diagnostic capacity, emergency care pathways, surgical service delivery, and pandemic-era disruptions. This period was also chosen to support internally consistent analyses of recent temporal trends, COVID-19-era deviations, and projections to 2030. The implications of using a relatively short contemporary time window were considered when interpreting trend estimates, counterfactual analyses, and forecasts.

We evaluated four burden metrics: incidence, prevalence, deaths, and disability-adjusted life-years (DALYs). For each metric, we analyzed both absolute numbers and age-standardized rates (ASRs) per 100,000 population, with corresponding 95% uncertainty intervals (UIs) for GBD-derived estimates. All primary analyses were conducted for both sexes combined.

Case definition and geographic scope

Appendicitis was defined according to the GBD 2023 cause hierarchy and corresponding disease modeling framework. We did not independently assign International Classification of Diseases (ICD) codes during data extraction. Instead, appendicitis estimates were extracted as the GBD appendicitis cause category, for which ICD-coded and other source data are mapped and harmonized within the GBD framework.

The geographic scope included the African Union and its 54 member states. For descriptive regional analyses and figure presentation, countries were grouped into standard GBD African subregions: North Africa, Central sub-Saharan Africa, Eastern sub-Saharan Africa, Southern sub-Saharan Africa, and Western sub-Saharan Africa.

Descriptive burden assessment

We first summarized appendicitis burden at the African Union level in 2010 and 2023, including incident cases, prevalent cases, deaths, DALYs, and the corresponding ASRs. We then mapped country-level age-standardized incidence, prevalence, death, and DALY rates for 2010 and 2023 to assess geographic variation and temporal changes in the spatial distribution of appendicitis burden across Africa.

Temporal trend analysis

To quantify temporal changes in ASRs from 2010 to 2023, we calculated the estimated annual percentage change (EAPC) for each country and burden metric. For each country-metric combination, finite positive ASR values were modeled using a log-linear regression model:

$$ln\left(\mathit{ASRt}\right)=\alpha +\beta t+\epsilon t$$

where ASRt denotes the age-standardized rate in year t, and β represents the annual change in the natural logarithm of the ASR. The EAPC was calculated as follows:

$$EAPC=100\times \left[exp\left(\beta \right)-1\right]$$

The 95% confidence interval (CI) for the EAPC was derived from the 95% CI of the regression coefficient β:

$$95\%CI=100\times \left[exp\left(\beta \pm 1.96\times S{E}_{\beta }\right)-1\right]$$

A positive EAPC indicated an increasing ASR over time, whereas a negative EAPC indicated a decreasing ASR. Statistical evidence for a temporal trend was assessed according to whether the 95% CI of the EAPC excluded zero. The 95% UIs reported for 2010 and 2023 burden estimates were derived from the GBD framework, whereas EAPC 95% CIs reflected uncertainty around the fitted log-linear temporal trend based on ASR point estimates.

Trajectory clustering of incidence trends

To characterize similarities in the longitudinal evolution of appendicitis incidence across countries, we applied dynamic time warping (DTW) to complete country-level age-standardized incidence series from 2010 to 2023. DTW is a distance-based method that allows flexible alignment of time series with different local shapes and timings, making it suitable for comparing temporal trajectories that may not evolve synchronously.

Country-level incidence series were converted into complete annual time-series vectors. The series were not standardized before clustering; therefore, the resulting clusters reflected both absolute burden level and temporal trajectory shape. We used partitional time-series clustering with DTW distance and dynamic time warping barycenter averaging centroids. A three-cluster solution was selected based on interpretability, visual separation of trajectory patterns, and avoidance of excessive fragmentation. Cluster labels were assigned according to the level and shape of the resulting trajectory groups, including low-stable, moderate-rising, and high-persistent trajectories. Cluster assignments were visualized using longitudinal trajectory plots and geographic maps.

Decomposition analysis

To quantify contributors to changes in appendicitis burden between 2010 and 2023, we used the Das Gupta decomposition method to partition changes in absolute numbers into three components: population growth, population aging or age-structure change, and epidemiological change in age-specific rates. This analysis was applied separately to incident cases, prevalent cases, deaths, and DALYs at the country level and summarized across the African Union.

For each country and burden metric, the expected number of events was expressed as follows:

$$N=\sum _{i}P\times A_i\times R_i\left./\phantom{R_{i}100000}\right)100000$$

where P is the total population, Ai is the proportion of the population in age group i, and Ri is the age-specific rate in age group i. The change in N between 2010 and 2023 was decomposed into contributions from changes in total population size, age structure, and age-specific rates. To avoid dependence on the order in which components were changed, we averaged contributions across all six possible ordering sequences of the three components. Positive values indicated contributions to increasing burden, whereas negative values indicated offsetting contributions.

Age–period–cohort analysis

We examined age, period, and cohort patterns of appendicitis burden using an exploratory age–period–cohort (APC) modeling approach, with a primary focus on incidence. Additional APC summaries for prevalence, deaths, and DALYs were reported in the Supplementary Material.

Age-specific numbers and rates were arranged by age group and calendar year. Population denominators for each age-period cell were derived from the relationship between number and rate estimates. Birth cohort was calculated as calendar year minus age midpoint. To reduce instability in sparse extreme age strata, age groups younger than 15 years and the 95 + years group were excluded from the APC model. Country-level data were aggregated to the African Union level for APC modeling.

We fitted generalized additive models with penalized cubic regression splines for age, period, and cohort. The model included an offset for the logarithm of the population denominator. A Poisson model with a log link was fitted first; when model fitting was unstable, a quasi-poisson model was used. Relative risks (RRs) for age, period, and cohort effects were obtained by exponentiating the corresponding smooth-term estimates, and uncertainty bands were derived from the standard errors of the model terms.

Because age, period, and cohort are linearly dependent, APC findings were interpreted as exploratory smoothed patterns rather than fully separable causal effects. The APC results were used to describe broad age gradients, period patterns, and cohort-related variation in appendicitis burden.

Counterfactual analysis during the COVID-19 period

To assess whether appendicitis burden during the COVID-19 period deviated from pre-pandemic expectations, we performed a counterfactual time-series analysis at the African Union level. For each burden metric, autoregressive integrated moving average (ARIMA) models were fitted to log-transformed annual ASRs from 2010 to 2019 and used to forecast expected values for 2020–2023 under a continuation of pre-pandemic trends.

Forecasted values were back-transformed to the original ASR scale. We calculated observed-minus-expected differences for each year from 2020 to 2023 and summarized cumulative excess or deficit burden descriptively. Ninety-five percent prediction intervals were generated for the counterfactual forecasts and used to visualize uncertainty around expected trajectories. Model diagnostics included Akaike information criterion corrected for small samples and Ljung–Box tests of residual autocorrelation.

Because the pre-pandemic training series was relatively short, observed-versus-expected differences were interpreted cautiously as descriptive ecological deviations rather than causal estimates of the effect of the COVID-19 pandemic.

Forecasting to 2030

We projected country-level appendicitis burden to 2030 using log-linear models fitted to annual ASRs. Main projections were generated for incidence, prevalence, deaths, and DALYs for all 54 African countries using the 2010–2023 period. Sensitivity analyses were conducted using alternative pre-pandemic training windows ending in 2019 and 2018.

For each country and burden metric, we fitted the following model:

$$ln\left(\mathit{ASRt}\right)=\alpha +\beta t+\epsilon t$$

The fitted model was used to generate predicted ASRs through 2030. To estimate uncertainty around projected values, we drew 2000 sets of model coefficients from a multivariate normal distribution defined by the fitted coefficient estimates and their variance–covariance matrix, and then back-transformed predicted log-ASRs to the original ASR scale.

Forecast results were presented as projected ASRs in 2030 and as absolute and relative changes from the model-fitted baseline ASR. For the main analysis, the baseline comparator was the fitted 2023 ASR, not the directly observed 2023 ASR. Absolute change was calculated as follows:

$$ProjectedASR2030-FittedASRbaseline$$

and was expressed per 100,000 population. Relative change was calculated as the absolute change divided by the fitted baseline ASR. For transparency, the supplementary forecast table reports both the observed ASR in the baseline year and the model-fitted baseline ASR. Projection results were interpreted as exploratory model-based forecasts rather than deterministic predictions.

Statistical analysis and visualization

All analyses were conducted in R.4.4.2.

Ethical considerations

This study used publicly available, de-identified, aggregate estimates from GBD 2023 and did not involve direct participant contact or access to identifiable personal information. Accordingly, institutional review board approval and individual informed consent were not required.

Results

Overall burden at the African Union level

At the African Union level, the absolute burden of appendicitis increased between 2010 and 2023 across all four metrics (Table 1). Incident cases rose from 2,445,266 (95% UI, 1,950,259–3,108,647) in 2010 to 3,729,868 (2,984,037–4,703,775) in 2023, and prevalent cases increased from 92,805 (74,232–117,491) to 141,711 (113,272–178,043). Deaths increased from 3,240 (1,551–6,276) to 3,984 (2,065–6,968), and DALYs increased from 182,086 (101,542–332,323) to 226,592 (137,512–371,835).

Table 1.

Deaths due to appendicitis in Africa, 2010 and 2023

Region	Location	Number_2010	ASR_2010	Number_2023	ASR_2023	EAPC_95CI
African Union	African Union	3240 (1551–6276)	0.5 (0.3–1.0)	3984 (2065–6968)	0.4 (0.2–0.8)	−1.23 (−1.47 to −1.00)
North Africa	Algeria	95 (45–149)	0.4 (0.2–0.6)	108 (50–195)	0.3 (0.1–0.6)	−1.73 (−2.26 to −1.20)
North Africa	Egypt	35 (19–63)	0.1 (0.0–0.1)	66 (35–117)	0.1 (0.0–0.2)	2.70 (2.08–3.33)
North Africa	Libya	12 (5–20)	0.3 (0.1–0.5)	11 (4–25)	0.2 (0.1–0.4)	−3.92 (−4.56 to −3.26)
North Africa	Morocco	101 (43–196)	0.4 (0.2–0.8)	78 (34–133)	0.2 (0.1–0.4)	−3.98 (−4.41 to −3.55)
North Africa	Sudan	81 (35–152)	0.5 (0.2–1.0)	83 (32–170)	0.4 (0.1–0.7)	−1.81 (−2.03 to −1.59)
North Africa	Tunisia	49 (26–72)	0.5 (0.3–0.8)	61 (36–95)	0.5 (0.3–0.8)	−2.98 (−5.39 to −0.51)
Central Sub-Saharan Africa	Angola	51 (13–107)	0.4 (0.1–0.9)	66 (20–134)	0.3 (0.1–0.7)	−1.40 (−1.61 to −1.18)
Central Sub-Saharan Africa	Cameroon	42 (22–78)	0.3 (0.2–0.6)	49 (25–84)	0.3 (0.1–0.4)	−2.02 (−2.32 to −1.73)
Central Sub-Saharan Africa	Central African Republic	19 (6–44)	0.7 (0.2–1.7)	26 (8–67)	0.7 (0.2–1.8)	0.20 (−0.09 to 0.49)
Central Sub-Saharan Africa	Chad	40 (23–66)	0.6 (0.4–1.1)	50 (27–88)	0.5 (0.3–0.9)	−1.43 (−1.72 to −1.14)
Central Sub-Saharan Africa	Congo	10 (5–20)	0.5 (0.2–0.9)	12 (5–24)	0.4 (0.2–0.7)	−1.50 (−1.65 to −1.35)
Central Sub-Saharan Africa	Democratic Republic of the Congo	188 (31–593)	0.5 (0.1–1.7)	229 (52–636)	0.5 (0.1–1.2)	−1.13 (−1.38 to −0.89)
Central Sub-Saharan Africa	Equatorial Guinea	2 (1–3)	0.3 (0.1–0.6)	2 (1–5)	0.3 (0.1–0.6)	−0.10 (−0.55 to 0.34)
Central Sub-Saharan Africa	Gabon	4 (1–6)	0.4 (0.1–0.7)	4 (2–8)	0.3 (0.1–0.6)	−1.54 (−1.91 to −1.16)
Central Sub-Saharan Africa	Sao Tome and Principe	1 (0–1)	0.6 (0.3–1.0)	1 (0–1)	0.4 (0.2–0.8)	−2.43 (−3.21 to −1.64)
Eastern Sub-Saharan Africa	Burundi	39 (8–118)	0.7 (0.1–2.1)	63 (13–174)	0.8 (0.2–2.2)	0.58 (0.25–0.91)
Eastern Sub-Saharan Africa	Comoros	3 (1–7)	0.6 (0.2–1.5)	4 (1–9)	0.5 (0.2–1.2)	−1.29 (−1.66 to −0.92)
Eastern Sub-Saharan Africa	Djibouti	4 (1–9)	0.8 (0.3–1.8)	7 (2–15)	0.8 (0.3–1.6)	−0.66 (−1.06 to −0.26)
Eastern Sub-Saharan Africa	Eritrea	35 (11–89)	1.0 (0.3–2.7)	44 (17–97)	1.0 (0.4–2.2)	−0.43 (−0.79 to −0.07)
Eastern Sub-Saharan Africa	Ethiopia	563 (159–1509)	1.4 (0.4–3.6)	737 (227–1680)	1.2 (0.4–2.7)	−0.95 (−1.35 to −0.55)
Eastern Sub-Saharan Africa	Kenya	55 (19–127)	0.2 (0.1–0.6)	61 (25–116)	0.2 (0.1–0.4)	−1.78 (−2.08 to −1.48)
Eastern Sub-Saharan Africa	Madagascar	104 (34–249)	1.0 (0.3–2.4)	145 (50–372)	0.9 (0.3–2.2)	−0.58 (−0.86 to −0.30)
Eastern Sub-Saharan Africa	Malawi	54 (17–134)	0.8 (0.3–1.9)	69 (24–150)	0.7 (0.2–1.5)	−0.82 (−1.16 to −0.48)
Eastern Sub-Saharan Africa	Mauritius	1 (1–1)	0.1 (0.1–0.1)	1 (1–2)	0.1 (0.1–0.1)	1.19 (0.34–2.05)
Eastern Sub-Saharan Africa	Mozambique	136 (48–352)	1.0 (0.4–2.6)	157 (61–382)	0.9 (0.3–2.1)	−1.40 (−1.89 to −0.91)
Eastern Sub-Saharan Africa	Rwanda	35 (9–94)	0.6 (0.1–1.5)	54 (16–132)	0.6 (0.2–1.5)	0.61 (0.23–0.99)
Eastern Sub-Saharan Africa	Seychelles	0 (0–1)	0.5 (0.3–0.7)	0 (0–1)	0.4 (0.2–0.6)	−2.64 (−3.29 to −1.99)
Eastern Sub-Saharan Africa	Somalia	55 (13–162)	0.7 (0.2–1.9)	81 (23–204)	0.7 (0.2–1.7)	−0.01 (−0.28 to 0.27)
Eastern Sub-Saharan Africa	South Sudan	34 (11–82)	0.7 (0.3–1.5)	42 (15–97)	0.7 (0.3–1.7)	0.85 (0.35–1.35)
Eastern Sub-Saharan Africa	Uganda	86 (25–217)	0.5 (0.1–1.3)	117 (39–270)	0.5 (0.2–1.1)	−0.88 (−1.43 to −0.32)
Eastern Sub-Saharan Africa	United Republic of Tanzania	210 (83–494)	0.8 (0.3–1.9)	229 (95–513)	0.6 (0.3–1.3)	−2.42 (−2.90 to −1.94)
Eastern Sub-Saharan Africa	Zambia	58 (27–112)	1.1 (0.5–2.1)	79 (34–134)	0.9 (0.4–1.5)	−1.62 (−1.93 to −1.32)
Southern Sub-Saharan Africa	Botswana	8 (4–15)	0.6 (0.3–1.1)	8 (4–16)	0.4 (0.2–0.8)	−2.48 (−2.66 to −2.30)
Southern Sub-Saharan Africa	Eswatini	6 (3–10)	0.7 (0.3–1.2)	5 (2–8)	0.4 (0.2–0.8)	−3.36 (−3.85 to −2.86)
Southern Sub-Saharan Africa	Lesotho	10 (6–20)	0.7 (0.4–1.4)	10 (5–20)	0.6 (0.3–1.3)	−1.47 (−1.58 to −1.35)
Southern Sub-Saharan Africa	Namibia	11 (6–20)	0.6 (0.3–1.1)	13 (7–24)	0.5 (0.3–0.9)	−1.96 (−2.34 to −1.58)
Southern Sub-Saharan Africa	South Africa	201 (142–263)	0.4 (0.3–0.6)	223 (143–341)	0.4 (0.2–0.6)	−1.93 (−2.48 to −1.39)
Southern Sub-Saharan Africa	Zimbabwe	71 (37–125)	0.7 (0.4–1.2)	81 (36–145)	0.6 (0.3–1.0)	−1.34 (−1.54 to −1.13)
Western Sub-Saharan Africa	Benin	26 (14–48)	0.5 (0.3–0.8)	32 (17–54)	0.4 (0.2–0.7)	−1.46 (−1.78 to −1.14)
Western Sub-Saharan Africa	Burkina Faso	61 (31–108)	0.6 (0.3–1.1)	72 (38–123)	0.5 (0.3–0.9)	−1.30 (−1.54 to −1.06)
Western Sub-Saharan Africa	Cabo Verde	0 (0–0)	0.1 (0.0–0.1)	0 (0–0)	0.1 (0.0–0.1)	−1.11 (−2.28 to 0.08)
Western Sub-Saharan Africa	Côte d'Ivoire	47 (25–74)	0.4 (0.2–0.6)	64 (37–106)	0.3 (0.2–0.5)	−1.76 (−2.23 to −1.29)
Western Sub-Saharan Africa	Gambia	7 (4–10)	0.6 (0.4–1.0)	7 (4–13)	0.5 (0.3–0.9)	−1.46 (−2.31 to −0.59)
Western Sub-Saharan Africa	Ghana	149 (83–233)	0.9 (0.5–1.4)	187 (91–325)	0.8 (0.4–1.3)	−0.51 (−0.77 to −0.25)
Western Sub-Saharan Africa	Guinea	30 (13–53)	0.4 (0.2–0.7)	31 (14–52)	0.3 (0.2–0.5)	−1.95 (−2.16 to −1.73)
Western Sub-Saharan Africa	Guinea-Bissau	7 (3–12)	0.8 (0.4–1.3)	7 (4–11)	0.6 (0.3–0.9)	−2.14 (−2.30 to −1.98)
Western Sub-Saharan Africa	Liberia	12 (6–23)	0.5 (0.3–1.0)	14 (7–25)	0.4 (0.2–0.7)	−2.77 (−3.50 to −2.04)
Western Sub-Saharan Africa	Mali	52 (26–94)	0.6 (0.3–0.9)	66 (33–118)	0.5 (0.2–0.8)	−1.43 (−1.97 to −0.89)
Western Sub-Saharan Africa	Mauritania	8 (3–16)	0.4 (0.2–0.9)	9 (3–19)	0.3 (0.1–0.8)	−2.39 (−2.88 to −1.89)
Western Sub-Saharan Africa	Niger	43 (15–82)	0.5 (0.2–0.8)	67 (25–137)	0.4 (0.2–0.8)	−0.79 (−0.97 to −0.61)
Western Sub-Saharan Africa	Nigeria	202 (114–317)	0.2 (0.1–0.3)	253 (148–410)	0.2 (0.1–0.3)	−0.67 (−1.07 to −0.27)
Western Sub-Saharan Africa	Senegal	44 (23–74)	0.5 (0.3–0.8)	52 (27–94)	0.4 (0.2–0.7)	−1.79 (−2.44 to −1.13)
Western Sub-Saharan Africa	Sierra Leone	24 (12–45)	0.6 (0.3–1.1)	25 (13–44)	0.5 (0.3–0.9)	−1.29 (−1.53 to −1.05)
Western Sub-Saharan Africa	Togo	19 (11–30)	0.6 (0.3–0.9)	22 (11–39)	0.4 (0.2–0.8)	−1.77 (−2.15 to −1.38)

Values are presented as counts and age-standardized rates (ASRs) with 95% uncertainty intervals; estimated annual percentage change (EAPC) is shown with its 95% confidence interval for 2010–2023

Age-standardized patterns differed by outcome. Age-standardized incidence increased from 217.0 to 239.5 per 100,000 population, and age-standardized prevalence increased from 8.3 to 9.1 per 100,000. In contrast, the age-standardized death rate declined from 0.5 to 0.4 per 100,000, and the age-standardized DALY rate declined from 20.0 to 17.6 per 100,000. Thus, while absolute counts increased across all outcomes, age-standardized nonfatal burden increased modestly, whereas age-standardized mortality- and disability-related rates declined (Table 1).

Geographic variation in appendicitis burden

Marked geographic heterogeneity was observed in both 2010 and 2023 (Supplementary Fig. 1; Fig. 1). In 2023, higher age-standardized incidence and prevalence were concentrated in parts of southern and western Africa, particularly Namibia and Ghana. Lower nonfatal rates were observed in several central and eastern African countries. By contrast, higher age-standardized death and DALY rates were concentrated mainly in eastern Africa, with Ethiopia and Eritrea among the visually highest-burden countries on the 2023 maps.

Fig. 1.

Geographical distribution of appendicitis burden across 54 African countries in 2023. Maps show age-standardized rates per 100,000 population for incidence, prevalence, deaths, and disability-adjusted life-years (DALYs) in both sexes. Darker shading indicates a higher burden. Country estimates were derived from the Global Burden of Disease Study 2023

Comparison with the 2010 maps suggested that broad spatial patterns of nonfatal burden remained relatively stable over time. Death and DALY burden also continued to show disproportionate concentration in eastern Africa, although country-level variation persisted within each subregion.

Temporal trends in age-standardized rates, 2010–2023

From 2010 to 2023, age-standardized incidence and prevalence increased in most African countries, whereas age-standardized death and DALY rates declined in the majority of settings (Fig. 2; Supplementary Table 1). On the EAPC maps, incidence and prevalence were predominantly positive across the continent, while deaths and DALYs were largely negative. This indicates a divergent continental pattern: increasing age-standardized occurrence of appendicitis but declining age-standardized severe-outcome rates in most countries.

Fig. 2.

Estimated annual percentage change in age-standardized appendicitis burden across 54 African countries, 2010–2023. Maps display the estimated annual percentage change (EAPC) in age-standardized rates (ASRs) for incidence, prevalence, deaths, and DALYs in both sexes. EAPCs were estimated using log-linear regression of the form log(ASR) ~ year. Positive values indicate increasing trends, whereas negative values indicate decreasing trends over time

For incidence, the largest increases were observed in Ghana (EAPC 1.50, 95% CI 1.40–1.60), Tunisia (1.42, 1.12–1.73), and Botswana (1.41, 1.17–1.64). The greatest declines were observed in Eswatini (− 1.65, − 1.74 to − 1.55), Seychelles (− 0.99, − 1.45 to − 0.52), and Mauritius (− 0.64, − 1.12 to − 0.17). The same countries showed the strongest increases and decreases for prevalence, and 49 of 54 countries had positive EAPCs for both incidence and prevalence (Fig. 2; Supplementary Table 1).

Declining trends predominated for deaths and DALYs. The largest increases in age-standardized death rates were observed in Egypt (2.70, 2.08–3.33), Mauritius (1.19, 0.34–2.05), and South Sudan (0.85, 0.35–1.35). The steepest declines occurred in Morocco (− 3.98, − 4.41 to − 3.55), Libya (− 3.92, − 4.56 to − 3.26), and Eswatini (− 3.36, − 3.85 to − 2.86). For DALYs, Egypt (1.66, 1.12–2.20), South Sudan (0.66, 0.23–1.08), and Rwanda (0.47, 0.02–0.91) showed the largest increases, whereas Morocco (− 3.30, − 3.60 to − 2.99), Eswatini (− 2.81, − 3.32 to − 2.30), and Libya (− 2.73, − 3.33 to − 2.13) showed the largest declines. Overall, 48 of 54 countries had negative EAPCs for both deaths and DALYs (Fig. 2; Supplementary Table 1).

Incidence trajectory clusters

Dynamic time warping identified three country-level trajectory clusters for age-standardized appendicitis incidence during 2010–2023 (Fig. 3). The largest cluster showed a low-stable trajectory, characterized by a modest mid-period decline followed by gradual rebound toward 2023. A second cluster showed a moderate-rising trajectory, with a higher overall incidence level and more evident upward movement in later years. The third cluster was a single-country high-persistent trajectory, remaining above all other clusters throughout the study period despite a decline after 2020.

Fig. 3.

Temporal trajectory clustering of age-standardized appendicitis incidence in Africa, 2010–2023. A Dynamic time warping identified three distinct trajectory clusters of country-level age-standardized incidence rates: low-stable, moderate-rising, and high-persistent trajectories. Thin lines represent individual countries and thick lines represent cluster mean trajectories. B Geographic distribution of the three trajectory clusters across Africa. Countries were grouped according to similarity in longitudinal incidence patterns rather than geographic proximity

Supplementary Table 2 showed that 33 countries were assigned to the low-stable cluster, 20 to the moderate-rising cluster, and 1 country, Namibia, to the high-persistent cluster. The low-stable cluster included much of North Africa as well as countries from central, eastern, and western Africa. The moderate-rising cluster was more common in parts of western, eastern, and southern Africa. Figure 3B shows that countries with similar incidence trajectories were distributed across multiple subregions rather than concentrated in a single geographic bloc.

Between-cluster differences reflected both incidence level and temporal pattern. Countries in the low-stable cluster generally had lower incidence rates at baseline and follow-up, while those in the moderate-rising cluster had higher rates and more evident increases by 2023. Namibia, the only country in the high-persistent cluster, had the highest incidence level at both time points and remained markedly above the rest of the continent. Because this was a single-country cluster, it should be interpreted descriptively as a distinct country-level trajectory rather than as evidence of a broader regional pattern (Fig. 3; Supplementary Table 2).

Decomposition of changes in absolute burden

Das Gupta decomposition showed that population growth contributed most to the increase in absolute appendicitis burden across Africa from 2010 to 2023, with a smaller contribution from population aging or age-structure change. The epidemiological component varied by outcome (Fig. 4; Supplementary Table 3).

Fig. 4.

Das Gupta decomposition of changes in appendicitis burden across 54 African countries, 2010–2023. Panels show decomposition of changes in the numbers of incident cases, prevalent cases, deaths, and DALYs into contributions from population growth (P), population aging or age structure (A), and epidemiological change in age-specific rates (R). Results are presented for both sexes and stratified by African subregions and countries. Positive values indicate factors contributing to burden increases, whereas negative values indicate offsetting effects

At the continental level, incident cases increased by 1,284,602.1, of which 972,962.9 were attributable to population growth, 47,209.3 to aging or age-structure change, and 264,429.9 to epidemiological change. Prevalent cases increased by 48,906.0, with corresponding contributions of 36,922.3, 1,943.2, and 10,040.5. Deaths increased by 744.7 and DALYs by 44,506.1. For both deaths and DALYs, however, the epidemiological component was negative (− 607.5 deaths and − 25,388.9 DALYs), indicating that increases in absolute counts mainly reflected demographic expansion rather than increasing age-specific severe-outcome rates.

Country-level patterns were consistent with the regional visualization in Fig. 4. For incidence, the largest absolute increases were observed in Nigeria (183,109.7), Ethiopia (123,080.2), and the Democratic Republic of the Congo (83,921.5), with population growth as the largest contributor in each country. A similar ranking was observed for prevalence, with Nigeria (6,943.9), Ethiopia (4,700.0), and the Democratic Republic of the Congo (3,189.7) contributing the largest increases. For deaths and DALYs, Ethiopia showed the greatest absolute increases (174.2 deaths and 8,298.1 DALYs), followed by Nigeria and the Democratic Republic of the Congo.

The epidemiological component was positive for incidence and prevalence but negative for deaths and DALYs at the continental level. This pattern suggests that age-specific occurrence increased, whereas age-specific death and DALY rates declined. Ethiopia and Nigeria had large demographic-related increases in deaths and DALYs despite negative epidemiological contributions, whereas Egypt was among the few countries with positive epidemiological contributions to both deaths and DALYs (Fig. 4; Supplementary Table 3).

Age–period–cohort patterns

Age–period–cohort analysis showed a strong age gradient in appendicitis incidence, with the highest modeled relative risk in late adolescence and early adulthood, followed by a decline through middle and older age (Fig. 5; Supplementary Table 4). In the incidence model, the age-effect RR was 2.16 at age 17.5 years and 2.15 at age 22.5 years. It approached unity by age 47.5 years (RR 1.10), fell below 1.0 from age 52.5 years onward, and reached 0.39 at age 72.5 years before a modest rebound in the oldest modeled ages. The age–period heatmap was consistent with this pattern, showing the highest incidence burden in younger age groups throughout the study period.

Fig. 5.

Age–period–cohort analysis of appendicitis incidence in Africa. A Age–period heatmap of appendicitis incidence rates per 100,000 population, illustrating variation across age groups and calendar periods. Diagonal gradients reflect the progression of birth cohorts over time. B Age effect, shown as relative risk (RR) across age groups. C Period effect, shown as RR across calendar periods. D Cohort effect, shown as RR across birth cohorts. Relative risks were derived from penalized cubic regression splines adjusted for age, period, and cohort. Shaded areas indicate uncertainty bands

The period effect for incidence was modest and followed a shallow U-shaped pattern, declining from 1.04 in 2010 to 0.97 in 2016, then rising again to 1.04 in 2023 (Fig. 5; Supplementary Table 4). The cohort effect increased across successive birth cohorts, with incidence RR rising from 0.49 in the 1917.5 birth cohort to 1.61 in the 2005.5 cohort. These results describe modeled APC patterns and should be interpreted as exploratory age, period, and cohort-related variation rather than fully separable causal effects.

Supplementary Table 4 showed that age patterns differed across outcomes. Incidence and prevalence shared a similar profile, with peak risks in younger age groups. Deaths and DALYs showed a different age pattern, with relative risks increasing again in older adults and reaching their highest levels in the oldest age groups. Period effects for deaths and DALYs were generally close to 1.0, indicating limited modeled period-related change in these severe outcomes over the study period.

COVID-19-era observed-versus-expected deviations

During 2020–2023, appendicitis burden at the African Union level showed different observed-versus-expected patterns for nonfatal and severe outcomes (Fig. 6; Supplementary Table 5). Compared with ARIMA-based counterfactual trajectories derived from 2010 to 2019 data, observed age-standardized incidence and prevalence rates were generally higher than expected, whereas observed death and DALY rates were lower than expected. These deviations were modest in rate terms and were interpreted descriptively.

Fig. 6.

COVID-19 counterfactual analysis of appendicitis burden in Africa, 2010–2023. Panels compare observed and counterfactual age-standardized rates per 100,000 population for A incidence, B prevalence, C deaths, and D DALYs. Counterfactual trajectories for 2020–2023 were estimated using ARIMA models trained on pre-pandemic data from 2010 to 2019. The vertical dashed line marks the onset of the COVID-19 period. Shaded areas represent projected uncertainty intervals and the difference between observed and expected values during 2020–2023

Across 2020–2023, the mean observed incidence rate was 232.79 per 100,000, compared with an expected 231.75, corresponding to a cumulative excess of 4.43 per 100,000. Prevalence showed a mean observed rate of 8.89 per 100,000 versus an expected 8.53, with a cumulative excess of 1.42 per 100,000. By contrast, the mean observed death rate was 0.448 per 100,000 versus an expected 0.475, corresponding to a cumulative deficit of 0.108 per 100,000. DALYs averaged 17.67 per 100,000 versus an expected 18.40, yielding a cumulative deficit of 2.94 per 100,000 (Supplementary Table 5).

Year-specific results showed that incidence was slightly below expectation in 2020 but exceeded expected values in 2021–2023, with the largest gap in 2023 (239.50 vs. 236.84 per 100,000). Prevalence remained above expected levels throughout 2020–2023, with the excess increasing from 0.04 per 100,000 in 2020 to 0.74 in 2023. Deaths and DALYs were below expected values in every pandemic-era year. For DALYs, the largest negative deviation occurred in 2021 (17.41 vs. 18.44 per 100,000), while the observed death rate declined to 0.44 versus an expected 0.47 per 100,000 in 2023 (Fig. 6; Supplementary Table 5). Given the short pre-pandemic training period, these observed-versus-expected differences should be regarded as exploratory ecological deviations rather than causal estimates of pandemic effects.

Projections to 2030

Under the main log-linear projection based on 2010–2023 trends, age-standardized incidence and prevalence were projected to increase in 49 of 54 African countries by 2030, whereas age-standardized death and DALY rates were projected to decrease in 48 of 54 countries (Fig. 7; Supplementary Table 6). In this analysis, projected changes were calculated from the model-fitted 2023 ASR to the projected 2030 ASR, not from the directly observed 2023 ASR.

Fig. 7.

Projected shift in appendicitis burden across 54 African countries from model-fitted 2023 ASRs to projected 2030 ASRs. Dumbbell plots compare model-fitted 2023 age-standardized rates (ASRs) and projected 2030 ASRs per 100,000 population for A incidence, B prevalence, C deaths, and D disability-adjusted life-years (DALYs) in both sexes. Blue points indicate model-fitted 2023 ASRs, red points indicate projected 2030 ASRs, and gray connecting lines indicate the direction and magnitude of projected change. Countries are grouped by African subregion: (1) North Africa, (2) Central Sub-Saharan Africa, (3) Eastern Sub-Saharan Africa, (4) Southern Sub-Saharan Africa, and (5) Western Sub-Saharan Africa. Projections were generated using log-linear models fitted to 2010–2023 ASRs. Baseline points represent model-fitted ASRs rather than directly observed ASRs

For incidence, the largest projected increases were observed in Ghana (fitted 2023 ASR 338.52 to projected 2030 ASR 375.66 per 100,000; absolute change + 37.13), Botswana (287.09–316.58; + 29.49), Sao Tome and Principe (303.58–329.62; + 26.03), Eritrea (299.03–324.94; + 25.91), and Tunisia (245.55–271.10; + 25.56). The same countries also showed among the largest projected increases in prevalence, led by Ghana (+ 1.42 per 100,000). By contrast, Eswatini showed the largest projected decline in both incidence (284.66–253.40 per 100,000; − 31.25) and prevalence (10.88–9.68 per 100,000; − 1.20), followed by Seychelles, Namibia, and Mauritius (Fig. 7; Supplementary Table 6).

For severe outcomes, projected decreases predominated. The largest absolute declines in death rates were seen in Zambia and the United Republic of Tanzania (both − 0.10 per 100,000), followed by Eswatini (− 0.09) and Mozambique (− 0.08). South Sudan showed the largest absolute projected increase in death rate (0.79–0.84 per 100,000; + 0.05). DALY rates showed a similar pattern: the greatest projected declines were observed in Eswatini (24.04–19.69 per 100,000; − 4.35), the United Republic of Tanzania (− 3.64), and Mozambique (− 3.10), whereas South Sudan showed the largest projected increase (30.36–31.79 per 100,000; + 1.43). Egypt showed the largest relative projected increase in deaths (+ 20.53%) and DALYs (+ 12.23%), although the corresponding absolute changes were smaller.

Sensitivity analyses using fitted 2019 and 2018 baseline ASRs provided alternative projection scenarios based on shorter pre-2023 training windows (Supplementary Table 6; Supplementary Figs. 2 and 3). These analyses supported the need to interpret country-level forecasts cautiously, particularly for nonfatal outcomes where projected directions and magnitudes were more sensitive to the baseline window. Overall, the projection results suggest continued model-based growth in nonfatal appendicitis burden across many African countries, alongside declining mortality- and disability-related ASRs in most settings.

Discussion

This study provides a standardized, continent-wide assessment of appendicitis burden across the African Union and 54 African countries from 2010 to 2023. The findings indicate a divergent pattern: absolute burden increased across all four outcomes, and age-standardized incidence and prevalence rose modestly, whereas age-standardized death and DALY rates declined in many countries. This pattern suggests that Africa may face increasing demand for diagnostic, emergency, and surgical services for appendicitis, even as age-standardized severe-outcome rates show declining trends in many settings.

The divergence between increasing nonfatal burden and declining mortality- and DALY-related rates is clinically important. Increasing age-standardized incidence and prevalence may reflect several non-mutually exclusive factors, including improved case ascertainment, greater access to diagnostic imaging, changes in care-seeking behavior, urbanization-associated epidemiologic transition, and evolving diagnostic thresholds. However, these explanations remain speculative because the present ecological analysis cannot directly evaluate individual-level exposures, diagnostic pathways, imaging use, or treatment decisions. Declining age-standardized death and DALY rates may be consistent with improvements in recognition, referral, antibiotic availability, perioperative care, anesthesia safety, and surgical management, but these mechanisms also cannot be confirmed using GBD data alone. Current clinical evidence emphasizes that appendicitis spans a heterogeneous disease spectrum and that outcomes depend strongly on timely diagnosis, risk stratification, source control, and access to appropriate surgical or nonoperative management [1, 2, 16].

The decomposition analysis showed that population growth was the largest contributor to the increase in absolute numbers of incident cases, prevalent cases, deaths, and DALYs. This finding is important because it indicates that increasing absolute burden does not necessarily imply worsening age-specific severe-outcome rates. In rapidly growing populations, even stable or declining age-standardized rates can translate into more patients requiring emergency evaluation and treatment. Therefore, health-system planning should consider both epidemiological trends and demographic expansion. For appendicitis, this means that emergency departments, imaging services, referral systems, operating theaters, antibiotic supply chains, anesthesia services, and postoperative monitoring capacity may need to expand even where mortality-related rates are declining [8, 13, 14].

Substantial geographic heterogeneity remained evident across the continent. Higher age-standardized nonfatal burden was observed in parts of southern and western Africa, whereas age-standardized death and DALY rates were more concentrated in parts of eastern Africa. These patterns may be related to differences in access to timely diagnosis, referral, emergency surgery, perioperative care, and postoperative rescue capacity. However, the ecological nature of the analysis means that these associations should not be interpreted causally. Previous literature from African and other low-resource settings has reported that delayed presentation, referral barriers, advanced disease at operation, postoperative complications, and failure to rescue can influence appendicitis outcomes [12–14, 17]. Our findings are consistent with these concerns, but they should be viewed as population-level signals that require further validation using clinical and health-system data.

The DTW clustering analysis provided an additional view of country-level incidence trajectories. Most countries were grouped into low-stable or moderate-rising trajectories, whereas Namibia formed a single-country high-persistent cluster. This single-country cluster should be interpreted cautiously. It may reflect Namibia’s distinct high-incidence trajectory in the GBD estimates, but it may also be sensitive to the clustering method, the short 2010–2023 time series, the use of non-standardized incidence series, and the modeled nature of GBD estimates. Therefore, the Namibia cluster should be regarded as a descriptive trajectory pattern rather than evidence of a broader regional category or a separate epidemiological mechanism.

The APC analysis showed that modeled appendicitis incidence was highest in late adolescence and early adulthood, which is consistent with the known age distribution of appendicitis. The cohort pattern suggested higher modeled incidence risk in more recent birth cohorts, but this finding should be interpreted carefully. APC models are affected by the linear dependency between age, period, and cohort, and the present analysis was exploratory. The observed cohort gradient could reflect true changes in disease occurrence, improved detection, diagnostic practice, population structure, environmental or lifestyle changes, or a combination of these factors. GBD data do not allow these possibilities to be separated. Therefore, the APC findings should be understood as smoothed descriptive patterns rather than causal age, period, or cohort effects.

The COVID-19 counterfactual analysis should also be interpreted cautiously. In this study, observed incidence and prevalence during 2020–2023 were generally higher than expected, whereas observed death and DALY rates were lower than expected. These observed-versus-expected differences were modest in rate terms and were based on ARIMA models trained on a relatively short pre-pandemic series from 2010 to 2019. As a result, the estimates may be sensitive to model specification and should not be considered causal estimates of pandemic effects. The findings also differ from many hospital-based pandemic studies, which reported fewer presentations and a higher proportion of complicated appendicitis during periods of disrupted healthcare access [18–21]. This discrepancy may reflect differences between continent-level modeled population estimates and facility-based clinical series. Hospital studies capture treated patients and presentation patterns, whereas GBD-based estimates aim to model population-level burden. Both perspectives are useful but answer different questions.

The 2030 projections suggest that age-standardized incidence and prevalence may continue to increase in many countries, while age-standardized death and DALY rates may continue to decline in most settings. These forecasts have practical implications, but they should be interpreted as exploratory model-based scenarios rather than deterministic predictions. The projection models assume continuity of recent log-linear trends, and the results may be sensitive to the selected training window. In addition, projected absolute changes were calculated using model-fitted baseline ASRs rather than directly observed baseline ASRs. This distinction is important for interpreting country-level results, including Eswatini, where a relatively high fitted 2023 incidence ASR makes a larger absolute projected decline possible despite a modest negative EAPC. The projections are therefore most useful for identifying possible future service pressures and countries that may merit closer monitoring, rather than for making definitive predictions about future burden.

From a health-system perspective, these findings support continued strengthening of emergency abdominal pain pathways and surgical capacity across African settings. Appendicitis is a time-sensitive condition, and delays in recognition, referral, diagnosis, and treatment can increase the risk of perforation, sepsis, prolonged hospitalization, and death. Investments in district-level diagnostic capacity, timely referral, safe anesthesia, antibiotic availability, appendectomy services, and postoperative monitoring may help reduce avoidable severe outcomes. However, the specific interventions needed will differ by country and should be guided by local data on presentation delays, imaging access, operative capacity, treatment patterns, complication rates, and mortality.

This study has several strengths. It provides a standardized assessment of appendicitis burden across all 54 African countries using GBD 2023 estimates and applies complementary analytical approaches, including temporal trend analysis, spatial mapping, DTW trajectory clustering, Das Gupta decomposition, APC modeling, COVID-era counterfactual analysis, and projections to 2030. These approaches allowed us to examine not only where appendicitis burden was concentrated, but also how burden changed over time and how demographic and epidemiological components contributed to changes in absolute numbers.

Several limitations should be acknowledged. First, all estimates were derived from the GBD framework and depend on the availability and quality of underlying data, which vary across African countries. In data-sparse settings, estimates rely more heavily on statistical modeling. Second, GBD estimates do not provide sufficient clinical detail to distinguish uncomplicated from complicated appendicitis, perforation status, imaging use, treatment modality, time to operation, antibiotic use, postoperative complications, or failure-to-rescue events. Third, the ecological design precludes causal inference regarding geographic variation, temporal changes, COVID-era deviations, or projected future trends. Fourth, the analysis was restricted to 2010–2023 to focus on contemporary burden and pandemic-era patterns; however, excluding earlier GBD years may reduce the ability to assess longer-term historical trends and may affect forecast stability. Fifth, the APC, ARIMA, DTW, and forecast analyses were exploratory and may be sensitive to model assumptions, time-series length, cluster selection, and baseline-window choice. Finally, the 2030 projections assume continuation of recent trends and should be interpreted as scenario-based estimates rather than definitive future outcomes.

Conclusion

Appendicitis burden in Africa increased in absolute terms from 2010 to 2023, while age-standardized patterns differed by outcome: incidence and prevalence increased modestly, whereas death and DALY rates declined in many countries. Population growth was the largest contributor to increases in absolute burden, and substantial geographic heterogeneity persisted across the continent. The COVID-era counterfactual and 2030 projection analyses should be interpreted cautiously because they are model-based and depend on relatively short recent time series. Overall, the findings highlight the need to strengthen timely diagnosis, referral, emergency surgical care, perioperative services, and surveillance systems to reduce preventable severe outcomes from appendicitis across African settings.

Author contributions

Hao Li: Conceptualization, Methodology, Data curation, Writing—original draft. Dajun Wang: Data analysis, Visualization, Writing—review & editing. Yanan Peng: Supervision, Project administration, Validation. Ri Cai: Data collection, Formal analysis. Yan Gao: Writing—review & editing, Visualization. Bing Zhou: Conceptualization, Methodology, Supervision. Guoyong Chen: Resources, Writing—review & editing. All authors have read and approved the final manuscript.

Funding

The authors received no specific funding for this work.

Data availability

All data used in this study are publicly available, anonymised modelled estimates from the Global Burden of Disease (GBD) Study 2023. No additional primary data were collected or generated for this analysis.

Declarations

Ethics approval and consent to participate

Ethics statement and informed consent: Not applicable. This study used publicly available aggregated data from the Global Burden of Disease (GBD) study, which does not require ethical approval or individual informed consent.

Competing interests

The authors declare no competing interests.