Prevalence and risk factors of Cryptosporidium in humans in China: A systematic review and meta-analysis

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Despite its growing recognition as an important zoonotic pathogen, large-scale epidemiological data specific to the Chinese population remain scarce. Methods We systematically retrieved articles on the occurrence of Cryptosporidium in humans in China, through a search in the following six databases: PubMed, Web of Science, ScienceDirect, Chinese National Knowledge Infrastructure, Wanfang Data, and VIP Chinese Journal Database, following PRISMA guidelines. Results A total of 193 studies were included, covering data from 27 provinces. Pooled prevalence and 95% confidence intervals (CI) were estimated using a random-effects model. The pooled prevalence of Cryptosporidium infection in humans in China was estimated at 5%. Higher infection rates were observed in Northern China (6.61%, 95% CI: 3.20–11.13) and among individuals aged 17–30 years (8.43%, 95% CI: 3.55–14.61). A significant decline in prevalence was noted in studies conducted after 2015. C. parvum and C. hominis were identified as the main infecting species. Additionally, higher infection rates in HIV-positive individuals (7.63%, 95% CI: 5.34–10.28) and rural populations (5.18%, 3.46–7.23). Conclusion These findings highlight the need for improved surveillance, accurate diagnostics, and targeted prevention strategies in high-risk regions and populations. In particular, attention should be given not only to young children but also to adults, who may face overlooked exposure risks. Cryptosporidium Cryptosporidiosis Prevalence Systematic review Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cryptosporidium is one of the most widespread intestinal protozoan parasites, representing a significant global public health concern, particularly in developing countries where water sanitation infrastructure is often inadequate [ 1 ]. These apicomplexan, oocyst-forming parasites have a direct fecal-oral transmission cycle and can infect a wide range of vertebrate hosts, including humans and livestock, thereby facilitating both zoonotic and anthroponotic transmission pathways [ 2 ]. Cryptosporidiosis, the disease caused by Cryptosporidium , is now recognized as a leading cause of diarrheal illness, especially among immunocompromised individuals such as those living with HIV/AIDS, young children, and the elderly [ 3 – 5 ]. Outbreaks are predominantly associated with contaminated drinking water, recreational water use, or foodborne exposure, although direct contact with infected humans or animals is also a known risk factor [ 6 ]. The clinical presentation commonly includes acute, self-limiting watery diarrhea; however, in immunodeficient individuals, infections can become chronic and life-threatening [ 5 ]. Globally, C. hominis and C. parvum are the two species most frequently implicated in human disease, accounting for approximately 90% of reported cases [ 7 ]. Other less prevalent species such as C. meleagridis , C. ubiquitum , C. felis , and C. canis have also been documented, particularly in specific host or geographic contexts [ 8 – 11 ]. The distribution of C. hominis and C. parvum is often region-specific [ 12 ]. In China, the first human case of cryptosporidiosis was reported in Jiangsu Province in 1987 [ 13 ]. Subsequent cases have been recorded in regions including Anhui, Chongqing, Taiwan, and Fujian [ 14 – 17 ]. By the late 1990s, human Cryptosporidium infections had been reported in at least 23 provinces. As of 2021, this number had risen to 29 provinces and 107 prefecture-level regions, with a total of 4,975 confirmed infections documented in the literature, underscoring a substantial yet likely underrecognized national burden [ 18 ]. To effective diagnosis of cryptosporidiosis and reduce misdiagnosis and underdiagnosis, the former National Health and Family Planning Commission issued the Diagnosis of Cryptosporidiosis (WS/T487–2016) in 2016, which provides a detailed description of laboratory tests and clinical diagnosis of cryptosporidiosis [ 18 ]. Efforts to control waterborne transmission of Cryptosporidium in China have made progress, notably with the 2006 implementation of the National Health Standard for Drinking Water (GB 5749–2006), which included mandatory testing for Cryptosporidium and Giardia as microbiological indicators. This regulatory shift has contributed to improved monitoring and risk mitigation in public water supplies [ 19 ]. Owing to its extensive distribution, environmental resilience, and clinical impact, Cryptosporidium was ranked fifth among the most significant global foodborne parasites by a joint FAO/WHO expert committee [ 20 – 21 ]. Despite these recognitions, cryptosporidiosis remains a neglected tropical disease in terms of research, surveillance, and funding. Many countries lack systematic monitoring programs, and the true burden of disease is likely underestimated due to underdiagnosis and underreporting. However, despite the increasing recognition of Cryptosporidium as a significant zoonotic pathogen, large-scale epidemiological data specific to the Chinese population remain limited. In particular, little is known about the geographic distribution and host-related risk factors influencing infection dynamics in China. To address this gap, our study conducted a meta-analysis to estimate the overall prevalence of Cryptosporidium infection in humans across China and to explore potential sources of heterogeneity. Subgroup analyses were performed based on region, age, sex, sampling year, distribution, season, diarrhea, HIV status, diagnostic methods, and Cryptosporidium species. These stratified factors provide a foundation for identifying high-risk populations and tailoring targeted prevention strategies. Materials and Methods Search Strategy and Study Selection This study was conducted in accordance with the PRISMA guidelines for systematic reviews and meta-analyses [ 22 ]. A comprehensive literature search was carried out across five major databases, including Web of Science, PubMed, ScienceDirect, Scopus, and Google Scholar, using the search string: (" Cryptosporidium OR cryptosporidiosis") AND ("human OR people OR person OR man OR men OR women OR woman OR patient") AND ("China") (Table. S1). All peer-reviewed studies reporting cryptosporidiosis infections in humans were considered, with no geographic restrictions, covering the period from database inception through 15 May 2025. All retrieved records were imported into EndNote (version X9; Clarivate Analytics, London, UK) for duplicate removal. An automated screening tool was used to eliminate clearly irrelevant entries. Two researchers performed the database search independently, and titles and abstracts were evaluated separately to minimize selection bias. Studies were eligible for inclusion if they (1) reported the prevalence of Cryptosporidium infection in humans, (2) provided both total sample size and the number of positive cases, (3) included a sample size greater than 20, (4) employed a cross-sectional study design, and (5) presented raw data suitable for epidemiological analysis. Articles not meeting the above criteria were excluded, such as review articles, case reports, those with no full-text and abstract, and those not in English. In cases of disagreement regarding the inclusion or exclusion of a study, discrepancies were resolved through team discussion to reach a consensus. Data Extraction Data from the eligible studies were systematically extracted using standardized data collection forms in Microsoft Excel (version 16.0.18429.20132; Microsoft Corporation, Redmond, WA, USA). Extracted variables included the first author, year of publication, sampling year, the geographical region of the study (including province, administrative regions, and the municipalities directly under the central government), age, sex, method of diagnosis, HIV status, presence of diarrhea, distribution, season, Cryptosporidium species, total number of individuals examined, and the number of positive cases for Cryptosporidium infection. When multiple diagnostic methods were reported, microscopy results were prioritized to maintain consistency across studies. Quality Assessment Study quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework [ 23 ]. One point was assigned for each of the following criteria: reporting the year of sampling, including a sample size greater than 60, providing a detailed sampling method, cross-sectional study design, and reporting data on four or more potential risk factors. Based on total scores ranging from 0 to 5, studies were categorized as high quality (4–5 points), moderate quality (2–3 points), or low quality (0–1 point). Statistical Analysis All quantitative analyses were conducted using the "meta" package in RStudio (version 2024.12.0 + 467). To identify the most appropriate transformation method for normalizing data distributions in the meta-analysis, five approaches were evaluated: raw proportions (PRAW), logarithmic transformation (PLN), logit transformation (PLOGIT), arcsine transformation (PAS), and double-arcsine transformation (PFT). Based on diagnostic metrics, PAS was selected for human data (W = 0.90862, p < 0.05). These transformations were chosen because they yielded W-values closest to 1 and p -values nearest to 0.05, indicating the best approximation to normality. Heterogeneity across studies was assessed using the I² statistic and Cochran’s Q test [ 24 ]. Forest plots were constructed to visualize the pooled prevalence estimates and the results of subgroup analyses. A random-effects model was employed to account for between-study variability. Potential publication bias was evaluated using funnel plots and Egger’s test. Sensitivity analyses were performed by sequentially excluding individual studies to assess their impact on the overall effect estimates. To investigate potential sources of heterogeneity, subgroup analyses and meta-regression were conducted [ 25 – 26 ]. Both univariable and multivariable meta-regression models were used to explore associations between covariates and the prevalence of Cryptosporidium infection. In multivariable analyses, the subgroup with the highest prevalence was used as the reference category. Geographical comparisons within China were based on seven major regions: Western China, Southwestern China, Northwestern China, Northern China, Northeastern China, Eastern China, and Central China. Sampling years were grouped into three periods: after 2015, 2005–2015, and 2004 or before. Temporal trends in prevalence were assessed using random-effects meta-regression, with the sampling year as the independent variable and the logit-transformed infection rate as the dependent variable. For studies covering multiple years, a weighted average year was calculated and used in the analysis. In multivariable meta-regression, the reference category for each covariate was defined as the subgroup with the highest observed prevalence. Analyses of host-related factors included comparisons among age groups ( 30) and sex (women vs. men). Clinical symptoms were assessed by comparing individuals with and without diarrhea. Immunocompromised status was examined by comparing participants with HIV infection vs. without HIV. Residential distribution was categorized as urban and rural. Seasonal variations in infection prevalence were compared across the four seasons: spring, summer, autumn, and winter. Diagnostic methods were assessed by comparing studies utilizing microscopy, molecular, and immunological. Results Search Results and Eligible Studies A total of 4,297 studies were retrieved from the five databases. According to the inclusion and exclusion criteria, a total of 193 full-text studies comprising 75 high quality, 105 middle quality, and 13 low quality articles, which covered 27 provinces were used for the meta-analysis (Fig. 1 ). References are cited in Supplementary Materials. Publication Bias and Sensitivity Analysis The forest plot illustrated the overall heterogeneity among the included studies (Fig. 2 ). Funnel plots suggested publication bias, which was further supported by trim-and-fill analysis. The improved symmetry after imputation indicated that the bias was likely due to missing studies (Fig. 3 ). Egger’s test further quantified this bias, revealing statistically significant publication bias in huamns studies ( p = 0.0082, Fig. S1 ). Sensitivity analysis demonstrated that the pooled prevalence estimates remained stable upon the exclusion of any single study, confirming the robustness and reliability of the meta-analysis results (Fig. S2 ). Pooling and Heterogeneity Analyses According to the meta-analysis, the overall pooled prevalence of Cryptosporidium infection was 5% in humans in China. Among the seven major regions, Northwestern China exhibited the highest prevalence at 6.61% (95% CI: 3.20–11.13), followed by Northern China 5.74% (95% CI: 2.46–10.28). The lowest prevalence was recorded in Northeastern China, at 1.35% (95% CI: 0.43–2.78; p = 0.0249). Cryptosporidium infection in humans was reported in 25 provinces, that Liaoning and Hebei exhibited the highest infection rates. The infection rate in Liaoning was 6.30% (95% CI: 3.53–9.78), followed by Hebei with an infection rate of 6.14% (95% CI: 4.83–7.59; Table 1 , Figs. 4 and 5 ). Table 1 Pooled prevalence of Cryptosporidium infection in humans in China. No. studies No. examined No. positive % (95% Cl) Heterogeneity Univariate meta-regression χ2 p -value I 2 (%) p -value Coefficient (95% CI) Humans Region Western China 23 19578 501 2.54 [1.30–3.96] 340.07 p < 0.0001 93.5 0.0779 -0.1034 (-0.2183 to 0.0115) Southwestern China 4 3090 289 5.01 [0.62–13.25] 164.30 p < 0.0001 98.2 0.7127 -0.0350 (-0.2211 to 0.1512) Northwestern China 13 6737 634 6.61 [3.20–11.13] 749.17 p < 0.0001 98.4 - - Northern China 17 8344 431 5.74 [2.46–10.28] 356.15 p < 0.0001 95.4 0.7061 -0.0192(-0.1422 to 0.1039) Northeastern China 12 5820 189 1.35 [0.43–2.78] 83.03 p < 0.0001 97.2 0.0249 -0.1872 (-0.3508 to 0.0294) Eastern China 66 106121 2825 4.45 [2.78–6.48] 3750.94 P = 0 98.3 0.3482 -0.0483 (-0.1492 to 0.0526) Central China 59 66584 3360 5.03 [3.28–7.13] 7174.22 P = 0 99.2 0.5102 -0.0342 (-0.1362 to 0.0677) Sampling year After 2015 24 22533 662 2.47 [1.07–4.43] 1246.66 p < 0.0001 98.2 0.0076 -0.0924 (-0.1603 to -0.0246) 2005–2015 48 77122 4847 6.41 [4.52–8.60] 4386.15 p = 0 98.9 - - 2004 or before 58 74221 2905 3.27 [2.19–4.55] 5372.30 p = 0 98.9 0.0135 -0.0661 (-0.1186 to -0.0137) Age < 3 30 20252 805 5.22 [2.49–8.77] 829.09 p < 0.0001 96.5 0.0604 -0.1114 (-0.2276 to 0.0047) 3–6 38 34478 1055 3.04 [2.22–3.98] 616.45 p < 0.0001 94.0 0.0070 -0.1565 (-0.2703 to -0.0427) 7–17 20 13873 435 1.09 [0.00-3.85] 454.28 p < 0.0001 95.8 0.0099 -0.1595 (-0.2807 to -0.0383) 17–30 7 1820 289 8.43 [3.55–14.61] 34.53 p 30 11 9613 377 7.51 [3.39–12.98] 1064.81 p < 0.0001 99.1 0.4111 -0.0549 (-0.1858 to 0.0760) Sex Women 94 117833 4816 6.60 [4.66–8.44] 4263.98 p = 0 97.8 0.6652 -0.0140 (-0.0775 to 0.0495) Man 99 131469 4993 7.66 [4.92–9.78] 5887.62 p = 0 98.3 - - Diarrhea Yes 57 28750 991 4.88 [3.48–6.50] 1694.72 p < 0.0001 96.7 - - No 150 235865 10741 4.33 [3.27–5.53] 15032.04 p = 0 99.0 0.5793 -0.0142 (-0.0643 to 0.0359) HIV Yes 35 43867 4125 7.63 [5.34–10.28] 1015.82 p < 0.0001 96.7 - - No 172 220748 7607 3.94 [3.04–4.95] 12650.74 p = 0 98.6 0.0075 -0.0803 (-0.1391 to -0.0214) Distribution Urban 19 31146 2002 1.50 [0.91–2.14] 88.69 p < 0.0001 79.7 < .0001 -0.1020 (-0.1506 to -0.0535) Roral 18 30218 1960 5.18 [3.46–7.23] 225.88 p < 0.0001 92.5 - - Season Spring 12 27854 714 2.68 [0.76–5.74] 486 − 28 p = 0 97.7 0.6060 -0.0239 (-0.1148 to 0.0670) Summer 14 29381 919 3.34 [1.44–6.24] 569.44 p < 0.0001 97.7 0.9890 -0.0006 (-0.0848 to 0.0837) Autumn 20 35186 925 3.44 [1.81–5.58] 502.73 p < 0.0001 96.2 - - Winter 14 29274 829 1.87 [0.52–4.04] 420.67 p < 0.0001 96.9 0.2490 -0.0497 (-0.1342 to 0.0348) Method of diagnosis Microscopy 146 165571 6275 4.20 [3.24–5.27] 11142.94 p = 0 98.7 0.2764 -0.0321 (-0.0898 to 0.0257) Molecular 33 39680 1283 5.65 [3.20–8.70] 1461.30 p < 0.0001 97.8 - - Immunological 19 46298 3907 4.57 [2.65–6.94] 1088.13 p < 0.0001 98.3 0.7239 -0.0156 (-0.1020 to 0.0708) Species C. parvum 7 11213 408 1.51 [0.15–4.29] 578.70 p < 0.0001 99.0 - - C. hominis 3 7048 107 1.11 [0.43–2.12] 7.39 p = 0.0248 72.9 0.7682 -0.0202 (-0.1547 to 0.1142) Other 10 7284 58 0.96 [0.13–2.54] 116.62 p < 0.0001 116.62 0.6026 -0.0255 (-0.1213 to 0.0704) CI - confidence interval, X2 - chi-squared Seasonal trends were modest: autumn and summer showed slightly higher prevalence estimates (3.44% and 3.34%, respectively), while winter had the lowest infection rate (1.87%, 95% CI: 0.52–4.04). In terms of sampling year, the highest prevalence was observed during 2005–2015, reaching 6.41% (95% CI: 4.52–8.60; Fig. 4 ). A statistically significant decline in prevalence over time was observed, with samples collected after 2015 showing a lower rate of 2.47% (95% CI: 1.07–4.43; p = 0.0076). To explore temporal trends, a random-effects meta-regression was conducted, with sampling year as the independent variable and logit-transformed prevalence as the dependent variable. The analysis revealed a negative slope for humans (0.0129), suggesting a minimal increase. However, neither trend was statistically significant ( p = 0.3008; Table 1 , Fig. 6 ). In the subgroup analysis by age, children under 3 years had an infection rate of 5.22% (95% CI: 2.49–8.77). Individuals aged 17–30 years showed the highest infection rate at 8.43% (95% CI: 3.55–14.61). For participants older than 30 years, the infection rate was 7.51% (95% CI: 3.39–12.98), with a statistically significant association ( p = 0.0070; Fig. 4 ). Regarding sex, men had a higher prevalence (7.66%, 95% CI: 4.92–9.78) compared to women (6.60%, 95% CI: 4.66–8.44). Host health status played a role in infection risk. Individuals with diarrhea exhibited a higher prevalence (4.88%, 95% CI: 3.48–6.50) than those without diarrhea (4.33%, 95% CI: 3.27–5.53). However, a significant difference was observed in HIV status: individuals with HIV had a significantly higher prevalence (7.63%, 95% CI: 5.34–10.28) than those without HIV (3.94%, 95% CI: 3.04–4.95; p = 0.0075). Geographically, individuals living in rural areas had a markedly higher prevalence (5.18%, 95% CI: 3.46–7.23) compared to those in urban areas (1.50%, 95% CI: 0.91–2.14), with a statistically significant association ( p < 0.0001; Table 1 , Fig. 4 ). In the subgroup analysis by Cryptosporidium species, C. parvum was associated with the highest prevalence at 1.51% (95% CI: 0.15–4.29), followed by C. hominis at 1.11% (95% CI: 0.43–2.12) and other species at 0.96% (95% CI: 0.13–2.54). Diagnostic methods influenced the detection rates. Studies using molecular techniques reported the highest prevalence at 5.65% (95% CI: 3.20–8.70), followed by immunological methods at 4.57% (95% CI: 2.65–6.94), and microscopy at 4.20% (95% CI: 3.24–5.27). However, differences across methods were not statistically significant (Table 1 ). Discussion Cryptosporidium is one of the important zoonotic diseases that cause life-threatening diarrhoea in young, immunodeficient, and malnourished hosts. It has been reported in animals and humans across more than 106 countries, with a higher prevalence in developing nations [ 21 , 27 – 29 ]. Despite the widespread distribution and zoonotic potential of Cryptosporidium species, comprehensive assessments of its prevalence and the epidemiological factors influencing infection in humans in China remain limited. Therefore, this study aims to systematically estimate the prevalence and associated risk factors of Cryptosporidium infection in humans in China through meta-analysis. In this study, the pooled prevalence of Cryptosporidium infection in humans in China was estimated at 4.0%, indicating a relatively low infection rate compared to the global average of 7.6%, and much lower than that reported in countries such as South Africa (18.1%) and Nigeria (15.0%) [ 21 , 30 – 31 ]. This suggests that the overall burden of Cryptosporidium infection in China is comparatively lower on a global scale. The reasons for this may include improvements in sanitation, drinking water safety, and public health awareness. Nevertheless, differences in study design, diagnostic methods, and population characteristics across countries should be considered when comparing prevalence rates. Continued monitoring is warranted to prevent potential outbreaks and to protect vulnerable populations. In our study, the pooled prevalence of Cryptosporidium infection in humans in Northwestern China was found to be 6.61% ( p < 0.05). This regional variation may be partly explained by the unique climatic and environmental conditions of Northwestern China, which is characterized by a semi-arid to arid climate, extreme seasonal temperature fluctuations, and limited water resources. In arid regions like Northwestern China, where water is scarce and often contaminated, the persistence of Cryptosporidium oocysts in the environment may be enhanced, increasing the likelihood of human and animal exposure [ 32 ]. The limited rainfall in these regions can lead to poor water quality, especially in rural areas that rely on untreated or minimally treated water sources. In contrast, several studies indicate that Cryptosporidium infection rates are positively correlated with rainfall, which facilitates the spread of oocysts through runoff and increases contamination in water sources. These climatic factors, combined with the environmental resilience of Cryptosporidium oocysts, emphasize the need for targeted public health interventions in Northwestern China. However, caution should be exercised when generalizing these findings, as other regions, such as Southwestern China, have limited data, which may not fully capture the regional variation or reflect the broader prevalence trends. Further studies focusing on how climatic and environmental factors influence Cryptosporidium transmission in arid regions will provide valuable insights for more effective control measures. Given the limited research in some areas, further investigation is needed to fully understand the regional transmission dynamics of Cryptosporidium across China. The highest prevalence of Cryptosporidium infection in humans was observed during the autumn season in China. This finding is interesting when compared to previous research that suggests temperature plays a significant role in the survival and infectivity of Cryptosporidium oocysts in the environment. Several studies have demonstrated that Cryptosporidium oocysts exhibit increased survival in soil at temperatures below 15°C, while exposure to higher temperatures (> 25°C) and increased UV-A/B insolation can cause oocyst degradation [ 33 – 36 ]. Autumn in China is typically characterized by moderate temperatures ranging, which may create an optimal environment for Cryptosporidium oocysts to persist in both soil and water sources. During this period, temperatures may be low enough to prevent oocyst degradation, but not high enough to cause significant damage to their structure. While Cryptosporidium survival and infectivity are influenced by multiple environmental factors, including temperature, more research is needed to fully understand how seasonal temperature fluctuations, along with other climatic conditions, impact the transmission dynamics of Cryptosporidium in different regions, especially in areas like China where seasonal variation plays a crucial role in disease transmission. Further studies focusing on how climatic and environmental factors influence Cryptosporidium transmission in arid regions will provide valuable insights for more effective control measures. Given the limited research in some areas, further investigation is needed to fully understand the regional transmission dynamics of Cryptosporidium across China. Our results showed that the prevalence of Cryptosporidium infection in humans in China was higher before 2015. One possible explanation for this trend is the historical limitation in diagnostic accuracy and standardization. In 2016, the former National Health and Family Planning Commission issued the Diagnosis of Cryptosporidiosis (WS/T487-2016), which provided standardized guidelines for clinical and laboratory diagnosis of Cryptosporidium infection, aiming to reduce misdiagnosis and missed diagnosis [ 37 ]. The implementation of this national guideline likely contributed to improvements in diagnostic consistency and case identification across the country. In addition, advances in detection technology may also explain the changes in prevalence estimates. Traditional detection methods such as microscopy and immunological assays have been widely used, but these approaches are often time-consuming, require skilled personnel, and have limited sensitivity, especially in early-stage or low-intensity infections. As shown in recent Chinese literature, microscopy can be subjective and antibody-based methods may suffer from cross-reactivity and low specifiUrban [ 38 ]. In recent years, molecular diagnostic techniques such as PCR have been increasingly applied for detecting Cryptosporidium in clinical and environmental samples. These methods offer higher sensitivity, specifiUrban, and the ability to distinguish between species and genotypes, thus providing more accurate epidemiological data [ 39 – 43 ]. Therefore, part of the observed decrease in prevalence after 2015 may reflect both improved surveillance and more precise detection rather than an actual decline in transmission. Together, these factors highlight the importance of standardized guidelines and modern diagnostic tools in understanding and controlling Cryptosporidium infection in China. Future efforts should continue to expand access to molecular diagnostics, particularly in rural and under-resourced areas, to ensure timely and accurate detection. Our meta-analysis showed that the epidemiological pattern of human cryptosporidiosis in China is similar to that observed in Nigeria, characterized by higher infection rates in younger children and older age groups [ 44 ]. Previous studies have suggested that Cryptosporidium infections are particularly prevalent among young children due to their developing immune systems and higher susceptibility to environmental exposures [ 45 ]. The observed elevated prevalence in Chinese adults older than 17 years further highlights potential age-specific behavioral or occupational factors contributing to increased risk. Therefore, preventive measures should not only target young children but also pay close attention to adults, as Cryptosporidium represents an important public health concern across various age groups. However, interpretations for certain age groups, such as individuals aged 17–30 years, should be made cautiously due to limited sample size (only seven studies included), which might not fully represent the actual prevalence. Future studies with larger sample sizes and detailed demographic analyses are warranted to clarify these age-related prevalence patterns and associated risk factors. Our meta-analysis indicates that C. parvum is the predominant species in China, demonstrating a higher prevalence compared to C. hominis . Nevertheless, due to the limited number of studies included, we refrained from performing extensive subgroup analyses or drawing overly generalized conclusions. The prevalence of Cryptosporidium infection in rural populations in China was significantly higher than in urban populations. This aligns with existing evidence that urban areas, with better sanitation, treated water supplies, and infrastructure, tend to have lower rates of infection [ 44 ]. One important factor likely contributing to the elevated prevalence in rural areas is the increased frequency of contact with livestock and other domestic animals. Moreover, existing literature has clearly shown that individuals involved in farming activities, such as feeding or milking livestock, are more susceptible to Cryptosporidium infection [ 29 ]. In rural China, individuals are more commonly engaged in farming and animal husbandry, including tasks such as feeding, cleaning, and milking, which increase the risk of zoonotic transmission. Thus, the higher infection rate in rural areas is likely driven not only by weaker sanitation and limited access to safe water, but also by greater exposure to parasite-carrying hosts. These findings highlight the need to strengthen preventive efforts in rural communities-through health education, safer livestock-handling practices, and improvements in water and sanitation systems-to reduce the risk of cross-species transmission. Future studies should also aim to collect more detailed exposure data to better assess the specific role of contact with animals in driving Cryptosporidium infection. The prevalence of Cryptosporidium infection was significantly higher among HIV-infected individuals compared to healthy controls in China. This finding is consistent with international studies, such as those conducted in Ethiopia, Iran, and Venezuela, where immunocompromised individuals-particularly people living with HIV/AIDS (PLWHA) -showed markedly higher infection rates [ 46 – 50 ]. In immunocompetent hosts, Cryptosporidium infection is usually self-limiting, but in HIV/AIDS patients, it can become chronic and severe, contributing to a substantial proportion of diarrheal cases [ 51 ]. We also observed a significantly higher prevalence of Cryptosporidium infection in individuals with diarrhea compared to those without, which aligns with previous findings showing that Cryptosporidium is a common etiological agent of diarrhea in HIV/AIDS patients. For instance, a study in Ethiopia reported that the prevalence of infection in diarrheic HIV patients was nearly seven times higher than in non-diarrheic individuals [ 48 ]. These results reinforce the importance of routine Cryptosporidium screening among HIV-infected individuals in China, particularly those presenting with diarrhea. They also highlight the need for improved diagnostic capacity in clinical settings, as early detection and management are crucial for reducing morbidity in this vulnerable population. Conclusion This systematic review and meta-analysis provides a comprehensive assessment of the prevalence and epidemiological patterns of Cryptosporidium infection in humans across China. While the overall infection rate remains moderate, certain populations, particularly young children, adults aged 17–30 years, HIV-positive individuals, and rural residents, are at significantly higher risk.The predominance of C. parvum and C. hominis further highlights the public health importance of these species. The observed decline in prevalence after 2015 may reflect improvements in diagnostic capacity and standardization, especially the adoption of molecular techniques and the implementation of national diagnostic guidelines. However, gaps remain in surveillance, particularly in under-resourced and rural settings. Future efforts should prioritize the use of sensitive molecular diagnostics, routine screening among immunocompromised individuals, and prevention strategies tailored to specific age groups and regions. Integrated approaches that consider environmental conditions, immune status, and exposure pathways are essential for the effective control of Cryptosporidium infection in humans in China. Declarations Funding Funding for this study was provided by Key R & D and Achievement Transformation Projects of Inner Mongolia, China (grant no. 2023YFDZ0048), Research Project Funding for First-class Disciplines at Inner Mongolia Education Department (grant no. YLXKZX-NND-012), Technology Support Project of Major Innovation Platform (Base) Construction (grant no. KCX2024016), National Center of Technology Innovation for Dairy (grant no. 2023-JSGG-5), the Natural Science Foundation of Inner Mongolia, China (grant no. 2023LHMS03022, and 2023LHMS03005) and the National Natural Science Foundation of China (grant no. 32160838). CRediT authorship contribution statement Writing – review & editing: Rui Shi, Wei Wei, Di Jiao, Rigai Sa, Guoshuai Li, Rui Wang; Writing – original draft: Rui Shi; Validation: Rui Shi; Investigation: Rui Shi, Rigai Sa; Formal analysis: Rui Shi, Jing Li; Conceptualization: Rui Shi; Supervision: Wei Wei, Rui Wang; Project administration: Wei Wei, Rui Wang; Data curation: Wei Wei, Di Jiao, Hua Bai, Risu Na, Rui Wang; Methodology: Rigai Sa; Funding acquisition: Rui Wang. Declaration of Competing Interest 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. Acknowledgments None. 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Parasitol Int 71: 194–206. https://doi.org/10.1016/j.parint.2019.04.007. Olson ME, Goh J, Phillips M, Guselle N, McAllister TA (1999) Giardia cyst and Cryptosporidium oocyst survival in water soil and cattle feces. J Environ Qual 28: 1991–1996. https://doi.org/10.2134/jeq1999.00472425002800060040x. Omolabi KF, Odeniran PO, Soliman ME (2022) A meta-analysis of Cryptosporidium species in humans from southern Africa (2000–2020). J Parasit Dis 46: 304–316. https://doi.org/10.1007/s12639-021-01436-4. Putignani L, Menichella D (2010) Global distribution public health and clinical impact of the protozoan pathogen Cryptosporidium . Interdiscip Perspect Infect Dis 2010 753512. https://doi.org/10.1155/2010/753512. Robertson LJ, Campbell AT, Smith HV (1992) Survival of Cryptosporidium parvum oocysts under various environmental pressures. Appl Environ Microbiol 58: 3494–3500. https://doi.org/10.1128/aem.58.11.3494-3500.1992. Ryan U, Fayer R, Xiao L (2014) Cryptosporidium species in humans and animals: current understanding and research needs. Parasitology 141: 1667–1685. https://doi.org/10.1017/S0031182014001085. Shirley DT, Moonah SN, Kotloff KL (2012) Burden of disease from cryptosporidiosis. Curr Opin Infect Dis. 25: 555–563. https://doi.org/10.1097/QCO.0b013e328357e569. Su QP, Chen DG, Hua XL, Chen S, Zhao ZQ, Huang MH, Wan JQ, Hua YH, Guo YH, Li SM (1989) Cryptosporidiosis in infants in Fuzhou. Chin J Zoonoses 5 (5): 35–36 65 (In Chinese). Tawana M, Onyiche TE, Ramatla T, Nkhebenyane SJ, Grab DJ, Thekisoe O (2024) Cryptosporidium species infections detected from fecal samples of animal and human hosts in South Africa: systematic review and meta-analysis. Microorganisms 12: 2426. https://doi.org/10.3390/microorganisms12122426. Thompson RCA, Ash A (2016) Molecular epidemiology of Giardia and Cryptosporidium infections. Infect Genet Evol 40: 315–323. https://doi.org/10.1016/j.meegid.2015.09.028. Tsaihong JC, Tang RB, Wu KK et al (1988) Pediatric cryptosporidiosis: a report of 2 cases. Taiwan Yi Xue Hui Za Zhi 87: 914–918. Vanathy K, Parija SC, Mandal J, Hamide A, Krishnamurthy S (2017) Cryptosporidiosis: a mini review. Trop Parasitol 7: 72–80. https://doi.org/10.4103/tp.TP_25_17. Wang X, Shen YJ, Cao JP (2022) Current epidemic situation and prevention progress of Cryptosporidium infection in China. J Trop Dis Parasitol 20: 136–148 (In Chinese). Wang X, Shen YJ, Cao JP (2022) Epidemic status and prevention progress of cryptosporidiosis in China. J Trop Dis Parasitol 20: 136–148 (In Chinese). Wang Y, Zhang B, Li J et al (2021) Development of a quantitative real-time PCR assay for detection of Cryptosporidium spp. infection and threatening caused by Cryptosporidium parvum subtype IIdA19G1 in diarrhea calves from northeastern China. Vector Borne Zoonotic Dis 21: 179–190. https://doi.org/10.1089/vbz.2020.2674. Wang ZD, Wang SC, Liu HH et al (2017) Prevalence and burden of Toxoplasma gondii infection in HIV-infected people: a systematic review and meta-analysis. Lancet HIV 4 e177–e188. https://doi.org/10.1016/S2352-3018(17)30005-X. Wei XY, Gong QL, Zeng A et al (2021) Seroprevalence and risk factors of Toxoplasma gondii infection in goats in China from 2010 to 2020: a systematic review and meta-analysis. Prev Vet Med 186 105230. https://doi.org/10.1016/j.prevetmed.2020.105230. Xiao L (2010) Molecular epidemiology of cryptosporidiosis: an update. Exp Parasitol 124: 80–89. https://doi.org/10.1016/j.exppara.2009.03.018. Xiao L, Bern C. Limor J, Sulaiman I, Roberts J, Checkley W, Cabrera L, Gilman RH, Lal AA (2001) Identification of 5 types of Cryptosporidium parasites in children in Lima Peru. J Infect Dis 183: 492–497. https://doi.org/10.1371/journal.pone.0253186. Zhang L, Chen CJ, Chen YY (2007) China's standards for drinking water hygiene. Chin J Public Health 23: 1281–1282 (In Chinese). Zhou Y (1987) Cryptosporidium infection and cryptosporidial enteritis. J Chongqing Med Univ 12 3 (In Chinese). Zu S, Du M (1987) Discovery of human Cryptosporidium infection in China (brief report). J Anhui Med Univ 22: 276 (In Chinese). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx FigureS1.tiff Fig. S1. Egger’s test for publication bias. FigureS2.tiff Fig. S2. Sensitivity analysis of Cryptosporidium infection in humans in the studies conducted in China. TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2","display":"","copyAsset":false,"role":"figure","size":1069263,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in huamns among studies conducted in China.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/4f4ac84a2856c87e673b4bb5.png"},{"id":90474379,"identity":"177c4b3e-4ba6-44f6-91a9-04799a15ea8d","added_by":"auto","created_at":"2025-09-03 06:46:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":885185,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Funnel plot showing the distribution of studies according to their arcsine-transformed prevalence estimates and standard errors, with pseudo 95% confidence interval limits. The asymmetrical pattern suggests the presence of potential publication bias. (B) Funnel plot after trim-and-fill analysis. Filled points indicate imputed studies used to adjust for asymmetry and estimate a more unbiased pooled effect size. The x-axis was expanded accordingly to accommodate the imputed values.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/b32eb5d0cb86299559660799.png"},{"id":90473015,"identity":"a8e4e4db-3a47-4af1-b250-ef883c3b22b7","added_by":"auto","created_at":"2025-09-03 06:38:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":689889,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of human \u003cem\u003eCryptosporidium \u003c/em\u003eprevalence in China based on major epidemiological risk factors, including region (A), sampling year (B), age (C), HIV (D), and residenc (E).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/1a66b1b528998d3341ef4802.png"},{"id":90473028,"identity":"87eb3db8-ef40-4b2b-ab14-56f1e6f709dd","added_by":"auto","created_at":"2025-09-03 06:38:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":909564,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing a geographic overview of the prevalence rates of \u003cem\u003eCryptosporidium \u003c/em\u003ein humans across various provinces in China.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/18fdd1c266592d7da526fa9f.png"},{"id":90474367,"identity":"a554d9d9-ef2a-447c-8d40-d37edcc6671d","added_by":"auto","created_at":"2025-09-03 06:46:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1024736,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-regression plots showing the temporal trend in the global prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans based on the weighted average collection year. Each circle represents a study, with circle size proportional to sample size. The dashed line indicates the fitted regression line, and the shaded area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/1b8dca94a775669bac4cbfb1.png"},{"id":91630819,"identity":"1fea06a2-4349-4716-af22-a274322e05f7","added_by":"auto","created_at":"2025-09-18 13:02:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7255834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/07c23bdb-bf3b-4153-8a1e-d207e7cc8cb9.pdf"},{"id":90473025,"identity":"f4ad4a08-fcbf-4ae1-857d-e8f6adcde568","added_by":"auto","created_at":"2025-09-03 06:38:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38533,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/17bdbd27a8d8443fcdb0f0b9.docx"},{"id":90473045,"identity":"336eb32c-3845-40eb-a217-17942f06e161","added_by":"auto","created_at":"2025-09-03 06:38:05","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":447762,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S1. Egger’s test for publication bias.\u003c/p\u003e","description":"","filename":"FigureS1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/2256ab0d44a804bb4588e777.tiff"},{"id":90473047,"identity":"bfc33ee7-0806-4c90-865e-ed55821b6cf3","added_by":"auto","created_at":"2025-09-03 06:38:05","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1152470,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S2. Sensitivity analysis of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in the studies conducted in China.\u003c/p\u003e","description":"","filename":"FigureS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/b72cb7666c478ce02800b3cd.tiff"},{"id":90474368,"identity":"43c89df7-f65f-44c9-8e4f-1d58c23363eb","added_by":"auto","created_at":"2025-09-03 06:46:03","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14239,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7425773/v1/36bed02b6899897fca72348b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and risk factors of Cryptosporidium in humans in China: A systematic review and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eCryptosporidium\u003c/em\u003e is one of the most widespread intestinal protozoan parasites, representing a significant global public health concern, particularly in developing countries where water sanitation infrastructure is often inadequate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These apicomplexan, oocyst-forming parasites have a direct fecal-oral transmission cycle and can infect a wide range of vertebrate hosts, including humans and livestock, thereby facilitating both zoonotic and anthroponotic transmission pathways [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cryptosporidiosis, the disease caused by \u003cem\u003eCryptosporidium\u003c/em\u003e, is now recognized as a leading cause of diarrheal illness, especially among immunocompromised individuals such as those living with HIV/AIDS, young children, and the elderly [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Outbreaks are predominantly associated with contaminated drinking water, recreational water use, or foodborne exposure, although direct contact with infected humans or animals is also a known risk factor [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The clinical presentation commonly includes acute, self-limiting watery diarrhea; however, in immunodeficient individuals, infections can become chronic and life-threatening [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, \u003cem\u003eC. hominis\u003c/em\u003e and \u003cem\u003eC. parvum\u003c/em\u003e are the two species most frequently implicated in human disease, accounting for approximately 90% of reported cases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other less prevalent species such as \u003cem\u003eC. meleagridis\u003c/em\u003e, \u003cem\u003eC. ubiquitum\u003c/em\u003e, \u003cem\u003eC. felis\u003c/em\u003e, and \u003cem\u003eC. canis\u003c/em\u003e have also been documented, particularly in specific host or geographic contexts [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The distribution of \u003cem\u003eC. hominis\u003c/em\u003e and \u003cem\u003eC. parvum\u003c/em\u003e is often region-specific [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In China, the first human case of cryptosporidiosis was reported in Jiangsu Province in 1987 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Subsequent cases have been recorded in regions including Anhui, Chongqing, Taiwan, and Fujian [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By the late 1990s, human \u003cem\u003eCryptosporidium\u003c/em\u003e infections had been reported in at least 23 provinces. As of 2021, this number had risen to 29 provinces and 107 prefecture-level regions, with a total of 4,975 confirmed infections documented in the literature, underscoring a substantial yet likely underrecognized national burden [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo effective diagnosis of cryptosporidiosis and reduce misdiagnosis and underdiagnosis, the former National Health and Family Planning Commission issued the Diagnosis of Cryptosporidiosis (WS/T487\u0026ndash;2016) in 2016, which provides a detailed description of laboratory tests and clinical diagnosis of cryptosporidiosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Efforts to control waterborne transmission of \u003cem\u003eCryptosporidium\u003c/em\u003e in China have made progress, notably with the 2006 implementation of the National Health Standard for Drinking Water (GB 5749\u0026ndash;2006), which included mandatory testing for \u003cem\u003eCryptosporidium\u003c/em\u003e and \u003cem\u003eGiardia\u003c/em\u003e as microbiological indicators. This regulatory shift has contributed to improved monitoring and risk mitigation in public water supplies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Owing to its extensive distribution, environmental resilience, and clinical impact, \u003cem\u003eCryptosporidium\u003c/em\u003e was ranked fifth among the most significant global foodborne parasites by a joint FAO/WHO expert committee [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite these recognitions, cryptosporidiosis remains a neglected tropical disease in terms of research, surveillance, and funding. Many countries lack systematic monitoring programs, and the true burden of disease is likely underestimated due to underdiagnosis and underreporting.\u003c/p\u003e\u003cp\u003eHowever, despite the increasing recognition of \u003cem\u003eCryptosporidium\u003c/em\u003e as a significant zoonotic pathogen, large-scale epidemiological data specific to the Chinese population remain limited. In particular, little is known about the geographic distribution and host-related risk factors influencing infection dynamics in China. To address this gap, our study conducted a meta-analysis to estimate the overall prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans across China and to explore potential sources of heterogeneity. Subgroup analyses were performed based on region, age, sex, sampling year, distribution, season, diarrhea, HIV status, diagnostic methods, and \u003cem\u003eCryptosporidium\u003c/em\u003e species. These stratified factors provide a foundation for identifying high-risk populations and tailoring targeted prevention strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSearch Strategy and Study Selection\u003c/h2\u003e\u003cp\u003eThis study was conducted in accordance with the PRISMA guidelines for systematic reviews and meta-analyses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A comprehensive literature search was carried out across five major databases, including Web of Science, PubMed, ScienceDirect, Scopus, and Google Scholar, using the search string: (\"\u003cem\u003eCryptosporidium\u003c/em\u003e OR cryptosporidiosis\") AND (\"human OR people OR person OR man OR men OR women OR woman OR patient\") AND (\"China\") (Table. S1). All peer-reviewed studies reporting cryptosporidiosis infections in humans were considered, with no geographic restrictions, covering the period from database inception through 15 May 2025.\u003c/p\u003e\u003cp\u003eAll retrieved records were imported into EndNote (version X9; Clarivate Analytics, London, UK) for duplicate removal. An automated screening tool was used to eliminate clearly irrelevant entries. Two researchers performed the database search independently, and titles and abstracts were evaluated separately to minimize selection bias. Studies were eligible for inclusion if they (1) reported the prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans, (2) provided both total sample size and the number of positive cases, (3) included a sample size greater than 20, (4) employed a cross-sectional study design, and (5) presented raw data suitable for epidemiological analysis. Articles not meeting the above criteria were excluded, such as review articles, case reports, those with no full-text and abstract, and those not in English. In cases of disagreement regarding the inclusion or exclusion of a study, discrepancies were resolved through team discussion to reach a consensus.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eData from the eligible studies were systematically extracted using standardized data collection forms in Microsoft Excel (version 16.0.18429.20132; Microsoft Corporation, Redmond, WA, USA). Extracted variables included the first author, year of publication, sampling year, the geographical region of the study (including province, administrative regions, and the municipalities directly under the central government), age, sex, method of diagnosis, HIV status, presence of diarrhea, distribution, season, \u003cem\u003eCryptosporidium\u003c/em\u003e species, total number of individuals examined, and the number of positive cases for \u003cem\u003eCryptosporidium\u003c/em\u003e infection. When multiple diagnostic methods were reported, microscopy results were prioritized to maintain consistency across studies.\u003c/p\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eStudy quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. One point was assigned for each of the following criteria: reporting the year of sampling, including a sample size greater than 60, providing a detailed sampling method, cross-sectional study design, and reporting data on four or more potential risk factors. Based on total scores ranging from 0 to 5, studies were categorized as high quality (4\u0026ndash;5 points), moderate quality (2\u0026ndash;3 points), or low quality (0\u0026ndash;1 point).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll quantitative analyses were conducted using the \"meta\" package in RStudio (version 2024.12.0\u0026thinsp;+\u0026thinsp;467). To identify the most appropriate transformation method for normalizing data distributions in the meta-analysis, five approaches were evaluated: raw proportions (PRAW), logarithmic transformation (PLN), logit transformation (PLOGIT), arcsine transformation (PAS), and double-arcsine transformation (PFT). Based on diagnostic metrics, PAS was selected for human data (W\u0026thinsp;=\u0026thinsp;0.90862, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These transformations were chosen because they yielded W-values closest to 1 and \u003cem\u003ep\u003c/em\u003e-values nearest to 0.05, indicating the best approximation to normality.\u003c/p\u003e\u003cp\u003eHeterogeneity across studies was assessed using the I\u0026sup2; statistic and Cochran\u0026rsquo;s Q test [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Forest plots were constructed to visualize the pooled prevalence estimates and the results of subgroup analyses. A random-effects model was employed to account for between-study variability. Potential publication bias was evaluated using funnel plots and Egger\u0026rsquo;s test. Sensitivity analyses were performed by sequentially excluding individual studies to assess their impact on the overall effect estimates. To investigate potential sources of heterogeneity, subgroup analyses and meta-regression were conducted [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Both univariable and multivariable meta-regression models were used to explore associations between covariates and the prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection. In multivariable analyses, the subgroup with the highest prevalence was used as the reference category.\u003c/p\u003e\u003cp\u003eGeographical comparisons within China were based on seven major regions: Western China, Southwestern China, Northwestern China, Northern China, Northeastern China, Eastern China, and Central China. Sampling years were grouped into three periods: after 2015, 2005\u0026ndash;2015, and 2004 or before. Temporal trends in prevalence were assessed using random-effects meta-regression, with the sampling year as the independent variable and the logit-transformed infection rate as the dependent variable. For studies covering multiple years, a weighted average year was calculated and used in the analysis. In multivariable meta-regression, the reference category for each covariate was defined as the subgroup with the highest observed prevalence. Analyses of host-related factors included comparisons among age groups (\u0026lt;\u0026thinsp;3, 3\u0026ndash;6, 7\u0026ndash;17, 17\u0026ndash;30, and \u0026gt;\u0026thinsp;30) and sex (women vs. men). Clinical symptoms were assessed by comparing individuals with and without diarrhea. Immunocompromised status was examined by comparing participants with HIV infection vs. without HIV. Residential distribution was categorized as urban and rural. Seasonal variations in infection prevalence were compared across the four seasons: spring, summer, autumn, and winter. Diagnostic methods were assessed by comparing studies utilizing microscopy, molecular, and immunological.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSearch Results and Eligible Studies\u003c/h2\u003e\u003cp\u003eA total of 4,297 studies were retrieved from the five databases. According to the inclusion and exclusion criteria, a total of 193 full-text studies comprising 75 high quality, 105 middle quality, and 13 low quality articles, which covered 27 provinces were used for the meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). References are cited in Supplementary Materials.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePublication Bias and Sensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eThe forest plot illustrated the overall heterogeneity among the included studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Funnel plots suggested publication bias, which was further supported by trim-and-fill analysis. The improved symmetry after imputation indicated that the bias was likely due to missing studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Egger\u0026rsquo;s test further quantified this bias, revealing statistically significant publication bias in huamns studies (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0082, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sensitivity analysis demonstrated that the pooled prevalence estimates remained stable upon the exclusion of any single study, confirming the robustness and reliability of the meta-analysis results (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePooling and Heterogeneity Analyses\u003c/h3\u003e\n\u003cp\u003eAccording to the meta-analysis, the overall pooled prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection was 5% in humans in China. Among the seven major regions, Northwestern China exhibited the highest prevalence at 6.61% (95% CI: 3.20\u0026ndash;11.13), followed by Northern China 5.74% (95% CI: 2.46\u0026ndash;10.28). The lowest prevalence was recorded in Northeastern China, at 1.35% (95% CI: 0.43\u0026ndash;2.78; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0249). \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans was reported in 25 provinces, that Liaoning and Hebei exhibited the highest infection rates. The infection rate in Liaoning was 6.30% (95% CI: 3.53\u0026ndash;9.78), followed by Hebei with an infection rate of 6.14% (95% CI: 4.83\u0026ndash;7.59; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePooled prevalence of Cryptosporidium infection in humans in China.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003cp\u003estudies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003cp\u003eexamined\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003cp\u003epositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e% (95% Cl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eHeterogeneity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eUnivariate meta-regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHumans\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWestern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e19578\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e501\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.54 [1.30\u0026ndash;3.96]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e340.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e93.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0779\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.1034 (-0.2183 to 0.0115)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSouthwestern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3090\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e289\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.01 [0.62\u0026ndash;13.25]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e164.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.7127\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0350 (-0.2211 to 0.1512)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNorthwestern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6737\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e634\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e6.61 [3.20\u0026ndash;11.13]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e749.17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNorthern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8344\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e431\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.74 [2.46\u0026ndash;10.28]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e356.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e95.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.7061\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0192(-0.1422 to 0.1039)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNortheastern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5820\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e189\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.35 [0.43\u0026ndash;2.78]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e83.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e97.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0249\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.1872 (-0.3508 to 0.0294)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEastern China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e106121\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2825\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4.45 [2.78\u0026ndash;6.48]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e3750.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.3482\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0483 (-0.1492 to 0.0526)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCentral China\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e66584\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3360\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.03 [3.28\u0026ndash;7.13]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e7174.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e99.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.5102\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0342 (-0.1362 to 0.0677)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSampling year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter 2015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e22533\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e662\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.47 [1.07\u0026ndash;4.43]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1246.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0076\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0924 (-0.1603 to -0.0246)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2005\u0026ndash;2015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e77122\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4847\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e6.41 [4.52\u0026ndash;8.60]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4386.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2004 or before\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e74221\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2905\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.27 [2.19\u0026ndash;4.55]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e5372.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0135\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0661 (-0.1186 to -0.0137)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e95.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0099\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.1595 (-0.2807 to -0.0383)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e17\u0026ndash;30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1820\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.60 [4.66\u0026ndash;8.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4263.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.6652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0140 (-0.0775 to 0.0495)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.66 [4.92\u0026ndash;9.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5887.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiarrhea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.88 [3.48\u0026ndash;6.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1694.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.33 [3.27\u0026ndash;5.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15032.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.5793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0142 (-0.0643 to 0.0359)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e43867\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4125\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e7.63 [5.34\u0026ndash;10.28]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1015.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e96.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e172\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e220748\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7607\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.94 [3.04\u0026ndash;4.95]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e12650.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e98.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0075\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e-0.0803 (-0.1391 to -0.0214)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e31146\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.50 [0.91\u0026ndash;2.14]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e88.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e79.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.1020 (-0.1506 to -0.0535)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRoral\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e30218\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1960\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.18 [3.46\u0026ndash;7.23]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e225.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e92.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.68 [0.76\u0026ndash;5.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e486\u0026thinsp;\u0026minus;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.6060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0239 (-0.1148 to 0.0670)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.34 [1.44\u0026ndash;6.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e569.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.9890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0006 (-0.0848 to 0.0837)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.44 [1.81\u0026ndash;5.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e502.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.87 [0.52\u0026ndash;4.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e420.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0497 (-0.1342 to 0.0348)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod of diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicroscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20 [3.24\u0026ndash;5.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11142.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0321 (-0.0898 to 0.0257)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.65 [3.20\u0026ndash;8.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1461.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.57 [2.65\u0026ndash;6.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1088.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0156 (-0.1020 to 0.0708)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. parvum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51 [0.15\u0026ndash;4.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e578.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. hominis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11 [0.43\u0026ndash;2.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0202 (-0.1547 to 0.1142)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96 [0.13\u0026ndash;2.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e116.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e116.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.6026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.0255 (-0.1213 to 0.0704)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cstrong\u003eCI - confidence interval, X2 - chi-squared\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSeasonal trends were modest: autumn and summer showed slightly higher prevalence estimates (3.44% and 3.34%, respectively), while winter had the lowest infection rate (1.87%, 95% CI: 0.52\u0026ndash;4.04). In terms of sampling year, the highest prevalence was observed during 2005\u0026ndash;2015, reaching 6.41% (95% CI: 4.52\u0026ndash;8.60; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A statistically significant decline in prevalence over time was observed, with samples collected after 2015 showing a lower rate of 2.47% (95% CI: 1.07\u0026ndash;4.43; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0076). To explore temporal trends, a random-effects meta-regression was conducted, with sampling year as the independent variable and logit-transformed prevalence as the dependent variable. The analysis revealed a negative slope for humans (0.0129), suggesting a minimal increase. However, neither trend was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3008; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the subgroup analysis by age, children under 3 years had an infection rate of 5.22% (95% CI: 2.49\u0026ndash;8.77). Individuals aged 17\u0026ndash;30 years showed the highest infection rate at 8.43% (95% CI: 3.55\u0026ndash;14.61). For participants older than 30 years, the infection rate was 7.51% (95% CI: 3.39\u0026ndash;12.98), with a statistically significant association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0070; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding sex, men had a higher prevalence (7.66%, 95% CI: 4.92\u0026ndash;9.78) compared to women (6.60%, 95% CI: 4.66\u0026ndash;8.44). Host health status played a role in infection risk. Individuals with diarrhea exhibited a higher prevalence (4.88%, 95% CI: 3.48\u0026ndash;6.50) than those without diarrhea (4.33%, 95% CI: 3.27\u0026ndash;5.53). However, a significant difference was observed in HIV status: individuals with HIV had a significantly higher prevalence (7.63%, 95% CI: 5.34\u0026ndash;10.28) than those without HIV (3.94%, 95% CI: 3.04\u0026ndash;4.95; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0075). Geographically, individuals living in rural areas had a markedly higher prevalence (5.18%, 95% CI: 3.46\u0026ndash;7.23) compared to those in urban areas (1.50%, 95% CI: 0.91\u0026ndash;2.14), with a statistically significant association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the subgroup analysis by \u003cem\u003eCryptosporidium\u003c/em\u003e species, \u003cem\u003eC. parvum\u003c/em\u003e was associated with the highest prevalence at 1.51% (95% CI: 0.15\u0026ndash;4.29), followed by \u003cem\u003eC. hominis\u003c/em\u003e at 1.11% (95% CI: 0.43\u0026ndash;2.12) and other species at 0.96% (95% CI: 0.13\u0026ndash;2.54). Diagnostic methods influenced the detection rates. Studies using molecular techniques reported the highest prevalence at 5.65% (95% CI: 3.20\u0026ndash;8.70), followed by immunological methods at 4.57% (95% CI: 2.65\u0026ndash;6.94), and microscopy at 4.20% (95% CI: 3.24\u0026ndash;5.27). However, differences across methods were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eCryptosporidium\u003c/em\u003e is one of the important zoonotic diseases that cause life-threatening diarrhoea in young, immunodeficient, and malnourished hosts. It has been reported in animals and humans across more than 106 countries, with a higher prevalence in developing nations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Despite the widespread distribution and zoonotic potential of \u003cem\u003eCryptosporidium\u003c/em\u003e species, comprehensive assessments of its prevalence and the epidemiological factors influencing infection in humans in China remain limited. Therefore, this study aims to systematically estimate the prevalence and associated risk factors of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in China through meta-analysis.\u003c/p\u003e\u003cp\u003eIn this study, the pooled prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in China was estimated at 4.0%, indicating a relatively low infection rate compared to the global average of 7.6%, and much lower than that reported in countries such as South Africa (18.1%) and Nigeria (15.0%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This suggests that the overall burden of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in China is comparatively lower on a global scale. The reasons for this may include improvements in sanitation, drinking water safety, and public health awareness. Nevertheless, differences in study design, diagnostic methods, and population characteristics across countries should be considered when comparing prevalence rates. Continued monitoring is warranted to prevent potential outbreaks and to protect vulnerable populations.\u003c/p\u003e\u003cp\u003eIn our study, the pooled prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in Northwestern China was found to be 6.61% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This regional variation may be partly explained by the unique climatic and environmental conditions of Northwestern China, which is characterized by a semi-arid to arid climate, extreme seasonal temperature fluctuations, and limited water resources. In arid regions like Northwestern China, where water is scarce and often contaminated, the persistence of \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts in the environment may be enhanced, increasing the likelihood of human and animal exposure [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The limited rainfall in these regions can lead to poor water quality, especially in rural areas that rely on untreated or minimally treated water sources. In contrast, several studies indicate that \u003cem\u003eCryptosporidium\u003c/em\u003e infection rates are positively correlated with rainfall, which facilitates the spread of oocysts through runoff and increases contamination in water sources. These climatic factors, combined with the environmental resilience of \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts, emphasize the need for targeted public health interventions in Northwestern China. However, caution should be exercised when generalizing these findings, as other regions, such as Southwestern China, have limited data, which may not fully capture the regional variation or reflect the broader prevalence trends. Further studies focusing on how climatic and environmental factors influence \u003cem\u003eCryptosporidium\u003c/em\u003e transmission in arid regions will provide valuable insights for more effective control measures. Given the limited research in some areas, further investigation is needed to fully understand the regional transmission dynamics of \u003cem\u003eCryptosporidium\u003c/em\u003e across China.\u003c/p\u003e\u003cp\u003eThe highest prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans was observed during the autumn season in China. This finding is interesting when compared to previous research that suggests temperature plays a significant role in the survival and infectivity of \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts in the environment. Several studies have demonstrated that \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts exhibit increased survival in soil at temperatures below 15\u0026deg;C, while exposure to higher temperatures (\u0026gt;\u0026thinsp;25\u0026deg;C) and increased UV-A/B insolation can cause oocyst degradation [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Autumn in China is typically characterized by moderate temperatures ranging, which may create an optimal environment for \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts to persist in both soil and water sources. During this period, temperatures may be low enough to prevent oocyst degradation, but not high enough to cause significant damage to their structure. While \u003cem\u003eCryptosporidium\u003c/em\u003e survival and infectivity are influenced by multiple environmental factors, including temperature, more research is needed to fully understand how seasonal temperature fluctuations, along with other climatic conditions, impact the transmission dynamics of \u003cem\u003eCryptosporidium\u003c/em\u003e in different regions, especially in areas like China where seasonal variation plays a crucial role in disease transmission. Further studies focusing on how climatic and environmental factors influence \u003cem\u003eCryptosporidium\u003c/em\u003e transmission in arid regions will provide valuable insights for more effective control measures. Given the limited research in some areas, further investigation is needed to fully understand the regional transmission dynamics of \u003cem\u003eCryptosporidium\u003c/em\u003e across China.\u003c/p\u003e\u003cp\u003eOur results showed that the prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in China was higher before 2015. One possible explanation for this trend is the historical limitation in diagnostic accuracy and standardization. In 2016, the former National Health and Family Planning Commission issued the Diagnosis of Cryptosporidiosis (WS/T487-2016), which provided standardized guidelines for clinical and laboratory diagnosis of \u003cem\u003eCryptosporidium\u003c/em\u003e infection, aiming to reduce misdiagnosis and missed diagnosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The implementation of this national guideline likely contributed to improvements in diagnostic consistency and case identification across the country. In addition, advances in detection technology may also explain the changes in prevalence estimates. Traditional detection methods such as microscopy and immunological assays have been widely used, but these approaches are often time-consuming, require skilled personnel, and have limited sensitivity, especially in early-stage or low-intensity infections. As shown in recent Chinese literature, microscopy can be subjective and antibody-based methods may suffer from cross-reactivity and low specifiUrban [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In recent years, molecular diagnostic techniques such as PCR have been increasingly applied for detecting \u003cem\u003eCryptosporidium\u003c/em\u003e in clinical and environmental samples. These methods offer higher sensitivity, specifiUrban, and the ability to distinguish between species and genotypes, thus providing more accurate epidemiological data [\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, part of the observed decrease in prevalence after 2015 may reflect both improved surveillance and more precise detection rather than an actual decline in transmission. Together, these factors highlight the importance of standardized guidelines and modern diagnostic tools in understanding and controlling \u003cem\u003eCryptosporidium\u003c/em\u003e infection in China. Future efforts should continue to expand access to molecular diagnostics, particularly in rural and under-resourced areas, to ensure timely and accurate detection.\u003c/p\u003e\u003cp\u003eOur meta-analysis showed that the epidemiological pattern of human cryptosporidiosis in China is similar to that observed in Nigeria, characterized by higher infection rates in younger children and older age groups [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Previous studies have suggested that \u003cem\u003eCryptosporidium\u003c/em\u003e infections are particularly prevalent among young children due to their developing immune systems and higher susceptibility to environmental exposures [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The observed elevated prevalence in Chinese adults older than 17 years further highlights potential age-specific behavioral or occupational factors contributing to increased risk. Therefore, preventive measures should not only target young children but also pay close attention to adults, as \u003cem\u003eCryptosporidium\u003c/em\u003e represents an important public health concern across various age groups. However, interpretations for certain age groups, such as individuals aged 17\u0026ndash;30 years, should be made cautiously due to limited sample size (only seven studies included), which might not fully represent the actual prevalence. Future studies with larger sample sizes and detailed demographic analyses are warranted to clarify these age-related prevalence patterns and associated risk factors.\u003c/p\u003e\u003cp\u003eOur meta-analysis indicates that \u003cem\u003eC. parvum\u003c/em\u003e is the predominant species in China, demonstrating a higher prevalence compared to \u003cem\u003eC. hominis\u003c/em\u003e. Nevertheless, due to the limited number of studies included, we refrained from performing extensive subgroup analyses or drawing overly generalized conclusions. The prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in rural populations in China was significantly higher than in urban populations. This aligns with existing evidence that urban areas, with better sanitation, treated water supplies, and infrastructure, tend to have lower rates of infection [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. One important factor likely contributing to the elevated prevalence in rural areas is the increased frequency of contact with livestock and other domestic animals. Moreover, existing literature has clearly shown that individuals involved in farming activities, such as feeding or milking livestock, are more susceptible to \u003cem\u003eCryptosporidium\u003c/em\u003e infection [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In rural China, individuals are more commonly engaged in farming and animal husbandry, including tasks such as feeding, cleaning, and milking, which increase the risk of zoonotic transmission. Thus, the higher infection rate in rural areas is likely driven not only by weaker sanitation and limited access to safe water, but also by greater exposure to parasite-carrying hosts. These findings highlight the need to strengthen preventive efforts in rural communities-through health education, safer livestock-handling practices, and improvements in water and sanitation systems-to reduce the risk of cross-species transmission. Future studies should also aim to collect more detailed exposure data to better assess the specific role of contact with animals in driving \u003cem\u003eCryptosporidium\u003c/em\u003e infection.\u003c/p\u003e\u003cp\u003eThe prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection was significantly higher among HIV-infected individuals compared to healthy controls in China. This finding is consistent with international studies, such as those conducted in Ethiopia, Iran, and Venezuela, where immunocompromised individuals-particularly people living with HIV/AIDS (PLWHA) -showed markedly higher infection rates [\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In immunocompetent hosts, \u003cem\u003eCryptosporidium\u003c/em\u003e infection is usually self-limiting, but in HIV/AIDS patients, it can become chronic and severe, contributing to a substantial proportion of diarrheal cases [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We also observed a significantly higher prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in individuals with diarrhea compared to those without, which aligns with previous findings showing that \u003cem\u003eCryptosporidium\u003c/em\u003e is a common etiological agent of diarrhea in HIV/AIDS patients. For instance, a study in Ethiopia reported that the prevalence of infection in diarrheic HIV patients was nearly seven times higher than in non-diarrheic individuals [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These results reinforce the importance of routine \u003cem\u003eCryptosporidium\u003c/em\u003e screening among HIV-infected individuals in China, particularly those presenting with diarrhea. They also highlight the need for improved diagnostic capacity in clinical settings, as early detection and management are crucial for reducing morbidity in this vulnerable population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis provides a comprehensive assessment of the prevalence and epidemiological patterns of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans across China. While the overall infection rate remains moderate, certain populations, particularly young children, adults aged 17\u0026ndash;30 years, HIV-positive individuals, and rural residents, are at significantly higher risk.The predominance of \u003cem\u003eC. parvum\u003c/em\u003e and \u003cem\u003eC. hominis\u003c/em\u003e further highlights the public health importance of these species. The observed decline in prevalence after 2015 may reflect improvements in diagnostic capacity and standardization, especially the adoption of molecular techniques and the implementation of national diagnostic guidelines. However, gaps remain in surveillance, particularly in under-resourced and rural settings. Future efforts should prioritize the use of sensitive molecular diagnostics, routine screening among immunocompromised individuals, and prevention strategies tailored to specific age groups and regions. Integrated approaches that consider environmental conditions, immune status, and exposure pathways are essential for the effective control of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in China.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eFunding for this study was provided by Key R \u0026amp; D and Achievement Transformation Projects of Inner Mongolia, China (grant no. 2023YFDZ0048), Research Project Funding for First-class Disciplines at Inner Mongolia Education Department (grant no. YLXKZX-NND-012), Technology Support Project of Major Innovation Platform (Base) Construction (grant no. KCX2024016), National Center of Technology Innovation for Dairy (grant no. 2023-JSGG-5), the Natural Science Foundation of Inner Mongolia, China (grant no. 2023LHMS03022, and 2023LHMS03005) and the National Natural Science Foundation of China (grant no. 32160838).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing: Rui Shi, Wei Wei, Di Jiao, Rigai Sa, Guoshuai Li, Rui Wang; Writing \u0026ndash; original draft: Rui Shi; Validation: Rui Shi; Investigation: Rui Shi, Rigai Sa; Formal analysis: Rui Shi, Jing Li; Conceptualization: Rui Shi; Supervision: Wei Wei, Rui Wang; Project administration: Wei Wei, Rui Wang; Data curation: Wei Wei, Di Jiao, Hua Bai, Risu Na, Rui Wang; Methodology: Rigai Sa; Funding acquisition: Rui Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eNone of the data were deposited in an official repository.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlonso JL, Amor\u0026oacute;s I, Guy RA (2014) Quantification of viable \u003cem\u003eGiardia\u003c/em\u003e cysts and \u003cem\u003eCryptosporidium\u003c/em\u003e oocysts in wastewater using propidium monoazide quantitative real-time PCR. 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Lancet HIV 4 e177\u0026ndash;e188. https://doi.org/10.1016/S2352-3018(17)30005-X.\u003c/li\u003e\n\u003cli\u003eWei XY, Gong QL, Zeng A et al (2021) Seroprevalence and risk factors of \u003cem\u003eToxoplasma gondii\u003c/em\u003e infection in goats in China from 2010 to 2020: a systematic review and meta-analysis. Prev Vet Med 186 105230. https://doi.org/10.1016/j.prevetmed.2020.105230.\u003c/li\u003e\n\u003cli\u003eXiao L (2010) Molecular epidemiology of cryptosporidiosis: an update. Exp Parasitol 124: 80\u0026ndash;89. https://doi.org/10.1016/j.exppara.2009.03.018.\u003c/li\u003e\n\u003cli\u003eXiao L, Bern C. Limor J, Sulaiman I, Roberts J, Checkley W, Cabrera L, Gilman RH, Lal AA (2001) Identification of 5 types of \u003cem\u003eCryptosporidium\u003c/em\u003e parasites in children in Lima Peru. J Infect Dis 183: 492\u0026ndash;497. https://doi.org/10.1371/journal.pone.0253186.\u003c/li\u003e\n\u003cli\u003eZhang L, Chen CJ, Chen YY (2007) China\u0026apos;s standards for drinking water hygiene. Chin J Public Health 23: 1281\u0026ndash;1282 (In Chinese).\u003c/li\u003e\n\u003cli\u003eZhou Y (1987) \u003cem\u003eCryptosporidium\u003c/em\u003e infection and cryptosporidial enteritis. J Chongqing Med Univ 12 3 (In Chinese).\u003c/li\u003e\n\u003cli\u003eZu S, Du M (1987) Discovery of human \u003cem\u003eCryptosporidium\u003c/em\u003e infection in China (brief report). J Anhui Med Univ 22: 276 (In Chinese).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cryptosporidium, Cryptosporidiosis, Prevalence, Systematic review, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-7425773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7425773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCryptosporidium is a globally distributed protozoan parasite and a major cause of diarrheal disease, particularly among immunocompromised individuals. Despite its growing recognition as an important zoonotic pathogen, large-scale epidemiological data specific to the Chinese population remain scarce.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe systematically retrieved articles on the occurrence of \u003cem\u003eCryptosporidium\u003c/em\u003e in humans in China, through a search in the following six databases: PubMed, Web of Science, ScienceDirect, Chinese National Knowledge Infrastructure, Wanfang Data, and VIP Chinese Journal Database, following PRISMA guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 193 studies were included, covering data from 27 provinces. Pooled prevalence and 95% confidence intervals (CI) were estimated using a random-effects model. The pooled prevalence of \u003cem\u003eCryptosporidium\u003c/em\u003e infection in humans in China was estimated at 5%. Higher infection rates were observed in Northern China (6.61%, 95% CI: 3.20–11.13) and among individuals aged 17–30 years (8.43%, 95% CI: 3.55–14.61). A significant decline in prevalence was noted in studies conducted after 2015. \u003cem\u003eC. parvum\u003c/em\u003e and \u003cem\u003eC. hominis\u003c/em\u003e were identified as the main infecting species. Additionally, higher infection rates in HIV-positive individuals (7.63%, 95% CI: 5.34–10.28) and rural populations (5.18%, 3.46–7.23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings highlight the need for improved surveillance, accurate diagnostics, and targeted prevention strategies in high-risk regions and populations. In particular, attention should be given not only to young children but also to adults, who may face overlooked exposure risks.\u003c/p\u003e","manuscriptTitle":"Prevalence and risk factors of Cryptosporidium in humans in China: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 06:37:58","doi":"10.21203/rs.3.rs-7425773/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba54cb7a-daf2-4ffa-afdb-d0c8b3288ed4","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T12:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 06:37:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7425773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7425773","identity":"rs-7425773","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}