Objective To investigate the species of sandflies and the prevalence of Leishmania infections in sandflies from selected areas of northern and northwestern China, so as to provide insights into identification of leishmaniasis vectors and assessment of epidemiological trends of leishmaniasis in China. Methods Sandfly samples were collected from Mentougou District of Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County of Karamay District of Karamay City, Gaochang District of Turpan City in Xinjiang Uygur Autonomous Region from July 2023 to July 2024. Approximately 100 intact female sandfly samples were randomly selected from each site and the species of sandflies was identified according to morphological characteristics and molecular assays. Female sandflies originating from the same habitat were grouped into pools of 10 individuals. Leishmania infection was detected using polymerase chain reaction (PCR) assay targeting the internal transcribed spacer 1 (ITS-1) gene, and the prevalence of Leishmania infection was calculated in sandflies from different sampling sites using the minimum infection rate (MIR) method. In addition, positive amplicons were sequenced and subjected to phylogenetic analysis. Results A total of 6 155 sandflies were collected from different environments at sampling sites across the six aforementioned regions from July 2023 to July 2024. Phlebotomus chinensis (96.00%) was the dominant sandfly species in Mentougou District, Beijing Municipality, with a small proportion of Ph. sergenti (4.00%), and only Ph. chinensis was found in Xiangning County, Linfen City, Shanxi Province. Ph. wui was the only sandfly species detected in Ejin Banner, Alxa League, Inner Mongolia Autonomous Region, and Payzawat County, Kashgar City, Xinjiang Uygur Autonomous Region, and Ph. caucasicus (97.70%) was the dominant sandfly species in Karamay District, Karamay City, Xinjiang Uygur Autonomous Region, with a small proportion of Ph. wui (2.30%), while Ph. alexandri was the only species in Gaochang District, Turpan City, Xinjiang Uygur Autonomous Region. A total of 40, 60, 34, 18, 18, and 22 pools of sandfly samples were tested from Mentougou District in Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, Payzawat County in Kashgar City, Karamay District in Karamay City, and Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region, respectively. L. infantum was detected in Ph. chinensis samples from Mentougou District in Beijing Municipality, and Xiangning County of Linfen City in Shanxi Province, with MIR of 0.25% to 1.00%, and L. donovani was detected in Ph. wui from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region, with MIR of 0.56% to 0.88%; however, no Leishmania infection was detected in Ph. caucasicus from Karamay District in Karamay City or Ph. alexandri from Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region. Phylogenetic analysis showed that the Leishmania ITS-1 gene sequences obtained from Mentougou District in Beijing Municipality and Xiangning County in Linfen City of Shanxi Province were clustered into the same clade with the reference sequences of L. infantum ITS-1 gene, while the Leishmania ITS-1 gene sequences obtained from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region were clustered into the same clade with the reference sequences of L. donovani ITS-1 gene. Conclusions There are variations in sandfly species in selected areas of northern and northwestern China, and variations in the species of Leishmania infecting sandflies. Improved surveillance of sandfly vectors and targeted control strategies with adaptations to geographical features and leishmaniasis vectors are recommended.

OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Objective To investigate the genetic diversity of sandfly populations in endemic areas of visceral leishmaniasis in China, so as to provide references insights into management of visceral leishmaniasis and the vector sandflies. MethodsSixteen sampling sites were selected from main endemic foci of visceral leishmaniasis in China from June to September 2024, including Shanxi Province, Shaanxi Province, Henan Province, Gansu Province, Sichuan Province, and Xinjiang Uygur Autonomous Region. Sandflies were captured using light traps and manual aspirators from sheep pens, chicken coops, cave dwellings, bovinesheds, and pig pens at each sampling site. A single sandfly sample was washed in phosphate-buffered saline (PBS), and genomic DNA was extracted from sandfly samples. Cytochrome oxidase subunit 1 (COI) gene was amplified using PCR assay with universal primers, and analyzed and retrieved with the nucleotide sequence analysis tool (BLAST) software, and the sequence of COI gene was aligned with the ClustalX 1.83 and MEGA 7.0 software. The base composition and variation site of the COI gene sequence were analyzed using the software MEGA 7.0, and the number of haplotypes, total number of segregating sites, haplotype diversity, nucleotide diversity, and average nucleotide differences were calculated in the COI gene sequence using the software DnaSP 5.10, followed by Tajima’s D test for neutrality. Haplotypes were screened using the software DnaSP 5.10, and the haplotype network map of sandfly samples was plotted using the software Network 5.0. MEGA 7.0 software was employed for gene sequence editing and alignment, and calculation of genetic distances among sandfly species sampled from different regions, and a phylogenetic tree was built with a neighbor-joining method. Results A total of 466 sandflies were captured from 16 sampling sites in China from June to September 2024, and 430 gene sequences were yielded following PCR amplification and sequencing of the COI gene, with 652 to 688 bp in the length of amplification fragments. The captured sandfly samples were characterized as Phlebotomus chinensis, Sergentomyia squamirostris, Se. koloshanensis, Ph. sichuanensis, and Ph. longiductus following the COI gene sequence alignment in BLAST. A total of 251 haplotypes were identified in the 430 gene sequences from sandfly samples (50.5%), and the average haplotype diversity, nucleotide diversity and average number of nucleotide difference were 0.885, 0.257 and 160.761, respectively. The Tajima’s D values were -0.92 for sandfly populations from Yangquan City, Shanxi Province and -1.73 for sandfly populations from Sanmenxia City, Henan Province, and were all more than 0 for sandfly populations from other sampling sites. Haplotype analysis identified 50 haplotypes, which were classified into two haplogroups. Heplogroup 1 included 29 haplotypes, which had a high homology, and heplogroup 2 included 21 haplotypes. The average genetic distance was 0.000 to 0.604 among sandfly samples from different sampling sites, and phylogenetic analysis revealed that the five sandfly species were clustered into distinct clades, all with 100% clade confidence. Conclusions There is a high genetic polymorphism in the COI gene from five sandfly populations in main endemic foci of visceral leishmaniasis in China, and COI gene may serve as a marker gene for analysis of the genetic structure of sandfly populations.

Objective:To explore the association between albumin-corrected serum calcium (ACSC) levels and 30-day all-cause mortality in patients hospitalized for community-acquired bacterial pneumonia (CABP).Methods:A secondary analysis was conducted on 1 899 patients with CABP from a Norwegian cohort study. The relationship between baseline ACSC levels and 30-day mortality was assessed using multivariable logistic regression models, adjusted for potential confounders.Results:A significant positive correlation was found between ACSC levels and 30-day mortality risk after adjusting for confounding variables ( OR=1.95, 95% CI=1.48-2.58). When ACSC levels were categorized into tertiles (T1-T3), a trend analysis revealed that the T2 and T3 groups had significantly higher mortality risks compared to the lowest tertile (T1), with odds ratios ( OR) and 95% confidence intervals ( CI) of 1.52(95% CI: 0.97-2.38) and 2.21 (95% CI: 1.44-3.39), respectively ( P for trend <0.001). Subgroup analyses demonstrated no significant interactions across predefined subgroups (all P for interaction > 0.05). Conclusions:In patients with CABP and admission ACSC levels of ≥8.6 mg/dL, higher ACSC levels were positively associated with an increased risk of 30-day mortality. These findings highlight the potential prognostic value of ACSC levels in CABP patients.

Objective:To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI).Methods:Based on the American Medical Information Mart for Intensive Care-Ⅳ (MIMIC-Ⅳ), clinical data of S-AKI patients from 2008 to 2021 were collected.Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7∶3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds.Results:Based on the survival status at 3 months after diagnosis, patients were divided into 7?768 (68.54%) survivors and 3?566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95% CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95% CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95% CI was 0.56-0.58) and SIRS score's 0.56 (95% CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. Conclusions:A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC-Ⅳ and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

Objective To analyze the diagnosis status of negative etiological pulmonary tuberculosis test re-sults in Wuhan,and to provide scientific basis for improving the diagnosis strategy of etiological negative pul-monary tuberculosis.Methods From January 1 to February 28,2019,a total of 241 patients with negative eti-ological tuberculosis who were registered,reported and treated in 1 municipal and 2 district-level designated hospitals were selected.The medical record data,chest imaging examination and laboratory examination re-sults of the selected patients were reviewed and extracted,and the quality of etiological examination and ima-ging examination of patients with negative etiological pulmonary tuberculosis were analyzed.Results Among the 241 patients,88.8％(214/241)of the patients met the diagnostic criteria for negative etiological pulmona-ry tuberculosis,and 96.3％(232/241)of the patients had chest imaging examinations that were consistent with the original diagnostic results.Patients received sputum smear examination,sputum culture,and molecu-lar biology test accounted for 97.9％(236/241),73.9％(178/241)and 65.6％(158/241),respectively.Patients received anti-tuberculosis antibody test,tuberculin skin test,y-interferon release and diagnostic anti-infection treatment accounted for 54.8％(132/241),46.5％(112/241),26.1％(63/241),and 53.1％(128/241),respec-tively.The sputum culture detection rate of urban area was higher than those of central and remote urban are-as,the rate of central urban area was higher than that of remote urban areas,and the molecular biology detec-tion rate of urban area was higher than those of central and remote urban area,with statistical significance(P＜0.001).The detection rate of anti-tuberculosis antibody of urban area was lower than that of central ur-ban area,and the differences were statistically significant(P＜0.001).The rate of diagnostic anti-infective therapy of central urban area was higher than that of urban area and the remote urban area,and the rate in ur-ban area was higher than that of remote urban area,and the differences were statistically significant(P＜0.001).Conclusion It is necessary to further standardize the diagnosis of negative etiological pulmonany tu-berculosis of designated tuberculosis hospitals.The proportion of diagnostic anti-infection treatment and auxil-iary examination at the municipal level needs to be increased,and the frequency and items of laboratory etio-logical examination at the district level need to be increased.

Objective To analyze and evaluate the implementation effect of tuberculosis prevention and control program in Wuhan, and to provide reference for scientific formulation of tuberculosis prevention and control measures. Methods Using the National Tuberculosis Information Management System, descriptive statistical analysis was carried out on the medical record information of pulmonary tuberculosis patients registered in Wuhan , 2016 - 2021. Results A total of 34 937 cases of pulmonary tuberculosis were registered in Wuhan , with an average annual incidence rate of 49.85/100 000. The incidence rate showed a downward trend year by year, with a statistically significant difference in 2016—2021 (χ²_trend = 708.387, P<0.001). The patients mainly came from referrals, accounting for 71.86%, and the proportion of referrals varied significantly among different years (χ²=355.541, P<0.001). The diagnosis type was mainly pathogenic negative, accounting for 49.12%. The proportion of pathogenic negative had statistically significant difference among different years (χ²=1 354.830, P<0.001). The proportion of patients cured and completed the course of treatment reached 93.98%, with statistically significant differences in the proportions among different years (cured, χ²=1 080.252, P<0.001; completed the treatment course, χ²= 933.655, P<0.001). The sputum examination rate of newly diagnosed patients in each year reached over 90%, and the overall completion rate reached over 95%. The proportion of positive pathogens showed an increasing trend year by year. Conclusion The overall epidemic situation of tuberculosis in Wuhan is declining year by year, and tuberculosis prevention and control work has achieved remarkable results. Active screening in key areas and populations should be strengthened, and prevention and control strategies should be formulated by emphasizing the key and difficult points.

Objective:To investigate the risk factors and construct a nomogram model for predicting the occurrence of cirrhotic portal vein thrombosis in patients combined with esophagogastric variceal bleeding (EVB).Methods:Clinical data on 416 cirrhotic PVT cases was collected from the First Hospital of Lanzhou University between January 2016 and January 2022. A total of 385 cases were included after excluding 31 cases for retrospective analysis. They were divided into an esophagogastric variceal bleeding group and a non-esophagogastric variceal bleeding group based on the clinical diagnosis. The esophagogastric variceal group was then further divided into an EVB group and a non-bleeding group. All patients underwent gastroscopy, serology, and imaging examinations. The risk factors of PVT combined with EVB were identified by univariate analysis using SPSS 26. The prediction model of cirrhotic PVT in patients combined with EVB was constructed by R 4.0.4. The prediction efficiency and clinical benefits of the model were evaluated by the C-index, area under the receiver operating characteristic curve, calibration plots, and decision curve. The measurement data were examined by a t-test or Mann-Whitney U test. The counting data were tested using the χ2 test or the Fisher exact probability method. Results:There were statistically significant differences in the etiology, Child-Pugh grade,erythrocyte count, hematocrit, globulin, and serum lipids between the esophageal and non-esophageal varices groups ( P < 0.05). There were statistically significant differences in etiology, erythrocyte count, hemoglobin, hematocrit, neutrophil percentage, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and neutrophil lymphocyte ratio (NLR) between the EVB and non-bleeding groups ( P < 0.05). Multivariate logistic regression analysis showed that etiology ( OR = 3.287, 95% CI: 1.497 ~ 7.214), hematocrit ( OR = 0.897, 95% CI: 0.853 ~ 0.943), and high-density lipoprotein cholesterol ( OR = 0.229, 95% CI: 0.071 ~ 0.737) were independent risk factors for cirrhotic PVT patients combined with EVB. The constructed normogram model predicted the probability of bleeding in patients. The nomogram model had shown good consistency and differentiation (AUC = 0.820, 95% CI: 0.707 ~ 0.843), as verified by 10-fold cross-validation (C-index = 0.799) and the Hosmer-Lemeshow goodness of fit test ( P = 0.915). The calibration plot and the decision curve suggested that the prediction model had good stability and clinical practicability. Conclusion:The risk factors for EVB occurrence include etiology, erythrocyte, hemoglobin, hematocrit, percentage of neutrophils, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and NLR in patients with cirrhotic liver. The constructed prediction model has good predictive value, and it can provide a reference for medical personnel to screen patients with high bleeding risk for targeted treatment.

Objective To construct an evaluation index system for the capability of comprehensive control of mountain-type zoonotic visceral leishmaniasis based on the One Health concept, so as to provide insights into the control and elimination of mountain-type zoonotic visceral leishmaniasis using the One Health approach. Methods A preliminary evaluation index system was constructed based on literature review, panel discussions and field surveys. Thirty-three experts were selected from 7 provincial disease control and prevention centers in Beijing Municipality, Hebei Province, Shanxi Province, Henan Province, Sichuan Province, Shaanxi Province and Gansu Province where mountain-type zoonotic visceral leishmaniasis was endemic, and two rounds of expert consultations were conducted to screen the indicators. The positive coefficient, degree of concentration, degree of coordination, and authority of the experts were calculated, and the normalized weights of each index were calculated with the analytic hierarchy process. Results The response rates of questionnaires during two rounds of expert consultation were both 100.00% (33/33), and the authority coefficients of the experts were 0.86 and 0.88, respectively. The coefficients of coordination among experts on the rationality, importance, and operability of the indicators were 0.392, 0.437, 0.258, and 0.364, 0.335, 0.263, respectively (all P values < 0.05). Following screening, the final evaluation index system included 3 primary indicators, 17 secondary indicators, and 50 tertiary indicators. The normalized weights of primary indicators “external environment”, “internal support” and “comprehensive control” were 16.98%, 38.73% and 44.29%, respectively. Among the secondary indicators of the primary indicator “external environment”, the highest weight was seen for natural environment (66.67%), and among the secondary indicators of the primary indicator “internal support”, the lowest weight was seen for the scientific research for visceral leishmaniasis control (8.26%), while other indicators had weights of 12.42% to 13.38%. Among the secondary indicators of the primary indicator “comprehensive control”, the weight was 16.67% for each indicator. Conclusions An evaluation index system has been constructed for the capability of comprehensive control of mountain-type zoonotic visceral leishmaniasis based on the One Health concept. In addition to assessment of the effect of conventional mountain-type zoonotic visceral leishmaniasis control measures, this index system integrates the importance of top-level design, organizational management, and implementation of control measures, and includes indicators related to multi-sectoral cooperation.