Tag: pubmed

J Health Popul Nutr. 2025 Mar 3;44(1):61. doi: 10.1186/s41043-025-00753-1.

ABSTRACT

BACKGROUND: Research on the distribution and association of depression with tobacco consumption among young population is commonly prioritised in India, while studies on tobacco use among middle-aged (45-59 years) and elderly (≥ 60 years) adults are noticeably lacking. Thus, we conducted this study with the objectives of estimating the prevalence, distribution and determining the association of depression and tobacco consumption among middle-aged and elderly Indian population; overall and stratified into age group, gender, and geographical location.

METHODS: Using dataset from Longitudinal Aging Study in India (LASI), a bivariate analysis was conducted among middle-aged (45-59 years) and elderly (≥ 60 years) Indians to estimate the prevalence of depression and tobacco consumption. States and Union Territories were categorised as low, medium, and high as per prevalence of depression and tobacco consumption, and spatial distribution maps were created. To reduce the confounding effects of demographic & socioeconomic and health-related & behavioural covariates; propensity score matching (PSM) was conducted. Nested multilevel regression modelling was employed to explore the association between depression (outcome variable) and tobacco consumption (explanatory variable) using STATA version 17. The p value < 0.05 was considered statistically significant.

RESULTS: Overall, 36.78% (36.03-37.55%) participants documented using any form of tobacco; with higher consumption of smokeless tobacco (SLT) (19.88%) than smoking (SM) (13.92%). The overall prevalence of depression was 7.62% irrespective of tobacco consumption, and 8.51% among participants consuming any form of tobacco. Mizoram had the highest consumption of tobacco in any form (78.21%), whereas Madhya Pradesh recorded the highest (14.62%) depression prevalence. Bihar, Uttar Pradesh, West Bengal, and Uttarakhand had both high prevalence of depression and any form of tobacco consumption. The average estimated treatment effect (ATE) indicated a positive association both between depression and any form of tobacco consumption (p value = 0.001) and with smokeless tobacco (p value = 0.001) consumption. Participants ever consuming any form of tobacco had 28% higher odds (aOR-1.28 (1.18-1.38). The odds of having depression were higher among females (aOR = 1.28 (1.17-1.41); richest (aOR-1.48 (1.32-1.65); living alone (aOR = 1.14 (1.01-1.33). Participants with comorbidity (aOR = 1.20 (1.10-1.30) and multimorbidity (aOR = 1.24 (1.13-1.36)) had higher odds of depression.

CONCLUSION: The study has established significant positive association between depression and tobacco consumption stratified into gender and age group. Prioritisation of mental health disorders like depression and tobacco prevention and cessation programmes must be implemented with focusing on females and the middle-aged population with community awareness and intersectoral collaborative effort irrespective of subnational-variations.

PMID:40033402 | DOI:10.1186/s41043-025-00753-1

Infect Dis Poverty. 2025 Mar 3;14(1):17. doi: 10.1186/s40249-025-01284-x.

ABSTRACT

BACKGROUND: Leptospirosis, a zoonotic disease caused by pathogenic species of the genus Leptospira, is an important public health concern globally. Leptospirosis has been notifiable under statute in China since 1955, and its epidemiological characteristics have evolved during near 70 years. This study aimed to describe the spatial and temporal patterns and demographic characteristics of leptospirosis from 1955 to 2022 in China, and explore the possible factors that influence leptospirosis transmission risk.

METHODS: Wavelet time series analysis, global Moran’s I coefficients, space-time scanning statistics, and so on were used to analyze temporal, seasonal, geographic, and demographic trends in leptospirosis using reported national surveillance data from Chinese mainland from 1955 to 2022. Additionally, a Bayesian spatiotemporal model was used in a preliminary analysis to explore potential factors associated with leptospirosis occurrence.

RESULTS: Between 1955 and 2022, China reported 25,236,601 leptospirosis cases, with 91% occurring from July to October. The annual incidence rate peaked at 38.28/100,000 during outbreaks in the 1960s-1980s but stabilized at a low level (0.07/100,000) between 2005 and 2022, with over 99% of cases in southern China. Clustering increased over time, being greatest during the period 2015-2022 (Moran’s I = 0.41, P < 0.01). Space-time cluster analysis indicated that the most likely clusters were in northern provincial-level administrative divisions (PLADs) from 1955 to 1984, in southern PLADs from 1985 to 2022. The main identified risk factors of leptospirosis occurrence were annual average precipitation (3.68, 95% CI: 2.50 to 5.12), GDP per capita (-3.70, 95% CI: – 5.97 to – 1.41), and the total power of agricultural machinery (- 2.51, 95% CI: – 3.85 to – 1.17).

CONCLUSIONS: Over past 70 years, leptospirosis in China has occurred as significant outbreaks but has ultimately declined to stable, low levels of occurrence. However, a clear north-south disparity persists, with tropical and subtropical regions in southern China remaining high-risk areas. The nearly 70-year dataset underscores the complex interplay of climate and socioeconomic factors influencing the disease’s occurrence. Targeted prevention and control measures are critical to prevent outbreaks, especially in regions prone to extreme climatic events like heavy rainfall and floods, which may signal the resurgence of leptospirosis.

PMID:40033390 | DOI:10.1186/s40249-025-01284-x

Infect Dis Poverty. 2025 Mar 3;14(1):17. doi: 10.1186/s40249-025-01284-x.

ABSTRACT

BACKGROUND: Leptospirosis, a zoonotic disease caused by pathogenic species of the genus Leptospira, is an important public health concern globally. Leptospirosis has been notifiable under statute in China since 1955, and its epidemiological characteristics have evolved during near 70 years. This study aimed to describe the spatial and temporal patterns and demographic characteristics of leptospirosis from 1955 to 2022 in China, and explore the possible factors that influence leptospirosis transmission risk.

METHODS: Wavelet time series analysis, global Moran’s I coefficients, space-time scanning statistics, and so on were used to analyze temporal, seasonal, geographic, and demographic trends in leptospirosis using reported national surveillance data from Chinese mainland from 1955 to 2022. Additionally, a Bayesian spatiotemporal model was used in a preliminary analysis to explore potential factors associated with leptospirosis occurrence.

RESULTS: Between 1955 and 2022, China reported 25,236,601 leptospirosis cases, with 91% occurring from July to October. The annual incidence rate peaked at 38.28/100,000 during outbreaks in the 1960s-1980s but stabilized at a low level (0.07/100,000) between 2005 and 2022, with over 99% of cases in southern China. Clustering increased over time, being greatest during the period 2015-2022 (Moran’s I = 0.41, P < 0.01). Space-time cluster analysis indicated that the most likely clusters were in northern provincial-level administrative divisions (PLADs) from 1955 to 1984, in southern PLADs from 1985 to 2022. The main identified risk factors of leptospirosis occurrence were annual average precipitation (3.68, 95% CI: 2.50 to 5.12), GDP per capita (-3.70, 95% CI: – 5.97 to – 1.41), and the total power of agricultural machinery (- 2.51, 95% CI: – 3.85 to – 1.17).

CONCLUSIONS: Over past 70 years, leptospirosis in China has occurred as significant outbreaks but has ultimately declined to stable, low levels of occurrence. However, a clear north-south disparity persists, with tropical and subtropical regions in southern China remaining high-risk areas. The nearly 70-year dataset underscores the complex interplay of climate and socioeconomic factors influencing the disease’s occurrence. Targeted prevention and control measures are critical to prevent outbreaks, especially in regions prone to extreme climatic events like heavy rainfall and floods, which may signal the resurgence of leptospirosis.

PMID:40033390 | DOI:10.1186/s40249-025-01284-x

Implement Sci Commun. 2025 Mar 3;6(1):24. doi: 10.1186/s43058-025-00707-y.

ABSTRACT

BACKGROUND: A framework to evaluate implementation of Major System Change (MSC) in healthcare has been developed and applied to implementation of longer-term system changes. This was the first study to apply the five domains of the MSC framework to rapid healthcare system change. We aimed to: i) evaluate implementation of rapid MSC, using England COVID-19 remote home monitoring services as a case study and ii) consider whether and how the MSC framework can be applied to rapid MSC.

METHODS: A mixed-methods rapid evaluation in England, across 28 primary and secondary healthcare sites (October 2020-November 2021; data collection: 4 months). We conducted 126 interviews (5 national leads, 59 staff, 62 patients/carers) and surveyed staff (n = 292) and patients/carers (n = 1069). Service providers completed cost surveys. Aggregated and patient-level national datasets were used to explore enrolment, service use and clinical outcomes. The MSC framework was applied retrospectively. Qualitative data were analysed thematically to explore key themes within each MSC framework domain. Descriptive statistics and multivariate analyses were used to analyse experience, costs, service use and clinical outcomes.

RESULTS: Decision to change/Decision on model: Service development happened concurrently: i) early local development motivated by urgent clinical need, ii) national rollout using standard operating procedures, and iii) local implementation and adaptation. Implementation approach: Services were tailored to local needs to consider patient, staff, organisational and resource factors. Implementation outcomes: Patient enrolment was low (59% services <10%). Service models and implementation approaches varied substantially. Intervention outcomes: No associations found between services and clinical outcomes. Patient and staff experiences were generally positive. However, barriers to delivery and engagement were found; with some groups finding it harder to engage.

CONCLUSIONS: Low enrolment rates and substantial variation due to tailoring services to local contexts meant it was not possible to conclusively determine service effectiveness. Process outcomes indicated areas of improvement. The MSC framework can be used to analyse rapid MSC. Implementation and factors influencing implementation may differ to non-rapid contexts (e.g. less uniformity, more tailoring). Our mixed-methods approach could inform future evaluations of large-scale rapid and non-rapid MSC in a range of conditions and services internationally.

PMID:40033389 | DOI:10.1186/s43058-025-00707-y

Implement Sci Commun. 2025 Mar 3;6(1):24. doi: 10.1186/s43058-025-00707-y.

ABSTRACT

BACKGROUND: A framework to evaluate implementation of Major System Change (MSC) in healthcare has been developed and applied to implementation of longer-term system changes. This was the first study to apply the five domains of the MSC framework to rapid healthcare system change. We aimed to: i) evaluate implementation of rapid MSC, using England COVID-19 remote home monitoring services as a case study and ii) consider whether and how the MSC framework can be applied to rapid MSC.

METHODS: A mixed-methods rapid evaluation in England, across 28 primary and secondary healthcare sites (October 2020-November 2021; data collection: 4 months). We conducted 126 interviews (5 national leads, 59 staff, 62 patients/carers) and surveyed staff (n = 292) and patients/carers (n = 1069). Service providers completed cost surveys. Aggregated and patient-level national datasets were used to explore enrolment, service use and clinical outcomes. The MSC framework was applied retrospectively. Qualitative data were analysed thematically to explore key themes within each MSC framework domain. Descriptive statistics and multivariate analyses were used to analyse experience, costs, service use and clinical outcomes.

RESULTS: Decision to change/Decision on model: Service development happened concurrently: i) early local development motivated by urgent clinical need, ii) national rollout using standard operating procedures, and iii) local implementation and adaptation. Implementation approach: Services were tailored to local needs to consider patient, staff, organisational and resource factors. Implementation outcomes: Patient enrolment was low (59% services <10%). Service models and implementation approaches varied substantially. Intervention outcomes: No associations found between services and clinical outcomes. Patient and staff experiences were generally positive. However, barriers to delivery and engagement were found; with some groups finding it harder to engage.

CONCLUSIONS: Low enrolment rates and substantial variation due to tailoring services to local contexts meant it was not possible to conclusively determine service effectiveness. Process outcomes indicated areas of improvement. The MSC framework can be used to analyse rapid MSC. Implementation and factors influencing implementation may differ to non-rapid contexts (e.g. less uniformity, more tailoring). Our mixed-methods approach could inform future evaluations of large-scale rapid and non-rapid MSC in a range of conditions and services internationally.

PMID:40033389 | DOI:10.1186/s43058-025-00707-y

Microbiome. 2025 Mar 3;13(1):64. doi: 10.1186/s40168-025-02041-w.

ABSTRACT

BACKGROUND: The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting.

RESULTS: We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE’s ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks.

CONCLUSIONS: LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract.

PMID:40033386 | DOI:10.1186/s40168-025-02041-w

Microbiome. 2025 Mar 3;13(1):64. doi: 10.1186/s40168-025-02041-w.

ABSTRACT

BACKGROUND: The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting.

RESULTS: We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE’s ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks.

CONCLUSIONS: LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract.

PMID:40033386 | DOI:10.1186/s40168-025-02041-w

Microbiome. 2025 Mar 3;13(1):64. doi: 10.1186/s40168-025-02041-w.

ABSTRACT

BACKGROUND: The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting.

RESULTS: We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE’s ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks.

CONCLUSIONS: LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract.

PMID:40033386 | DOI:10.1186/s40168-025-02041-w

BMC Med Inform Decis Mak. 2025 Mar 3;25(1):106. doi: 10.1186/s12911-025-02927-7.

ABSTRACT

BACKGROUND: Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.

METHODS: eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.

RESULTS: On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.

CONCLUSION: Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.

TRIAL REGISTRATION: The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916.

PMID:40033378 | DOI:10.1186/s12911-025-02927-7

BMC Med Inform Decis Mak. 2025 Mar 3;25(1):106. doi: 10.1186/s12911-025-02927-7.

ABSTRACT

BACKGROUND: Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context.

METHODS: eXtreme Gradient Boosting (XGBoost) and multivariable linear regression were used for predictions. Both models were applied to both the whole dataset which included multiple hospitals (3,704 patients), and a single-hospital dataset (1,815 patients) of the hospital with the highest case-volumes of our sample. Data was split into training (75%) and test data (25%) for both datasets. Models were trained using 5-fold cross-validation (CV) on the training datasets and applied to test data for performance comparison.

RESULTS: On test data in the multi-hospital setting, the mean absolute error (MAE) was 12.13 min (HA) / 13.61 min (KA) for XGBoost. In the single-hospital analysis, performance on test data was MAE 10.87 min (HA) / MAE 12.53 min (KA) for XGBoost. Predictive ability of XGBoost was tended to be better than of regression in all setting, however not statistically significantly. Important predictors for XGBoost were physician experience, age, body mass index, patient reported outcome measures and, for the multi-hospital analysis, the hospital.

CONCLUSION: Machine learning can predict DOS in both a multi-hospital and single-hospital setting with reasonable performance. Performance between regression and machine learning differed slightly, however insignificantly, while larger datasets may improve predictive performance. The study found that hospital indicators matter in the multi-hospital setting despite controlling for various variables, highlighting potential quality differences between hospitals.

TRIAL REGISTRATION: The study was registered at the German Clinical Trials Register (DRKS) under DRKS00019916.

PMID:40033378 | DOI:10.1186/s12911-025-02927-7