Pub Date : 2024-09-10DOI: 10.1101/2024.09.10.24313390
Jessica Y. Wong, Wey Wen Lim, Justin K. Cheung, Caitriona Murphy, Eunice Y.C. Shiu, Jingyi Xiao, Dongxuan Chen, Yanmin Xie, Mingwei Li, Hualei Xin, Michelle Szeto, Sammi Choi, Benjamin J. Cowling
Background: Influenza pandemic plans often recommend non-pharmaceutical interventions (NPIs) in household settings, including hand hygiene and face masks. We reviewed the evidence supporting the recommendations of these measures to prevent the spread of influenza in households. Methods: We performed systematic reviews between 26 May and 30 August 2022 in Medline, PubMed, EMBASE, and CENTRAL to identify evidence for the effectiveness of selected measures recommended by representative national influenza pandemic plans. We prioritized evidence from randomized controlled trials. Fixed-effects models were used to estimate the overall effects. Systematic reviews were registered in the OSF registry (https://osf.io/8kyth). Results: We selected 9 NPIs for evidence review. We identified 9 randomized-controlled trials related to hand hygiene and face masks in household settings. 2 studies reported that measures could delay the introduction of influenza virus infections into households. However, we did not identify evidence from randomized controlled trials that indicated a substantial effect of hand hygiene and face masks in preventing the spread of pandemic influenza within households. Conclusions: Limited evidence indicated that within-household measures may likely be effective only when implemented before or as soon as possible after symptom onset in an infected case. Improving the evidence base for NPIs in households and elsewhere is a continuing priority.
{"title":"Non-pharmaceutical interventions to reduce influenza transmission in households: a systematic review and meta-analysis","authors":"Jessica Y. Wong, Wey Wen Lim, Justin K. Cheung, Caitriona Murphy, Eunice Y.C. Shiu, Jingyi Xiao, Dongxuan Chen, Yanmin Xie, Mingwei Li, Hualei Xin, Michelle Szeto, Sammi Choi, Benjamin J. Cowling","doi":"10.1101/2024.09.10.24313390","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313390","url":null,"abstract":"Background: Influenza pandemic plans often recommend non-pharmaceutical interventions (NPIs) in household settings, including hand hygiene and face masks. We reviewed the evidence supporting the recommendations of these measures to prevent the spread of influenza in households.\u0000Methods: We performed systematic reviews between 26 May and 30 August 2022 in Medline, PubMed, EMBASE, and CENTRAL to identify evidence for the effectiveness of selected measures recommended by representative national influenza pandemic plans. We prioritized evidence from randomized controlled trials. Fixed-effects models were used to estimate the overall effects. Systematic reviews were registered in the OSF registry (https://osf.io/8kyth).\u0000Results: We selected 9 NPIs for evidence review. We identified 9 randomized-controlled trials related to hand hygiene and face masks in household settings. 2 studies reported that measures could delay the introduction of influenza virus infections into households. However, we did not identify evidence from randomized controlled trials that indicated a substantial effect of hand hygiene and face masks in preventing the spread of pandemic influenza within households.\u0000Conclusions: Limited evidence indicated that within-household measures may likely be effective only when implemented before or as soon as possible after symptom onset in an infected case. Improving the evidence base for NPIs in households and elsewhere is a continuing priority.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Household overcrowding has increased in England. However, there is limited synthesis of evidence about what can be done to reduce the impact of overcrowding on health/well-being. We undertook a rapid realist review of English language peer-reviewed and grey literature of interventions from comparable settings to urban contexts in England that addressed household overcrowding/health outcomes. A search was conducted (01.06.23) in MEDLINE, EMBASE, Web of Science, SCOPUS. Two expert panels informed the review. The first comprised individuals with lived experience of overcrowding in London; the second local and regional government representatives from London, Salford and Doncaster (England). Both panels contributed at two stages to guide the scope/literature identification and test/refine programme theories. Final full-text screening and quality appraisal were completed by two independent researchers. Thirty-one peer-reviewed papers and 27 documents from participating local authorities were included. The peer-reviewed literature, emanating from multiple geographical contexts and of variable study designs and quality, contained 15 evaluated interventions across three categories: Rehousing (n=7 interventions); Home improvements, e.g. renovations/retrofitting (n=6); Co-ordination with healthcare and wider services (combined with home improvements) (n=2). A synthesis of the peer-reviewed literature with expert panel comments and grey literature, identified contexts and mechanisms that could facilitate or hinder achievement of positive health outcomes. There was reluctance to be rehoused elsewhere, with residents fearing the loss of social networks in available properties often located far away from their current homes. Home improvements may alleviate the worst impacts of overcrowding, and residents living in unhealthy conditions can benefit from better healthcare co-ordination.
{"title":"Interventions that could mitigate the adverse effects of household overcrowding: A rapid realist review with stakeholder participation from urban contexts in England","authors":"Kristoffer Halvorsrud, Elizabeth Eveleigh, Mathilda O'Donoghue, Pratima Singh, Rose-Marie McDonald, Marcella Ucci, Jessica Sheringham","doi":"10.1101/2024.09.10.24313301","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313301","url":null,"abstract":"Household overcrowding has increased in England. However, there is limited synthesis of evidence about what can be done to reduce the impact of overcrowding on health/well-being. We undertook a rapid realist review of English language peer-reviewed and grey literature of interventions from comparable settings to urban contexts in England that addressed household overcrowding/health outcomes. A search was conducted (01.06.23) in MEDLINE, EMBASE, Web of Science, SCOPUS. Two expert panels informed the review. The first comprised individuals with lived experience of overcrowding in London; the second local and regional government representatives from London, Salford and Doncaster (England). Both panels contributed at two stages to guide the scope/literature identification and test/refine programme theories. Final full-text screening and quality appraisal were completed by two independent researchers. Thirty-one peer-reviewed papers and 27 documents from participating local authorities were included. The peer-reviewed literature, emanating from multiple geographical contexts and of variable study designs and quality, contained 15 evaluated interventions across three categories: Rehousing (n=7 interventions); Home improvements, e.g. renovations/retrofitting (n=6); Co-ordination with healthcare and wider services (combined with home improvements) (n=2). A synthesis of the peer-reviewed literature with expert panel comments and grey literature, identified contexts and mechanisms that could facilitate or hinder achievement of positive health outcomes. There was reluctance to be rehoused elsewhere, with residents fearing the loss of social networks in available properties often located far away from their current homes. Home improvements may alleviate the worst impacts of overcrowding, and residents living in unhealthy conditions can benefit from better healthcare co-ordination.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1101/2024.09.10.24313389
Naomi R Waterlow, Tom Ashfield, Gwenan M Knight
Objectives The drivers of antimicrobial resistance (AMR) likely vary substantially by different demographics. However, few complete open national detailed data exist on how antibiotic use (ABU) varies by both age and sex. Methods Here, prescriptions of antibiotics from General Practices in England for 2015-2023 disaggregated by 5-year age bands and sex were analysed at the national and Integrated Care Board (ICB) level. From a total of 249,578,795 prescriptions (across 9 years), 63% were given to women and the most prescribed were amoxicillin, nitrofurantoin and flucloxacillin sodium. Prescriptions per 100K population varied substantially across sex, age, geographical region, season, year, COVID-19 pandemic period and drug. Results Most antibiotics were prescribed more to women across most age bands (84% of antibiotics had more prescriptions to females across 50% of age bands). We show how this variation requires a more nuanced approach to comparing ABU across geographies and highlight that AWaRe targets are not met uniformly (young men have a higher proportion of Watch antibiotic prescriptions). We also show the impact on ABU of time-sensitive interruptions, including differential age-targeted influenza vaccination, COVID-19 restrictions and a shortage of amoxicillin combined with a Streptococcus A outbreak. Comparing to open access AMR data (MRSA in bloodstream infections) highlights the complexity of the link between ABU and AMR. Conclusions These detailed differences in ABU across England suggest that there should be large variation in AMR burden by age and sex, which now need to be quantified with detailed open access AMR data for a better intervention design.
目标抗菌药耐药性（AMR）的驱动因素可能因人口结构的不同而有很大差异。然而，关于抗生素使用（ABU）如何因年龄和性别而异的完整、公开的全国性详细数据却寥寥无几。在此，我们在国家和综合护理委员会（ICB）层面分析了 2015-2023 年英格兰全科医生按 5 岁年龄段和性别开具的抗生素处方。在总共 249,578,795 份处方（跨越 9 年）中，63% 的处方给了女性，处方最多的是阿莫西林、硝基呋喃妥因和氟氯西林钠。每 10 万人口的处方量在性别、年龄、地理区域、季节、年份、COVID-19 大流行时期和药物方面存在很大差异。结果 大多数抗生素在大多数年龄段的处方中女性占多数（84%的抗生素在 50%的年龄段中女性处方占多数）。我们展示了这种差异如何要求采用更细致的方法来比较不同地区的 ABU，并强调了 AWaRe 目标并未统一实现（年轻男性的 Watch 抗生素处方比例更高）。我们还展示了时间敏感性中断对 ABU 的影响，包括针对不同年龄段的流感疫苗接种、COVID-19 限制以及阿莫西林短缺和甲型链球菌爆发。与开放存取的 AMR 数据（血流感染中的 MRSA）相比，ABU 与 AMR 之间的联系更加复杂。结论英格兰各地 ABU 的这些详细差异表明，不同年龄和性别的 AMR 负担应该存在很大差异，现在需要利用详细的开放式 AMR 数据对这些差异进行量化，以便更好地设计干预措施。
{"title":"Antibiotic prescribing patterns by age and sex in England: why we need to take this variation into account to evaluate antibiotic stewardship and AMR selection","authors":"Naomi R Waterlow, Tom Ashfield, Gwenan M Knight","doi":"10.1101/2024.09.10.24313389","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313389","url":null,"abstract":"Objectives\u0000The drivers of antimicrobial resistance (AMR) likely vary substantially by different demographics. However, few complete open national detailed data exist on how antibiotic use (ABU) varies by both age and sex. Methods\u0000Here, prescriptions of antibiotics from General Practices in England for 2015-2023 disaggregated by 5-year age bands and sex were analysed at the national and Integrated Care Board (ICB) level. From a total of 249,578,795 prescriptions (across 9 years), 63% were given to women and the most prescribed were amoxicillin, nitrofurantoin and flucloxacillin sodium. Prescriptions per 100K population varied substantially across sex, age, geographical region, season, year, COVID-19 pandemic period and drug. Results\u0000Most antibiotics were prescribed more to women across most age bands (84% of antibiotics had more prescriptions to females across 50% of age bands). We show how this variation requires a more nuanced approach to comparing ABU across geographies and highlight that AWaRe targets are not met uniformly (young men have a higher proportion of Watch antibiotic prescriptions). We also show the impact on ABU of time-sensitive interruptions, including differential age-targeted influenza vaccination, COVID-19 restrictions and a shortage of amoxicillin combined with a Streptococcus A outbreak. Comparing to open access AMR data (MRSA in bloodstream infections) highlights the complexity of the link between ABU and AMR. Conclusions\u0000These detailed differences in ABU across England suggest that there should be large variation in AMR burden by age and sex, which now need to be quantified with detailed open access AMR data for a better intervention design.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1101/2024.09.07.24313248
Jinyao Li, Mingcong Tang, Ziqi Deng, Yanchen Feng, Xue Dang, Lu Sun, Yunke Zhang, Jianping Yao, Min Zhao, Feixiang Liu
Background: Hypertension (HTN), a globally prevalent chronic condition, poses a significant public health challenge. Concurrently, abnormalities in bone metabolism, such as reduced bone mineral density (BMD) and osteoporosis (OP), profoundly affect the quality of life of affected individuals. This study aims to comprehensively investigate the relationship between HTN and bone metabolism abnormalities using data from the National Health and Nutrition Examination Survey (NHANES) and advanced machine learning techniques. Methods: Data were sourced from the NHANES database, covering the years 2009 to 2018. Specifically, femur and spine BMD measurements were obtained via dual-energy X-ray absorptiometry (DXA) for the 2009-2010 period, given the lack of full-body data. A predictive model was developed to estimate total body BMD from femur and spine measurements. The initial dataset comprised 49,693 individuals, and after rigorous data cleaning and exclusion of incomplete records, 7,566 participants were included in the final analysis. Data were processed and analyzed using SPSS, which facilitated descriptive statistical analysis, multivariate logistic regression, and multiple linear regression, alongside subgroup analyses to explore associations across different demographic groups. Machine learning algorithms, including neural networks, decision trees, random forests, and XGBoost, were utilized for cross-validation and hyperparameter optimization. The contribution of each feature to the model output was assessed using SHAP (Shapley Additive Explanations) values, enhancing the model's accuracy and robustness. Results: Baseline characteristic analysis revealed that compared to the non-HTN group, the HTN group was significantly older (44.37 vs. 34.94 years, p < 0.001), had a higher proportion of males (76.8% vs. 60.7%, p < 0.001), higher BMI (31.21 vs. 27.77, p < 0.001), a higher smoking rate (54.4% vs. 41.2%, p < 0.001), and notably lower BMD (1.1507 vs. 1.1271, p < 0.001). When comparing the low bone mass group with the normal bone mass group, the former was older (36.02 vs. 34.5 years, p < 0.001), had a lower proportion of males (41.8% vs. 63.3%, p < 0.001), lower BMI (25.28 vs. 28.25, p < 0.001), and a higher incidence of HTN (10.9% vs. 8.6%, p = 0.006). Overall logistic and multiple linear regression analyses demonstrated a significant negative correlation between HTN and bone metabolism abnormalities (adjusted model Beta = -0.007, 95% CI: -0.013 to -0.002, p = 0.006). Subgroup analysis revealed a more pronounced association in males (Beta = -0.01, p = 0.004) and in the 40-59 age group (Beta = -0.01, p = 0.012). The machine learning models corroborated these findings, with SHAP value analysis consistently indicating a negative impact of HTN on BMD across various feature controls, thus demonstrating high explanatory power and robustness across different models. Conclusion: This study comprehensively confirms the significant ass
背景：高血压（HTN）是一种全球流行的慢性疾病，对公共卫生构成了重大挑战。与此同时，骨代谢异常，如骨矿物质密度（BMD）降低和骨质疏松症（OP），也会严重影响患者的生活质量。本研究旨在利用美国国家健康与营养调查（NHANES）的数据和先进的机器学习技术，全面研究高血压与骨代谢异常之间的关系。研究方法数据来源于 NHANES 数据库，时间跨度为 2009 年至 2018 年。具体来说，由于缺乏全身数据，2009-2010 年期间的股骨和脊柱 BMD 测量是通过双能 X 射线吸收测定法（DXA）获得的。根据股骨和脊柱测量结果开发了一个预测模型来估算全身 BMD。初始数据集包括 49,693 人，经过严格的数据清理和排除不完整记录后，最终分析包括 7,566 名参与者。数据使用 SPSS 系统进行处理和分析，该系统可进行描述性统计分析、多变量逻辑回归和多元线性回归，并可进行亚组分析，以探讨不同人口统计群体之间的关联。交叉验证和超参数优化采用了机器学习算法，包括神经网络、决策树、随机森林和 XGBoost。使用 SHAP（夏普利相加解释）值评估了每个特征对模型输出的贡献，从而提高了模型的准确性和稳健性。结果基线特征分析表明，与非 HTN 组相比，HTN 组的年龄明显偏大（44.37 岁 vs. 34.94 岁，p < 0.001），男性比例更高（76.8% vs. 60.7%，p <0.001）、体重指数更高（31.21 vs. 27.77，p <0.001）、吸烟率更高（54.4% vs. 41.2%，p <0.001）、骨密度明显更低（1.1507 vs. 1.1271，p <0.001）。低骨量组与正常骨量组相比，前者年龄更大（36.02 岁 vs. 34.5 岁，p < 0.001），男性比例更低（41.8% vs. 63.3%，p < 0.001），BMI 更低（25.28 vs. 28.25，p < 0.001），高血压发病率更高（10.9% vs. 8.6%，p = 0.006）。整体逻辑和多元线性回归分析表明，高血压和骨代谢异常之间存在显著的负相关（调整模型 Beta = -0.007，95% CI：-0.013 至 -0.002，p = 0.006）。亚组分析显示，男性（Beta = -0.01，p = 0.004）和 40-59 岁年龄组（Beta = -0.01，p = 0.012）的相关性更为明显。机器学习模型证实了这些发现，SHAP 值分析一致表明，在不同的特征对照中，高血压对 BMD 有负面影响，因此在不同的模型中显示出较高的解释力和稳健性。结论本研究利用 NHANES 数据和机器学习算法，全面证实了高血压和骨代谢异常之间的显著关联。
{"title":"A Comprehensive Study on the Impact of Hypertension on Bone Metabolism Abnormalities Based on NHANES Data and Machine Learning Algorithms","authors":"Jinyao Li, Mingcong Tang, Ziqi Deng, Yanchen Feng, Xue Dang, Lu Sun, Yunke Zhang, Jianping Yao, Min Zhao, Feixiang Liu","doi":"10.1101/2024.09.07.24313248","DOIUrl":"https://doi.org/10.1101/2024.09.07.24313248","url":null,"abstract":"Background: Hypertension (HTN), a globally prevalent chronic condition, poses a significant public health challenge. Concurrently, abnormalities in bone metabolism, such as reduced bone mineral density (BMD) and osteoporosis (OP), profoundly affect the quality of life of affected individuals. This study aims to comprehensively investigate the relationship between HTN and bone metabolism abnormalities using data from the National Health and Nutrition Examination Survey (NHANES) and advanced machine learning techniques. Methods: Data were sourced from the NHANES database, covering the years 2009 to 2018. Specifically, femur and spine BMD measurements were obtained via dual-energy X-ray absorptiometry (DXA) for the 2009-2010 period, given the lack of full-body data. A predictive model was developed to estimate total body BMD from femur and spine measurements. The initial dataset comprised 49,693 individuals, and after rigorous data cleaning and exclusion of incomplete records, 7,566 participants were included in the final analysis. Data were processed and analyzed using SPSS, which facilitated descriptive statistical analysis, multivariate logistic regression, and multiple linear regression, alongside subgroup analyses to explore associations across different demographic groups. Machine learning algorithms, including neural networks, decision trees, random forests, and XGBoost, were utilized for cross-validation and hyperparameter optimization. The contribution of each feature to the model output was assessed using SHAP (Shapley Additive Explanations) values, enhancing the model's accuracy and robustness. Results: Baseline characteristic analysis revealed that compared to the non-HTN group, the HTN group was significantly older (44.37 vs. 34.94 years, p < 0.001), had a higher proportion of males (76.8% vs. 60.7%, p < 0.001), higher BMI (31.21 vs. 27.77, p < 0.001), a higher smoking rate (54.4% vs. 41.2%, p < 0.001), and notably lower BMD (1.1507 vs. 1.1271, p < 0.001). When comparing the low bone mass group with the normal bone mass group, the former was older (36.02 vs. 34.5 years, p < 0.001), had a lower proportion of males (41.8% vs. 63.3%, p < 0.001), lower BMI (25.28 vs. 28.25, p < 0.001), and a higher incidence of HTN (10.9% vs. 8.6%, p = 0.006). Overall logistic and multiple linear regression analyses demonstrated a significant negative correlation between HTN and bone metabolism abnormalities (adjusted model Beta = -0.007, 95% CI: -0.013 to -0.002, p = 0.006). Subgroup analysis revealed a more pronounced association in males (Beta = -0.01, p = 0.004) and in the 40-59 age group (Beta = -0.01, p = 0.012). The machine learning models corroborated these findings, with SHAP value analysis consistently indicating a negative impact of HTN on BMD across various feature controls, thus demonstrating high explanatory power and robustness across different models. Conclusion: This study comprehensively confirms the significant ass","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1101/2024.09.05.24313130
Melino Ndayizigiye, Afom Andom, Palesa Thabane, Mphatso Tsoka, Francis Sambani, Tumelo Monyane, Juliana Lawrence, Ninza Sheyo, Mpho Pholoanyane, Jessica Parker, William Haggerty, Emily Gingras, Tiara Calhoun, Joia Mukherjee, Paul Sonenthal
Background Despite its essential and life-saving role in the treatment of many medical conditions, access to medical oxygen remains limited in many countries. In 2021, Partners In Health established an oxygen distribution network in Lesotho to increase medical oxygen access. Methods We conducted a longitudinal analysis of the implementation of an oxygen distribution network in Lesotho from November 2022 to January 2024. Oxygen delivery data were abstracted from tracking logs and analysed in Stata. Continuous and ordinal variables were summarized by medians and ranges. Categorical variables were described using frequencies and proportions. Results Over the 15-month study period, the network expanded from 1 oxygen production hub serving 5 recipients to 4 hubs and 21 recipients located across nine of Lesotho's ten districts. The network delivered 1,565 filled cylinders containing 9,619.23 m3 oxygen. For the 13 recipients with inpatient beds, the median monthly volume of oxygen delivered per bed was 1.43 m3 (IQR: 0.57 to 2.31). Conclusion This study demonstrates the feasibility and impact of an oxygen distribution network in Lesotho, providing proof-of-concept for an intervention to improve oxygen access in LMICs. By employing real-time monitoring and redundant sourcing, the network provided a reliable oxygen supply responsive to variations in demand and periods of oxygen plant downtime. This study also provides insights into facility-level oxygen consumption, which may help policymakers improve quantification and prediction of oxygen demand. Future efforts should focus on enhancing data collection, characterizing oxygen usage, and strengthening infrastructure to promote sustainable oxygen security.
{"title":"Implementation of a national oxygen distribution network in Lesotho: a longitudinal analysis","authors":"Melino Ndayizigiye, Afom Andom, Palesa Thabane, Mphatso Tsoka, Francis Sambani, Tumelo Monyane, Juliana Lawrence, Ninza Sheyo, Mpho Pholoanyane, Jessica Parker, William Haggerty, Emily Gingras, Tiara Calhoun, Joia Mukherjee, Paul Sonenthal","doi":"10.1101/2024.09.05.24313130","DOIUrl":"https://doi.org/10.1101/2024.09.05.24313130","url":null,"abstract":"<strong>Background</strong>\u0000Despite its essential and life-saving role in the treatment of many medical conditions, access to medical oxygen remains limited in many countries. In 2021, Partners In Health established an oxygen distribution network in Lesotho to increase medical oxygen access. <strong>Methods</strong>\u0000We conducted a longitudinal analysis of the implementation of an oxygen distribution network in Lesotho from November 2022 to January 2024. Oxygen delivery data were abstracted from tracking logs and analysed in Stata. Continuous and ordinal variables were summarized by medians and ranges. Categorical variables were described using frequencies and proportions. <strong>Results</strong>\u0000Over the 15-month study period, the network expanded from 1 oxygen production hub serving 5 recipients to 4 hubs and 21 recipients located across nine of Lesotho's ten districts. The network delivered 1,565 filled cylinders containing 9,619.23 m<sup>3</sup> oxygen. For the 13 recipients with inpatient beds, the median monthly volume of oxygen delivered per bed was 1.43 m<sup>3</sup> (IQR: 0.57 to 2.31). <strong>Conclusion</strong>\u0000This study demonstrates the feasibility and impact of an oxygen distribution network in Lesotho, providing proof-of-concept for an intervention to improve oxygen access in LMICs. By employing real-time monitoring and redundant sourcing, the network provided a reliable oxygen supply responsive to variations in demand and periods of oxygen plant downtime. This study also provides insights into facility-level oxygen consumption, which may help policymakers improve quantification and prediction of oxygen demand. Future efforts should focus on enhancing data collection, characterizing oxygen usage, and strengthening infrastructure to promote sustainable oxygen security.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1101/2024.09.08.24313212
Xintao Li, Sibei Liu
Background Readmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. The Centers for Medicare & Medicaid Services (CMS) implemented the Hospital Readmissions Reduction Program (HRRP) to tackle the problem of readmission by penalizing to hospitals with excessive 30-day readmissions. Our study is to analyze hospital readmissions among Medicare patients and provide some suggestions of readmission prevention furthermore. Using LSTM networks with feature engineering, our proposed deep learning model would help us understand the contribution of the features. Design The MIMIC-III clinical database contains information from 53,423 patient admissions at Beth Israel Deaconess Medical Center. We restricted our analysis to 21,002 patient admissions with Medicare coverage, and selected variables from admission-level data, inpatient medical history and patient demography. The baseline model is a logistic-regression model based on the LACE index, and the LSTM model was another model that designed to capture temporal dynamic in the data from admission-level and patient-level data. We used Area Under the Curve (AUC) metric to evaluate the model's performance and leveraged the precision and recall to evaluate how the two models perform in predicting top 10% high-risk decile patients. Results The LSTM model outperformed the logistic regression baseline, accurately leveraging temporal features (time-series data) to predict readmission. The major features used by the model were the Charlson Comorbidity Index, hospital length of stay, the hospital admissions over the past 6 months or the number of medications before discharge, while demographic variables were less impactful Limitations The use of a single-center database from the MIMIC-III database further limits the generalizability of the findings: patients from this database might not fully represent the wider Medicare population. Additionally, the focus on all-cause hospital readmissions without accounting for specific chronic conditions, such as heart failure or diabetes, limits the model's ability to capture the complexities of chronic diseases known to impact readmission rates in older adults. Lastly, the exclusion of external factors, such as environmental quality, proximity to healthcare facilities, and patient behaviors, further constrains the LSTM's predictive accuracy. Conclusions This work suggests that LSTM networks will be a more promising approach to aid efforts to predict the readmission of Medicare patients. Capturing temporal interactions in patient databases can help us arrive at a more nuanced understanding of potential hospital readmission and help healthcare providers make their prediction models better than current approaches do. Implications Adoption of such predictive models into clinical practice may be more effective in identifying high-risk patients to provide earlier and
{"title":"Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model","authors":"Xintao Li, Sibei Liu","doi":"10.1101/2024.09.08.24313212","DOIUrl":"https://doi.org/10.1101/2024.09.08.24313212","url":null,"abstract":"Background Readmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. The Centers for Medicare & Medicaid Services (CMS) implemented the Hospital Readmissions Reduction Program (HRRP) to tackle the problem of readmission by penalizing to hospitals with excessive 30-day readmissions. Our study is to analyze hospital readmissions among Medicare patients and provide some suggestions of readmission prevention furthermore. Using LSTM networks with feature engineering, our proposed deep learning model would help us understand the contribution of the features. Design The MIMIC-III clinical database contains information from 53,423 patient admissions at Beth Israel Deaconess Medical Center. We restricted our analysis to 21,002 patient admissions with Medicare coverage, and selected variables from admission-level data, inpatient medical history and patient demography. The baseline model is a logistic-regression model based on the LACE index, and the LSTM model was another model that designed to capture temporal dynamic in the data from admission-level and patient-level data. We used Area Under the Curve (AUC) metric to evaluate the model's performance and leveraged the precision and recall to evaluate how the two models perform in predicting top 10% high-risk decile patients. Results The LSTM model outperformed the logistic regression baseline, accurately leveraging temporal features (time-series data) to predict readmission. The major features used by the model were the Charlson Comorbidity Index, hospital length of stay, the hospital admissions over the past 6 months or the number of medications before discharge, while demographic variables were less impactful Limitations The use of a single-center database from the MIMIC-III database further limits the generalizability of the findings: patients from this database might not fully represent the wider Medicare population. Additionally, the focus on all-cause hospital readmissions without accounting for specific chronic conditions, such as heart failure or diabetes, limits the model's ability to capture the complexities of chronic diseases known to impact readmission rates in older adults. Lastly, the exclusion of external factors, such as environmental quality, proximity to healthcare facilities, and patient behaviors, further constrains the LSTM's predictive accuracy. Conclusions This work suggests that LSTM networks will be a more promising approach to aid efforts to predict the readmission of Medicare patients. Capturing temporal interactions in patient databases can help us arrive at a more nuanced understanding of potential hospital readmission and help healthcare providers make their prediction models better than current approaches do. Implications Adoption of such predictive models into clinical practice may be more effective in identifying high-risk patients to provide earlier and ","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1101/2024.09.08.24313277
Chenchang Xiao, Jingyan Yan, Hanjia Li, Changmian Ding, Bin Yu
Abstract Objectives: There is an increasing number of international students in China. Acculturation strategies are the way students cope with different cultures, including integration, assimilation, separation, and marginalization. This study aims to investigate the acculturation strategies and associated factors, and the effect of these strategies on mental health status among international students in China. Study design: Cross-sectional study. Methods: Study data were collected from 567 international students attending universities in China. Acculturation strategies, acculturative stress, depressive symptoms were measured using reliable and valid scales. Linear and multinomial logistic regression were used for analysis. Results: Study findings revealed that integration (31.57%) was the most preferred acculturation strategy, followed by marginalization (28.92%), separation (21.87%) and assimilation (17.54%). Females were more likely to choose separation strategy than marginalization, while students with religions had higher likelihood to choose integration strategy. Students majoring in Literature/Art and liking their major were more likely to use assimilation strategy. Students with more studying time in weekdays and medium studying time in weekends were more likely to prefer integration strategy. Students with separation and integration strategy had higher acculturative stress. Conclusions: Integration is the most popular acculturation strategy among international students in China. Students with separation and integration strategy had worse mental health status. Gender, major, religion, daily study time were significantly associated with the preference of acculturation strategies.
{"title":"Associated factors of acculturation strategies and mental health outcomes among international students in China","authors":"Chenchang Xiao, Jingyan Yan, Hanjia Li, Changmian Ding, Bin Yu","doi":"10.1101/2024.09.08.24313277","DOIUrl":"https://doi.org/10.1101/2024.09.08.24313277","url":null,"abstract":"Abstract\u0000Objectives: There is an increasing number of international students in China. Acculturation strategies are the way students cope with different cultures, including integration, assimilation, separation, and marginalization. This study aims to investigate the acculturation strategies and associated factors, and the effect of these strategies on mental health status among international students in China.\u0000Study design: Cross-sectional study.\u0000Methods: Study data were collected from 567 international students attending universities in China. Acculturation strategies, acculturative stress, depressive symptoms were measured using reliable and valid scales. Linear and multinomial logistic regression were used for analysis.\u0000Results: Study findings revealed that integration (31.57%) was the most preferred acculturation strategy, followed by marginalization (28.92%), separation (21.87%) and assimilation (17.54%). Females were more likely to choose separation strategy than marginalization, while students with religions had higher likelihood to choose integration strategy. Students majoring in Literature/Art and liking their major were more likely to use assimilation strategy. Students with more studying time in weekdays and medium studying time in weekends were more likely to prefer integration strategy. Students with separation and integration strategy had higher acculturative stress.\u0000Conclusions: Integration is the most popular acculturation strategy among international students in China. Students with separation and integration strategy had worse mental health status. Gender, major, religion, daily study time were significantly associated with the preference of acculturation strategies.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-07DOI: 10.1101/2024.09.06.24313223
April Kimmel, Zhongzhe Pan, Ellen Brazier, Gad Murenzi, Benjamin Muhoza, Marcel Yotebieng, Kathryn Anastos, Denis Nash, Central Africa International epidemiology Databases to Evaluate AIDS (CA-IeDEA)
Background: We developed and calibrated the Central Africa-International epidemiology Databases to Evaluate AIDS (CA-IeDEA) HIV policy model to inform equitable achievement of global goals, overall and across sub-populations, in Rwanda. Methods: We created a deterministic dynamic model to project adult HIV epidemic and care continuum outcomes, overall and for 25 subpopulations (age group, sex, HIV acquisition risk, urbanicity). Data came from the Rwanda cohort of CA-IeDEA, 2004–2020; Rwanda Demographic and Health Surveys, 2005, 2010, 2015; Rwanda Population-based HIV Impact Assessment, 2019; and the literature and reports. We calibrated the model to 47 targets by selecting the 50 best-fitting parameter sets among 20,000 simulations. Calibration targets reflected epidemic (HIV prevalence, incidence), global goals (percentage on antiretroviral therapy (ART) among diagnosed, percentage virally suppressed among on ART) and other (number on ART, percentage virally suppressed) indicators, overall and by sex. Best-fitting sets minimized the summed absolute value of the percentage deviation (AVPD) between model projections and calibration targets. Good model performance was mean AVPD <5% across the 50 best-fitting sets and/or projections within the target confidence intervals; acceptable was mean AVPD >5% and <15%. Results: Across indicators, 1,841 of 2,350 (78.3%) model projections were a good or acceptable fit to calibration targets. For HIV epidemic indicators, 256 of 300 (85.3%) projections were a good fit to targets, with the model performing better for women (83.3% a good fit) than for men (71.7% a good fit). For global goals indicators, 96 of 100 (96.0%) projections were a good fit; model performance was similar for women and men. For other indicators, 653 of 950 (68.7%) projections were a good or acceptable fit. Fit was better for women than for men (percentage virally suppressed only) and when restricting targets for number on ART to 2013 and beyond. Conclusions: The CA-IeDEA HIV policy model fits historical data and can inform policy solutions for equitably achieving global goals to end the HIV epidemic in Rwanda. High-quality, unbiased population-based data, as well as novel approaches that account for calibration target quality, are critical to ongoing use of mathematical models for programmatic planning.
{"title":"Development and calibration of a mathematical model of HIV outcomes among Rwandan adults: informing equitable achievement of targets in Rwanda","authors":"April Kimmel, Zhongzhe Pan, Ellen Brazier, Gad Murenzi, Benjamin Muhoza, Marcel Yotebieng, Kathryn Anastos, Denis Nash, Central Africa International epidemiology Databases to Evaluate AIDS (CA-IeDEA)","doi":"10.1101/2024.09.06.24313223","DOIUrl":"https://doi.org/10.1101/2024.09.06.24313223","url":null,"abstract":"Background: We developed and calibrated the Central Africa-International epidemiology Databases to Evaluate AIDS (CA-IeDEA) HIV policy model to inform equitable achievement of global goals, overall and across sub-populations, in Rwanda.\u0000Methods: We created a deterministic dynamic model to project adult HIV epidemic and care continuum outcomes, overall and for 25 subpopulations (age group, sex, HIV acquisition risk, urbanicity). Data came from the Rwanda cohort of CA-IeDEA, 2004–2020; Rwanda Demographic and Health Surveys, 2005, 2010, 2015; Rwanda Population-based HIV Impact Assessment, 2019; and the literature and reports. We calibrated the model to 47 targets by selecting the 50 best-fitting parameter sets among 20,000 simulations. Calibration targets reflected epidemic (HIV prevalence, incidence), global goals (percentage on antiretroviral therapy (ART) among diagnosed, percentage virally suppressed among on ART) and other (number on ART, percentage virally suppressed) indicators, overall and by sex. Best-fitting sets minimized the summed absolute value of the percentage deviation (AVPD) between model projections and calibration targets. Good model performance was mean AVPD <5% across the 50 best-fitting sets and/or projections within the target confidence intervals; acceptable was mean AVPD >5% and <15%. Results: Across indicators, 1,841 of 2,350 (78.3%) model projections were a good or acceptable fit to calibration targets. For HIV epidemic indicators, 256 of 300 (85.3%) projections were a good fit to targets, with the model performing better for women (83.3% a good fit) than for men (71.7% a good fit). For global goals indicators, 96 of 100 (96.0%) projections were a good fit; model performance was similar for women and men. For other indicators, 653 of 950 (68.7%) projections were a good or acceptable fit. Fit was better for women than for men (percentage virally suppressed only) and when restricting targets for number on ART to 2013 and beyond.\u0000Conclusions: The CA-IeDEA HIV policy model fits historical data and can inform policy solutions for equitably achieving global goals to end the HIV epidemic in Rwanda. High-quality, unbiased population-based data, as well as novel approaches that account for calibration target quality, are critical to ongoing use of mathematical models for programmatic planning.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 has negative repercussions on psychological aspects of the individual. Provision of personalized assistance to each individual is a task which cannot be fulfilled by the health system. In this context religion remains to be an effective tool. A qualitative study was conducted to find the spiritual practices and coping mechanisms of COVID-19 infected people(N=15, mean age =46.3 years) in Kurunegala district. Data collection was conducted using in-depth interviews and thematic analysis was conducted. The main themes found were Fatalism and perceived risk of COVID 19, Adherence to health care guidelines despite fatalistic beliefs, Religious coping in COVID 19, Spiritual practices for coping with stress/ distress and Fatalism as a positive force for self-care. It is imperative to manage the pandemic with the assistance of religion and spiritual practices.
{"title":"Religion and COVID 19 Pandemic for better or for worse?","authors":"Sameera Upashantha Ranasinghe, Lakshitha Iroshan Ranasinghe, Indika Pathiraja","doi":"10.1101/2024.09.04.24312776","DOIUrl":"https://doi.org/10.1101/2024.09.04.24312776","url":null,"abstract":"COVID-19 has negative repercussions on psychological aspects of the individual. Provision of personalized assistance to each individual is a task which cannot be fulfilled by the health system. In this context religion remains to be an effective tool. A qualitative study was conducted to find the spiritual practices and coping mechanisms of COVID-19 infected people(N=15, mean age =46.3 years) in Kurunegala district. Data collection was conducted using in-depth interviews and thematic analysis was conducted. The main themes found were Fatalism and perceived risk of COVID 19, Adherence to health care guidelines despite fatalistic beliefs, Religious coping in COVID 19, Spiritual practices for coping with stress/ distress and Fatalism as a positive force for self-care. It is imperative to manage the pandemic with the assistance of religion and spiritual practices.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1101/2024.09.04.24313091
Muna Barakat, Roa’a Abuarab, Baraah Alkharabsheh, Nada Budair, Mais Fareed, Rahma Kharabsheh, Dana Oshroq Laban, Malik Sallam
The prevalence of vaping (e-cigarette use) surged among young individuals, especially university students, highlighting the necessity to explore the factors influencing this behavior. This study aimed to develop and validate the Vaping Attitude and Perceptions Scale (VAPeS), based on the Theory of Planned Behavior (TPB). A cross-sectional design was employed using a self-administered electronic questionnaire to collect data from university students in Jordan. The questionnaire assessed demographic variables and key constructs of the VAPeS: Social Influence, Perceived Harms, Pleasure Derived from Vaping, Behavioral Influences, and Economic Factors and Self-Efficacy. The survey engaged 671 university students, with 201 (30.0%) reporting the use of tobacco products. Among these tobacco users, a majority (n=126, 62.7%) reported the current use of e-cigarettes. The exploratory factor analysis (EFA) using Principal Component Analysis (PCA) revealed a five-factor solution explaining 64.5% of the variance. These factors—Social Influence, Perceived Harms, Vaping Pleasure, Behavioral Influences, and Economic and Self-Efficacy—demonstrated high internal consistency. Regression analysis showed that Social Influence, Behavioral Influences, and sex were significant predictors of vaping behavior, with standardized coefficients of 0.494, 0.206, and −0.154, respectively (P<.001, P=.008, and P=.018). The VAPeS displayed satisfactory psychometric properties, making it a valuable tool for investigating vaping behaviors among the youth. Factors such as Social Influence and Behavioral Influences were significant factors in shaping vaping behaviors among the participants. These insights emphasize the need to consider these dimensions in public health initiatives aimed at managing the rising vaping prevalence among university students. The VAPeS instrument can be utilized for better delineation of vaping behaviors among the younger demographic. Subsequently, the insights gained through VAPeS-based research can help devise targeted educational campaigns aimed at addressing the perceived social acceptability and the supposed safety of vaping.
{"title":"Exploratory Validation of a Survey Instrument Based on the Theory of Planned Behavior to Assess Vaping Attitude and Perceptions","authors":"Muna Barakat, Roa’a Abuarab, Baraah Alkharabsheh, Nada Budair, Mais Fareed, Rahma Kharabsheh, Dana Oshroq Laban, Malik Sallam","doi":"10.1101/2024.09.04.24313091","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313091","url":null,"abstract":"The prevalence of vaping (e-cigarette use) surged among young individuals, especially university students, highlighting the necessity to explore the factors influencing this behavior. This study aimed to develop and validate the Vaping Attitude and Perceptions Scale (VAPeS), based on the Theory of Planned Behavior (TPB). A cross-sectional design was employed using a self-administered electronic questionnaire to collect data from university students in Jordan. The questionnaire assessed demographic variables and key constructs of the VAPeS: Social Influence, Perceived Harms, Pleasure Derived from Vaping, Behavioral Influences, and Economic Factors and Self-Efficacy. The survey engaged 671 university students, with 201 (30.0%) reporting the use of tobacco products. Among these tobacco users, a majority (n=126, 62.7%) reported the current use of e-cigarettes. The exploratory factor analysis (EFA) using Principal Component Analysis (PCA) revealed a five-factor solution explaining 64.5% of the variance. These factors—Social Influence, Perceived Harms, Vaping Pleasure, Behavioral Influences, and Economic and Self-Efficacy—demonstrated high internal consistency. Regression analysis showed that Social Influence, Behavioral Influences, and sex were significant predictors of vaping behavior, with standardized coefficients of 0.494, 0.206, and −0.154, respectively (<em>P</em><.001, <em>P</em>=.008, and <em>P</em>=.018). The VAPeS displayed satisfactory psychometric properties, making it a valuable tool for investigating vaping behaviors among the youth. Factors such as Social Influence and Behavioral Influences were significant factors in shaping vaping behaviors among the participants. These insights emphasize the need to consider these dimensions in public health initiatives aimed at managing the rising vaping prevalence among university students. The VAPeS instrument can be utilized for better delineation of vaping behaviors among the younger demographic. Subsequently, the insights gained through VAPeS-based research can help devise targeted educational campaigns aimed at addressing the perceived social acceptability and the supposed safety of vaping.","PeriodicalId":501276,"journal":{"name":"medRxiv - Public and Global Health","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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