Health Services and Outcomes Research Methodology最新文献_第6页

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis. 用数据包络分析评价各县提供糖尿病预防保健的效率。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-09-01 Epub Date: 2021-01-06 DOI: 10.1007/s10742-020-00237-1

Hyojung Kang, Soyoun Kim, Kevin Malloy, Timothy L McMurry, Rajesh Balkrishnan, Roger Anderson, Anthony McCall, Min-Woong Sohn, Jennifer Mason Lobo

{"title":"Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis.","authors":"Hyojung Kang, Soyoun Kim, Kevin Malloy, Timothy L McMurry, Rajesh Balkrishnan, Roger Anderson, Anthony McCall, Min-Woong Sohn, Jennifer Mason Lobo","doi":"10.1007/s10742-020-00237-1","DOIUrl":"https://doi.org/10.1007/s10742-020-00237-1","url":null,"abstract":"For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores < 1) in Group 1, Group 2, and Group 3, respectively. For inefficient counties, foot and eye exams were often identified as sources of inefficiency. Available health professionals in some counties were not fully utilized to provide diabetes preventive care. Identifying benchmarking targets from counties with similar resources can help counties and policy makers develop actionable strategies to improve performance.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 3","pages":"324-338"},"PeriodicalIF":1.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00237-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39660437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Characterizing Bias Due to Differential Exposure Ascertainment in Electronic Health Record Data. 电子健康记录数据中差异暴露确定的特征偏差。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-09-01 Epub Date: 2021-01-04 DOI: 10.1007/s10742-020-00235-3

Rebecca A Hubbard, Elle Lett, Gloria Y F Ho, Jessica Chubak

{"title":"Characterizing Bias Due to Differential Exposure Ascertainment in Electronic Health Record Data.","authors":"Rebecca A Hubbard, Elle Lett, Gloria Y F Ho, Jessica Chubak","doi":"10.1007/s10742-020-00235-3","DOIUrl":"https://doi.org/10.1007/s10742-020-00235-3","url":null,"abstract":"Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and five-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 3","pages":"309-323"},"PeriodicalIF":1.5,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00235-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39292782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying random forest in a health administrative data context: a conceptual guide 在健康管理数据上下文中应用随机森林:概念指南

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-07-17 DOI: 10.1007/s10742-021-00255-7

Caroline A. King, E. Strumpf

引用次数: 3

Measuring spatial access to emergency general surgery services: does the method matter? 测量急诊普外科服务的空间通道:方法重要吗?

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-06-16 DOI: 10.1007/s10742-021-00254-8

Neng Wan, M. McCrum, Jiuying Han, S. Lizotte, Dejun Su, Ming Wen, Shue Zeng

引用次数: 5

Bias Reduction Methods for Propensity Scores Estimated from Error-Prone EHR-Derived Covariates. 从易出错的ehr衍生协变量估计倾向得分的偏倚减少方法。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-06-01 Epub Date: 2020-09-10 DOI: 10.1007/s10742-020-00219-3

Joanna Harton, Ronac Mamtani, Nandita Mitra, Rebecca A Hubbard

{"title":"Bias Reduction Methods for Propensity Scores Estimated from Error-Prone EHR-Derived Covariates.","authors":"Joanna Harton, Ronac Mamtani, Nandita Mitra, Rebecca A Hubbard","doi":"10.1007/s10742-020-00219-3","DOIUrl":"https://doi.org/10.1007/s10742-020-00219-3","url":null,"abstract":"As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance. These comparisons were conducted across a range of scenarios featuring variation in outcome type, validation sample size, main sample size, strength of confounding, and structure of the error in the mismeasured covariate. We then applied these approaches to a real-world EHR-based comparative effectiveness study of alternative treatments for metastatic bladder cancer. This head-to-head comparison of measurement error correction methods in the context of a propensity score-adjusted analysis demonstrated that multiple imputation for propensity scores performs best when the outcome is continuous and regression calibration-based methods perform best when the outcome is binary.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 ","pages":"169-187"},"PeriodicalIF":1.5,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00219-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39249436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims. 用倾向评分法对医疗索赔有效性比较研究的实证因果推断。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-06-01 Epub Date: 2020-10-20 DOI: 10.1007/s10742-020-00222-8

Ryan D Ross, Xu Shi, Megan E V Caram, Pheobe A Tsao, Paul Lin, Amy Bohnert, Min Zhang, Bhramar Mukherjee

{"title":"Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims.","authors":"Ryan D Ross, Xu Shi, Megan E V Caram, Pheobe A Tsao, Paul Lin, Amy Bohnert, Min Zhang, Bhramar Mukherjee","doi":"10.1007/s10742-020-00222-8","DOIUrl":"https://doi.org/10.1007/s10742-020-00222-8","url":null,"abstract":"Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally-varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 2","pages":"206-228"},"PeriodicalIF":1.5,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00222-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39022715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Identifying Cohabiting Couples in Administrative Data: Evidence from Medicare Address Data. 在行政数据中识别同居伴侣:来自医疗保险地址数据的证据。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-06-01 Epub Date: 2020-11-12 DOI: 10.1007/s10742-020-00229-1

Sasmira Matta, Joanne W Hsu, Theodore J Iwashyna, Micah Y Baum, Kenneth M Langa, Lauren Hersch Nicholas

引用次数: 1

Inferring patient transfer networks between healthcare facilities 推断医疗机构之间的患者转移网络

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-05-07 DOI: 10.1007/s10742-021-00249-5

Samuel Justice, Daniel K. Sewell, A. Miller, J. Simmering, P. Polgreen

引用次数: 1

Adjustment for biased sampling using NHANES derived propensity weights 使用NHANES衍生的倾向权重调整有偏抽样

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-04-21 DOI: 10.1007/s10742-022-00283-x

O. Bernstein, Brian G. Vegetabile, C. R. Salazar, J. Grill, D. Gillen

引用次数: 0

Distinguishing frontloading: an examination of medicare home health claims 区分前负荷:医疗保险家庭健康索赔的检查

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-04-18 DOI: 10.1007/s10742-021-00247-7

Brant Morefield, L. Tomai

引用次数: 0