Health Services and Outcomes Research Methodology最新文献_第7页

Authentic assessments: a method to detect anomalies in assessment response patterns via neural network 真实评估:一种利用神经网络检测评估反应模式异常的方法

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-03-09 DOI: 10.1007/s10742-021-00245-9

Katelyn Cordell, H. Rao, John Lyons

引用次数: 2

Estimating Heterogeneous Effects of a Policy Intervention across Organizations when Organization Affiliation is Missing for the Control Group: Application to the Evaluation of Accountable Care Organizations. 当控制组缺乏组织隶属关系时，评估跨组织政策干预的异质效应:应用于责任关怀组织的评估。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-03-01 Epub Date: 2021-01-04 DOI: 10.1007/s10742-020-00230-8

Guanqing Chen, Valerie A Lewis, Daniel Gottlieb, A James O'Malley

{"title":"Estimating Heterogeneous Effects of a Policy Intervention across Organizations when Organization Affiliation is Missing for the Control Group: Application to the Evaluation of Accountable Care Organizations.","authors":"Guanqing Chen, Valerie A Lewis, Daniel Gottlieb, A James O'Malley","doi":"10.1007/s10742-020-00230-8","DOIUrl":"https://doi.org/10.1007/s10742-020-00230-8","url":null,"abstract":"First introduced in early 2000s, the accountable care organization (ACO) is designed to lower health care costs while improving quality of care and has become one of the most important coordinated care technologies in the United States. In this research, we use the Medicare fee-for-service claims data from 2009-2014 to estimate the heterogeneous effects of Medicare ACO programs on hospital admissions across hospital referral regions (HRRs) and provider groups. To conduct our analysis, a model for a difference-in-difference (DID) study is embellished in multiple ways to account for intricacies and complexity with the data not able to be accounted for using existing models. Of particular note, we propose a Gaussian mixture model to account for the inability to observe the practice group affiliation of physicians if the organization they worked for did not become an ACO, which is needed to ensure appropriate partitioning of variation across the different units. The results suggest that the ACO programs reduced the rate of readmission to hospital, that the ACO program may have reduced heterogeneity in readmission rates, and that the effect of joining an ACO varied considerably across medical groups.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 1","pages":"54-68"},"PeriodicalIF":1.5,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00230-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25426299","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: 1

A multivariate spatio-temporal model of the opioid epidemic in Ohio: A factor model approach. 俄亥俄州阿片类药物流行的多变量时空模型:一个因素模型方法。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-03-01 Epub Date: 2020-11-05 DOI: 10.1007/s10742-020-00227-3

David Kline, Yixuan Ji, Staci Hepler

{"title":"A multivariate spatio-temporal model of the opioid epidemic in Ohio: A factor model approach.","authors":"David Kline, Yixuan Ji, Staci Hepler","doi":"10.1007/s10742-020-00227-3","DOIUrl":"https://doi.org/10.1007/s10742-020-00227-3","url":null,"abstract":"Opioid misuse is a significant public health issue and a national epidemic with a high prevalence of associated morbidity and mortality. The epidemic is particularly severe in Ohio which has some of the highest overdose rates in the country. It is important to understand spatial and temporal trends of the opioid epidemic to learn more about areas that are most affected and to inform potential community interventions and resource allocation. We propose a multivariate spatio-temporal model to leverage existing surveillance measures, opioid-associated deaths and treatment admissions, to learn about the underlying epidemic for counties in Ohio. We do this using a temporally varying spatial factor that synthesizes information from both counts to estimate common underlying risk which we interpret as the burden of the epidemic. We demonstrate the use of this model with county-level data from 2007-2018 in Ohio. Through our model estimates, we identify counties with above and below average burden and examine how those regions have shifted over time given overall statewide trends. Specifically, we highlight the sustained above average burden of the opioid epidemic on southern Ohio throughout the 12 years examined.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 ","pages":"42-53"},"PeriodicalIF":1.5,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00227-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39219285","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: 1

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures. 使用熵平衡权对连续暴露的总体平均剂量-反应曲线进行非参数估计。

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-03-01 Epub Date: 2021-02-13 DOI: 10.1007/s10742-020-00236-2

Brian G Vegetabile, Beth Ann Griffin, Donna L Coffman, Matthew Cefalu, Michael W Robbins, Daniel F McCaffrey

{"title":"Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures.","authors":"Brian G Vegetabile, Beth Ann Griffin, Donna L Coffman, Matthew Cefalu, Michael W Robbins, Daniel F McCaffrey","doi":"10.1007/s10742-020-00236-2","DOIUrl":"https://doi.org/10.1007/s10742-020-00236-2","url":null,"abstract":"Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 1","pages":"69-110"},"PeriodicalIF":1.5,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10742-020-00236-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39385609","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: 42

Heterogeneous treatment effects and bias in the analysis of the stepped wedge design 阶梯式楔形设计分析中的异质处理效应和偏差

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-02-26 DOI: 10.1007/s10742-021-00244-w

S. Lindner, K. McConnell

引用次数: 1

Conditional power, predictive power and probability of success in clinical trials with continuous, binary and time-to-event endpoints 在具有连续、二元和事件时间终点的临床试验中，条件能力、预测能力和成功概率

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-02-26 DOI: 10.1007/s10742-023-00302-5

M. G. Kundu, S. Samanta, Shoubhik Mondal

引用次数: 3

Which patients benefit most from completing health risk assessments: comparing methods to identify heterogeneity of treatment effects 哪些患者从完成健康风险评估中获益最大:比较方法以确定治疗效果的异质性

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-02-24 DOI: 10.1007/s10742-021-00243-x

M. Olsen, K. Stechuchak, E. Oddone, L. Damschroder, M. Maciejewski

引用次数: 2

Using a spatiotemporal model to estimate the impact of suicide prevention in small areas 利用时空模型估算小区域自杀预防的影响

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-02-07 DOI: 10.1007/s10742-021-00242-y

Lucas Godoy-Garraza, S. Campos, C. Walrath

引用次数: 0

Using Synthetic Data to Replace Linkage Derived Elements: A Case Study. 使用合成数据替换链接派生元素：一个案例研究。

IF 1.6

Health Services and Outcomes Research Methodology Pub Date : 2021-02-03 DOI: 10.1007/s10742-021-00241-z

Dean M Resnick, Christine S Cox, Lisa B Mirel

{"title":"Using Synthetic Data to Replace Linkage Derived Elements: A Case Study.","authors":"Dean M Resnick, Christine S Cox, Lisa B Mirel","doi":"10.1007/s10742-021-00241-z","DOIUrl":"10.1007/s10742-021-00241-z","url":null,"abstract":"While record linkage can expand analyses performable from survey microdata, it also incurs greater risk of privacy-encroaching disclosure. One way to mitigate this risk is to replace some of the information added through linkage with synthetic data elements. This paper describes a case study using the National Hospital Care Survey (NHCS), which collects patient records under a pledge of protecting patient privacy from a sample of U.S. hospitals for statistical analysis purposes. The NHCS data were linked to the National Death Index (NDI) to enhance the survey with mortality information. The added information from NDI linkage enables survival analyses related to hospitalization, but as the death information includes dates of death and detailed causes of death, having it joined with the patient records increases the risk of patient re-identification (albeit only for deceased persons). For this reason, an approach was tested to develop synthetic data that uses models from survival analysis to replace vital status and actual dates-of-death with synthetic values and uses classification tree analysis to replace actual causes of death with synthesized causes of death. The degree to which analyses performed on the synthetic data replicate results from analysis on the actual data is measured by comparing survival analysis parameter estimates from both data files. Because synthetic data only have value to the degree that they can be used to produce statistical estimates that are like those based on the actual data, this evaluation is an essential first step in assessing the potential utility of synthetic mortality data.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"21 ","pages":"389-406"},"PeriodicalIF":1.6,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563018/pdf/nihms-1670775.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39680445","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

Standard electronic health record (EHR) framework for Indian healthcare system 印度医疗保健系统标准电子健康记录(EHR)框架

IF 1.5

Health Services and Outcomes Research Methodology Pub Date : 2021-01-27 DOI: 10.1007/s10742-020-00238-0

M. Pai, R. Ganiga, R. Pai, R. Sinha

引用次数: 36