A Bayesian Difference-in-Difference Framework for the Impact of Primary Care Redesign on Diabetes Outcomes

被引:5
作者
Normington, James [1 ]
Lock, Eric [1 ]
Carlin, Caroline [2 ]
Peterson, Kevin [2 ]
Carlin, Bradley [3 ]
机构
[1] Univ Minnesota, Sch Publ Hlth, Div Biostat, A460 Mayo Bldg,MMC 303,420 Delaware St SE, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Dept Family Med & Community Hlth, Minneapolis, MN USA
[3] Counterpoint Stat Consulting LLC, Minneapolis, MN USA
来源
STATISTICS AND PUBLIC POLICY | 2019年 / 6卷 / 01期
基金
美国国家卫生研究院;
关键词
Bayesian hierarchical modeling; Diabetes; Difference-in-differences; Errors in covariates; Patient-centered medical home; Primary care redesign; INFERENCE; WAGES; MODEL;
D O I
10.1080/2330443X.2019.1626310
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
Although national measures of the quality of diabetes care delivery demonstrate improvement, progress has been slow. In 2008, the Minnesota legislature endorsed the patient-centered medical home (PCMH) as the preferred model for primary care redesign. In this work, we investigate the effect of PCMH-related clinic redesign and resources on diabetes outcomes from 2008 to 2012 among Minnesota clinics certified as PCMHs by 2011 by using a Bayesian framework for a continuous difference-in-differences model. Data from the Physician Practice Connections-Research Survey were used to assess a clinic's maturity in primary care transformation, and diabetes outcomes were obtained from the MN Community Measurement program. These data have several characteristics that must be carefully considered from a modeling perspective, including the inability to match patients over time, the potential for dynamic confounding, and the hierarchical structure of clinics. An ad-hoc analysis suggests a significant correlation between PCMH-related clinic redesign and resources on diabetes outcomes; however, this effect is not detected after properly accounting for different sources of variability and confounding. Supplementary materials for this article are available online.
引用
收藏
页码:55 / 66
页数:12
相关论文
共 27 条
[11]   USING REGIONAL VARIATION IN WAGES TO MEASURE THE EFFECTS OF THE FEDERAL MINIMUM-WAGE [J].
CARD, D .
INDUSTRIAL & LABOR RELATIONS REVIEW, 1992, 46 (01) :22-37
[12]  
DHaultfoeuille X., 2018, Two-way fixed effects estimators with heterogeneous treatment effects
[13]   ERROR DETECTING AND ERROR CORRECTING CODES [J].
HAMMING, RW .
BELL SYSTEM TECHNICAL JOURNAL, 1950, 29 (02) :147-160
[14]   AN INVARIANT FORM FOR THE PRIOR PROBABILITY IN ESTIMATION PROBLEMS [J].
JEFFREYS, H .
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL AND PHYSICAL SCIENCES, 1946, 186 (1007) :453-461
[15]  
Jolliffe I. T., 2002, PRINCIPAL COMPONENT
[16]  
Lechner M., 2010, FDN TRENDS ECONOMETR, V4, P167
[17]  
LINDLEY DV, 1972, J ROY STAT SOC B, V34, P1
[18]   JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES [J].
Lock, Eric F. ;
Hoadley, Katherine A. ;
Marron, J. S. ;
Nobel, Andrew B. .
ANNALS OF APPLIED STATISTICS, 2013, 7 (01) :523-542
[19]  
Minnesota Statutes, 2008, 2008 MINN SESS LAWS
[20]  
Montgomery J., 2017, How conditioning on post-treatment variables can ruin your experiment and what to do about it