Effects of Continuity of Care on Medication Duplication Among the Elderly
ABSTRACT The effects of continuity of care on health care outcomes are well documented. However, little is known about the effect of continuity at the physician or the site level on the process of care for patients with multiple chronic conditions (MCCs).
The objective of this study was to examine the effects of physician continuity versus site continuity on duplicated medications received by patients with and without MCCs.
This study utilized a longitudinal design with an 8-year follow-up from 2004 to 2011 of patients aged 65 or older under a universal health insurance program in Taiwan (55,573 subjects and 389,011 subject-years). Generalized estimating equation models with propensity score method were conducted to assess the association between continuity and medication duplication.
The rates of subjects receiving duplicated medications ranged from 40.38% to 43.50% with 1.45-1.62 duplicated medications during the study period. The findings revealed that better continuity, either at the physician level or the site level, was significantly associated with fewer duplicated medications. This study also indicated that the physician continuity had a stronger effect on medication duplication than did site continuity. Furthermore, the magnitude of the protective effect of continuity against duplicated medications increased when the patients had more chronic conditions [physician continuity: the marginal effect ranged from -10.7% to -52.9% (all P<0.001); site continuity: the marginal effect ranged from -0.4% (P=0.063) to -31.4% (P<0.001)].
Improving either physician continuity or site continuity may result in fewer duplicated medications, particularly for patients with MCCs.
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ABSTRACT: This study investigated the relationship between continuity of care (having one's own doctor and a regular site of care), and receipt of preventive services in a population of adult fee-for-service Medicaid enrollees with physical disabilities. A random sample of 555 physically disabled Rhode Island Medicaid enrollees aged 18 to 64 years were surveyed by telephone. Respondents were asked about receipt of six preventive services in the previous year. They were also asked whether they had their own doctor and whether they had a regular site of care. Regression analyses with propensity score corrections for selection bias were used to test the associations between care continuity measures and the number of preventive services received, as well as the receipt of each individual service. After adjustment for predisposing, enabling, and need factors, respondents with their own doctor received 0.73 more preventive services than peers without their own doctor, and respondents who had a usual site of care received 0.85 more services than peers who received care at the emergency department or who had no regular site. The influences of having a regular doctor and a usual site of care varied according to type of preventive service, and these influences appear to be largely complementary rather than overlapping. Study findings suggest that care models for adults with physical disabilities should include mechanisms to ensure both physician and site continuity. A strong primary care component that links individual patients with a personal doctor, as well as care protocols that ensure receipt of preventive services, appear to be optimal for medically needy populations.Disability and Health Journal 10/2009; 2(4):180-7. DOI:10.1016/j.dhjo.2009.06.004 · 1.50 Impact Factor
Medical Care 05/1977; 15(4):347-9. DOI:10.1097/00005650-197704000-00010 · 2.94 Impact Factor
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ABSTRACT: The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.Multivariate Behavioral Research 05/2011; 46(3):399-424. DOI:10.1080/00273171.2011.568786 · 2.97 Impact Factor