Suchergebnisse

To increase use of medical service across the country, the Chinese government has tried to improve equity in health care access and reduce patients' medical expenses. For this purpose, the National Essential Medicine Policy (NEMP) was introduced in 2009 to mandate the distribution of medicines to health care facilities at a low cost and without profit. This study aims to evaluate the effect of the essential medicine policy on average per-visit expenses for outpatient and inpatient services. The annual national surveillance system data covering all the grassroots-level primary health care facilities (PHFs) in 2675 counties and 31 provinces in China during 2008 to 2012 were used in this study. The 4-level hierarchical random effects models were utilized to deal with possible dose-response effects of the policy and possible variations of such effects at the provincial, county, and facility levels. Our research findings suggest that the NEMP had positive effects in reducing both outpatient and inpatient expenses at grassroots level, and the policy effects tended to be greater as the exposure time increased. This study provides implications on reforming China's health system and its medicine cost control policies.

AbstractWe developed a novel method to address multicollinearity in linear models called average ordinary least squares (OLS)‐centered penalized regression (AOPR). AOPR penalizes the cost function to shrink the estimators toward the weighted‐average OLS estimator. The commonly used ridge regression (RR) shrinks the estimators toward zero, that is, employs penalization prior in the Bayesian view, which contradicts the common real prior . Therefore, RR selects small penalization coefficients to relieve such a contradiction and thus makes the penalizations inadequate. Mathematical derivations remind us that AOPR could increase the performance of RR and OLS regression. A simulation study shows that AOPR obtains more accurate estimators than OLS regression in most situations and more accurate estimators than RR when the signs of the true s are identical and is slightly less accurate than RR when the signs of the true s are different. Additionally, a case study shows that AOPR obtains more stable estimators and stronger statistical power and predictive ability than RR and OLS regression. Through these results, we recommend using AOPR to address multicollinearity more efficiently than RR and OLS regression, especially when the true s have identical signs.