ArticleBMJ open2017
Assessing dose-response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure.
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- A systematic environmental intervention, nidotherapy, given to whole communities: protocol for a randomised stepped-wedge trial.BJPsych open · 2025Article
- Health shocks and changes in preventive behaviors: Results from the China Health and Retirement Longitudinal Study.Frontiers in public health · 2022Article
- Gender differences in caregiving among a schizophrenia population.Psychology research and behavior management · 2019Article
- The Effect of China's National Essential Medicine Policy on Health Expenses: Evidence From a National Study.Inquiry : a journal of medical care organization, provision and financingArticle
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7 authors.
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Abstract
objectivesTo introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose-response effect) for data from a stepped-wedge design with a hierarchical structure.
designThe implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects.
settingRoutinely and annually collected national data on China from 2008 to 2012.
participants34 506 primary healthcare facilities in 2675 counties of 31 provinces. OUTCOME MEASURES: Agreement and differences in estimates of dose-response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV).
resultsThe estimated dose-response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2-4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose-response among provinces, counties and facilities were estimated, and the 'lowest' or 'highest' units by their dose-response effects were pinpointed only by the multilevel RM model.
conclusionsFor examining dose-response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models.
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