Polypharmacy and its correlates in village health clinics across 10 provinces of Western China.
BACKGROUND
Polypharmacy represents a growing public health concern for its associations with drug-related adverse events, poor adherence, medication errors and increased healthcare costs.


OBJECTIVE
To assess the occurrence of polypharmacy prescribing and to identify the correlates in terms of doctors and patients characteristics in rural Western China.


METHODS
20,125 prescriptions were collected from 680 primary health clinics in villages from 40 counties in 10 provinces of Western China. Two outcome variables are used in the analysis: number of drug items prescribed and use of five or more drugs (polypharmacy). A multilevel Poisson regression model and a multilevel logistic regression model were employed to examine the socio-economic, demographic determinants of the above two outcome variables, respectively.


RESULTS
The average drug number per prescription was 2.36, and the percentage of polypharmacy was 5.8%. The odds for polypharmacy were higher for village doctors with a heavier workload than those with less workload (OR=1.70; 95% CI 1.26 to 2.29). Patients with injections prescribed had higher odds for polypharmacy than patients without injections (OR=4.61; 95% CI 3.93 to 5.42). Village doctor's workload, subsidy for doctors from government and injection in prescriptions, patient's age and gender were also significant predictors of number of drug items prescribed in the studied areas.


CONCLUSIONS
Polypharmacy seems to be associated with the village doctors' and patients' background and government policy. Intervention studies are needed to promote rational use of drugs in rural China.
Dong
Lifang
L
Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University College of Medicine, Xi'an, Shaanxi 710061, PR China.
Yan
Hong
H
Wang
Duolao
D
D000328:Adult; D000368:Aged; D002681:China; D003151:Community Health Centers; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008508:Medication Errors; D008875:Middle Aged; D016012:Poisson Distribution; D019338:Polypharmacy; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012424:Rural Population; D016526:Workload