The percentage use of antibiotics is high in China. The excessive use of antibiotics is particularly more problematic in lower-level hospitals and in less developed western China. The implementation and impact of the national efforts to control the excessive use of antibiotics should be appropriately evaluated.

BackgroundPatient adherence is an important issue for health service providers and health researchers. However, the knowledge structure of diverse research on treatment adherence is unclear. This study used co-word analysis and social network analysis techniques to analyze research literature on adherence, and to show their knowledge structure and evolution over time.MethodsPublished scientific papers about treatment adherence were retrieved from Web of Science (2000 to May 2011). A total of 2308 relevant articles were included: 788 articles published in 2000–2005 and 1520 articles published in 2006–2011. The keywords of each article were extracted by using the software Biblexcel, and the synonym and isogenous words were merged manually. The frequency of keywords and their co-occurrence frequency were counted. High frequency keywords were selected to yield the co-words matrix. Finally the decomposition maps were used to comb the complex knowledge structures.ResultsResearch themes were more general in the first period (2000 to 2005), and more extensive with many more new terms in the second period (2006 to 2011). Research on adherence has covered more and more diseases, populations and methods, but other diseases/conditions are not as hot as HIV/AIDS and have not become specialty themes/sub-directions. Most studies originated from the United States.ConclusionThe dynamic of this field is mainly divergent, with increasing number of new sub-directions of research. Future research is required to investigate specific directions and converge as well to construct a general paradigm in this field.

Inactive HBsAg carrier status and possible occult HBV infection may increase the risk of PaC. Large population-based multicenter prospective studies are required to further confirm this finding.

BackgroundMedication adherence is critical in Tuberculosis (TB) treatment success, but existing tools are inadequate in identifying non-adherents, reasons for non-adherence or interventions to improve adherence. This study intended to fill the gap by developing and validating a TB medication adherence scale (TBMAS).MethodsAn initial 41-item TBMAS was designed through review of literature, consultation from an 8-member clinical expert panel and a 15-patient focus group, and pilot-testing in 25 TB patients. The questionnaire was validated in 438 patients who visited 23 community health centers for TB treatment in Wuhan from September 1, 2010, to August 31, 2011, using pharmacy refill records in a 15-week period as external criteria for medication adherence. After removing redundant and cross-loading items, the internal consistency, reliability and validity of TBMAS in identifying non-adherents were examined.ResultsThe final TBMAS included 30 items scored on a 5-point Likert scale, and these items were loaded in nine distinct factors that explained 65% of cumulative variance among respondents. Cronbach's alpha, test-retest reliability and split-half reliability were 0.87, 0.83, and 0.85, respectively. Convergent validity was supported by statistically significant associations between TBMAS scores and adherence measured by pharmacy refill records. Receiver Operating Characteristics curve analysis suggested a cut-off point at 113, with which TBMAS showed a positive predictive value of 65.5% and sensitivity of 82.9% in identifying non-adherents.ConclusionTBMAS demonstrated satisfactory internal consistency, reliability and validity in identifying TB patients with poor adherence and potential causes for non-adherence.

Private CHCs are better equipped and better staffed than public CHCs but are less compliant with national policy on essential medicines and have poorer prescribing quality in China, warranting more rigorous government supervision.

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