Providers’ perceptions of the key issues affecting the delivery of quality services at a Dental Clinic in Port Moresby, Papua New Guinea: a qualitative descriptive study

@#The aim of this study was to identify factors that affect the dental service delivery in a dental clinic from the context of Papua New Guinean dental providers and to make practical recommendations to improve dental service delivery. This qualitative descriptive study used individual interviews with seven dental providers to identify the factors that were affecting quality dental service delivery in a dental clinic in Papua New Guinea. The factors influencing the quality of dental service fell into three categories and 8 themes. The categories were environmental factors with themes of resources and facilities, leadership and management, partnership development and collaboration; provider-related factors with themes provider competence, provider motivation and satisfaction; and required measures with the themes of a new dental clinic building, renovation of the main clinic area and organizational structure. Quality dental service is achieved when there is a supportive working environment with availability of (new) physical amenities and dental resources, and proper management and organizational structures with supportive visionary leadership. This would encourage teamwork and partnership development internally within the dental team and externally with its stakeholders. Furthermore, it would increase staff motivation and satisfaction internally and partnership development internally within the dental staff and externally with its stakeholders. Consider creating a new dental clinic. Develop a contract between the University and the Hospital to resolve the property ownership issues. Create a clear organizational structure and improve management interaction with clinical staff so that management is supportive. Improve the quality of dental supplies and create an efficient material ordering system. Ensure fairness to staff and respect to procedures are compliantly maintained to encourage teamwork. Reward clinical staff for mentoring and teaching dental students. Create access to continuing professional development for the clinical staff.

Disparity in Health Screening and Health Utilization according to Economic Status / 가정의학회지

BACKGROUND: Cardiovascular disease (CVD) has become the most common cause of mortality and morbidity worldwide. Health screening is associated with higher outpatient visits for detection and treatment of CVD-related diseases (diabetes mellitus, hypertension, and dyslipidemia). We examined the association between health screening, health utilization, and economic status. METHODS: A sampled cohort database from the National Health Insurance Corporation was used. We included 306,206 participants, aged over 40 years, without CVD (myocardial infarction, stroke, and cerebral hemorrhage), CVD-related disease, cancer, and chronic renal disease. The follow-up period was from January 1, 2003 through December 31, 2005. RESULTS: Totally, 104,584 participants received at least one health screening in 2003–2004. The odds ratio of the health screening attendance rate for the five economic status categories was 1.27 (95% confidence interval [CI], 1.24 to 1.31), 1.05 (95% CI, 1.02 to 1.08), 1, 1.16 (95% CI, 1.13 to 1.19) and 1.50 (95% CI, 1.46 to 1.53), respectively. For economic status 1, 3, and 5, respectively, the diagnostic rate after health screening was as follows: diabetes mellitus: 5.94%, 5.36%, and 3.77%; hypertension: 32.75%, 30.16%, and 25.23%; and dyslipidemia: 13.43%, 12.69%, and 12.20%. The outpatient visit rate for attendees diagnosed with CVD-related disease was as follows for economic status 1, 3, and 5, respectively: diabetes mellitus: 37.69%, 37.30%, and 43.70%; hypertension: 34.44%, 30.09%, and 32.31%; and dyslipidemia: 18.83%, 20.35%, and 23.48%. CONCLUSION: Thus, higher or lower economic status groups had a higher health screening attendance rate than the middle economic status group. The lower economic status group showed lower outpatient visits after screening, although it had a higher rate of CVD diagnosis.

The supply and utilization of vision care in rural primary Health / 中国卫生政策研究

Objective:To identify the vision care access in rural primary health institutions and the utilization of vision inspections among rural residents. Methods:Survey data was collected from primary health institutions ( town-ship health centers and village clinics) and households. The descriptive statistics and multivariate regression analysis were applied to analyze data. Results:The household survey data shows that 33. 2% of rural residents self-reported having poor vision, and 22. 1% of rural residents stated that they had ever used vision care (vision screening or vison examinations) . The health facilities survey data shows that 84% of township health centers and 44% of village clinics can provide vision care. The multivariate analysis shows that the vision care access in township health centers is sig-nificantly correlated with the probability of residents’ utilization of vision care, but there is no significant correlation between the provision of vision care in village clinics and its utilization. Conclusions:The vision care access is low in rural China though there is a huge demand therefore. Inadequate supply of primary vision care services in primary health institutions may result in low utilization among rural residents. It is suggested that the government further pro-mote the National Public Health Service Program and strengthen the capacity of primary health facilities to provide primary vision care. To do so, an increase in the utilization of vision care among rural residents can be expected, which would thereby reduce potential losses caused by further vision impairment.

An Application of Discrete Probability Models to the Number of Outpatient Visits

It is hypothesized that the number of outpatient visits can be represented by three different probability models: the truncated Poisson distribution, the Zeta distribution and the logarithmic series distribution. Maximum likelihood estimates of parameters of the above distributions were obtained by using grouped data according to the number of visits. A goodness-of-fit test was also made to compare the fit of the three distributions and the value of this statistic was classified and compared according to the types of medical care facilities. Based on the likelihood ratio statistic as a test criterion, both the truncated Poisson and Zeta distributions were not appropriate for the model of the number of outpatient visits. However, the logarithmic series distribution provides a good fit to data in the case of university hospitals, general hospitals and hospitals. When we apply this distribution in the 10 most common diseases, the estimates of the parameter vary from 0.39567 to 0.54176 for university hospitals, from 0.45329 to 0.65387 for general hospitals, and from 0.55104 to 0.77625 for hospitals. On the other hand in the case of clinics, even the logarithmic series distribution cannot be fitted to the data well. A characteristic of clinic utilization with almost homogeneous treatment patterns, in spite of the fact that there are a great many clinics, could be the reason for the above results.