RESUMO
Our study aims to measure outpatient waiting times at Vietnam health facilities according to the socioeconomic characteristics. We employed the 2015 Vietnam District and Commune Health Facility Survey which was a cross-sectional study designed by the World Bank in collaboration with the Vietnam Health Strategy and Policy Institute. This survey was designed to be representative of six provinces (Dien Bien, Hanoi, Binh Dinh, Dak Lak, Dong Nai, and Dong Thap) drawn from six distinct geographical regions of Vietnam. Data from 4949 outpatients at district hospitals (DHs) and 1724 outpatients at commune health centers (CHCs) were extracted for final analysis. We recorded average outpatient waiting times of 32.58 min at DHs and of 11.58 min at CHCs. Four hundred and forty-five outpatients at DHs (9.0%) and 720 those at CHCs (42.8%) were examined immediately (waiting time = 0 min). Outpatient waiting times were various in six distinct geographical regions. With an investigation according to several socioeconomic characteristics, significant differences in outpatient waiting times were observed at both two levels of health facilities as measured by province, age, self-reported health status, patient's wealth, ethnicity, and health insurance. Conclusions. Outpatient waiting times from arrival at health facility until receiving care were significantly distinct amongst two health facility levels, revealing longer at DHs compared to at CHCs. There was significantly higher proportion of outpatients examined immediately at CHCs compared to at DHs. Our study suggests that, vulnerable populations, with longer outpatient waiting time, should be dealt with in appropriate models towards each medical facility according to key socioeconomic factors to contribute to simplify the process of medical examination and treatment for outpatients.
RESUMO
BACKGROUND: The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. RESULTS: We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. CONCLUSIONS: Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .
