The occurrence of out-of-hospital cardiac arrest (OHCA) is a critical life-threatening event which frequently warrants early defibrillation with an automated external defibrillator (AED). The optimization of allocating a limited number of AEDs in various types of communities is challenging. We aimed to propose a two-stage modeling framework including spatial accessibility evaluation and priority ranking to identify the highest gaps between demand and supply for allocating AEDs. In this study, a total of 6135 OHCA patients were defined as demand, and the existing 476 publicly available AEDs locations and 51 emergency medical service (EMS) stations were defined as supply. To identify the demand for AEDs, Bayesian spatial analysis with the integrated nested Laplace approximation (INLA) method is applied to estimate the composite spatial risks from multiple factors. The population density, proportion of elderly people, and land use classifications are identified as risk factors. Then, the multi-criterion two-step floating catchment area (MC2SFCA) method is used to measure spatial accessibility of AEDs between the spatial risks and the supply of AEDs. Priority ranking is utilized for prioritizing deployment of AEDs among communities because of limited resources. Among 6135 OHCA patients, 56.85 % were older than 65 years old, and 79.04 % were in a residential area. The spatial distribution of OHCA incidents was found to be concentrated in the metropolitan area of Kaohsiung City, Taiwan. According to the posterior mean estimated by INLA, the spatial effects including population density and proportion of elderly people, and land use classifications are positively associated with the OHCA incidence. Utilizing the MC2SFCA for spatial accessibility, we found that supply of AEDs is less than demand in most areas, especially in rural areas. Under limited resources, we identify priority places for deploying AEDs based on transportation time to the nearest hospital and population size of the communities. The proposed method will be beneficial for optimizing resource allocation while considering multiple local risks. The optimized deployment of AEDs can broaden EMS coverage and minimize the problems of the disparity in urban areas and the deficiency in rural areas.

Ambulance response times and resuscitation efforts are critical predictors of the survival rate after out-of-hospital cardiac arrests (OHCA). On the other hand, rural-urban differences in the OHCA survival rates are an important public health issue. We retrospectively reviewed the January 2011–December 2013 OHCA registry data of Kaohsiung City, Taiwan. With particular focus on geospatial variables, we aimed to unveil risk factors predicting the overall OHCA survival until hospital admission. Spatial analysis, network analysis, and the Kriging method by using geographic information systems were applied to analyze spatial variations and calculate the transport distance. Logistic regression was used to identify the risk factors for OHCA survival. Among the 4,957 patients, the overall OHCA survival to hospital admission was 16.5%. In the multivariate analysis, female sex (adjusted odds ratio:, AOR, 1.24 [1.06–1.45]), events in public areas (AOR: 1.30 [1.05–1.61]), exposure to automated external defibrillator (AED) shock (AOR: 1.70 [1.30–2.23]), use of laryngeal mask airway (LMA) (AOR: 1.35 [1.16–1.58]), non-trauma patients (AOR: 1.41 [1.04–1.90]), ambulance bypassed the closest hospital (AOR: 1.28 [1.07–1.53]), and OHCA within the high population density areas (AOR: 1.89 [1.55–2.32]) were positively associated with improved OHCA survival. By contrast, a prolonged total emergency medical services (EMS) time interval was negatively associated with OHCA survival (AOR: 0.98 [0.96–0.99]). Resuscitative efforts, such as AED or LMA use, and a short total EMS time interval improved OHCA outcomes in emergency departments. The spatial heterogeneity of emergency medical resources between rural and urban areas might affect survival rate.

Taiwan has integrated the previous vertical tuberculosis (TB) control system into the general health care system. With the phase out of the specialized TB care system and the declining TB incidence, it is likely that clinical workers become less familiar with the presentation of TB, resulting in delay in TB diagnosis and treatment. We used the detailed information of health care visits in the Taiwan National Health Insurance database to analyze the temporal pattern of the health system delay (HSD) among 3,117 patients with TB between 2003 and 2010. The median HSD was 29 days (interquartile range 5–73 days), and the median delay increased from 26 days in 2003 to 33.5 days in 2008, thereafter slightly decreased to 32 days in 2010. Patient factors associated with a longer HSD included: aged 45–64 and ≧65 years (as compared to aged <30 years); females (as compared to males); an initial visit as an outpatient (as compared to an inpatient). Provider factors were an initial visit to a provider not specialized in TB (as compared to a TB-related provider), to a primary care clinic or to a medical center (as compared to a district hospital), and in Central region, Northern region, KaoPing region, Southern region and Taipei region (as compared to in Eastern region). Longer distances from the point of initial visit to that of treatment were associated with longer HSD. Patients who switched among different levels or different types of medical care services during their illness exhibited the longest HSD. In countries where the TB care systems are being restructured from a vertical to a horizontal system, it is critical to monitor HSD and be aware of its increase. The potential increase in the HSD from 2003 to 2008 observed in this study is concerning and the decline of HSD after 2008 might be attributed to the launch of contact investigation. Our results call for actions to improve the efficiency of TB diagnosis in the health care system and to increase the awareness of TB among physicians and the general public.

Cases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level. A total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space–time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC’s surveillance statistics. Among the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map. We designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.

While studies have examined the association between weather variables and acute diarrhea in a city, region, or country, less evidence is available on the temperature effect across countries. The objective of this study is to elucidate the nonlinear and lagged association between ambient temperature and acute diarrhea in Hong Kong, Taiwan, and Japan. We collected weekly surveillance statistics on acute diarrhea with the corresponding meteorological data from 12 regions of Hong Kong, Taiwan, and Japan during 2012–2016. Firstly, we fitted the region-specific counts of acute diarrhea in a distributed lag nonlinear model (DLNM) which accounts for the non-linearity and lagged effect of temperature. Secondly, we applied meta-analysis to pool estimates across 12 regions. A total of 5,992,082 acute diarrhea cases were identified. We found that (1) the pooled overall cumulative relationship between the relative risk (RR) of acute diarrhea and temperature was the greatest (RR = 1.216; 95% CI: 1.083, 1.364) at 11 °C; (2) a pooled predictor-specific summary association at lower temperatures (12 °C or 25th percentile) began immediately and vanished after four weeks. Predictions and error analysis for new onsets of acute diarrhea in 2017 were evaluated. An early warning system based on the information of temperature variation was suggested for acute diarrhea control management.

Background: The decline of the incidence rate of tuberculosis in Taiwan has been partly attributed to the launch of the directly observed therapy short course (DOTS) program in 2006, followed by the DOTS-Plus in 2007. However, with the phasing out of the specialized tuberculosis care system and the declining incidence, clinical workers in Taiwan might become less familiar with the presentation of tuberculosis. Complementing the patient-pathway analysis with health system delay estimates, the objective of this study is twofold: to estimate the alignment between patient care initiation and the availability of prompt diagnostic and treatment services, and to identify the risk factors of delayed tuberculosis treatment. Methods: The study population included all Taiwanese patients with incident tuberculosis in 2013. We (1) identified 11,507 incident tuberculosis patients from the 2013 National TB Registry, and (2) linked 10,932 Taiwanese from the registry to the 2012–2013 National Health Insurance Research Database. We assessed patient’s care-seeking pathways and associated the determinants of health system delay in a Cox model. Results: The overall health system delay was 46 days. We found that 20.5 and 3.5% of 10,932 tuberculosis patients were diagnosed and treated respectively at the initial visit to seek care for TB-related symptoms. Risk factors related to the prolonged health system delay included female gender (adjusted HR = 0.921, 95% CI: 0.884, 0.960), age > =65 years (adjusted HR = 0.720, 95% CI: 0.692, 0.750), non-severe (chest X-ray without cavities) (adjusted HR =0.721, 95% CI 0.683–0.760), chronic respiratory diseases (adjusted HR = 0.544, 95% CI: 0.522, 0.566), living in long-term care facilities (adjusted HR = 0.580, 95% CI: 0.525,0.640), an initial visit at a primary care clinic (adjusted HR = 0.588, 95% CI: 0.565, 0.612), and living in southern Taiwan (adjusted HR = 0.887, 95% CI: 0.798, 0.987). Conclusions: The low access to TB diagnostic and treatment services at the initial visit and the prolonged health system delay indicate inefficiency in the health care system. Strengthening training of physicians at public hospitals and health workers at nursing homes might improve the efficiency and timeliness of tuberculosis diagnosis and treatment in Taiwan.