There are few studies comparing proportion, frequency, mortality and mortality rate of antimicrobial-resistant (AMR) bacterial infections between tertiary-care hospitals (TCHs) and secondary-care hospitals (SCHs) in low and middle-income countries (LMICs) to inform infection control strategies. We evaluated bloodstream infections (BSIs) from 2012 to 2015 in 15 TCHs and 34 SCHs in Thailand. There were differences in the proportions (%) of BSI caused by AMR strains for some pathogens between TCHs and SCHs. Of 19,110 patients with AMR BSI, 6,491 (34.0%) died. Among patients with AMR BSI, there were no or minimal differences in mortality proportion for all AMR pathogens between TCHs and SCHs. However, the frequency and mortality rates of AMR BSI were considerably higher in TCHs for most pathogens. For example, the mortality rate of hospital-origin carbapenem-resistant Acinetobacter baumannii BSI in TCHs was two and a half times higher than that in SCHs (8.4 vs. 3.6 per 100,000 patient-days at risk, mortality rate ratio 2.51; 95% confidence interval 1.44 to 4.37, p=0.0012). Targets of and resources for antimicrobial stewardship and infection control programs in LMICs may need to be tailored based on hospital type and size, as burden of AMR infections could differ by hospital setting.

There is little evidence to describe the burden of skin diseases in developing countries and its accuracy remained uncertain. We aimed to examine prevalence and disability adjusted life years (DALYs) of skin diseases in a Thai general population in Ubonratchathani. Based on real-world healthcare service data (diagnoses, prevalence, and cause-specific mortality) retrieved from the National Health Security Office reimbursement database, we used a simplified prevalence-based approach adopted in the Global Burden of Diseases to compute disease burden, measured as DALYs, of skin diseases. DALYs was calculated as the sum of years lost due to disability and years of life lost due to skin diseases, with adoption of previously published averaged disability weights and a 95% uncertainty interval (UI) estimated using a Bayesian bootstrap technique. From a total population of 1,503,945, 110,205 people were affected by skin disease in 2018—an overall prevalence of 7%. The prevalence varied across sex, age group and geographic areas. The most common skin diseases treated in Ubonratchathani’s healthcare services were dermatitis, bacterial skin diseases and urticaria (prevalence of 2.35%, 2.21% and 0.89% respectively). Overall DALYs of skin diseases in Ubonratchathani population was 26,125 (95%UI 24,783–27,467), and this was relatively higher in men than women. (DALYs 13,717 (12,846–14,588) and 12,408 (11,417–13,399) for men and women respectively). The greatest contributors of DALYs were cellulitis, decubitus ulcer and contact dermatitis (11,680, 4,806 and 1,598 years respectively). In conclusion, skin disease caused substantial disease burden in this Thai population, with cellulitis being the largest contributor.

Introduction: Most stroke risk prediction equations were developed in Western populations. These risk scores perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new stroke risk algorithm for estimating 5-year risk of developing stroke in a Thai general population using large electronic health records. Methods: This retrospective cohort was derived from the linkage of health checks data with diagnostic information from electronic health records of 483,285 men and women residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of stroke over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on stroke risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 241,643 individuals aged ≥30 years, who contributed 1,393,990 person-years of follow-up and 1,884 incident cases of stroke, while the validation cohort comprised of 241,642 individuals (1,393,420 person-years), with 1,915 incident cases of stroke. The risk equation was 0.0584 x Age (years) + 0.4538 x Sex (Male=1) + 0.0040 x Systolic BP (mmHg) + 0.2886 x Hypertension (present=1) + 0.5214 x Diabetes (present=1) + 0.0266 x Body mass index (kg/m 2 ) - 0.0039 (if exercise 1-2 days/week) or -0.2827 (if exercise 3-5 days/week) or -0.3179 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort, with the area under the receiver operating characteristic curve of 0.756 (95%CI 0.750-0.772). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 701.43 (p<0.001). Conclusions: This simple stroke risk score is the first risk algorithm to estimate the 5-year risk of stroke in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and also by the public.

Introduction: Body mass index (BMI) and waist circumference has been reported to have a positive association with risk of coronary heart disease (CHD) and their optimal levels have been proposed. However, the association was less well described in Asian population. Hypothesis: This study aimed to examine the risk of developing CHD across different levels of BMI and waist circumference in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 708,544 men and women aged 20 years and above residing in Ubon Ratchathani. We examined the incidence of CHD over 6 years of follow-up in individuals with different levels of BMI defined by the WHO Asia-Pacific cut-offs and central obesity defined as waist circumference higher than half of each individual’s height. Corresponding hazard ratios were computed using Cox proportional hazards regression. Results: Over 3,514,681 person-years, 2,562 CHD cases developed, an overall incidence of 0.73 (95%CI 0.70-0.76) per 1,000 person-years. BMI had a J-shape association with CHD risk, with those with a BMI of 20-22.4 kg/m2 showing the lowest CHD incidence. Waist circumference had a curvilinear relationship with CHD risk, with CHD risk starting to increase after waist circumference of 80 and 85 cm in women and men respectively. CHD risk increased with higher levels of BMI and waist circumference (Table 1). Compared to those with BMI of 20.0-22.9 kg/m 2 and without central obesity, those with BMI higher than 30 kg/m 2 and with and without central obesity had a 1.8 and 2.4 fold increased risk of CHD (Adjusted hazards ratio 1.80 (1.46-2.24) and 2.39 (1.38-4.13) respectively). Conclusions: Different levels of BMI and waist circumference conferred different CHD risk. Change in optimal cut-off of BMI and waist circumference for the Thai population should be considered.

Introduction: Most heart risk prediction equations were developed in Western populations. These risk scores are likely to perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new risk algorithm for estimating 5-year risk of developing coronary heart disease (CHD) in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 608,544 men and women aged 20 years and above residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of CHD over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on CHD risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 304,272 individuals, who contributed 1,757,369 person-years of follow-up and 1,272 incident cases of CHD, while the validation cohort comprised of 304,272 individuals (1,757,312 person-years), with 1,290 incident cases of stroke. The risk equation was 0.0580 x Age (years) + 0.5739 x Sex (Male=1) + 0.3850 x Hypertension (present=1) + 0.7080 x Diabetes (present=1) + 0.0386 x Body mass index (kg/m 2 ) + 0.2117 x Central obesity (present=1) - 0.1389 (if exercise 1-2 days/week) or -0.3975 (if exercise 3-5 days/week) or - 0.5598 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort with the area under the receiver operating characteristic curve of 0.790 (95%CI 0.779-0.801). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 809.45 (p<0.001). Conclusions: This simple heart risk score is the first risk algorithm to estimate the 5-year risk of CHD in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and by the public.