METHODS: The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases.
RESULTS: The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20-29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia.
CONCLUSIONS: Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.
METHODS: A retrospective analysis of the 14-year data from 2005-2018 of confirmed S.suis patients admitted at Chiang Mai University Hospital (CMUH) was conducted. Descriptive statistics were used to summarize the data of patients' characteristics, healthcare utilization and costs. The multiple imputation with predictive mean matching strategy was employed to deal with missing Glasgow Coma Scale (GCS) data. Generalized linear models (GLMs) were used to forecast costs model and identify determinants of costs associated with S.suis treatment. The modified Park test was adopted to determine the appropriate family. All costs were inflated applying the consumer price index for medical care and presented to the year 2019.
RESULTS: Among 130 S.suis patients, the average total direct medical cost was 12,4675 Thai baht (THB) (US$ 4,016), of which the majority of expenses were from the "others" category (room charges, staff services and medical devices). Infective endocarditis (IE), GCS, length of stay, and bicarbonate level were significant predictors associated with high total treatment costs. Overall, marginal increases in IE and length of stay were significantly associated with increases in the total costs (standard error) by 132,443 THB (39,638 THB) and 5,490 THB (1,715 THB), respectively. In contrast, increases in GCS and bicarbonate levels were associated with decreases in the total costs (standard error) by 13,118 THB (5,026 THB) and 7,497 THB (3,430 THB), respectively.
CONCLUSIONS: IE, GCS, length of stay, and bicarbonate level were significant cost drivers associated with direct medical costs. Patients' clinical status during admission significantly impacts the outcomes and total treatment costs. Early diagnosis and timely treatment were paramount to alleviate long-term complications and high healthcare expenditures.
MATERIALS AND METHODS: A systematic literature search was performed through SCOPUS database and Google Scholar from January till March 2018. All published articles which developed stature estimation from different types of bone, methods and type of statures (i.e. living stature, forensic stature and cadaveric stature) were included in this study. Risks of biases were also assessed. Population studies with no regression equations were excluded from the study.
RESULTS: Seven studies that met the inclusion criteria were identified. In the South-East Asia region, regression equations for stature estimation were developed in Thailand and Malaysia. In these studies, bone measurements were done either by radiography, direct bone measurement, or palpation on body surface for anatomical bony prominence. All of these studies used various parts of bones for stature estimation.
CONCLUSION: The most widely used regression equations for stature estimation in South-East Asian population were from the Thailand population. Further research is recommended to develop regression equations for other South-East Asian countries.