Predictive Factors of Growth Among Preterm MigrantChildren in Kanchanaburi Province, Thailand

This research soughtto study the predictive factors of the duration of exclusive breastfeeding, mother’sfeeding behaviors,and health service accessibility onthe growth of preterm migrant children in Kanchanaburi Province, Thailand. The subjects were 156 Burmese migrant mothers with preterm children 18-24 months old who received health check-upsat well-baby clinics in Kanchanaburi Province. The data analysis was performed utilizingdescriptive and multiple logistic regression statistics. The results revealed that although premature infants should reach normal growth the same as full-term children at 2 years old,41%of the child subjects were underweight, and 46.8%had stunted growth. Also, 53.2% of the mothers exclusively breastfed for less than 6 months due to the mothers having to returnto work. Upon performing multiple logistic regression analysis, it was found that exclusive breastfeeding from birth to the age of 6 months old and the feeding behaviors of the mothers were the most significantpredictorsinfluencing the growth of premature migrant children.In comparison, health service accessibility did not affectthe growth of premature migrant childrensince the majority of migrant mothers (88.5%) regularly brought their preterm children forhealth check-ups. Ourfindings strongly suggested that the education of migrant mothers should be continuously promoted to emphasize the importance of exclusive breastfeeding of their preterm children, including in their workplace, and to encourage migrant mothers to recognize the importance of providing complementary foods to suit their migrant Burmese lifestyle.

Economic Conditions and Mental Health in Thailand

BackgroundThere is a continual rise in the number of people suffered from mental disorder around the world. Several factors are believed to be connected to the development of mental disorders. Apart from health factors, mental disorders could also be caused by the economic and social factors associated with the patients. However, none of the existing literature has provided the analysis by taking into account all the possible causes together. This paper attempts to fill this gap in the literature by ascertaining the relationship between economic and social factors in relation to mental disorders.MethodsUsing the number of mental disorder cases in all 77 provinces in Thailand during the period 2015 to 2017, this paper uses either a fixed effects or a random effects model, depending on the results of the Hausman test, to analyse the effect of economic and social factors on the number of mental disorder cases in Thailand.ResultsGenerally, we found that better economic situations, as measured by higher GPP per capita, higher employment rates and lower household debt, help reduce mental disorder rates, while social factors such as greater health service accessibility and lower technology accessibility also reduce the number of mental disorder cases. However, death and divorce rates are not consistent in their effect on mental disorder rates, their impact seeming to depend on what type of mental disorder is being considered. ConclusionsWhen considering different types of disorders, we found that they could be affected by each of these factors in different ways.

Assessing The Youth-Friendliness Of Youth Clinics In Northern Sweden: A Survey Analyzing The Perspective Of The Youth

Background: Sweden has nearly 300 youth clinics that have been offering services since the 1970s. However, no evaluation has been done to assess their youth-friendliness. This study aims to assess: i) to what extent youth clinics are perceived as youth-friendly by the young people using them; and ii) if the level of youth friendliness is equally perceived across different sociodemographic groups of users. Methods: The four northernmost counties of Sweden were included in the study. Of the total identified 22 youth clinics, 20 participated by giving out questionnaires to the youth after their visits to the respective youth clinics. In total 1,110 youth participated in the study and answered questions according to the World Health Organization’s criteria of accessibility, equity, respect, privacy and confidentiality, no judgement, and quality. Means and frequencies were calculated, and t-test and ANOVA were used to compare means by sociodemographic variables. Results: Participants perceived the youth clinics as very youth friendly across the measured domains, with scores as high as 4.8 and 4.9 (of a maximum of 5).Youth clinics were perceived in a similar way regardless of gender, but other sociodemographic factors influenced some of the domains, especially ethnic background. Conclusions: The perception of youth friendliness in youth clinics was very high. Nonetheless, younger users; users who did not categorize themselves as either heterosexual, homosexual, or bisexual; users with trans-experiences; and users with non-Swedish backgrounds gave youth clinics lower scores for certain domains. Keywords: youth clinic, Sweden, youth friendly, health services, health service accessibility, youth, questionnaire

Travel times to hospitals in Australia

Travel time to hospital is a key measure of health service accessibility, and impacts patients’ experiences of care and health outcomes. Methods used to estimate travel time vary across studies. In Australia the smallest geographical areas defined by the Australian Bureau of Statistics for the release of population counts are mesh blocks (MBs) and the smallest geographical areas for the release of health-related statistics are statistical areas level 2 (SA2). SA2s are built up from whole MBs. This project used the Open Source Routing Machine (OSRM) HTTP server to compute estimated travel times between the centroid of each inhabited MB and each hospital in Australia, as well as the shortest travel times between MBs and any hospital. By computing population-weighted averages across MBs, the average travel times to hospitals and the shortest travel time to any hospital were estimated for each SA2. This dataset will promote consistency across studies investigating geographic influences on health care in Australia, and the methods are applicable to generating similar datasets for other countries.