Promoting Smoke-Free Environments: The Impact of Thirdhand Smoke Awareness on Smoking Bans at Home.

OBJECTIVE: A creating smoke-free home is a way to protect a vulnerable group from being exposed to secondhand smoke in the home, such as children, infants, and non-smokers. Studies reported an intervention for promoting a smoke-free home by using secondhand smoke messages and smoking cessation messages. However, the thirdhand smoke (THS) message has rarely been found. Therefore, this study aimed to explore the prevalence and correlations of smoking bans in homes. METHODS: Secondary data from a community-based cross-sectional design survey was used for this study. Smoking ban status was defined as a smoking ban or no smoking ban. We used multiple logistic regression to test the association between factors and smoking ban status. An adjusted odd ratio and 95% confidence interval were reported. RESULTS: Of the 882 participants included in this analysis, 38.66% (95%CI: 35.43, 41.97) had a smoking ban at home. A multiple logistic regression analysis showed that participants who believe that secondhand or THS harms children had a greater correlation with developing a smoking ban in the home compared with those who did not believe (odd ratio: 3.94, 95% confidence interval: 2.35, 6.60 and odd ratio:4.22, 95% confidence interval:2.6, 6.86, respectively). CONCLUSION: This study found that the belief that thirdhand smoke and secondhand smoke exposure harm children. The smoke-free home message's relevance to the harms of thirdhand smoke exposure should be adapted to be promoted, especially in homes.

The prevalence of secondhand smoke exposure and related factors among schoolchildren in Northeast Thailand.

Background: The prevalence of tobacco consumption in Thailand has gradually declined; however, the prevalence of exposure to secondhand smoke (SHS) is still high. The objective of this study is to estimate the prevalence of SHS exposure and examine the association between exposure to SHS and depressive symptoms among schoolchildren, and test for moderation by the number of smokers in household. Methods: We conducted a cross-sectional study of 1105 schoolchildren. Socioeconomics factors, depressive symptoms and exposure to SHS variables were collected. We used the chi-square test for testing the factors associated to SHS exposure. In addition, we used the Mantel Haenszel test for testing interaction effect of depression to SHS exposure by the number of smokers in home. Multiple logistic regression was used to test the factors related to SHS exposure adjusted for confounders. Results: The prevalence of exposure to SHS was 58.2% (95%CI: 55.2, 61.1). The schoolchildren with abnormal depression status were 1.8 times more likely to have been exposed to SHS (95%CI: 1.3, 2.5). In addition, the number of smokers in the home did not modify the association between exposure to SHS and depressive symptoms (P: 0.964). Conclusions: An association between exposure to SHS and depressive in schoolchildren was observed, but this relationship was not affected by the number of smokers in children's homes.

Socioeconomic Inequality in Concurrent Tobacco and Alcohol Consumption

Background: Whilst several studies have examined inequity of tobacco use and inequity of alcohol drinking individually, comparatively little is known about concurrent tobacco and alcohol consumption. The present study therefore investigated inequity of concurrent tobacco and alcohol consumption in Thailand. Methods: The 2015 Health and Welfare Survey was obtained from Thailand's National Statistical Office and used as a source of national representative data. Concurrent tobacco and alcohol consumption was defined as current and concurrent use of both tobacco and alcohol. The wealth assets index was used as an indicator of socioeconomic inequity. Socioeconomic status included 5 groups ranging from poorest (Q1) to richest (Q5). A total of 55,920 households and 113,705 participants aged 15 years or over were included and analyzed. A weighted multiple logistic regression was performed. Results: The prevalence of concurrent tobacco and alcohol consumption, tobacco consumption only, and alcohol consumption only were 15.2% (95% CI: 14.9, 15.4), 4.7% (95% CI: 4.5, 4.8), and 18.9% (95% CI: 18.7, 19.1), respectively. Weighted multiple logistic regression showed that concurrent tobacco and alcohol consumption was high in the poorest socioeconomic group (P for trend: <0.001), and tobacco consumption only was also high in the poorest group (P for trend: <0.001). A high prevalence of alcohol consumption was observed in the richest group (P for trend: <0.001). Conclusions: These findings suggest that tobacco and alcohol consumption prevention programs would be more effective if they considered socioeconomic inequities in concurrent tobacco and alcohol consumption rather than focusing on single drug use.

Disease patterns among Thai adult population: an analysis of data from the hospitalization National Health Insurance System 2010.

BACKGROUND: Disease pattern is an important informational tool used by policymakers in setting priorities, strategies and allocating budgets to address the precursors or causes of health problems. OBJECTIVE: To analyze the common diseases in the adult population using in-patient information from the three health insurance coverage schemes in the fiscal year 2010. MATERIAL AND METHOD: The authors analyzed the data on in-patients with 23 major disease groups as per ICD-10 coding. The data were analyzed to obtain the number of patients, number of admissions, number of hospital mortalities, mortality rates and length of hospital stays. RESULTS: The total number of adult in-patients was 3,876,792 presenting for admission 4,863,935 times. Infectious and parasitic diseases were the most common causes of admission. Diseases of the circulatory system resulted in the highest number of mortality rate (8.72%). Intracerebral hemorrhage, neoplasm, septicemia, liver failure, coronary heart disease, HIV/AIDS, status epilepticus, pneumonia, accidents and acute renal failure were the top ten diseases with a high mortality rate. CONCLUSION: The review indicated communicable diseases are the most common disease group although non-communicable diseases were also important because of their high mortality rate.

Health insurance system and healthcare provision: nationwide hospital admission data 2010.

BACKGROUND: The three major health insurance systems are different in their medical service coverage, reimbursement process and choice of providers; leading to the question of how great are the variations in the healthcare offered and disease outcomes. OBJECTIVE: To assess whether differences exist and to analyze the effects of on healthcare provision and disease outcomes in the adult population across the three health insurance systems. MATERIAL AND METHOD: The authors analyzed the disease outcomes of the 23 major ICD-10 disease groups among the three major health insurance systems to obtain the death rates, levels of healthcare provision and the hospital charges. Factors influencing mortality rates were evaluated by multiple logistic regression analysis. RESULTS: The community, general, tertiary care and private hospitals provided hospitalization for 41.4%, 22%, 27.3% and 9.3% of hospitalized adult patients, respectively. Infectious & parasitic diseases were the most common causes of admissions. Disease of the digestive system was the most common cause of admission in general hospitals while malignancy was the most common in the tertiary care hospitals. Patients with congenital malformation, neoplasm, mental and behavioral disorder and diseases of the eye were commonly treated at tertiary care hospitals. The mean and median of hospital charges were highest in the Civil Servant Medical Benefit System (CSMBS) (26,668; 10,209 Baht), followed by the Social Security System (SSS) (21,455; 9,713 Baht) and the Universal Coverage System (UC) (13,086; 5,246 Baht). The respective overall mortality rates for the CSMBS, SSS and UC were 4.40%, 1.38% and 3.32%. After adjustment, however a significant association between UC and mortality was found with an odds ratio of 1.43 (1.40-1.45) as compared to CSMBS. In addition, other factors most influencing mortality rates were male sex, elderly age, and the levels of healthcare. CONCLUSION: The differences in charges for some groups of diseases and significantly different clinical outcomes across schemes existed. The differences in disease outcomes were not adjusted for socioeconomic status and disease severity, requiring a cautious interpretation; nevertheless, an association with a higher mortality rate under the UC scheme for inpatient services need prompt further study