Treatment-seeking behaviour among persons with chronic diseases in Ghana: Does national health insurance status matter?

2021 ◽  
pp. 1-13
Author(s):  
Kofi Osei Adu
2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Razak Mohammed Gyasi

This paper examines the relationship between national health insurance status and the pattern of traditional medicine (TRM) use among the general population in Ghana. A retrospective cross-sectional survey of randomly sampled adults, aged ≥18 years (N=324), was conducted. The results indicate that TRM use was high with prevalence of over 86%. The study found no statistically significant association between national health insurance status and TRM utilisation (P>0.05). Paradoxically, major sources of TRM, frequency of TRM use, comedical administration, and disclosure of TRM use to health care professionals differed significantly between the insured and uninsured subgroups (P<0.001). Whereas effectiveness of TRM predicted its use for both insured [odds ratio (OR) = 4.374 (confidence interval (CI): 1.753–10.913;P=0.002)] and uninsured [OR = 3.383 CI: 0.869–13.170;P=0.039)], work experience predicted TRM use for the insured [OR = 1.528 (95% CI: 1.309–1.900;P=0.019)]. Cultural specific variables and health philosophies rather than health insurance status may influence health care-seeking behaviour and TRM use. The enrollment of herbal-based therapies on the national health insurance medicine plan is exigent to ensure monitoring and rational use of TRM towards intercultural health care system in Ghana.


Sci ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 25
Author(s):  
Jesse Patrick ◽  
Philip Q. Yang

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA, but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. Government in deciding the fate of the ACA.


Sci ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 30
Author(s):  
Patrick ◽  
Yang

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. government in deciding the fate of the ACA.


Author(s):  
Atina Husnayain ◽  
Nopryan Ekadinata ◽  
Dedik Sulistiawan ◽  
Emily Chia-Yu Su

Given the increasing burden of chronic diseases in Indonesia, characteristics of chronic multimorbidities have not been comprehensively explored. Therefore, this research evaluated chronic multimorbidity patterns among Indonesians using Indonesian National Health Insurance (INHI) sample data. We included 46 chronic diseases and analyzed their distributions using population-weighted variables provided in the datasets. Results showed that chronic disease patients accounted for 39.7% of total patients who attended secondary health care in 2015–2016. In addition, 43.1% of those were identified as having chronic multimorbidities. Findings also showed that multimorbidities were strongly correlated with an advanced age, with large numbers of patients and visits in all provinces, beyond those on Java island. Furthermore, hypertension was the leading disease, and the most common comorbidities were diabetes mellitus, cerebral ischemia/chronic stroke, and chronic ischemic heart disease. In addition, disease proportions for certain disease dyads differed according to age group and gender. Compared to survey methods, claims data are more economically efficient and are not influenced by recall bias. Claims data can be a promising data source in the next few years as increasing percentages of Indonesians utilize health insurance coverage. Nevertheless, some adjustments in the data structure are accordingly needed to utilize claims data for disease control and surveillance purposes.


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