METHODS: This study was based on data obtained from 3716 Malaysians aged ≥60 years as part of the National Health and Morbidity Survey (NHMS) 2018. QoL was measured using the Control, Autonomy, Self-realization and Pleasure 19-item (CASP-19) questionnaire. UI was measured using the Questionnaire Urinary Incontinence Diagnosis (QUID) score. Association between UI and QoL were examined using linear regression analysis, after controlling socio-demographic variables and comorbidities.
RESULTS: Overall, the prevalence of UI was 5.2%. By subtypes, the prevalence of stress UI and urge UI were both 2.0%, while that of mixed UI was 1.3%. The UI group rated their lives more negatively in all four domains of QoL compared with non-UI group. Those who were incontinent had lower standardized scores on control and autonomy domains of CASP-19 as well as total score. Results from linear regression analysis indicated that UI had a significantly negative impact on control and autonomy domains of QoL after controlling for socio-demographic factors and comorbidities.
CONCLUSION: UI contributes to a significant reduction on QoL of older persons. Healthcare providers need to be sensitive in evaluating and discussing UI, particularly with their older patients. Geriatr Gerontol Int 2020; 20: 38-42.
Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE.
Results: Adjusting for controls, both MPI (β =-0.245, P<0.001) and IPG at 3.1 USD (β=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=-0.210, P<0.001) than male (β=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity.
Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.
Methods: This observational study employs secondary data from various official sources of 12 states and one federal territory in Malaysia (2002-2014). Panel data of 78 observations (13 cross-sections at six points in time) were used in multivariate, fixed-effect, regressions to estimate the effects of socioeconomic variables on life expectancy at birth for male, female and both-gender.
Results: Poverty and income significantly determine female, male, and total life expectancies. Unemployment significantly determines female and total life expectancies, but not male. Income inequality and public spending on health (as a percentage of total health spending) do not significantly determine life expectancy. The coefficients of the multivariate regressions suggest that a 1% reduction in poverty, 1% reduction in unemployment, and around USD 23.20 increase in household monthly income prolong total life expectancy at birth by 17.9, 72.0, and 16.3 d, respectively. The magnitudes of the effects of the socioeconomic variables on life expectancy vary somewhat by gender.
Conclusion: Life expectancy in Malaysia is higher than the world average and higher than that in some developing countries in the region. However, it is far lower than the advanced world. Reducing poverty and unemployment and increasing income are three effective channels to enhance longevity.
METHODS: We estimated mortality using natural history models for acute hepatitis infections and GBD's cause-of-death ensemble model for cirrhosis and liver cancer. We used meta-regression to estimate total cirrhosis and total liver cancer prevalence, as well as the proportion of cirrhosis and liver cancer attributable to each cause. We then estimated cause-specific prevalence as the product of the total prevalence and the proportion attributable to a specific cause. Disability-adjusted life-years (DALYs) were calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs).
FINDINGS: Between 1990 and 2013, global viral hepatitis deaths increased from 0·89 million (95% uncertainty interval [UI] 0·86-0·94) to 1·45 million (1·38-1·54); YLLs from 31·0 million (29·6-32·6) to 41·6 million (39·1-44·7); YLDs from 0·65 million (0·45-0·89) to 0·87 million (0·61-1·18); and DALYs from 31·7 million (30·2-33·3) to 42·5 million (39·9-45·6). In 2013, viral hepatitis was the seventh (95% UI seventh to eighth) leading cause of death worldwide, compared with tenth (tenth to 12th) in 1990.
INTERPRETATION: Viral hepatitis is a leading cause of death and disability worldwide. Unlike most communicable diseases, the absolute burden and relative rank of viral hepatitis increased between 1990 and 2013. The enormous health loss attributable to viral hepatitis, and the availability of effective vaccines and treatments, suggests an important opportunity to improve public health.
FUNDING: Bill & Melinda Gates Foundation.