- The Burden of Stroke in Kurdistan Province, Iran From 2011 to 2017
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Shahram Moradi, Ghobad Moradi, Bakhtiar Piroozi
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J Prev Med Public Health. 2021;54(2):103-109. Published online February 1, 2021
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DOI: https://doi.org/10.3961/jpmph.20.335
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Abstract
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- Objectives
The aim of this study was to calculate the burden of stroke in Kurdistan Province, Iran between 2011 and 2017.
Methods Incidence data extracted from the hospital information system of Kurdistan Province and death data extracted from the system of registration and classification of causes of death were used in a cross-sectional study. The World Health Organization method was used to calculate disability-adjusted life years (DALYs).
Results The burden of stroke increased from 2453.44 DALYs in 2011 to 5269.68 in 2017, the years of life lost increased from 2381.57 in 2011 to 5109.68 in 2017, and the years of healthy life lost due to disability increased from 71.87 in 2011 to 159.99 in 2017. The DALYs of ischaemic stroke exceeded those of haemorrhagic stroke. The burden of disease, new cases, and deaths doubled during the study period. The age-standardised incidence rate of ischaemic stroke and haemorrhagic stroke in 2017 was 21.72 and 20.72 per 100 000 population, respectively.
Conclusions The burden of stroke is increasing in Kurdistan Province. Since health services in Iran are based on treatment, steps are needed to revise the current treatment services for stroke and to improve the quality of services. Policy-makers and managers of the health system need to plan to reduce the known risk factors for stroke in the community. In addition to preventive interventions, efficient and up-to-date interventions are recommended for the rapid diagnosis and treatment of stroke patients in hospitals. Along with therapeutic interventions, preventive interventions can help reduce the stroke burden.
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Summary
- What Explains Socioeconomic Inequality in Health-related Quality of Life in Iran? A Blinder-Oaxaca Decomposition
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Satar Rezaei, Mohammad Hajizadeh, Yahya Salimi, Ghobad Moradi, Bijan Nouri
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J Prev Med Public Health. 2018;51(5):219-226. Published online August 7, 2018
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DOI: https://doi.org/10.3961/jpmph.18.012
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This study aimed to explain the health-related quality of life (HRQoL) gap between the poorest and the wealthiest quintiles in the capitals of Kermanshah and Kurdistan Provinces (Kermanshah and Sanandaj), in western Iran.
Methods This was a cross-sectional study conducted among 1772 adults. Data on socio-demographic characteristics, socioeconomic status (SES), lifestyle factors, body mass index, and HRQoL of participants were collected using a self-administered questionnaire. The slope and relative indices of inequality (SII and RII, respectively) were employed to examine socioeconomic inequality in poor HRQoL. Blinder-Oaxaca (BO) decomposition was used to quantify the contribution of explanatory variables to the gap in the prevalence of poor HRQoL between the wealthiest and the poorest groups.
Results The overall crude and age-adjusted prevalence of poor HRQoL among adults was 32.0 and 41.8%, respectively. The SII and RII indicated that poor HRQoL was mainly concentrated among individuals with lower SES. The absolute difference (%) in the prevalence of poor HRQoL between the highest and lowest SES groups was 28.4. The BO results indicated that 49.9% of the difference was explained by different distributions of age, smoking behavior, physical inactivity, chronic health conditions, and obesity between the highest and lowest SES groups, while the remaining half of the gap was explained by the response effect.
Conclusions We observed a pro-rich distribution of poor HRQoL among adults in the capitals of Kermanshah and Kurdistan Provinces. Policies and strategies aimed at preventing and reducing smoking, physical inactivity, chronic health conditions, and obesity among the poor may reduce the gap in poor HRQoL between the highest and lowest SES groups in Iran.
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Summary
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Citations
Citations to this article as recorded by
- Why is there a gap in self-rated health among people with hypertension in Zambia? A decomposition of determinants and rural‒urban differences
Chris Mweemba, Wilbroad Mutale, Felix Masiye, Peter Hangoma BMC Public Health.2024;[Epub] CrossRef - Health-related quality of life variation by socioeconomic status: Evidence from an Iranian population-based study
Sulmaz Ghahramani, Maryam Hadipour, Payam Peymani, Sahar Ghahramani, KamranB Lankarani Journal of Education and Health Promotion.2023; 12(1): 287. CrossRef - What explains the large disparity in child stunting in the Philippines? A decomposition analysis
Valerie Gilbert T Ulep, Jhanna Uy, Lyle Daryll Casas Public Health Nutrition.2022; 25(11): 2995. CrossRef - Health-related quality of life by household income in Chile: a concentration index decomposition analysis
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- Socioeconomic Inequalities in the Oral Health of People Aged 15-40 Years in Kurdistan, Iran in 2015: A Cross-sectional Study
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Ghobad Moradi, Ardavan Moinafshar, Hemen Adabi, Mona Sharafi, Farideh Mostafavi, Amjad Mohamadi Bolbanabad
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J Prev Med Public Health. 2017;50(5):303-310. Published online July 25, 2017
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DOI: https://doi.org/10.3961/jpmph.17.035
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The aim of this study was to evaluate socioeconomic inequalities in the prevalence of dental caries among an urban population. Methods: This study was conducted among 2000 people 15-40 years of age living in Kurdistan, Iran in 2015. Using a questionnaire, data were collected by 4 trained dental students. The dependent variable was the decayed, missing, and filled teeth (DMF) index. Using principal component analysis, the socioeconomic status (SES) of families was determined based on their household assets. Inequality was measured using the concentration index; in addition, the Oaxaca analytical method was used to determine the contribution of various determinants to the observed inequality. Results: The concentration index for poor scores on the DMF index was -0.32 (95% confidence interval [CI], -0.40 to -0.36); thus, poor DMF indices had a greater concentration in groups with a low SES (p<0.001). Decomposition analysis showed that the mean prevalence of a poor DMF index was 43.7% (95% CI, 40.4 to 46.9%) in the least privileged group and 14.4% (95% CI, 9.5 to 9.2%) in the most privileged group. It was found that 85.8% of the gap observed between these groups was due to differences in sex, parents’ education, and the district of residence. A poor DMF index was less prevalent among people with higher SES than among those with lower SES (odds ratio, 0.31; 95% CI, 0.19 to 0.52). Conclusions: An alarming degree of SES inequality in oral health status was found in the studied community. Hence, it is suggested that inequalities in oral health status be reduced via adopting appropriate policies such as the delivery of oral health services to poorer groups and covering such services in insurance programs.
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