The Effect of Human Development Index on Obesity Prevalence at the Global Level: A Spatial Analysis.

Background: Obesity is one of the major public health concerns, and its prevalence is increasing worldwide. This study aimed to investigate the effect of human development index on the prevalence of obesity across 152 countries. Methods: Country-level data on obesity prevalence and its influencing variables related to 152 countries were obtained during 2000-2019 from several sources. A Spatial Bayesian Hierarchical model was employed in this research, and the analyses were performed using R statistical software (version 3.6.1). Results: We found a positive relation between HDI and obesity prevalence, in such a way if low HDI countries advance to high HDI countries, the obesity rate is expected to increase significantly by 7.45%. Moreover, the association between obesity prevalence and the percentage of people aged 40-59 (ß=0.07), urbanization rate (ß=0.11), percentage of internet users (ß=0.01), percentage of alcohol users (ß=0.16), milk consumption per capita (ß=0.15) and Percentage of depression (ß=0.58) was significantly positive. Conversely, per capita consumption of fruits and vegetables (ß=-0.15), and smoking rate (ß=-0.02) was negatively associated with obesity prevalence. Conclusion: The prevalence of obesity is growing across all countries, especially in the countries with high and very high HDI. Therefore, policymakers must also pay attention to the negative effects of development when trying to improve the welfare of society.

Hospital Expenditure at the End-of-Life: A Time-to-Death Approach.

BACKGROUND: In recent years the use of time to death (TTD) variables in the modeling of individual health expenditures has been of interest to health economics researchers. The aim of this study was to investigate the effect of age and TTD on hospital inpatient expenditure (HIE). METHODS: We used a claims database from Iran Health Insurance Organization of Tehran city that includes considerable proportion of Tehran residents and contains information on insured individuals' HIE. We included HIE of all insured decedents (30 to 90 years old) who died during March 2013 and March 2014 (n=1018). No sampling was required. According to the decedents' date of death, we extracted their last 24 months HIE. The period of time March 30, 2011 until March 30, 2014 (3 years) was used to guarantee a full 24 months of observations for decedents. A two-part econometric model was employed to investigate the effect of age, TTD, and some demographic variables on probability and conditional amount of individuals' hospital expenditure. Stata software (version 16.0) was used for data processing and analysis. RESULTS: Our results demonstrated that the month-based TTDs especially near months before death of decedents (TTD1 to TTD10) significantly affected both probability and conditional amount of HIE. One month before death incurred more HIE than the rest of the months. A further interesting finding is that after including TTD, age variable as a conditional driver of HIE loses its direct effect on decedents' HIE, but age TTD interaction effect on HIE is still positive and statistically significant. CONCLUSION: The results confirm that TTD as a proxy of mortality indicator has a considerable effect on decedents' HIE. The age variable has not directly affected decedents' HIE but indirectly and through its interaction with TTD has a statistically significant effect on HIE. In addition to age, policy-makers should consider TTD to make better predictions of future HIE.

Ageing and inpatient hospital expenditures: A study of steepening.

Background: Considering the health economics aspect, the effect of population aging on the growth of medical expenditures is of great importance. Thus, the aim of this study was to investigate the growth rate of inpatient hospital expenditures (IHE) of older age groups compared to younger age groups or testing of steepening hypothesis. Methods: In this descriptive-analytic study, monthly IHE data of insured patients from April 2011 to March 2014 was collected from Iran Health Insurance Organization database. The sample consisted of 297,145 patients who were selected using stratified random sampling among the patients insured by different health insurance funds. Using the Age-cut method, patients were classified into two main age groups: older than 60 and under 60 years old. The average IHE of the two groups and the growth rate of their expenditures were estimated. At the 95% significance level, the difference in average growth rate of IHE was tested between the two age groups using tstatistic in Stata (version 13.0) software. Results: The findings of this study revealed that per capita IHE for the older and younger groups was $469 and $399, respectively. The monthly average growth rate of IHE for the older (60-90 yrs.) and younger groups (30-59 yrs.) was 2.43% and 2.38%, respectively. The difference between the monthly average growth rate of the older and younger groups was not statistically significant (t = - 0.0332; P value = 0.9736). Conclusion: Our study results rejected the steepening hypothesis. The monthly average growth rates of IHE in both older and younger age groups have risen to a similar extent over the period of 36 months. Further research should focus on the inpatient hospital expenditures of younger age groups to explain the causes of their increased expenditures.

An Estimation of the Potential Utilization in Iranian Pharmaceutical Industry Involved in the Stock Exchange, 2008-2012.

The aim of this study was to measure the potential of production and the capacity used in the pharmaceutical industry. Capacity use is the actual production rate to the potential output, which reflects the gap between actual production and production capacity. Through econometric methods, translog cost function in the short run along with functions of share cost of production factors is estimated through seemingly unrelated repeated regression (SURE) as a multivariate regression analysis provided by zeller. During the study the capacity used is decreasing. The capacity used, which calculated by weighted average, also decreased and the amount during the study period is much less than the simple average of the industry. Average capacity utilization in the industry over five years of study is equal to 57% while the average capacity used calculated by the weighted of industry average is 37%. To enhance the economic potential requires a proper use of resources, creation of favorable economic structure and productivity of the industry. Due to the large amount of unused capacity in the pharmaceutical industry there is no need to invest anymore unless in new grounds and it is obvious that more investment will change using capacity.

Predictors of treatment retention in a major methadone maintenance treatment program in iran: a survival analysis.

BACKGROUND: To identify correlates related to retention time of a cohort study of the opioid-dependent patients participating in the Methadone Maintenance Treatment (MMT) program offered by a major addiction treatment clinic in Tehran, Iran between April 2007 and March 2011. METHODS: Several parametric Survival models assuming Weibull, Log-normal and Log-logistic distributions were compared to search for association between covariates and risk of relapse and dropping out of treatment among 198 patient participants. RESULTS: According to Akaike Information Criterion (AIC), Log-normal model had the best fitting. Estimates of this model indicated that increase in average methadone dosage was associated with longer retention time. Correlates associated with shorter retention time were suffering from mental disorders, using stimulant drugs, being poly-substance dependents and having prior treatments. CONCLUSIONS: Findings of this study provide support for giving more attention to patients who are poly-substance or stimulant-drug dependents, have non-substance psychiatric comorbidity and the ones with addiction treatment history. Independent of patient characteristics, retention improved as the dose of methadone increased.

Stated time preferences for health: a systematic review and meta analysis of private and social discount rates.

BACKGROUND: The present study aimed to provide better insight on methodological issues related to time preference studies, and to estimate private and social discount rates, using a rigorous systematic review and meta-analysis. METHODS: We searched PubMed, EMBASE and Proquest databases in June 2013. All studies had estimated private and social time preference rates for health outcomes through stated preference approach, recognized eligible for inclusion. We conducted both fixed and random effect meta-analyses using mean discount rate and standard deviation of the included studies. I-square statistics was used for testing heterogeneity of the studies. Private and social discount rates were estimated separately via Stata11 software. RESULTS: Out of 44 screened full texts, 8 population-based empirical studies were included in qualitative synthesis. Reported time preference rates for own health were from 0.036 to 0.07 and for social health from 0.04 to 0.2. Private and social discount rates were estimated at 0.056 (95% CI: 0.038, 0.074) and 0.066 (95% CI: 0.064, 0.068), respectively. CONCLUSIONS: Considering the impact of time preference on healthy behaviors and because of timing issues, individual's time preference as a key determinant of policy making should be taken into account. Direct translation of elicited discount rates to the official discount rates has been remained questionable. Decisions about the proper discount rate for health context, may need a cross-party consensus among health economists and policy makers.

Analysis of economic determinants of fertility in Iran: a multilevel approach.

BACKGROUND: During the last three decades, the Total Fertility Rate (TFR) in Iran has fallen considerably; from 6.5 per woman in 1983 to 1.89 in 2010. This paper analyzes the extent to which economic determinants at the micro and macro levels are associated with the number of children in Iranian households. METHODS: Household data from the 2010 Household Expenditure and Income Survey (HEIS) is linked to provincial data from the 2010 Iran Multiple-Indicator Demographic and Health Survey (IrMIDHS), the National Census of Population and Housing conducted in 1986, 1996, 2006 and 2011, and the 1985-2010 Iran statistical year books. Fertility is measured as the number of children in each household. A random intercept multilevel Poisson regression function is specified based on a collective model of intra-household bargaining power to investigate potential determinants of the number of children in Iranian households. RESULTS: Ceteris paribus (other things being equal), probability of having more children drops significantly as either real per capita educational expenditure or real total expenditure of each household increase. Both the low- and the high-income households show probabilities of having more children compared to the middle-income households. Living in provinces with either higher average amount of value added of manufacturing establishments or lower average rate of house rent is associated to higher probability of having larger number of children. Higher levels of gender gap indices, resulting in household's wife's limited power over household decision-making, positively affect the probability of having more children. CONCLUSION: Economic determinants at the micro and macro levels, distribution of intra-household bargaining power between spouses and demographic covariates determined fertility behavior of Iranian households.

Avoidable Mortality Differences between Rural and Urban Residents During 2004-2011: A Case Study in Iran.

BACKGROUND: Avoidable mortality as an indicator for assessing the health system performance has caught the attention of researchers for a long time. In this study we aimed to compare the health system performance using this indicator in rural and urban areas of one of Iran's southern provinces. METHODS: All deaths (29916) which happened during 2004-2011 in Bushehr province were assessed. Nolte and McKee's avoidable deaths model was used to distinguish avoidable and unavoidable conditions. Accordingly, all deaths were classified into four categories including three avoidable death categories and one unavoidable death category. STATA software was used to conduct Poisson Regression Test and age-standardized death rate. RESULTS: Findings showed that avoidable mortality rates declined in both urban and rural areas at 3.33% per year, but decline rates were influenced by Ischemic Heart Disease (IHD) and preventable death categories to treatable death category. Annual decline rate for IHD category in rural and urban areas was nearly the same as 8%, but in preventable death category, rural areas experienced more decreases than urban ones (7% vs 5% respectively). However, decline rate in treatable mortality neither in urban and nor in rural areas was statistically significant. CONCLUSION: Despite the annual decline in the rate of avoidable deaths, policy making initiatives especially screening and inter-sectoral measures targeting cause of deaths such as colon and breast cancers, hypertension, lung cancer and traffic accidents, can still further decrease avoidable deaths in both areas.