Socioeconomic inequality in informal payments for health services among Iranian households: a national pooled study.

BACKGROUND: There is limited evidence on the prevalence and socioeconomic inequality in informal payments (IP) of households in the Iranian health system. This study was conducted to investigate the prevalence of IP and related socioeconomic inequalities among Iranian households in all provinces. METHOD: Data on Household Income and Expenditure Surveys (HIES) for 91,360 households were used to examine the prevalence and inequality in informal health sector payments in the years 2016 to 2018. The Normalized Concentration Index (NC) was used to examine inequality in these payments and the decomposition analysis by the Wagstaff approach was used to determine the share of variables affecting the measured inequality. RESULTS: Of the total households, 7,339 (7.9%) reported IP for using health services. Urban households had higher IP (10%) compared to rural ones (5.42%). Also, the proportion of households with IP in 2016 (11.69%) was higher than in 2017 (9.9%), and 2018 (4.60%). NC for the study population was 0.129, which shows that the prevalence of IP is significantly higher in well-off households. Also, NC was 0.213 (p < 0.0001) and -0.019 for urban and rural areas, respectively (p > 0.05). Decomposition analysis indicated that income, sex of head of household, and the province of residence have the highest positive contribution to measured inequality (with contributions of 156.2, 45.8, and 25.6%, respectively). CONCLUSION: There are a significant prevalence and inequality in IP in Iran's health system and important variables have shaped it. On the whole, inequality was pro-rich. This may lead to increasing inequality in access to quality services in the country. Our findings showed that previous health policies such as regulatory tools, and the health transformation plan (HTP) have not been able to control IP in the health sector in the desired way. It seems that consumer-side policies focusing on affluent households, and high-risk provinces can play an important role in controlling this phenomenon.

Which patients bring the most costs for hospital? A study on the cost determinants among COVID-19 patients in Iran.

BACKGROUND: Accurate information on the cost determinants in the COVID-19 patients could provide policymakers a valuable planning tool for dealing with the future COVID-19 crises especially in the health systems with limited resources. OBJECTIVES: This study aimed to determine the factors affecting direct medical cost of COVID-19 patients in Hamadan, the west of Iran. METHODS: This study considered 909 confirmed COVID-19 patients with positive real-time reverse-transcriptase polymerase-chain-reaction test which were hospitalized from 1 March to 31 January 2021 in Farshchian (Sina) hospital in Hamadan, Iran. A checklist was utilized to assess the relationship of demographic characteristics, clinical presentation, medical laboratory findings and the length of hospitalization to the direct hospitalization costs in two groups of patients (patients with hospitalization ≤ 9 days and > 9 days). Statistical analysis was performed using chi-square, median test and multivariable quantile regression model at 0.05 significance levels with Stata 14 software program. RESULTS: The median cost of hospitalization in patients was totally 134.48 dollars (Range: 19.19-2397.54) and respectively 95.87 (Range: 19.19-856.63) and 507.30 dollars (Range: 68.94-2397.54) in patients with hospitalization ≤ 9 days and > 9 days. The adjusted estimates presented that in patients with 9 or less hospitalization days history of cardiovascular disease, wheezing pulmonary lung, SPO2 lower than 90%, positive CRP, LDH higher than 942 U/L, NA lower than 136 mEq/L, lymphosite lower than 20% and patients with ICU experience had significantly positive relationship to the median of cost. Moreover, in patients with more than 9 hospitalization days, history of cardiovascular disease and ICU experience was statistically positive association and age older than 60 years and WBC lower than 4.5 mg/dL had statistically negative relationship to the median of hospitalization cost. CONCLUSION: As the length of hospital stay, which can be associated with the severity of the disease, increases, health systems become more vulnerable in terms of resource utilization, which in turn can challenge their responsiveness and readiness to meet the specialized treatment needs of individuals.

Socio-economic inequality in reported dental self-care behaviour among Iranian households: A national pooled study.

BACKGROUND: Setting out effective prevention strategies in dental diseases needs recognition related factors of the prevention behaviours and targeting the most disadvantaged groups in the term of dental hygiene. This study aimed to investigate socio-economic inequality in the dental self-care status (DSS) of Iranian households and decompose the measured inequality into its contributors. METHOD: In this cross-sectional study, pooled data were extracted from Households Income and Expenditure Surveys (HIESs) conducted in Iran from 2012 to 2017. The index of socio-economic status (SES) for each household was constructed using principal components analysis (PCA). We used Wagstaff normalized concentration index as a measure of socio-economic inequality in dental self-care. Decomposition analysis was applied to determine the main factors contributed to the measured inequality. RESULTS: The prevalence of dental self-care in the whole population was 40.56%. The total concentration index was 0.271 (CI: 0.266, 0.275). The results of decomposition analysis for the measured inequality showed that SES, was the highest positive contributors (90.19%) followed by sex of household's head (12.15%), place of residence (11.79%) and education level of household's head (11.71%). Furthermore, the province of residence had the highest negative contribution (-11.37) to the inequality. CONCLUSION: The findings of this study showed that a huge portion of the observed inequality was explained by SES that might give us a policy recommendation: There is room for improving dental health and reducing inequality in dental self-care by paying more attention to SES-disadvantaged households.

Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree.

OBJECTIVES: Chronic kidney disease (CKD) is one of the main causes of morbidity and mortality worldwide. Detecting survival modifiable factors could help in prioritizing the clinical care and offers a treatment decision-making for hemodialysis patients. The aim of this study was to develop the best predictive model to explain the predictors of death in Hemodialysis patients by data mining techniques. METHODS: In this study, we used a dataset included records of 857 dialysis patients. Thirty-one potential risk factors, that might be associated with death in dialysis patients, were selected. The performances of four classifiers of support vector machine, neural network, logistic regression and decision tree were compared in terms of sensitivity, specificity, total accuracy, positive likelihood ratio and negative likelihood ratio. RESULTS: The average total accuracy of all methods was over 61%; the greatest total accuracy belonged to logistic regression (0.71). Also, logistic regression produced the greatest specificity (0.72), sensitivity (0.69), positive likelihood ratio (2.48) and the lowest negative likelihood ratio (0.43). CONCLUSIONS: Logistic regression had the best performance in comparison to other methods for predicting death among hemodialysis patients. According to this model female gender, increasing age at diagnosis, addiction, low Iron level, C-reactive protein positive and low urea reduction ratio (URR) were the main predictors of death in these patients.

Decomposition of Health Inequality Determinants in Shiraz, South-west Iran.

BACKGROUND: Despite the enhancement in health outcomes worldwide, health inequity and inequality is one of the most relevant topics both for health policy and public health. This research was designed to decompose the health inequality of people living in Shiraz, south-west Iran. METHODS: Data were obtained from a multistage-sample survey conducted in Shiraz from April to May 2012, to find determinants of health related quality of life (HRQoL). General health (GH) and mental health (MH) were used as health status. As a measure of socioeconomic inequality, a concentration index of GH and MH was used and decomposed into its determinants. RESULTS: The overall concentration indices of MH and GH in Shiraz were 0.023 (95% CI: 0.015, 0.031) and 0.016 (95% CI: 0.009, 0.022), respectively. Decomposition of the concentration indices indicated that income made the largest contribution (39.92% for GH and 39.82% for MH) to income-related health inequality. Education (about 25% for GH and 34% for MH), insurance (about 14% for GH and 11% for MH), and occupation (about 12% for GH and 11% for MH) also proved important contributors to the health inequality in Shiraz. CONCLUSIONS: There exist MH and GH inequalities in Shiraz. Apart from insurance, most of the health inequalities in Shiraz can be explained through factors beyond the health sector. Hence, implementing redistributive policies and education expansion programs as well as providing an insurance scheme and secure career conditions could decrease these unethical health inequalities.

Horizontal inequity in access to outpatient services among Shiraz City residents, Iran.

BACKGROUND: lack of access to health services has been mentioned as one of the main causes of health inequity in health system. The aim of this study was to measure horizontal inequity in access to outpatient services in Shiraz. METHODS: This household survey was conducted among 1608 participants above 18 years in Shiraz in 2012. Four-stage sampling was used. According to high amount of zero-valued of outpatient services utilization, Zero inflated regression model was established. We computed concentration index (CI) for determining actual (CIM) and indirect standardized utilization (CIIS) of outpatient services in order to compute horizontal inequity index (HII). The results were analyzed using Stata software, version 8. RESULTS: The CIM was not statistically significant (-0.016, 95% CI: -0.097, 0.066). But the CIIS was statistically significant and favored the rich (0.06, 95% CI: 0.010 to 0.001). The horizontal inequity index was -0.076. CONCLUSIONS: There was no inequality in actual amount of outpatient utilization, maybe High subsidization to health care by government in public sector, high insurance coverage, low prices of health services in the public sector, quality of services and opportunity cost of high income groups were the reasons for our results.