Introduction:In recent decades, the development of medical and pharmaceutical science has led to a heavy financial burden on the government, insurance companies, and the general population. Due to the increasing the cost of pharmaceutical products in the Kermanshah Province, policy makers have tried to identify the factors that resulted in the increases. The aim of this study was to determine the main factors that affect the expenditures for pharmaceutical products by urban households in Kermanshah Province, Iran. Methods:This analytical-descriptive study was conducted using time series method. The study population was urban households of Kermanshah Province from 1991 to 2013. The explanatory variables of the log-log model were drug price index (LnDPI), the average income of urban households (LnINC), the number of physicians per 1,000 people (LnPH), and the number of hospital beds per 1,000 people (LnBE).The required data were acquired from the Statistical Center of the Ministry of Health and Medical Education. The unit root was evaluated by the Dickey-Fuller test. Stata v.11 software was used for the statistical analysis. Results:Coefficients of LnDPI and LnPH were 0.97 and 0.77, respectively, and they were statistically significant (p 0.05). Conclusion:The results showed that drugs are non-elastic and essential for households. It should be noted that the health policy makers in Iran should conduct appropriate planning to ensure both the physical and financial accessibility to drugs by urban households. The development of basic and supplementary health insurance coverage, especially for poor populations and urban areas where there are patients with chronic diseases, can be a suitable solution to reduce barriers to acquiring the required drugs.
BACKGROUND: Deciding on pharmaceutical subsidy is regarded as a challenging issue for healthcare policymakers in Iran in most times. Public preferences, rarely attended in Iran, could be invaluable for including a particular drug in the list of subsidized medications. OBJECTIVES: The current study aims to elicit the public preferences to develop an evidence-based decision-making framework for entering a drug into the list of subsidies in Iran. METHODS: Discrete Choice Experiment (DCE) was employed to elicit the public preferences. Around 34 attributes were identified based on the systematic review and interview with 51 experts. By holding an expert panel, 7 attributes were finalized, namely: the survival after treatment, quality of life after treatment (QoL), alternative treatment, age group of the target population, cost burden for the government, disease severity, and drug manufacturer country. Next, 1224 households were selected for the survey in the city of Tehran, using random cluster sampling. Data were analyzed using conditional logit model. RESULTS: The survival after treatment (β = 1.245; SE = 0.053) and disease severity (β =− 0.143; SE = 0.043) had the highest and lowest priority, respectively, in the preferences for allocating subsidy to a drug. In developed region, unlike the other two regions, the level of domestic drug production (β =− 0.302; SE = 0.073) was inversely associated with preferences toward allocating subsidy to a drug. In contrast to other districts, those living in district number one (β = 2.053; SE = 0.138) gave the highest value to promoting the QoL after treatment. CONCLUSIONS: It is suggested that policymakers pay more attention to attributes such as effectiveness and alternative treatment when developing an evidence-based framework for entering a drug into the list of subsidies. This study highlighted the public belief in the government's subsidy for medicines, provided that, this results in an increased survival and QoL. SUPPLEMENTARY INFORMATION: The online version contains ...
The results of previous Iranian studies on the protective effect of health insurance on catastrophic health expenditures (CHE) are inconsistent. Therefore, the aim of this meta‐analysis was to summarize all existent evidence. We searched international and Iranian scientific databases for relevant literature. Using a Mantel–Haenszel random‐effects model, the pooled odds ratios (OR) were calculated. Subgroup meta‐analyses were performed considering the type of insurance and study population. The pooled OR for the protective effect of health insurance risk on facing CHE was 0.93 (95% confidence interval [CI], 0.68–1.28). The protective effect of the two types of insurance was statistically insignificant. Health insurance does not effectively provide financial protection against CHE in Iran. Expanding the prepayment mechanism and integrating health insurance funds can be good strategies in protecting people from CHE. More attention should be paid to the design of health insurance benefit package to cover chronic diseases.
BACKGROUND: Global concerns regarding the significant burden of non-communicable diseases and injuries (NCDIs) exist from both public health and economic perspectives. Our research focuses on the reduction of fatal risks due to NCDIs and the citizens' preferences about health programs and intervention to reduce premature death due to NCDIs. Governments and health authorities need reliable evidence and information to prioritize the interests of their citizens. One crucial piece of evidence to justify the resources spent on NCDIs is the value derived from the interventions on prevention and NCDIs control. This concept is usually called "Value of Statistical Life" (VSL), meaning the monetary value that individuals place on changes in the risk levels of life- threatening events. To the best of our knowledge, for the first time, our study will estimate the statistical value of life for selected interventions for the prevention and control of NCDIs at both national and sub-national levels in the context of Iran. This paper reports the development of a national protocol through Discrete Choice Experiments (DCEs) method. METHODS AND DESIGNS: Our study comprises several stages: (a) a literature review to identify the attributes and levels of the prevention programs and Willingness to Pay (WTP) for reducing the NCDI's fatal risks; (b) experimental design to assessing, prioritizing, and finalizing the identified attributes and levels; (c) instrumental design to conduct face-to-face structured survey interviews of 3180 respondents aged 18–69 across the entire country; (d) statistical analysis to estimate the results through the Mixed Multinomial logit (MMNL) model. DISCUSSION: We anticipate that our findings will help build a stronger empirical basis for monetizing the value of small changes in selected fatality risks. It paves the way for other national or vast VSL estimates for NCDIs, as well as other major causes of morbidity and mortality in the context of Iran, and perhaps other low and middle-income countries (LMICs).
BACKGROUND: The demand for cosmetic surgery is on the rise worldwide, making it the common form of surgery globally while the use of cosmetic surgery being exponentially high in Iran. The aim of this study was to investigate inequality in the use of cosmetic services and surgery (CSS) among Iranian households concerning demographic and socio-economic characteristics. METHODS: This study used data of 38960 Iranian household from the income-expenditure survey of the statistical center of Iran (SCI) in 2019. Concentration index (C) was used to measure inequalities in the use of CSS. Microsoft Excel sheet 2019 was used to extract the data, and the analysis was performed using Stata statistical package version 14.2. RESULTS: Households with female head, with single head, households with 3 - 4 people, headed with undergraduate education person, households with insurance coverage, with higher socio-economic quintiles, rural households and residents of northwestern Iran were accounted for the highest use of CSS. Also, according to the decomposition analysis, wealth and education level are the two main factors in creating inequality, with wealth, having the highest positive share (88.11%) and education level having the most negative share (-5.26%) in creating measured inequality. CONCLUSION: The use of CSS is more concentrated in well-off households in Iran. As the resources of health system are limited, the government and the policy makers should have defined plans with regards to CSS use especially taking factors like socioeconomic status and education status of target groups in to account.
BACKGROUND: Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. METHODS: We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories-government, out-of-pocket, and prepaid private health spending-and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. FINDINGS: Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89-4·12) annually, although it grew slower in per capita terms (2·72% [2·61-2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18-5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10-4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8-8·1) in 2016 (comprising 8·6% [8·4-8·7] of the global economy and $10·3 trillion [10·1-10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184-5319) in high-income countries, $491 (461-524) in upper-middle-income countries, $81 (74-89) in lower-middle-income countries, and $40 (38-43) in low-income countries. In 2016, 0·4% (0·3-0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0-16·0) by 2050 (reaching 9·4% [7·6-11·3] of the global economy and $21·3 trillion [19·8-23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68-2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6-0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9-136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7-138·1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. INTERPRETATION: Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. FUNDING: Bill & Melinda Gates Foundation. ; Bill & Melinda Gates Foundation ; Sí
High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations
High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations