Determinants of Inequity in Health Care Services Utilization in Markazi Province of Iran

AUTHORS

Jafar Hassanzadeh 1 , Abolfazl Mohammadbeigi 2 , * , Babak Eshrati 3 , Abbas Rezaianzadeh 1 , Abdolreza Rajaeefard 1

1 Department of Epidemiology, School of Health and Nutrition, Shiraz University of Medical Sciences, Shiraz, IR Iran

2 Department of Epidemiology, School of Health, Qom University of Medical Sciences, Qom, IR Iran

3 Department of Epidemiology, School of Health, Arak University of Medical Sciences, Arak, IR Iran

How to Cite: Hassanzadeh J, Mohammadbeigi A, Eshrati B, Rezaianzadeh A, Rajaeefard A. Determinants of Inequity in Health Care Services Utilization in Markazi Province of Iran, Iran Red Crescent Med J. 2013 ; 15(5):363-370. doi: 10.5812/ircmj.3525.

ARTICLE INFORMATION

Iranian Red Crescent Medical Journal: 15 (5); 363-370
Published Online: May 5, 2013
Article Type: Research Article
Revised: February 27, 2012
Accepted: March 10, 2012

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Abstract

Background: National and international statistics from Iran have indicated that progresses and achievements have been made for most health indicators, but there are differences in some indicators in special groups and at the provincial level.

Objectives: Our aim was to assess the main predictors of inequity in seeking Health Care Utilities (HCU) locally.

Patients and Methods: Data gathered from the HCU survey, which was conducted in the Markazi province of Iran during 2008, was used in the present study. A systematic sampling method, based on the Iranian household framework, was applied in order to choose 758 households as well as 2711 individuals. The household wealth index constructed by Principle Component Analysis (PCA) and robust login link function in Generalized Estimation Equation (GEE) model were used in order to determine the predictors of inequity.

Results: 66.4% of those in need sought outpatient health care from which 97.7% received appropriate services. After adjustment for the clustering effect of household as well as confounding effect of the covariates, GEE model showed that there were inequities in HCU for females (OR = 2.44, CI; 1.24- 4.81) and subjects with inpatient need (OR = 2.14, CI: 1.23-3.72). Being in the lowest quintile of household wealth index was associated with the lower use of outpatient health services (OR = 0.45, CI; 0.23- 0.88).

Conclusions: In spite of improvements in the national health indicator, different groups of people use health care services differently, but these inequities in HCU are related to social and individual factors. Also, it is clear that health sector facilities and the accessibility to health services are not the only predictors.

Keywords

Quality Indicators, Health Care Utilization Iran

Copyright © 2013, Iranian Red Crescent Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Equal access for equal need is defined as equity (1). It is widely recognized that health systems are highly important determinants for improvement of the population health and performance (2). One of the most important duties of health organizations and authorities is complete coverage as well as preparation of sufficient and cost effective health care (3). In Iran, the 29th article of the constitution states that every Iranian has the right to utilize the highest attainable level of health that the Ministry of Health and Medical Education – the executor of this goal – entrusts to the medical universities at the provincial level (4). After the revolution, the coverage of social insurance was increased in Iran which resulted in an improvement in both access and accessibility to health services for over 90% of the population (5). Health care and public health services have been presented to people through a health net, which provides primary, secondary, and tertiary health services. In addition to public sectors, private sectors have a significant role in secondary and tertiary health as well as non-governmental organization work in health activities of special fields (4). National and international statistics from Iran indicated that progresses and achievements have been made in most health indicators such as the coverage of vaccination, life expectancy, infant and prenatal mortality ratios, the proportion of births attended by skilled health personnel, safe injection, combating infectious diseases such as Malaria, TB, and HIV/AIDS, as well as other indicators (3, 4, 6-10). Although public and private health sectors’ activities have improved some health indicators and despite the achievements of the Millennium Development Goals (MDGs) at the national level, there are differences at the provincial level in some indicators (11-15). Therefore, serious attempts should be taken into consideration in order to fully improve and achieve the MDG (8, 13). Health care utilization (HCU) is one of the indicators in which – despite the universal coverage and provision –inequities still exist throughout the whole country as well as among the provinces (16, 17). Based on the results of other studies, a number of predisposing factors such as sex, location of dwelling, occupation and education, enabling factors e.g. health insurance coverage, distance and availability of health care system, and factors related to the need for health care have been recognized as significant determinants of HCU (15, 18, 19). Another classification has categorized these factors into three main categories: demographic/socio-economic, personal and parental/family characteristics (18, 19). It has been assumed that these factors could create inequities in different groups of people. However, one of the most important factors, which are related to HCU is the socioeconomic position (19, 20). However, utilization of health care services can be considered as a type of individual behavior; consequently, most researchers have paid much attention to individual characteristics and have been less concerned about the impact of societal factors (21). Recently, decrease of health inequities in countries, especially in developing countries, has become a major objective for policy makers in national governments and international organizations. Determination of these disparities and differences as well as their extensiveness is a prerequisite for achieving this goal (22). In addition to inequity, monitoring and identification of health utilities and resources is necessary due to rapid increase in costs of medical care (23). Results of a study showed that there was an inequity in infant mortality at the provincial level in Iran, and that the Markazi province experienced a high inequity (24).

2. Objectives

The aim of this study was to assess the main socioeconomic and demographic determinants for seeking HCU in the local level rather than the entire country in order to find the main factors, which contribute to inequity.

3. Patients and Methods

3.1. Study Setting

This cross sectional study was conducted in the Markazi province, which has ten cities and is located in the center of Iran, 293 km southwest of Tehran, the capital (12). According to the latest census, 1,351,257 people including 682,367 males (50.49%) and 668,890 females (49.51%) live in this province (25). This study was conducted in all cities and a number of villages for a duration of two weeks from 16 February to 1 March 2008, based on the sampling schedule (26).

3.2. Subjects and Sampling

The data for the present study was gathered from the national HCU survey, which was conducted in 2008 in the Markazi province, Iran. Systematic sampling method was used to choose 760 households living in this province. The sampling method was based on the Iranian household framework available from the Health Promotion and Network Development Center of Ministry of Health and Medical Education (26). In the selected households, all members were interviewed and finally data from 2711 individuals were recorded in pre-coded and pretested questionnaires (26). In order to gather the data from subjects under 15 years old, interviewers had to contact their parents; therefore, these subjects were excluded from the study. Also written informed consent was taken from all of the subjects and the Ethics Committee in MHME approved the research protocol.

3.3. Instrument

The data was collected using a valid and reliable questionnaire. This questionnaire was used for the latest HCU survey of Iran in 2002 and was revised with regards to its advantages and disadvantages. The questionnaire included some individual factors such as age, sex, marital status, job, education, socio-economic factors such as income level, and a list of assets which were used for factor analysis. In addition, seeking outpatient health services (including any type of health care such as primary health, dentistry, specialist, laboratory, et.) by people with outpatient needs within the two weeks before the interview was the outcome variable. All 15 or more years old subjects were interviewed by trained interviewers (26).

3.4. Statistical Analysis and Creation of the Household Wealth Index

The statistical process was done in three steps. First, we used the Principle Component Analysis (PCA) method to construct a household wealth index – based on recommendations of other studies (20, 22, 27). It was assumed that the economical status of a household reflects the socioeconomic situation of the subjects under study, thus this was the first component to be considered (20). Due to the fact that the asset data are claimed to be more reliable than other variables such as income or expenditure (27), data from the twenty five asset variables were gathered and used in the factor analysis of the present study. Number of people per number of rooms per capita, having a separate kitchen, bathroom, toilet, fully hygienic toilet, using the kitchen stove, safe heating and cooling devices, having living facilities including fridge, freezer, refrigerator, television (black and white, color, or LCD), mobile phone, clothes and dish washing machines, microwave, vacuum cleaner, computer, Internet access at home, motorcycle, car, own villa and own house were the asset variables which were included in the PCA. Scores from this primary assessment were sorted in an ascending manner. After calculating the quintiles, the lowest quintile was considered as the poorest, while the highest quintile was considered as the richest one. Second, the outcome variable was considered as a binary variable for seeking outpatient health services within two weeks prior to the interview. In this way, the percentage of the subjects who were seeking outpatient services to those who needed them was obtained. Finally, the robust logit link in the Generalized Estimation Equation (GEE) model with the exchangeable correlation matrix was used for computing the Odds Ratio (OR) as well as their 95% confidence interval (95% CI) and was applied for interpretation of the results. Robust logistic regression in the GEE model was used to adjust the clustering effect of sampling units and remove the correlation of the subjects (28, 29). The GEE model considered two sources of variances in obtaining the estimates; the interacluster and intercluster variances (30). In the GEE model, we included all socio-demographic characteristics which had a significant relationship with seeking out-patient health services in addition to those variables which had a significance probability lower than 0.35. Income level variable, however, was excluded from the model because it had a significant correlation with the household wealth index (r = 0.548, P < 0.001). We conducted all statistical analyses using the STATA statistical package (10.0). All tests were considered as two-tail and P values below 0.05 were considered as statistically significant.

4. Results

The response rate was 99.7%, and from all 2711 subjects who were interviewed, 2131 were 15 or older and, consequently, were included in the analysis. From the 2131 subjects, 779 needed outpatient health care. It was reported that 66.4% (517 subjects) sought outpatient health care and 97.7% (503 subjects) received it. Among the subjects who were analyzed, 59.8% were females, 45.3% lived in cities, and 73.4% were married. The mean of age and years of education of the participants was 45.33 ± 19.4 years old and 5.63 ± 5.13 years, respectively. Table 1 summarizes the findings of the bivariate analysis of socio demographic characteristics of the two group of subjects based on seeking outpatient health services. The results show that subjects that were female and had a higher household wealth index, fewer years of education, higher income level, as well as those who were insured tended to use the outpatient health services more than other subjects. Also, In-patient need was significantly related to seeking outpatient health services (P < 0.05); so that inpatient need increased outpatient utilization of health care services. However, no significant relationship was found between seeking outpatient health services and marital status, age group, supplementary insurance, insurance validity, location of dwelling, and the subjects’ job (P > 0.05). Table 2 provides the crude and the adjusted estimates of the relationship between seeking outpatient health services and the related factors. Unadjusted OR for the variables of sex, household wealth index, and inpatient need was significant in the GEE model after removing the clustering effect of the sampling method. The multivariate analysis, also showed that only three variables have significant relationships with seeking outpatient health services after adjusting the effect of other variables. According to the GEE estimates, the likelihood of seeking outpatient health services in females was 2.4 folds higher than that of males (OR = 2.44, CI; 1.24- 4.81). Inpatient need for health care services increases the likelihood of seeking outpatient health services by 2.1 folds (OR = 2.14, CI: 1.23-3.72). The relationship between seeking outpatient health services and quintile of household wealth index showed that the subjects in the first quintile had 55% lower likelihood than the subjects in the fifth quintile (OR = 0.45, CI; 0.23- 0.88).

Table 1. Univariate Analysis of Socio-Demographic Characteristics and Seeking out-Patient Health Services of the Subjects Under Study
VariablesSeeking Out-patient Care, No. (%)P Value
NoYes
Sex0.004 a
Male39.3 (123)60.7 (190)
Female29.8 (139)70.2 (327)
Marital status0.90
Married33.6 (192)66.4 (380)
Divorced and widow35.1(27)64.9 (50)
Never married33.1(43)66.9 (87)
Household wealth index0.006 b
1 St Quintile44.4 (69)55.8 (87)
2nd Quintile36.5 (57)63.5 (99)
3rd Quintile31.4 (49)68.6 (107)
4th Quintile30.8 (48)69.2 (108)
5th Quintile25.2 (39)74.8 (116)
Age Group0.124
15-2928.3 (58)71.7 (147)
30-4432.3 (65)67.7 (136)
45-5939.6 (67)60.4 (102)
60 and Upper35.3 (72)64.7 (132)
The insured0.028 b
Yes32.3 (219)67.7 (459)
No42.6 (43)57.4 (58)
Supplementary Insured0.528
Yes32.2 (28)67.5 (392)
No67.8 (59)32.5 (189)
Insurance Validity0.186
Yes31.7 (200)68.3 (430)
No39.5 (17)60.5 (26)
Location0.218
City 31.4 (111)68.6 (242)
Rural (main) 36.5 (137)63.5 (238)
Years of education0.028 b
None (illiterature)37.1 (95)62.9 (161)
1-5 (primary school)31.1 (56)68.9 (124)
6-10 (guidance and high school)38 (63)62 (103)
Pre-university or Diploma20.8 (21)72.9 (80)
College and seminary35.5 (27)65.5 (49)
Job0.312
Employed38.8 (88)61.2 (139)
Housewife or retired31.5 (136)68.5 (296)
Student32.8 (19)67.2 (39)
Unemployed34.6 (18)65.4 (34)
Income0.003 b
Lower 200 $39.7 (152)60.3 (231) 200-500$28 (94)72 (242)
Upper 500 \$29.4 (15)70.6 (36)
In-patient need0.006 b
Yes24.7 (37)75.3 (113)
No35.8(225)64.2 (404)

aBased on chi-square test

bSignificant at the P < 0.05

Table 2. Crude and Adjusted or For Seeking Outpatient Health Services In GEE Model
OR (95% CI)P valueOR (95% CI )P value
Gender
Female 1.6 (1.15- 3.17)< 0.00012.44 (1.24- 4.81)0.010
Household wealth index
1 St Quintile 0.51(0.3-1.35)0.0110.45 (0.23- 0.88)0.019
2nd Quintile 0.65 (0.38 -1.47)0.1010.68 (0.37-1.27)0.229
3rd Quintile 0.83 (0.49- 1.63)0.5030.76 (0.41-1.42)0.386
4th Quintile 0.79 (0.47-1.6)0.3820.77 (0.42-1.41)0.398
5th Quintile 11
In-patient need
Having 2.02 (1.24-3.47)< 0.00012.14 (1.23-3.72)0.007
Validity of insurance
Having 1.19 (0.6-1.82)0.6091.09 (0.54- 2.21)0.809
Place of Residency
City 1.23 (0.89-2.43)0.2071.2 (0.77-1.85)0.423
Years of education
None (illiterate) 0.97 (0.53-1.71)0.9191.38 (0.56-3.42)0.491
1-5 (primary school) 1 (0.54-1.72)0.9911.32 (0.6 -2.92)0.496
6-10 (guidance and high school) 0.74 (0.4-1.49)0.3320.98 (0.46-2.06)0.953
Pre-university or Diploma 1.69 (0.82- 2.27)0.1551.71 (0.72-4.06)0.226
College and seminary 11
Age Group, y
15-29 1.12 (0.71-2.03)0.6240.99 (0.45-2.16)0.974
30-44 0.94 (0.59-1.81)0.7780.85 (0.44-1.68)0.649
45-60 0.77 (0.48-1.61)0.2810.72 (.41-1.28)0.266
Upper 60 11
Job
Employed 0.88 (0.43-1.54)0.7180.57 (0.21-1.55)0.273
Housewife or retired 1.17 (0.59-1.81)0.6560.35 (0.12-1.09)0.070
Student 0.99 (0.42-1.52)0.9750.50 (0.15-1.67)0.262
Unemployed 11

aAdjusted estimates for clustering effect of sampling method

bAdjusted estimates for clustering effect of sampling method and all included covariates in the GEE model

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