ORIGINAL ARTICLE
Development of a Socioeconomic Status Index to Interpret Inequalities in Oral Health in Developing Countries Zahra Ghorbania/Arezoo Ebn Ahmadyb/Harry A. Landoc/Shahram Yazdanid/ Zohreh Amirie Purpose: To develop an instrument to measure socioeconomic status (SES) in order to assess SES-related inequalities in oral health in a developing country. Materials and Methods: In order to develop a SES measurement tool, an expert panel generated a primary item pool from which the items were revised after validity and reliability testing. The final instrument was used in a 1100-sample survey in Tehran. SES was calculated using the weights produced by both principal component analysis (PCA) and expert panel two-stage paired comparisons (TSPC) methods. Results: The final instrument contained 10 items. Standardised SES scores derived from TSPC and PCA methods were significantly correlated (r = 0.749, P < 0.001). Five-level SES stratification by the two methods revealed a correlation coefficient of 0.701 (P < 0.001) for SES class. Conclusion: The newly developed SES index was appropriate to be used in exploring oral health inequalities in the studied sample of the Iranian population. When formulating SES, domestic experts’ opinions could help the researchers explore and weight sub-construct factors. Key words: developing countries, inequality, oral health, paired comparisons, principal component analysis, socioeconomic status Oral Health Prev Dent 2013; 11: 9-15
O
ral health is one of the socioeconomic disparity issues clearly evident in most of the developed nations (Watt and Sheiham, 1999; Sanders et al, 2006; Petersen, 2007; Locker, 2009; Dye, 2010). There are four theoretical explanations for social inequalities in oral health: 1) the materialist explanation, which highlights the role of external environmental factors outside the individual’s control; 2) a
Assistant Professor, Community Oral Health Department, Dental School, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
b
Assistant Professor, Dental Research Center, Dental School, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
c
Professor, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
d
Associate Professor, Educational Development Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
e
Assistant Professor, National Nutrition and Food Technology Research Institute, Department of Basic Sciences, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Correspondence: Dr. Arezoo Ebn Ahmady, Community Oral Health Department, Dental School, Shahid Beheshti University of Medical Sciences, Daneshjo Blvd, Velenjak, Tehran, Iran. Tel: +98-212-2421813. Email:
[email protected]
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Submitted for publication: 28.01.12; accepted for publication: 16.05.12
cultural/behavioural explanations which imply that people from low socioeconomic backgrounds are more likely to engage in behaviours that are damaging to their health, in turn leading to higher levels of disease; 3) the psychosocial perspective, which focuses on the experience of psychological stress; 4) the life-course perspective, which argues that health inequalities result from the interaction of materialist, behavioural and psychosocial factors over different stages of life with a cumulative effect (Sisson, 2007). The relationship between socioeconomic status and disease takes the form of a social gradient from the top to the bottom of the social hierarchy. According to the gradient theory, inequality of health is not only confined to the poorest members of society, but runs across the social spectrum (Sanders and Spencer, 2004). The shape of the socioeconomic-oral health gradient differs according to the measure used to index social status (Sanders et al, 2006); therefore, selecting a suitable socioeconomic status (SES) measure for the target population is critical. An ideal SES measure for public health surveys should at a minimum 1) be based on a unified and
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well-developed theoretical framework, 2) have sound psychometric properties, 3) permit analyses across the life course, 4) be amenable, if not restricted, to ‘aggregation’ for analyses at higher levels such as household or neighbourhood, 5) be adaptable to temporal changes in the socioeconomic structure, 6) employ terms/concepts that policymakers understand, and perhaps most importantly, 7) be practical and useful in applied public health and epidemiological surveys (Oakes and Rossi, 2003). Many different approaches have been used to construct SES measures for health surveys in previous decades, most of them focusing on occupation, education and/or income. In 2003, a different theoretical approach was identified; it specified SES as a function of ‘capital,’ and was thus termed CAPSES. The establishers of the CAPSES theory defined SES as a differential access tool (realised and potential) to reach desired resources. They believed that SES is a function of 1) material capital (e.g. income, real property and other fungible goods), 2) human capital (skills, ability and knowledge) and 3) social capital (one’s social network and status) (Oakes and Rossi 2003). A wide range of single or combined variables have been used to address socioeconomic inequalities in oral health. Most documented evidence arises from developed countries and focuses on occupation (Petersen, 2007), education, income (Sanders et al, 2006; Locker 2009; Bernabe and Marcenes, 2011) or a combination of the three measures (Sanders and Spencer, 2004; Mundt et al, 2009). The family affluence scale (FAS) – including items of car ownership, own bedroom, family holidays and computer ownership – was used to determine the predictors of adolescents’ tooth brushing in Scotland (Currie et al, 2008). However, measuring household economic status in developing countries presents considerable challenges. Data on two frequently used indicators of wealth, i.e. household income and expenditure levels, are often unavailable or unreliable (Houweling et al, 2003). High non-response rates on items assessing income, although evident in high-income countries, have been even greater in developing countries due to cultural factors (Bayat et al, 2006; Shavers, 2007). Occupation has its own disadvantages in SES measurement, as there is a lack of precision in measurement and difficulty with the classification of homemakers and retirees (Shavers, 2007). The problem worsens in developing countries where women are less likely to have a formal job.
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Evidence suggests that in developing countries, the assets that households have acquired are a good indicator of their ‘long-run’ economic status (Houweling et al, 2003). There is, however, a lack of well designed, theory-driven and appropriately planned oral health-related SES measures for developing countries. Therefore, the aim of this study was to establish a socioeconomic status measure for oral health inequality studies in a developing country context. This study was the first phase of a larger study which measured inequalities in oral health and dental care utilisation in Iran.
METHODS AND PARTICIPANTS The Iran Centre for Dental Research granted ethical approval for the present study. Fifteen experts in two groups took part in the instrument development project. Group A consisted of five experts in health economics (one), epidemiology (one), community oral health (two) and sociology (one). Group B included 10 experts in community oral health (five), research methodology (three) and epidemiology (two). The initial SES measure was developed based on related recent studies and the ideas of the expert group using the CAPSES theory (Oakes and Rossi, 2003). The evolution process of the SES measure is shown in Fig 1. The initial developed instrument was then reviewed by group B experts regarding relevancy, readability and clarity using the Waltz and Bausell method (Polit and Beck, 2006). To examine the reliability of the questionnaire, a test-retest study was performed on 35 participants with a two-week interval. The final instrument was applied in a 1100-sample telephone oral health survey in Tehran. Expert opinion-based two-stages paired comparisons (David; 1963; Garber et al, 2009) (TSPC) and principal component analysis (PCA) were used as two alternative methods to weight the item effects. In the TSPC method, the group B experts were asked to estimate the relative effects of paired items on SES in two stages. First, they were asked to weight the four main fields of education, housing, having car and home appliances for predicting the SES of adults in Tehran City. The second step was to weight dimensions of housing and several home appliances. The results were scored from 0.1 (very unimportant) to 10 (very important), including a score of 1 which indicated two equally important
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Item pooling Primary 14-item instrument: 1. income 2. education 3. job 4. family size 5. house area 6. house location 7. house ownership 8. having a car 9. having bathroom 10. kitchen 11. vacuum cleaner 12. washing machine 13. computer 14. access to internet
Validity testing 4 items deleted: 1. income 2. job 3. bathroom 4. kitchen
Pilot study 2 items deleted: 1. vacuum cleaner 2. washing machine
3 items added: 1. dish washer 2. steam washer 3. microwave 2 items combined: 1. family size 2. house area
Final instrument Final 10-item instrument: 1. education 2. house area per capita 3. house location 4. house ownership 5. having a car 6. computer 7. access to internet 8. dish washer 9. steam washer 10. microwave
Fig 1 The changes of socioeconomic measure in each stage of development.
items. Finally, the SES score (between 0 and 1) was formulated as a function of weights multiplied into standardised values of its sub-constructs using Microsoft Excel 2007. For the PCA method, factor analysis was done using SPSS Version 16 and the first component scores were applied to construct the SES measure. The Pearson correlation between SES scores and SES classifications was estimated for the two weighting approaches. SES classification was done by cluster analysis (Kaufman et al, 1990) using the data-driven approach. Additionally, the odds ratios of self-reported number of extracted teeth, oral self-care and dental visits were calculated for the top and bottom SES classes using logistic regression analysis. The questions about oral self-care covered the frequency of tooth brushing, flossing, the consumption of sugary snacks between meals and tobacco use.
RESULTS The Tehran telephone oral health survey was performed in 2010–2011 with 5271 randomly generated telephone numbers. 3771 telephone numbers were not reached (184 busy, 1406 no answer, 60 fax, 1549 line blocked, 572 commercial). Of the 1500 subjects who answered the phone calls, 400 refused to participate, leaving 1100 adults (response rate: 73% among those who answered) in
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the final sample. Of the respondents, 50.8% were women and the mean ± SD age was 39 ± 13.82 years. As shown in Fig 1, the 14-item primary instrument was modified to a 12-item instrument after validity testing and group B expert panel revision. The experts decided to eliminate four items considering the study population characteristics: 1) income, because of the expected low response rate and lack of reliability; 2) job, due to existing multiple careers and many women being jobless; 3) and 4) bathroom and kitchen, since these were expected to be afforded by urban populations. The two items of family size and house area were combined to a single measure labeled as house area per capita. Finally, the panel added three new items: having a dishwasher, steam washer (an electric device to clean surfaces using steam power) and microwave, which were regarded to be more discriminative in different SES classes of the urban population. Primary descriptive analysis of the 1100 samples’ raw data resulted in elimination of two factors which had more than 99% frequency in the population. Neither of these were good differentiators of SES because nearly all people owned them. Finally, from the 10-item SES indicator, material capital was measured by asking about housing (house area per capita, ownership status and house location), having a car and home appliances (including microwave, dishwasher, steam washer and computer), while human capital and social capital were
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inequalities in developing countries. SES measurement is the basic issue in addressing inequalities in health. The selection of an inappropriate and unreliable scale causes misleading interpretations of inequalities (Lindelow, 2006). Onwujekwe et al (2006) reported that some commonly used SES indicators have only low to moderate reliability. Furthermore, the choice of equivalence scale can sometimes systematically affect absolute and relative levels of poverty, inequality and therefore rankings of countries (or population subgroups within countries) (Buhmann et al, 1988). Many researchers have used income as an SES measure to demonstrate worldwide oral health inequalities. In the present study, the panel of experts eliminated income because of low expected response rates (McIsaac and Wilkinson, 1997; Bayat et al, 2008) and the likelihood that reported answers (Mehryar and Tashakkori, 1984; O’Donnell et al, 2008) in developing countries would be unreliable. Using a paired-comparison method, the SES function revealed a much stronger weight for education, followed by housing. Education is the most widely used measure of SES in epidemiological studies (Kaplan and Keil, 1993). Because of the high price of both buying and renting a house in large cities, housing could be a very good economic status indicator for urban populations. PCA weights did not confirm TSPC factor impacts. PCA resulted in three principal components, but SES was computed using only the first component
measured by asking about education (in years) and internet usage, respectively. Test-retest reliability analysis showed kappa > 0.98 for binary variables and an overall correlation coefficient of over 0.95 for numerical variables. The weights of the TSPC method are listed in Fig 2. From the experts’ view, education was determined as the most important factor in prediction of SES, followed by housing status. The PCA revealed three components with consideration of an Eigen-value > 1; the first one covered 34% of variance. Table 1 presents the mean, standard deviation and the first principal component factor scores. Standardized SES scores derived from TSPC and PCA methods were significantly correlated (r = 0.749, P < 0.001). In addtion, 5-level SES stratification by the two methods revealed a 0.701 correlation coefficient (P < 0.001) for SES class. The mean SES scores of each SES class by the two methods are shown in Table 2. Table 3 presents the odds ratios (20% highest to 20% lowest SES scores) of oral health-related indicators for the TSPC and PCA methods.
DISCUSSION The present study introduces a new, mixed SES measure designed for interpretation of oral health
First Stage
Socioeconomic status
Second Stage
Housing
0.23
Education
0.56
Having a car
0.09
Home appliances
0.12
House area per capita
0.14
Ownership
0.28
House price per m2
0.58
Having a computer
0.30
Access to internet
0.34
Having a dishwasher
0.15
Having a microwave
0.08
Having a steam washer
0.13
Fig 2 The factor weights resulting from two-stage paired comparisons in socioeconomic status.
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weights. Therefore, some variables which were not included in the first component had a very low or even negative correlation with the first component and thus had a low or negative weight in the final SES function. The high correlation of the two methods of formulating SES and also good agreements in SES stratification suggest that TSPC can be used as an alternative to the conventional PCA method. Both TSPC and PCA SES scores revealed oral health inequalities consistent with the literature. Prior evidence indicates that individuals with high SES are expected to have fewer missing teeth (Gilbert, 2003; Sanders, 2004; Pizarro et al, 2006; Haugejorden et al, 2008; Bernabe and Marcenes
2011), take better care of their teeth (Astrom and Rise, 2001; Levin and Currie, 2009) and visit a dentist more frequently (Nguyen and Hakkinen, 2004; Grignon et al, 2008; Roberts-Thomson et al, 2008; Maharani, 2009; Somkotra and Detsomboonrat, 2009). Social determinants interact with biological and personal determinants at a collective level to shape individual biology, individual risk behaviours, environmental exposures and access to resources that promote health (Patrick et al, 2006). Health status at any given age is not only a result of current conditions, but is also the embodiment of prior living conditions from conception onwards. Health ine-
Table 1 Components extracted from principal component analysis, % of variance, mean, standard deviation (SD) and the first principal component factor scores of items for Tehran adults (n = 1100) Components
Item
Mean
SD
Factor Score
Component 1 v1 = 34.7%
Having computer*
0.68
0.46
0.419
Access to internet*
0.66
0.47
0.419
Having car*
0.63
0.48
0.23
Education (in years)
11.78
3.59
0.191
Having steam-washer*
0.29
0.45
–0.059
Having dishwasher*
0.22
0.41
–0.017
House area per capita (m of area/family size)
31.89
18.83
–0.154
Having microwave*
0.54
0.49
–0.002
2.18
0.82
–0.049
0.57
0.49
0
Component 2 v = 14.9%
2
House price (thousand dollars) per m Component 3 v = 11.6% 1
2
Own home *
Percent of variance explained by the component. * The variables are dichotomous (0 = no, 1 = yes).
Table 2 The mean standardised socioeconomic status (SES) scores of SES classes by two-stage paired comparisons (TSPC) and principal component analysis (PCA) Method
Poorest
Second
Middle
Fourth
Richest
TSPC
–1.51
–0.46
0.09
0.59
1.29
PCA
–1.6
–0.58
0.47
0.73
0.97
Table 3 Odds ratio (20% highest to 20% lowest SES scores) and 95% CI of extracted teeth, dental care behaviours and dental visits by two-stage paired comparisons (TSPC) and principal component analysis (PCA) OR (CI 95%) in TSPC
OR (CI 95%) in PCA
Extracted teeth
0.638 (0.556, 0.732)
0.737 (0.654, 0.832)
Dental care behaviour
2.563 (1.882, 3.491)
2.068 (1.527, 2.802)
Dental visit
2.33 (1.585, 3.428)
1.671 (1.142, 2.447)
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qualities therefore result from the interaction of environmental, behavioural and psychosocial factors over time (Sisson, 2007). The children of the low SES classes have more dental caries and fewer dental restorations than those of high SES classes in both primary and permanent teeth (Pine et al, 2004a; Pine et al, 2004b; Saied-Moallemi et al, 2006; Jason, 2007). Conversely, there is an association between higher childhood socioeconomic status (SES) and lower risk of unhealthy behaviours in adulthood (Bernabe et al, 2009). It seems that people born in low SES families are victims of poor oral health not only because of their childhood disadvantages, but also because of their lifelong socioeconomic position, which is affected by their history. The results of the present study imply that TSPC could better discriminate oral health disparities (extracted teeth, dental care behaviour and dental visits) than PCA does. Two reasons could be presented for this; first, PCA weights are based on the first component scores and thus ignore the other scores. Secondly, TSPC weights are based on local expert views of the society, which could be more representative of the target population’s living situations. Some limitations affected the present study. First, the principal component analysis approach in SES measurement can obscure the meaning of the final index and is statistically inappropriate for use with discrete data (Howe et al, 2008; Kolenikov and Angeles, 2009). There are some other common weighting methods: PCA using dichotomized versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item and multiple correspondence analysis, all of which have their own disadvantages (Howe et al, 2008). However, PCA is widely applied and is approved for use for discrete data or the combination of both discrete and continuous data (Montgomery et al, 2000; Filmer and Pritchett, 2001; Jolliffe, 2005; McKenzie, 2005; Vyas and Kumaranayake, 2006). Second, in the present study, only the first component scores were used to measure SES. It would be more comprehensive if all principal component scores were considered in SES calculation. Third, the data used were survey based; therefore, they may be affected to some extent by reporting bias. The information on assets and possessions is somewhat confidential, and dissemination of these is influenced by the community culture.
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CONCLUSION The newly developed SES index is appropriate for use in exploring oral health inequalities in the studied sample of Tehran population, and this has implications for testing and use in the context of other developing countries. When formulating measures of SES, domestic experts’ opinions can help researchers explore and weight sub-construct factors.
ACKNOWLEDGEMENTS This study reported the results of a PhD thesis at the Dental School of Shahid Beheshti University of Medical Sciences, Tehran, Iran. This study was supported by the Dental Research Center of Shahid Beheshti University M.C. We wish to thank the survey interviewer, Mrs Golnaz Jafari, and all of the experts for their participation. The authors also thank Dr Fariborz Bayat for his useful comments.
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