Abstract
Objective: This study evaluated social inequalities in adult oral health across several low- and middle-income countries. Methods: We used data from 40 countries that participated in the World Health Surveys. Participants’ socio-economic position was assessed using the wealth index. Oral health was assessed using two perceived measures, namely total tooth loss and whether they had any problems with their mouth and/or teeth during the last 12 months (perceived needs). Absolute and relative wealth inequalities in oral health were measured using the slope index of inequality (SII) and the relative index of inequality (RII), respectively, after adjusting for participants’ sex, age and education. Results: There were wealth inequalities in total tooth loss and perceived needs in most countries. However, significant monotonic gradients were found in 21 countries for total tooth loss and in 18 countries for perceived needs. Two distinctive patterns of social inequality in oral health were found across countries using the RII and the SII. For total tooth loss, pro-rich inequality was found in 25 countries (significant RII/SII in eight countries) and pro-poor inequality was found in 15 (significant RII/SII in three countries). For perceived needs, pro-poor inequality was found in 26 countries (significant RII/SII in six countries) and pro-rich inequality was found in 14 (significant RII/SII in five countries). Conclusions: The well-documented social gradient in adult oral health favouring the rich was not present in all low- and middle-income countries. Pro-poor inequalities in total tooth loss, and particularly in perceived dental-treatment needs, were observed in some countries.
Key words: Socio-economic factors, oral health, tooth loss
INTRODUCTION
There is overwhelming evidence of socio-economic inequalities in adult oral health. Oral diseases are disproportionately represented in adults of low socio-economic position (SEP). However, poor oral health is not limited to the lower end of the social scale as there is a social gradient in oral health that is determined by an individuals’ position on the social ladder1., 2.. Most evidence on social inequalities in adult oral health comes from developed countries3., 4., 5., 6.. A robust association between SEP and adult oral health has been found regardless of which SEP indicator is used, which oral health outcome is assessed and whether all the population or only a segment (such as senior adults) is evaluated.
Recent literature has focused on monitoring social inequalities in adult oral health by assessment of trends within countries or comparisons between countries. Monitoring social inequalities in health is important to improve understanding of the social determinants of health and to evaluate policies to promote health and reduce health inequalities7., 8.. Within countries, despite large declines in the prevalence and incidence of oral diseases at global, regional and country levels over the past two decades9., 10., 11., social inequalities in oral health persist and may be widening3., 4., 5., 6.. There is also evidence of variations in social gradients in oral health between countries, even among rich neighbouring countries, like those in Europe3., 5. and North America4. Based on these combined findings, some have argued that social inequalities in adult oral health are universal1., 2..
Despite the paucity of studies monitoring social inequalities in oral health in developing countries, a few national surveys in developing countries show contradicting evidence12., 13., 14., 15., 16.. A significant social gradient in caries experience was found among Vietnamese adults ≥18 years of age12. Although a significant gradient in self-reported worse oral health status, according to household consumption, was found among 15- to 75-year-old Thai subjects during bivariate analysis, this association was fully attenuated after controlling for sociodemographic factors13. On the other hand, education was not related to severe caries (defined for the study as having 16 or more decayed or missing teeth) among Pakistani adults ≥25 years of age14. Moreover, a significant interaction was found between the count of durable goods in the household and community development: in communities with low development it was the more advantaged who were more likely to have severe caries, whereas in communities with a high level of development it was those with few foods who were most likely to have severe caries14. In Mexico, the prevalence of edentulism decreased with increasing household wealth in adults ≥35 years of age15, whereas the opposite trend was found for the prevalence of self-reported oral/dental problems in adults ≥18 years of age16. There is also evidence in medicine showing that the shape of the social gradient in health varies according to economic development and the stage in which the country is in regarding demographic, epidemiologic and nutrition change17., 18., 19.. Therefore, this study aimed to evaluate social inequalities in adult oral health across several low- and middle-income countries, using a comparable data set and measurement method.
METHODS
Data source
Data were obtained from the World Health Survey (WHS) conducted in 2002–2004, which was sponsored by the World Health Organization (WHO) to provide valid, reliable and comparable information across 70 countries from all world regions regarding health status and health systems. In each country, the target population was adults ≥18 years of age living in private households. Participants were selected using multistage stratified cluster sampling with the intention of collecting nationally representative samples. However, in six countries the survey was carried out in geographically limited regions and random sampling was not used. Sample size varied from 1,000 to 10,000 between countries whilst ensuring that the sample was nationally representative of the population20.
Fifty of the 70 countries included in the WHS were classified as low- and middle-income economies, according to the 2003 classification of the World Bank, and were initially selected for this analysis. We excluded China, Comoros, Congo, India, Ivory Coast and the Russian Federation because their samples were not nationally representative; Zambia and Guatemala because their data files had no survey information needed to produce nationally representative estimates; and Tunisia and Mauritania because their study samples (participants with complete data in relevant variables) represented only 20.4% and 45.4% of the full sample of the participants, respectively.
Variables selection
Participants’ SEP was determined using the wealth index21., 22., which classifies households based on their ownership of a range of permanent income indicators (household assets) ranging from a bicycle, mobile phone, fixed-line telephones and refrigerator to a computer, dishwasher, washing machine and car. Country-specific items were also added to the list of assets to fit the standard of living of particular countries, and the final list included between 11 and 20 items. A principal components analysis (PCA) was then performed separately for each country to determine the weights to create an index of the asset variables. The weights for the first component were then applied to each person’s data thus giving a continuous asset index measure21. Because the PCA was performed separately for each country, the absolute value of the wealth index cannot be compared between countries. We thus categorised this index into tertiles to improve cross-country comparability of social gradients.
Two perceived oral health indicators were the outcome variables. The first measured total tooth loss through the question ‘Have you lost all of your natural teeth?’ and the second measured dental treatment needs through the question ‘During the last 12 months, did you have any problems with your mouth and/or teeth?’ Binary response options (no/yes) were used with the two items.
Covariates were participants’ sex, age and education. Age was categorised as 18–29, 30–39, 40–49, 50–59, 60–69 and ≥70 years of age. Education was measured using a seven-point response scale, and responses were collapsed into three categories (primary school or less; secondary school; and college and higher education) to enhance cross-country comparability. For one country (Turkey), the categorical classification of education was missing and years of education was converted into three categories based on the Turkish Ministry of Education classification. Although education is a common SEP indicator in high-income countries, we treated it as a confounder because it reflects childhood SEP (i.e. it happened before the creation of wealth in adult life), more so in developing than in developed countries23. Furthermore, education has its own effects on health status, which may offset low economic status; more education, however, does not necessarily lead to greater wealth in low- and middle-income countries.
Statistical analysis
stata/ic 12 for Windows (Stata Corp., College Station, TX, USA), using the survey command, was used for data analysis. All analyses considered the complex survey design (stratification and clustering), as well as the sample weights, to produce nationally representative estimates. Of the 214,240 respondents in the 40 countries, 28,458 (13.3%) had missing data on total tooth loss, 27,097 (12.6%) on problems with mouth and/or teeth, and 6,147 (2.9%) on one or more covariates. As there is ongoing debate on whether multiple imputation methods are useful with missing outcome data24., 25. (the two oral health measures explained the largest proportion of missing data), we opted to exclude participants with missing data from the analysis (casewise deletion).
We first presented the crude prevalence of total tooth loss and problems with mouth and/or teeth in the full sample of each country and then stratified the data according to household wealth. Linear trends for the association of household wealth with each oral health outcome were assessed, fitting the former as a continuous variable in survey logistic regression models. Results were presented for low-, lower-middle and upper-middle-income countries (LIC, LMIC and UMIC, respectively).
The Slope Index of Inequality (SII) and the Relative Index of Inequality (RII) were used to measure, respectively, the magnitude of absolute and relative inequalities in oral health according to household wealth. These regression-based indicators take the whole socio-economic distribution into account, rather than only comparing the two most extreme groups8., 26.. To that end, wealth tertiles were transformed into a summary measure (ridit score) that was scaled from 0 (first/bottom tertile) to 1 (third/top tertile) and were weighted to reflect the share of the sample at each wealth tertile. Ridit scores reflect the average cumulative frequency of the group, a midpoint of the range in the cumulative distribution, as described in detail elsewhere. For instance, if the first wealth group included 34% of the population, the range of participants in this category would be 0.00–0.34 and assigned a ridit score of 0.17 (= 0.34/2); if the second wealth group included 32% of the population the range of participants would be 0.34–0.66 and the corresponding ridit score would be 0.50 (= 0.34 + 0.32/2); and if the third wealth group included 34% of the population the range of participants would be 0.66–1.00 and the corresponding ridit score would be 0.83 (= 0.66 + 0.34/2). Ridit scores, instead of the wealth tertiles, were used in regression models to estimate SII and RII27.
Linear and logistic regressions were used to estimate SII and RII, respectively, in models adjusting for sex, age and education. SII and RII were calculated with their corresponding 95% confidence interval (CI). SII represents the absolute difference in total tooth loss and problems with mouth and/or teeth when moving from the bottom wealth tertile to the top wealth tertile. On the other hand, RII measures the odds of reporting total tooth loss or problems with mouth and/or teeth in the top tertile compared with the bottom tertile8., 26.. An SII value lower than 0 (or an RII value lower than 1) indicates that the oral health outcome is more common among the worse-off, whereas an SII value higher than 0 (or an RII value higher than 1) indicates that the oral health outcome is more prevalent among the better-off8., 26..
RESULTS
We used data from 180,996 adults, ≥18 years of age, living in 40 low- and middle-income countries (17 LIC, 13 LMIC and 10 UMIC). The number of adults participating in the WHS in these countries ranged from 929 in Latvia to 38,746 in Mexico, and the analytical sample used for each country represented between 61.0% and 99.5% of all WHS participants. Those excluded because of missing data were significantly older, more educated and wealthier than those with complete data.
The prevalence of total tooth loss ranged from 1.1% in Kenya and Myanmar to 15.7% in Hungary (Table 1). There were wealth-related inequalities in total tooth loss in most countries. Significant monotonic gradients in total tooth loss according to wealth tertiles were found in 21 of 40 countries and they were more common in more developed economies (35% of LIC, 46% of LIMC and 90% of UMIC). Two distinctive patterns were found based on the adjusted RII and SII values (Table 2). For the majority of countries (nine LIC, eight LMIC and eight UMIC), RII was lower than 1 (ranging from 0.13 for Swaziland to 0.94 for Paraguay) and SII was lower than 0 (ranging from −16.8% for Zimbabwe to −0.2% for Burkina Faso), suggesting that the prevalence of total tooth loss was higher in the bottom wealth tertile than in the top wealth tertile. For the remaining countries (eight LIC, five LMIC and two UMIC), RII was higher than 1 (ranging from 1.05 for Senegal to 7.08 for Vietnam) and SII was higher than 0 (ranging from 0.3% for Senegal to 12.8% for Namibia), suggesting that total tooth loss was more prevalent in the top wealth tertile than in the bottom wealth tertile. However, RII and SII were significant in 11 countries (three LIC, six LMIC and two UMIC), with total tooth loss being more common among the worse-off in Lao, Zimbabwe, Bosnia and Herzegovina, the Dominican Republic, Swaziland, Turkey, Latvia and Uruguay and among the better-off in Vietnam, Namibia and the Philippines.
Table 1.
Crude prevalence of total tooth loss among adults ≥ 18 years of age (n = 179,763), according to household wealth tertiles (World Health Survey, 2002–2004)
| Group | Country | n* | All sample (%) | Lowest tertile (%) | Middle tertile (%) | Highest tertile (%) | P value for trend† |
|---|---|---|---|---|---|---|---|
| LIC | Bangladesh | 5,411 | 1.2 | 0.8 | 1.6 | 1.2 | 0.489 |
| Burkina Faso | 4,694 | 1.6 | 1.8 | 1.5 | 1.3 | 0.291 | |
| Chad | 4,128 | 5.1 | 7.5 | 4.7 | 3.2 | 0.001 | |
| Ethiopia | 4,789 | 1.2 | 1.7 | 1.1 | 0.5 | 0.016 | |
| Georgia | 2,718 | 12.9 | 17.0 | 13.2 | 9.2 | <0.001 | |
| Ghana | 3,448 | 1.6 | 1.6 | 1.7 | 1.6 | 0.994 | |
| Kazakhstan | 4,460 | 10.7 | 11.7 | 9.8 | 10.7 | 0.759 | |
| Kenya | 4,189 | 1.1 | 1.1 | 1.4 | 0.6 | 0.262 | |
| Lao | 4,831 | 1.8 | 2.3 | 2.3 | 0.8 | 0.005 | |
| Malawi | 5,117 | 2.5 | 2.3 | 3.1 | 2.1 | 0.798 | |
| Mali | 3,379 | 1.9 | 1.7 | 2.2 | 2.0 | 0.708 | |
| Myanmar | 5,886 | 1.1 | 1.6 | 0.9 | 1.0 | 0.278 | |
| Nepal | 8,657 | 1.7 | 1.8 | 1.9 | 1.6 | 0.556 | |
| Pakistan | 5,798 | 5.3 | 5.8 | 5.7 | 4.0 | 0.147 | |
| Senegal | 2,295 | 5.4 | 4.8 | 6.8 | 4.8 | 0.907 | |
| Vietnam | 3,261 | 2.1 | 1.5 | 1.4 | 3.2 | 0.018 | |
| Zimbabwe | 3,644 | 15.1 | 22.1 | 11.7 | 8.6 | <0.001 | |
| LMIC | Bosnia & Herzegovina | 1,026 | 15.2 | 21.7 | 9.2 | 11.1 | 0.003 |
| Brazil | 4,960 | 14.7 | 18.4 | 15.8 | 11.0 | <0.001 | |
| Dominican Republic | 4,376 | 8.1 | 13.0 | 8.0 | 6.2 | <0.001 | |
| Ecuador | 3,876 | 8.1 | 8.0 | 10.1 | 6.2 | 0.162 | |
| Morocco | 4,466 | 9.6 | 8.7 | 10.4 | 9.4 | 0.767 | |
| Namibia | 3,675 | 15.5 | 13.3 | 12.5 | 21.3 | 0.003 | |
| Paraguay | 5,079 | 4.7 | 4.4 | 6.1 | 3.6 | 0.125 | |
| Philippines | 10,019 | 6.4 | 5.5 | 6.5 | 7.0 | 0.097 | |
| South Africa | 1,992 | 8.8 | 9.3 | 6.9 | 10.4 | 0.783 | |
| Sri Lanka | 5,372 | 4.2 | 6.8 | 4.0 | 3.4 | 0.117 | |
| Swaziland | 1,905 | 7.7 | 12.8 | 7.2 | 3.2 | <0.001 | |
| Turkey | 10,828 | 13.6 | 16.3 | 15.1 | 9.8 | <0.001 | |
| Ukraine | 2,195 | 10.1 | 12.6 | 10.3 | 8.0 | 0.329 | |
| UMIC | Croatia | 967 | 11.6 | 21.0 | 7.8 | 9.3 | 0.003 |
| Czech Republic | 875 | 11.4 | 22.6 | 8.2 | 2.6 | <0.001 | |
| Estonia | 991 | 11.8 | 19.3 | 10.6 | 5.3 | <0.001 | |
| Hungary | 1,386 | 15.7 | 28.2 | 13.2 | 6.1 | <0.001 | |
| Latvia | 839 | 10.3 | 17.9 | 9.7 | 1.7 | <0.001 | |
| Malaysia | 5,842 | 9.0 | 10.2 | 9.2 | 7.7 | 0.042 | |
| Mauritius | 3,726 | 12.0 | 15.9 | 11.8 | 8.9 | <0.001 | |
| Mexico | 24,075 | 7.3 | 7.2 | 7.2 | 7.6 | 0.665 | |
| Slovakia | 1,679 | 2.9 | 6.3 | 1.2 | 1.0 | 0.001 | |
| Uruguay | 2,909 | 7.3 | 11.1 | 6.5 | 4.3 | <0.001 |
Counts are unweighted.
P value for trend was derived from unadjusted survey logistic regression models.
LIC, low-income countries; LMIC, lower-middle-income countries; UMIC, upper-middle-income countries.
Table 2.
Absolute and relative measures of inequalities in total tooth loss, according to household wealth, in adults ≥ 18 years of age (World Health Survey, 2002–2004)
| Group | Country | RII† | (95% CI) | SII† | (95% CI) |
|---|---|---|---|---|---|
| LIC | Bangladesh | 2.47 | (0.99 to 6.21) | 1.0 | (−0.1 to 2.1) |
| Burkina Faso | 0.86 | (0.31 to 2.37) | −0.2 | (−1.6 to 1.2) | |
| Chad | 0.40 | (0.16 to 1.01) | −4.0 | (−8.3 to 0.2) | |
| Ethiopia | 0.21 | (0.04 to 1.08) | −1.7 | (−3.2 to −0.1) | |
| Georgia | 0.82 | (0.35 to 1.93) | −1.7 | (−8.5 to 5.0) | |
| Ghana | 1.38 | (0.41 to 4.65) | 0.5 | (−1.3 to 2.3) | |
| Kazakhstan | 2.36 | (0.94 to 5.96) | 7.0 | (−1.7 to 15.7) | |
| Kenya | 1.60 | (0.34 to 7.59) | 0.4 | (−1.0 to 1.8) | |
| Lao | 0.18 | (0.07 to 0.46)** | −2.9 | (−4.5 to −1.3)** | |
| Malawi | 1.14 | (0.48 to 2.71) | 0.4 | (−1.7 to 2.4) | |
| Mali | 1.66 | (0.53 to 5.18) | 1.0 | (−1.2 to 3.3) | |
| Myanmar | 0.51 | (0.15 to 1.75) | −0.8 | (−2.3 to −0.6) | |
| Nepal | 0.71 | (0.34 to 1.48) | −0.6 | (−1.9 to 0.7) | |
| Pakistan | 0.67 | (0.25 to 1.80) | −1.5 | (−5.8 to 2.8) | |
| Senegal | 1.05 | (0.45 to 2.45) | 0.3 | (−3.9 to 4.5) | |
| Vietnam | 7.08 | (1.89 to 26.46)** | 3.8 | (0.8 to 6.8)* | |
| Zimbabwe | 0.23 | (0.14 to 40.0)*** | −16.8 | (−22.7 to −0.9)*** | |
| LMIC | Bosnia & Herzegovina | 0.31 | (0.14 to 0.70)** | −8.7 | (−15.7 to −1.7)* |
| Brazil | 0.65 | (0.41 to 1.04) | −3.6 | (−8.1 to 0.8) | |
| Dominican Republic | 0.38 | (0.18 to 0.82)* | −6.0 | (−10.4 to −1.6)** | |
| Ecuador | 0.50 | (0.25 to 1.00) | −4.5 | (−9.1 to 0.2) | |
| Morocco | 1.11 | (0.53 to 2.32) | 0.8 | (−5.2 to 6.7) | |
| Namibia | 2.77 | (1.58 to 4.87)** | 12.8 | (5.6 to 20.0)** | |
| Paraguay | 0.94 | (0.52 to 1.72) | −0.4 | (−3.1 to 2.2) | |
| Philippines | 1.82 | (1.11 to 2.98)* | 3.5 | (0.7 to 6.3)* | |
| South Africa | 1.92 | (0.76 to 4.87) | 5.0 | (−2.4 to 12.3) | |
| Sri Lanka | 0.61 | (0.19 to 1.92) | −1.8 | (−5.4 to 1.8) | |
| Swaziland | 0.13 | (0.05 to 0.31)* | −13.5 | (−19.4 to −7.5)* | |
| Turkey | 0.67 | (0.46 to 0.97)*** | −4.0 | (−7.5 to −0.6)*** | |
| Ukraine | 1.42 | (0.29 to 6.88) | 2.4 | (−8.7 to 13.5) | |
| UMIC | Croatia | 2.08 | (0.73 to 5.94) | 7.8 | (−2.3 to 17.9) |
| Czech Republic | 0.44 | (0.09 to 2.07) | −3.2 | (−14.5 to 8.0) | |
| Estonia | 0.64 | (0.24 to 1.72) | −3.2 | (−10.5 to 4.1) | |
| Hungary | 0.54 | (0.24 to1.24) | −5.2 | (−12.7 to 2.2) | |
| Latvia | 0.18 | (0.05 to 0.69)* | −9.7 | (−18.8 to −0.6)* | |
| Malaysia | 0.81 | (0.48 to 1.37) | −1.7 | (−5.1 to 1.8) | |
| Mauritius | 0.78 | (0.50 to 1.20) | −2.4 | (−6.1 to 1.4) | |
| Mexico | 0.94 | (0.65 to 1.36) | −0.3 | (−2.4 to 1.9) | |
| Slovakia | 2.66 | (0.47 to 14.96) | 0.8 | (−1.4 to 3.0) | |
| Uruguay | 0.29 | (0.16 to 0.53)*** | −6.4 | (−10.2 to −2.7)** |
P < 0.05; **P < 0.01; ***P < 0.001.
Estimates were adjusted for participants’ sex, age groups and education.
LIC, low-income countries; LMIC, lower-middle-income countries; UMIC, upper-middle-income countries; RII, relative index of inequality; SII, slope index of inequality.
The prevalence of problems with mouth and/or teeth ranged from 12.8% in Myanmar to 63.7% in Kazakhstan (Table 3). In most countries there were inequalities in problems with mouth and/or teeth when this was stratified according to household wealth. However, significant monotonic wealth gradients in problems with mouth and/or teeth were present in 18 of 40 countries and they were more common in less-developed economies (47%, 46% and 40% for LIC, LMIC and UMIC, respectively). The adjusted RII and SII values showed two opposite patterns (Table 4). For 26 countries (eight LIC, 11 LMIC and seven UMIC), RII (ranging from 1.02 for Mauritius to 2.19 for Uruguay) and SII (ranging from 0.4% for Mauritius to 16.7% for Slovakia) suggested that the prevalence of problems with mouth and/or teeth was higher in the top wealth tertile than in the bottom wealth tertile. For the second group of countries (nine LIC, two LMIC and three UMIC), RII (ranging from 0.49 for Ethiopia to 0.92 for Latvia) and SII (ranging from −10.6% for Malawi to −2.0% for Latvia) indicated that problems with mouth and/or teeth were more prevalent in the bottom wealth tertile than in the top wealth tertile. However, the adjusted RII and SII values were only significant in 11 countries (seven LIC, two LMIC and two UMIC), with problems with mouth and/or teeth being more prevalent among the worse-off in Ethiopia, Ghana, Malawi, Nepal and the Philippines and more prevalent among the better-off in Kazakhstan, Lao, Pakistan, the Dominican Republic, Mexico and Uruguay.
Table 3.
Crude prevalence of problems with mouth and/or teeth in adults ≥ 18 years of age (n = 180,996), according to household wealth tertiles (World Health Survey, 2002–2004)
| Group | Country | n* | All samples (%) | Lowest tertile (%) | Middle tertile (%) | Highest tertile (%) | P value for trend† |
|---|---|---|---|---|---|---|---|
| LIC | Bangladesh | 5,425 | 42.6 | 43.7 | 42.5 | 41.8 | 0.365 |
| Burkina Faso | 4,697 | 24.9 | 24.3 | 25.9 | 24.4 | 0.756 | |
| Chad | 4,157 | 29.3 | 33.1 | 29.4 | 25.4 | 0.001 | |
| Ethiopia | 4,851 | 19.4 | 22.2 | 17.9 | 15.4 | 0.001 | |
| Georgia | 2,709 | 49.4 | 47.0 | 51.0 | 50.4 | 0.447 | |
| Ghana | 3,496 | 17.8 | 20.8 | 19.2 | 14.3 | <0.001 | |
| Kazakhstan | 4,469 | 63.7 | 63.0 | 64.1 | 64.0 | 0.741 | |
| Kenya | 4,231 | 27.8 | 31.5 | 28.9 | 23.0 | 0.005 | |
| Lao | 4,835 | 21.9 | 19.8 | 19.3 | 26.1 | 0.004 | |
| Malawi | 5,146 | 37.3 | 41.6 | 38.1 | 31.6 | <0.001 | |
| Mali | 3,460 | 25.3 | 25.3 | 26.2 | 24.6 | 0.735 | |
| Myanmar | 5,886 | 12.8 | 12.4 | 13.2 | 12.6 | 0.890 | |
| Nepal | 8,623 | 34.0 | 36.6 | 35.1 | 31.2 | <0.001 | |
| Pakistan | 5,884 | 18.7 | 17.5 | 17.7 | 22.7 | 0.039 | |
| Senegal | 2,332 | 29.9 | 34.0 | 25.8 | 28.5 | 0.180 | |
| Vietnam | 3,366 | 21.0 | 21.1 | 19.8 | 21.6 | 0.868 | |
| Zimbabwe | 3,686 | 33.6 | 32.7 | 33.2 | 34.9 | 0.387 | |
| LMIC | Bosnia & Herzegovina | 1,020 | 33.9 | 33.1 | 34.9 | 34.2 | 0.831 |
| Brazil | 4,960 | 35.3 | 32.8 | 34.3 | 38.0 | 0.007 | |
| Dominican Republic | 4,383 | 27.9 | 23.5 | 26.6 | 30.8 | 0.007 | |
| Ecuador | 3,901 | 23.6 | 18.3 | 25.8 | 25.2 | 0.014 | |
| Morocco | 4,467 | 43.4 | 39.6 | 43.3 | 46.3 | 0.040 | |
| Namibia | 3,731 | 22.1 | 23.3 | 21.6 | 21.4 | 0.363 | |
| Paraguay | 5,086 | 40.9 | 37.6 | 39.8 | 44.1 | 0.002 | |
| Philippines | 10,029 | 38.0 | 41.7 | 38.5 | 34.4 | <0.001 | |
| South Africa | 1,964 | 17.1 | 17.7 | 18.1 | 15.2 | 0.510 | |
| Sri Lanka | 5,685 | 22.0 | 19.2 | 21.3 | 23.9 | 0.168 | |
| Swaziland | 1,918 | 21.6 | 22.5 | 17.5 | 24.0 | 0.669 | |
| Turkey | 11,026 | 34.2 | 33.3 | 34.4 | 34.3 | 0.587 | |
| Ukraine | 2,219 | 51.3 | 49.0 | 51.6 | 52.9 | 0.445 | |
| UMIC | Croatia | 968 | 40.0 | 33.7 | 40.9 | 42.9 | 0.060 |
| Czech Republic | 876 | 46.3 | 47.1 | 42.7 | 49.6 | 0.719 | |
| Estonia | 991 | 52.8 | 51.0 | 53.5 | 53.8 | 0.507 | |
| Hungary | 1,386 | 34.2 | 24.4 | 35.8 | 42.2 | <0.001 | |
| Latvia | 842 | 47.5 | 42.7 | 50.8 | 49.8 | 0.178 | |
| Malaysia | 5,845 | 20.5 | 19.8 | 18.5 | 22.9 | 0.041 | |
| Mauritius | 3,733 | 23.8 | 21.7 | 26.3 | 23.1 | 0.503 | |
| Mexico | 24,075 | 27.0 | 22.8 | 28.8 | 31.5 | <0.001 | |
| Slovakia | 1,728 | 41.3 | 35.8 | 42.2 | 46.3 | 0.111 | |
| Uruguay | 2,910 | 27.8 | 20.4 | 27.1 | 35.8 | <0.001 |
Counts are unweighted.
P value for trend derived from unadjusted survey logistic regression models.
LIC, low-income countries; LMIC, lower-middle-income countries; UMIC, upper-middle-income countries.
Table 4.
Absolute and relative measures of inequalities in problems with mouth and/or teeth, according to household wealth, in adults ≥ 18 years of age (World Health Survey, 2002–2004)
| Group | Country | RII† | (95% CI) | SII† | (95% CI) |
|---|---|---|---|---|---|
| LIC | Bangladesh | 0.90 | (0.83 to 1.53) | −2.4 | (−4.6 to 9.4) |
| Burkina Faso | 1.27 | (0.95 to 1.69) | 4.2 | (−0.9 to 9.2) | |
| Chad | 0.80 | (0.54 to 1.17) | −4.5 | (−12.1 to 3.1) | |
| Ethiopia | 0.49 | (0.30 to 0.80)** | −10.4 | (−17.4 to −3.5)** | |
| Georgia | 1.29 | (0.79 to 2.12) | 6.3 | (−5.8 to 18.5) | |
| Ghana | 0.59 | (0.39 to 0.90)* | −7.3 | (−13.0 to −1.6)* | |
| Kazakhstan | 1.52 | (1.01 to 2.27)* | 9.3 | (0.1 to 18.6)* | |
| Kenya | 0.82 | (0.51 to 1.30) | −4.0 | (−12.8 to 4.9) | |
| Lao | 1.61 | (1.09 to 2.38)* | 8.0 | (1.4 to 14.6)* | |
| Malawi | 0.62 | (0.44 to 0.87)** | −10.6 | (−18.3 to −3.0)** | |
| Mali | 1.13 | (0.76 to 1.69) | 2.2 | (−5.0 to 9.5) | |
| Myanmar | 1.05 | (0.70 to 1.59) | 0.6 | (−4.0 to 5.1) | |
| Nepal | 0.71 | (0.57 to 0.87)** | −7.4 | (−12.0 to −2.9)** | |
| Pakistan | 1.85 | (1.13 to 3.04)* | 8.5 | (1.4 to 15.7)* | |
| Senegal | 0.68 | (0.34 to 1.37) | −7.8 | (−22.2 to 6.5) | |
| Vietnam | 1.27 | (0.73 to 2.20) | 4.0 | (−4.6 to 12.6) | |
| Zimbabwe | 0.76 | (0.52 to 1.11) | −5.7 | (−13.6 to 2.1) | |
| LMIC | Bosnia & Herzegovina | 1.05 | (0.47 to 2.34) | 1.0 | (−16.7 to 18.7) |
| Brazil | 1.13 | (0.85 to 1.49) | 2.7 | (−3.5 to 8.8) | |
| Dominican Republic | 1.83 | (1.15 to 2.89)** | 11.9 | (2.9 to 20.9)** | |
| Ecuador | 1.32 | (0.88 to 1.99) | 4.9 | (−2.2 to 11.9) | |
| Morocco | 1.39 | (0.86 to 2.23) | 7.8 | (−3.5 to 19.2) | |
| Namibia | 1.04 | (0.68 to 1.59) | 0.5 | (−6.5 to 7.5) | |
| Paraguay | 1.17 | (0.87 to 1.55) | 3.6 | (−3.2 to 10.3) | |
| Philippines | 0.69 | (0.53 to 0.88)** | −8.8 | (−14.6 to −3.0)** | |
| South Africa | 0.73 | (0.39 to 1.35) | −4.1 | (−12.5 to 4.2) | |
| Sri Lanka | 1.71 | (0.80 to 3.65) | 9.1 | (−4.5 to 22.6) | |
| Swaziland | 1.54 | (0.73 to 3.24) | 7.3 | (−5.2 to 19.8) | |
| Turkey | 1.07 | (0.84 to 1.35) | 1.5 | (−3.8 to 6.7) | |
| Ukraine | 1.11 | (0.60 to 2.07) | 2.6 | (−12.6 to 17.7) | |
| UMIC | Croatia | 1.47 | (0.75 to 2.89) | 9.1 | (−6.7 to 25.0) |
| Czech Republic | 0.91 | (0.40 to 2.09) | −2.3 | (−22.5 to 18.0) | |
| Estonia | 0.77 | (0.40 to 1.48) | −6.2 | (−21.8 to 9.4) | |
| Hungary | 1.59 | (0.93 to 2.71) | 10.0 | (−1.5 to 21.6) | |
| Latvia | 0.92 | (0.44 to 1.93) | −2.0 | (−19.9 to 15.9) | |
| Malaysia | 1.15 | (0.84 to 1.58) | 2.1 | (−2.7 to 7.2) | |
| Mauritius | 1.02 | (0.75 to 1.41) | 0.4 | (−5.3 to 6.1) | |
| Mexico | 1.83 | (1.54 to 2.16)*** | 11.7 | (8.4 to 15.1) *** | |
| Slovakia | 2.06 | (0.91 to 4.69) | 16.7 | (−2.2 to 35.5) | |
| Uruguay | 2.19 | (1.26 to 3.80)** | 15.0 | (4.2 to 25.8)** |
P < 0.05; **P < 0.01; ***P < 0.001.
Estimates were adjusted for participants’ sex, age groups and education.
LIC, low-income countries; LMIC, lower-middle-income countries; UMIC, upper-middle-income countries; RII, relative index of inequality; SII, slope index of inequality.
DISCUSSION
Our results indicate that wealth-related inequalities in self-reported total tooth loss and perceived dental-treatment needs (problems with mouth and/or teeth in the past year) were present in countries from different WHO regions and at different levels of national income. Significant gradients were found in 11 of 40 countries evaluated, with evidence of both pro-rich and pro-poor wealth inequalities in oral health (gradients in total tooth loss and treatment needs favouring the better-off and the worse-off, respectively). These findings were not accounted for by participants’ sex, age and level of education.
These results should be interpreted with consideration of some study limitations. First, data on total tooth loss and dental-treatment needs were based on self-reports, which may raise concerns about their validity when compared with objective clinical assessments. However, self-reported measures are valid and reliable indicators of individuals’ oral health status and are positively correlated with disease measures28., 29.. Self-reported tooth counts can be used to estimate the number of remaining teeth accurately28., 30., and self-assessed needs are positively correlated with disease measures and are valuable in assessing the needs of adults31., 32.. In addition, similar results were found in previous surveys conducted in some of these countries15., 16., even when using clinical measures33. Second, we used the wealth index to measure participants’ SEP. The wealth index is considered a stable and effective indicator for monitoring long-term SEP of individuals and their households in developing countries where data on education and occupation are often inaccurate and not likely to capture the full extent of an individual’s SEP21., 22.. Household income and consumption expenditure are other alternatives but have their limitations compared with wealth21. In addition, the decision to use wealth tertiles was empirical because quartiles and quintiles did not provide equal-size groups or enough participants for meaningful comparisons in some countries. Third, we used linear and logistic regression to estimate SII and RII, respectively, despite recent suggestions to use log-binomial regression with a logarithmic link function to calculate the RII and with an identity link function to calculate the SII27., 34.. We encountered convergence issues for some countries when using log-binomial regression which persisted even when resorting to robust Poisson regression as an alternative. We compared our results with those from log-binomial regression for countries in which the latter model converged and found that the results were similar for RII and slightly higher for SII (when using logistic regression) but in the same direction. Fourth, no attempt to control for dental behaviours was carried out. As the aim was to assess the overall impact of SEP on oral health, it was deemed inappropriate to adjust for behaviours. Indeed, dental behaviours are considered as merely intermediates of the relationship between socio-economic indicators and oral health1., 35..
The existence of wealth inequalities in adult oral health favouring the poor contradicts the a-priori assumption that social gradients in oral health are universal1., 2.. Pro-poor inequalities in total tooth loss may be explained by differences in life expectancy between the rich and the poor: tooth loss is age-dependent and will be more common among the rich if they live longer. Another explanation is that the poor may have less caries – the main reason for tooth loss – than the rich because sugar is still a commodity in some developing countries and, as such, only accessible to the better-off17. A final explanation combines high costs of treatment and delay in seeking care. Dental services in developing countries are mainly financed via out-of-pocket spending, driving individuals to seek dental care only when there is an acute problem. Individuals may arrive at a dental practice with more severe disease, when tooth extraction might be the only possible care pathway. Under these circumstances, the poor could have more teeth (including tooth remnants) because they could not afford to have teeth extracted.
Wealth inequalities in perceived dental-treatment needs favouring the poor were more common than those for total tooth loss. Indeed, more countries reported pro-poor than pro-rich inequalities in perceived needs. A possible explanation for these findings is that the priorities of the poor tend to diverge from those of the rich; the poor having more urgent needs in life to be met than those related to the condition of their mouth and teeth, whereas the rich could identify their oral health needs through enhanced access to information and health education16. This is in addition to evidence suggesting that people with the same state of health judge their quality of life differently according to their social standing36. It is also possible that adults with oral diseases, who are over-represented in lower social groups, may have learned how to cope with frequent symptoms during the course of their condition, which in turn become less distressing with every recurrence, leading to changes in internal standards, values and beliefs (response shift)37.
This is the first study exploring social inequalities in adult oral health in subjects from low- and middle-income countries. Governments can use these baseline data to track their own progress relative to geographic neighbours, economically-comparable countries or a development reference group. The data could also inform policy action to address oral health inequalities, although we need to understand country-specific conditions and tailor policies that take due consideration of these country-specific circumstances7., 8.. As the WHS data are relatively old, future studies should evaluate whether the present findings are replicated when using alternative SEP indicators and clinical oral-health indices.
In conclusion, this multicountry comparison provides evidence of the presence of social inequalities in adult oral health, according to household wealth, in low- and middle-income countries, regardless of economic development. However, the well-documented social gradient in adult oral health favouring the rich was not observed in all low- and middle-income countries. Pro-poor inequalities in total tooth loss, and particularly in perceived dental-treatment needs, were seen in several countries.
Acknowledgements
The authors have received no financial support for this study.
Competing interest
The authors declare no competing interest.
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