Abstract
Objectives
Most studies on factors influencing dental attendance are cross‐sectional and focus on specific age groups. The associations between private ancillary health insurance, tobacco smoking, alcohol consumption and overweight/obesity with dental attendance were examined in three cohorts of Australian women of different ages using multiple waves of data over similar time periods.
Methods
Data from 10 233, 12 378 and 7892 women born in 1973–1978, 1946–1951 and 1921–1926 participating in the Australian Longitudinal Study on Women's Health were used. Poisson regression using generalized estimating equations was used to investigate factors associated with self‐report of not visiting the dentist in the 12 months before completing each wave.
Results
The role of dental non‐attendance was higher in women without insurance (versus those with insurance) in all cohorts with adjusted rate ratios (RR) of 1.52 95% CI 1.48–1.57, RR 1.45 95% CI 1.41–1.49 and RR 1.32 95% CI 1.28–1.36 in the 1973–78, 1946–51 and 1921–26 cohorts respectively. Current smokers at any intensity (versus never smokers) had a higher risk of non‐attendance and the risk was strongest for women in the 1946–51 cohort who smoked ≥20 cigarettes/day (RR 1.35 95% CI 1.30–1.41). Compared with low‐risk drinkers, non‐drinkers were more likely to be non‐attenders, but only in the two older cohorts. Women who were overweight or obese (versus healthy weight) were more likely to be non‐attenders in all cohorts, with the risk of non‐attendance higher with increasing BMI.
Conclusions
This study emphasizes the continued need to address socioeconomic inequities in access to dental care, along with strategies to overcome barriers for those who are obese or smoke. In this study, barriers to access existed for women of all ages, indicating that interventions need to be appropriate across age groups.
Keywords: Australia, dental health services, obesity, socioeconomic factors, tobacco smoking, women
1. INTRODUCTION
It is well established that oral health has an impact on overall health and well‐being, with poor oral health associated with a number of chronic diseases in adults of all ages including cardiovascular disease, 1 head and neck cancers 2 and adverse pregnancy outcomes. 3 People who regularly visit a dentist for a check‐up (at least annually) have better oral health than people who do not see the dentist regularly. 4
Currently, there is no publicly funded universal dental insurance scheme in Australia, with most adults self‐funding all (or at least some) of the costs incurred visiting a private dentist. Adults who hold health care or pensioner concession cards are eligible for dental treatment provided in the public sector, although there are long waiting lists for these services. 4 Private ancillary health insurance may partly cover the costs of dental treatment; however, there is considerable variation between policies in the types of services covered and the level of reimbursement provided. Factors such as income and education level influence the uptake of private health insurance and those without insurance are less likely to access dental care. 5 , 6 , 7 , 8 , 9
Obesity, smoking and high alcohol consumption contribute to poor oral health are more prevalent in those with low income and education levels and have been associated with a higher likelihood of dental non‐attendance. 4 , 9 , 10 , 11 , 12 Reasons for non‐attendance may be multi‐faceted and may differ across the factors but potentially include access difficulties (e.g. due to cost, dental anxiety, 13 ill health or physical function limitations 14 ) or a reluctance to undertake preventive health care. 15
Most research investigating associations between private health insurance and these specific health factors and dental non‐attendance has been cross‐sectional, 5 , 10 , 11 , 16 , 17 while the fewer longitudinal studies have focused mostly on specific age groups (e.g. adults >65 years 6 , 7 , 9 or adolescents 18 ). In addition, most studies considering obesity, smoking and alcohol consumption have used dichotomous variables, 7 , 9 , 10 , 11 limiting the degree to which associations at different intensity levels can be assessed. Longitudinal studies with measures repeated at multiple waves at different timepoints provide the opportunity to study associations between exposures and outcome. Understanding whether barriers to dental attendance are similar or different across age groups is important to inform the design and targeting of interventions to overcome them. To the best of our knowledge, no prior study has looked at the associations between private health insurance and key health factors (with at least four ordinal categories) with dental non‐attendance across different age cohorts. Hence, the aim of this study was to compare the associations between private health insurance and modifiable health factors and dental non‐attendance across three cohorts of Australian women of different ages using repeated waves of data over several years.
2. METHODS
2.1. Participants
Data from the Australian Longitudinal Study on Women's Health (ALSWH) were used. The ALSWH is a population‐based cohort study designed to assess women's physical and mental health, as well as their use of health services. 19 A summary of the sampling, recruitment, retention and data collection methods is provided in Appendix S1. The study began in 1996 and included three cohorts of women born in 1973–78, 1946–51 and 1921–26. Participants within each cohort were randomly selected from the Medicare database, with women living in rural and remote areas sampled at twice the rate of women in urban areas to allow statistical comparisons between these groups. 19 Women who participated in the baseline (1996) wave have been followed up approximately every 3 years.
The ALSWH has been granted ethics clearance by the University of Newcastle (ethics approval H0760795) and the University of Queensland (ethics approval 2004000224). All participants provided informed, written consent at each wave.
2.2. Exposures of interest
Using directed acyclic graphs (DAGs), three separate models were conceptualized for the associations between (1) private health insurance, (2) alcohol and smoking and (3) body mass index and dental non‐attendance (Figure S1).
2.2.1. Private ancillary health insurance
At each wave, women were asked if they had private ancillary health insurance; women in the 1945–51 and 1921–26 cohorts who held Australian Department of Veterans' Affairs gold cards were considered to have the equivalent of health insurance. This was a dichotomous variable (yes/no).
2.2.2. Smoking status
Women were asked about their smoking habits at all waves for the 1973–78 and 1946–51 cohorts and at Wave 2 for the 1921–26 cohort. A five‐category variable was used in the analysis (‘Never smoker’, ‘Former smoker’, ‘Current smoker <10 cigarettes/day’, ‘Current smoker 10–19 cigarettes/day’ and ‘Current smoker ≥20 cigarettes/day).
2.2.3. Alcohol consumption
At each wave (except Wave 3 for the 1946–51 cohort and Wave 4 for the 1921–26 cohort), a variable for alcohol consumption (categorized according to the Australian National Health and Medical Research Council Guidelines 20 ) was derived from questions about the frequency and quantity of alcohol consumed. A four‐category variable was used in the analysis: ‘Non‐drinker’, ‘Rarely drinker (<1 drink/month)’, ‘Low‐risk drinker (1 to ≤2 drinks/day)’, ‘Risky/high risk drinker (≥3 drinks/day)’.
2.2.4. Body mass index
Body mass index (BMI) was calculated from self‐reported height and weight (kg/m2) at each wave, and categorized according to the World Health Organization classifications (<18.5 kg/m2 ~ underweight, 18.5–24.9 kg/m2 ~ healthy weight, 25.0–29.9 kg/m2 ~ overweight, 30.0–34.9 kg/m2 ~ obese class I, 35.0–39.9 kg/m2 ~ obese class II, ≥40.0 kg/m2 ~ obese class III). 21
2.3. Dental non‐attendance
Questions about dental attendance were asked in at least three waves in all cohorts (Figure 1). Women were asked whether they had visited a dentist in the 12 months prior to completing the questionnaire. A time varying binary variable (dental attendance in the previous 12 months/dental non‐attendance in the previous 12 months) was used in the analysis.
FIGURE 1.
Flowchart of participants included in the analysis at each wave. Women who did not have complete information on all covariates at a wave were excluded from that wave
2.4. Other covariates
Models were adjusted for likely confounding factors of the relationships between the four exposures of interest and dental attendance (see Figure S1). Unless stated, all covariates were time‐varying, and the same measures were used at all applicable waves for each cohort. Categories for each variable are included in Table 1. Further details on the measurement and categorization of covariates are included in Appendix S2. Socioeconomic covariates included the following: area of residence, 22 ability to manage on the income available and education level. The physical functioning (PF) and mental health (MH) sub‐scales of the Medical Outcomes Study Short Form Health Survey (SF‐36) 23 were included as continuous variables. In addition to reporting the mean and standard deviation of these subscales in the descriptive characteristics at study baseline for each cohort, the PF sub‐scale was also dichotomized at the first quartile (Q1) to indicate the poorest levels of physical functioning and the MH sub‐scale was dichotomized at 60, with a score <60 indicating moderate or severe depressive symptoms. 24
TABLE 1.
Descriptive characteristics of women (at respective study baseline year) in the 1973–78, 1946–51 and 1921–26 cohorts of the Australian Longitudinal Study on Women's Health (ALSWH) stratified by dental attendance (yes/no) in the prior 12 months
| 1973–1978 cohort (Wave 4 2006, Aged 28–33 years) n = 8573 | 1946–1951 cohort (Wave 2 1998, Aged 47–52 years) n = 10 109 | 1921–1926 cohort (Wave 2 1999, Aged 73–78 years) n = 6508 | ||||
|---|---|---|---|---|---|---|
| Dental attendance in prior 12 months | Dental attendance in prior 12 months | Dental attendance in prior 12 months | ||||
| Yes n (%) a | No n (%) a | Yes n (%) a | No n (%) a | Yes n (%) a | No n (%) a | |
| Area of residence | ||||||
| Major cities | 2794 (57.1) | 2110 (42.9) | 2117 (64.4) | 1181 (35.6) | 1138 (43.9) | 1464 (56.1) |
| Inner regional | 1098 (51.5) | 1095 (48.5) | 2256 (55.5) | 1873 (44.5) | 904 (34.3) | 1737 (65.7) |
| Outer regional/remote/very remote | 744 (51.5) | 732 (48.5) | 1420 (52.2) | 1262 (47.8) | 393 (32.0) | 872 (68.0) |
| Ability to manage on income | ||||||
| Not too bad/Easy | 2881 (58.6) | 2137 (41.4) | 3459 (64.4) | 2091 (35.6) | 1905 (41.6) | 3030 (58.4) |
| Impossible/Difficult sometimes/often | 1755 (50.4) | 1800 (49.6) | 2334 (55.6) | 2080 (44.4) | 530 (36.8) | 1043 (63.2) |
| Education level | ||||||
| High level of education | 2374 (59.0) | 1693 (41.0) | 3433 (68.1) | 1827 (31.9) | 1992 (45.9) | 2655 (54.1) |
| Low level of education | 2262 (51.3) | 2244 (48.7) | 2360 (51.7) | 2490 (48.3) | 443 (26.1) | 1418 (73.9) |
| Private ancillary health insurance status | ||||||
| Has ancillary health insurance | 2786 (63.5) | 1659 (36.5) | 3224 (70.6) | 1542 (29.4) | 1293 (55.4) | 1163 (44.6) |
| Does not have ancillary health insurance | 1850 (45.5) | 2278 (54.5) | 2569 (50.8) | 2774 (49.2) | 1142 (30.3) | 2910 (69.7) |
| Mental Health Index Score | ||||||
| Mean (standard deviation) | 72.5 (16.7) | 71.8 (16.7) | 73.8 (18.3) | 72.1 (18.4) | 79.3 (16.4) | 79.1 (15.4) |
| 60+ | 3791 (55.8) | 3175 (44.2) | 4785 (62.1) | 3417 (37.9) | 2198 (40.5) | 3672 (59.5) |
| <60 | 845 (53.1) | 762 (46.9) | 1008 (55.8) | 899 (44.2) | 237 (40.1) | 401 (59.9) |
| Physical Function Score | ||||||
| Mean (standard deviation) | 91.4 (15.1) | 90.7 (14.8) | 85.1 (18.2) | 82.5 (18.5) | 65.3 (25.5) | 61.5 (24.9) |
| ≥25th percentile | 3662 (56.2) | 3024 (43.8) | 4578 (62.3) | 3173 (37.7) | 1951 (41.7) | 3088 (58.3) |
| <25th percentile | 974 (52.3) | 913 (47.7) | 1215 (55.9) | 1143 (44.1) | 484 (36.3) | 985 (63.7) |
| Smoking status | ||||||
| Never smoker | 2734 (56.3) | 2258 (43.7) | 3411 (63.5) | 2263 (36.5) | 1519 (40.2) | 2588 (59.8) |
| Former smoker | 1027 (54.2) | 894 (45.8) | 1575 (62.2) | 1142 (37.8) | 824 (43.2) | 1235 (56.8) |
| Current smoker <10 cigarettes/day | 438 (55.5) | 373 (44.5) | 232 (61.7) | 160 (38.3) | 34 (27.2) | 83 (72.8) |
| Current smoker 10–19 cigarettes/day | 302 (54.5) | 259 (45.5) | 212 (50.0) | 226 (50.0) | 43 (28.8) | 116 (71.2) |
| Current smoker ≥20 cigarettes/day | 135 (46.6) | 153 (53.4) | 363 (43.7) | 525 (56.3) | 15 (27.2) | 51 (72.8) |
| Alcohol consumption | ||||||
| Low‐risk drinker (>1 drink/month to ≤2 drinks/day) | 2863 (56.6) | 2294 (43.4) | 3293 (64.8) | 2056 (35.2) | 1068 (47.7) | 1328 (52.3) |
| Non‐drinker | 434 (51.1) | 444 (48.9) | 686 (55.3) | 620 (44.7) | 616 (31.9) | 1464 (68.1) |
| Rarely drinker (<1 drink/month) | 1168 (54.0) | 1051(46.0) | 1511 (56.4) | 1370 (43.6) | 627 (39.3) | 1137 (60.7) |
| Risky/High‐risk drinker (≥3 drinks/day) | 171 (54.3) | 148 (45.7) | 300 (55.9) | 270 (44.1) | 124 (47.5) | 144 (52.5) |
| Body Mass Index (BMI) | ||||||
| Underweight (<18.5 kg/m2) | 169 (56.6) | 133 (43.4) | 80 (55.2) | 64 (44.8) | 83 (46.8) | 127 (53.2) |
| Healthy weight (18.5–24.9 kg/m2) | 2603 (58.1) | 1998 (41.9) | 2910 (64.9) | 1808 (35.1) | 1317 (43.3) | 1945 (56.7) |
| Overweight (25.0–29.9 kg/m2) | 1107 (53.3) | 1011 (46.7) | 1780 (59.3) | 1409 (40.7) | 753 (37.7) | 1381 (62.3) |
| Obese Class I (30.0–34.5 kg/m2) | 470 (50.3) | 455 (49.7) | 678 (54.5) | 643 (45.5) | 229 (37.3) | 460 (62.7) |
| Obese Class II (35.0–39.9 kg/m2) | 180 (50.0) | 192 (50.0) | 236 (49.8) | 273 (50.2) | 48 (30.1) | 125 (69.9) |
| Obese Class III (≥40.0 kg/m2) | 107 (43.2) | 148 (56.8) | 109 (54.0) | 119 (46.0) | 5 (10.2) | 35 (89.8) |
Abbreviation: n = number.
percentages and means weighted by area of resident to account for over‐sampling in rural areas.
2.5. Statistical Analysis
The characteristics of participants at study baseline were described for each cohort stratified by whether a woman reported dental attendance in the previous 12 months. The impact of loss to follow‐up was assessed for each cohort by comparing differences at Wave 1 of those included and not included in the analysis. Percentages for descriptive characteristics were weighted by area of residence to account for oversampling in rural and remote areas. Separately for each of the cohorts, risk ratios (RRs) with 95% confidence intervals (CI) were estimated for the associations between (1) health insurance, (2) levels of smoking, (3) levels of alcohol consumption and (4) BMI and self‐report of dental non‐attendance in the previous 12 months compared with dental attendance during this period. Poisson regression using generalized estimating equations with robust error variance to account for repeated measures was used. 25 A woman could change exposure, outcome and/or covariate category at each wave. Initially, RRs adjusted for age and time were calculated for each exposure (Model 1). The effect of adding confounders into the model was then assessed (Model 2).
Descriptive and regression analyses were done using SAS software, Version 9.4 (TS1M5) of the SAS system for Windows copyright © 2016 by SAS Institute Inc (Carey).
3. RESULTS
The analysis included 10 233, 12 378 and 7892 women from the 1973–78, 1941–56 and 1921–26 cohorts, respectively, who had complete information on dental attendance and all covariates at one or more waves (Figure 1). In all cohorts, compared with women included in the analysis, women not included were more likely to have a low education level, income difficulties, no health insurance, poorer physical and mental health and be smokers. They were less likely to be low‐risk drinkers and a healthy weight (Table S1).
Characteristics of the women in each cohort at study baseline, stratified by dental attendance are described in Table 1. In all cohorts, women who lived in regional/remote areas, had difficulties managing on their income, a low education level, no health insurance, poorer physical function and those who were smokers or overweight/obese were more likely be non‐attenders (Table 1).
Over time, the percent of women who were non‐attenders decreased in both the 1973–78 cohort (46% to 33% between 2006 and 2018) and 1946–51 cohort (43% to 30%, 1998 to 2016) but was stable in the 1921–26 cohort at around 61% (Table S2).
3.1. Private ancillary health insurance
In the fully adjusted model (Model 2), the likelihood of dental non‐attendance was higher in women without health insurance in all cohorts than in women with insurance; however, the strength of the association was weaker in the 1921–26 cohort (RR 1.32 95% CI 1.28–1.36) than in both the 1973–78 cohort (RR 1.52 95% CI 1.48–1.57) and the 1945–51 cohort (RR 1.45 95% CI 1.41–1.49, Table 2).
TABLE 2.
Risk ratios (RR) and 95% confidence intervals (CI) for the associations between ancillary private health insurance and self‐report of dental non‐attendance in the 12 months prior to completing each wave (compared with dental attendance) in the 1973–78, 1946–51 and 1921–26 cohorts of the Australian Longitudinal Study on Women's Health
| Model 1 a RR (95% CI) | Model 2 b RR (95% CI) | |
|---|---|---|
| PRIVATE ANCILLARY HEALTH INSURANCE | ||
| 1973–78 cohort | ||
| Has private ancillary health insurance | ref. | ref. |
| Does not have private ancillary health insurance | 1.65 (1.55–1.65) | 1.52 (1.48–1.57) |
| 1946–51 cohort | ||
| Has private ancillary health insurance | ref. | ref. |
| Does not have private ancillary health insurance | 1.55 (1.51–1.60) | 1.45 (1.41–1.49) |
| 1921–26 cohort | ||
| Has private ancillary health insurance | ref. | ref. |
| Does not have private ancillary health insurance | 1.37 (1.33–1.41) | 1.32 (1.28–1.36) |
Model 1: adjusted for age and wave.
Model 2: as for Model 1 and additionally adjusted for area of residence, level of education, ability to manage on income, Mental Health Index Score, Physical Function Score, Smoking Status, Alcohol consumption, body mass index (BMI).
3.2. Smoking status and alcohol consumption
In the model adjusted for confounders (Model 2), compared with never smoking, current smoking at any intensity was associated with a higher risk of non‐attendance. The association was strongest for women in the 1946–51 cohort who smoked ≥20 cigarettes/day (RR 1.35 95% CI 1.30–1.41, Table 3).
TABLE 3.
Risk ratios (RR) and 95% confidence intervals (CI) for the associations between smoking status and alcohol consumption and self‐report of dental non‐attendance in the 12 months prior to completing each wave (compared with dental attendance) in the 1973–78, 1946–51 and 1921–26 cohorts of the Australian Longitudinal Study on Women's Health
| Model 1 a RR (95% CI) | Model 2 b RR (95% CI) | |
|---|---|---|
| SMOKING STATUS | ||
| 1973–78 cohort | ||
| Never smoker | ref. | ref. |
| Former smoker | 1.06 (1.02–1.10) | 1.02 (0.98–1.06) |
| Current smoker <10 cigarettes/day | 1.11 (1.05–1.17) | 1.06 (1.00–1.11) |
| Current smoker 10–19 cigarettes/day | 1.18 (1.12–1.26) | 1.08 (1.02–1.15) |
| Current smoker ≥20 cigarettes/day | 1.28 (1.19–1.38) | 1.14 (1.06–1.23) |
| 1946–51 cohort | ||
| Never smoker | ref. | ref. |
| Former smoker | 1.12 (1.09–1.16) | 1.11 (1.07–1.14) |
| Current smoker <10 cigarettes/day | 1.20 (1.14–1.27) | 1.19 (1.12–1.26) |
| Current smoker 10–19 cigarettes/day | 1.34 (1.27–1.41) | 1.29 (1.23–1.36) |
| Current smoker ≥20 cigarettes/day | 1.42 (1.36–1.49) | 1.35 (1.30–1.41) |
| 1921–26 cohort | ||
| Never smoker | ref. | ref. |
| Former smoker | 0.99 (0.95–1.02) | 0.99 (0.96–1.03) |
| Current smoker <10 cigarettes/day | 1.15 (1.05–1.26) | 1.14 (1.04–1.25) |
| Current smoker 10–19 cigarettes/day | 1.23 (1.14–1.32) | 1.22 (1.13–1.31) |
| Current smoker ≥20 cigarettes/day | 1.24 (1.09–1.40) | 1.24 (1.10–1.39) |
| ALCOHOL CONSUMPTION STATUS | ||
| 1973–78 cohort | ||
| Low‐risk drinker (>1 drink/month to ≤2 drinks/day) | ref. | ref. |
| Non‐drinker | 1.09 (1.04–1.13) | 1.05 (1.01–1.10) |
| Rarely drinker (<1 drink/month) | 1.09 (1.05–1.12) | 1.05 (1.02–1.09) |
| Risky/High‐risk drinker (≥3 drinks/day) | 1.08 (1.02–1.15) | 1.07 (1.01–1.13) |
| 1946–51 cohort | ||
| Low‐risk drinker (>1 drink/month to ≤2 drinks/day) | ref. | ref. |
| Non‐drinker | 1.25 (1.21–1.29) | 1.19 (1.15–1.24) |
| Rarely drinker (<1 drink/month) | 1.14 (1.11–1.17) | 1.11 (1.08–1.14) |
| Risky/High‐risk drinker (≥3 drinks/day) | 1.06 (1.01–1.11) | 1.05 (1.00–1.10) |
| 1921–26 cohort | ||
| Low‐risk drinker (>1 drink/month to ≤2 drinks/day) | ref. | ref. |
| Non‐drinker | 1.22 (1.18–1.26) | 1.18 (1.15–1.22) |
| Rarely drinker (<1 drink/month) | 1.12 (1.08–1.16) | 1.10 (1.06–1.14) |
| Risky/High‐risk drinker (≥3 drinks/day) | 0.96 (0.89–1.04) | 0.97 (0.90–1.05) |
Model 1: adjusted for age and wave and mutually adjusted for smoking status and alcohol consumption.
Model 2: as for Model 1 and additionally adjusted for area of residence, level of education, ability to manage on income, Mental Health Index Score.
Compared with low‐risk drinkers, women who were non‐drinkers or rarely drinkers were more likely to be non‐attenders with the associations strongest in the two older cohorts (Table 3). The associations were highest in non‐drinkers in both the 1946–51 cohort (RR 1.19 95% CI 1.15–1.24) and 1926–21 cohort (RR 1.18 95% CI 1.15–1.22, Table 3). In the two younger cohorts, risky/high risk drinkers also had a higher likelihood of non‐attendance, but the effect estimates were small (Table 3).
3.3. Body mass index (BMI)
In the model adjusted for confounders (Model 2), compared with having a healthy weight, women who were overweight or obese were more likely to be non‐attenders in all cohorts. The strength of the associations was similar across the cohorts and was higher with increasing BMI. In women with a BMI ≥40 kg/m2 (Obese Class III) the RRs were 1.23 95% CI 1.15–1.31 (1973–78 cohort), 1.24 95% CI 1.16–1.31 (1946–51 cohort) and 1.29 95% CI 1.17–1.43 (1921–1926 cohort, Table 4).
TABLE 4.
Risk ratios (RR) and 95% confidence intervals (CI) for the associations between body mass index (BMI) and self‐report of dental non‐attendance in the 12 months prior to completing each wave (compared with dental attendance) in the 1973–78, 1946–51 and 1921–26 cohorts of the Australian Longitudinal Study on Women's Health
| Model 1 a RR (95% CI) | Model 2 b RR (95% CI) | |
|---|---|---|
| BODY MASS INDEX | ||
| 1973–78 cohort | ||
| Underweight (<18.5 kg/m2) | 0.92 (0.84–1.02) | 0.91 (0.82–1.00) |
| Healthy weight (18.5–24.9 kg/m2) | ref. | ref. |
| Overweight (25.0–29.9 kg/m2) | 1.12 (1.08–1.16) | 1.08 (1.04–1.12) |
| Obese Class I (30.0–34.9 kg/m2) | 1.20 (1.15–1.25) | 1.13 (1.08–1.18) |
| Obese Class II (35.0–39.9 k/gm2) | 1.35 (1.28–1.42) | 1.24 (1.17–1.31) |
| Obese Class III (≥40.0 k/gm2) | 1.38 (1.29–1.47) | 1.23 (1.15–1.31) |
| 1946–51 cohort | ||
| Underweight (<18.5 kg/m2) | 1.04 (0.94–1.15) | 0.99 (0.90–1.10) |
| Healthy weight (18.5–24.9 kg/m2) | ref. | ref. |
| Overweight (25.0–29.9 kg/m2) | 1.10 (1.07–1.13) | 1.07 (1.04–1.10) |
| Obese Class I (30.0–34.9 kg/m2) | 1.21 (1.17–1.26) | 1.15 (1.11–1.19) |
| Obese Class II (35.0–39.9 k/gm2) | 1.32 (1.27–1.38) | 1.21 (1.16–1.31) |
| Obese Class III (≥40.0 k/gm2) | 1.39 (1.31–1.48) | 1.24 (1.16–1.31) |
| 1921–26 cohort | ||
| Underweight (<18.5 kg/m2) | 0.99 (0.92–1.05) | 0.97 (0.91–1.04) |
| Healthy weight (18.5–24.9 kg/m2) | ref. | ref. |
| Overweight (25.0–29.9 kg/m2) | 1.07 (1.04–1.10) | 1.06 (1.03–1.09) |
| Obese Class I (30.0–34.9 kg/m2) | 1.11 (1.07–1.15) | 1.07 (1.03–1.12) |
| Obese Class II (35.0–39.9 k/gm2) | 1.23 (1.16–1.31) | 1.16 (1.10–1.24) |
| Obese Class III (≥40.0 k/gm2) | 1.40 (1.27–1.54) | 1.29 (1.17–1.43) |
Model 1: adjusted for age and wave.
Model 2: as for Model 1 and additionally adjusted for area of residence, level of education, ability to manage on income, Mental Health Index Score, Physical Function Score, Smoking Status, Alcohol consumption.
4. DISCUSSION
To the best of our knowledge, this is the first study that has looked at selected socioeconomic and health factors associated with dental non‐attendance for Australian women of different ages. The study found that, in all cohorts, women without health insurance, those who were current smokers, never/rarely drinkers, or who were overweight/obese had a higher risk of dental non‐attendance. However, for health insurance, these associations were strongest for women in the 1973–78 and 1946–51 cohorts; and for smoking and alcohol consumption these associations were strongest for women in the 1946–1951 and 1921–26 cohorts.
Although context‐specific both cross‐sectional and longitudinal studies in Australia, 5 , 9 , 26 Sweden, 7 the United States 6 , 8 , 16 and South America 27 have consistently found that those without health insurance have fewer dental visits. Comparatively few studies have looked at health factors, and most of these are cross‐sectional and used dichotomous health factor variables. Three studies (from the United States and Australia) found that smokers were less likely to visit a dentist, 9 , 10 , 28 while two studies (from Greece and Belgium) found that obese people were less likely to visit a dentist for a regular check‐up. 11 The results of the current study are consistent with this previous research; however, this study demonstrated that these associations persist across time and are present in older and younger cohorts of women.
A cross‐sectional Belgian study found that among adults aged 55+ years, abstainers/rarely drinkers were more likely to be non‐attenders than moderate level drinkers (<15 drinks/week). In contrast to the current study, this association was not present in younger adults, and high‐level drinking (>15 drinks/week) was not associated with dental attendance at any age, 12 although the Belgian study did not consider males and females separately.
Women without health insurance were more likely to be dental non‐attenders, particularly in the two younger cohorts. Even though insurance rarely covers the full cost of dental care, it appears to be a key enabler, and in this study, the effect could not be explained by perceived income constraints, education level or location. The motivators for purchasing insurance may vary across the cohorts. In the two younger cohorts, Australian Government policies to incentivize young people to take out insurance for hospital cover, and private health insurance tax rebates may have influenced the decision to purchase insurance over time. 29 In contrast, as people age and leave the workforce their income level may drop and they may forgo private health insurance (particularly ancillary coverage that includes dental coverage) due to affordability issues. 30
Current smokers at any intensity in all cohorts were more likely to be non‐attenders. Other studies have demonstrated that smoking is associated with lower use of preventive health services, 15 lower rates of cancer screening 31 and less uptake of health insurance, 32 but more hospitalizations and outpatient visits, 32 indicating that smokers may be more likely to seek care when the need is urgent, rather than on a regular basis for the purposes of maintenance and prevention.
The strength of the associations between alcohol consumption and dental non‐attendance differed across cohorts. Non‐drinkers in the 1946–51 and 1921–26 cohorts were the most likely to be non‐attenders; in the 1973–78 cohort, while the associations were statistically significant the effect estimates were small. While unable to differentiate between never drinkers and former drinkers in the analysis, the authors speculate that in line with other research, the two older cohorts may have a higher proportion of women who give up drinking due to health reasons (with poorer health impacting on the ability to attend the dentist) than the 1973–78 cohort, who may be more likely to be non‐drinkers for lifestyle and/or social reasons. 33
Women in all the cohorts who were overweight/obese were more likely to be non‐attenders, with the highest risk of non‐attendance in the bigger BMI categories. While obesity is generally associated with higher use of healthcare services, 34 barriers to dental treatment may include difficulties of physical access (e.g., difficulties getting in and out of the dental chair or maximum lifting weight of the dental chair 14 , 35 ), difficulties in receiving optimal care due to extra weight around the neck and mouth 13 or higher levels of dental anxiety. 13 Of note, there are currently no guidelines for dental care of obese patients in Australia, and the only bariatric dental chairs are located within the public dental health system. 14
Strengths of this study were the use of multiple waves of data, large community‐based sample, the use of health‐related variables with at least four levels (reflecting increasing intensity), and that the survey questions used to measure the variables of interest were repeated across the waves and were the same for all three cohorts, strengthening the credibility of our comparisons. The analysis was adjusted for a range of sociodemographic variables to take into account the different socioeconomic contexts of each cohort. However, there are potentially other unmeasured factors (e.g., childhood nutrition, dental hygiene practices and water fluoridation) that may have differentially impacted each cohort in their attitudes and need for dental services. There were missing data through loss to follow‐up, with women who were not included in all waves more likely to be smokers, have a higher BMI and not have health insurance, potentially biasing our effect estimates towards the null. Another limitation is that all information was self‐reported, which may be an additional source of bias in the analysis. While the validity of self‐report of dental visits has been shown to be reasonable, 36 information on why a woman visited the dentist was not available (i.e., for review or acute problems), nor was it possible to ascertain whether a dental visit in the 12 months prior to completing each questionnaire reflected a regular 12‐month visiting pattern between waves. Also, women who wore dentures were unable to be excluded from the analysis. Even though people who wear dentures are recommended to maintain regular dental visits, 37 in practice this group have a much less regular visiting pattern. 38 In this analysis, the proportion of women with dentures would be highest in the 1921–1926 cohort; including them in the analysis may at least partially explain why this cohort was less likely to visit a dentist than the younger cohorts. Finally, this study was based on cohorts that were more highly educated and predominantly of white, Anglo‐Celtic descent, which may limit the generalizability of the findings to women with lower education levels or diverse cultural backgrounds.
This study was a cohort comparison of Australian women; to the best of our knowledge, no equivalent studies have been done in men, nor looked at factors associated with dental visits comparing men with women. While Australian women visit the dentist more frequently than men, 4 it is likely that the socioeconomic and health factors associated with dental visits will be the same (although the magnitude of the associations may differ). Testing this assumption in future research involving men is warranted.
In conclusion, the findings of this study add to the substantial evidence that previously exists that public health responses are needed to facilitate better use of dental care, and further emphasizes the need to address socioeconomic inequities in access to dental care in Australia as a priority. Work should also be undertaken to overcome the barriers to dental visits for those who are obese or smoke. This study has shown that these barriers to access exist for women of all ages, indicating that interventions need to be appropriate across all age groups.
AUTHOR CONTRIBUTIONS
All authors meet the ICMJE authorship criteria. LFW contributed to conception, design, analysis, data curation, drafted and critically revised the manuscript. XZ contributed to the design and critically revised the manuscript. JD contributed to conception, design and critically revised the manuscript. GDM contributed to conception, design and critically revised the manuscript. AJD contributed to conception, design and critically revised the manuscript.
FUNDING INFORMATION
The ALSWH is funded by the Australian Government Department of Health. LW and ZX were supported by an Australian National Health and Medical Research Council (NHMRC) Centres for Research Excellence grant (APP1153420) and GM was supported by an NHMRC Principal Research Fellowship (APP1121844). The funding bodies played no role in the design; in the collection, analysis or interpretation of data; in the writing of the manuscript or in the decision to submit the manuscript for publication.
CONFLICT OF INTEREST
The authors declare that there are no competing interests.
Supporting information
Appendix S1
ACKNOWLEDGEMENTS
The research on which this paper is based was conducted as part of the Australian Longitudinal Study on Women's Health (ALSWH) by the University of Queensland and the University of Newcastle. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data. Open access publishing facilitated by The University of Queensland, as part of the Wiley ‐ The University of Queensland agreement via the Council of Australian University Librarians.
Wilson LF, Xu Z, Doust J, Mishra GD, Dobson AJ. Associations of socioeconomic and health factors with dental non‐attendance: A comparison of three cohorts of women. Community Dent Oral Epidemiol. 2023;51:452‐461. doi: 10.1111/cdoe.12776
DATA AVAILABILITY STATEMENT
ALSWH survey data are owned by the Australian Government Department of Health and due to the personal nature of the data collected, release by ALSWH is subject to strict contractual and ethical restrictions. Ethical review of ALSWH is by the Human Research Ethics Committees at The University of Queensland and The University of Newcastle. De‐identified data are available to collaborating researchers where a formal request to make use of the material has been approved by the ALSWH Data Access Committee. The committee is receptive of requests for datasets required to replicate results. Information on applying for ALSWH data is available from https://alswh.org.au/for‐data‐users/applying‐for‐data/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1
Data Availability Statement
ALSWH survey data are owned by the Australian Government Department of Health and due to the personal nature of the data collected, release by ALSWH is subject to strict contractual and ethical restrictions. Ethical review of ALSWH is by the Human Research Ethics Committees at The University of Queensland and The University of Newcastle. De‐identified data are available to collaborating researchers where a formal request to make use of the material has been approved by the ALSWH Data Access Committee. The committee is receptive of requests for datasets required to replicate results. Information on applying for ALSWH data is available from https://alswh.org.au/for‐data‐users/applying‐for‐data/.