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Heart disease in women in remote Australia: urban-rural differences after adjusting for lifestyle behaviours and socio-demographic factors

Authors


Correspondence to: Dr Deirdre McLaughlin, School of Population Health, The University of Queensland, Herston, Queensland, 4006; e-mail: deirdre.mclaughlin@uq.edu.au

Rural-urban differences in the prevalence of a number of chronic conditions have been identified, with rural residents more likely to have higher morbidity than their urban counterparts.1–3 However, differences are not always consistent and variations may exist in particular diseases.1 One possible reason is that many studies rely on a simple rural-urban dichotomy that may conceal variations in health across rural areas. This study aimed to compare differences in selected chronic conditions in urban, inner regional, outer regional and remote dwelling mid-aged Australian women, and to consider the impact of known socio-demographic and lifestyle factors.

Data for this paper were drawn from surveys 1 to 6 (1996–2010) of the 1946–1951 cohort (n=13,715) of the Australian Longitudinal Study on Women's Health. This is a prospective study of factors affecting the health and well-being of three cohorts of women, aged 18–23 years (‘1973–1978 cohort’), 45–50 years (‘1946–1951 cohort’) and 70–75 years (‘1921–1926 cohort’) at the time of Survey 1 in 1996. The method of recruitment and response rates have been described elsewhere4 and details of the study can be found at http://www.alswh.org.au.

Participants were asked at each survey if they had ever been diagnosed (yes/no) by a doctor with heart disease, diabetes or hypertension. Area of residence was classified according to the Australian Standard Geographic Classification (ASGC) as urban, inner regional, outer regional, remote and very remote. BMI was calculated from height and weight (kg/m2) and categorised as ‘underweight’ (<18.5), ‘normal’ (18.5 to <25), ‘overweight’ (25 to <30), ‘obese’ (30+). Smoking status was categorised as ‘non-smoker’, ‘ex-smoker’ and ‘current smoker.’ An estimate of total physical activity in MET minutes per week (METs) was obtained and categorised as ‘nil/sedentary’ (0-<40 METs), ‘low’ (40-<600 METs), ‘moderate’ (600-<1200 METs) or ‘high’ (1200+ METs). Alcohol consumption was categorised as ‘non-drinker’, ‘rarely drinks alcohol’, ‘low risk drinker’ (≤14 drinks/week), ‘risky drinker’ (≤28 drinks/week). Demographics included age, country of birth, marital status, and highest educational qualification completed. Ability to manage on income was categorised on a five-point scale ranging from ‘it is easy’ to ‘it is impossible’.

Table 1 presents odds ratios for incidence of conditions by area of residence. Women living in inner regional (OR 1.24, 95%CI 1.05–1.47) and remote/very remote (OR 1.74, 95%CI 1.24–2.44) areas were more likely to report heart disease than women in urban areas, after adjusting for survey and age. These differences remained for women in remote/very remote areas after further adjusting for demographic factors alone (OR 1.73, 95%CI 1.23–2.44), demographics and BMI (OR 1.66, 95%CI 1.17–2.34) and demographics, BMI, smoking, alcohol and exercise (OR 1.63, 95%CI 1.16–2.31).

Table 1.  Odds ratio for incidence of selected chronic conditions by remoteness – ALSWH 1946–51 cohort 1996 to 2010.
AreaDiabetesaDiabetesbHypertenaHypertenbHeart diseaseaHeart diseaseb
  1. Notes:

  2. a Adjusted for survey and age.                   b Adjusted for survey, age, BMI, smoking, alcohol, PA, COB, education, marital status and income

  3. 95% confidence interval in parenthesis.              Heart disease and diabetes = relatively small numbers, hypertension much larger

Urban1.001.001.001.001.001.00
Inner Regional1.01 (0.86,1.19)0.94 (0.80,1.12)1.09 (0.98,1.20)1.02 (0.92,1.13)1.24 (1.05,1.47)1.17 (0.98,1.39)
Outer Regional1.05 (0.86,1.27)0.92 (0.75,1.12)1.13 (1.00,1.28)1.00 (0.88,1.13)1.12 (0.90,1.38)1.02 (0.82,1.27)
Remote/Very Remote1.34 (0.95,1.90)1.14 (0.80,1.63)1.18 (0.94,1.49)1.04 (0.78,1.27)1.74 (1.24,2.44)1.63 (1.16,2.31)

Our results indicate that after controlling for socio-demographic and lifestyle factors, differences among the women across the three conditions disappeared, with the exception of heart disease in women living in remote/very remote areas.

These results indicate that the higher morbidity related to hypertension and heart disease in women living in outer and inner regional areas is mainly due to potentially modifiable lifestyle factors. However, differences in heart disease remain in women living in more remote areas. Our results indicate that these differences are not driven by socio-demographic factors as suggested by other authors5 nor generally related to poor lifestyle behaviours. These findings suggest that any subsequent discrepancy in mortality between women living in urban and remote areas is most likely the result of differences in access to health services and available treatments.

A potential limitation of this study is that the women participating in the Australian Longitudinal Study on Women's Health may be a biased sample having better health. Very few participants in the study reported being of Aboriginal or Torres Strait Islander origin so Indigenous disadvantage is unlikely to be an explanation for the morbidity differences observed.

This paper highlights the need to focus on potentially modifiable risk factors in public health interventions across regional Australia. Further, our results suggest that differences in health service access and treatments may affect the health of women living in remote areas.

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