• Open Access

Self-rated health status in an urban indigenous primary care setting: implications for clinicians and public health policy

Authors


Correspondence to:
Dr Geoffrey Spurling, Discipline for General Practice, University of Queensland, Inala Indigenous Health, 64 Wirraway Pde, Inala, QLD 4077. Fax: (07) 3278 9987; e-mail g.spurling@uq.edu.au

Abstract

Background: Self-rated health status provides insights into the health beliefs of a population. This will be important for framing public health messages in the context of the need to ‘close the gap’ for Australian Indigenous people. Our primary objectives were to describe the self-rated health status of Indigenous people attending the Inala Indigenous Health service, identify associations with positive and negative self-rated health status and identify targets for public health awareness raising activity.

Methods: Using a convenience sample, we approached all Indigenous patients attending the Inala Indigenous Health Service for an Indigenous adult health check between June 2007 and July 2008. From Indigenous adult health check data we analysed self-rated health status and chronic disease risk factors.

Results: Out of a possible 509, 413 patients were recruited (response rate 81%). The number of participants who rated their health as fair or poor was 47%. The association of greatest magnitude and statistical significance with Indigenous patients’ self rated health status (negative versus positive) was waist circumference followed by smoking, depression and age. Chronic disease risk factors not associated with self-rated health status included systolic blood pressure, harmful alcohol use, marijuana use, presence of diabetes and lack of exercise.

Conclusions: High rates of negative self-rated health status were found. Public health awareness-raising for Indigenous audiences should consider targeting chronic disease risk factors such as systolic blood pressure, harmful alcohol use, marijuana use, presence of diabetes and lack of exercise.

‘Closing the Gap’ will require successful public health promotion targeting Australian Indigenous audiences.1 Analysis of self-rated health status has previously identified factors contributing to negative and positive perceptions of health among Indigenous people in the community setting.2 The adult Indigenous health check was introduced in part to detect risk factors for the chronic diseases largely responsible for the mortality gap.3 In the primary care context, the opportunity has arisen to examine the relationship between these chronic disease risk factors identified by the recently introduced Indigenous adult health check and self-rated health status. Insights into this relationship may lead to improved targeting and prioritisation of public health messages in advertising for Indigenous audiences.

Using a convenience sample of Indigenous patients, the primary objectives of this study were to describe self-rated health status and detect associations with negative (versus positive) self-rated health status.

Methods

Inala is the postcode (4077) area with the highest concentration of Indigenous people in Brisbane, with 5.4% of the 22 337 people living in Inala being Indigenous.4 The Inala Indigenous Health service, bulk billed 508 adult health checks in the 2008 calendar year. All Indigenous participants presenting opportunistically for an Indigenous adult health check were approached as they attended the health service. After obtaining informed consent, registered nurses used a customised Indigenous Adult Health Check adapted for research to collect demographic and health status data. We asked participants at the beginning of the health check to self assess their health status on the same five point likert scale reported by the Australian Institute of Health and Welfare (excellent, very good, good, fair and poor).5 Negative self-rated health status was defined by fair and poor responses with positive self-rated health status being the remainder. Completed adult health checks were entered into a database (Microsoft Access, 2003) by a registered nurse with experience and training in data entry. Multi variable logistic regression was used to determine associations with negative versus positive self-rated health status. We calculated that using the smallest sample size of 173 males, we would be able to detect a difference of 5 cm in waist between negative and positive self-rated health to a significance level of 5%. We assumed the mean for those with negative self-rated health would be 100 cm with a standard deviation of 20. For the categorical variable of current smoking we calculated that for the 173 males we would be able to detect a relative risk of 2.91 significant at the 5% level assuming 50% of the positive self-rated health group being current smokers. For both continuous and categorical analyses we assumed a power of 0.8, a two-sided test and that the ratio of the two groups is 1:1 given the overall prevalence of negative self-rated health was 47%. These values apply to unadjusted analyses and multi variable logistic regression may require a greater sample size.

Variables were excluded after adjustment for age with a significance level greater than 20% using logistic regression. Remaining potential association variables of negative self-rated health status were introduced in step-wise fashion to the multi variable model if their age adjusted logistic regression significance level was less than 20%. Variables with an unadjusted or adjusted significance level less than 5% (for either men, women or combined datasets) were retained in the final multi variable model. All data were analysed using Stata, version 10.0 (StataCorp, College Station, Tex, US). Graphs were prepared using Microsoft Excel 2003.

The local Inala Indigenous Elders Incorporated were consulted and supported the project. The University of Queensland's Behavioural and Social Science Ethical Research Committee also approved the study.

Results

Between June 2007 and July 2008, we recruited 413 participants who had Indigenous adult health checks at the Inala Indigenous Health Service. The total number of adult health checks undertaken in this time period was 509 giving a response rate of 81%. According to the 2006 census, the age and sex (46% of the Inala Indigenous census population were male) distribution of Indigenous people living in postcode 4077 aged 15–54 (51% of the Inala Indigenous census population) was similar to the age and sex distribution of our cohort (Table 1 includes all postcodes).4 The adult health check also revealed high rates of unemployment, overcrowding and chronic disease risk factors (Table 1). Negative self-rated health status (poor or fair) was reported by 184 (47%) participants with similar results for men and women (Figure 1). After adjustment for confounding factors, the association of greatest magnitude and statistical significance with negative self-rated health status for men was being unemployed, while for women it was increasing waist circumference. (Table 2) Using combined male and female data, the most important adjusted association by magnitude and statistical significance was waist circumference. Other significant factors presented as likelihood ratios included current smoking, depression and increasing age (Figure 1). The variance in negative self-rated health status explained by this model was 11% (pseudo r2). Using 10 deciles, the Hosmer-Lemeshow goodness of fit test found a chi-squared (8) result of 7.96 (p=0.44) confirming that the model was a satisfactory fit. Chronic disease risk factors which were not associated with self-rated health status in the multi variable model included systolic blood pressure, harmful alcohol use, marijuana use, diabetes and lack of exercise.

Table 1.  Socio-demographics of the Inala adult Indigenous health check cohort.
Demographic (total participants=413)Result n (%)
  1. Note: a) Fourteen missing values for this category (3%)

Ethnicity
  Aboriginal386 (93)
  Torres Strait Islander15 (4)
  Both Aboriginal and Torres Strait Islander11 (3)
Gender
  Male173 (42)
Age
  teens71 (17)
  20s125 (30)
  30s114 (28)
  40s80 (19)
  50s23 (6)
Employment statusa
  Unemployed89 (22)
  Full-time105 (25)
  Pensioner61 (15)
  Student48 (12)
  Part-time/Casual49 (12)
  Home Duties37 (9)
  Volunteer/Carer10 (2)
Housing status
  No problem identified350 (85)
  Overcrowding39 (9)
  Homeless12 (3)
  Conflict at home12 (3)
Chronic disease risk factors
  Depression96 (23)
  Current smoking275 (67)
  Overweight or obese (BMI>25)251 (61)
  Waist >100 cm (male), >90 cm (female)218 (53)
  Blood pressure >140/9049 (12)
Exercise
  none122 (31)
  <30 minutes per day95 (24)
  >30 minutes per day173 (44)
Substance use
  No harmful substance use (including non-smokers)109 (26)
  Harmful alcohol use (NHMRC definition)149 (46)
  Marijuana use118 (29)
  Amphetamine use23 (6)
  Opiate use18 (4)
  Inhalent use6 (1)
Figure 1.

Self-rated perception of healthstatus in adult Indigenous health checks (n=413).

Table 2.  Predictors of negative (poor/fair) versus positive (good/very good/excellent) self-rated health status using multi-variable logistic regression.
 Unadjusted odds ratio (95% CI)Adjusted odds ratio (95% CI)
VariableMaleFemaleCombinedMaleFemaleCombined
  1. Note: a) Statistically significant results (p<0.05) are in bold

Age (ref teens)
20s1.67 (0.57,4.90)1.55 (0.68,3.51)1.59 (0.84,3.01)1.06 (0.26,4.34)1.44 (0.58,3.56)1.44 (0.69,3.02)
30s2.89 (0.94,8.95)3.20 (1.45,7.07)a3.06 (1.61,5.84)a1.58 (0.36,7.04)1.60 (0.62,4.13)1.83 (0.84,4.01)
40s3.93 (1.23,12.53)a3.77 (1.54,9.26)a3.81 (1.89,7.66)a1.97 (0.41,9.37)1.82 (0.63, 5.27)2.28 (0.97,5.32)
50s2.50 (0.47,13.39)2.75 (0.78,9.66)2.64 (0.97,7.20)2.74 (0.35,21.4)1.33 (0.31,5.63)1.86 (0.58,5.96)
Gender (ref male)NANA1.02 (0.68,1.53)NANA1.46 (0.87,2.45)
Unemployed3.35 (1.56,7.22)a0.80 (0.41,1.56)1.51 (0.93,2.47)2.99 (1.26,7.05)a0.80 (0.36,1.76)1.47 (0.84,2.59)
Waist (cm)1.02 (1.00,1.04)a1.03 (1.01,1.04)a1.03 (1.01,1.04)a1.02 (0.99,1.05)1.02 (1.00,1.04)a1.02 (1.00,1.04)a
Sys BP (mm Hg)1.02 (1.00,1.04)1.01 (1.00,1.04)1.01 (1.00,1.03)a1.02 (1.00,1.05)1.00 (0.98,1.03)1.01 (0.99,1.03)
Exercise (>30min)0.44 (0.24,0.84)a0.65 (0.38,1.10)0.55 (0.37,0.83)a0.52 (0.23,1.16)0.90 (0.49,1.68)0.74 (0.46, 1.18)
Current smoker2.05 (1.03,4.09)a2.10 (1.20,3.67)a2.06 (1.34,3.18)a1.89 (0.74,4.78)1.83 (0.93,3.61)1.71 (1.00,2.92)a
Marijuana use1.84 (0.98,3,46)1.94 (1.01,3.72)a1.84 (1.18,2.86)a1.56 (0.64,3.85)1.86 (0.82,4.19)1.70 (0.95,3.04)
Depression2.34 (1.01,5.44)a2.94 (1.58,5.47)a2.68 (1.63, 4.41)a1.54 (0.53,4.45)1.99 (0.99,3.98)1.79 (1.01,3.16)a

Discussion

Surprisingly high rates of negative self-rated health status were found in this study. Urban Indigenous participants attending the Inala Indigenous Health service became more concerned about their health as their waist increased, they smoked or were depressed.

Strengths of this study included a high response rate with an even spread of age and sex, comparable with census data. We are not aware of previous studies examining self-rated health status in urban Indigenous primary care context or in conjunction with the adult health check. There is potential for selection bias in a clinic based population with a non-random sample. Type II error may occur especially when considering the smaller numbers in male and female sub-populations. We estimate that this study may have sufficient power to assess clinically important differences for continuous variables but may be underpowered to detect clinically significant differences for categorical variables especially in the smaller samples for each gender.

Associations, where they were statistically significant, were small. Generalisations from this study should be treated with caution as other Indigenous populations may reveal different associations with self-rated health status.

This study's rate of 47% reporting fair or poor health is of concern in the context of “closing the gap” given poor self-rated health has been associated with a two to three fold increase in mortality compared to those with self-rated excellent health.6 This is substantially higher than community based national data, showing 23% of non-remote Indigenous Australians aged 15 and above reporting poor/fair health (versus 19% for remote and twice the non-Indigenous rate).5 Proportions of negative self-rated health status rise with proximity to health infrastructure.2 Community-based data have shown that simply seeing a health professional increases the adjusted odds ratio of reporting poor/fair health status by 1.7 times for males and 2.4 times for females.2 However, better access to primary healthcare has been shown to improve negative self-rated health status by more than 5%.7 While variance in negative self-rated health status predicted by our model was a significant 11%, other variables identified in the national community based survey included low socio-economic status, number of children borne, identifying with a tribe, clan or language group and being taken away as a child.2 This suggests that there are a number of social and cultural factors important to our Indigenous participants’ self-perception of their health, which the adult health check does not address. As with this study, Cunningham et al. also found an association between negative self-rated health status for age, smoking and unemployment but did not identify depression or increasing waist.2 It is unlikely that lack of awareness of weight and smoking and their health problems are barriers to clinicians or public health campaigns dealing with chronic disease risk factors. It can be useful for clinicians using the adult health check in an Indigenous primary healthcare context to know what is influencing their patients’ perceptions of their own health. Motivational interviewing to increase dissonance between health goals and risk behaviours such as smoking and poor nutrition will be more likely to be successful if the patient already is concerned and less ambivalent about the impact of these behaviours on their health.8 Public health campaigns targeting Indigenous audiences and primary healthcare clinicians need to consider awareness-raising for chronic disease risk factors such as systolic blood pressure, harmful alcohol use, marijuana use and lack of exercise.

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