4707.0 - Occasional Paper: Self-Assessed Health Status, Indigenous Australians, 1994
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 11/08/1997
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Joan Cunningham
CHAPTER THREE - RESULTS Overall, about 17% of Indigenous males and females aged 15 and over reported their health as fair or poor (table 3.1). This is similar to what was observed for all Australians in the 1995 National Health Survey (NHS), but this overall similarity obscures differences in self-assessed health status within particular age groups, as is discussed below
Age Reported fair or poor health generally increased with age for both males and females (table 3.2, graphs 3.3 and 3.4). The proportion of people who reported poor or fair health was similar in the NATSIS and the NHS among males and females aged 15-34 years and among those aged 65 years or more (graphs 3.3 and 3.4). However, a large difference was observed between the two surveys among people aged 35-64 years, with Indigenous people in those age groups in the NATSIS about twice as likely to report fair or poor health as their all-Australian counterparts in the NHS. Because of the importance of age with respect to self-assessed health status, and because age may also be associated with other factors of interest, such as employment, relative weight, smoking, health conditions and health actions, and culture and language, the effect of age was taken into account by adjusting for it in statistical models. The results of the modelling are presented in the technical notes. The figures presented in the tables in this chapter have not been adjusted for age, but the results of such an adjustment are noted in the text when relevant. Area of residence Reports of fair or poor health varied from region to region (maps 3.5 and 3.6). People living in rural areas were significantly less likely than those in capital cities to report their health as fair or poor (table 3.2). People in other urban areas were slightly less likely to report fair or poor health than those in capital cities, but the difference was not statistically significant. Adjustment for age did not appreciably alter the relationship between place of residence and self-assessed health status.
3.3 REPORTED POOR/FAIR HEALTH STATUS IN TWO SURVEYS, Males 3.4 REPORTED POOR/FAIR HEALTH STATUS IN TWO SURVEYS, Females 3.5 REPORTED POOR/FAIR HEALTH STATUS, Males 3.6 REPORTED POOR/FAIR HEALTH STATUS, Females Labour force status Labour force status was significantly associated with self-assessed health status (table 3.2), even after adjusting for age (table T.1 in Technical Notes). For both males and females, people who were unemployed or not in the labour force were significantly more likely to report fair or poor health than those employed in mainstream jobs (that is, jobs other than Community Development Employment Projects (CDEP) scheme jobs). Females who were employed in CDEP scheme jobs were somewhat more likely than those in non-CDEP jobs to report fair or poor health. Although the opposite was true for males, the difference was not statistically significant after adjustment for age. Educational attainment Males and females who did not complete year 10 were more likely to report poor or fair health than those with a higher level of educational attainment (table 3.2). People still attending school were the least likely to report poor or fair health. Much of the difference can be explained by differences in the age distributions of people in particular categories of educational attainment (table T.1). For example, people still attending school tend to be younger than those who have left school. Household income Males and females with reported household incomes of less than $20,000 per year were significantly more likely to report poor or fair health than those with $40,000 or more in annual household income (table 3.2), even after adjusting for age (table T.1). People with a household income of $20,000-39,999 were intermediate in their reporting of poor or fair health. Housing Males and females who lived in dwellings owned or being purchased by their occupants were significantly less likely to report fair or poor health after adjustment for age than were those living in rented dwellings or with some other housing tenure (tables 3.2 and T.1). Residents of more crowded households were significantly less likely to report fair or poor health than were those living in less crowded accommodation. Number of children borne Women who said they had never borne children were similar in their reporting of poor or fair health to women who had borne one child (table 3.2). Reported poor or fair health increased with the number of children ever borne. Although the differences were less marked after adjusting for age (table T.1), women who had borne four or more children were significantly more likely to report poor or fair health than those who had borne 2-3 children, while women who had borne one child were significantly less likely to report poor or fair health. Cultural factors Males and females who said they had been removed from their natural family as children were significantly more likely to report fair or poor health than were those not taken away (table 3.7), even after adjusting for age (table T.2). Those who said they recognised homelands or traditional country were more likely to report poor or fair health, but the differences were only statistically significant among males after adjustment for age. Participation in cultural activities in the last year and perceiving the role of elders as important were not significantly associated with the reporting of poor or fair health status. Reported identification with a clan, tribal or language group was only significant among males, and only after adjustment for age (table T.2). Males who lived in households which included non-Indigenous members were not significantly different in their reporting of poor or fair health than those in households with Indigenous members only, but females who lived in households with non-Indigenous members were significantly less likely to report fair or poor health than those who lived only with other Indigenous people (table 3.7), even after adjusting for age (table T.2). Although Torres Strait Islanders appeared less likely to report poor or fair health than Aboriginal people, the number of Torres Strait Islanders in the NATSIS was relatively small, and the differences were not statistically significant.
Main language Males and females who said they spoke English as their main language were significantly more likely to report poor or fair health than were those whose main language was not English (table 3.7), even after adjustment for age (table T.2). A trend of generally increased reporting of poor or fair health with increased age was found regardless of main language spoken, especially among males (graphs 3.8 and 3.9), but the proportions were lower in absolute terms among those who did not use English as their main language. 3.8 REPORTED POOR/FAIR HEALTH STATUS AND MAIN LANGUAGE, Males 3.9 REPORTED POOR/FAIR HEALTH STATUS AND MAIN LANGUAGE, Females HEALTH RISK FACTORS Relative weight and food security Males and females who were classified as underweight or of acceptable weight were less likely to report poor or fair health than those who were categorised as overweight or obese (table 3.10). After adjusting for age, however, relative weight was not significantly associated with reports of poor or fair health (table T.3). This lack of an association may be related to different perceptions of overweight and obesity in different cultures; in some cultures, overweight and obesity may be viewed positively as a sign of good health. Although reporting that household members had gone without food in the last four weeks was not significantly associated with reported poor or fair health (table 3.10), people who said they worried about going without food were significantly more likely to report poor or fair health than those who said they did not worry, even after adjusting for age (table T.3). Smoking and alcohol consumption Cigarette smoking was associated with significantly higher reporting of poor or fair health among males and females (table 3.10), even after adjusting for age (table T.3). Self-reported non-drinkers were significantly less likely to report poor or fair health after adjusting for age than were those who said they drank alcohol within the week before they were interviewed (tables 3.10 and T.3). Among those who reported any alcohol use, females who said their last drink was more than a week ago were significantly less likely to report poor or fair health than were those who drank more recently, but there was no significant difference among males. No information was available about level of alcohol consumption. Personal security Males and females who said they had been attacked or verbally threatened in the past year were significantly more likely to report poor or fair health than were those who said they had not been attacked or threatened (table 3.10), a difference that was more pronounced after adjusting for age (table T.3).
HEALTH ACTIONS AND CONDITIONS Recent health actions People who reported taking any health-related action in the two weeks prior to being interviewed were significantly more likely to report poor or fair health than those who did not (table 3.11), even after adjusting for age. Many people reported taking more than one health-related action in the period specified. For example, some people were admitted to hospital, saw a doctor, nurse and/or Aboriginal Health Worker and used medications, all as part of the same illness or condition. Males and females who reported a greater number of different types of health-related actions were more likely to report poor or fair health than those who reported fewer or no actions (table 3.11), even after adjustment for age (table T.4). Among people who reported a recent health-related action, males and females who said they had been admitted to hospital were the most likely to report poor or fair health (tables 3.11 and T.4, graph 3.12).
3.12 HEALTH-RELATED ACTIONS(a) AND REPORTED POOR/FAIR HEALTH
Long-term health conditions Even after adjustment for age, males and females who reported that they had a long-term condition or disability for which they required assistance were significantly more likely to report poor or fair health than those who said they did not have such a condition or did not need assistance (tables 3.13 and T.4). Respondents were also asked to indicate whether they had any of a number of specified long-term (lasting for six months or more) health conditions, including: asthma; diabetes; heart problems; chest problems; skin problems; high blood pressure; ear or hearing problems; eye problems not correctable by glasses; and kidney problems. For each of these conditions, males and females who reported that they had that particular condition were significantly more likely to report poor or fair health than were those who said they did not have that condition, even after adjusting for age (tables 3.13 and T.4). Among males and females who reported having at least one of these conditions, those who said they had heart problems were the most likely to report poor or fair health (table 3.13, graph 3.14), followed by kidney problems and diabetes among males, and high blood pressure, diabetes and chest problems among females. Some people reported more than one of the conditions listed above. An increase in the number of reported long-term conditions was significantly associated with an increase in reporting of poor or fair health even after adjusting for age (tables 3.13 and T.4, graph 3.15). Over half of females and almost two-thirds of males who reported three or more long-term conditions reported their health as poor or fair. Similar results were observed when limiting the conditions to those more likely to be life-threatening, i.e. asthma, diabetes, heart problems, chest problems, high blood pressure and kidney problems.
3.14 HEALTH CONDITIONS(a) AND REPORTED POOR/FAIR HEALTH
3.15 NUMBER OF HEALTH CONDITIONS AND REPORTED POOR/FAIR HEALTH
Main language The reporting of poor or fair health increased with increasing number of reported long-term health conditions regardless of main language spoken. However, the level of reported poor or fair health was higher for people who speak English as their main language for every category of number of reported conditions (graphs 3.16 and 3.17). A similar pattern was observed for category of health-related actions, with generally higher reporting of poor or fair health for more serious actions regardless of main language spoken, and higher levels of reported poor or fair health among those for whom English is the main language spoken, for each category of type of action taken (graphs 3.18 and 3.19). Among people who said they do not speak English as their main language, less than 1% reported that their only health-related action in the last two weeks was a reduction in activity. Thus the proportion of people in this category who reported poor or fair health is subject to a large degree of error. 3.16 HEALTH CONDITIONS, LANGUAGE AND POOR/FAIR HEALTH, Males
3.17 HEALTH CONDITIONS, LANGUAGE AND POOR/FAIR HEALTH, Males
3.18 HEALTH ACTIONS(a), LANGUAGE AND POOR/FAIR HEALTH, Males
3.19 HEALTH ACTIONS(a), LANGUAGE AND POOR/FAIR HEALTH, Females
Adjusting for other factors Although age is an important predictor of self-assessed health status and is associated with many other factors of interest, it is not the only factor which may obscure the relationship of the variables of interest. As discussed above, several factors were associated with self-assessed health status after adjusting for age. Some of these factors may be related to one another, and it is useful to examine relationships after adjusting for other variables in addition to age. Multiple logistic regression (see technical notes) was used to adjust simultaneously for the effects of many factors. Variables which were significantly associated with self-assessed health status after adjusting for age were included in a number of models to assess their relationship with reported poor or fair health while adjusting for other factors. Because of the exploratory nature of the analysis, the purpose of the modelling was to identify variables which were associated with reported poor or fair health status, rather than to quantify precisely the magnitude of the associations. The adjusted odds ratios should therefore be interpreted with caution. More work is needed to distinguish, for example, between the relative contributions of long-term health conditions and recent health actions. The results of the modelling are described in more detail in the technical notes. Summary of results from multiple logistic regression models Reported long-term health conditions, the need for assistance due to a long-term condition or disability and recent health actions were among the most important factors with respect to self-assessed health status. It is not surprising that people take such things into account when they rate their own health. The number of reported conditions, the presence of a disability or condition for which assistance is required and the number and type of reported recent health actions were significantly and independently associated with reported poor or fair health even after adjusting for other factors (table T.5). Age continued to be significantly associated with reported poor or fair health even after adjusting for many other factors of interest, including health actions and conditions (tables T.5). Language also remained significant, with those who said they used English as their main language more likely to report poor or fair health than those who said they used another language even after adjustment for other factors (table T.5). The difference was larger among males, but was still significant among females. Labour force status was also associated with self-reported health status after adjusting for other factors, with males and females who were not in the labour force significantly more likely to report poor or fair health than those in non-CDEP employment (table T.5). Although long-term health conditions may prevent people from participating in paid employment, the relationship was independent of reported health conditions and health actions. Females who were unemployed or who worked in CDEP scheme jobs were also significantly more likely to report poor or fair health than their counterparts in non-CDEP jobs after adjustment for other factors. Other reported factors, such as home ownership, level of educational attainment, number of people per bedroom, smoking, having been attacked or threatened in the past year, worrying about going without food, number of children borne, recognising homelands, identifying with a clan, tribal or language group and having been taken away as a child, were also significantly associated with reported poor or fair health among males, females or both, but the associations were generally less strong than those observed for reported health conditions and actions, age, main language and employment status (table T.5). Factors such as area of residence, household income, alcohol consumption, whether the household included non-Indigenous people, whether the role of elders is considered important, participation in cultural activities, whether household members went without food and relative weight were not significantly associated with reported poor or fair health after adjusting for age, health actions, health conditions, the need for assistance, main language, home ownership, labour force status and other variables in the final model. CHAPTER FOUR - DISCUSSION VARIABLES ASSOCIATED WITH SELF-ASSESSED HEALTH STATUS The first aim of this study was to explore the relationships of a number of variables with self-assessed health status. The results of the analysis showed that, among Indigenous adults, there is a relationship between self-assessed health and several objective health measures, such as number of reported long-term conditions, recent health-related actions and the presence of a disability for which assistance is required. As expected, age was significantly associated with reported poor or fair health, but this relationship was attenuated after adjustment for other factors. A number of social and cultural variables were also independently associated with self-assessed health in the NATSIS, and some of these variables, such as employment status, have been found to be associated with self-assessed health status in other studies (AIHW 1996, Macran, Clarke et al. 1994). The observed relationship between main language spoken and reported poor or fair health is a potentially important one and will be discussed at greater length below. It should be noted that all the data used in this analysis were self-reported, and the results should therefore be interpreted with caution. For example, no validation of self-reported health conditions was made. It is possible that some people reported having conditions which they did not actually have. Conversely, it is likely that some people did not report conditions which they did have. This may have occurred because they did not wish to report a condition, because they were not aware they had the condition and/or had not received a diagnosis, because they did not recognise the name of the condition when asked or otherwise misunderstood the question, or for some other reason. THE MANY DIMENSIONS OF HEALTH Some NATSIS respondents reported their health to be better than might be expected on objective grounds. For example, 40% of males and 54% of females with heart problems reported themselves to be in good, very good or excellent health, as did 43% of males and 64% of females with kidney problems and 48% of males and 59% of females with diabetes. This is consistent with the findings of other research. For example, in one British study, only 12% of respondents reported their health as fair or poor, even though 30% reported chronic illnesses or long-standing disabilities. Of those reporting long-term chronic illness only 28% reported their health as poor (Jenkinson 1994). This lends support to theoretical developments which indicate that health is fundamentally a social construct with multiple dimensions, only one of which is illness identified within a Western biomedical paradigm (see, for example, Mobbs 1991; Segovia, Bartlett et al. 1989; and Jylha 1994). The SF-36, a widely used instrument for measuring health status, has scales relating to general health, bodily pain, physical functioning, physical roles, mental health, social functioning and emotional roles (Ware & Sherbourne 1992). With the exception of developmental work for the SF-36, little research has been undertaken to investigate the cross-cultural robustness of these constructs. This is further complicated by the fact that, with global questions, all dimensions are collapsed into a single item which raises issues about the relative salience of different constructs when health status is assessed. For example, work by Krause and Jay (Krause & Jay 1994) has shown that both within and between cultures, people use different referents when answering a global question about self-rated health; some think about specific health problems while others think about general physical functioning or health behaviours. In addition, referents vary with age, and may also vary with education and race. Smith and others (Smith, Shelley et al. 1994) found that self-assessed health reported as worse than one's peers reflected physical experience of ill-health, while reports of better health than one's peers reflected not only absence of disease but sociodemographic advantage and self-image. LANGUAGE AS A PROXY MEASURE OF ACCESS TO SERVICES The extent to which the presence of disease is a salient construct in self-assessment of health raises an important issue in the context of the NATSIS. In Indigenous populations, there may be a relationship between access to Western medicine and knowledge of disease; that is, the closer social proximity one has to health infrastructure, the more likely it is that disease, if present, will be detected (Anderson & Sibthorpe 1996). We use the term social proximity to emphasise the socio-cultural and economic dimensions of a relationship that is also in part determined by the geographic distribution of health care services which favours urban and metropolitan centres. Although it may be counter-intuitive, improvements in access to health services may result in a decline in self-assessed health status, at least in the short term, as the likelihood of detection of existing disease increases. Better access to services may also increase people's expectations of good health; if reality fails to keep pace with such changes in expectation, self-assessed health status might worsen, even in the absence of changes in objective measures of health. Main language spoken could be considered a proxy measure of access to health care infrastructure in the NATSIS, and this may help to explain why those whose main language was English were more likely to report their health as poor or fair than those who spoke some other language, a finding which is perhaps contrary to expectations. Those who did not speak English as their main language may have been less likely to know that they had a disease because they had less access to relevant diagnostic services. In addition to being a possible indicator of reduced access to health care infrastructure, speaking a main language other than English is a marker, albeit an imperfect one, of a more traditional lifestyle, of cultural differences in the meaning of 'health', and of social as well as physical remoteness. Indeed, maps 3.5-3.6 indicate that regions in which a low proportion of people reported poor or fair health were primarily situated in the Northern Territory and Western Australia, in areas which are more remote and in which Indigenous people are less likely to speak English as their main language and are more likely to comprise a relatively high proportion of the regional population (Australian Bureau of Statistics 1997). Thus the distribution of health infrastructure and a number of socio-cultural variables are inter-related and it is therefore difficult to distinguish clearly their relative influence with respect to self-assessed health. These factors, alone or in combination, do appear to result in a raised threshold for the self-reporting of poor or fair health status. It must be noted that language may also have had a more direct influence on reported self-assessed health status. That is, it is possible that some people who use a main language other than English misunderstood the question and/or what was expected by way of response. ASSESSING THE POTENTIAL USEFULNESS OF SELF-ASSESSED HEALTH STATUS The second aim of this study was to assess the potential usefulness of self-assessed health to make comparisons and examine trends over time within the Indigenous population, and to make comparisons between Indigenous and non-Indigenous populations. The analysis reported here indicates that a global measure of self-assessed health status may be of use in these areas, but that there are limitations which must be acknowledged. Although it is clear that the Indigenous people surveyed in the NATSIS used objective measures such as long-term health conditions, disability and recent health actions in their assessment of their own health, the data suggest that the context in which poor or fair health was reported may have differed for different groups of Indigenous people (such as those who speak English as their main language versus those who do not), thus making comparisons between such groups difficult. Comparisons within a group over time may control for these differences. However, when using a global measure it must be remembered that improvements in one or more dimensions may be obscured by deterioration in others. Thus an overall change may be discernible, but the extent to which different dimensions have contributed to that change will not. Concerns about the usefulness of a global question on self-assessed health to make comparisons between Indigenous and non-Indigenous populations are partly allayed by the results of the analysis. Indigenous people clearly take account of a number of objective health factors when assessing their own health. An apparent similarity in the rates of poor or fair health for Indigenous people in the NATSIS and their all-Australian counterparts in the NHS (both at around 17% overall) would seem at first glance to suggest that the thresholds for reporting poor or fair health are different, making comparisons of little value, because the available objective measures clearly indicate much higher levels of morbidity and mortality among Indigenous people. However, the analysis showed that the expected differences did appear after accounting for the age structures of the two populations. The differences were most pronounced in the 35-44 and 45-64 year age groups, which roughly correspond with the age groups which have the largest differentials between Indigenous and non-Indigenous mortality (Anderson, Bhatia and Cunningham 1996). The current focus on outcome measures in health research means that measures of self-assessed health status remain highly relevant. The simple global measure used in the NATSIS has, for the first time, provided information on a national level about the subjective health of Indigenous Australians. Limitations such as those described above do not necessarily mean that these data are not useful for making comparisons between groups and over time. Rather, more research is needed to enhance our understanding of the meaning of differences in the reporting of poor or fair health both within and between groups to ensure that measures of self-assessed health status are used and interpreted appropriately. CHAPTER FIVE - TECHINICAL NOTES THE LOGISTICS REGRESSION MODEL The dependent variable of interest in this analysis was reported poor or fair health status. Because people without adequate information on self-assessed health status were excluded from consideration, all those included in the analysis could be assigned to one of two categories: poor/fair self-assessed health or good/very good/excellent self-assessed health. In cases such as this, where the probability of falling into one of two categories is of interest, the logistic regression model is commonly used, especially in the area of health research. Logistic regression overcomes the fact that probabilities are limited in range from 0 to 1. By using a logit transformation, the dependent variable has a range from negative infinity to positive infinity, thus facilitating modelling. In its simplest form, the logistic regression model can be described as follows: Logit Pi = log [Pi / (1-Pi)] = alpha + biXi +ei where Pi is the probability of the outcome occurring (e.g. reporting poor or fair health), alpha is an intercept term, the bi's are coefficients, Xi's the independent variables of interest, and ei is the error term. Logit Pi is the natural logarithm of the 'odds ratio', which is commonly used in the field of health research as a measure of the magnitude of the relationship between two variables. More information on logistic regression is available elsewhere (e.g., Hosmer & Lemeshow 1989). As discussed in chapters 2 and 3, several independent variables were of interest in the analysis. The relationship of a variable of interest with reported poor or fair health can be obscured if other factors are related both to the variable and to self-reported health status. For example, as is discussed in chapter 3, age is an important predictor of reported poor or fair health. Age is also associated with other variables of interest, such as labour force status, education, language spoken, etc. Thus an observed relationship between one of these factors and reported poor or fair health may be wholly or partly due to the effect of age. Conversely, a failure to observe any association may also be due to differences in age. The same may be true for variables other than age. Therefore, it is important to adjust for other variables when examining the relationship between a factor of interest and self-assessed health status. In the current analysis, both unadjusted and adjusted models have been generated and compared. In tables T.1-T.5, odds ratios are presented for selected variables. Odds ratios have been estimated relative to an appropriate set of reference characteristics. For example, in table T.5, the reference characteristics are as follows:
The results of adjustment for age were discussed in the text in chapter 3 and will not be repeated here. Tables T.1-T.4 present crude and age-adjusted odds ratios for the variables of interest. In addition to age, the factors most strongly associated with reported poor or fair health included reported long-term health conditions, recent health actions, the need for assistance due to a long-term disability or condition, main language spoken, home ownership and labour force status, which were all significant even in a model which included all of these factors. These factors were then used as a baseline model to assess the significance of other factors after adjustment. The other variables of interest were added one at a time to this baseline model and the results were examined. A number of variables were significantly associated with reported poor or fair health among males and/or females when added by themselves to the baseline model. These variables were then added all together to the baseline model. All of the variables remained significant for either males or females, with the exception of household income and alcohol consumption, which were not included in the final model. The factors included in the final model are listed in table T.5, along with the adjusted relative odds of reported poor or fair health and 95% confidence intervals. Several variables were not significantly associated with poor or fair health among either males or females when added to the baseline model, including relative weight, area of residence, whether a household member went without food, participation in cultural activities, whether the household included non-Indigenous people, and considering the role of elders to be important. These variables were not included in the final model. Results of multiple logistic regression As is shown in table T.5, reported long-term health conditions, the need for assistance due to a long-term condition or disability and recent health actions were among the most important factors with respect to self-assessed health status. These factors were significantly associated with reported poor or fair health even after adjusting for all the other factors listed. As noted in chapter 3, the analysis was primarily exploratory in nature, and the purpose of the modelling was to identify variables which were associated with reported poor or fair health status, rather than to quantify precisely the magnitude of the associations. Thus the odds ratios for particular health variables after adjustment for other health variables should be interpreted with caution, as the models were not intended to assess the relative contributions of particular health variables. That is, while the results indicate that long-term health conditions, recent health-related actions and the presence of a disability for which assistance is required are all important factors with respect to reported poor or fair health, more work would be required to adequately determine the relative importance of these three variables, as well as identify any interactions between them. Age, main language and labour force status also continued to be significantly associated with reported poor or fair health even after adjustment for many other factors of interest. Although long-term health conditions may prevent people from participating in paid employment, the relationship between labour force status and self-assessed health was independent of reported health conditions, health actions and other factors. Other variables included in the final model, such as home ownership, level of educational attainment, number of people per bedroom, smoking, having been attacked or threatened in the past year, worrying about going without food, number of children borne, recognising homelands, identifying with a clan, tribal or language group and having been taken away as a child, were also significantly associated with reported poor or fair health among males, females or both, but the associations were generally less strong than those observed for reported health conditions and actions, the need for assistance, age, main language and employment status (table T.5).
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