Resultados de caracterización geoquímica de rocas

Perceived general health status was measured in each study area population through the use of the SF-36. As discussed in Chapter 4, each subscale is scored to provide an indication of general health status. Mean SF-36 scores for each of the participants were calculated for each study area to determine general health status across the three study areas. This was done for each of the eight domains of the SF-36. Responses to the health transition question were also analysed for each study area to determine self-reported changes in health status.

3.3.1 Mean scores as a measure of general health

Mean scores for the eight subscales are presented in Figure 5.3, as well as the physical and mental component summaries for each of the study areas, alongside the Australian normative data. The

assumption of homogeneity of variances was not met; therefore, the Welch F-ratio was used with the

Games-Howell post-hoc test. Across the three study areas, the lowest mean scores were for the ‘vitality’

subscale, suggesting that all three areas have poorer general health in this domain.

Figure 5.3. Mean Short-Form-36 (SF-36) scores for respondents in the three study areas across the SF-36 subscales, including a reference line for SF-36 scores for Australia using 2007 norms (Note:

PF = ‘physical function’, RP = ‘role–physical’, BP = ‘bodily pain’, GH = ‘general health’, VT = ‘vitality’, SF = ‘social function’, RE = ‘role–emotional’, MH = ‘mental health’, PCS = ‘physical component summary’, and MCS = ‘mental component summary’). The three study areas are: CSG = coal seam gas; CM = coal mining; and RA = rural/agricultural.

Overall, SF-36 scores for all three study areas were generally higher than the Australian norms.

The Australian normative data have slight variation due to weighting of the data, ranging from 49.8 to 50.0 (Hawthorne et al., 2007), and most of the scores fall at the mid-point, below the CSG, CM, and RA mean scores. Therefore, a reference line has been added at the mid-point in Figure 5.3 for visualisation purposes (the full set of scores can be found in Appendix H). The mid-point score of 50 is the value from which to compare outcomes as better or poorer than the general population. While there were no

significant differences between the CSG study area and the CM or RA areas, comparison with the normative data suggests that respondents from all three study areas may have better self-reported general health compared to a person in the Australian general population.

The ‘physical function’ subscale differed significantly between the three study areas

[F (2, 245.39) = 3.19, p = 0.043]. Games-Howell post-hoc tests revealed that CM respondents reported higher scores than RA respondents (Games-Howell: p = 0.033; 95% CI: 0.56-16.32), but scores did not differ significantly between CSG and CM areas (p > 0.05). This suggests that the RA area respondents had the poorest health in terms of physical function.

No other significant between-group differences were observed on any of the other subscales or in the physical and mental component summary scores (p > 0.05).

All of the mean scores for each subscale can be found in Appendix H.

3.3.2 SF-36 health transition

In terms of the SF-36 question on ‘health transition’, the majority of respondents for all three areas reported that their current health was about the same at the present time as at one year ago. This was also true for the respondents on which the Australian normative data (from 1995) were based.

The percentage of respondents reporting their health as ‘much better than one year ago’,

‘somewhat better than one year ago’, ‘about the same as one year ago’, ‘somewhat worse than one year ago’, and ‘much worse than one year ago’ is shown in Figure 5.4. The percentage of respondents recording each response (from the sample used to calculate the 1995 Australian normative data) for the Australian general population is also shown in the figure.

Figure 5.4. Percentage of respondents from each study area, as well as the Australian general population, and the responses for the Short-Form-36 (SF-36) health transition question (Note: MB = ‘much better than one year ago’, SB = ‘somewhat better than one year ago’, Same = ‘about the same as one year ago’, SW = ‘somewhat worse than one year ago’, and MW = ‘much worse than one year ago’). The three study areas are: CSG = coal seam gas; CM = coal mining; and

RA = rural/agricultural.

The CM area had a higher percentage of respondents reporting that their health was much better at the present time than it was one year ago (10.5%). Conversely, the RA area had a higher percentage of respondents reporting that their health was much worse at the present time compared to one year ago (7.8%), which was closely followed by the CSG area (7.4%). However, these differences were not significant (p > 0.05). The three study areas were more similar to the Australian general population with respect to the percentage of respondents noting that their health was much better or somewhat better compared to one year ago; however, the CSG area respondents were more similar to the Australian general population for reporting health currently being somewhat worse, when compared to that of one year ago. CM area respondents were more similar to the Australian general population for reporting health currently being much worse than it was one year ago.

3.3.3 Discussion of general health (SF-36 outcomes)

SF-36 scores from the three study areas were higher than the Australian norms, indicating overall better general health when compared to the Australian general population, but the areas were similar to one another. Norm-based comparisons allow for interpretation as to whether observed scores within a group are better or worse than those of the general population (Gandek, 2002). Due to the fact that norm-based scoring equates all scores, scores below 50 are considered worse than the general population average (Gandek, 2002). The three study areas had scores that were above 50 for all measures, indicating scores were better than the Australian general population average.

There are few studies that have examined all of the SF-36 subscales in rural and remote populations in Australia for which to compare these results. However, a few studies have used the SF-12 (a subset of the SF-36), with a particular focus on mental health. These included predictors of mental health outcomes (Eckert, Wilkinson, Taylor, Stewart, & Tucker, 2006) and prevalence of mental health outcomes

categorised by remoteness categories (in South Australia) (Eckert, Taylor, Wilkinson, & Tucker, 2004).

The former study found that presence of a physical health condition (as measured through the SF-12) is an important predictor of mental health outcomes (OR: 1.90; 95% CI: 1.20-3.00), but odds of mental health outcomes did not vary across remoteness categories (Eckert et al., 2006). Likewise, the latter study used the SF-12 to assess depression and found that, while the prevalence of depression was high (overall prevalence: 12.9%; 95% CI: 11.6%-14.2%), there was no evidence for substantial variation across remoteness categories (Eckert et al., 2004).

The Household, Income and Labour Dynamics in Australia survey used a panel study design and measured SF-36 outcomes over time (Butterworth & Crosier, 2004). The datasets were pooled to analyse mental health outcomes categorised by remoteness categories (Dennis & Skelton, 2015). This analysis noted that residents living in remote and very remote areas self-reported significantly better mental health (i.e., higher SF-36 scores) than people in major cities, inner regional, and outer regional areas (Dennis &

Skelton, 2015). While these studies have used parts of the SF-36 in rural and remote areas of Australia, it is difficult to compare the results from all of these studies to the results presented here as inner-area remoteness classification variation is present in the three study areas. Therefore, in the future, it would be useful to examine the data by SLA, if possible, and compare such data to an area classified as a major city (i.e., Brisbane) to see if those findings are similar to the studies presented above.

The RA area reported poorer general physical health, with the lowest mean scores for the ‘physical function’ subscale. For the remaining domains, the three areas were not significantly different, meaning that self-reported general health status was similar across the three study areas. As discussed in

Section 3.2 above, it could be expected that the RA area respondents might perceive their physical health status as poorer compared to that of respondents in the CSG or CM areas due to respondents in this area generally being older, which is associated with poorer health outcomes (National Rural Health Alliance Inc., 2013) and physical decline (Chappell & Cooke, 2010). However, as discussed previously, additional factors (e.g., education, income, housing status, and overcrowding) can contribute to health outcomes. It was thought the CM area respondents may have lower scores for some sub-scales due to the differences in

housing status, but this was not the case and could be attributed to other factors that should be explored in the future.

Some studies have used the SF-36 with respect to residents living near wind farms (Mroczek, Kurpas, & Karakiewicz, 2012; Nissenbaum, Aramini, & Hanning, 2012) and traffic or noise annoyance (Hansen & Neller, 2005; Nitschke, Tucker, Simon, Hansen, & Pisaniello, 2014), but overall, literature related to resource development and use of the SF-36 in communities is limited. One study conducted in India compared women in a coal mining community to women in an agricultural community and found that the agricultural area respondents had scores that were significantly higher (indicating better health) than those of the coal mining area respondents for certain measures such as ‘role–physical’, ‘mental health’, and ‘general health’ (D'Souza, Karkada, & Somayaji, 2013). While this is an example of the SF-36 being used in relation to resource development, it is not plausible to compare outcomes reported in developing countries to those from developed countries due to other differences such as socioeconomic status, culture, access to health services, and other factors. Therefore, a conclusion here is that more research is needed where the SF-36 is applied in similar settings so the outcomes from this study can be compared to outcomes from other resource development settings, as well as rural and remote areas.