Analysis Of Inequality In Riverlands

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Analysis Of Inequality In Riverlands

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Analysis Of Inequality In Riverlands

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Analysis the Inequality in Riverland.

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Introduction
Several factors contribute to health inequalities in different regions in Australia. Each region has certain unique factors that contribute to the quality of health provided in that specific region. The quality differs in terms of the gender and the age. Some of the factors that affect the quality of healthcare in Riverlands include socioeconomic disadvantage, education, and occupation. Socioeconomic disadvantage is rated by the low income, high unemployment and low academic attainment (Uphoff & Wright, 2013).Health inequalities in relation to the education level are measured by the level of education attained by the different individuals. These levels include a bachelor degree, a diploma, and lack of post-school qualification. The final factor is occupation, which involved the analysis of those with white collar jobs and those with blue-collar jobs (Peres, Sabbah & Antunes 2015). This paper will be analyzing the findings on health inequalities within Riverlands
Socioeconomic Factors
The socioeconomic disadvantage has been associated with high mortality rates because of ill health. This is because they are less likely to prevent a disease or detect it in its early stages (Mithen & Kavanagh, 2015). The disadvantaged groups are also more likely to engage in risky behaviors that may affect their health. This applies for both females and males of different ages. The inequalities in socioeconomic health are looked at in 4 stages, children and infants from 0-14 years, young adults from 15-24 years, middle age working adults between 25-64 and the older people from age 65 and older. With the use of the geographic measure known as the Index of Relative Socioeconomic Disadvantage index (IRSD), the data is categorized based on the socioeconomic characteristics. For people aged between 0-14 years, the males from these disadvantaged backgrounds were found to have higher rates of asthma and bronchitis. For both male and female infants, there were lower chances of having been breastfed. In terms of the duration of breastfeeding, the females were more disadvantaged as they were breastfed for less than duration of 12 months. This group of people was also likely to consult a doctor but less likely to seek medical help from a specialist such as a dentist. This was higher for females compared to the males. All these factors contribute to a difference in the kind of healthcare received by this age group in the socioeconomically disadvantaged areas.

Individuals between the ages of 15-24 years were also assessed to show the existing differences in healthcare. Females in this age from disadvantaged areas have a higher rate of bronchitis. People of this age group from disadvantaged groups are more likely to engage in risky behaviors that may affect their health. Females have a high likelihood9of risking their health through alcohol consumption. The males in this group had higher smoking rates although the difference was not significant when compared to the rates in females. Both sexes had the likelihood of being obese and experiencing food insecurity are some of the major that contribute to health inequalities in Riverlands.
Persons between ages 25-64 years from disadvantaged socioeconomic areas rate their health as being poor and have reported several instances of being ill compared to the people who are from areas that are least disadvantaged. Most males from this age group reported missing work because of an illness especially illnesses like arthritis compared to the females. Diabetes was also common among the individuals in this group (González & Stocks, 2017). Risky behaviors are also a contributing factor in this age in the disadvantaged areas with alcohol use, smoking, and lack of physical activity being among these factors. The males in this age were more likely to drink higher levels of alcohol compared to the women. Hypertension was also a major occurrence among this group of people. For those aged from 65 years and above, they reported a number of long-term illnesses compared to those in the least disadvantaged areas. Therefore, poor individuals in Riverlands are more likely to experience health inequality because of the high cost of medicine and hospital accommodation (Gunasekara, Carter & McKenzie, 2013).
Education Factors
The next factor is health inequalities from the perspective of education. This determines an individual’s health through the occupational opportunities that may result from their level of education and the income potential that accompanies their occupation (Turrell & Giles-Corti, 2013). The knowledge and skills one gains from education can also help individuals in maintaining and improving their health. Studies in Australia have indicated that those who are less educated have poorer health. Higher education levels have been associated with lower mortality rates and lower rates of self-reported illnesses (Zhang & Oldenburg, 2014). The risk behaviors among people with low education include; smoking, high blood pressure, obesity, and insufficient physical activity are the major factors are crucial in determining an individual’s health, which are less likely to be experienced among the educated people because of their knowledge and understanding of the risks involved in engaging in such behaviors (Badland, Aye & Butterworth, 2014). Therefore, the level of education between individuals and sections in the society are some of the major factors that contribute to the increased cases of health inequalities in Riverlands

Individuals between the ages of 25-64 years with low educational qualifications had poor health ratings. In both males and females, those with low educational qualifications reported more cases of bronchitis and arthritis. Diabetes was more prevalent among the females who lacked post-school qualifications (Mithen & Kavanagh, 2015). The number of males who engaged in risky alcohol intake was higher compared to the number of females in this particular group. Both males and females of lower educational qualifications reported higher rates of smoking. Health risks such as obesity were also higher among this group with it being more prominent among the males than the females. In this case, those women in the ages of 50 and 64 have not had a mammogram. They are also less likely to have a pap smear. These two health processes are very important for early detection of cancer and therefore, lack of these exposes the women with low educational qualifications to diseases like cancer (Meier & Brennan, 2016). In this case, the findings indicate that poor education can deny one an opportunity of accessing healthcare.
The individuals who are 65 years of age and above with low educational qualifications are also more likely to have adverse health problems compared to those with higher educational qualifications. The number of males with bronchitis was higher compared to the males who had a bachelor degree or higher. Those with higher educational qualifications at this age are also less likely to engage in behaviors that put their health at risks such as smoking and drinking. Therefore, older individuals in Riverlands are more exposed to health inequalities based on their level of education, which hinders their communication (Uphoff & Wright, 2013).
Occupation Factors
The third factor to be considered in determining the inequality in healthcare is the individual’s occupation. An individual’s occupation is a great indicator of an individual’s socioeconomic status, which also determines the healthcare of the individual (Batterham & Osborne, 2016) the impacts of an individual’s occupation on their health can be direct or indirect. The direct impact is through the exposure to hazards at work and the indirect impact is the association of the occupation with a particular income level and living standards (Durand & Elwyn, 2014).

People who work in low-status occupations have a lower rating of their health compared to individuals in higher status occupations. The number of males that do manual labor who report chronic illnesses is higher compared to the number of females who do manual labor. Females who are in the low-status occupations were however reported lower incidences of breast cancer. Males in blue-collar occupations also have higher risks of occupational injuries (Gibney & Leder, 2017). Both the males and females in blue-collar occupations are likely to have health-related issues from smoking. They are also less likely to participate in physical activities and as a result, they develop health complications. Males with lower occupations are more likely to drink high levels of alcohol compared to females in the same occupation (González-Chica & Stocks, 2017). Most of the individuals in the low-status jobs also have higher intakes of cholesterol with the main source of energy being derived from fats and sugars (Mather & Korda, 2014). Therefore, the nature of work between individuals can hinder one from accessing healthcare based on the expenses that are associated with healthcare provision.
Those between age 25-64 and working in blue collar jobs reported a lower rating in their health and reported more illnesses compared to those in the white collar jobs. In both females and males, those in blue-collar jobs reported higher rates of arthritis. The number of males in the blue-collar jobs with bronchitis was higher compared to the females. Both males and females are likely to engage in risky behaviors that may harm their health. Risky behaviors such as risky alcohol intake were reported to be higher in males compared to the females (Peres, Sabbah & Antunes, 2015). Both the male and the female had higher health risks from smoking. However, males with blue-collar occupations reported fewer cases of hypertension compared to those in the white-collar jobs. Those in the blue-collar jobs are likely to consult a doctor as compared to those with white-collar jobs. Blue-collar male workers were less likely to consult specialists and dentists compared to the white-collar male workers. Women from age 50-64 in blue-collar occupations are more likely to have never had a mammogram or a pap smear (Turrell & Giles-Corti, 2013). Individuals in blue-collar occupations with a lower status or level of skills had poorer health and engaged in risky behaviors compared to the individuals in the white collar occupations. In this case, the occupation of individuals and the level of income are some of the contributing factors that lead to health disparities in Riverlands (Zhang & Oldenburg, 2014).

In conclusion, the above analysis has indicated the factors that contribute to disparities in healthcare in River lands. Socioeconomic disadvantage, education, and occupation are some of the contributing factors to health care inequalities. These factors affect the kind of healthcare the individuals seek depending on their financial capabilities. These factors also determine whether these individuals engage in risky health behaviors. Therefore, in dealing with inequality in healthcare, it is important to put into the consideration the underlying factors that affect the health-related behaviors of individuals. These factors are important in understanding the inequality in healthcare by getting a better view of the contributing factors.

References
Badland, H., Aye, L., &Butterworth, I (2014). Urban liveability: emerging lessons from Australia for exploring the potential for indicators to measure the social determinants of health. Social science & medicine, 111, 64-73.
Batterham, R. W.,& Osborne, R. H. (2016). Health literacy: applying current concepts to improve health services and reduce health inequalities. Public health, 132, 3-12.
Durand, M. A., & Elwyn, G. (2014). Do interventions designed to support shared decision-making reduce health inequalities? A systematic review and meta-analysis. PloS one, 9(4), e94670.
Falster, K., Banks, & Jorm, L. (2016). Inequalities in pediatric avoidable hospitalizations between Aboriginal and non-Aboriginal children in Australia: a population data linkage study. BMC pediatrics, 16(1), 169.
Gibney, K. B., &Leder, K. (2017). Sociodemographic and geographical inequalities in notifiable infectious diseases in Australia: a retrospective analysis of 21 years of national disease surveillance data. The Lancet Infectious Diseases, 17(1), 86-97.
González-Chica, D. A.,& Stocks, N. (2017). Lower educational level and unemployment increase the impact of cardiometabolic conditions on the quality of life: results of a population-based study in South Australia. Quality of Life Research, 1-10.
Gunasekara, F. I., Carter, K., & McKenzie, S. (2013). Income?related health inequalities in working age men and women in Australia and New Zealand. Australian and New Zealand journal of public health, 37(3), 211-217.
Mather, T., & Korda, R. J. (2014). Variation in health inequalities according to measures of socioeconomic status and age. Australian and New Zealand journal of public health, 38(5), 436-440.
Meier, P. & Brennan, A. (2016). Estimated effects of different alcohol taxation and price policies on health inequalities: a mathematical modelling study. PLoS Med, 13(2), e1001963.
Mithen, J.,& Kavanagh, A. M. (2015). Inequalities in social capital and health between people with and without disabilities. Social Science & Medicine, 126, 26-35.
Uphoff, E. P., & Wright, J. (2013). A systematic review of the relationships between social capital and socioeconomic inequalities in health: a contribution to understanding the psychosocial pathway of health inequalities. International Journal for Equity in Health, 12(1), 54.
Peres, M. A., Sabbah, W., & Antunes, J. L., (2015). Income?related inequalities in inadequate dentition over time in Australia, Brazil and USA adults. Community dentistry and oral epidemiology, 43(3), 217-225.
Turrell, G., & Giles-Corti, B. (2013). Can the built environment reduce health inequalities? A study of neighborhood socioeconomic disadvantage and walking for transport. Health & place, 19, 89-98.
Zhang, J., & Oldenburg, B. F. (2014). Socioeconomic inequalities in utilization of preventive health services in relation to cardiovascular disease and diabetes. Journal of Behavioral Health, 3(2), 87-94.

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