Health Survey in Australia - Essay Example

Title: Health Survey in Australia Name: Institution Instructor: Date of Submission: Introduction Australian Health Ministry has shown its concern on the health status of its population. In order to get accurate information about the health of the population, the Australian Department of health conducts surveys to establish the health status of its citizens based on established body characteristics2. Various characteristics of health are measured such as amount of energy expenditure, BMI, weight and waist size. In addition, nutritional characteristics are established such as the constituent of foods consumed by the population. This involves establishing the amount of carbohydrates, proteins, vitamins, fats and oils and mineral composition of the bodies of its population1. This paper involves a report on a study that was conducted in a sample population in Australia and comparing the data with the expected health conditions of Australian population so that a recommendation could be made about their health status. Participants and Methods In this study, 120 participants were involved. The participants were high school female students and the methods involved measuring various health characteristics of the students. The first characteristics to be measured was body composition in terms of weight, waist circumference, BMI, percentage of fats in the body using skin fold method and using BIA method. The other characteristics that were measured include the amount of energy expenditure in Kj, the quantity of various components of a diet such as proteins, carbohydrates, vitamins and minerals such as calcium, folate, zinc, iron and alcohol content. The strategies used to get these measurements were the FFQ method and 3 day method. Descriptive characteristics pertaining to the parameters measured such as mean, variance, maximum values, minimum values and percentiles were computed. The amount of various nutrients taken were measured in mg and recorded. Descriptive statistics relating to these nutrients were calculated. The results of nutrients intake were compared with the recommended nutrients intake to establish nutrients that were not taken adequately by the sample population. This was followed by computation of the correlation between measurement of percentage fat using the skin-fold method and using the BIA method. In measurements of the quantities of calcium, FFQ and 3DFR were used and descriptive statistics relating to them such as mean and standard deviation obtained. Pearson correlation between Calcium FFQ and Calcium 3DFR were performed. In order to understand the balance of consumption of various minerals, normal distribution curves were used to present the results of the quantities of each mineral consumed. The results were analyzed and compared with the expected values by the Australian Ministry of Health. This enabled getting a recommendation on the course of action that need to be taken to improve health of the population. Results The results for body composition were represented as shown below: In the measurements of body composition, the average height of participants was found to be 4.6 m, the average weight was 63.1 kg, the average waist circumference was found to be 72.6 cm, the average BMI was found to be 23.5 kg/m2, the mean % fat in skinfolds was found to be 26.4% and % fat in BIA was 24.5%. Other descriptive statistics relating to these compositions such as maximum, minimum and media were tabulated as shown in appendix A. According to the Australian department of health survey, the normal BMI for a Caucasian woman should be 25.1kg/m27.The BMI results were compared with the WHO classifications of BMI and it was found that 7.8% were underweight, 84.5% were within the normal range and 2.6% were obese. Figure 1. Descriptive Statistics of body compositions of participants Figure 2. Age specific BMI based on Australian Health statistics. According to the Australian health survey department, the mimimum wasit size for a caucasian woman should be 80cm in circumference4.It was found that the total number of women whose waist circumference were below the minimum size required for a healthy woman was 85 or 69.1%, this shows that most women in the samnple had wasit sizes below the normal size. In order to achieve accurate results in t-tests, there should be a positive correlation between the methods used to find the results3.The results of the T-test of the difference between % fat in skinfold and % fat in BIA were compared in a Pearson Correlation as shown below. Figure 3. Correlation between % Fat (skinfold) and % Fat (BIA) The descriptive statistics of the comparison of Calcium FFQ and calcium 3DFR were compared and represented as shown below. Figure 4. Descriptive Statistics of Calcium FFQ and Calcium 3DFR The corresponding correlation coefficient table was obtained as shown below. Correlation between Calcium FFQ vs Calcium 3DFR   Calcium FFQ Calcium3D   Pearson Correlation 1 .422** Sig. (2-tailed)   .000 N 123 123 Figure 5. Correlation between Calcium FFQ and Calcium 3DFR The results of energy expenditure in the AAs process resulted into a mean of 2580.05 kj and in the use of d day diary resulted into a mean of 9663.90. The results of the paired sample tests testing difference between EE (AAS) versus EE (3DD) resulted into a mean of -7083.85 standard deviation of 5257.07 and a t value of -14.94.Other descriptive characteristics were determined and tabulated as shown in appendix B. The results of constituents of diet consumed by the participants were tabulated as shown in appendix B. finally, the number of participants whose level of consumption of various minerals were below the expected average rates EAR of various minerals was 84.5% for calcium, 72.87% for Folate, 10.08% for Zinc and 51.13% for iron in women. These results are tabulated in Appendix C. Discussion The results of the average height was close to normal height of Australian population of 4.5 m. the average waist circumference was 72.2 cm while that of a b normal woman in Australia is expected to be 80 cm. this shows that the sample did not have the waist size of an expected healthy woman. Consequently, the values of waist circumference show that the sample involved in the study faces the risk of developing chronic illnesses. The average BMI of 22.5kg/m2 was an indication that the population was healthy due to its closeness to the recommended BMI value of 21.3 by the Australian health Survey reports3. The correlation between % fat using skinfold and using BIA methods is positive thus, the results from the two methods of measuring % fats are important in approaching closeness to the exact measurement of fat composition of the bodies of participants. The average % fat composition of the population of the two methods is within the required limits of fats composition for a healthy woman by the Australian Ministry of Health8. In measuring the level of calcium in the bodies of patients using the Calcium FFQ method and Calcium 3DFR method, it was found that both the results for calcium FFQ method were lower than that of calcium 3DFR method. These methods are both suitable for establishing the quantity of calcium in the body because they resulted into a value of 800mg/liter that is recommend by the Australian Ministry of Health6. In the analysis of energy expenditure, the results shows that the case of 3 day activity diary resulted into high energy loss compared with the AAS method. This is because during a 3 day energy measurement, more energy was lost compared to the AAs method. The comparison of various minerals consumption with the estimated average requirements shows that the minerals that are not adequately consumed by the population include calcium, folate and iron. This is because the values of these mineral compositions of 840mg/day for calcium, 30mg/day of vitamin C, 320µg/day of folate, 6.5mg of Zinc and 8mg/day of Iron were not attained. Thus, it is recommended that foods should be taken by the sample population so that these values are attained. Conclusion This study contributes significantly towards understanding health conditions of Australian population. From the results, the Australian population is generally within a better health status condition. This illustrated by proper intake of various food groups such as proteins, carbohydrates and some minerals in adequate amounts. It contributes to understanding the factors that can contribute to chronic diseases such as poor nutrition. This assists in coming up with a decision regarding the action that can be taken to protect the population from particular illnesses. For instance, in this study, it is possible to come up with a number of recommendations that are important for the help of the sample population. These include the recommendation to increase consumption of foods rich in minerals such as calcium, folate, iron, zinc and reduce energy expenditure in the activities they are involved in. if these recommendations are im0plemented, there is a high possibility of improvement in health of citizens in Australia and chances of suffering from chronic illnesses are reduced. References 1. Australian Institute of Health and Welfare. A Picture of Australia's Children 2012. Canberra: Australian Institute of Health and Welfare, 2012; 935(1-2):40-6.  2. Australian Institute of Health and Welfare. Australia's Health 2012: The Thirteenth Biennial Health Report of the Australian Institute of Health and Welfare. Canberra: The Institute, 2012; 347(4):284-7. 3. Australian Institute of Health and Welfare. Palliative Care Services in Australia 2012. Canberra: Australian Institute of Health and Welfare, 2012; 127(2):179-86.  4. Australian Institute of Health and Welfare. Risk Factor Trends Age Patterns in Key Health Risks Over Time. Canberra: Australian Institute of Health and Welfare, 2012; 88(3):276-9. 5. Australian Institute of Health and Welfare. Social Distribution of Health Risks and Health Outcomes: Preliminary Analysis of the National Health Survey 2007-08. Canberra, A.C.T.: Australian Institute of Health and Welfare, 2012; 40(5):679-86. 6. Britt, Helena. General Practice Activity in Australia 2012-13: BEACH: Bettering the Evaluation and Care of Health. 2013; 169(6):2257-61. 7. Culyer, A. J., and Joseph P. Newhouse. Handbook of Health Economics. Volume 1A Volume 1A. Amsterdam: Elsevier, 2000; 13(9 Pt 1):923-8. 8. Eccles, R., and Olaf F. Weber. Common Cold. Basel: Birkhäuser, 2009; 2:933-6.   9. Gray, Matthew, John Taylor, and Boyd Hunter. Health Expenditure, Income and Health Status Among Indigenous and Other Australians. Canberra: ANU E Press, 2004; 79:8-15. 10. Hunter, Boyd, and Nicholas Biddle. Survey Analysis for Indigenous Policy in Australia Social Science Perspectives. Acton, A.C.T.: ANU E Press, 2012; 298:784-8.  11. Klevmarken, Anders, and Björn Lindgren. Simulating an Ageing Population: A Microsimulation Approach Applied to Sweden. Bingley: Emerald, 2008; 127(2):179-86. 12. Lupton, Gillian M., and Jakob M. Najman. Sociology of Health and Illness: Australian Readings. South Melbourne: Macmillan Education Australia, 1995; 88(3):276-9. 13. Merwin, Elizabeth. Focus on Rural Health. New York: Springer, 2008; 169(6):2257-61. 14. Moreno Aznar, Luis, Iris Pigeot, and Wolfgang Ahrens. Epidemiology of Obesity in Children and Adolescents: Prevalence and Etiology. New York: Springer, 2011; 122(8):785-7. 15. National Centre for Monitoring Cancer (Australia). National Centre for Monitoring Cancer: Framework 2012. Canberra, ACT: Australian Instiute of Health and Welfare, 2012; 83(Pt 2):491-5. 16. Reynolds, Christopher. Public and Environmental Health Law. Annandale, N.S.W.: Federation Press, 2011 ;( 401):230-8. 17. Sassi, F. Obesity and the Economics of Prevention Fit Not Fat. Paris: OECD, 2010; 13(12):2507-18. Appendices A. Descriptive statistics of participants   Height (m) Weight (kg) Waist Cir (cm) BMI (kg/m2) % Fat (Skinfolds) % Fat (BIA) Number Obs 120 120 120 120 122 122 Missing 0 0 0 0 1 0 Min 1.4 44.4 26.9 15.5 8.8 8.4 Max 168.0 104.7 116.0 36.0 47.4 48.3 Median 1.8 62.8 74.0 23.4 25.5 24.6 Mean 4.6 63.9 71.6 23.5 25.2 24.4 SD 21.1 13.3 11.3 4.4 6.7 7.6 SEM 11.6 6.3 5.8 1.9 3.4 3.6 5th percentile 1.6 48.2 62.1 17.7 13.5 11.2 25th percentile 1.8 54.7 67.3 22.1 22.6 18.7 50th percentile 1.5 62.8 72.0 21.4 28.6 25.3 75th percentile 1.8 69.0 79.3 23.8 28.0 26.3 95th percentile 1.9 88.0 94.0 30.6 68.2 34.1 A. Comparison of Energy expenditure under (AAS) and 3 day diary methods Descriptives Energy Expenditure (kJ) (AAS Q) Energy Expenditure (kJ) (3 day diary) Number Obs 120 120 Missing 0 0 Min 34.2 255.5 Max 19400 43600 Median 1173.9 9160 Mean 2584.05 9753.90 SD 3478.13 4707.63 SEM 321.81 432.67 5th percentile 190.54 3630.12 25th percentile 572.3 7300 50th percentile 1160.9 9160 75th percentile 2580 11306.7 95th percentile 8500.28 18500 B. Composition of various nutrients in the bodies of participants Variable N Missing Mean Std. Dev Std Error Serves Dairy 120 0 5.5 38.3 3.4 Calcium (mg/d) FFQ 120 0 746.5 470.4 42.8 Energy Intake (kJ) 3 d diary 120 0 7736.9 3069.4 272.3 % Fat 3 d diary 120 0 31.5 8.6 0.8 % Carb 3 d diary 120 0 44.9 13.5 1.3 % Prot 3 d diary 120 0 22.5 9.8 0.9 % Alcoh 3 d diary 120 0 0.7 2.1 0.3 Calcium (mg/d) 3 d diary 120 0 490.2 288.0 25.5 Iron (mg/d ) 3 d diary 120 0 9.4 5.6 0.6 Folate (ug/d) 3-d diary 120 0 271.8 191.9 17.8 Vit C (mg/d) 3 d diary 120 0 112.8 97.2 8.6 Zinc (mg/d) 3 d diary 120 0 9.4 16.5 1.5 C. Tabulation of the number of participants with mineral consumption below estimated average requirements (EAR). Read More