Data Analysis Healthcare Concern

Question: Discuss about the Data Analysis for Healthcare Concern. Answer: Introduction Healthcare is a major concern across Singapore. Analysing the data for healthcare in Singapore is an important aspect enabling in understanding the quality of health status of the country. Number of admissions to hospitals across different years and the genders reveal about the health condition of the county over the years. In this assignment, the data for hospital admission rate by sex and age had been collected from the Ministry of Health, of Singapore (LaHuis et al. 2014). This data would be used for analysis and it would reveal the trend of admissions to the hospitals over the years. This would give an idea about the probable causes and suggestions would be provided for the improvement of the health sector. Analysis and interpretation On analysing the data of the given years across different age groups and gender, it was found that the average number of males admitted in public sector hospital for the age group 0-14 years was 75. The median value for this variable was 75. The standard deviation was 7.13 and its variance was 50.90. It was seen that the number of males admitted to the public sector hospital of this age group was increasing with the years. There were large variations among the rate of admission over the years for this variable. The average number of admissions of male of age group 15-64 years to the public sector hospitals was found to be 62 and its median was 62. The standard deviation of this variable was 1.676 while its variance was 2.809. This suggests that the number of admissions of males of this age group varied slightly across the years. The mean number of males of the age group 65 years and above was found to be 325. The median value was 328. The standard deviation was found to be 8.960 and its variance was 80.285. This suggests that there was a high variation among the number of admissions of males of this age group across the years. The number of males of this age group was found to be increasing with the change in years. It was seen that the average number of males admitted to the public sector was 87 and its median was 87. The standard deviation was 5.14 and its variance was 26.47. Thus, the number of males admitted to the public sector hospital had a moderate variation over the years. This variation was found to be less than the number of males admitted to the hospital for the age group 0-14 years and 65 years and above. Figure 1: Number of males admitted to the public sector hospitals across different age groups over the years 2008-2014 (Source: As created by author) The average number of females admitted to the public sector hospital was found to be 83, its median was 81, its standard deviation was 6.18 and its variance is 38.2381. There was a moderate variation among the number of females admitted over the years. The average number of females admitted to public sector hospitals for the age group 0-14 years was 62, the median was 63, the standard deviation was 5.859 and the variance was 34.33. The average number of females for the age group 15-64 years was 59 and median was 56. The standard deviation was 4.029 and its variance was 16.238 (Elvarsson et al. 2014). The average number of females for the age group 65 years and above was 282, median was 283, standard deviation was 6.74 and variance was 45.476. It was seen that the average number of females was highest for the age group 65 years and above and this age group had the maximum variation in the number of admissions over the years. Figure 2: number of females admitted to the public sector hospitals across different age groups over the years 2008-2014 (Source: As created by author) The average number of males admitted to the private sector hospital during the period 2008 to 2014 was found to be 15. The median was 15, standard deviation was 1.952 and its variance was 3.809. The average value of the males of age group 0-14 years admitted to the private sector hospital in this period was 37, median was 37, standard deviation was 3.891 and variance was 15.143 (Thongpanja et al. 2013). The average for the number of males of 15-64 years admitted to this hospital was 8, median was 7, standard deviation was 1.676 and variance was 2.809. The average number of males of age group 65 years and above admitted to this hospital was 31, median was 31, standard deviation was 0.9754 and variance was 0.952. This suggests that there was high variation among the males admitted to the private sector hospitals over the years. The age group of 0-14 years had the most variation for this variable. Figure 3: Number of males admitted to the private sector hospitals across different age groups over the years 2008-2014 (Source: As created by author) The average of the females admitted to the private sector hospitals was 27, median was 25, standard deviation was 3.162 and variance was 10. The average number of females of age group 0-14 years admitted to private hospital was 33, median was 33, standard deviation was 3.258 and variance was 10.619 (Jerom et al. 2015). The average number of females admitted to this hospital for age group 15-64 years was 24, median was 22, standard deviation was 3.690 and variance was 13.619. The average number of females of age group 65 years and above was 35, median was 35, standard deviation was 1.414 and variance was 2. Thus, it was seen that most of the variation in admission was found in the age group of 15-64 years and there was also moderate variation for the age group of 0-14 years and the number of females over the years 2008-2014. It was seen that there was an increase in the number of admissions to the hospitals over the years for different age groups. This could be because of increase in pollution and unhealthy lifestyles. With the advancement of years, there is modification of lifestyle and food habits. These may not be suitable for the Singaporeans and their body cannot adapt to this lifestyle, which has resulted in the increase in the number of admissions to the private and public hospitals across different age groups. This could be reduced by adapting a healthy lifestyle and maintaining proper balance of diet. Reduction in pollution and adulteration of food would also help people not to land in hospitals. Conclusion It was seen that there were different number of patients admitted to different hospitals for various age groups. There was difference in the number of admissions to the public and private hospitals across genders. An increasing trend in the number of admissions in the hospitals was seen for different age groups and across the genders. Increase in number of admissions to hostipals might be due to pollutions, adulteration of foods, change in life styles and other unhealthy factors. On eradicating, these factors would reduce the chance of disease and the health index of Singapore would increase eventually. People of Singapore could have a healthy and happy life. References Elvarsson, B.., Taylor, L., Trenkel, V.M., Kupca, V. and Stefansson, G., 2014. A bootstrap method for estimating bias and variance in statistical fisheries modelling frameworks using highly disparate datasets.African Journal of Marine Science,36(1), pp.99-110. Jerome, N.P., Orton, M.R., dArcy, J.A., Feiweier, T., Tunariu, N., Koh, D.M., Leach, M.O. and Collins, D.J., 2015. Use of the temporal median and trimmed mean mitigates effects of respiratory motion in multiple-acquisition abdominal diffusion imaging.Physics in medicine and biology,60(2), p.N9. LaHuis, D.M., Hartman, M.J., Hakoyama, S. and Clark, P.C., 2014. Explained variance measures for multilevel models.Organizational Research Methods,17(4), pp.433-451. Thongpanja, S., Phinyomark, A., Phukpattaranont, P. and Limsakul, C., 2013. Mean and median frequency of EMG signal to determine muscle force based on time-dependent power spectrum.Elektronika ir Elektrotechnika,19(3), pp.51-56.