Background Pneumonia and influenza (P&I) infections have been increasing substantially over the past decade and have been attributed to high morbidity and mortality rates. P&I are particularly prevalent amongst the young and elderly populations, but there may be additional vulnerable groups due to socioeconomic disparities that are also at high risk. Health inequalities can oftentimes be linked to socioeconomic inequities. Identifying these groups is essential for lowering P&I mortality rates, which can be addressed via hand washing, vaccination and effective pharmaceuticals. In Hong Kong, the income gap between the poor and wealthy has been on the rise. Inequalities that exist in one socioeconomic factor like income may also exist among other factors as well. Thus constructing a socioeconomic deprivation index (SDI) can be a more powerful and comprehensive indicator than income alone. Objective To examine the effect of tertiary planning unit (TPU)-level socioeconomic deprivation on pneumonia and influenza mortality in Hong Kong. Method This is an ecological study using population census statistics from 2001, 2006 and 2011, along with P&I mortality data from the beginning of 2001 to the end of 2010, which were retrieved from the Hong Kong Census and Statistics Department. The census data was used to construct individual TPU SDI scores. Crude and directly standardised mortality rates by age for the underlying cause of P&I were calculated and stratified by gender. The SDI scores were divided into quantiles, which were then analysed for associations with P&I mortality rates using ANOVA. Standardised mortality ratios were also derived to estimate relative risk across SDI. Results A total of 186 TPU clusters from each year were used in the analysis. There were a total number of 42208 deaths with the underlying cause of pneumonia and influenza from 2001 to 2010. The crude rate for both genders in 2001 was 42.4 per 100,000 persons. In 2010 this rate had increased to 84.3 per 100,000 persons. After adjusting for age, the rate increased from 60.4 to 86.6 per 100,000 persons, respectively. A positive trend was observed in both men and women, though rates were higher in men than in women. There was a strong positive association between SDI and CMRs (p<0.001) for the entire study period. However, after adjusting for age the effect was no longer statistically significant (p=0.376). Conclusion The statistically significant results from the crude mortality rates were due to the confounding effects by age. The high-SDI TPUs seem to a higher proportion of older age groups, which contributed to such high crude mortality rates, but lower standardised mortality rates. Even though P&I mortality was not observed to be associated with SDI, it was associated with age. Therefore geographical areas with a high proportion of older people should be more strongly targeted for public health strategies and health promotion. / published_or_final_version / Public Health / Master / Master of Public Health

Lee Suk-Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 69-71). / Abstracts in English and Chinese. / 摘要 --- p.i / Abstract --- p.iii / Acknowledgement --- p.v / Table of Contents --- p.vi / Tables and Figures --- p.vii / Chapter Chapter One --- Introduction --- p.1 / Chapter Chapter Two --- Background and Literature Review --- p.5 / Chapter 2.1 --- Background Review --- p.5 / Chapter 2.2 --- Literature Review - Vaccination of FFLI --- p.12 / Chapter 2.3 --- Literature Review - Social impact and productivity loss of FFLI --- p.23 / Chapter Chapter Three --- Conduct of Survey --- p.29 / Chapter 3.1 --- Study Design --- p.29 / Chapter 3.2 --- Questionnaire --- p.32 / Chapter Chapter Four --- Data Description --- p.36 / Chapter 4.1 --- Participants --- p.36 / Chapter 4.2 --- Incidence and impact of FFLI during the three months period (February - April 2001) --- p.38 / Chapter 4.3 --- Incidence and impact of the most recent FFLI --- p.39 / Chapter Chapter Five --- Economic Loss --- p.45 / Chapter 5.1 --- Estimators - EDPH loss and Productivity loss --- p.45 / Chapter 5.2 --- Density Estimation --- p.50 / Chapter 5.3 --- Hypothesis Testing --- p.51 / Chapter 5.4 --- Sensitivity Analysis --- p.53 / Chapter 5.5 --- Regression Analysis --- p.54 / Chapter 5.6 --- Long Haul flights and Long flights --- p.57 / Chapter 5.7 --- Company Profit --- p.58 / Chapter 5.8 --- Society Loss - GDP --- p.59 / Chapter Chapter Six --- Discussion --- p.61 / Chapter Chapter Seven --- Conclusion --- p.67 / Reference --- p.69 / Tables --- p.72 / Figures --- p.83 / Appendix --- p.87