Environmental Health and Biostatistics and Computing GroupsDepartment of Community MedicineThe University of Hong Kong

Short-term effects of ambient air pollution

on public health in Hong Kong - a follow-up study

A consultancy report submitted to the

Environmental Protection Department, Hong Kong

(Operation Manual)
 
 

February 1998

Department of Community Medicine

The University of Hong Kong

OPERATION MANUAL

Data management and data analysis procedures

a) input data format and medium

The data was then read into S-plus objects in a sub-directory (one for each batch of data). The source programmes are attached (Source Programmes 1.1, 2.1, 3.1 and 3.2). The format of the data can be found from these Source Programmes.

b) examination and cleaning data

- tabulation of monthly average of meteorological measures by year

- tabulation of monthly average of air pollution data by monitoring stations by year

iii) hospital admission data

- tabulation of total number of in-patient by hospitals discharges for financial year

c) missing data definition and replacement

After checking for completeness of the hospital admission data, the only missing data arise from the unavailability of discharge diagnosis at the time of data analysis. The scope of missing data of this kind can be minimized by waiting for several months so as to capture those who have a long length of stay in hospital as well as those in hospital, which return the diagnosis and the ICD code late. (It is advisable to obtain data at a date three months later than the required period of the study.)

d) descriptive statistics and graphs

ii) summary statistics of meteorological data with pollution data and hospital admission data (Source Programmes 2.4)

iii) Spearman's rank correlation coefficient between any two pollution, meteorological and health outcome data

iv) time series plots of each data

e) data analysis

iii) multiple regression used for obtaining the multiple R squared value
- S-plus command used: lsfit()

iv) Poisson adjusted with over-dispersion regression used for modelling the various health outcomes with air pollution concentrations and other covariates.
- S-Plus command used: glm(..., family=quasi(link="log",var="mu"),...)

v) Akaike Information Criterion (AIC) was computed and used to identify the model with best cumulative lag. The AIC is the sum of the deviance residuals and twice the number of degrees of freedom used in fitting the model. It can be thought of as the deviance with penalty added to take account of the number of parameters in the model. In choosing models, the model with the smaller AIC is preferred.
- S-plus command and self-written S-Plus function used:

AIC ? deviance(glm.object) + 2*(length(glm.object$fitted) - glm.object$df.resid)

where glm.object can be obtained from above procedure (iv).

Then, min(AIC).

vi) Principal component analysis used for generating a composite score of 4 different pollutants
- S-plus command used: princomp()

vii) Interaction effects between co-pollutant, pollutant and 4 different seasons were performed

viii) Autocorrelation function used for estimating the serial correlations among residuals of health outcome after modelling.
- S-Plus command used: acf(resid(glm.object))

Advice for future study

Suggestion and guidelines for future study

To be in line with the new trend in developing new hypotheses and methodological insights in the APHEA II project, the following should be the focus in any future study relevant to the Hong Kong situation:

2. exploration of new methodological approaches to develop a better understanding of how premature deaths caused by air pollution (harvesting or mortality displacement) and what is the effect of harvesting on estimating the size of the effect parameters; and

3. investigation of regional differences and explanation of heterogeneous effect estimates via modelling techniques by taking advantage of the extended data-base.