OBJECTIVEPreviously documented indirect adjustment methods could only adjust for the confounding effects from cigarette smoking. The aim of this paper is to introduce a novel method for dealing with the effects of smoking in occupational, epidemiological cohort studies using a 'smoking adjustment factor'.

METHODSA retrospective cohort study among male silicotic workers purely exposed to silica dusts in Hong Kong (1981 - 1999) was used as an example. 'Smoking adjustment factor' in occupationally exposed smoking and nonsmoking sub-cohorts was expressed as 1/(1- PAR% )xRR and 1/1- PAR% respectively. Relative exposure effect and Synergy index were estimated to assess the multiplicative and additive interactions.

RESULTS'Smoking adjustment factor' for non-smoking and smoking silicotic workers was 1/0.33 and 1/1.62 respectively. Lung cancer standardized mortality ratio(SMR) of all cohort members was reduced from 1.61(95% CI: 1.22-2.10) to 1.08(95% CI:0.81-1.41) after indirectly adjusted for smoking effect. Results from our novel indirect method were in line with that from Axelson' s approach. Relative silicosis effect and synergy index were estimated to be 0.63 (95% CI:0.08-0.79) and 0.90 (95 % CI:0.42-1.94) ,suggesting a significant but negative multiplicative interaction between smoking and silicosis on the risk of lung cancer mortality.

CONCLUSIONThe merit of this new method was the ability to adjust for the confounding effect and evaluate the interactive effect with smoking. However, comparability of age distribution between occupationally exposed smoking and nonsmoking sub-cohorts was a prerequisite for the accurate estimations of the smoking indirectly adjusted SMR, relative exposure effect, and/or synergy index.

Cohort Studies ; Epidemiologic Methods ; Hong Kong ; epidemiology ; Humans ; Lung Diseases ; epidemiology ; mortality ; Male ; Occupational Exposure ; Retrospective Studies ; Silicosis ; epidemiology ; mortality ; Smoking ; adverse effects ; epidemiology

Population attributable fraction (PAF) is an important public health parameter that is familiar to epidemiologists. The calculation of PAF is frequently based on the relative risk (RR) of a risk factor for a certain disease and the prevalence of exposure to the risk factor in the total population (Pe). We introduce here the method for making a nomogram linking PAF to RR and Pe. The nomogram will be very handy for public health professionals in estimating PAF quickly and accurately when information on RR and Pe is available.
Epidemiologic Methods ; Humans ; Risk Factors

OBJECTIVESTo assess the association of socioeconomic indicators with various chronic and acute illnesses and the utilization of public health care in a new town in Hong Kong, China.

METHODSIllness experience and socioeconomic and demographic data of 7570 residents from 2022 randomly selected households were collected through telephone interviews. The relationships between socioeconomic indicators and illnesses/choice of health care were explored using stepwise logistic regressions after adjusting for sex and age.

RESULTSSignificant positive associations were noted between low household income and diabetes mellitus, any chronic illnesses among adults and flu among younger subjects; low educational level and accident-related illness among adults; being born in Chinese mainland and flu, any acute illness in adults. For the utilization of public health care, low household income was the most consistent risk factor.

CONCLUSIONThis study did not demonstrate a unidirectional socioeconomic gradient in health but supported the hypothesis that socioeconomic deprivation was associated with the utilization of public health care.

Acute Disease ; economics ; epidemiology ; Adolescent ; Adult ; Age Factors ; China ; Chronic Disease ; economics ; epidemiology ; Cities ; Cross-Sectional Studies ; Delivery of Health Care ; utilization ; Educational Status ; Employment ; Female ; Health Surveys ; Hong Kong ; epidemiology ; Housing ; Humans ; Income ; Male ; Prevalence ; Residence Characteristics ; Sex Factors ; Socioeconomic Factors

Rothman argued that interaction estimated as departure from additivity better reflected the biological interaction. In a logistic regression model, the product term reflects the interaction as departure from multiplicativity. So far, literature on estimating interaction regarding an additive scale using logistic regression was only focusing on two dichotomous factors. The objective of the present report was to provide a method to examine the interaction as departure from additivity between two continuous variables or between one continuous variable and one categorical variable.We used data from a lung cancer case-control study among males in Hong Kong as an example to illustrate the bootstrap re-sampling method for calculating the corresponding confidence intervals.Free software R (Version 2.8.1) was used to estimate interaction on the additive scale.

When study on epidemiological causation is carried out,logistic regression has been commonly used to estimate the independent effects of risk factors.as well as to examine possible interactions among individual risk factor by adding one or more product terms to the regression model.In logistic or Cox's regression model.the regression coefficient of the product term estimates the interaction on a muhiplicative scale while statistical significance indicates the departure from multiplicativity.Rothman argues that when biologic interaction iS examined,we need to focus on interaction as departure from additivity rather than departure from multiplicativity.He presents three indices to measure interaction on an additive scale or departure from additivity.using logarithmic models such aS logistic or Cox's regression model.In this paper,we use data from a case-control study of female lung cancer in Hong Kong to calculate the regression coefficients and covariance matrix of logistie model in SPSS.We then introduce an Excel spreadsheet set up by Tomas Andersson to calculate the indices of interaction on an additive scale and the corresponding confidence intervals.The results can be used as reference by epidemiologists to assess the biologic interaction between factors.The proposed method is convenient and the Excel spreadsheet is available online for free.

OBJECTIVEThis study examined the status of health-related behaviors among rural residents and the factors influencing the practice of such behaviors.

METHODSOne thousand and ninety subjects aged 15 years or over in a rural community, Anhui Province, China were surveyed. A questionnaire was used to collect information on the health knowledge, attitude and behavior of the subjects. Information on health behavior included smoking, drinking, dietary habits, regular exercises, sleeping pattern and oral health behavior.

RESULTSThe prevalence of smoking and drinking in the male subjects was 46.5% and 46.9%, respectively. There was a positive significant association between smoking and drinking. Only 8.3% of all subjects ate three regular meals a day regularly. Among subjects who ate two meals a day, 89.7% did not have breakfast. Only 1.7% of subjects took part in regular exercise. About 85% of subjects slept 6 to 8 h per day. Only 38.4% of the respondents had the habit of hand washing before eating and after using the lavatory. 79.3% of the subjects brushed their teeth every day, and among them, only 10.6 percent brushed their teeth twice a day. Further analyses showed that 64.8% of subjects had 3-5 items of positive health behaviors out of 8 items and only 16.9% had six or more items. Logistical regression analyses suggested that better health behavior was affected by sex, age, years of education, income and health knowledge.

CONCLUSIONThe status of health behaviors among rural residents was generally poor. It is thus urgent to reinforce health education in rural communities in China.

Adolescent ; Adult ; Aged ; Alcohol Drinking ; epidemiology ; Attitude to Health ; China ; epidemiology ; Cross-Sectional Studies ; Diet ; Exercise ; Female ; Health Behavior ; Health Surveys ; Humans ; Male ; Middle Aged ; Oral Health ; Risk-Taking ; Rural Population ; Sleep ; Smoking ; epidemiology