Individual and environmental predictors of health risk behaviours among Dutch adolescents: the HBSC study.

OBJECTIVE: To examine unique and common predictors of tobacco smoking, binge drinking, cannabis smoking, early sexual intercourse and multiple health risk behaviours. STUDY DESIGN: Cross-sectional survey study. METHODS: The Dutch Health Behaviour in School-aged Children (HBSC) study was used to provide data on 1742 adolescents aged 15 and 16 years of age. This study focused on a variety of individual and environmental predictors of health risk behaviours, tapping into four domains (mental health, family, peers and school), retrieved by adolescent self-reports and corrected for sociodemographic variables. Logistic and linear regression analyses were performed. RESULTS: Unique predictors (i.e., gender, low and very low education level, general health, hyperactivity problems, conduct problems, incomplete family, religion, knowledge of mother, parental rules on alcohol drinking, time spent with friends, number of friends, perceived tobacco use of classmates, truancy) were identified. In addition, common predictors (i.e., permissive rules on alcohol drinking and much time spent with friends) were also identified, explaining an increase in engagement in all investigated health risk behaviours in adolescence, including multiple risk behaviours. CONCLUSIONS: A prevention strategy targeting restrictive parenting and time spent with friends may be effective to reduce/discourage engagement in health risk behaviours.

Occupational level of the father and alcohol consumption during adolescence; patterns and predictors.

STUDY OBJECTIVE: This paper describes and attempts to explain the association between occupational level of the father and high alcohol consumption among a cohort of New Zealand adolescents from age 11 to 21. DESIGN: Data were obtained from the longitudinal Dunedin multidisciplinary health and development study. At each measurement wave, those who then belonged to the quartile that reported the highest usual amount of alcohol consumed on a typical drinking occasion were categorised as high alcohol consumers. Potential predictors of high alcohol consumption included environmental factors, individual factors, and educational achievement measured at age 9, 11, or 13. Longitudinal logistic GEE analyses described and explained the relation between father's occupation and adolescent alcohol consumption. SETTING: Dunedin, New Zealand. PARTICIPANTS: About 1000 children were followed up from birth in 1972 until adulthood. MAIN RESULTS: A significant association between fathers' occupation and adolescent alcohol consumption emerged at age 15. Overall adolescents from the lowest occupational group had almost twice the odds of being a large consumer than the highest occupational group. The association between father's occupation and high alcohol consumption during adolescence was explained by the higher prevalence of familial alcohol problems and friends approving of alcohol consumption, lower intelligence scores, and lower parental attachment among adolescents from lower occupational groups. CONCLUSIONS: Socioeconomic background affects adolescent alcohol consumption substantially. This probably contributes to cumulation of disadvantage. Prevention programmes should focus on adolescents from lower socioeconomic groups and make healthier choices the easier choices by means of environmental change.

Determinants of levels and changes of physical functioning in chronically ill persons: results from the GLOBE Study.

STUDY OBJECTIVE: Declines in physical functioning are a common result of chronic illness, but relatively little is known about factors not directly related to severity of disease that influence the occurrence of disability among chronically ill persons. The aim of this study was to assess the effect of a large number of potential determinants (sociodemographic factors, health related behaviour, structural living conditions, and psychosocial factors). DESIGN: Longitudinal study of levels and changes of physical functioning among persons suffering from four chronic diseases (asthma/chronic obstructive pulmonary disease (COPD), heart disease, diabetes, chronic low back pain). In 1991, persons suffering from one or more of these diseases were identified in a general population survey. Self reported disabilities, using a subset of the OECD disability indicator, were measured six times between 1991 and 1997. These data were analysed using generalised estimating equations, relating determinants measured in 1991 to disability between 1991 and 1997, and controlling for a number of potential confounders (age, gender, year of measurement, and type and severity of chronic disease). SETTING: Region of Eindhoven (south eastern Netherlands). PARTICIPANTS: 1784 persons with asthma/COPD, heart disease, diabetes mellitus and/or low back pain. MAIN RESULTS: In a "repeated prevalence" model, statistically significant (p<0.05) and strong associations were found between most of the determinants and the prevalence of disabilities. In a "longitudinal change" model, statistically significant (p<0.05) predictors of unfavourable changes in physical functioning were low income and excessive alcohol consumption, while we also found indications for effects of marital status, degree of urbanisation, smoking, and external locus of control. CONCLUSIONS: Other factors than characteristics of the underlying disease have an important influence on levels and changes of physical functioning among chronically ill persons. Reduction of the prevalence of disabilities in the population not only depends on medical interventions, but may also require social interventions, health education, and psychological interventions among chronically ill persons.

Educational level and decreases in leisure time physical activity: predictors from the longitudinal GLOBE study.

STUDY OBJECTIVE: This study describes educational differences in decreases in leisure time physical activity among an adult, physically active population and additionally attempts to identify predictors of these differences from information on health status and individual and environmental factors. DESIGN: Prospective population based study. Baseline measurement were carried out in 1991 and follow up in 1997. SETTING: South eastern part of the Netherlands. PARTICIPANTS: The study included 3793 subjects who were physically active in 1991 and who participated in the follow up. METHODS: Potential predictors of decreasing physical activity were measured in 1991. Logistic regression analyses were carried out for two age groups (< 45 years; > or = 45 years) separately. MAIN RESULTS: Lower educated respondents experienced statistically significant higher odds to decrease physical activity during follow up, compared with respondents with higher vocational schooling or a university degree. Perceived control was the main predictor of educational differences in decreasing physical activity in both age groups. In the older group, material problems and a poor perceived health experienced by lower educated people additionally predicted educational differences in decreases in physical activity during leisure time. CONCLUSIONS: These findings have important implications for health promotion practice and policy to prevent socioeconomic differences in physical inactivity and health. There is a need for evidence-based interventions that improve perceived control and reduce material problems in lower educated groups.

Socio-demographic variables and 6 year change in body mass index: longitudinal results from the GLOBE study.

BACKGROUND: Body mass index (BMI) differs by socio-demographic variables, but the origin of these associations remains relatively unknown. OBJECTIVE: To investigate the association between socio-demographic variables and the subsequent change in BMI over six years. DESIGN: A Dutch prospective cohort study (GLOBE) from which data were used from initially 20-49-year-old subjects (males: n=362; females: n=405). BMI was calculated from self-reported body height and weight data obtained in 1991 and 1997. Socio-demographic variables used were sex, age, educational level and the occupational level of the main breadwinner, family income, marital status, religious affiliation and degree of urbanization and measured in 1991. RESULTS: Cross-sectionally, BMI was higher in males than in females. BMI was positively associated with age and negatively associated with educational level in both sexes, after adjustment for the other socio-demographic variables. A positive association of BMI with family income was found in males and a negative association with occupational level was found in females. During follow-up, BMI increased significantly more in females (from 23.0 (s.d. 3.3) to 24. 2 (s.d. 3.8)) than in males (from 24.3 (s.d. 2.9) to 25.1 (s.d. 3. 5)). With the exception of a significant lesser increase in BMI in initially 30-39-year-old women compared to initially 40-49-year-old women, no other statistically significant associations were found between socio-demographic variables and the 6-year change in BMI. CONCLUSIONS: Cross-sectional differences in BMI by socio-demographic variables are not due to different 6-year changes in BMI for categories of these variables in adulthood. Cross-sectional differences in BMI by educational level are probably established at the end of adolescence.

Educational differences in excessive alcohol consumption: the role of psychosocial and material stressors.

BACKGROUND: Socioeconomic differences in health are determined mainly by socioeconomic differences in unhealthy behavior. Little is known, however, about the mechanisms that account for socioeconomic differences in unhealthy behavior, such as excessive alcohol consumption. In this paper we examined educational differences in excessive alcohol consumption in The Netherlands and whether these may be explained by educational differences in experienced stress and stress-moderating factors. METHODS: Data were obtained from the baseline survey of the Longitudinal Study on Socio Economic Health Differences in 1991. Excessive drinking was defined as drinking more than six glasses on 3 or more days a week or more than four glasses on 5 or more days a week. Socioeconomic status was indicated by educational level. Stressors were divided into psychosocial and material factors. Analyses were performed for women (n = 756) and men (n = 1,006) separately, among drinkers only. RESULTS: Excessive alcohol consumption was more common among lower educational groups. Material stressors, such as financial problems, deprivation, and income, were related to part of the educational gradient in excessive alcohol consumption. Differences in stress-moderating factors were not related to the educational gradient in excessive drinking. CONCLUSIONS: Our results suggest that improvement of material conditions among the lower educational groups could result in a reduction of socioeconomic differences in excessive alcohol consumption.

The influence of adult ill health on occupational class mobility and mobility out of and into employment in the The Netherlands.

In the debate about the explanation of socio-economic health inequalities one of the important issues is the relative importance of health selection. The aim of this study was to investigate to what extent occupational class mobility and mobility out of and into employment are health-related, and in addition, to estimate the contribution of health-related social mobility to socio-economic health differences in the working population. Data were taken from the Longitudinal Study on Socio-Economic Health Differences in the Netherlands, which started in 1991; follow-up data were collected in 1995. The analysis is based on 2533 persons aged 15-59 at baseline. The influence of health problems in 1991 (perceived general health, health complaints and chronic conditions) on changes in occupational class between 1991 and 1995 was negligible. Neither upward nor downward mobility was affected by health problems. However, health problems in 1991 were significantly associated with a higher risk of mobility out of employment and a lower risk of mobility into employment in 1995. For example, for mobility out of employment among persons that reported at least one chronic condition in 1991, the odds ratio was 1.46. Health-related mobility out of employment substantially influences the estimate of socio-economic health inequalities in the working population (measured by current occupation). For manual workers, as compared to non-manual workers, the odds ratio for a less-than-good perceived general health was underestimated by 34% in 1995. Selective mobility into employment overestimates socio-economic inequalities in health in the working population by 9%. Respondents that moved into and out of employment were healthier than those that remained economically inactive, but their health was worse than of those that remained employed (both manual and non-manual). Implications for health policy are that the prospects for people with health problems to stay in paid employment should be improved.

Explaining educational differences in mortality: the role of behavioral and material factors.

OBJECTIVES: This study examined the role of behavioral and material factors in explaining educational differences in all-cause mortality, taking into account the overlap between both types of factors. METHODS: Prospective data were used on 15,451 participants in a Dutch longitudinal study. Relative hazards of all-cause mortality by educational level were calculated before and after adjustment for behavioral factors (alcohol intake, smoking, body mass index, physical activity, dietary habits) and material factors (financial problems, neighborhood conditions, housing conditions, crowding, employment status, a proxy of income). RESULTS: Mortality was higher in lower educational groups. Four behavioral factors (alcohol, smoking, body mass index, physical activity) and 3 material factors (financial problems, employment status, income proxy) explained part of the educational differences in mortality. With the overlap between both types of factors accounted for, material factors were more important than behavioral factors in explaining mortality differences by educational level. CONCLUSIONS: The association between educational level and mortality can be largely explained by material factors. Thus, improving the material situation of people might substantially reduce educational differences in mortality.

Educational differences in leisure-time physical inactivity: a descriptive and explanatory study.

In this study we aim to explain educational differences in leisure-time physical inactivity in terms of psychosocial and material factors. Cross-sectional data were obtained from the baseline of the Dutch GLOBE study in 1991, including 2598 men and women, aged 15-74 years. Physical inactivity during leisure time was defined as not participating in any activity, such as sports, gardening, walking or cycling. Psychosocial factors included in the study were coping resources, personality, and stressors. Material factors were financial situation, employment status, and living conditions. Logistic regression models were used to calculate educational differences in physical inactivity. Physical inactivity was more prevalent in lower educational groups. Psychosocial factors related to physical inactivity were locus of control, parochialism, neuroticism, emotional social support, active problem focussing, optimistic and palliative coping styles. Material factors associated with physical inactivity were income, employment status and financial problems. All correlates of physical inactivity were unequally distributed over educational groups, except optimistic and palliative coping. Personality and coping style were the main contributors to the observed educational differences in physical inactivity. That is to say, parochialism, locus of control, neuroticism and active problem focussing explained about half of elevated odds ratios of physical inactivity in the lower educational groups. The material factors, equivalent income and employment status explained about 40% of the elevated odds ratios. Psychosocial and material correlates together reduced the odds ratios of lower educational groups by on average 75%. These results have practical consequences for the design of more effective interventions to promote physical activity. In particular, personality and coping style of risk groups, such as lower educational groups, should be taken into consideration at the future development of these interventions, as well as inequalities in material restrictions related to engaging in physical activity. Supplementary interventions focussing on childhood conditions which, partly, influence both personality and physical inactivity may also contribute to a reduction of socio-economic differences in physical inactivity.

Socioeconomic inequalities in health in the working population: the contribution of working conditions.

BACKGROUND: The aim was to study the impact of different categories of working conditions on the association between occupational class and self-reported health in the working population. METHODS: Data were collected through a postal survey conducted in 1991 among inhabitants of 18 municipalities in the southeastern Netherlands. Data concerned 4521 working men and 2411 working women and included current occupational class (seven classes), working conditions (physical working conditions, job control, job demands, social support at work), perceived general health (very good or good versus less than good) and demographic confounders. Data were analysed with logistic regression techniques. RESULTS: For both men and women we observed a higher odds ratio for a less than good perceived general health in the lower occupational classes (adjusted for confounders). The odds of a less than good perceived general health was larger among people reporting more hazardous physical working conditions, lower job control, lower social support at work and among those in the highest category of job demands. Results were similar for men and women. Men and women in the lower occupational classes reported more hazardous physical working conditions and lower job control as compared to those in higher occupational classes. High job demands were more often reported in the higher occupational classes, while social support at work was not clearly related to occupational class. When physical working conditions and job control were added simultaneously to a model with occupational class and confounders, the odds ratios for occupational classes were reduced substantially. For men, the per cent change in the odds ratios for the occupational classes ranged between 35% and 83%, and for women between 35% and 46%. CONCLUSIONS: A substantial part of the association between occupational class and a less than good perceived general health in the working population could be attributed to a differential distribution of hazardous physical working conditions and a low job control across occupational classes. This suggests that interventions aimed at improving these working conditions might result in a reduction of socioeconomic inequalities in health in the working population.

Socioeconomic status and comorbidity among newly diagnosed cancer patients.

BACKGROUND: Many studies found better cancer survival in patients with a high socioeconomic status (SES) than in patients with a low SES. Comorbidity at the time of diagnosis may be more frequent in patients of lower SES, and negatively influences their survival. The authors studied the association between SES and serious comorbidity at the time of diagnosis among newly diagnosed cancer patients in The Netherlands. METHODS: Included in the analyses were patients registered in 1993 in the population-based Eindhoven Cancer Registry (southeastern Netherlands) with one of the most common carcinomas: breast (n = 457), lung (n = 442), colorectum (n = 384), prostate (n = 240), and stomach (n = 118). Information regarding comorbidity came from medical records. The SES of the patients was derived from their postal code of residence and stratified into three categories. The risk of being diagnosed with at least one other chronic condition was calculated using logistic regression analyses. RESULTS: The risk of being diagnosed with at least one other chronic condition was higher among patients with a low or intermediate SES than among those with a high SES for the five sites combined as well as for carcinomas of the breast or lung. The gradient was less clear for patients with colorectal carcinoma, whereas no socioeconomic variation in comorbidity was found for patients with carcinomas of the prostate or stomach. CONCLUSIONS: Socioeconomic variation in the prevalence of serious comorbidity at the time of diagnosis does exist in some cancer sites, which may explain (partly) the socioeconomic gradient in survival observed in patients with tumors in these sites.

Deprivation, stage at diagnosis and cancer survival.

The association between an area-based measure of deprivation and survival from the 10 most common cancers was studied in 155,682 patients diagnosed between 1980 and 1989 in the area covered by the South Thames Regional Health Authority. Furthermore, the impact of stage of disease at diagnosis on this association was studied. The measure of deprivation was the Carstairs Index of the census enumeration district of each patient's residence at diagnosis (5 categories) and the cancers studied were: lung, breast, colorectum, bladder, prostate, stomach, pancreas, ovary, uterus and cervix. In the univariate analyses the measure of outcome was the relative survival rate and in the multivariate analyses it was the hazard ratio. Both univariate and multivariate analyses showed that patients from affluent areas had better survival than patients from deprived areas for cancers of the lung, breast, colorectum, bladder, prostate, uterus and cervix. Stage of disease at diagnosis did not explain the survival differences by deprivation category. For cancers of the stomach, pancreas and ovary, no variation in survival by deprivation category was found. For most cancer sites, a clear gradient in survival by deprivation category was observed, which implies a large potential reduction of cancer mortality among the lower socioeconomic groups. Future studies need to incorporate other possible explanatory factors, besides stage, of the association between deprivation and survival.

Deprivation and survival from breast cancer.

We studied the association between deprivation and survival from breast cancer in 29,676 women aged 30 and over who were diagnosed during the period 1980-89 in the area covered by the South Thames Regional Health Authority. The measure of deprivation was the Carstairs Index of the census enumeration district of each woman's residence at diagnosis. We studied the impact of stage at diagnosis, morphology and type of treatment on this association, with the relative survival rate and the hazard ratio as measures of outcome. There was a clear gradient in survival, with better survival for women from more affluent areas. At all ages, women in the most deprived category had a 35% greater hazard of death than women from the most affluent areas after adjustment for stage at diagnosis, morphological type and type of treatment. In younger women (30-64 years), the survival gradient by deprivation category cannot be explained by these prognostic factors. In older women (65-99 years), part of the unadjusted gradient in survival can be explained by differences in the stage of disease: older women in the most deprived category were more often diagnosed with advanced disease. Other factors, so far unidentified, are responsible for the gradient in breast cancer survival by deprivation category. The potential effect on breast cancer mortality of eliminating the gradient in survival by deprivation category is substantial (7.4%). In women aged 30-64 years, 10% of all deaths within 5 years might be avoidable, while in older women this figure is 5.8%.

Socioeconomic status and breast cancer survival in the southeastern Netherlands, 1980-1989.

Socioeconomic differences in breast cancer survival in the southeastern Netherlands between 1980 and 1989 were studied (n = 3928), as was the impact of prognostic factors (stage at diagnosis, morphology, and treatment) on such differences. An area-based measure of socioeconomic status (SES) in five groups, based on the postcode of residence at the time of diagnosis, was used. In univariate analyses the relative survival rate was used to correct for causes of death other than breast cancer. The measure of outcome in multivariate analyses was the hazard ratio. The results of both univariate and multivariate analyses suggested a small survival advantage for the higher SES groups. In a model with follow-up period, SES and age, the hazard ratios with 95% confidence intervals (CI) for SES groups from high to low were: 1.00, 1.06 (0.84-1.33), 1.04 (0.86-1.26), 1.15 (0.96-1.38), 1.18 (0.99-1.42). After a correction for stage at diagnosis, differences in survival were reduced substantially. Morphology and treatment were not important explanatory factors of the SES survival association. We conclude that small socioeconomic differences in breast cancer survival exist in The Netherlands and that stage at diagnosis is the most important determinant of such differences.

Socioeconomic variation in cancer survival in the southeastern Netherlands, 1980-1989.

BACKGROUND: The survival rates of patients with cancer by socioeconomic status (SES) has never been investigated in the Netherlands, a country characterized by good general access to health care services. The association between socioeconomic status and survival from cancer of the lung (n = 4591), breast (n = 3928), colorectum (n = 3558), prostate (n = 1484), and stomach (n = 1455) was studied, and the impact of some prognostic factors (stage at diagnosis, histologic type, and treatment) on this association was assessed. METHODS: Subjects were patients who were diagnosed from 1980 to 1989 and included in the population-based Eindhoven Cancer Registry in the Southeastern Netherlands. The patients were classified by socioeconomic status based on their postal code of residence at the time of diagnosis (3 or 5 categories). The follow-up ended July 1, 1991, at which time relative survival rates and hazard ratios were calculated. RESULTS: A more favorable relative survival for patients living in high SES areas was found for those with cancer of the lung, breast, colorectum, and prostate, whereas for those with stomach cancer, lower survival was found for patients living in high SES areas. For cancer of the lung, colorectum, and prostate, the socioeconomic variation in survival could not be explained by the distribution of the prognostic factors stage, histologic type, and treatment. For patients with breast and stomach cancer, the socioeconomic variation in survival could be ascribed mainly to differences in the percentage of patients diagnosed with a metastasis. CONCLUSIONS: Socioeconomic variation in survival from a number of common cancer sites exists in the Netherlands, despite the fairly equal access to health care services for different socioeconomic groups. Most of the variation could not be explained by the differential distribution of stage, histologic type, and treatment across SES categories.

Cancer patient survival by socioeconomic status in seven countries: a review for six common cancer sites [corrected].

STUDY OBJECTIVE: To study the size and consistency of socioeconomic differences in cancer patient survival as reported in published studies. METHODS: A systematic review was conducted. Several criteria were developed to select the study material, which resulted in 14 reports on socioeconomic differences in survival for cancers of the colon, rectum, lung, prostate, breast, and cervix. These present results on patients from the United States, Japan, Australia, United Kingdom, Sweden, Finland, and Germany. The results are summarised in a relative risk of dying or survival ratio for the lowest socioeconomic status group compared with the highest. RESULTS: For cancers of the colon, rectum, breast, and cervix, patients from higher socioeconomic status groups had a better survival. For lung cancer and cancer of the prostate, results were unclear. CONCLUSION: Socioeconomic differences in cancer survival are generally small and their contribution to socioeconomic differences in cancer mortality is probably small too. These findings have implications for the type of health policy measures which should be taken to reduce socioeconomic differences in cancer mortality.

Validation of cancer prevalence data from a postal survey by comparison with cancer registry records.

Data on self-reported cancer from a health interview survey carried out in 1991 in the southeastern Netherlands by means of a postal questionnaire (n = 17,940) were validated against records from a population-based cancer registry. The sensitivity of the questionnaire was 0.552 (95% confidence interval (CI) 0.507-0.597), and the specificity was 0.995 (95% CI 0.994-0.996). The survey underestimated cancer prevalence in the population by 25%. Of the 212 false negative cases, 46% were registered with non-melanoma skin cancer. After the exclusion of nonmelanoma skin cancer from cancer registry records, cancer prevalence was overestimated by the survey by a negligible 2%. The misclassification of cancer by the postal survey was differential according to age, sex, education, and degree of urbanization. The survey overestimated cancer prevalence ratios for men versus women, old respondents versus young respondents, and urban residents versus rural residents. The prevalence ratios for respondents with a low educational level versus those with a high level were underestimated using survey data. These patterns remained essentially the same after exclusion of nonmelanoma skin cancer from the cancer registry records. This study shows that both overall cancer prevalence and differences in cancer prevalence between subgroups of the population may be biased when health interview survey data are used. If explicit attention is paid to nonmelanoma skin cancer in survey questions, this might improve the validity of overall cancer prevalence estimates, but not that of comparisons between subgroups of the population.