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Background:As a result of increasing urbanisation, people face the prospect of living in environments with few green spaces. There is increasing evidence for a positive relation between green space in people’s living environment and self-reported indicators of physical and mental health. This study investigates whether physician-assessed morbidity is also related to green space in people’s living environment.Methods:Morbidity data were derived from electronic medical records of 195 general practitioners in 96 Dutch practices, serving a population of 345 143 people. Morbidity was classified by the general practitioners according to the International Classification of Primary Care. The percentage of green space within a 1 km and 3 km radius around the postal code coordinates was derived from an existing database and was calculated for each household. Multilevel logistic regression analyses were performed, controlling for demographic and socioeconomic characteristics.Results:The annual prevalence rate of 15 of the 24 disease clusters was lower in living environments with more green space in a 1 km radius. The relation was strongest for anxiety disorder and depression. The relation was stronger for children and people with a lower socioeconomic status. Furthermore, the relation was strongest in slightly urban areas and not apparent in very strongly urban areas.Conclusion:This study indicates that the previously established relation between green space and a number of self-reported general indicators of physical and mental health can also be found for clusters of specific physician-assessed morbidity. The study stresses the importance of green space close to home for children and lower socioeconomic groups.

To investigate the construct validity of morbidity severity scales based on routine consultation data by studying their associations with sociodemographic factors and physical health. Study participants were 11,232 English adults aged 50 years and over and 9,664 Dutch adults aged 18 years and over, and their consulting morbidity data in a 12-month period were linked to their physical health data. Consulters with any of 115 morbidities classified on four ordinal scales of severity (“chronicity,” “time course,” “health care use,” and “patient impact”) were compared to all other consulters. As hypothesized, in both countries, morbidity severity was associated with older age, female gender, more deprivation (all comparisons P ≤ 0.05), and poor physical health (all trends P < 0.001). The estimated strengths of association of poor physical health with the highest severity category expressed as odds ratios, for each of the four scales, were 5.4 for life-threatening on the “chronicity” scale, 1.8 for time course, 2.8 for high health care use, and 3.7 for high patient impact. Four scales of morbidity severity have been validated in English and Dutch settings, and they offer the potential to use simple routine consultation data as an indicator of physical health status in populations from general practice.

To assess socioeconomic disparities in stroke incidence and in the quality of preventive care for stroke in the Netherlands. A total of 190,664 patients who registered in 96 general practices were followed up for 12 months. Data were collected on diagnoses, referrals, prescriptions, and diagnostic procedures. Hazard ratios (HR) were calculated to assess the association between educational level and stroke incidence. Multilevel logistic regression was used to assess socioeconomic disparities in the quality of preventive care for stroke precursors. Lower educational level was associated with higher incidence of stroke in men (HR = 1.36, 95% CI = 1.06–1.74) but not in women. Among both men and women, there were socioeconomic disparities in the prevalence of hypertension, hypercholesterolemia, diabetes, angina pectoris, heart failure, and peripheral artery disease. Lower educated hypercholesterolemia patients under medication were less likely to be prescribed statins (odds ratio = 0.62, 95% CI = 0.42–0.91). However, for other precursors of stroke, there were no major disparities in the quality of preventive care. There are socioeconomic disparities in stroke incidence among men but not among women. Socioeconomic differences in factors such as hypertension and diabetes are likely to contribute to stroke disparities. However, general practitioners(GPs) provide care of a similar quality to patients from different socioeconomic groups.

This article presents methods for using administrative data to study multimorbidity in hospitalized individuals and indicates how the findings can be used to gain a deeper understanding of hospital multimorbidity. A Dutch nationwide hospital register (n=4,521,856) was used to calculate age- and sex-standardized observed/expected ratios of disease-pairing prevalences with corresponding confidence intervals. The strongest association was found for the combination between alcoholic liver and mental disorders due to alcohol abuse (observed/expected=39.2). Septicemia was found to cluster most frequently with other diseases. The consistency of the ratios over time depended on the number of observed cases. Furthermore, the ratios also depend on the length of the time frame considered. Using observed/expected ratios calculated from the administrative data set, we were able to (1) better quantify known morbidity pairings while also revealing hitherto unnoticed associations, (2) find out which pairings cluster most strongly, and (3) gain insight into which diseases cluster frequently with other diseases. Caveats with this method are finding spurious associations on the basis of too few observed cases and the dependency of the ratio magnitude on the length of the time frame observed.

Background: For the second time a plan to monitor public health and health inequalities in the Netherlands through general practice was put into action: the Second National Survey of General Practice (DNSGP-2, 2001). The first aim of this paper is to describe the general design of DNSGP-2. Secondly, to describe self assessed health inequalities in the Netherlands. Thirdly, to present differences in prevalence of chronic conditions by educational attainment using both self-assessed health and medical records of GPs. Finally, inequalities in 1987 (DNSGP-1) and 2001 will be compared. Methods: Data were collected from 96 (1987) and 104 (2001) general practices. The data include background information on patients collected via a census, approximately 12 000 health interview surveys per time point and more than one million recorded contacts of patients with their GPs in both years. The method of statistical analysis is logistic regression. Results: The analyses shows that the lower educated have significantly higher odds of feeling unhealthy and having chronic conditions in 2001. Diabetes and myocardial infarction (GP data) showed the largest difference in prevalence between educational groups (OR 2.5 and 2.4, self-reported data). The way the data is collected (self-assessment versus GP registration) hardly affects the magnitude of the educational differences in the prevalence of chronic conditions. The pattern of health inequalities across chronic conditions in 1987 and 2001 hardly differs. Diabetes doubled in prevalence and health inequalities were not significant in 1987, but compared to the other conditions were largest in 2001 (OR 1.1 versus 2.5). Conclusion: Health inequalities were shown to be substantial in 2001 and persistent over time. Socio-economic differences were shown to be similar using self-assessed health data and GP data. Hence, a person's educational attainment did not appear to play a part in presenting health problems to the GP. Key points Socio-economic differences showed to be similar using self-assessed health data and GP data. Educational attainment plays no part in presenting health problems to the GP in the Netherlands. Between 1987 and 2001 diabetes doubled in prevelance and shows large educational differences.

•We studied the additional effect of cancer and chronic disease on GP contact rates.•Cancer leads to a similar increase in GP contacts as having a chronic disease.•Cancer does not seem to affect the impact of chronic diseases.•Cancer does not affect the impact of age and number of chronic diseases. More than two-thirds of cancer patients have one or more chronic diseases besides cancer. The purpose of this study was to get detailed insight into the combined effect of cancer and chronic diseases on general practitioner (GP) consultation rates. From the NIVEL Primary Care Database we identified cancer patients with diabetes mellitus (n=629), osteoarthritis (n=425), coronary artery disease (n=466), COPD (n=383) or without a chronic disease (n=1507), diagnosed with cancer between 2002 and 2010. They were matched on sex, age, practice and chronic disease to 6645 non-cancer controls. 2–5 years after diagnosis, cancer patients without a chronic disease had on average 6.5 GP contacts per year, those with a comorbid disease almost twice as many (ranging from 10 for osteoarthritis to 12.4 for COPD). A similar difference was seen in non-cancer controls. The number of GP contacts for chronic diseases did not differ between cancer patients and controls. The increase in the number of GP consultations with age and number of chronic diseases was similar in cancer patients and controls. Consultation rates were similar in cancer patients and controls if they were stratified by number of chronic diseases while counting cancer as a chronic disease. Two to five years after diagnosis, cancer leads to an increase in GP contacts that is similar to having a chronic disease. This increase does not differ between those with and without a chronic disease and cancer does not seem to increase the impact of having a chronic disease.

Background General practice based registration networks (GPRNs) provide information on morbidity rates in the population. Morbidity rate estimates from different GPRNs, however, reveal considerable, unexplained differences. We studied the range and variation in morbidity estimates, as well as the extent to which the differences in morbidity rates between general practices and networks change if socio-demographic characteristics of the listed patient populations are taken into account. Methods The variation in incidence and prevalence rates of thirteen diseases among six Dutch GPRNs and the influence of age, gender, socio economic status (SES), urbanization level, and ethnicity are analyzed using multilevel logistic regression analysis. Results are expressed in median odds ratios (MOR). Results We observed large differences in morbidity rate estimates both on the level of general practices as on the level of networks. The differences in SES, urbanization level and ethnicity distribution among the networks' practice populations are substantial. The variation in morbidity rate estimates among networks did not decrease after adjusting for these socio-demographic characteristics. Conclusion Socio-demographic characteristics of populations do not explain the differences in morbidity estimations among GPRNs.

Background: The prevalence of diabetes has been proposed as a European Community Health Indicator. The prevalence of diabetes known to general practitioners (GPs) in different European countries has been investigated and the usefulness of sentinel practice networks in delivering prevalence data on diabetes has been evaluated. Methods: Patients presenting with diabetes in a 12 month period (1999/2000) to GPs in established European sentinel practice surveillance networks in eightEuropean countries were registered. Estimates of prevalence were standardized to the 1998 European population. Results: All-age prevalence reported in the network populations was lowest in Slovenia (male 16, female 16 per 1000) and highest in Belgium (male 31, female 34). The range of estimates obtained in this study was narrower than that published by the WHO in the Health For All database. The range was further reduced by age standardization. In males aged 45 years and over, age standardized prevalence ranged from 39 (Slovenia) to 76 (Belgium) and in females from 37 (Slovenia) to 75 (Belgium). There were no consistent gender differences in national prevalence rates. Conclusions: The study demonstrates the capacity of sentinel practice networks to deliver data on the prevalence of known diabetes in persons over 45 years. National differences in prevalence are less than hitherto reported. Prevalence in Belgium measured in all ages and in 45 years and over males andfemales was higher than in the seven other countries.

Background The high prevalence of chronic diseases in Western countries implies that the presence of multiple chronic diseases within one person is common. Especially at older ages, when the likelihood of having a chronic disease increases, the co-occurrence of distinct diseases will be encountered more frequently. The aim of this study was to estimate the age-specific prevalence of multimorbidity in the general population. In particular, we investigate to what extent specific pairs of diseases cluster within people and how this deviates from what is to be expected under the assumption of the independent occurrence of diseases (i.e., sheer coincidence). Methods We used data from a Dutch health survey to estimate the prevalence of pairs of chronic diseases specified by age. Diseases we focused on were diabetes, myocardial infarction, stroke, and cancer. Multinomial P-splines were fitted to the data to model the relation between age and disease status (single versus two diseases). To assess to what extent co-occurrence cannot be explained by independent occurrence, we estimated observed/expected co-occurrence ratios using predictions of the fitted regression models. Results Prevalence increased with age for all disease pairs. For all disease pairs, prevalence at most ages was much higher than is to be expected on the basis of coincidence. Observed/expected ratios of disease combinations decreased with age. Conclusion Common chronic diseases co-occur in one individual more frequently than is due to chance. In monitoring the occurrence of diseases among the population at large, such multimorbidity is insufficiently taken into account.

Background: The health monitoring programme of the European Commission has proposed a set of health indicators whereby the health status of member states can be measured. As part of that programme we considered how primary care might contribute relevant data. Methods: Using a questionnaire distributed to personal contacts and health authorities, we investigated the activities of sentinel practice networks and sought opinions on the place of primary care as a provider of information on health indicators. Studies on the prevalence of diabetes mellitus and on the incidence of chickenpox were undertaken within selected networks. Results: 33 networks were found who provided relevant information on a timely and continuing basis. Contributions varied; some were limited to monitoring influenza but others recorded morbidity data from every consultation. Recording methods ranged from the paper based to fully automated systems in which all morbidity was coded electronically at data entry. The study of diabetes mellitus showed less variation between national networks than currently suggested on the WHO database. For chickenpox we estimated the incidence of cases not presenting to general practitioners ranged between 3 and 27%. Conclusions: Information on health indicators needs to come from the place where relevant care is delivered; for many conditions that is from primary care. It can be delivered from appropriately resourced practices where the population is defined, the practice populations are nationally representative and data collection is automated.