Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.
Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.
Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.
Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.
incidence; prevalence; Monte Carlo simulation; uncertainty
Substantial variation in antibiotic prescribing rates between general practices persists, but remains unexplained at national level.
To establish the degree of variation in antibiotic prescribing between practices in England and identify the characteristics of practices that prescribe higher volumes of antibiotics.
Design of study
8057 general practices in England.
A dataset was constructed containing data on standardised antibiotic prescribing volumes, practice characteristics, patient morbidity, ethnicity, social deprivation, and Quality and Outcomes Framework achievement (2004–2005). Data were analysed using multiple regression modelling.
There was a twofold difference in standardised antibiotic prescribing volumes between practices in the 10th and 90th centiles of the sample (0.48 versus 0.95 antibiotic prescriptions per antibiotic STAR-PU [Specific Therapeutic group Age-sex weightings-Related Prescribing Unit]). A regression model containing nine variables explained 17.2% of the variance in antibiotic prescribing. Practice location in the north of England was the strongest predictor of high antibiotic prescribing. Practices serving populations with greater morbidity and a higher proportion of white patients prescribed more antibiotics, as did practices with shorter appointments, non-training practices, and practices with higher proportions of GPs who were male, >45 years of age, and qualified outside the UK.
Practice and practice population characteristics explained about one-sixth of the variation in antibiotic prescribing nationally. Consultation-level and qualitative studies are needed to help further explain these findings and improve our understanding of this variation.
antibiotics; prescriptions; primary care
From a public health perspective and for the appropriate allocation of resources it is important to understand the differences in health between areas. This paper examines the variations in morbidity and mortality between urban and rural areas.
This is a cohort study looking at morbidity levels of the population of Northern Ireland at the time of the 2001 census, and subsequent mortality over the following four years. Individual characteristics including demographic and socio-economic factors were as recorded on census forms. The urban-rural nature of residence was based on census areas (average population c1900) classified into eight settlement bands, ranging from cities to rural settlements with populations of less than 1000.
The study shows that neither tenure nor car availability are unbiased measures of deprivation in the urban-rural context. There is no indication that social class is biased. There was an increasing gradient of poorer health from rural to urban areas, where mortality rates were about 22% (95% Confidence Intervals 19%–25%) higher than the most rural areas. Differences in death rates between rural and city areas were evident for most of the major causes of death but were greatest for respiratory disease and lung cancer. Conversely, death rates in the most rural areas were higher in children and adults aged less than 20.
Urban areas appear less healthy than the more rural areas and the association with respiratory disease and lung cancer suggests that pollution may be a factor. Rural areas however, have higher death rates amongst younger people, something which requires further research. There is also a need for additional indicators of deprivation that have equal meaning in urban and rural areas.
The increasing proportion of skin diseases encountered in general practice represents a substantial part of morbidity in children. Only limited information is available about the frequency of specific skin diseases. We aimed to compare incidence rates of skin diseases in children in general practice between 1987 and 2001.
We used data on all children aged 0–17 years derived from two consecutive surveys performed in Dutch general practice in 1987 and 2001. Both surveys concerned a longitudinal registration of GP consultations over 12 months. Each disease episode was coded according to the International Classification of Primary Care. Incidence rates of separate skin diseases were calculated by dividing all new episodes for each distinct ICPC code by the average study population at risk. Data were stratified for socio-demographic characteristics.
The incidence rate of all skin diseases combined in general practice decreased between 1987 and 2001. Among infants the incidence rate increased. Girls presented more skin diseases to the GP. In the southern part of the Netherlands children consulted their GP more often for skin diseases compared to the northern part. Children of non-Western immigrants presented relatively more skin diseases to the GP. In general practice incidence rates of specific skin diseases such as impetigo, dermatophytosis and atopic dermatitis increased in 2001, whereas warts, contact dermatitis and skin injuries decreased.
The overall incidence rate of all skin diseases combined in general practice decreased whereas the incidence rates of bacterial, mycotic and atopic skin diseases increased.
Many patients experience difficulties in following treatment recommendations. This study's objective is to identify nonadherence risk profiles regarding medication (antidepressants, antihypertensives, and oral hypoglycemics) from a combination of patients' socio-demographic characteristics, morbidity presented within general practice and medication characteristics. An additional objective is to explore differences in nonadherence among patients from different general practices.
Data were obtained by linkage of a Dutch general practice registration database to a dispensing registration database from the year 2001. Subjects included in the analyses were users of antidepressants (n = 4,877), antihypertensives (n = 14,219), or oral hypoglycemics (n = 2,428) and their GPs. Outcome variables were: 1) early dropout i.e., a maximum of two prescriptions and 2) refill nonadherence (in patients with 3+ prescriptions); refill adherence < 80% was considered as nonadherence. Multilevel modeling was used for analyses.
Both early dropout and refill nonadherence were highest for antidepressants, followed by antihypertensives. Risk factors appeared medication specific and included: 1) non-western immigrants being more vulnerable for nonadherence to antihypertensives and antidepressants; 2) type of medication influencing nonadherence in both antihypertensives and antidepressants, 3) GP consultations contributing positively to adherence to antihypertensives and 4) somatic co-morbidity influencing adherence to antidepressants negatively. There was a considerable range between general practices in the proportion of patients who were nonadherent.
No clear risk profiles for nonadherence could be constructed. Characteristics that are correlated with nonadherence vary across different types of medication. Moreover, both patient and prescriber influence adherence. Especially non-western immigrants need more attention with regard to nonadherence, for example by better monitoring or communication. Since it is not clear which prescriber characteristics influence adherence levels of their patients, there is need for further research into the role of the prescriber.
Health professionals, policy-makers and researchers need to be able to explore potential associations between prevalence rates and quality of care with a range of possible determinants including socio-economic deprivation and morbidity levels to determine the impact of commissioning and service delivery. In the UK, data in England are only available nationally at practice postcode level. In Scotland, such data are available based on an aggregate of the practices population's postcodes. The use of data assigned to the practice postcode may underestimate the association between ill health and income deprivation. Here, we report on the impact of using data assigned to the practice population by comparing analyses using English and Scottish data.
Income deprivation based on data assigned to the practice postcode under-estimated deprivation compared to using income deprivation data assigned to the practice population for the five least deprived deciles, and over-estimated deprivation for the five most deprived deciles. The biggest differences were found for the most deprived decile. A similar trend was found for limiting long-term illness (LLTI). Differences between the QOF prevalence rates of the least and most deprived deciles using practice postcode data were similar (0.2% points or less) in England and Scotland for 8 out of 10 clinical domains. Using practice population assigned deprivation, differences in the prevalence rate between the least and most deprived deciles increase for all clinical domains. A similar trend was again found for LLTI. Using practice population assigned deprivation, differences for population achievement increase for all CHD quality indicators with the exception of beta-blockers (CHD10). With practice postcode assigned deprivation, significant differences between the least and most deprived deciles were found for 2 out 8 indicators, compared to 5 using practice population assigned deprivation. For LLTI differences between the lowest and most deprived deciles increased for all indicators when ill health assigned to the practice population was used.
We have found, through comparing deprivation and ill health data assigned to either the practice postcode or the practice population postcode in Scotland, that analyses based on practice postcode assigned data under-estimated the relationship between deprivation and ill health for both prevalence and quality care. Given the importance of understanding the effect of deprivation and ill health on a range of determinants related to health care, policy makers should ensure that practice population data are available and used at national level in England and elsewhere where possible.
OBJECTIVE: To examine whether the sociodemographic and morbidity characteristics of populations influence their use of the following community heath services: district nursing, health visiting, chiropody, community maternity, community mental illness, and the professions allied to medicine. DESIGN: Observational study. SETTING: Nationally representative sample of provider trusts in England. MAIN OUTCOME MEASURES: Activity levels for each service calculated for enumeration districts within the catchment areas of the sample of trusts and standardised to allow for differences in age structure. Regression analysis to determine whether the standardised activity rates for each service could be predicted by a range of socio-demographic and morbidity proxies. RESULTS: Morbidity or deprivation, or both, seemed to influence the use of services in each of the care programmes examined. CONCLUSIONS: The allocation of funds for community health services should allow for differences in the health and socio-demographic characteristics of health authorities.
Social networks and social support are supposed to contribute to the development of unequal health within populations. However, little is known about their socio-economic distribution. In this study, we explore this distribution.
This study analyses the association of two indicators of socio-economic position, education and income, with different measures of social networks and support. Cross-sectional data have been derived from the baseline examination of an epidemiological cohort study of 4.814 middle aged urban inhabitants in Germany (Heinz Nixdorf Recall Study). Bivariate and multivariate logistic regression analysis were carried out to estimate the risk of having poor social networks and support across socio-economic groups.
Socially disadvantaged persons more often report poor social networks and social support. In multivariate analyses, based on education, odds ratios range from 1.0 (highest education) to 4.9 (lowest education) in a graded way. Findings based on income show similar effects, ranging from 1.0 to 2.5. There is one exception: no association of SEP with close ties living nearby and regularly seen was observed.
Poor social networks and low social support are more frequent among socio-economically disadvantaged people. To some extent, this finding varies according to the indicator chosen to measure these social constructs.
The burden of patients with heart failure on health care systems is widely recognised, although there have been few attempts to quantify individual patterns of care and differences in health service utilisation related to age, socio-economic factors and the presence of co-morbidities. The aim of this study was to assess the typical profile, trajectory and resource use of a cohort of Australian patients with heart failure using linked population-based, patient-level data.
Using hospital separations (Admitted Patient Data Collection) with death registrations (Registry of Births, Deaths and Marriages) for the period 2000–2007 we estimated age- and gender-specific rates of index admissions and readmissions, risk factors for hospital readmission, mean length of stay (LOS), median survival and bed-days occupied by patients with heart failure in New South Wales, Australia.
We identified 29,161 index admissions for heart failure. Admission rates increased with age, and were higher for males than females for all age groups. Age-standardised rates decreased over time (256.7 to 237.7/100,000 for males and 235.3 to 217.1/100,000 for females from 2002–3 to 2006–7; p = 0.0073 adjusted for gender). Readmission rates (any cause) were 27% and 73% at 28-days and one year respectively; readmission rates for heart failure were 11% and 32% respectively. All cause mortality was 10% and 28% at 28 days and one year. Increasing age was associated with more heart failure readmissions, longer LOS and shorter median survival. Increasing age, increasing Charlson comorbidity score and male gender were risk factors for hospital readmission. Cohort members occupied 954,888 hospital bed-days during the study period (any cause); 383,646 bed-days were attributed to heart failure admissions.
The rates of index admissions for heart failure decreased significantly in both males and females over the study period. However, the impact on acute care hospital beds was substantial, with heart failure patients occupying almost 200,000 bed-days per year in NSW over the five year study period. The strong age-related trends highlight the importance of stabilising elderly patients before discharge and community-based outreach programs to better manage heart failure and reduce readmissions.
Heart failure; Hospitalization; Health services research; Australia
The estimated life expectancy at birth for Indigenous Australians is 10-11 years less than the general Australian population. The mean family income for Indigenous people is also significantly lower than for non-Indigenous people. In this paper we examine poverty or socioeconomic disadvantage as an explanation for the Indigenous health gap in hospital morbidity in Australia.
We utilised a cross-sectional and ecological design using the Northern Territory public hospitalisation data from 1 July 2004 to 30 June 2008 and socio-economic indexes for areas (SEIFA) from the 2006 census. Multilevel logistic regression models were used to estimate odds ratios and confidence intervals. Both total and potentially avoidable hospitalisations were investigated.
This study indicated that lifting SEIFA scores for family income and education/occupation by two quintile categories for low socio-economic Indigenous groups was sufficient to overcome the excess hospital utilisation among the Indigenous population compared with the non-Indigenous population. The results support a reframing of the Indigenous health gap as being a consequence of poverty and not simplistically of ethnicity.
Socio-economic disadvantage is a likely explanation for a substantial proportion of the hospital morbidity gap between Indigenous and non-Indigenous populations. Efforts to improve Indigenous health outcomes should recognise poverty as an underlying determinant of the health gap.
Background: Studies in the USA have shown ethnic inequalities in quality of hospital care, but in Europe, this has never been analysed. We explored variations in indicators of quality of hospital care by ethnicity in the Netherlands. Methods: We analysed unplanned readmissions and excess length of stay (LOS) across ethnic groups in a large population of hospitalized patients over an 11-year period by linking information from the national hospital discharge register, the Dutch population register and socio-economic data. Data were analysed with stepwise logistic regression. Results: Ethnic differences were most pronounced in older patients: all non-Western ethnic groups > 45 years had an increased risk for excess LOS compared with ethnic Dutch patients, with odds ratios (ORs) (adjusted for case mix) varying from 1.05 [95% confidence intervals (95% CI) 1.02–1.08] for other non-Western patients to 1.14 (95% CI 1.07–1.22) for Moroccan patients. The risk for unplanned readmission in patients >45 years was increased for Turkish (OR 1.24, 95% CI 1.18–1.30) and Surinamese patients (OR 1.11, 95% CI 1.07–1.16). These differences were explained partially, although not substantially, by differences in socio-economic status. Conclusion: We found significant ethnic variations in unplanned readmissions and excess LOS. These differences may be interpretable as shortcomings in the quality of hospital care delivered to ethnic minority patients, but exclusion of alternative explanations (such as differences in patient- and community-level factors, which are outside hospitals’ control) requires further research. To quantify potential ethnic inequities in hospital care in Europe, we need empirical prospective cohort studies with solid quality outcomes such as adverse event rates.
Changes in the burden of chronic obstructive pulmonary disease (COPD) and its exacerbations on primary health care are not well studied.
To identify trends in the prevalence of physician-diagnosed COPD and exacerbation rates by age, sex, and socioeconomic status in a general practice population.
Design of study
Trend analysis of COPD data from a 27-year prospective cohort of a dynamic general practice population.
Data were taken from the Continuous Morbidity Registration Nijmegen.
For the period 1980–2006, COPD and COPD exacerbation data were extracted for patients aged ≥40 years. Data were standardised for the composition of the Continuous Morbidity Registration population in the year 2000. Regression coefficients for trends were estimated by sex, age, and socioeconomic status. Rate ratios were calculated for prevalence differences in different demographic subgroups.
During the study period, the overall COPD prevalence decreased from 72.7 to 54.5 per 1000 patients per year. The exacerbation rate decreased from 44.1 to 31.5 per 100 patients, and the percentage of patients with COPD who had exacerbations declined from 27.6% to 21.0%. The prevalence of COPD increased significantly in women, in particular those aged ≥65 years with low socioeconomic status. Decreases in exacerbation rates and percentages of patients with exacerbations were independent of sex, age, and socioeconomic status.
The decline in COPD prevalence and exacerbation rates suggests a reduction of the burden on Dutch primary care. The increase of the prevalence in women indicates a need to focus on this particular subgroup in COPD management and research.
chronic obstructive pulmonary disease; family practice; prevalence; trends
The persistence of the black health disadvantage has been a puzzling component of health in the United States in spite of general declines in rates of morbidity and mortality over the past century. Studies that have focused on well-established individual-level determinants of health such as socio-economic status and health behaviors have been unable to fully explain these disparities. Recent research has begun to focus on other factors such as racism, discrimination, and segregation. Variation in neighborhood context — socio-demographic composition, social aspects, and built environment —has been postulated as an additional explanation for racial disparities, but few attempts have been made to quantify its overall contribution to the black/white health gap. This analysis is an attempt to generate an estimate of place effects on explaining health disparities by utilizing data from the US National Health Interview Survey (NHIS) (1989−1994), combined with a methodology for identifying residents of the same blocks both within and across NHIS survey cross-sections. Our results indicate that controlling for a single point-in-time measure of residential context results in a roughly 15 to 76 percent reduction of the black/white disparities in self-rated health that were previously unaccounted for by individual-level controls. The contribution of residential context toward explaining the black/white self-rated health gap varies by both age and gender such that contextual explanations of disparities decline with age and appear to be smaller among females.
racial disparities; neighborhood effects; USA; age; gender
This study assesses the behavioural and socio-economic factors associated with avoiding mosquitoes and preventing malaria in urban environments in Kenya.
Data from two cities in Kenya were gathered using a household survey and a two-stage cluster sample design. The cities were stratified based on planning and drainage observed across the urban areas. This helped control for the strong environmental and topographical variation that we assumed influences mosquito ecology. Individual interviews given to each household included questions on socio-economic status, education, housing type, water source, rubbish disposal, mosquito-prevention practices and knowledge of mosquitoes. In multivariate regression, factors measuring wealth, education level, and the communities' level of planning and drainage were used to estimate the probability that a household engages in multiple mosquito-avoidance activities, or has all members sleeping under a bed net.
Our analysis shows that people from wealthier, more educated households were more likely to sleep under a net, in Kisumu (OR = 6.88; 95% CI = 2.56,18.49) and Malindi (OR = 3.80; 95% CI = 1.91,7.55). Similarly, the probability that households use several mosquito-prevention activities was highest among the wealthiest, best-educated households in Kisumu (OR = 5.15; 95% CI = 2.04,12.98), while in Malindi household wealth alone is the major determinant.
We demonstrate the importance of examining human-mosquito interaction in terms of how access to resources may enhance human activities. The findings illustrate that the poorest segments of society are already doing many things to protect themselves from being bitten, but they are doing less than their richer neighbours.
The aim of this study was to explore possible differences in health care seeking behaviour among a rural and urban African population.
A cross sectional design was followed using the infrastructure of the PURE-SA study. Four rural and urban Setswana communities which represented different strata of urbanisation in the North West Province, South Africa, were selected. Structured interviews were held with 206 participants. Data on general demographic and socio-economic characteristics, health status, beliefs about health and (access to) health care was collected.
The results clearly illustrated differences in socio-economic characteristics, health status, beliefs about health, and health care utilisation. In general, inhabitants of urban communities rated their health significantly better than rural participants. Although most urban and rural participants consider their access to health care as sufficient, they still experienced difficulties in receiving the requested care. The difference in employment rate between urban and rural communities in this study indicated that participants of urban communities were more likely to be employed. Consequently, participants from rural communities had a significantly lower available weekly budget, not only for health care itself, but also for transport to the health care facility. Urban participants were more than 5 times more likely to prefer a medical doctor in private practice (OR:5.29, 95% CI 2.83-988).
Recommendations are formulated for infrastructure investments in rural communities, quality of health care and its perception, improvement of household socio-economical status and further research on the consequences of delay in health care seeking behaviour.
Accessibility; Health care seeking behaviour; Quality; Rural community; Urban community
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality caused by cigarette smoking and other environmental exposures. While variation in exposures may affect COPD morbidity and mortality, little is known about geographic variation, a surrogate of exposures. The objective of this manuscript is to explore the geographic variation in COPD hospitalization rates among the Texas population in 2006.
The study population consisted of all Texas residents with COPD hospitalizations in the 2006 Texas Health Care Information Council (THCIC) data. County population estimates stratified by race, age, gender were linked to THCIC data to calculate county level COPD hospitalization rates per 100,000 admissions. The data were merged with Urban Influence Codes by county, and metropolitan status was determined by United States Department of Agriculture (USDA) criteria. Variation in COPD hospitalization rates were analyzed using Poisson Regression.
Overall, non-Hispanic (NH) Whites had the highest rate of hospitalization, followed by NH Blacks (rate ratio=0.42) and Hispanics (RR=0.17), the 65+ age category had the highest rates of hospitalization. In the metropolitan counties COPD hospitalization rates are lower than non metropolitan counties, however in metropolitan counties the rates of hospitalization are significantly higher (p<0.0001) in females compared to males. The rates were significantly higher in males in public health regions 10 and 11, which are predominantly non-metropolitan counties.
In Texas there is substantial geographic variation in hospitalization rates associated with gender and race/ethnicity. Other factors that may contribute to the variation and require further investigation include differences in smoking and exposure to other environmental risk factors, access to primary care, medical practice patterns, and coding practices.
The metabolic syndrome (MetSyn) places individuals at increased risk for type 2 diabetes and cardiovascular disease. Prevalence rates of the population of the MetSyn are still scarce. Moreover, the impact of different definitions of the MetSyn on the prevalence is unclear. Aim here is to assess the prevalence of the MetSyn in primary health care and to investigate the impact of four different definitions of the MetSyn on the determined prevalence with regard to age, gender and socio-economic status.
The German-wide cross-sectional study was conducted during two weeks in October 2005 in 1.511 randomly selected general practices. Blood samples were analyzed, blood pressure and waist circumference assessed, data on lifestyle, medication, chronic disorders, and socio-demographic characteristics collected. MetSyn prevalence was estimated according to the definitions of NCEP ATP III (2001), AHA/NHLBI (2004, 2005), and IDF (2005). Descriptive statistics and prevalence rate ratios using the PROG GENMOD procedure, were calculated. Cohen's kappa was used as measure for interreliability between the different prevalence estimates.
Data of 35,869 patients (age range: 18–99, women 61.1%) were included. The prevalence was lowest using the NCEP ATP III- (all: 19.8%, men 22.7%, women: 18.0%), highest according to the IDF-definition (32.7%, 40.3%, 28.0%). The increase in prevalence with recent definitions was more pronounced for men than for women, and was particularly high for men and women aged 60–79 years. The IDF-definition resulted in a higher prevalence especially in those with the highest educational status. Agreement (kappa) between the NCEP ATP III- and IDF-definition was 0.68 (men 0.61, women 0.74), between the updated the AHA/NHLBI- (2005) and IDF-definition 0.85 (men 0.79, women 0.89).
The prevalence of metabolic syndrome is associated with age, gender, and educational status and increases considerably with each newly published definition. Our data highlight the need for a better evidence regarding thresholds of the components of the metabolic syndrome, especially with regard to the IDF-definition – according to which in some populations a majority of subjects are diagnosed with the metabolic syndrome.
STUDY OBJECTIVE—To examine the geographical variation in self perceived morbidity in the south west of England, and assess the associations with rurality and social deprivation.
DESIGN—A geographically based cross sectional study using 1991 census data on premature Limiting Long Term Illness (LLTI). The urban-rural and intra-rural variation in standardised premature LLTI ratios is described, and correlation and regression analyses explore how well this is explained by generic deprivation indices. Multilevel Poisson modelling investigates whether Customised Deprivation Profiles (CDPs) and area characteristics improve upon the generic indices.
SETTING—Nine counties in the south west of England
PARTICIPANTS—The population of the south west enumerated in the 1991 census.
MAIN RESULTS—Intra-rural variation is apparent, with higher rates of premature LLTI in remoter areas. Together with high rates in urban areas and lower rates in the semi-rural areas this indicates the existence of a U shaped relation with rurality. The generic deprivation indices have strong positive relations with premature LLTI in urban areas, but these are a lot weaker in semi-rural and rural locations. CDPs improve upon the generic indices, especially in the rural settings. A substantial reduction in unexplained variation in rural areas is seen after controlling for the level of local isolation, with higher isolation, at the wider geographical scale, being related to higher levels of LLTI.
CONCLUSIONS—This study highlights the need to treat rural areas as heterogeneous, although this has not been the tendency in health research. Generic deprivation indices are unlikely to be a true reflection of levels of deprivation in rural environments. The importance of CDPs that are specific to the area type and health outcome is emphasised. The significance of physical isolation suggests that accessibility to public and health services may be an important issue, and requires further research.
Keywords: rural health; limiting long term illness; deprivation indices
Objective To examine whether patient level morbidity based measure of clinical case mix explains variations in prescribing in general practice.
Design Retrospective study of a cohort of patients followed for one year.
Setting UK General Practice Research Database.
Participants 129 general practices, with a total list size of 1 032 072.
Main outcome measures Each patient was assigned a morbidity group on the bases of diagnoses, age, and sex using the Johns Hopkins adjusted clinical group case mix system. Multilevel regression models were used to explain variability in prescribing, with age, sex, and morbidity as predictors.
Results The median number of prescriptions issued annually to a patient is 2 (90% range 0 to 18). The number of prescriptions issued to a patient increases with age and morbidity. Age and sex explained only 10% of the total variation in prescribing compared with 80% after including morbidity. When variation in prescribing was split between practices and within practices, most of the variation was at the practice level. Morbidity explained both variations well.
Conclusions Inclusion of a diagnosis based patient morbidity measure in prescribing models can explain a large amount of variability, both between practices and within practices. The use of patient based case mix systems may prove useful in allocation of budgets and therefore should be investigated further when examining prescribing patterns in general practices in the UK, particularly for specific therapeutic areas.
People with higher socio-economic status (SES) are generally in better health. Less is known about when these socio-economic health differences set in during childhood and how they develop over time. The goal of this study was to prospectively study the development of socio-economic health differences in the Netherlands, and to investigate possible explanations for socio-economic variation in childhood health.
Data from the Dutch Prevention and Incidence of Asthma and Mite Allergy (PIAMA) birth cohort study were used for the analyses. The PIAMA study followed 3,963 Dutch children during their first eight years of life. Common childhood health problems (i.e. eczema, asthma symptoms, general health, frequent respiratory infections, overweight, and obesity) were assessed annually using questionnaires. Maternal educational level was used to indicate SES. Possible explanatory lifestyle determinants (breastfeeding, smoking during pregnancy, smoking during the first three months, and day-care centre attendance) and biological determinants (maternal age at birth, birthweight, and older siblings) were analysed using generalized estimating equations.
This study shows that socio-economic differences in a broad range of health problems are already present early in life, and persist during childhood. Children from families with low socio-economic backgrounds experience more asthma symptoms (odds ratio (OR) 1.27; 95% Confidence Interval (CI) 1.08-1.49), poorer general health (OR 1.36; 95% CI 1.16-1.60), more frequent respiratory infections (OR 1.57; 95% CI 1.35-1.83), more overweight (OR 1.42; 95% CI 1.16-1.73), and more obesity (OR 2.82; 95% CI 1.80-4.41). The most important contributors to the observed childhood socio-economic health disparities are socio-economic differences in maternal age at birth, breastfeeding, and day-care centre attendance.
Socio-economic health disparities already occur very early in life. Socio-economic disadvantage takes its toll on child health before birth, and continues to do so during childhood. Therefore, action to reduce health disparities needs to start very early in life, and should also address socio-economic differences in maternal age at birth, breastfeeding habits, and day-care centre attendance.
Previous studies of inter-practice variation of the prevalence of hypertension and diabetes mellitus showed wide variations between practices. However, in these studies inter-practice variation was calculated without controlling for clustering of patients within practices and without adjusting for patient and practice characteristics. Therefore, in the present study inter-practice variation of diagnosed hypertension and diabetes mellitus prevalence rates was calculated by 1) using a multi-level design and 2) adjusting for patient and practice characteristics.
Data were used from the Netherlands Information Network of General Practice (LINH) in 2004. Of all 168.045 registered patients, the presence of hypertension, diabetes mellitus and all available ICPC coded symptoms and diseases related to hypertension and diabetes, were determined. Also, the characteristics of practices were used in the analyses. Multilevel logistic regression analyses were performed.
The 95% prevalence range for the practices for the prevalence of diagnosed hypertension and diabetes mellitus was 66.3 to 181.7 per 1000 patients and 22.2 to 65.8 per 1000 patients, respectively, after adjustment for patient and practice characteristics. The presence of hypertension and diabetes was best predicted by patient characteristics. The most important predictors of hypertension were obesity (OR = 3.5), presence of a lipid disorder (OR = 3.0), and diabetes mellitus (OR = 2.6), whereas the presence of diabetes mellitus was particularly predicted by retinopathy (OR = 8.5), lipid disorders (OR = 2.8) and hypertension (OR = 2.7).
Although not the optimal case-mix could be used in this study, we conclude that even after adjustment for patient (demographic variables and risk factors for hypertension and diabetes mellitus) and practice characteristics (practice size and presence of a practice nurse), there is a wide difference between general practices in the prevalence rates of diagnosed hypertension and diabetes mellitus.
BACKGROUND: Although the link between depression, unemployment, and measures of deprivation and morbidity has been previously documented, the relationship between general practice prescribing of antidepressants, morbidity, and the social demography of general practice populations is poorly understood. AIM: To consider whether morbidity and the social demography of general practice populations influence the prescribing costs of individual practices. METHOD: Data were analysed, using a forward stepwise regression procedure, of all 78 practices served by the Cornwall and Isles of Scilly Health Authority. Data on prescribing for antidepressants were provided by the Prescription Pricing Authority for the period from July to December 1995 and converted into defined daily doses (DDDs) to standardize for the variation in prescribing practice between general practitioners. RESULTS: A significant positive correlation exists between the rates of prescribing DDDs of antidepressants by general practices and the prevalence of permanent sickness in the areas in which these practices serve. CONCLUSION: Demonstrating an association between morbidity and prescribing rates for depression may prove helpful in setting prescribing budgets.
The aim of this study was to recognize factors associated with cancer of oral cavity considering socio-demographic characteristics. The cases were 350 with squamous-cell carcinoma of oral cavity diagnosed between 2005 and 2006 in Morbai, Narandia, Budharani Cancer Institute, Pune, India. Similar number of controls match for age and sex selected from the background population. Cases and controls were interviewed for tobacco related habits and general characteristics; age, gender, education and possible socio-demographic factors. Chi-square test in uni-variate analysis and estimate for risk showed that education, occupation and monthly household income were significantly different between cases and controls (P < 0.001). Irrespective to gender, relative risk, here odds ratio, (OR) of low level of education (OR = 5.3, CI 3.7–7.6), working in field as a farmer (OR = 2.5, CI 1.7–3.7), and monthly household income less than 5000 Indian Rupees currency (OR = 1.7, CI 1.2–2.3) were significant risk factors for oral cancer. While, there was no significant relationship between religious and or marital status either in males or females.
socio-demographic factors; oral cancer
The aim of the study was to investigate the differences in presented morbidity and use of health services among boys and girls in early childhood. The study was performed using data collected by the continuous morbidity registration project of the department of general practice at Nijmegen University. All recorded morbidity, referrals to specialists and admissions to hospitals were recorded by the registration project. The study population included children born in four practices from 1971 to 1984. The children were followed up until the age of five years and if possible until the age of 10 years. The morbidity of the children had been categorized into three levels of seriousness of diagnosis and 15 diagnostic groups as part of the registration project. Boys presented more morbidity than girls in the first years of their lives. For the age group 0-4 years this was true for all levels of seriousness of diagnosis except the most serious. In this younger age group significantly more boys than girls suffered respiratory diseases, behaviour disorders, gastroenteritis and accidents. Girls suffered from more episodes of urinary infection than boys in both age groups. More boys were referred to specialists and admitted to hospital than girls. The findings of this study suggest that not only inborn factors can explain the sex differences in presented morbidity and use of health services in early childhood. In particular, differences between girls and boys in terms of non-serious morbidity and referral and admission rates suggest a different way of handling health problems in boys and girls in early childhood both by parents and doctors.(ABSTRACT TRUNCATED AT 250 WORDS)
Tiruppuvanam, with a population of 15,668 a semi-urban area near Madurai was chosen for a door to door survey to assess the psychiatric and physical morbidity in all those aged 60 and above. There were 686, in this age group. The socio economic status, family structure, social integration, occupation, literacy, physical illness and handicaps in the total population were assessed and compared with the psychiatrically morbid group. The prevalence of psychiatric morbidity was estimated at 89/1000. 48.84% suffered from physical morbidity. 57% of the psychiatric group suffered physical morbidity and 85% from sensory handicaps. The findings indicated that lack of social integration rather than social isolation, and lack of occupation were significantly related to psychiatric morbidity. The type of family structure did not relate to the degree of social integration. Depressive illness contributed to 67% of total psychiatric morbidity, some intervention measures are suggested.