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1.  Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty 
BMC Public Health  2011;11:163.
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.
PMCID: PMC3064641  PMID: 21406092
incidence; prevalence; Monte Carlo simulation; uncertainty
2.  Which practices are high antibiotic prescribers? A cross-sectional analysis 
The British Journal of General Practice  2009;59(567):e315-e320.
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
Cross-sectional 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.
PMCID: PMC2751935  PMID: 19843411
antibiotics; prescriptions; primary care
3.  Increasing incidence of skin disorders in children? A comparison between 1987 and 2001 
BMC Dermatology  2006;6:4.
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.
PMCID: PMC1435925  PMID: 16551358
4.  Urban and rural variations in morbidity and mortality in Northern Ireland 
BMC Public Health  2007;7:123.
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.
PMCID: PMC1913506  PMID: 17594471
5.  Patient risk profiles and practice variation in nonadherence to antidepressants, antihypertensives and oral hypoglycemics 
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.
PMCID: PMC1855317  PMID: 17425792
6.  A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study 
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.
PMCID: PMC2658517  PMID: 18625598
7.  A multilevel analysis of the effects of rurality and social deprivation on premature limiting long term illness 
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
PMCID: PMC1731764  PMID: 11112950
8.  Practice postcode versus patient population: a comparison of data sources in England and Scotland 
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.
PMCID: PMC2490685  PMID: 18631388
9.  Sociodemographic and morbidity indicators of need in relation to the use of community health services: observational study. 
BMJ : British Medical Journal  1997;315(7114):994-996.
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.
PMCID: PMC2127650  PMID: 9365299
10.  Population-based prevention of influenza in Dutch general practice. 
BACKGROUND: Although the effectiveness of influenza vaccination in high-risk groups has been proven, vaccine coverage continues to be less than 50% in The Netherlands. To improve vaccination rates, data on the organizational factors, which should be targeted in population-based prevention of influenza, is essential. AIM: To assess the organizational factors in Dutch general practice, which were associated with the influenza vaccination rate in 1994. METHOD: A retrospective questionnaire study was undertaken in 1586 of the 4758 Dutch general practices, which were randomly selected. A total of 1251 (79%) practices returned a questionnaire. The items verified were practice profile, urbanization, delegation index, use of computer-based patient records, influenza vaccination characteristics and influenza vaccination rate. RESULTS: No differences were found with regard to the percentage of single-handed practices (65%), practices situated in urban area (38%), practices with a pharmacy (12%), patients insured by the National Health Service (59%) and use of computer-based patient records (57%) when compared with national statistics. The mean overall influenza vaccination rate was 9.0% (SD 4.0%). Using a logistic regression analysis, a high vaccination rate (> or = 9%) was associated with the use of personal reminders (odds ratio (OR) 1.7, 1.3-2.2), monitoring patient compliance (OR 1.8, 1.3-2.4), marking risk patients in computer-based patient records (OR 1.3, 1.0-1.6), a small number of patients per full-time practice assistant (OR 1.5, 1.1-1.9), urban areas (OR 1.6, 1.3-2.1) and single-handed practices (OR 1.5, 1.1-1.9). CONCLUSION: Improvement of vaccination rates in high-risk patients may be achievable by promoting the use of personal reminders and computer-based patient records, as well as monitoring patient compliance. In addition, the role of practice assistants with regard to preventive activities should be developed further. Practices situated in rural areas and group practices may need more support with a population-based approach for the prevention of influenza.
PMCID: PMC1313027  PMID: 9231470
11.  Case-mix and variation in specialist referrals in general practice 
The potential of a comprehensive measure of patient morbidity to explain variation in referrals to secondary care has not previously been examined in the UK.
To examine the relative role of age, sex and morbidity as defined by the Johns Hopkins ACG Case-Mix System in explaining variations in specialist referrals in general practice.
Design of study
Retrospective study of a cohort of patients followed for 1 year.
Two hundred and two general practices, with a total list size of 1 161 892, contributing data to the General Practice Research Database.
Each patient was assigned an ACG and morbidity group, based on their diagnoses, age and sex. The variability in referrals explained by these factors was examined using multilevel logistic regression models by splitting it into variation between practices and variation between patients within practices.
The annual median (range) percentage of patients referred was 14.8% (range = 2.4–24.4%). The percentage of patients referred increased with age and morbidity. Morbidity explained 30.4% of the total variation in referrals (composed of variability between and within practices). Age and sex only explained 5.3% of the total variation. The variation attributable to practices was approximately 5%, thus most of the variation occurred within practices. Morbidity was also identified as a better predictor of referral compared to age and sex.
Morbidity explains almost six times more of the variation in general practice referrals than age and sex, although about two-thirds of the variation remains unexplained. Most of the unexplained variation is due to differences within rather than between practices. The amount of variability in referrals between practices may be less than implied by previous studies based on aggregate information. The implications are that any investigation of specialist referrals from general practice should be interpreted cautiously, even after adjustment for age, sex and morbidity.
PMCID: PMC1472770  PMID: 16004738
case-mix; morbidity; referral and consultation
12.  The health services burden of heart failure: an analysis using linked population health data-sets 
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.
PMCID: PMC3413515  PMID: 22533631
Heart failure; Hospitalization; Health services research; Australia
13.  Derivation of a needs based capitation formula for allocating prescribing budgets to health authorities and primary care groups in England: regression analysis 
BMJ : British Medical Journal  2000;320(7230):284-288.
To develop a weighted capitation formula for setting target allocations for prescribing expenditures for health authorities and primary care groups in England.
Regression analysis relating prescribing costs to the demographic, morbidity, and mortality composition of practice lists.
8500 general practices in England.
Data from the 1991 census were attributed to practice lists on the basis of the place of residence of the practice population.
Main outcome measures
Variation in age, sex, and temporary resident originated prescribing units (ASTRO(97)-PUs) adjusted net ingredient cost of general practices in England for 1997-8 modelled for the impact of health and social needs after controlling for differences in supply.
A needs gradient based on the four variables: permanent sickness, percentage of dependants in no carer households, percentage of students, and percentage of births on practice lists. These, together with supply characteristics, explained 41% of variation in prescribing costs per ASTRO(97)-PU adjusted capita across practices. The latter alone explained about 35% of variation in total costs per head across practices.
The model has good statistical specification and contains intuitively plausible needs drivers of prescribing expenditure. Together with adjustments made for differences in ASTRO(97)-PUs the model is capable of explaining 62% (35%+0.65% (41%)) of variation in prescribing expenditure at practice level. The results of the study have formed the basis for setting target budgets for 1999-2000 allocations for prescribing expenditure for health authorities and primary care groups.
PMCID: PMC27276  PMID: 10650026
14.  Trends in COPD prevalence and exacerbation rates in Dutch primary care 
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.
PMCID: PMC2784530  PMID: 19891824
chronic obstructive pulmonary disease; family practice; prevalence; trends
Respiratory medicine  2011;105(5):734-739.
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.
PMCID: PMC3064740  PMID: 21255991
16.  Low socio-economic position is associated with poor social networks and social support: results from the Heinz Nixdorf Recall Study 
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.
PMCID: PMC2424055  PMID: 18457583
17.  Socio-cultural factors in maternal morbidity and mortality: a study of a semi-urban community in southern Nigeria 
STUDY OBJECTIVE: To understand community based or socio-cultural factors that determine maternal morbidity and mortality in a semi-urban setting. DESIGN: The study is an exploratory multidisciplinary operations research and the instruments were focus groups and interviews. SETTING: Ekpoma, a semi-urban community with a population of 70,000 in central part of Edo state in southern Nigeria. PARTICIPANTS: Thirteen groups of women, two groups of men, and two groups of traditional birth attendants. RESULTS: There is a fairly good knowledge of haemorrhage but this is circumscibed by attitudes, practices, and situations that keep women away from or delay the decision to seek modern obstetric care. CONCLUSIONS: For a fuller understanding of maternal morbidity and mortality, it is important to consider factors outside the hospital and formal medical practice. Furthermore, a change of existing knowledge, attitudes, practices, and situations can be enhanced through modelling on them.
PMCID: PMC1756717  PMID: 9764279
18.  Inter-practice variation in diagnosing hypertension and diabetes mellitus: a cross-sectional study in general practice 
BMC Family Practice  2009;10:6.
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.
PMCID: PMC2632987  PMID: 19159455
19.  Morbidity, deprivation, and antidepressant prescribing in general practice. 
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.
PMCID: PMC1313558  PMID: 10818653
20.  External Validation of EPICON: A Grouping System for Estimating Morbidity Rates Using Electronic Medical Records 
To externally validate EPICON, a computerized system for grouping diagnoses from EMRs in general practice into episodes of care. These episodes can be used for estimating morbidity rates.
Comparative observational study.
Morbidity rates from an independent dataset, based on episode-oriented EMRs, were used as the gold standard. The EMRs in this dataset contained diagnoses which were manually grouped by GPs. The authors ungrouped these diagnoses and regrouped them automatically into episodes using EPICON. The authors then used these episodes to estimate morbidity rates that were compared to the gold standard. The differences between the two sets of morbidity rates were calculated and the authors analyzed large as well as structural differences to establish possible causes.
In general, the morbidity rates based on EPICON deviate only slightly from the gold standard. Out of 675 diagnoses, 36 (5%) were considered to be deviating diagnoses. The deviating diagnoses showed differences for two main reasons: “differences in rules between the two methods of episode construction” and “inadequate performance of EPICON.”
The EPICON system performs well for the large majority of the morbidity rates. We can therefore conclude that EPICON is useful for grouping episodes to estimate morbidity rates using EMRs from general practices. Morbidity rates of diseases with a broad range of symptoms should, however, be interpreted cautiously.
PMCID: PMC2585534  PMID: 18755995
21.  Examining the determinants of mosquito-avoidance practices in two Kenyan cities 
Malaria Journal  2002;1:14.
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.
PMCID: PMC149385  PMID: 12495438
22.  A Comparative Analysis of Influenza Vaccination Programs 
PLoS Medicine  2006;3(10):e387.
The threat of avian influenza and the 2004–2005 influenza vaccine supply shortage in the United States have sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus.
Methods and Findings
We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we assume that vaccine supplies are limited and then evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions.
We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies.
If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities.
A comparative analysis of two classes of suggested vaccination strategies, mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations.
Editors' Summary
Influenza—a viral infection of the nose, throat, and airways that is transmitted in airborne droplets released by coughing or sneezing—is a serious public health threat. Most people recover quickly from influenza, but some individuals, especially infants, old people, and individuals with chronic health problems, can develop pneumonia and die. In the US, seasonal outbreaks (epidemics) of flu cause an estimated 36,000 excess deaths annually. And now there are fears that avian influenza might start a human pandemic—a global epidemic that could kill millions. Seasonal outbreaks of influenza occur because flu viruses continually change the viral proteins (antigens) to which the immune system responds. “Antigenic drift”—small changes in these proteins—means that an immune system response that combats flu one year may not provide complete protection the next winter. “Antigenic shift”—large antigen changes—can cause pandemics because communities have no immunity to the changed virus. Annual vaccination with vaccines based on the currently circulating viruses controls seasonal flu epidemics; to control a pandemic, vaccines based on the antigenically altered virus would have to be quickly developed.
Why Was This Study Done?
Most countries target vaccination efforts towards the people most at risk of dying from influenza, and to health-care workers who are likely come into contact with flu patients. But is this the best way to reduce the burden of illness (morbidity) and death (mortality) caused by influenza, particularly at the start of a pandemic, when vaccine would be limited? Old people and infants are much less likely to catch and spread influenza than school children, students, and employed adults, so could vaccination of these sections of the population—instead of those most at risk of death—be the best way to contain influenza outbreaks? In this study, the researchers used an analytical method called “contact network epidemiology” to compare two types of vaccination strategies: the currently favored mortality-based strategy, which targets high-risk individuals, and a morbidity-based strategy, which targets those segments of the community in which most influenza cases occur.
What Did the Researchers Do and Find?
Most models of disease transmission assume that each member of a community is equally likely to infect every other member. But a baby is unlikely to transmit flu to, for example, an unrelated, housebound elderly person. Contact network epidemiology takes the likely relationships between people into account when modeling disease transmission. Using information from Vancouver, British Columbia, Canada, on household size, age distribution, and occupations, and other factors such as school sizes, the researchers built a model population of a quarter of a million interconnected people. They then investigated how different vaccination strategies controlled the spread of influenza in this population. The optimal strategy depended on the level of viral transmissibility—the likelihood that an infectious person transmits influenza to a susceptible individual with whom he or she has contact. For moderately transmissible flu viruses, a morbidity-based vaccination strategy, in which the people most likely to catch the flu are vaccinated, was more effective at containing seasonal and pandemic outbreaks than a mortality-based strategy, in which the people most likely to die if they caught the flu are vaccinated. For highly transmissible strains, this situation was reversed. The level of transmissibility at which this reversal occurred depended on several factors, including whether vaccination was delayed and how many times influenza was introduced into the community.
What Do These Findings Mean?
The researchers tested their models by checking that they could replicate real influenza epidemics and pandemics, but, as with all mathematical models, they included many assumptions about influenza in their calculations, which may affect their results. Also, because the contact network used data from Vancouver, their results might not be applicable to other cities, or to nonurban areas. Nevertheless, their findings have important public health implications. When there are reasonable estimates of the viral transmission rate, and it is known how often influenza is being introduced into a community, contact network models could help public health officials choose between morbidity- and mortality-based vaccination strategies. When the viral transmission rate is unreliable or unavailable (for example, at the start of a pandemic), the best policy would be the currently preferred strategy of mortality-based vaccination. More generally, the use of contact network models should improve estimates of how infectious diseases spread through populations and indicate the best ways to control human epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at
US Centers for Disease Control and Prevention information about influenza for patients and professionals, including key facts on vaccination
US National Institute of Allergy and Infectious Diseases feature on seasonal, avian, and pandemic influenza
World Health Organization fact sheet on influenza, with links to information on vaccination
UK Health Protection Agency information on seasonal, avian, and pandemic influenza
MedlinePlus entry on influenza
PMCID: PMC1584413  PMID: 17020406
23.  Morbidity in early childhood: differences between girls and boys under 10 years old. 
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)
PMCID: PMC1372113  PMID: 1457171
24.  Relationship between Selected Socio-Demographic Factors and Cancer of Oral Cavity - A Case Control Study 
Cancer Informatics  2010;9:163-168.
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.
PMCID: PMC2935817  PMID: 20838608
socio-demographic factors; oral cancer
25.  Interrelations between three proxies of health care need at the small area level: an urban/rural comparison 
Study objective: To examine the relations between geographical variations in mortality, morbidity, and deprivation at the small area level in the south west of England and to assess whether these relations vary between urban and rural areas.
Design: A geographically based cross sectional study using 1991 census data on premature limiting long term illness (LLTI) and socioeconomic characteristics, and 1991–1996 data on all cause premature mortality. The interrelations between the three widely used proxies of health care need are examined using correlation coefficients and scatterplots. The distribution of standardised LLTI residuals from a regression analysis on mortality are mapped and compared with the distribution of urban and rural areas. Multilevel Poisson modelling investigates whether customised deprivation profiles improve upon a generic deprivation index in explaining the spatial variation in morbidity and mortality after controlling for age and sex. These relations are examined separately for urban, fringe, and rural areas.
Setting: Nine counties in the south west of England.
Participants: Those aged between 0–64 who reported having a LLTI in the 1991 census, and those who died during 1991–1996 aged 0–74.
Main results: Relations between both health outcomes and generic deprivation indices are stronger in urban than rural areas. The replacement of generic with customised indices is an improvement in all area types, especially for LLTI in rural areas. The relation between mortality and morbidity is stronger in urban than rural areas, with levels of LLTI appearing to be greater in rural areas than would be predicted from mortality rates. Despite the weak direct relations between mortality and morbidity, there are strong relations between the customised deprivation indices computed to predict these outcomes in all area types.
Conclusions: The improvement of the customised deprivation indices over the generic indices, and the similarity between the mortality and morbidity customised indices within area types highlights the importance of modelling urban and rural areas separately. Stronger relations between mortality and morbidity have been revealed at the local authority level in previous research providing empirical evidence that the inadequacy of mortality as a proxy for morbidity becomes more marked at lower levels of aggregation, especially in rural areas. Higher levels of LLTI than expected in rural areas may reflect different perceptions or differing patterns of illness. The stronger relations between the three proxies in urban than rural areas suggests that the choice of indicator will have less impact in urban than rural areas and strengthens the argument to develop better measures of health care need in rural areas.
PMCID: PMC1732023  PMID: 12239201

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