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1.  Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 
BMC Family Practice  2014;15(1):176.
Background
General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs.
Methods
We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN.
Results
Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package ‘Medicom’ and the province ‘Groningen’ showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices.
Conclusion
Practice characteristics do not explain the differences in morbidity estimates between GPRNs.
Electronic supplementary material
The online version of this article (doi:10.1186/s12875-014-0176-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12875-014-0176-7
PMCID: PMC4231185  PMID: 25358247
Family practice; Incidence; Electronic medical records; Practice characteristics; Population health; Prevalence
2.  Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty 
BMC Public Health  2011;11:163.
Background
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.
Methods
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.
Results
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.
Conclusion
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.
doi:10.1186/1471-2458-11-163
PMCID: PMC3064641  PMID: 21406092
incidence; prevalence; Monte Carlo simulation; uncertainty
3.  Patterns of physical co-/multi-morbidity among patients with serious mental illness: a London borough-based cross-sectional study 
BMC Family Practice  2014;15:117.
Background
Serious mental illness (SMI) is associated with elevated mortality compared to the general population; the majority of this excess is attributable to co-occurring common physical health conditions. There may be variation within the SMI group in the distribution of physical co/multi-morbidity. This study aims to a) compare the pattern of physical co- and multi-morbidity between patients with and without SMI within a South London primary care population; and, b) to explore socio-demographic and health risk factors associated with excess physical morbidity among the SMI group.
Methods
Data were obtained from Lambeth DataNet, a database of electronic patient records derived from general practices in the London borough of Lambeth. The pattern of 12 co-morbid common physical conditions was compared by SMI status. Multivariate ordinal and logistic regression analyses were conducted to assess the strength of association between each condition and SMI status; adjustments were made for potentially confounding socio-demographic characteristics and for potentially mediating health risk factors.
Results
While SMI patients were more frequently recorded with all 12 physical conditions than non-SMI patients, the pattern of co-/multi-morbidity was similar between the two groups. Adjustment for socio-demographic characteristics – in particular age and, to a lesser extent ethnicity, considerably reduced effect sizes and accounted for some of the associations, though several conditions remained strongly associated with SMI status. Evidence for mediation by health risk factors, in particular BMI, was supported.
Conclusions
SMI patients are at an elevated risk of a range of physical health conditions than non-SMI patients but they do not appear to experience a different pattern of co-/multimorbidity among those conditions considered. Socio-demographic differences between the two groups account for some of the excess in morbidity and known health risk factors are likely to mediate the association. Further work to examine a wider range of conditions and health risk factors would help determine the extent of excess mortality attributable to these factors.
doi:10.1186/1471-2296-15-117
PMCID: PMC4062514  PMID: 24919453
Serious mental illness; Mental health; Physical health; Comorbidity; Multimorbidity
4.  Maternal Clinical Diagnoses and Hospital Variation in the Risk of Cesarean Delivery: Analyses of a National US Hospital Discharge Database 
PLoS Medicine  2014;11(10):e1001745.
Katy Kozhimannil and colleagues use a national database to examine the extent to which variability in cesarean section rates across the US from 2009–2010 was attributable to individual women's clinical diagnoses.
Please see later in the article for the Editors' Summary
Background
Cesarean delivery is the most common inpatient surgery in the United States, where 1.3 million cesarean sections occur annually, and rates vary widely by hospital. Identifying sources of variation in cesarean use is crucial to improving the consistency and quality of obstetric care. We used hospital discharge records to examine the extent to which variability in the likelihood of cesarean section across US hospitals was attributable to individual women's clinical diagnoses.
Methods and Findings
Using data from the 2009 and 2010 Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project—a 20% sample of US hospitals—we analyzed data for 1,475,457 births in 1,373 hospitals. We fitted multilevel logistic regression models (patients nested in hospitals). The outcome was cesarean (versus vaginal) delivery. Covariates included diagnosis of diabetes in pregnancy, hypertension in pregnancy, hemorrhage during pregnancy or placental complications, fetal distress, and fetal disproportion or obstructed labor; maternal age, race/ethnicity, and insurance status; and hospital size and location/teaching status.
The cesarean section prevalence was 22.0% (95% confidence interval 22.0% to 22.1%) among women with no prior cesareans. In unadjusted models, the between-hospital variation in the individual risk of primary cesarean section was 0.14 (95% credible interval 0.12 to 0.15). The difference in the probability of having a cesarean delivery between hospitals was 25 percentage points. Hospital variability did not decrease after adjusting for patient diagnoses, socio-demographics, and hospital characteristics (0.16 [95% credible interval 0.14 to 0.18]). A limitation is that these data, while nationally representative, did not contain information on parity or gestational age.
Conclusions
Variability across hospitals in the individual risk of cesarean section is not decreased by accounting for differences in maternal diagnoses. These findings highlight the need for more comprehensive or linked data including parity and gestational age as well as examination of other factors—such as hospital policies, practices, and culture—in determining cesarean section use.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In an ideal world, all babies would be delivered safely and naturally through their mother's vagina. However, increasing numbers of babies are being delivered by cesarean section, a surgical operation in which the baby is delivered through a cut made in the mother's abdomen and womb. In the US, a third of all babies (about 1.3 million babies in 2011) are delivered this way. A cesarean section is usually performed when a vaginal birth would endanger the life of the mother or her unborn child because, for example, the baby is in the wrong position or the labor is not progressing normally. Some cesarean sections are performed as emergency procedures, but others are planned in advance when the need for the operation becomes clear during pregnancy. Although cesarean sections can save lives, women who deliver this way have higher rates of infection, pain, and complications in future pregnancies than women who deliver vaginally, and their babies can have breathing problems.
Why Was This Study Done?
Currently, cesarean section rates vary widely from country to country and from hospital to hospital within countries. Careful assessment of the risks and benefits of cesarean delivery in individual patients can help to ensure that cesarean sections are used only when necessary, but changes to clinical and policy guidelines are also needed to ensure that cesarean delivery is neither overused nor underused. To guide these changes, we need to know whether cesarean section rates vary among hospitals because of case-mix differences (some hospitals may have high rates because they admit many women with complicated pregnancies, for example) or because of differences in modifiable nonclinical factors such as hospital policies and practices. In this retrospective multilevel analysis, the researchers examine whether the current wide variation in cesarean section rates across US hospitals is attributable to differences in maternal clinical diagnoses and patient characteristics or to hospital-level differences in the use of cesarean delivery.
What Did the Researchers Do and Find?
For their study, the researchers used hospital discharge data on nearly 1.5 million births in 1,373 hospitals collected by the 2009 and 2010 US Nationwide Inpatient Sample database, which captures administrative data (for example, length of stay in hospital and clinical complications) from a representative sample of 20% of US hospitals. To assess the chances of cesarean delivery based on hospital and patient characteristics, researchers fitted these data to multilevel logistic regression statistical models. Among women with no prior cesarean deliveries, the (primary) cesarean section rate was 22%, whereas among the whole study population, it was 33% (women who have one cesarean delivery often have a cesarean section for subsequent deliveries). In unadjusted models that compared cesarean section rates between hospitals without considering patient characteristics, the between-hospital variance for primary cesarean section rate was 0.14. Put another way, the likelihood of an individual having a first cesarean delivery varied between 11% and 36% across the hospitals considered. After adjustment for maternal clinical diagnoses, maternal age and other socio-demographic factors, and hospital characteristics such as size, the between-hospital variance for the primary cesarean section rate was 0.16.
What Do These Findings Mean?
The finding that the between-hospital variance for primary cesarean section rate did not decrease after adjusting for maternal characteristics (and other findings presented by the researchers) suggests that differences in case mix or pregnancy complexity may not drive the wide variability in cesarean section rates across US hospitals. However, the lack of information in the US Nationwide Inpatient Sample database on parity (the number of babies a woman has had) or gestational age (the length of time the baby has spent developing inside its mother) limits the strength of this conclusion. Both parity and gestational age strongly predict a woman's risk of a cesarean delivery. Thus, unmeasured differences in the parity of women admitted to different hospitals and/or the gestational age of their babies may be driving some of the variability in cesarean section rates across US hospitals. The lack of hospital-level information on obstetric care policies in the database also means that the many possible administrative explanations for variations across hospitals cannot be assessed. These findings therefore highlight the need for more comprehensive patient data to be collected (including information on parity and gestational age) and on hospital policies, practices, and culture before the variation in cesarean section rate across US hospitals can be fully understood and the use of cesarean delivery can be optimized.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001745.
This study is further discussed in a PLOS Medicine Perspective by Gordon C. S. Smith
The American College of Obstetricians and Gynecologists provides a fact sheet for patients on cesarean birth
The American College of Nurse-Midwives provides a fact sheet for pregnant women on preventing cesarean birth
The US-based Childbirth Connection Project of the non-profit National Partnership for Women and Families has a booklet called “What Every Woman Should Know about Cesarean Section”
The US-based non-profit Nemours Foundation provides detailed information about cesarean sections (in English and Spanish)
The UK National Health Service Choices website provides information for patients about delivery by cesarean section
MedlinePlus provides links to additional resources about cesarean section (in English and Spanish)
The UK non-profit organization Healthtalkonline provides personal stories about women's experiences of cesarean delivery
Information about the US Nationwide Inpatient Sample database is available
doi:10.1371/journal.pmed.1001745
PMCID: PMC4205118  PMID: 25333943
5.  Patient risk profiles and practice variation in nonadherence to antidepressants, antihypertensives and oral hypoglycemics 
Background
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.
Methods
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.
Results
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.
Conclusion
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.
doi:10.1186/1472-6963-7-51
PMCID: PMC1855317  PMID: 17425792
6.  Reducing the Impact of the Next Influenza Pandemic Using Household-Based Public Health Interventions 
PLoS Medicine  2006;3(9):e361.
Background
The outbreak of highly pathogenic H5N1 influenza in domestic poultry and wild birds has caused global concern over the possible evolution of a novel human strain [1]. If such a strain emerges, and is not controlled at source [2,3], a pandemic is likely to result. Health policy in most countries will then be focused on reducing morbidity and mortality.
Methods and Findings
We estimate the expected reduction in primary attack rates for different household-based interventions using a mathematical model of influenza transmission within and between households. We show that, for lower transmissibility strains [2,4], the combination of household-based quarantine, isolation of cases outside the household, and targeted prophylactic use of anti-virals will be highly effective and likely feasible across a range of plausible transmission scenarios. For example, for a basic reproductive number (the average number of people infected by a typically infectious individual in an otherwise susceptible population) of 1.8, assuming only 50% compliance, this combination could reduce the infection (symptomatic) attack rate from 74% (49%) to 40% (27%), requiring peak quarantine and isolation levels of 6.2% and 0.8% of the population, respectively, and an overall anti-viral stockpile of 3.9 doses per member of the population. Although contact tracing may be additionally effective, the resources required make it impractical in most scenarios.
Conclusions
National influenza pandemic preparedness plans currently focus on reducing the impact associated with a constant attack rate, rather than on reducing transmission. Our findings suggest that the additional benefits and resource requirements of household-based interventions in reducing average levels of transmission should also be considered, even when expected levels of compliance are only moderate.
Voluntary household-based quarantine and external isolation are likely to be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is moderate.
Editors' Summary
Background.
Naturally occurring variation in the influenza virus can lead both to localized annual epidemics and to less frequent global pandemics of catastrophic proportions. The most destructive of the three influenza pandemics of the 20th century, the so-called Spanish flu of 1918–1919, is estimated to have caused 20 million deaths. As evidenced by ongoing tracking efforts and news media coverage of H5N1 avian influenza, contemporary approaches to monitoring and communications can be expected to alert health officials and the general public of the emergence of new, potentially pandemic strains before they spread globally.
Why Was This Study Done?
In order to act most effectively on advance notice of an approaching influenza pandemic, public health workers need to know which available interventions are likely to be most effective. This study was done to estimate the effectiveness of specific preventive measures that communities might implement to reduce the impact of pandemic flu. In particular, the study evaluates methods to reduce person-to-person transmission of influenza, in the likely scenario that complete control cannot be achieved by mass vaccination and anti-viral treatment alone.
What Did the Researchers Do and Find?
The researchers developed a mathematical model—essentially a computer simulation—to simulate the course of pandemic influenza in a hypothetical population at risk for infection at home, through external peer networks such as schools and workplaces, and through general community transmission. Parameters such as the distribution of household sizes, the rate at which individuals develop symptoms from nonpandemic viruses, and the risk of infection within households were derived from demographic and epidemiologic data from Hong Kong, as well as empirical studies of influenza transmission. A model based on these parameters was then used to calculate the effects of interventions including voluntary household quarantine, voluntary individual isolation in a facility outside the home, and contact tracing (that is, asking infectious individuals to identify people whom they may have infected and then warning those people) on the spread of pandemic influenza through the population. The model also took into account the anti-viral treatment of exposed, asymptomatic household members and of individuals in isolation, and assumed that all intervention strategies were put into place before the arrival of individuals infected with the pandemic virus.
  Using this model, the authors predicted that even if only half of the population were to comply with public health interventions, the proportion infected during the first year of an influenza pandemic could be substantially reduced by a combination of household-based quarantine, isolation of actively infected individuals in a location outside the household, and targeted prophylactic treatment of exposed individuals with anti-viral drugs. Based on an influenza-associated mortality rate of 0.5% (as has been estimated for New York City in the 1918–1919 pandemic), the magnitude of the predicted benefit of these interventions is a reduction from 49% to 27% in the proportion of the population who become ill in the first year of the pandemic, which would correspond to 16,000 fewer deaths in a city the size of Hong Kong (6.8 million people). In the model, anti-viral treatment appeared to be about as effective as isolation when each was used in combination with household quarantine, but would require stockpiling 3.9 doses of anti-viral for each member of the population. Contact tracing was predicted to provide a modest additional benefit over quarantine and isolation, but also to increase considerably the proportion of the population in quarantine.
What Do These Findings Mean?
This study predicts that voluntary household-based quarantine and external isolation can be effective in limiting the morbidity and mortality of an influenza pandemic, even if such a pandemic cannot be entirely prevented, and even if compliance with these interventions is far from uniform. These simulations can therefore inform preparedness plans in the absence of data from actual intervention trials, which would be impossible outside (and impractical within) the context of an actual pandemic. Like all mathematical models, however, the one presented in this study relies on a number of assumptions regarding the characteristics and circumstances of the situation that it is intended to represent. For example, the authors found that the efficacy of policies to reduce the rate of infection vary according to the ease with which a given virus spreads from person to person. Because this parameter (known as the basic reproductive ratio, R0) cannot be reliably predicted for a new viral strain based on past epidemics, the authors note that in an actual influenza pandemic rapid determinations of R0 in areas already involved would be necessary to finalize public health responses in threatened areas. Further, the implementation of the interventions that appear beneficial in this model would require devoting attention and resources to practical considerations, such as how to staff isolation centers and provide food and water to those in household quarantine. However accurate the scientific data and predictive models may be, their effectiveness can only be realized through well-coordinated local, as well as international, efforts.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030361.
• World Health Organization influenza pandemic preparedness page
• US Department of Health and Human Services avian and pandemic flu information site
• Pandemic influenza page from the Public Health Agency of Canada
• Emergency planning page on pandemic flu from the England Department of Health
• Wikipedia entry on pandemic influenza with links to individual country resources (note: Wikipedia is a free Internet encyclopedia that anyone can edit)
doi:10.1371/journal.pmed.0030361
PMCID: PMC1526768  PMID: 16881729
7.  What Can We Conclude from Death Registration? Improved Methods for Evaluating Completeness 
PLoS Medicine  2010;7(4):e1000262.
Julie Rajaratnam and colleagues evaluate the performance of a suite of demographic methods that estimate the fraction of deaths registered and counted by civil registration systems, and identify three variants that generally perform the best.
Background
One of the fundamental building blocks for determining the burden of disease in populations is to reliably measure the level and pattern of mortality by age and sex. Where well-functioning registration systems exist, this task is relatively straightforward. Results from many civil registration systems, however, remain uncertain because of a lack of confidence in the completeness of death registration. Incomplete registration systems mean not all deaths are counted, and resulting estimates of death rates for the population are then underestimated. Death distribution methods (DDMs) are a suite of demographic methods that attempt to estimate the fraction of deaths that are registered and counted by the civil registration system. Although widely applied and used, the methods have at least three types of limitations. First, a wide range of variants of these methods has been applied in practice with little scientific literature to guide their selection. Second, the methods have not been extensively validated in real population conditions where violations of the assumptions of the methods most certainly occur. Third, DDMs do not generate uncertainty intervals.
Methods and Findings
In this paper, we systematically evaluate the performance of 234 variants of DDM methods in three different validation environments where we know or have strong beliefs about the true level of completeness of death registration. Using these datasets, we identify three variants of the DDMs that generally perform the best. We also find that even these improved methods yield uncertainty intervals of roughly ± one-quarter of the estimate. Finally, we demonstrate the application of the optimal variants in eight countries.
Conclusions
There continues to be a role for partial vital registration data in measuring adult mortality levels and trends, but such results should only be interpreted alongside all other data sources on adult mortality and the uncertainty of the resulting levels, trends, and age-patterns of adult death considered.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Accurate worldwide information on the levels and patterns of mortality (deaths) is essential for planning and monitoring global public-health initiatives. The gold standard method for collecting such information is death registration. In high-income countries, death registration is effectively 100% complete, but the situation in many developing countries is very different. In most African countries, for example, less than one-quarter of deaths are officially recorded. Although other data sources such as household surveys can be used to estimate mortality levels in such countries, partial registration data could provide useful information about mortality levels in developing countries if its completeness could be evaluated. One way to do this is to use demographic methods called “death distribution methods” (DDMs). Demography is the study of the size, growth, and other characteristics of human populations; DDMs compare the age distribution of recorded deaths (the relative proportion of deaths in each age group) with the age distribution of the population in which they occurred to provide a correction factor that can be used to calculate corrected mortality levels from registered deaths. DDMs are used by the World Health Organization to monitor adult mortality in nearly 100 countries.
Why Was This Study Done?
Although widely used, few studies have compared the performance of the many available DDM variants, and DDMs have not been extensively validated by testing them in populations for which the completeness of death registration is known. In addition, because DDMs are mathematical in nature, they do not provide any indication of the uncertainty associated with the correction factors they yield. This means that public-health officials using estimates of mortality levels generated from partial registration data using DDMs have no idea of the limits between which the true mortality levels of their populations lie. In this study, the researchers systematically evaluate the performance of 234 DDM variants and use the optimal variants that they identify to analyze registration completeness over time in six developing countries.
What Did the Researchers Do and Find?
The researchers constructed 234 DDM variants by combining each of three general types of DDMs with 78 different “age trims”; demographers often age-trim—drop older and/or younger age groups—when using DDMs to estimate correction factors for observed death rates. The researchers then evaluated the performance of the variants in three “validation” datasets for which the completeness of death registration is known—a microsimulation of a population of 10 million people followed for 150 years, population data from US counties between 1990 and 2000, and population data from high-income OECD (Organisation for Economic Co-operation and Development) countries with populations of more than 5 million between 1950 and 2000. Detailed analyses of the performance of the DDM variants with all three datasets identified three optimal DDMs, one of each type. However, even with these optimal DDMs, the uncertainty intervals associated with estimates of relative completeness of registration were roughly +/− one-quarter of the estimate. Finally, the researchers applied their optimal DDMs to six developing countries over time. This analysis showed that death registration for adults has been relatively complete since 1970 in Mexico, for example, whereas in Tunisia, death registration has improved from nearly 50% in 1965 to complete by 1980. It also indicated that the three DDMs can give consistent results in some contexts.
What Do These Findings Mean?
By using multiple validation databases, these findings identify three optimal DDMs for the estimation of completeness of death registration. The researchers recommend that analysts apply all three methods when estimating the completeness of death registration data and look at the consistency of the results produced. They warn that the level of uncertainty associated with the estimation of completeness of registration means that results yielded by DDMs should be interpreted with considerable caution. In particular, they note that although correction factors provided by DDMs may be a good way of estimating mortality levels, the uncertainty in these factors may make them unsuitable for analyzing trends over time in mortality levels. Overall, the researchers conclude that partial death registration data have a role to play in measuring adult mortality levels, provided that they are analyzed alongside other data sources.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000262.
This study and two related PLoS Medicine Research Articles—by Obermeyer et al. and by Rajaratnam et al. —are further discussed in a PLoS Medicine Perspective by Mathers and Boerma
The Institute for Health Metrics and Evaluation makes available high-quality information on population health, its determinants, and the performance of health systems
Grand Challenges in Global Health provides information on research into better ways for developing countries to measure their health status
The World Health Organization Statistical Information System (WHOSIS) is an interactive database that brings together core health statistics for WHO member states, including information on vital registration of deaths; the WHO Health Metrics Network is a global collaboration focused on improving sources of vital statistics
doi:10.1371/journal.pmed.1000262
PMCID: PMC2854130  PMID: 20405002
8.  Health inequalities among older men and women in Africa and Asia: evidence from eight Health and Demographic Surveillance System sites in the INDEPTH WHO-SAGE Study 
Global Health Action  2010;3:10.3402/gha.v3i0.5420.
Background
Declining rates of fertility and mortality are driving demographic transition in all regions of the world, leading to global population ageing and consequently changing patterns of global morbidity and mortality. Understanding sex-related health differences, recognising groups at risk of poor health and identifying determinants of poor health are therefore very important for both improving health trajectories and planning for the health needs of ageing populations.
Objectives
To determine the extent to which demographic and socio-economic factors impact upon measures of health in older populations in Africa and Asia; to examine sex differences in health and further explain how these differences can be attributed to demographic and socio-economic determinants.
Methods
A total of 46,269 individuals aged 50 years and over in eight Health and Demographic Surveillance System (HDSS) sites within the INDEPTH Network were studied during 2006–2007 using an abbreviated version of the WHO Study on global AGEing and adult health (SAGE) Wave I instrument. The survey data were then linked to longitudinal HDSS background information. A health score was calculated based on self-reported health derived from eight health domains. Multivariable regression and post-regression decomposition provide ways of measuring and explaining the health score gap between men and women.
Results
Older men have better self-reported health than older women. Differences in household socio-economic levels, age, education levels, marital status and living arrangements explained from about 82% and 71% of the gaps in health score observed between men and women in South Africa and Kenya, respectively, to almost nothing in Bangladesh. Different health domains contributed differently to the overall health scores for men and women in each country.
Conclusion
This study confirmed the existence of sex differences in self-reported health in low- and middle-income countries even after adjustments for differences in demographic and socio-economic factors. A decomposition analysis suggested that sex differences in health differed across the HDSS sites, with the greatest level of inequality found in Bangladesh. The analysis showed considerable variation in how differences in socio-demographic and economic characteristics explained the gaps in self-reported health observed between older men and women in African and Asian settings. The overall health score was a robust indicator of health, with two domains, pain and sleep/energy, contributing consistently across the HDSS sites. Further studies are warranted to understand other significant individual and contextual determinants to which these sex differences in health can be attributed. This will lay a foundation for a more evidence-based approach to resource allocation, and to developing health promotion programmes for older men and women in these settings.
doi:10.3402/gha.v3i0.5420
PMCID: PMC2958198  PMID: 20967141
ageing; survey methods; public health; burden of disease; demographic transition; disability; well-being; health status; INDEPTH WHO-SAGE
9.  GEOGRAPHIC DISPARITY IN COPD HOSPITALIZATION RATES AMONG THE TEXAS POPULATION 
Respiratory medicine  2011;105(5):734-739.
Summary
Background
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.
Methods
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.
Results
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.
Conclusions
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.
doi:10.1016/j.rmed.2010.12.019
PMCID: PMC3064740  PMID: 21255991
10.  A Comparative Analysis of Influenza Vaccination Programs 
PLoS Medicine  2006;3(10):e387.
Background
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.
Conclusions
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
Background.
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 http://dx.doi.org/10.1371/journal.pmed.0030387.
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
doi:10.1371/journal.pmed.0030387
PMCID: PMC1584413  PMID: 17020406
11.  The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study 
Background
Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern.
Methods
The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses.
Results
Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status.
Conclusions
Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups.
Trial registration
ISRCTN89818205
doi:10.1186/1472-6963-12-89
PMCID: PMC3348059  PMID: 22471952
12.  First Diagnosis and Management of Incontinence in Older People with and without Dementia in Primary Care: A Cohort Study Using The Health Improvement Network Primary Care Database 
PLoS Medicine  2013;10(8):e1001505.
Robert Grant and colleagues used the British THIN primary care database to determine rates of first diagnosis of urinary and faecal incontinence among people aged 60–89 with dementia compared with those without dementia, and the use of medication or indwelling catheters for urinary incontinence in those with and without dementia.
Please see later in the article for the Editors' Summary
Background
Dementia is one of the most disabling and burdensome diseases. Incontinence in people with dementia is distressing, adds to carer burden, and influences decisions to relocate people to care homes. Successful and safe management of incontinence in people with dementia presents additional challenges. The aim of this study was to investigate the rates of first diagnosis in primary care of urinary and faecal incontinence among people aged 60–89 with dementia, and the use of medication or indwelling catheters for urinary incontinence.
Methods and Findings
We extracted data on 54,816 people aged 60–89 with dementia and an age-gender stratified sample of 205,795 people without dementia from 2001 to 2010 from The Health Improvement Network (THIN), a United Kingdom primary care database. THIN includes data on patients and primary care consultations but does not identify care home residents. Rate ratios were adjusted for age, sex, and co-morbidity using multilevel Poisson regression.
The rates of first diagnosis per 1,000 person-years at risk (95% confidence interval) for urinary incontinence in the dementia cohort, among men and women, respectively, were 42.3 (40.9–43.8) and 33.5 (32.6–34.5). In the non-dementia cohort, the rates were 19.8 (19.4–20.3) and 18.6 (18.2–18.9). The rates of first diagnosis for faecal incontinence in the dementia cohort were 11.1 (10.4–11.9) and 10.1 (9.6–10.6). In the non-dementia cohort, the rates were 3.1 (2.9–3.3) and 3.6 (3.5–3.8).
The adjusted rate ratio for first diagnosis of urinary incontinence was 3.2 (2.7–3.7) in men and 2.7 (2.3–3.2) in women, and for faecal incontinence was 6.0 (5.1–7.0) in men and 4.5 (3.8–5.2) in women. The adjusted rate ratio for pharmacological treatment of urinary incontinence was 2.2 (1.4–3.7) for both genders, and for indwelling urinary catheters was 1.6 (1.3–1.9) in men and 2.3 (1.9–2.8) in women.
Conclusions
Compared with those without a dementia diagnosis, those with a dementia diagnosis have approximately three times the rate of diagnosis of urinary incontinence, and more than four times the rate of faecal incontinence, in UK primary care. The clinical management of urinary incontinence in people with dementia with medication and particularly the increased use of catheters is concerning and requires further investigation.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Globally, more than 35 million people have dementia, brain disorders that are characterized by an irreversible decline in cognitive functions such as language and memory. Alzheimer's disease and other forms of dementia mainly affect older people and, because people are living longer than ever, experts estimate that by 2050 more than 115 million people will have dementia. The earliest sign of dementia is usually increasing forgetfulness but, as the disease progresses, people gradually lose their ability to deal with normal daily activities such as dressing, they may become anxious or aggressive, and they may lose control of their bladder (urinary incontinence), bowels (bowel or fecal incontinence), and other physical functions. As a result, people with dementia require increasing amounts of care as the disease progresses. Relatives and other unpaid carers provide much of this care—two-thirds of people with dementia are cared for at home. However, many people with dementia end their days in a care or nursing home.
Why Was This Study Done?
Incontinence in people with dementia is distressing for the person with dementia and for their carers and often influences decisions to move individuals into care homes. However, little is known about the diagnosis and treatment of urinary and/or fecal incontinence among people with dementia living at home. This information is needed to help policymakers commission the services required for this section of society and insurers recognize the needs such patients have, as well as helping to raise clinicians' awareness of the issue. In this cohort study (an investigation that compares outcomes in groups of people with different characteristics), the researchers use data routinely collected from general practices (primary care) in the UK to determine the rate of first diagnosis of urinary and fecal incontinence in elderly patients with and without dementia and to find out whether a diagnosis of dementia affects the rate of use of drugs or of indwelling urinary catheters (tubes inserted into the bladder to drain urine from the body) for the treatment of urinary incontinence.
What Did the Researchers Do and Find?
The researchers extracted data collected between 2001 and 2010 on incontinence for nearly 55,000 people aged 60–89 with a diagnosis of dementia (the dementia cohort) and for more than 200,000 individuals without a diagnosis of dementia (the non-dementia cohort) from The Health Improvement Network (THIN) primary care database, which includes anonymized consultation records from nearly 500 UK general practices. In the dementia cohort, the rates of first diagnosis of urinary incontinence were 42.3 and 33.5 per 1,000 person-years at risk among men and women, respectively. In the non-dementia cohort, the corresponding rates were 19.8 and 18.6. The rates of first diagnosis of fecal incontinence were 11.1 and 10.1 in the dementia cohort, and 3.1 and 3.6 in the non-dementia cohort among men and women, respectively. After adjusting for age, sex and other diseases, the adjusted rate ratio for the first diagnosis of urinary incontinence in people with dementia compared to people without dementia was 3.2 in men and 2.7 in women; for fecal incontinence, it was 6.0 in men and 4.5 in women; the adjusted rate ratio was 2.2 for both men and women for drug treatment of urinary incontinence and 1.6 in men and 2.3 in women for use of indwelling urinary catheters.
What Do These Findings Mean?
These findings indicate that, in primary care in the UK, dementia is associated with a three-fold higher rate of diagnosis of urinary incontinence and a greater than four-fold higher rate of diagnosis of fecal incontinence. Moreover, the authors suggest that some aspects of clinical management of urinary continence vary between people with and without dementia. In particular, the use of indwelling urinary catheters appears to be more common among people with dementia than among people without dementia, increasing the risk of infection. Thus, health care practitioners providing care for people with dementia may be prioritizing ease of management over risk avoidance, a possibility that requires further investigation. Although the accuracy of these findings is limited by certain aspects of the study design (for example, the THIN database does not identify which patients are living in care homes), they nevertheless suggest that policymakers and insurers involved in planning and providing services for people with dementia living at home need to provide high levels of help with incontinence, including the provision of advice and support for carers.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001505.
The UK not-for-profit organization Alzheimers Society provides information for patients and carers about dementia, including information on coping with incontinence and personal stories about living with dementia
The US not-for-profit organization Alzheimers Association also provides information for patients and carers about dementia and about incontinence, and personal stories about dementia
The UK National Health Service Choices website provides information (including personal stories) about dementia, urinary incontinence, and bowel incontinence
MedlinePlus provides links to further resources about dementia, urinary incontinence and fecal incontinence (in English and Spanish)
The International Continence Society and the International Consultation on Urological Diseases provide independent advice on products to manage incontinence
More information about the THIN database is available
doi:10.1371/journal.pmed.1001505
PMCID: PMC3754889  PMID: 24015113
13.  Which practices are high antibiotic prescribers? A cross-sectional analysis 
The British Journal of General Practice  2009;59(567):e315-e320.
Background
Substantial variation in antibiotic prescribing rates between general practices persists, but remains unexplained at national level.
Aim
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.
Setting
8057 general practices in England.
Method
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.
Results
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.
Conclusion
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.
doi:10.3399/bjgp09X472593
PMCID: PMC2751935  PMID: 19843411
antibiotics; prescriptions; primary care
14.  Migration and child immunization in Nigeria: individual- and community-level contexts 
BMC Public Health  2010;10:116.
Background
Vaccine-preventable diseases are responsible for severe rates of morbidity and mortality in Africa. Despite the availability of appropriate vaccines for routine use on infants, vaccine-preventable diseases are highly endemic throughout sub-Saharan Africa. Widespread disparities in the coverage of immunization programmes persist between and within rural and urban areas, regions and communities in Nigeria. This study assessed the individual- and community-level explanatory factors associated with child immunization differentials between migrant and non-migrant groups.
Methods
The proportion of children that received each of the eight vaccines in the routine immunization schedule in Nigeria was estimated. Multilevel multivariable regression analysis was performed using a nationally representative sample of 6029 children from 2735 mothers aged 15-49 years and nested within 365 communities. Odds ratios with 95% confidence intervals were used to express measures of association between the characteristics. Variance partition coefficients and Wald statistic i.e. the ratio of the estimate to its standard error were used to express measures of variation.
Results
Individual- and community contexts are strongly associated with the likelihood of receiving full immunization among migrant groups. The likelihood of full immunization was higher for children of rural non-migrant mothers compared to children of rural-urban migrant mothers. Findings provide support for the traditional migration perspectives, and show that individual-level characteristics, such as, migrant disruption (migration itself), selectivity (demographic and socio-economic characteristics), and adaptation (health care utilization), as well as community-level characteristics (region of residence, and proportion of mothers who had hospital delivery) are important in explaining the differentials in full immunization among the children.
Conclusion
Migration is an important determinant of child immunization uptake. This study stresses the need for community-level efforts at increasing female education, measures aimed at alleviating poverty for residents in urban and remote rural areas, and improving the equitable distribution of maternal and child health services.
doi:10.1186/1471-2458-10-116
PMCID: PMC2847974  PMID: 20211034
15.  Hospital Differences in Cesarean Deliveries in Massachusetts (US) 2004–2006: The Case against Case-Mix Artifact 
PLoS ONE  2013;8(3):e57817.
Objective
We examined the extent to which differences in hospital-level cesarean delivery rates in Massachusetts were attributable to hospital-level, rather than maternal, characteristics.
Methods
Birth certificate and maternal in-patient hospital discharge records for 2004–06 in Massachusetts were linked. The study population was nulliparous, term, singleton, and vertex births (NTSV) (n = 80,371) in 49 hospitals. Covariates included mother's age, race/ethnicity, education, infant birth weight, gestational age, labor induction (yes/no), hospital shift at time of birth, and preexisting health conditions. We estimated multilevel logistic regression models to assess the likelihood of a cesarean delivery
Results
Overall, among women with NTSV births, 26.5% births were cesarean, with a range of 14% to 38.3% across hospitals. In unadjusted models, the between-hospital variance was 0.103 (SE 0.022); adjusting for demographic, socioeconomic and preexisting medical conditions did not reduce any hospital-level variation 0.108 (SE 0.023).
Conclusion
Even after adjusting for both socio-demographic and clinical factors, the chance of a cesarean delivery for NTSV pregnancies varied according to hospital, suggesting the importance of hospital practices and culture in determining a hospital's cesarean rate.
doi:10.1371/journal.pone.0057817
PMCID: PMC3601117  PMID: 23526952
16.  Increasing incidence of skin disorders in children? A comparison between 1987 and 2001 
BMC Dermatology  2006;6:4.
Background
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.
Methods
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.
Results
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.
Conclusion
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.
doi:10.1186/1471-5945-6-4
PMCID: PMC1435925  PMID: 16551358
17.  Annual night visiting rates in 129 general practices in one family health services authority: association with patient and general practice characteristics. 
BACKGROUND. Rates of night visiting by general practitioners have increased steadily over the last 30 years and vary widely between general practices. AIM. An ecological study was carried out to examine night visiting rates by general practices in one family health services authority, and to determine the extent to which differences in night visiting rates between practices could be explained by patient and practice characteristics. METHOD. The study examined the variation in annual night visiting rates, based on night visit fees claimed between April 1993 and March 1994, among 129 general practices in Merton, Sutton and Wandsworth Family Health Services Authority, London. RESULTS. Practices' annual night visiting rates varied from three per 1000 to 75 per 1000 patients. The percentages of the practice population aged under five years and aged five to 14 years were both positively correlated with night visiting rates (r = 0.38 and r = 0.35, respectively), as were variables associated with social deprivation such as the estimated percentage of the practice population living in one-parent households (r = 0.24) and in households where the head of household was classified as unskilled (r = 0.20). The percentage of the practice population reporting chronic illness was also positively associated with night visiting rates (r = 0.26). The percentages of the practice population aged 35 to 44 years and 45 to 54 years were both negatively associated with night visiting rates (r = -0.34 and r = -0.31, respectively) as was the estimated list inflation for a practice (r = -0.31). There was no significant correlation between night visiting rates and the distance of the main practice surgery from the nearest hospital accident and emergency department. There was also no association between night visiting rates and permission to use a deputizing service. In a stepwise multiple regression model, the multiple correlation coefficient was 0.56 with four factors (percentage of the practice population aged under five years, percentage aged 35-44 years, percentage who were chronically ill and estimated list inflation) explaining 32% of the variation in night visiting rates. CONCLUSION. Only about one third of the variation in night visiting rates between practices could be explained by patient and practice variables derived from routine data. Population-based research using data collected on individual patients and practices is required to improve current understanding of the patient and practice characteristics that influence the demand for night visits and of why night visiting rates vary so widely between practices.
PMCID: PMC1239404  PMID: 7492422
18.  Suicides in the indigenous and non-indigenous populations in the Nenets Autonomous Okrug, Northwestern Russia, and associated socio-demographic characteristics 
International Journal of Circumpolar Health  2014;73:10.3402/ijch.v73.24308.
Background
To describe suicide rates in the indigenous and non-indigenous populations of the Nenets Autonomous Okrug (NAO) in 2002–2012, as well as associated socio-demographic characteristics.
Study design
Retrospective population-based mortality study.
Methods
Data from autopsy reports were used to identify 252 cases of suicide in the NAO in 2002–2012. Data on socio-demographic characteristics of these cases were obtained from passports and medical records at local primary health care units, and were then linked to total population data from the Censuses in 2002 and 2010. Suicide rates for the indigenous Nenets population and the non-indigenous population were standardized to the European standard population. The rates were also estimated according to different socio-demographic characteristics and compared by calculating relative risks.
Results
The crude suicide rates were 79.8 per 100,000 person-years (PYs) in the Nenets population and 49.2 per 100,000 PYs in the non-indigenous population. The corresponding standardized estimates were 72.7 per 100,000 PYs and 50.7 per 100,000 PYs. The highest suicide rates in the Nenets population were observed in the age group 20–29 years (391 per 100,000 PYs), and in females aged 30–39 years (191 per 100,000 PYs). Socio-demographic characteristics associated with high suicide rates in the Nenets population were age 20–39 years, male, urban residence, having secondary school or higher education, being an employee or employer, and being single or divorced. Males aged 20–29 years, and females aged 30–39 and aged 70 years and above had the highest suicide rates in the non-indigenous population (137.5, 21.6 and 29.9 per 100,000 PYs, respectively). The elevated suicide rates observed in the non-indigenous population were associated with male sex, rural residence, secondary school education, being an employee or employer, and being single or divorced.
Conclusions
Suicide rates in the NAO were substantially higher among the indigenous Nenets population than the non-indigenous population, and were associated with different socio-demographic characteristics.
doi:10.3402/ijch.v73.24308
PMCID: PMC4013488  PMID: 25006556
suicide rates; relative risks; person-years; indigenous Nenets
19.  ASSET (Age/Sex Standardised Estimates of Treatment): A Research Model to Improve the Governance of Prescribing Funds in Italy 
PLoS ONE  2007;2(7):e592.
Background
The primary objective of this study was to make the first step in the modelling of pharmaceutical demand in Italy, by deriving a weighted capitation model to account for demographic differences among general practices. The experimental model was called ASSET (Age/Sex Standardised Estimates of Treatment).
Methods and Major Findings
Individual prescription costs and demographic data referred to 3,175,691 Italian subjects and were collected directly from three Regional Health Authorities over the 12-month period between October 2004 and September 2005. The mean annual prescription cost per individual was similar for males (196.13 euro) and females (195.12 euro). After 65 years of age, the mean prescribing costs for males were significantly higher than females. On average, costs for a 75-year-old subject would be 12 times the costs for a 25–34 year-old subject if male, 8 times if female. Subjects over 65 years of age (22% of total population) accounted for 56% of total prescribing costs. The weightings explained approximately 90% of the evolution of total prescribing costs, in spite of the pricing and reimbursement turbulences affecting Italy in the 2000–2005 period. The ASSET weightings were able to explain only about 25% of the variation in prescribing costs among individuals.
Conclusions
If mainly idiosyncratic prescribing by general practitioners causes the unexplained variations, the introduction of capitation-based budgets would gradually move practices with high prescribing costs towards the national average. It is also possible, though, that the unexplained individual variation in prescribing costs is the result of differences in the clinical characteristics or socio-economic conditions of practice populations. If this is the case, capitation-based budgets may lead to unfair distribution of resources. The ASSET age/sex weightings should be used as a guide, not as the ultimate determinant, for an equitable allocation of prescribing resources to regional authorities and general practices.
doi:10.1371/journal.pone.0000592
PMCID: PMC1899227  PMID: 17611624
20.  Urban and rural variations in morbidity and mortality in Northern Ireland 
BMC Public Health  2007;7:123.
Background
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.
Methods
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.
Results
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.
Conclusion
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.
doi:10.1186/1471-2458-7-123
PMCID: PMC1913506  PMID: 17594471
21.  Case-mix and variation in specialist referrals in general practice 
Background
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.
Aim
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.
Setting
Two hundred and two general practices, with a total list size of 1 161 892, contributing data to the General Practice Research Database.
Method
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.
Results
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.
Conclusions
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
22.  Methodological Challenges in Collecting Social and Behavioural Data Regarding the HIV Epidemic among Gay and Other Men Who Have Sex with Men in Australia 
PLoS ONE  2014;9(11):e113167.
Background
Behavioural surveillance and research among gay and other men who have sex with men (GMSM) commonly relies on non-random recruitment approaches. Methodological challenges limit their ability to accurately represent the population of adult GMSM. We compared the social and behavioural profiles of GMSM recruited via venue-based, online, and respondent-driven sampling (RDS) and discussed their utility for behavioural surveillance.
Methods
Data from four studies were selected to reflect each recruitment method. We compared demographic characteristics and the prevalence of key indicators including sexual and HIV testing practices obtained from samples recruited through different methods, and population estimates from respondent-driven sampling partition analysis.
Results
Overall, the socio-demographic profile of GMSM was similar across samples, with some differences observed in age and sexual identification. Men recruited through time-location sampling appeared more connected to the gay community, reported a greater number of sexual partners, but engaged in less unprotected anal intercourse with regular (UAIR) or casual partners (UAIC). The RDS sample overestimated the proportion of HIV-positive men and appeared to recruit men with an overall higher number of sexual partners. A single-website survey recruited a sample with characteristics which differed considerably from the population estimates with regards to age, ethnically diversity and behaviour. Data acquired through time-location sampling underestimated the rates of UAIR and UAIC, while RDS and online sampling both generated samples that underestimated UAIR. Simulated composite samples combining recruits from time-location and multi-website online sampling may produce characteristics more consistent with the population estimates, particularly with regards to sexual practices.
Conclusion
Respondent-driven sampling produced the sample that was most consistent to population estimates, but this methodology is complex and logistically demanding. Time-location and online recruitment are more cost-effective and easier to implement; using these approaches in combination may offer the potential to recruit a more representative sample of GMSM.
doi:10.1371/journal.pone.0113167
PMCID: PMC4237373  PMID: 25409440
23.  Urbanicity and Lifestyle Risk Factors for Cardiometabolic Diseases in Rural Uganda: A Cross-Sectional Study 
PLoS Medicine  2014;11(7):e1001683.
Johanna Riha and colleagues evaluate the association of lifestyle risk factors with elements of urbanicity, such as having a public telephone, a primary school, or a hospital, among individuals living in rural settings in Uganda.
Please see later in the article for the Editors' Summary
Background
Urban living is associated with unhealthy lifestyles that can increase the risk of cardiometabolic diseases. In sub-Saharan Africa (SSA), where the majority of people live in rural areas, it is still unclear if there is a corresponding increase in unhealthy lifestyles as rural areas adopt urban characteristics. This study examines the distribution of urban characteristics across rural communities in Uganda and their associations with lifestyle risk factors for chronic diseases.
Methods and Findings
Using data collected in 2011, we examined cross-sectional associations between urbanicity and lifestyle risk factors in rural communities in Uganda, with 7,340 participants aged 13 y and above across 25 villages. Urbanicity was defined according to a multi-component scale, and Poisson regression models were used to examine associations between urbanicity and lifestyle risk factors by quartile of urbanicity. Despite all of the villages not having paved roads and running water, there was marked variation in levels of urbanicity across the villages, largely attributable to differences in economic activity, civil infrastructure, and availability of educational and healthcare services. In regression models, after adjustment for clustering and potential confounders including socioeconomic status, increasing urbanicity was associated with an increase in lifestyle risk factors such as physical inactivity (risk ratio [RR]: 1.19; 95% CI: 1.14, 1.24), low fruit and vegetable consumption (RR: 1.17; 95% CI: 1.10, 1.23), and high body mass index (RR: 1.48; 95% CI: 1.24, 1.77).
Conclusions
This study indicates that even across rural communities in SSA, increasing urbanicity is associated with a higher prevalence of lifestyle risk factors for cardiometabolic diseases. This finding highlights the need to consider the health impact of urbanization in rural areas across SSA.
Please see later in the article for the Editors' Summary
Editors’ Summary
Background
Cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels and metabolic diseases that affect the cellular chemical reactions needed to sustain life—are a growing global health concern. In sub-Saharan Africa, for example, the prevalence (the proportion of a population that has a given disease) of adults with diabetes (a life-shortening metabolic disease that affects how the body handles sugars) is currently 3.8%. By 2030, it is estimated that the prevalence of diabetes among adults in this region will have risen to 4.6%. Similarly, in 2004, around 1.2 million deaths in sub-Saharan Africa were attributed to coronary heart disease, heart failure, stroke, and other cardiovascular diseases. By 2030, the number of deaths in this region attributable to cardiovascular disease is expected to double. Globally, cardiovascular disease and diabetes are now responsible for around 17.3 million and 1.3 million annual deaths, respectively, together accounting for about one-third of all deaths.
Why Was This Study Done?
Experts believe that increased consumption of saturated fats, sugar, and salt and reduced physical activity are partly responsible for the increasing global prevalence of cardiometabolic diseases. These lifestyle changes, they suggest, are related to urbanization—urban expansion into the countryside and migration from rural to urban areas. If this is true, the prevalence of unhealthy lifestyles should increase as rural areas adopt urban characteristics. Sub-Saharan Africa is the least urbanized region in the world, with about 60% of the population living in rural areas. However, rural settlements across the subcontinent are increasingly adopting urban characteristics. It is important to know whether urbanization is affecting the health of rural residents in sub-Saharan Africa to improve estimates of the future burden of cardiometabolic diseases in the region and to provide insights into ways to limit this burden. In this cross-sectional study (an investigation that studies participants at a single time point), the researchers examine the distribution of urban characteristics across rural communities in Uganda and the association of these characteristics with lifestyle risk factors for cardiometabolic diseases.
What Did the Researchers Do and Find?
For their study, the researchers used data collected in 2011 by the General Population Cohort study, a study initiated in 1989 to describe HIV infection trends among people living in 25 villages in rural southwestern Uganda that collects health-related and other information annually from its participants. The researchers quantified the “urbanicity” of the 25 villages using a multi-component scale that included information such as village size and economic activity. They then used statistical models to examine associations between urbanicity and lifestyle risk factors such as body mass index (BMI, a measure of obesity) and self-reported fruit and vegetable consumption for more than 7,000 study participants living in those villages. None of the villages had paved roads or running water. However, urbanicity varied markedly across the villages, largely because of differences in economic activity, civil infrastructure, and the availability of educational and healthcare services. Notably, increasing urbanicity was associated with an increase in lifestyle risk factors for cardiovascular diseases. So, for example, people living in villages with the highest urbanicity scores were nearly 20% more likely to be physically inactive and to eat less fruits and vegetables and nearly 50% more likely to have a high BMI than people living in villages with the lowest urbanicity scores.
What Do These Findings Mean?
These findings indicate that, across rural communities in Uganda, even a small increase in urbanicity is associated with a higher prevalence of potentially modifiable lifestyle risk factors for cardiometabolic diseases. These findings suggest, therefore, that simply classifying settlements as either rural or urban may not be adequate to capture the information needed to target strategies for cardiometabolic disease management and control in rural areas as they become more urbanized. Because this study was cross-sectional, it is not possible to say how long a rural population needs to experience a more urban environment before its risk of cardiometabolic diseases increases. Longitudinal studies are needed to obtain this information. Moreover, studies of other countries in sub-Saharan Africa are needed to show that these findings are generalizable across the region. However, based on these findings, and given that more than 553 million people live in rural areas across sub-Saharan Africa, it seems likely that increasing urbanization will have a substantial impact on the future health of populations throughout sub-Saharan Africa.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001683.
This study is further discussed in a PLOS Medicine Perspective by Fahad Razak and Lisa Berkman
The American Heart Association provides information on all aspects of cardiovascular disease and diabetes; its website includes personal stories about heart attacks, stroke, and diabetes
The US Centers for Disease Control and Prevention has information on heart disease, stroke, and diabetes (in English and Spanish)
The UK National Health Service Choices website provides information about cardiovascular disease and diabetes (including some personal stories)
The World Health Organization’s Global Noncommunicable Disease Network (NCDnet) aims to help low- and middle-income countries reduce illness and death caused by cardiometabolic and other non-communicable diseases
The World Heart Federation has recently produced a report entitled “Urbanization and Cardiovascular Disease”
Wikipedia has a page on urbanization (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001683
PMCID: PMC4114555  PMID: 25072243
24.  The effect of co-morbidities on health-related quality of life in patients placed on the waiting list for total joint replacement 
Background
Co-morbidity is a powerful predictor of health care outcomes and costs, as well as an important cofounder in epidemiologic studies. The effect of co-morbidities is generally related to mortality or complications. This study evaluated the association between co-morbidity and health-related quality of life (HRQoL) in patients awaiting total joint replacement.
Methods
A total of 893 patients were recruited to the study between August 2002 and November 2003 in four Finnish hospitals. The effect of co-morbidity on HRQoL was measured by the generic 15D instrument and by a Visual Analog Scale (VAS). Comparative variance analysis of socio-demographic and clinical characteristics was described by using either an independent samples t-test or the Chi-square test. The differences in each of the 15D dimensions and the overall 15D single index score for patients were calculated. Two-sided p-values were calculated with the Levene Test for Equality of Variances.
Results
Patients with co-morbidity totaled 649; the incidence of co-morbidity was 73%. The mean number of co-morbidities among the patients was two. At baseline the 15D score in patients with and without co-morbidity was 0.778 vs 0.816, respectively. The difference of the score (0.038) was clinically and statistically significant (P < 0.001). The patients' scores with and without co-morbidity on the different 15D dimensions related to osteoarthritis-moving, sleeping, usual activities, discomfort and symptoms, vitality and sexual activity–were low in both groups. Patients with co-morbidity scored lower on the dimensions of moving, vitality and sexual activity compared to the patients without co-morbidity. Co-morbidity was significantly associated with a reduced HRQoL. Patients without co-morbidity had poorer VAS, arthritis had strong effect to their quality of life compared to the patients with co-morbidity.
Conclusion
Assessing co-morbidity in patients placed on the waiting list for joint replacement may be useful method to prioritization in medical decision-making for healthcare delivery. The assessment of co-morbidities during waiting time is important as well as evaluating how the co-morbidity may affect the final outcomes of the total joint replacement.
doi:10.1186/1477-7525-5-16
PMCID: PMC1831765  PMID: 17362498
25.  Do Social Network Characteristics Predict Mammography Screening Practices? 
Background
Many breast cancer screening programs are based on the assumption that dissemination of information through social networks and the provision of social support are effective strategies for promoting mammography use. This paper examines the prospective relationship between social network characteristics and breast cancer screening practices among employed women.
Methods
Women age 40 and over employed in 26 worksites participating in a randomized intervention trial completed baseline and two-year follow-up assessments. These analyses include women in the embedded cohort (n = 1,475). Measures included social network characteristics (size, social influences and support), breast cancer screening practices, and socio-demographic characteristics. Adherence to screening guidelines at follow-up (mammogram within the past two years) was modeled as a function of social network characteristics at baseline.
Results
The majority of women in this sample were adherent with mammography screening guidelines at baseline. Baseline adherence explained the vast majority of variation in screening practices at follow-up. Only after removing the effects of previous mammography screening did we find statistically significant relationships between network characteristics and screening status. Specifically, among women age 40–51, subjective norms and encouragement by family/friends to have a mammogram at baseline were each significantly associated with screening adherence at follow-up (OR = 2.20 and 1.18, respectively). For women age 52+, the perception that screening was normative among one’s peers was associated with increased likelihood of recent mammography at follow-up (OR = 1.46).
Conclusions
Previous mammography use is strongly predictive of future screening. Among employed women with high baseline screening rates, the impact of social network characteristics was modest. As previous use of screening is highly associated with future use, programs should focus on reaching those who have underutilized mammography in the past. In addition, further exploration of the prospective relationships between social network characteristics and mammography within more at-risk and disadvantaged populations is warranted.
doi:10.1177/1090198107303251
PMCID: PMC2859725  PMID: 17620665

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