Estimating CHD prevalence by small area: integrating information from health surveys and area mortality

Influences of socioeconomic deprivation on GPs' decisions to refer patients to cardiology: a qualitative study

BACKGROUND

Variation in GP referral practice may be a factor contributing to the lower uptake of cardiology specialist services for people living in socioeconomic deprivation. Cardiology referrals were chosen for this study due to higher rates of premature death and emergency admissions resulting from coronary heart disease for patients living in more deprived areas.

AIM

To find out how socioeconomic deprivation influences GP referral practice.

DESIGN AND SETTING

A qualitative study of GPs working in affluent and deprived areas of one large city in the UK.

METHOD

The authors used purposive and snowball sampling to recruit 17 GP participants to interviews and a focus group. Participants were asked to reflect on their own experience of making referrals. The authors used a framework approach to the analysis, with differences in themes for GPs working in least and most deprived areas being highlighted.

RESULTS

The authors identified four main themes by which socioeconomic deprivation influenced GP referral practice: identifying problems; making decisions about referral; navigating the healthcare system; and external pressures. Using a published framework of consultation complexity, the authors then examined the data in relation to a fifth theme of complexity. Referrals from areas of high socioeconomic deprivation involved greater complexity in the majority of the domains of this framework.

CONCLUSION

Socioeconomic deprivation influences GP referral decisions and navigation of the healthcare system in multiple ways. Referral practice for GPs working in deprived areas is more complex than for their peers working in more affluent areas.

The spatial structure of chronic morbidity: evidence from UK census returns

Background

Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity.

Methods

2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power.

Results

The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions.

Conclusions

Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0057-5) contains supplementary material, which is available to authorized users.

Equity of utilisation of cardiovascular care and mental health services in England: a cohort-based cross-sectional study using small-area estimation

Background

A strong policy emphasis on the need to reduce both health inequalities and unmet need in deprived areas has resulted in the substantial redistribution of English NHS funding towards deprived areas. This raises the question of whether or not socioeconomically disadvantaged people continue to be disadvantaged in their access to and utilisation of health care.

Objectives

To generate estimates of the prevalence of cardiovascular disease (CVD) and common mental health disorders (CMHDs) at a variety of scales, and to make these available for public use via Public Health England (PHE). To compare these estimates with utilisation of NHS services in England to establish whether inequalities of use relative to need at various stages on the health-care pathway are associated with particular sociodemographic or other factors.

Design

Cross-sectional analysis of practice-, primary care trust- and Clinical Commissioning Group-level variations in diagnosis, prescribing and specialist management of CVD and CMHDs relative to the estimated prevalence of those conditions (calculated using small-area estimation).

Results

The utilisation of CVD care appears more equitable than the utilisation of care for CMHDs. In contrast to the reviewed literature, we found little evidence of underutilisation of services by older populations. Indeed, younger populations appear to be less likely to access care for some CVD conditions. Nor did deprivation emerge as a consistent predictor of lower use relative to need for either CVD or CMHDs. Ethnicity is a consistent predictor of variations in use relative to need. Rates of primary management are lower than expected in areas with higher percentages of black populations for diabetes, stroke and CMHDs. Areas with higher Asian populations have higher-than-expected rates of diabetes presentation and prescribing and lower-than-expected rates of secondary care for diabetes. For both sets of conditions, there are pronounced geographical variations in use relative to need. For instance, the North East has relatively high levels of use of cardiac care services and rural (shire) areas have low levels of use relative to need. For CMHDs, there appears to be a pronounced ‘London effect’, with the number of people registered by general practitioners as having depression, or being prescribed antidepressants, being much lower in London than expected. A total of 24 CVD and 41 CMHD prevalence estimates have been provided to PHE and will be publicly available at a range of scales, from lower- and middle-layer super output areas through to Clinical Commissioning Groups and local authorities.

Conclusions

We found little evidence of socioeconomic inequality in use for CVD and CMHDs relative to underlying need, which suggests that the strong targeting of NHS resources to deprived areas may well have addressed longstanding concerns about unmet need. However, ethnicity has emerged as a significant predictor of inequality, and there are large and unexplained geographical variations in use relative to need for both conditions which undermine the principle of equal access to health care for equal needs. The persistence of ethnic variations and the role of systematic factors (such as rurality) in shaping patterns of utilisation deserve further investigation, as does the fact that the models were far better at explaining variation in use of CVD than mental health services.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

National prevalence of coronary heart disease and its relationship with human development index: A systematic review

BACKGROUND

Coronary heart disease has become a major health concern over the past several decades. Several reviews have assessed the effects of socioeconomic status on the coronary heart disease epidemic in communities and countries, but only a few reviews have been performed at a global level. This study was to explore the relationship between the prevalence of coronary heart disease and socioeconomic development worldwide using the Human Development Index.

DESIGN

Systematic review.

METHODS

The data in this study were collected from the MEDLINE database. Cross-sectional studies reporting the prevalence of coronary heart disease until November 2014 were collected. The Human Development Index was sourced from the United Nations Development Programme Database and was used to measure the socioeconomic achievements of countries. Each country was classified as a developing or developed country based on its level of development according to the Human Development Index value.

RESULTS

Based on the data analysis on the global level, coronary heart disease prevalence had no association with the national Human Development Index (rho = 0.07). However, there was a positive association between coronary heart disease prevalence and the national Human Development Index in developing countries, although a negative association existed in developed countries (rho = 0.47 and -0.34, respectively). In addition, the past decades have witnessed a growing coronary heart disease epidemic in developing countries, with reverse trends observed in developed countries (P = 0.021 and 0.002, respectively).

CONCLUSIONS

With the development of socioeconomic status, as measured by the Human Development Index, the prevalence of coronary heart disease is growing in developing countries, while declining in developed countries. Future research needs to pay more attention to the reasonable allocation of medical resources and control of coronary heart disease risk factors.

Socio-economic disparities in mortality due to pandemic influenza in England

OBJECTIVES

This study examines variations in mortality between socio-economic groups due to the pandemic Influenza (H1N1) 2009 virus in England.

METHODS

We established a system to identify all deaths related to pandemic (H1N1) 2009 influenza. We collected the postcode of every individual who died, and through this determined the socio-economic deprivation, urban-rural characteristics and region of their residence. Across England, we were therefore able to examine how mortality rates varied by socio-economic group, between urban and rural areas, and between regions.

RESULTS

People in the most deprived quintile of England's population had an age and sex-standardised mortality rate three times that experienced by the least deprived quintile (RR = 3.1, 95% CI 2.2-4.4). Mortality was also higher in urban areas than in rural areas (RR = 1.7, 95% CI 1.2-2.3). Mortality rates were similar between regions of the country.

CONCLUSION

Tackling socio-economic health inequalities is a central concept within public health, but has not always been a part of emergency preparedness plans. These data demonstrate the opportunity to reduce the overall impact and narrow inequalities by considering socio-economic disparities in future pandemic planning.

Generating small-area prevalence of psychological distress and alcohol consumption: validation of a spatial microsimulation method

PURPOSE

Public mental health surveillance data are rarely available at a fine geographic scale. This study applies a spatial microsimulation procedure to generate small-area (lower super outputs areas [LSOA]) estimates of psychological distress and alcohol consumption. The validity of LSOA estimates and their associations with proximal and broader socioeconomic conditions are examined.

METHODS

A deterministic reweighting methodology assigns prevalence estimates for psychological distress and heavy alcohol consumption through a process of matching individuals from a large, population-representative dataset (Health Survey for England) to known LSOA populations (from the 2001 population Census). 'goodness-of-fit' of LSOA estimates is assessed by their comparison to observed prevalence of these health indicators at higher levels of aggregation (local authority districts [LAD]). Population prevalence estimates are correlated to the mental health needs index (MINI) and other health indicators; ordered logistic regression is applied to investigate their associations with proximal and broader socioeconomic conditions.

RESULTS

Performance of microsimulation models is high with no more than 10% errors in at least 90% of LAD for psychological distress and moderate and heavy alcohol consumption. The MINI is strongly correlated with psychological distress (r = 0.910; p value < 0.001) and moderately with heavy drinking (r = 0.389; p value < 0.001). Psychological distress and heavy alcohol consumption are differently associated with socioeconomic and rurality indicators at the LSOA level. Associations further vary at the LAD level and regional variations are apparent.

CONCLUSION

Spatial microsimulation may be an appropriate methodological approach for replicating social and demographic health patterns at the local level.

Spatial clustering of non-transported cardiac decedents: the results of a point pattern analysis and an inquiry into social environmental correlates

Background

People who die from heart disease at home before any attempt at transport has been made may represent missed opportunities for life-saving medical intervention. In this study, we undertook a point-pattern spatial analysis of heart disease deaths occurring before transport in a large metropolitan area to determine whether there was spatial clustering of non-transported decedents and whether there were significant differences between the clusters of non-transported cardiac decedents and the clusters of transported cardiac decedents in terms of average travel distances to nearest hospital and area socioeconomic characteristics. These analyses were adjusted for individual predictors of transport status.

Methods

We obtained transport status from the place of death variable on the death certificate. We geocoded heart disease decedents to residential street addresses using a rigorous, multistep process with 97% success. Our final study population consisted of 11,485 adults aged 25-74 years who resided in a large metropolitan area in west-central Florida and died from heart disease during 1998-2002. We conducted a kernel density analysis to identify clusters of the residential locations of cardiac decedents where there was a statistically significant excess probability of being either transported or not transported prior to death; we controlled for individual-level covariates using logistic regression-derived probability estimates.

Results

The majority of heart disease decedents were married (53.4%), male (66.4%), white (85.6%), and aged 65-74 years at the time of death (54.7%), and a slight majority were transported prior to death (57.7%). After adjustment for individual predictors, 21 geographic clusters of non-transported heart disease decedents were observed. Contrary to our hypothesis, clusters of non-transported decedents were slightly closer to hospitals than clusters of transported decedents. The social environmental characteristics of clusters varied in the expected direction, with lower socioeconomic and household resources in the clusters of non-transported heart disease deaths.

Conclusions

These results suggest that in this large metropolitan area unfavorable household and neighborhood resources played a larger role than distance to hospital with regard to transport status of cardiac patients; more research is needed in different geographic areas of the United States and in other industrialized nations.

Modeling coronary heart disease prevalence in regional and sociodemographic contexts

The mortality rates from coronary heart disease (CHD) have been documented extensively in the United States and substantial disparities in CHD mortality rates exist by age, sex, race/ethnicity, socioeconomic status, and geographic location. The prevalence of people living with CHD, however, is relatively unexplored in part due to the lack of data. Using data from the Behavioral Risk Factor Surveillance System for 2005-2007, this study develops a logistic regression model to estimate the probability of CHD prevalence while incorporating key factors associated with CHD mortality rates. The findings highlight that older white males with lower socioeconomic status are more likely to be diagnosed with CHD, compared to their black counterparts. Areas of higher probability of CHD prevalence coincide with areas of higher CHD mortality rates. The lower probability of CHD prevalence among blacks, however, may be directly influenced by their higher CHD mortality rates.

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates

BACKGROUND

Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease.

METHODS

A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates.

RESULTS

To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit.

CONCLUSION

Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables.