Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature

Associations between dimensions of the social environment and cardiometabolic health outcomes: a systematic review and meta-analysis

OBJECTIVES

The social environment (SE), that is, the social relationships and social context in which groups of people live and interact, is an understudied element of the broader living environment which impacts health. We aim to summarise the available evidence on the associations between SE and cardiometabolic disease (CMD) outcomes.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, Scopus and Web of Science Core Collection were searched from inception to 28 February 2024.

ELIGIBILITY CRITERIA

We included studies for which determinants were SE factors such as area-level deprivation and social network characteristics and outcomes were type 2 diabetes mellitus and cardiovascular diseases incidence and prevalence.

DATA EXTRACTION AND SYNTHESIS

Titles and abstracts and full text were screened in duplicate. Data appraisal and extraction were based on the study protocol published in PROSPERO. Methodological quality was assessed with the Newcastle-Ottawa Scale. We synthesised the data through vote counting and meta-analyses.

RESULTS

From 10 143 records screened, 281 studies reporting 1108 relevant associations are included in this review. Of the 384 associations included in vote counting, 271 (71%) suggested that a worse SE is associated with a higher risk of CMD. 14 meta-analyses based on 180 associations indicated that worse SE was associated with increased odds of CMD outcomes, with 4 of them being statistically significant. For example, more economic and social disadvantage was associated with higher heart failure risk (OR 1.58, 95% CI 1.08 to 1.61; n=18; I2=95%). With the exception of two meta-analyses for men, meta-analysed sex-specific associations consistently showed results in the same direction as the overall meta-analyses.

CONCLUSION

Worse SE seems to be associated with increased odds of CMD outcomes, although certain SE dimensions are underexplored in relation to CMD.

PROSPERO REGISTRATION NUMBER

CRD42021223035.

The Association Between Cardiometabolic Risk and Cognitive Function Among Older Americans and Chinese

BACKGROUND

Cardiometabolic risk (CMR) is associated with cognitive health, but the association can be affected by broader social, economic, and medical contexts. The United States and China have very different developmental and epidemiological histories, and thus CMR among older people could be linked to cognitive function differently in the 2 countries.

METHODS

Cross-sectional and longitudinal ordinary least squares regression models were estimated for each country using nationally representative samples of populations over age 50: 7 430/4 474 Americans and 6 108/3 655 Chinese in the cross-sectional/longitudinal samples.

RESULTS

In the United States, higher CMR is associated with worse cognitive function (b = -0.08, p < .016). Longitudinally, CMR increase is associated with worse cognitive function at a marginally significant level (b = -0.10, p = .055). No relationship between CMR level or change and cognitive function is observed in China. Higher education levels are linked to better cognitive function and slower cognitive decline in both countries. Unlike older Americans, relative to those with very low education levels, among older Chinese with the highest education level, a higher CMR links to better cognitive function (b = 0.63, p = .013) and slower cognitive decline (b = 0.35, p = .062); Nevertheless, a rapid increase in CMR is additionally harmful (b = -0.54, p = .050) for cognitive function and may lead to faster cognitive decline (b = -0.35, p = .079).

CONCLUSIONS

The significant relationship between CMR and cognitive function in the United States suggests the importance of monitoring and controlling CMR factors at older ages. The insignificant relationship in China may be explained by the high CMR among those with high education levels, highlighting the need for improving cardiometabolic health through education and promoting healthy lifestyles.

Spatial analysis of 10-year predicted risk and incident atherosclerotic cardiovascular disease: the CoLaus cohort

Whether cardiovascular risk scores geographically aggregate and inform on spatial development of atherosclerotic cardiovascular diseases (ASCVD) remains unknown. Our aim is to determine the spatial distribution of 10-year predicted cardiovascular risk and ASCVD, and to compare the overlap of the resulting spatial distributions. Using prospective data from the CoLaus|PsyCoLaus cohort study (2003-2021) we computed SCORE2 in participants free from ASCVD. Geographical distributions of predicted risk and events were determined using the Gi* Getis-Ord autocorrelation statistic. 6203 individuals (54% women, mean age 52.5 ± SD 10.7, ASCVD incidence rate 5.7%) were included. We identified clusters of high versus low predicted risk (4%, 6%, respectively) and ASCVD (5%, 5% respectively) at baseline. They persisted at follow-up. Overlap of SCORE2 and ASCVD clusters was marginal. Body-mass index and alcohol consumption explained most of the predicted risk distribution. For ASCVD, high clusters persisted or were reinforced after multivariate adjustment, while low incidence clusters were reduced, multifactorial determinants. Incidence rate of ASCVD was 2.5% higher (IC 95%, 1.4-3.7) in clusters of higher incidence of ASCVD. To develop up-to-date, geographically targeted prevention strategies, there is a need to study novel geographically risk factors affecting ASCVD and to update commonly used prediction models for a population approach.

[Primary Care Inequalities to the Disadvantage of the Population of Socially Deprived Urban Areas: A Case Study of Essen, Germany]

Background As a large city in the middle of a metropolitan area, the city of Essen is characterized by strong social segregation and a north-south divide in the social status of its population. The conventional demand planning does not take such strong disparities within a planning unit into account. The present study aims to analyze the distribution of primary care structures using the example of two large urban areas within the city of Essen and to identify perspectives for strengthening primary care in disadvantaged urban areas. Methods First, a secondary data analysis was carried out at the district level and the study area Essen North (258,790 residents), consisting of 19 districts, was defined on the basis of two inclusion criteria - 1. location of the district north of the A40 freeway and 2. a mean subsistence rate>17.20 percent (average value of the city of Essen). Subsequently the study area was compared with the rest of the city (332,242 residents) with regard to the indicators a) social status, and b) available general practioners and pediatricians. The data of the selected indicators is based on the social reporting of the city of Essen and the "Online Practice Search" (Online-Praxissuche) of the Association of Statutory Health Insurance Physicians North Rhine. The resulting database (last update: 10/20212) was then evaluated at the district level and the primary care situation of the two urban areas was compared. Results The results revealed a significantly lower primary care coverage in the socially disadvantaged study area Essen North by about 25 percent for general practioners and by almost half for pediatricians. Thus, higher social disadvantage was associated with poorer access to primary care close to home, especially for children and adolescents. Conclusion The distribution of primary care providers in the city of Essen increases the risk of contributing to the manifestation of geographic disparities and health inequalities. The existing planning system in particular is not suitable for addressing the threatening or already existing undersupply of primary health care to the population in disadvantaged urban districts.

Neighborhood socioeconomic status and cardiometabolic risk: mediating roles of domain-specific physical activities and sedentary behaviors

PURPOSE

We examined the potential mediating roles of domain-specific physical activities and sedentary behaviors in the relationship between area-level socioeconomic status (SES) and cardiometabolic risk.

METHODS

Data were from the 2011/2012 Australian Diabetes, Obesity and Lifestyle study (n = 3431). The outcome was a clustered cardiometabolic risk (CCR) score, and the exposure was suburb-level SES. Potential mediators were domain-specific physical activities and sedentary behaviors. Multilevel linear regression models examined associations between SES and potential mediators (α) and between mediators and CCR (β). Mediation was assessed using the joint-significance test.

RESULTS

Higher SES was associated with a lower CCR score. Lower SES was associated with less frequent walking for transport, lower vigorous-intensity recreational physical activity, and higher TV time, which were associated with higher CCR scores. However, higher SES was associated with longer transport-related sitting time (all modes and in cars), which were associated with higher CCR scores.

CONCLUSIONS

The SES-cardiometabolic risk relationship may be partially explained by walking for transport, vigorous-intensity recreational physical activity, and TV viewing. These findings, which require corroboration from prospective evidence and clarification of the roles of transport-related sitting and occupational physical activity, can inform initiatives addressing socioeconomic inequalities in cardiometabolic health.

Major and minor ECG abnormalities depending on regional living conditions in Russia

The goal of our study was to explore the effect of living conditions on the odd of major and minor ECG abnormalities on a large region scale in Russia. For the analysis, cross-sectional data of the Russian study, ESSE-RF 2013-2014, were used. They were collected on a sample of 16,400 subjects from 10 regions of the Russia. ECG abnormalities were grouped into two categories: Major and Minor (sensu the 2009 version of the Minnesota Code Classification System). Regional living conditions were considered comprehensively via five indices combining 33 characteristics of the regions. The estimates were presented as odds ratios and their 95% confidence intervals. The prevalence values of major abnormalities in the sample were 8.4% among women and 9.4% among men (p = 0.021). The prevalence of minor abnormalities constituted 34.1% and 45.9%, respectively (p < 0.001). In men, the odd of major ECG abnormalities increased with the demographic depression growth (1.08: 1.04-1.12) and with industrial development growth in the region (1.12: 1.07-1.17). In women, an increase in the odd of major ECG abnormalities was directly associated with industrial development (1.12: 1.07-1.16) and inversely related to the economic development in the region (0.94: 0.89-0.99). The odd of minor ECG abnormalities in men and women declined with the growth of the regional economic development: OR of 0.95: 0.93-0.98, and OR of 0.92: 0.87-0.99, respectively. The study demonstrated an effect of regional living conditions of the Russian population on the odd of major and minor ECG abnormalities. The most stable and logically explainable relationships were obtained for industrial and economic characteristics of living conditions.

Socioeconomic deprivation and cardiometabolic risk factors in individuals with type 1 diabetes: T1D exchange clinic registry

AIMS

Social determinants of health (SDOH) influence cardiovascular health in the general population; however, the degree to which this occurs in individuals with type 1 diabetes (T1D) is not well understood. We evaluated associations among socioeconomic deprivation and cardiometabolic risk factors (hemoglobin A1c, low-density lipoprotein, blood pressure, body mass index, physical activity) in individuals with T1D from the T1D Clinic Exchange Registry.

METHODS

We evaluated the association between the social deprivation index (SDI) and cardiometabolic risk factors using multivariable and logistic regression among 18,754 participants ages 13 - 90 years (mean 29.2 ± 17) in the T1D Exchange clinic registry from 6,320 zip code tabulation areas (2007-2017).

RESULTS

SDI was associated with multiple cardiometabolic risk factors even after adjusting for covariates (age, biological sex, T1D duration, and race/ethnicity) in the multivariable linear regression models. Those in the highest socially deprived areas had 1.69 (unadjusted) and 1.78 (adjusted) times odds of a triple concomitant risk burden of poor glycemia, dyslipidemia, and hypertension.

CONCLUSIONS

Persistent SDOH differences could account for a substantial degree of poor achievement of cardiometabolic targets in individuals with T1D. Our results suggest the need for a broader framework to understand the association between T1D and adverse cardiometabolic outcomes.

Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation

BACKGROUND

Obesity is a serious public health problem. Existing research has shown a strong association between obesity and an individual's diet and physical activity. If we extend such an association to the neighborhood level, information about the diet and physical activity of the residents of a neighborhood may improve the estimate of neighborhood-level obesity prevalence and help identify the neighborhoods that are more likely to suffer from obesity. However, it is challenging to measure neighborhood-level diet and physical activity through surveys and interviews, especially for a large geographic area.

METHODS

We propose a method for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data, and examine the extent to which the derived measurements can enhance obesity estimation, in addition to the socioeconomic and demographic variables typically used in the literature. We conduct case studies in three different U.S. cities, which are New York City, Los Angeles, and Buffalo, using anonymized mobile phone location data from the company SafeGraph. We employ five different statistical and machine learning models to test the potential enhancement brought by the derived measurements for obesity estimation.

RESULTS

We find that it is feasible to derive neighborhood-level diet and physical activity measurements from anonymized mobile phone location data. The derived measurements provide only a small enhancement for obesity estimation, compared with using a comprehensive set of socioeconomic and demographic variables. However, using these derived measurements alone can achieve a moderate accuracy for obesity estimation, and they may provide a stronger enhancement when comprehensive socioeconomic and demographic data are not available (e.g., in some developing countries). From a methodological perspective, spatially explicit models overall perform better than non-spatial models for neighborhood-level obesity estimation.

CONCLUSIONS

Our proposed method can be used for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone data. The derived measurements can enhance obesity estimation, and can be especially useful when comprehensive socioeconomic and demographic data are not available. In addition, these derived measurements can be used to study obesity-related health behaviors, such as visit frequency of neighborhood residents to fast-food restaurants, and to identify primary places contributing to obesity-related issues.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Regional living conditions and the prevalence, awareness, treatment, control of hypertension at the individual level in Russia

Background

The objective of our study was to investigate the associations of characteristics inherent in large Russian Federation (RF) regions with prevalence, awareness, treatment and control of hypertension at the individual level.

Methods

Regional characteristics were obtained from the official website of the Federal State Statistics Service of the RF. We employed principal component analysis to reduce the dimensionality of data, which allowed defining five integral regional indices. Prevalence, awareness, treatment and control of hypertension were assessed from the data of the cross-sectional stage of ESSE-RF study conducted in 2013–2014. The final sample included 19,791 patients from 12 RF regions. Generalized estimating equations were used to identify the associations of regional indices with prevalence, awareness, treatment and control of hypertension at the individual level, taking into consideration nested data structures (study subjects in the regions).

Results

The index characterizing deterioration of social living conditions and societal marginalization exhibited positive associations with the prevalence of hypertension among men (OR = 1.18; 95% CI: 1.05–1.32) and elderly people (OR = 1.16; 95% CI: 1.02–1.32). Moreover, deterioration in the social environment was associated with a reduction in treatment (OR = 0.76; 95% CI: 0.64–0.90) and control of hypertension (OR = 0.79; 95% CI: 0.69–0.90). Hypertension awareness was directly connected with demographic crisis (OR = 1.13; 95% CI: 1.02–1.25) and augmented industrial development (OR = 1.15; 95% CI: 1.01–1.33) in the regions. The association of regional living conditions with the prevalence of hypertension is relatively weak, compared to predictors at the individual level, but this influence is important for awareness, treatment and control of hypertension.

Conclusion

The study contributed to evaluating the associations of the vital characteristics inherent in population of large RF regions with arterial hypertension prevalence, as well as with awareness, treatment and control of this disease. Our results provided original insights from the standpoint of cardiovascular disease epidemiology in the RF, as well as in the context of investigating the impact of living conditions on population health.

Educational Level, but Not Income or Area Deprivation, is Related to Macrovascular Disease: Results From Two Population-Based Cohorts in Germany

Objectives: An inverse relationship between education and cardiovascular risk has been described, however, the combined association of education, income, and neighborhood socioeconomic status with macrovascular disease is less clear. The aim of this study was to evaluate the association of educational level, equivalent household income and area deprivation with macrovascular disease in Germany.

Methods: Cross-sectional data from two representative German population-based studies, SHIP-TREND (n = 3,731) and KORA-F4 (n = 2,870), were analyzed. Multivariable logistic regression models were applied to estimate odds ratios and 95% confidence intervals for the association between socioeconomic determinants and macrovascular disease (defined as self-reported myocardial infarction or stroke).

Results: The study showed a higher odds of prevalent macrovascular disease in men with low and middle educational level compared to men with high education. Area deprivation and equivalent income were not related to myocardial infarction or stroke in any of the models.

Conclusion: Educational level, but not income or area deprivation, is significantly related to the macrovascular disease in men. Effective prevention of macrovascular disease should therefore start with investing in individual education.

Effect of regional living conditions on middle-term cardiovascular outcomes: data from prospective stage of the ESSE-RF study

Aim. To assess regional living conditions with cardiovascular outcomes based on prospective stage of the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study.

Material and methods. We used data from a 3- and 5-year prospective stage of the ESSE-RF study (2012-2013). For initial screening, 16210 people aged 25-64 were included. To characterize the regions of residence, 5 regional indices (RIs) were used. The following endpoints were considered: cardiovascular death, myocardial infarction, stroke, composite endpoint (cardiovascular death + myocardial infarction + stroke). To assess the correlations, generalized estimating equations with nested data structure (individuals in the regions) were used. The studied associations were adjusted for a wide range of potential effect modifiers.

Results. There are multiple direct associations of RIs with the likelihood of all endpoints. The exception was the reverse association of Industrial RI with cardiovascular death. The most stable associations regarded Mixed RI, Industrial RI and Socio-geographical RI, with a high RI significance in endpoint development, compared with well-known individual cardiovascular risk factors. Separate associations were noted for Economic RI and Demographic RI.

Conclusion. The results indicate a significant contribution of a number of regional living conditions to individual risk of fatal and non-fatal cardiovascular outcomes.

Changes in the Prevalence of Metabolic Syndrome and Its Components as Well as in Relevant Preventive Medication between 2006 and 2018 in the Northeast Hungarian Population

Metabolic syndrome (MetS) is a worldwide problem with severe health consequences. In this study, we examine the changes in the prevalence of MetS and its components in two disadvantaged counties of Northeastern Hungary. Two health examination surveys were performed in the Hungarian population aged 20–64 years in 2006 (n = 450) and 2018 (n = 397) and the data were compared to each other. It was found that the prevalence of MetS increased significantly in the period examined (from 34.9% to 42.2%, p = 0.035) due to the increased prevalence of raised blood pressure (from 45.6% to 57.0%, p = 0.002) and raised fasting glucose concentration (13.2% vs. 24.8%, p < 0.001). The increase mainly affects the younger (20–34 years old) age group (12.1% in 2006 vs. 31.6% in 2018, p = 0.001). It is quite alarming that the prevalence of MetS and its components has increased significantly in the last decade, while the prevalence of preventive medication is unchanged (antihypertensive and antidiabetic treatments) or even significantly decreased (lipid-lowering medication). Consequently, the number of individuals untreated for hypertension and metabolic disturbances is severely increased. A targeted public health strategy is desperately needed to prevent further worsening the situation.

Impact of area deprivation on the cardiac mortality in the UK between1991 and 2010: evidence from a population-based longitudinal study

AIMS

Evidence from longitudinal studies on the influence of area deprivation in cardiac mortality is limited. We aimed to examine the impact of area deprivation on cardiac mortality in a large representative Scottish population. We also examined differences between women and men.

METHODS AND RESULTS

Retrospective analysis was performed by using linked data from Scottish Longitudinal Study from 1991 to 2010. The main exposure variable was socioeconomic status using the Carstairs deprivation scores, a composite score of area-level factors. Cox proportional-hazards models were constructed to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiac mortality and all-cause mortality associated with area-based deprivation. Subgroup analyses were stratified by sex. In a representative population of 217 965 UK adults, a total of 58 770 deaths occurred over a median of 10 years of follow-up period. The risk of cardiac mortality and all-cause mortality showed a consistent graded increased across the deprived groups. Compared to the least deprived group, the adjusted HR of cardiac mortality in the most deprived group was 1.27 (1.15-1.39, P < 0.000). There was strong evidence that women from more deprived areas had significantly higher cardiac death risk than those from the least deprived areas (HR 1.42, 95% CI 1.22-1.65), while this observation was not strong in men with same background.

CONCLUSION

Our study demonstrated area deprivation was the strong predictor of long-term cardiac mortality and all-cause mortality. The inequalities were substantially greater in women from more deprived areas than men from the same background.

A decision support framework for prediction of avian influenza

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

Geographic variation in cardiometabolic risk factor prevalence explained by area-level disadvantage in the Illawarra-Shoalhaven region of the NSW, Australia

Cardiometabolic risk factors (CMRFs) demonstrate significant geographic variation in their distribution. The study aims to quantify the general contextual effect of the areas on CMRFs; and the geographic variation explained by area-level socioeconomic disadvantage. A cross sectional design and multilevel logistic regression methods were adopted. Data included objectively measured routine pathology test data between years 2012 and 2017 on: fasting blood sugar level; glycated haemoglobin; total cholesterol; high density lipoprotein; urinary albumin creatinine ratio; estimated glomerular filtration rate; and body mass index. The 2011 Australian census based Index of Relative Socioeconomic Disadvantage (IRSD) were the area-level study variables, analysed at its smallest geographic unit of reporting. A total of 1,132,029 CMRF test results from 256,525 individuals were analysed. After adjusting for individual-level covariates, all CMRFs significantly associated with IRSD and the probability of higher risk CMRFs increases with greater area-level disadvantage. Though the specific contribution of IRSD in the geographic variation of CMRF ranged between 57.8 and 14.71%, the general contextual effect of areas were found minimal (ICCs 0.6-3.4%). The results support universal interventions proportional to the need and disadvantage level of populations for the prevention and control of CMRFs, rather than any area specific interventions as the contextual effects were found minimal in the study region.

Role of Area-Level Access to Primary Care on the Geographic Variation of Cardiometabolic Risk Factor Distribution: A Multilevel Analysis of the Adult Residents in the Illawarra-Shoalhaven Region of NSW, Australia

BACKGROUND

Access to primary care is important for the identification, control and management of cardiometabolic risk factors (CMRFs). This study investigated whether differences in geographic access to primary care explained area-level variation in CMRFs.

METHODS

Multilevel logistic regression models were used to derive the association between area-level access to primary care and seven discrete CMRFs after adjusting for individual and area-level co-variates. Two-step floating catchment area method was used to calculate the geographic access to primary care for the small areas within the study region.

RESULTS

Geographic access to primary care was inversely associated with low high density lipoprotein (OR 0.94, CI 0.91-0.96) and obesity (OR 0.91, CI 0.88-0.93), after adjusting for age, sex and area-level disadvantage. The intra-cluster correlation coefficient (ICCs) of all the fully adjusted models ranged between 0.4-1.8%, indicating low general contextual effects of the areas on CMRF distribution. The area-level variation in CMRFs explained by primary care access was ≤10.5%.

CONCLUSION

The findings of the study support proportionate universal interventions for the prevention and control of CMRFs, rather than any area specific interventions based on their primary care access, as the contextual influence of areas on all the analysed CMRFs were found to be minimal. The findings also call for future research that includes other aspects of primary care access, such as road-network access, financial affordability and individual-level acceptance of the services in order to gain an overall picture of the area-level contributing role of primary care on CMRFs in the study region.

A diabetes risk index for small areas in England

UK and international studies point to significant area variation in diabetes risk, and summary indices of diabetic risk are potentially of value in effective targeting of health interventions and healthcare resources. This paper aims to develop a summary measure of the diabetic risk environment which can act as an index for targeting health care resources. The diabetes risk index is for 6791 English small areas (which provide entire coverage of England) and has advantages in incorporating evidence from both diabetes outcomes and area risk factors, and in including spatial correlation in its construction. The analysis underlying the risk index shows that area socio-economic status, social fragmentation and south Asian ethnic concentration are all positive risk factors for diabetes risk. However, urban-rural and regional differences in risk intersect with these socio-demographic influences.

Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia

INTRODUCTION

Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches.

METHOD

A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA).

RESULTS

A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.

CONCLUSION

The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.