Population effects on individual systolic blood pressure: a multilevel analysis of the World Health Organization MONICA Project

Prevalence and correlates of multidimensional child poverty in India during 2015-2021: A multilevel analysis

Despite increasing research and programs to eradicate poverty, poverty still exists and is a far greater concern for children than adults, leading child poverty to become a political, economic, and social issue worldwide and in India. The current study aims to find variations in the prevalence of child poverty and associated factors in India during 2015-21. In the current study, we used two consecutive rounds of the National Family Health Survey (NFHS-4, 2015-16 & NFHS-5, 2019-21) to estimate child poverty (aged 0-59 months) using the Alkire-Foster method. The multilevel logistic regression analyses were performed to find the important cofounder and cluster level variation in child poverty. The results show that about 38 percent of children were multidimensionally poor in 2015-16, which reduced to 27 percent in 2019-21. The decomposition analysis suggests that contribution of nutrition domain to child poverty increases over time, whereas the standard of living substantially declines from NFHS-4 to NFHS-5. The multilevel analysis results show that the age and sex of the child, age and years of schooling of the mother, children ever born, religion, caste, wealth quintile and central, northeast, north and west regions are significantly associated with child poverty over time. Further, the variance participation coefficient statistics show that about 12 percent of the variation in the prevalence of child poverty could be attributed to differences at the community level. The prevalence of child poverty significantly declines over time, and the community-level variation is higher than the district-level in both surveys. However, the community-level variation shows increases over time. The finding suggests a need to improve the nutritional status and standard of living of most deprived households by promoting a child-centric and dimension-specific approach with more focus on PSU-level intervension should adopt in order to lessen child poverty in India.

Multilevel Understanding of the Impact of Individual- and School-Level Determinants on Lipid Profiles in Adolescents: The Cross-Level Interaction of Food Environment and Body Mass Index

Adolescents with comparable personal risk factors may have different lipid profiles because of the school’s context. Lipid determinants in adolescents should be considered using a multilevel perspective. This multilevel study investigated the effects of individual-level and school-level factors on lipid profiles in adolescents and evaluated the cross-level influence of lipid determinants. A representative adolescent cohort (n = 2727) was randomly selected from 36 schools in three diverse economic areas in Taiwan and assessed for their personal dietary patterns, physical parameters, and lipid profiles. For individual-level factors, both low physical activity and high body mass index (BMI) were associated with elevated triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels, and a sugar-sweetened beverage intake of >500 mL/day was associated with increases of 5.97 and 6.12 mg/dL in LDL-C and TC levels, respectively, compared with abstinence. Regarding school-level factors, students in schools with ≥2 health promotion programs per year had a 5.27 mg/dL lower level of LDL-C than those in schools with 0−1 program, and students in schools with ≥46 food outlets within 600 m of the school had 6.90 and 13.3 mg/dL higher levels of TG and TC, respectively, than those in schools with <46 food outlets. School context modified the individual-level positive correlation between BMI and TG level (the p-value for the random-slope effect was 0.003). In conclusion, individual-level and school-level factors exert a multilevel effect on adolescent lipid profiles. The food environment near the school has a stronger cross-level impact on individual TG levels in adolescents with a high BMI than in those with a normal BMI.

Contextual and individual factors associated with knowledge, awareness and attitude on liver diseases: A large-scale Asian study

There are limited data to provide better understanding of the knowledge/awareness of general population towards liver health in Asia. We sought to identify the knowledge gaps and attitudes towards liver health and liver diseases as well as evaluate associated individual-level and macro-level factors based on contextual analysis. An online survey assessing knowledge, awareness and attitudes towards liver health and disease was conducted among 7500 respondents across 11 countries/territories in Asia. A liver index was created to measure the respondents' knowledge level and the degree of awareness and attitudes. Multilevel logistic regression was performed to identify individual factors and contextual effects that were associated with liver index. The overall liver index (0-100-point scale) was 62.4 with 6 countries/territories' liver indices greater than this. In the multilevel model, the inclusion of geographical information could explain for 9.6% of the variation. Residing in a country/territory with higher HBV prevalence (80% IOR: 1.20-2.79) or higher HCV death rate (80% IOR: 1.35-3.13) increased the individual probability of obtaining a high overall liver index. Individual factors like age, gender, education, household income, disease history and health screening behaviour were also associated with liver index (all p-values<0.001). The overall liver index was positively associated with the two macro-level factors viz. HBV prevalence and HCV death rate. There is a need to formulate policies especially in regions of lower HBV prevalence and HCV death rate to further improve the knowledge, awareness and attitudes of the general public towards liver diseases.

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.

Risk Factors and Comorbidities in Young Indian Patients with Hypertension: REAL YOUNG (Hypertension) Study

Purpose

To analyze the risk factors and comorbidities among the young Indian adults with hypertension.

Patients and Methods

This was a retrospective, multicentric real-world study which included patients diagnosed with and receiving treatment for hypertension. Data were collected from the medical records of clinics/hospitals across 623 study sites in India. Patients of either sex and aged 18–45 years were included. Demographic details (age, sex, anthropometric measurement), medical and family history, sedentary lifestyle, smoking status and alcohol consumption data were extracted. Descriptive and comparative analysis (Mann–Whitney U and chi-squared test) was done.

Results

Out of 15,006 young patients diagnosed with hypertension (men=63.6%), 65.7% belonged to the age group of >35–45 years. The median body mass index was 27.0 kg/m². Patients diagnosed with only hypertension were 29.1% while other predominant comorbidities with hypertension were diabetes mellitus (42.4%) and dyslipidemia (7.8%). Hypertension with diabetes mellitus were prevalent in the age group of >35–45 years (43.8%). More than half of the patients with hypertension (n=7656) had a sedentary lifestyle. Overall, 35.6%, 47.3%, and 56.7% of the patients were alcoholic, smokers (present and former), and had a family history of hypertension, respectively.

Conclusion

The results showed that among the young population, hypertension was common in the age group of >35–45 years and diabetes mellitus and dyslipidemia were common comorbidities. Family history, sedentary lifestyle, smoking, alcohol consumption, and body mass index may also contribute to hypertension.

The Relationship Between Alcohol Consumption With Vascular Structure and Arterial Stiffness in the Spanish Population: EVA Study

BACKGROUND

Controversy exists regarding the way alcohol use is associated with vascular structure and arterial stiffness parameters. The purpose of this study is to evaluate the association between alcohol consumption with vascular structure and arterial stiffness in an adult population.

METHODS

We conducted a cross-sectional study. Five hundred and one participants were recruited by random sampling from an urban population of 43,946 individuals aged 35 to 75 years, assigned from 5 healthcare centers located in Salamanca, Spain. This was a subanalysis of the EVA study. Arterial stiffness was assessed by measuring cardio-ankle vascular index (CAVI) and brachial-ankle pulse wave velocity (ba-PWV) with the VaSera VS-1500^® device, and carotid-femoral pulse wave velocity (cf-PWV) with the Sphygmocor^® . Vascular structure was evaluated by measuring the carotid intima-media thickness (c-IMT) with a Sonosite Micromax^® ultrasound. Alcohol consumption was calculated using a standardized questionnaire and quantified in g/wk.

RESULTS

In the multiple regression analysis adjusted for sex, age, smoking status, and systolic blood pressure, the c-IMT and cf-PWV values of individuals drinking > 70 g/wk were higher than those of the reference group (nondrinkers) by 0.02 mm (p = 0.030) and 0.42 m/s (p = 0.021), respectively. The association between alcohol consumption with vascular structure and arterial stiffness only attains statistical significance for cf-PWV (p = 0.039) and c-IMT (p = 0.019), showing an association which suggests a J-shaped association. This association was not statistically significant for ba-PWV and CAVI (p = 0.446 and p = 0.141, respectively), although a similar trend was observed with ba-PWV.

CONCLUSIONS

The results of this study suggest that heavy alcohol consumption (>70 g/wk) is associated with increased c-IMT and cf-PWV. Moreover, we found a J-shaped association between alcohol consumption and c-IMT and cf-PWV values as markers of vascular structure and arterial stiffness, with no association with ba-PWV and CAVI being observed.

Comparisons of body mass index, waist circumference, waist-to-height ratio and a body shape index (ABSI) in predicting high blood pressure among Malaysian adolescents: a cross-sectional study

OBJECTIVE

To compare the performance of different anthropometric indices including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and a body shape index to predict high blood pressure (BP) in adolescents using the 90th and 95th percentiles as two different thresholds.

DESIGN

Cross-sectional study.

SETTING

Probability proportionate to size was used to randomly select two schools in Selangor state, Malaysia.

PARTICIPANTS

A total of 513 adolescents (58.9% women and 41.1% men) aged 12-16 years were recruited.

PRIMARY AND SECONDARY OUTCOME MEASURES

Weight, height, WC and BP of the adolescents were measured. The predictive power of anthropometric indices was analysed by sex using the receiver operating characteristic curve.

RESULTS

BMI and WHtR were the indices with higher areas under the curve (AUCs), yet the optimal cut-offs to predict high BP using the 95th percentile were higher than the threshold for overweight/obesity. Most indices showed poor sensitivity under the suggested cut-offs. In contrast, the optimal BMI and WHtR cut-offs to predict high BP using the 90th percentile were lower (men: BMI-for-age=0.79, WHtR=0.46; women: BMI-for-age=0.92, WHtR=0.45). BMI showed the highest AUC in both sexes but had poor sensitivity among women. WHtR presented good sensitivity and specificity in both sexes.

CONCLUSIONS

These findings suggested that WHtR might be a useful indicator for screening high blood pressure risk in the routine primary-level health services for adolescents. Future studies are warranted to involve a larger sample size to confirm these findings.

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.

Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

BACKGROUND

In light of the opioid epidemic in the United States, there is growing concern about the use of opioids in Sweden as it may lead to misuse and overuse and, in turn, severe public health problems. However, little is known about the distribution of opioid use across different demographic and socioeconomic dimensions in the Swedish general population. Therefore, we applied an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), to obtain an improved mapping of the risk heterogeneity of and socioeconomic inequalities in opioid prescription receipt.

METHODS AND FINDINGS

Using data from 6,846,106 residents in Sweden aged 18 and above, we constructed 72 intersectional strata from combinations of gender, age, income, cohabitation status, and presence or absence of psychological distress. We modelled the absolute risk (AR) of opioid prescription receipt in a series of multilevel logistic regression models distinguishing between additive and interaction effects. By means of the Variance Partitioning Coefficient (VPC) and the area under the receiver operating characteristic curve (AUC), we quantified the discriminatory accuracy (DA) of the intersectional strata for discerning those who received opioid prescriptions from those who did not. The AR of opioid prescription receipt ranged from 2.77% (95% CI 2.69-2.86) among low-income men aged 18-34, living alone, without psychological distress, to 28.25% (95% CI 27.95-28.56) among medium-income women aged 65 and older, living alone, with psychological distress. In a model that conflated both additive and interaction effects, the intersectional strata had a fair DA for discerning opioid users from non-users (VPC = 13.2%, AUC = 0.68). However, in the model that decomposed total effects into additive and interaction effects, the VPC was very low (0.42%) indicating the existence of small interaction effects for a number of the intersectional strata.

CONCLUSIONS

The intersectional MAIHDA approach aligns with the aims of precision public health, through improving the evidence base for health policy by increasing understanding of both health inequalities and individual heterogeneity. This approach is particularly relevant for socioeconomically conditioned outcomes such as opioid prescription receipt. We have identified intersections of social position within the Swedish population at greater risk for opioid prescription receipt.

Measuring variability between clusters by subgroup: An extension of the median odds ratio

Investigating clustered data requires consideration of the variation across clusters, including consideration of the component of the total individual variance that is at the cluster level. The median odds ratio and analogues are useful intuitive measures available to communicate variability in outcomes across clusters using the variance of random intercepts from a multilevel regression model. However, the median odds ratio cannot describe variability across clusters for different patient subgroups because the random intercepts do not vary by subgroup. To empower investigators interested in equity and other applications of this scenario, we describe an extension of the median odds ratio to multilevel regression models employing both random intercepts and random coefficients. By example, we conducted a retrospective cohort analysis of variation in care limitations (goals of care preferences) according to ethnicity in patients admitted to intensive care. Using mixed-effects logistic regression clustered by hospital, we demonstrated that patients of non-Caucasian ethnicity were less likely to have care limitations but experienced similar variability across hospitals. Limitations of the extended median odds ratio include the large sample sizes and computational power needed for models with random coefficients. This extension of the median odds ratio to multilevel regression models with random coefficients will provide insight into cluster-level variability for researchers interested in equity and other phenomena where variability by patient subgroup is important.

Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data

BACKGROUND

Most epidemiological studies have not simultaneously quantified variance in health within and between populations. We aimed to estimate the extent to which basic socioeconomic factors contribute to variation in body-mass index (BMI) across different populations.

METHODS

We pooled data from the cross-sectional Demographic and Health Surveys (2005-16) for 15-49 year old women with complete data for anthropometric measures in 58 low-income and middle-income countries (LMICs). We compared estimates from multilevel variance component models for BMI before and after adjusting for age and socioeconomic factors (place of residence, education, household wealth, and marital status). The hierarchical structure of the sample included three levels with women at level 1, communities at level 2, and countries at level 3. The primary outcome was BMI. We did a sensitivity analysis using the 2002-03 World Health Surveys.

FINDINGS

Of 1 212 758 women nested within 64 764 communities and 58 countries, we found that most unexplained variation for BMI was attributed to between-individual differences (80%) and the remaining was between-population differences (14% for countries and 6% for communities). Socioeconomic factors explained a large proportion of between-population variance in BMI (14·8% for countries and 47·1% for communities), but only about 2% of interindividual variance. In country-specific models, we found substantial variation in the magnitude of between-individual differences (variance estimates ranging from 7·6 to 31·4, or 86·0-98·6% of the total variation) and the proportion explained by socioeconomic factors (0·1-6·4%). The disproportionately large unexplained between-individual variance in BMI was consistently found in additional analyses including more comprehensive set of predictor variables, both men and women, and populations from low-income and high-income countries.

INTERPRETATION

Our findings on variance decomposition in BMI and explanation by socioeconomic factors at population and individual levels indicate that inferential questions that target within and between populations are importantly inter-related and should be considered simultaneously.

FUNDING

None.

Characteristics of neighborhood environment (social cohesion and safety) and common mental disorders in ELSA-Brasil study: a multilevel analysis

The purpose of this study was to determine if self-reported characteristics of social cohesion and local neighborhood safety positively affect the mental health of their residents, regardless of individual characteristics. A sample of participants in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline was used. The Clinical Interview Schedule-Revised (CIS-R) instrument was used for tracking common mental disorders (CMD). Social cohesion and safety were measured by validated scales of neighborhood environment self-reported characteristics. The multilevel logistic regression model was used to estimate the effect in neighborhoods (level 2) and individuals (level 1), as well as the odds ratios for each neighborhood explanatory variable and social characteristics in the CMD. The results showed that part of the variance (2.3%), in the common mental disorders prevalence is attributed to local neighborhoods. The characteristics of social cohesion and safety remained significant, even after the adjustment of individual explanatory variables. This study confirmed the hypothesis that individuals living in neighborhoods where they perceive low social cohesion and safety present a higher chance of developing CMD.

Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.

General contextual effects on neglected tropical disease risk in rural Kenya

The neglected tropical diseases (NTDs) are characterized by their tendency to cluster within groups of people, typically the poorest and most marginalized. Despite this, measures of clustering, such as within-group correlation or between-group heterogeneity, are rarely reported from community-based studies of NTD risk. We describe a general contextual analysis that uses multi-level models to partition and quantify variation in individual NTD risk at multiple grouping levels in rural Kenya. The importance of general contextual effects (GCE) in structuring variation in individual infection with Schistosoma mansoni, the soil-transmitted helminths, Taenia species, and Entamoeba histolytica/dispar was examined at the household-, sublocation- and constituency-levels using variance partition/intra-class correlation co-efficients and median odds ratios. These were compared with GCE for HIV, Plasmodium falciparum and Mycobacterium tuberculosis. The role of place of residence in shaping infection risk was further assessed using the spatial scan statistic. Individuals from the same household showed correlation in infection for all pathogens, and this was consistently highest for the gastrointestinal helminths. The lowest levels of household clustering were observed for E. histolytica/dispar, P. falciparum and M. tuberculosis. Substantial heterogeneity in individual infection risk was observed between sublocations for S. mansoni and Taenia solium cysticercosis and between constituencies for infection with S. mansoni, Trichuris trichiura and Ascaris lumbricoides. Large overlapping spatial clusters were detected for S. mansoni, T. trichiura, A. lumbricoides, and Taenia spp., which overlapped a large cluster of elevated HIV risk. Important place-based heterogeneities in infection risk exist in this community, and these GCEs are greater for the NTDs and HIV than for TB and malaria. Our findings suggest that broad-scale contextual drivers shape infectious disease risk in this population, but these effects operate at different grouping-levels for different pathogens. A general contextual analysis can provide a foundation for understanding the complex ecology of NTDs and contribute to the targeting of interventions.

General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A conceptual tutorial

To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs).

Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but “statistically significant” SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.

•

The intra-context correlation is a measure of the general contextual effect (GCE).

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Contextual measures of association inform on specific contextual effects (SCEs).

•

Many multilevel regression analyses only report SCEs.

•

Paradoxically, the lower the GCE the easier it is to detect “statistically significant” SCEs.

•

Multilevel regression analysis need to consider both GCEs and SCEs.

Prevalence and correlates of hypertension in Maharashtra, India: A multilevel analysis

BACKGROUND AND AIM

In the last few decades, the prevalence of hypertension has been drastically increased in India. The present study estimates the current prevalence of hypertension and its correlates in the state of Maharashtra. The variation in the prevalence of hypertension associated with individual-level characteristics is explained at the community and district level.

METHODS

Data is used from the recent round of District Level Household & Facility Survey (DLHS-4), 2012-13. The DLHS-4 has used the nationally representative sample, collected through multistage stratified sampling procedure. A similar sampling frame, used in NSSO-2007-08, has been followed. The chi-square test is used to show the significance level of the association between the estimated prevalence of hypertension and its correlates. Multilevel regression analysis is carried out to investigate the effects of individual and community level factors on the prevalence of hypertension.

RESULTS

The overall prevalence of hypertension is 25% in Maharashtra, and a huge variation in the prevalence of hypertension is found across the districts. Dhule, Gadchiroli (with a low HDI rank), Mumbai and Satara (with higher HDI rank) are the districts with the higher (above 30%) prevalence of high blood pressure. The prevalence also significantly varies according to different correlates. The prevalence of high blood pressure is higher among elderly population (40%), among males (28%), in the urban areas (27%) and in the richest wealth quintile (28%). The prevalence is also higher among cigarette smokers (31%), alcohol consumers (30%) and people with obesity (38%) as compared to their counterparts. The results of the multilevel analysis show that the older and obese persons are at four-time higher risk of hypertension. Again, age, sex, marital status, place of residence, wealth status, unhealthy habits (i.e. smoking and alcohol consumption) and BMI are significantly associated with hypertension. The results of VPC statistics show that 14% of hypertension prevalence could be attributed to differences at the community level.

CONCLUSION

The prevalence of hypertension largely varies in the districts of Maharashtra irrespective of their level of socio-economic development (i.e. HDI rank). The variation in the rate of prevalence of hypertension is higher in the community (PSU) level as compared to the variation in the prevalence rate at the district level. Hypertension is attributable to the modifiable factors like risky lifestyle practices.

Patterning of individual heterogeneity in body mass index: evidence from 57 low- and middle-income countries

Modeling variation at population level has become increasingly valued, but no clear application exists for modeling differential variation in health between individuals within a given population. We applied Goldstein's method (in: Everrit, Howell (eds) Encyclopedia of statistics in behavioral science, Wiley, Hoboken, 2005) to model individual heterogeneity in body mass index (BMI) as a function of basic sociodemographic characteristics, each independently and jointly. Our analytic sample consisted of 643,315 non-pregnant women aged 15-49 years pooled from the latest Demographic Health Surveys (rounds V, VI, or VII; years 2005-2014) across 57 low- and middle-income countries. Individual variability in BMI ranged from 9.8 (95% CI: 9.8, 9.9) for the youngest to 23.2 (95% CI: 22.9, 23.5) for the oldest age group; 14.2 (95% CI: 14.1, 14.3) for those with no formal education to 19.7 (95% CI: 19.5, 19.9) for those who have completed higher education; and 13.6 (95% CI: 13.5, 13.7) for the poorest quintile to 20.1 (95% CI: 20.0, 20.2) for the wealthiest quintile group. Moreover, variability in BMI by age was also different for different socioeconomic groups. Empirically testing the fundamental assumption of constant variance and identifying groups with systematically large differentials in health experiences have important implications for reducing health disparity.

Country of residence, gender equality and victim blaming attitudes about partner violence: a multilevel analysis in EU

Background

Intimate partner violence against women (IPVAW) is a global and preventable public health problem. Public attitudes, such as victim-blaming, are important for our understanding of differences in the occurrence of IPVAW, as they contribute to its justification. In this paper, we focus on victim-blaming attitudes regarding IPVAW within the EU and we apply multilevel analyses to identify contextual determinants of victim-blaming attitudes. We investigate both the general contextual effect of the country and the specific association between country level of gender equality and individual victim-blaming attitudes, as well as to what extend a possible general contextual effect was explained by county level gender equality.

Methods

We analyzed data from 26 800 respondents from 27 member states of the European Union who responded to a survey on public perceptions of domestic violence. We applied multilevel logistic regression analysis and measures of variance (intra-class correlation (ICC)) were calculated, as well as the discriminatory accuracy by calculating the area under the receiver operator characteristic curve.

Results

Over and above individual characteristics, about 15% of the individual variance in the propensity for having victim-blaming attitudes was found at the country level, and country level of gender equality did not affect the general contextual effect (i.e. ICC) of the country on individual victim-blaming attitudes.

Conclusion

The present study shows that there are important between-country differences in victim-blaming attitudes that cannot be explained by differences in individual-level demographics or in gender equality at the country level. More research on attitudes towards IPVAW is needed.

Association of workplace and population characteristics with prevalence of hypertension among Brazilian industry workers: a multilevel analysis

BACKGROUND

Exposure to risk factors for hypertension may be influenced by the characteristics of the workplace, where workers spend most of their daily time.

OBJECTIVES

To evaluate the association between features of the companies, particularly the presence of facilities to provide meals, and of population characteristics and the prevalence of hypertension, taking into account individual risk factors for hypertension.

MATERIAL AND METHODS

This multilevel analysis was based on a cross-sectional study with individual and company data from the SESI (Serviço Social da Indústria-Social Service of Industries) study and population-based data from the national census statistics. Workers aged ≥15 years were randomly selected from small (20-99), medium (100-499) and large (≥500 employees) companies per state using multistage sampling. Logistic regression was used to analyse the association between hypertension and individual, workplace and population variables, with odds ratios (ORs; 95% CI) adjusted for three-level variables.

RESULTS

4818 Workers from 157 companies were interviewed and their blood pressure, weight and height were measured. Overall, 77% were men, aged 35.4 ±10.7 years, with 8.7 ±4.1 years of schooling and mostly worked in companies with a staff canteen (66%). Besides individual characteristics-being male, ageing, low schooling, alcohol abuse and higher BMI-a workplace with no staff canteen (OR=1.28; 95% CI 1.08 to 1.52), small companies (OR=1.31; 95% CI 1.07 to 1.60) and living in cities with higher economic inequality (OR=1.47; 95% CI 1.23 to 1.76) were associated with a higher risk for hypertension.

CONCLUSION

Among Brazilian workers, the prevalence of hypertension is associated with individual risk factors, lack of a canteen at the workplace, small companies and higher economic inequalities of cities. These three-level characteristics help to interpret differences in the prevalence of hypertension between regions or countries.

The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease

Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average “risk” between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors’ epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991–1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.

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There is a plethora of differences in “average” risk between exposed and unexposed groups of individuals.

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Individual heterogeneity around average values is seldom considered in Public Health.

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Measures of discriminatory accuracy (DA) informs on the underlying individual heterogeneity.

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Most know risk factors and other categorizations associated with diseases have low DA.

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We need a fundamental change in the way we investigate risk factors and other categorizations in Public Health.

Comparing multilevel and multiscale convolution models for small area aggregated health data

In spatial epidemiology, data are often arrayed hierarchically. The classification of individuals into smaller units, which in turn are grouped into larger units, can induce contextual effects. On the other hand, a scaling effect can occur due to the aggregation of data from smaller units into larger units. In this paper, we propose a shared multilevel model to address the contextual effects. In addition, we consider a shared multiscale model to adjust for both scale and contextual effects simultaneously. We also study convolution and independent multiscale models, which are special cases of shared multilevel and shared multiscale models, respectively. We compare the performance of the models by applying them to real and simulated data sets. We found that the shared multiscale model was the best model across a range of simulated and real scenarios as measured by the deviance information criterion (DIC) and the Watanabe Akaike information criterion (WAIC).

Role of Obesity Variables in Detecting Hypertension in an Iranian Population

INTRODUCTION

As the high incidence of hypertension has been in conjunction with dramatic increase in the prevalence of obesity, many studies have suggested obesity as its underlying cause in diverse race and ethnic groups.

AIM

The present study was designed to quantify the relationship between obesity variables and hypertension in Iranian population. A ROC curve analysis was also used to determine an optimal BMI cutoff for obesity with the aim of representing elevated incidence of hypertension in this population.

METHODS

The study population comprised of apparently healthy men and women who participated in the Iranian Multi-centric Osteoporosis Studies (IMOS), a multi-centric cross-sectional study carried out in urban areas of five great cities (Tehran, Tabriz, Mashhad, Shiraz and Bushehr). The anthropometric (weight, height, waist and hip circumferences) and blood pressure measures were reported in some 5724 subjects. The influence of these factors on systolic and diastolic blood pressure was assessed based on a list-wise method.

RESULTS

There was a significant difference in the studied subjects anthropometric (weight classes (BMI), WC and HC, and WHR) and blood pressure variables; age, gender and weight, however, were the only factors significantly influencing SBP and DBP. Furthermore, BMI showed a significant impact on the overall risk of developing hypertension.

CONCLUSION

General obesity rather than abdominal obesity is directly linked with higher blood pressure levels in Iranian population.

Intermediate and advanced topics in multilevel logistic regression analysis

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R² measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches

Intersectionality theory can contribute to epidemiology and public health by furthering understanding of power dynamics driving production of health disparities, and increasing knowledge about heterogeneities within, and overlap between, social categories. Drawing on McCall, we relate the first of these potential contributions to categorical intersectionality and the second to anti-categorical intersectionality. Both approaches are used in study of risk of ischemic heart disease (IHD), based on register data on 3.6 million adults residing in Sweden by 2010, followed for three years. Categorical intersectionality is here coupled with between-group differences in average risk calculation, as we use intersectional categorizations while estimating odds ratios through logistic regressions. The anti-categorical approach is operationalized through measurement of discriminatory accuracy (DA), i.e., capacity to accurately categorize individuals with or without a certain outcome, through computation of the area under the curve (AUC). Our results show substantial differences in average risk between intersectional groupings. The DA of social categorizations is found to be low, however, due to outcome variability within and overlap between categories. We argue that measures of DA should be used for proper interpretation of differences in average risk between social (or any other) categories. Tension between average between-group risk and the DA of categorizations, which can be related to categorical and anti-categorical intersectional analyses, should be made explicit and discussed to a larger degree in epidemiology and public health.

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health

BACKGROUND AND AIM

Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between "specific" (measures of association) and "general" (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health.

METHODS

We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics.

RESULTS

For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%.

CONCLUSION

Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood "effects" are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.

Prewarming neurosurgical patients to minimize hypotension on induction of anesthesia: a randomized trial

PURPOSE

Prewarming prior to surgery is effective in preventing perioperative hypothermia. There is a paucity of evidence, however, regarding the hemodynamic effects of prewarming. We hypothesized that the nadir mean arterial pressure during anesthesia induction would be higher after prewarming than after no prewarming.

METHODS

We randomized 32 patients prior to elective neurosurgery to receive either one hour of forced-air convective warming at 46°C or routine care (full body blanket with convective warmer attached but not turned on). All patients had invasive blood pressure, heart rate, and core temperature monitoring before and during warming and underwent a protocolized intravenous anesthetic induction with propofol and remifentanil target-controlled infusions. The primary endpoint was the nadir mean arterial blood pressure (MAP) during induction. Hypotension was defined as systolic blood pressure (SBP) < 90 mmHg, MAP < 60 mmHg, or a reduction in either SBP or MAP > 20% from baseline values.

RESULTS

No difference was found in the mean (SD) nadir MAP between the prewarmed group and the control group [64 (11) mmHg vs 68 (16) mmHg, respectively; mean difference, 5 mmHg; 95% confidence interval (CI), -6 to 15; P = 0.36]. Similarly, there was no difference between groups in the incidence of hypotension (100% of prewarmed vs 93% of control patients; relative risk, 1.07; 95% CI, 0.94 to 1.23; P = 0.32) or in the requirement for vasopressors during induction (four patients in each group required metaraminol; P = 1.00).

CONCLUSION

Prewarming with convective forced air for one hour prior to intravenous anesthetic induction did not prevent hypotension during the induction period (Australian New Zealand Clinical Trials Registry [ANZCTR] ACTRN12615000431527).

Ethnic differences in asthma treatment among Swedish adolescents: A multilevel analysis of individual heterogeneity

AIMS

Adolescents with immigrant or ethnic minority background suffering from asthma receive on average less appropriate anti-asthmatic medication (AAM) than the majority population. However, those findings are based on analyses of differences between group averages which prevents our understanding of individual heterogeneity around the averages. Taking into account individual socioeconomic factors and medical needs, we performed multilevel analysis in order to evaluate if maternal country of birth (MCOB) accurately identifies adolescents with inappropriate AAM use.

METHODS

Using the Swedish Medical Birth Register, we identified all singleton children born between 1988 and 1991 who were residing in the country until they turned 17 and had complete information on the study variables. We applied a two-level multilevel logistic regression analysis with 62 MCOBs at the second and 755,894 children at the first level. Adjusting for socioeconomic and medical factors using a risk score, and including the socioeconomic characteristics of the MCOBs, we obtained both measures of association (odds ratio (OR)) and measures of variance (Intra-class correlation (ICC)).

RESULTS

Comparing with adolescents born from Swedish mothers, all other children had a lower AAM use, especially those whose mothers were from upper-middle- and low-income countries (OR = 0.47, 95% confidence interval: 0.35-0.61). However, the ICC was low (i.e., ICC ≈ 3%) for both preventive and relief AAM.

CONCLUSIONS

MCOB was associated to adolescent use of AAM. However, the small ICC indicates that MCOB is an inaccurate categorization for identifying inappropriate use of AAM among Swedish adolescents.

Questioning the discriminatory accuracy of broad migrant categories in public health: self-rated health in Sweden

Background: Differences between natives and migrants in average risk for poor self-rated health (SRH) are well documented, which has lent support to proposals for interventions targeting disadvantaged minority groups. However, such proposals are based on measures of association that neglect individual heterogeneity around group averages and thereby the discriminatory accuracy (DA) of the categories used (i.e. their ability to discriminate the individuals with poor and good SRH, respectively). Therefore, applying DA measures rather than only measures of association our study revisits the value of broad native and migrant categorizations for predicting SRH. Design, setting and participants: We analyzed 27 723 individuals aged 18–80 who responded to a 2008 Swedish public health survey. We performed logistic regressions to estimate odds ratios (ORs), predicted risks and the area under the receiver operating characteristic curve (AU-ROC) as a measure of epidemiological DA. Results: Being born abroad was associated with higher odds of poor SRH (OR = 1.75), but the AU-ROC of this variable only added 0.02 units to the AU-ROC for age alone (from 0.53 to 0.55). The AU-ROC increased, but remained unsatisfactorily low (0.62), when available social and demographic variables were included. Conclusions: Our results question the use of broad native/migrant categorizations as instruments for forecasting individual SRH. Such simple categorizations have a very low DA and should be abandoned in public health practice. Measures of association and DA should be reported together whenever an intervention is being considered, especially in the area of ethnicity, migration and health.

Does Maternal Country of Birth Matter for Understanding Offspring's Birthweight? A Multilevel Analysis of Individual Heterogeneity in Sweden

Background

Many public health and epidemiological studies have found differences between populations (e.g. maternal countries of birth) in average values of a health indicator (e.g. mean offspring birthweight). However, the approach based solely on population-level averages compromises our understanding of variability in individuals’ health around the averages. If this variability is high, the exclusive study of averages may give misleading information. This idea is relevant when investigating country of birth differences in health.

Methods and Results

To exemplify this concept, we use information from the Swedish Medical Birth Register (2002–2010) and apply multilevel regression analysis of birthweight, with babies (n = 811,329) at the first, mothers (n = 571,876) at the second, and maternal countries of birth (n = 109) at the third level. We disentangle offspring, maternal and maternal country of birth components of the total offspring heterogeneity in birthweight for babies born within the normal timespan (37–42 weeks). We found that of such birthweight variation about 50% was at the baby level, 47% at the maternal level and only 3% at the maternal countries of birth level.

Conclusion

In spite of seemingly large differences in average birthweight among maternal countries of birth (range 3290–3677g), knowledge of the maternal country of birth does not provide accurate information for ascertaining individual offspring birthweight because of the high inter-offspring heterogeneity around country averages. Our study exemplifies the need for a better understanding of individual health diversity for which group averages may provide insufficient and even misleading information. The analytical approach we outline is therefore relevant to investigations of country of birth (and ethnic) differences in health in general.

Blood pressure response to a community-based program and long-term cardiovascular outcome

BACKGROUND

The significance in terms of the cardiovascular outcome of different patterns of blood pressure (BP) response to community-based activities has not been previously studied. The aim of our study was to evaluate the relationship between changes of BP observed during a community-based program and long-term rate of cardiovascular disease (CVD) events.

METHODS

A program focused on the prevention of CVDs was implemented in a small city in Argentina over a 6-year period (1997-2003). The program was evaluated using a cohort of 1,526 randomly selected individuals. The population's mean systolic BP (SBP) decreased approximately 5mm Hg, but BP changes were heterogeneous. Eight years after the intervention had stopped, 1,124 individuals of the cohort were surveyed to evaluate incident CVD events. The sample was divided into 3 SBP change categories during the intervention: decrease (< -5mm Hg), no change (-5 to 5mm Hg) and increase (> 5mm Hg). Cox models were used to estimate the hazard ratio (HR) of CVD events for each category adjusted for age, sex, and baseline BP.

RESULTS

At the end of the intervention, individuals who had increased their SBP showed an adjusted HR for CVD that was double those whose SBP levels decreased or did not change. An inverse relationship between baseline SBP and ΔSBP was observed: individuals with an increase in BP during the intervention had lower SBP at the beginning of the study.

CONCLUSIONS

BP increase during the community-based program was an independent predictor of CVD events.

Invited commentary: multilevel analysis of individual heterogeneity-a fundamental critique of the current probabilistic risk factor epidemiology

In this issue of the Journal, Dundas et al. (Am J Epidemiol. 2014;180(2):197-207) apply a hitherto infrequent multilevel analytical approach: multiple membership multiple classification (MMMC) models. Specifically, by adopting a life-course approach, they use a multilevel regression with individuals cross-classified in different contexts (i.e., families, early schools, and neighborhoods) to investigate self-reported health and mental health in adulthood. They provide observational evidence suggesting the relevance of the early family environment for launching public health interventions in childhood in order to improve health in adulthood. In their analyses, the authors distinguish between specific contextual measures (i.e., the association between particular contextual characteristics and individual health) and general contextual measures (i.e., the share of the total interindividual heterogeneity in health that appears at each level). By doing so, they implicitly question the traditional probabilistic risk factor epidemiology including classical "neighborhood effects" studies. In fact, those studies use simple hierarchical structures and disregard the analysis of general contextual measures. The innovative MMMC approach properly responds to the call for a multilevel eco-epidemiology against a widespread probabilistic risk factors epidemiology. The risk factors epidemiology is not only reduced to individual-level analyses, but it also embraces many current "multilevel analyses" that are exclusively focused on analyzing contextual risk factors.

Inference of cross-level interaction between genes and contextual factors in a matched case-control metabolic syndrome study: a Bayesian approach

Genes, environment, and the interaction between them are each known to play an important role in the risk for developing complex diseases such as metabolic syndrome. For environmental factors, most studies focused on the measurements observed at the individual level, and therefore can only consider the gene-environment interaction at the same individual scale. Indeed the group-level (called contextual) environmental variables, such as community factors and the degree of local area development, may modify the genetic effect as well. To examine such cross-level interaction between genes and contextual factors, a flexible statistical model quantifying the variability of the genetic effects across different categories of the contextual variable is in need. With a Bayesian generalized linear mixed-effects model with an unconditional likelihood, we investigate whether the individual genetic effect is modified by the group-level residential environment factor in a matched case-control metabolic syndrome study. Such cross-level interaction is evaluated by examining the heterogeneity in allelic effects under various contextual categories, based on posterior samples from Markov chain Monte Carlo methods. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor. That is, even among individuals with the same genetic component of PPARG_Pro12Ala, living in a residential area with low availability of exercise facilities may result in higher risk. The modification of the group-level environment factors on the individual genetic attributes can be essential, and this Bayesian model is able to provide a quantitative assessment for such cross-level interaction. The Bayesian inference based on the full likelihood is flexible with any phenotype, and easy to implement computationally. This model has a wide applicability and may help unravel the complexity in development of complex diseases.

Long term use of drugs affecting the renin-angiotensin system and the risk of cancer: a population-based case-control study

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• A recent meta-analysis has suggested an increased risk of cancer among users of angiotensin receptor blockers.

WHAT THIS STUDY ADDS

• Within the limitations of an observational study there is no difference in the cancer incidence between users of drugs affecting the renin-angiotensin system and users of other antihypertensives. • No consistent dose or duration dependency could be demonstrated for angiotensin reeptor blockers and angiotensin converting enzyme inhibitors.

AIMS

A recent meta-analysis of clinical trials has demonstrated a small excess of cancers in persons who had been allocated to angiotensin receptor blockers (ARBs). We undertook this observational study to look at dose-response and dose-duration effects and look for specificity with respect to outcome. Use of angiotensin converting enzyme inhibitors (ACEIs) was included in the main analysis since ACEIs share pharmacological properties with ARBs.

METHODS

We identified 149 417 incident cancer cases in Denmark during the period 2000-2005. Four controls, matched by age and gender, were selected for each case by a risk-set sampling. Data on medication were retrieved from the Danish National Prescription Registry. We defined long term exposure as at least 1000 defined daily doses redeemed within the past 5 years. Confounders were controlled by conditional logistic regression.

RESULTS

The odds ratio (OR) associating long term drug use with incident cancer was 1.12 (95% CI 1.06, 1.18), 1.17 (95% CI 1.14, 1.20), 1.23 (95% CI 1.20, 1.26), 1.18 (95% CI 1.14, 1.22), 1.25 (95% CI 1.22, 1.28), 1.37 (95% CI 1.21, 1.54), 1.29 (95% CI 1.22, 1.37) for ARBs, ACEIs, calcium channel blockers, β-adrenoceptor blockers, thiazide diuretics and α-adrenoceptor blockers. No consistent dose-duration or dose-response association could be demonstrated for ARBs or ACEIs.

CONCLUSIONS

The indication or possibly threshold for prescribing antihypertensives appears to be related to a small increase in cancer risk. The ARB-cancer association is probably too weak to be addressed in observational studies, given their limitations.

Revisiting causal neighborhood effects on individual ischemic heart disease risk: a quasi-experimental multilevel analysis among Swedish siblings

Neighborhood socioeconomic disadvantage is associated to increased individual risk of ischemic heart disease (IHD). However, the value of this association for causal inference is uncertain. Moreover, neighborhoods are often defined by available administrative boundaries without evaluating in which degree these boundaries embrace a relevant socio-geographical context that condition individual differences in IHD risk. Therefore, we performed an analysis of variance, and also compared the associations obtained by conventional multilevel analyses and by quasi-experimental family-based design that provides stronger evidence for causal inference. Linking the Swedish Multi-Generation Register to several other national registers, we analyzed 184,931 families embracing 415,540 full brothers 45-64 years old in 2004, and residing in 8408 small-area market statistics (SAMS) considered as "neighborhoods" in our study. We investigated the association between low neighborhood income (categorized in groups by deciles) and IHD risk in the next four years. We distinguished between family mean and intrafamilial-centered low neighborhood income, which allowed us to investigate both unrelated individuals from different families and full brothers within families. We applied multilevel logistic regression techniques to obtain odds ratios (OR), variance partition coefficients (VPC) and 95% credible intervals (CI). In unrelated individuals a decile unit increase of low neighborhood income increased individual IHD risk (OR = 1.04, 95% CI: 1.03-1.07). In the intrafamilial analysis this association was reduced (OR = 1.02, 95% CI: 1.02-1.04). Low neighborhood income seems associated with IHD risk in middle-aged men. However, despite the family-based design, we cannot exclude residual confounding by genetic and non-shared environmental factors. Besides, the low neighborhood level VPC = 1.5% suggest that the SAMS are a rather inappropriate construct of the socio-geographic context that conditions individual variance in IHD risk. In contrast the high family level VPC = 20.1% confirms the relevance of the family context for understanding IHD risk.

Bringing the individual back to small-area variation studies: a multilevel analysis of all-cause mortality in Andalusia, Spain

We performed a multilevel analysis (including individuals, households, census tracts, municipalities and provinces) on a 10% sample (N=230,978) from the Longitudinal Database of the Andalusian Population (LDAP). We aimed to investigate place effects on 8-year individual mortality risk. Moreover, besides calculating association (yielding odds ratios, ORs) between area socio-economic circumstances and individual risk, we wanted to estimate variance and clustering using the variance partition coefficient (VPC). We explicitly proclaim the relevance of considering general contextual effects (i.e. the degree to which the context, as a whole, affects individual variance in mortality risk) under at least two circumstances. The first of these concerns the interpretation of specific contextual effects (i.e. the association between a particular area characteristic and individual risk) obtained from multilevel regression analyses. The second involves the interpretation of geographical variance obtained from classic ecological spatial analyses. The so-called "ecological fallacy" apart, the lack of individual-level information renders geographical variance unrelated to the total individual variation and, therefore, difficult to interpret. Finally, we stress the importance of considering the familial household in multilevel analyses. We observed an association between percentage of people with a low educational level in the census tract and individual mortality risk (OR, highest v. lowest quintile=1.14; 95% confidence interval, CI 1.08-1.20). However, only a minor proportion of the total individual variance in the probability of dying was at the municipality (M) and census tract (CT) levels (VPC(M)=0.2% and VPC(CT)=0.3%). Conversely, the household (H) level appeared much more relevant (VPC(H)=18.6%) than the administrative geographical areas. Without considering general contextual effects, both multilevel analyses of specific contextual effects and ecological studies of small-area variation may provide a misleading picture that overstates the role of administrative areas as contextual determinants of individual differences in mortality.

Socioeconomic inequalities in the prevalence of nine established cardiovascular risk factors in a southern European population

The evaluation of the gender-specific prevalence of cardiovascular risk factors across socioeconomic position (SEP) categories may unravel mechanisms involved in the development of coronary heart disease. Using a sample of 1704 community dwellers of a Portuguese urban center aged 40 years or older, assessed in 1999–2003, we quantified the age-standardized prevalence of nine established cardiovascular risk factors (diabetes mellitus, hypertension, hypercholesterolemia, smoking, sedentariness, abdominal obesity, poor diet, excessive alcohol intake and depression) across SEP and gender categories. Data on individual education and occupation were collected by questionnaire and used to characterize SEP. The prevalence of seven out of nine well-established risk factors was higher in men. Among women, the prevalence of most of the studied risk factors was higher in lower SEP groups. The main exception was smoking, which increased with education and occupation levels. Among men, socioeconomic gradients were less clear, but lower SEP was associated with a higher prevalence of diabetes, excessive alcohol intake and depression in a graded mode. The historical cultural beliefs and practices captured throughout the lifecourse frame the wide socioeconomic gradients discernible in our study conducted in an unequal European developed population. While men were more exposed to most risk factors, the clearer associations between SEP and risk factors among women support that their adoption of particular healthy behaviors is more dependent on material and symbolic conditions. To fully address the issue of health inequalities, interventions within the health systems should be complemented with population-based policies specifically designed to reduce socioeconomic gradients.

Place effects for areas defined by administrative boundaries: a life course analysis of mortality and cause specific morbidity in Scania, Sweden

To understand the origin of disease risk in adulthood, factors in all stages of life and on different contextual levels should be considered. Therefore, the aim of this study was to investigate the relevance of a person's area of residence over their life course with regard to four outcomes: all-cause mortality; ischemic heart disease mortality and morbidity; cancer mortality and morbidity; and respiratory diseases and related mortality. We applied a cross-classified multilevel model for three age groups on a longitudinal data set spanning a 35 year period in Scania, Sweden. According to our analyses, the proportion of the total variance at the district level for all the outcomes studied was below 2% for the 65 to 84 age group, below 4.5% for those ages 50-64 years, and below 6.5% for those 30-49 years old. Our results suggest that the parish of residence, at four different time points during the individual life course, had little influence on individual all-cause mortality, or on mortality or morbidity from IHD, cancer, and respiratory diseases; i.e., knowing when and where an individual resided during their life course gives little indication of future mortality and morbidity. Such knowledge is essential in assisting decision makers determine the relevant geographical level of intervention (in our case whether to direct interventions toward the entire region of Scania or to specific parishes) needed. Valuable information for planning public health interventions might be obtained by considering measures of variance and clustering from specific contexts before implementing strategic programs.

Multilevel analysis of habitual physical activity and metabolic syndrome in Northern Taiwan

OBJECTIVE

This study examines the relationship between the availability of public facilities for habitual physical activity in the community and metabolic syndrome in northern Taiwan, one of most densely populated countries in the world.

METHODS

Subjects consisted of 14,658 participants (43.3% men and 56.7% women) ≥40 years old (mean=59.5) from 10 districts of Taoyuan County in a health check-up program in 2004-2005. Public facilities for habitual physical activity included school campuses and parks, and the density of such facilities was categorized into four levels. Multilevel logistic regression models were created to examine the effect on metabolic syndrome at both the individual and the contextual level using MLwiN software.

RESULTS

The addition of the contextual variable to the model that included individual characteristics led to a further reduction of 7.2% in the variance. Using the facility density level I as the reference, the odds ratios (95% confidence interval) of metabolic syndrome for levels II, III, and IV were 0.87 (0.71-1.07), 0.87 (0.68-1.12), and 0.78 (0.61-0.99), respectively, with the trend test reaching significance.

CONCLUSION

Greater availability of free facilities for habitual physical activity in a district was associated with a lower risk of metabolic syndrome among its residents.

Gender differences in acute myocardial infarction, twenty-five years registration

BACKGROUND/OBJECTIVES

The French-speaking Community of Belgium has set up a register of ischaemic cardiopathies (1983-2007). The aim consists in analyzing the evolution of fatal and non-fatal acute coronary events rates as well as the 28 days case fatality on a 25-year period and examine sex differences in lethality.

METHODS

This register assures a standardized procedure according to the MONICA criteria. For each period, we present attack rates and trends analysis. Hospital lethality takes again in-patients and community lethality is calculated starting from all the cases.

RESULTS

The total attack rate is rather stable between 1983 and 2007 for women (from 12 to 19 per 10,000 residents). For men, there is a distinct decline of the total attack rate since 1991 till 1993 (63 to 43 per 10,000 residents). We systematically observe a reduction in risk between men and women according to the age. For each 5-year period, this risk decreases significantly with age and this difference is strongest during the periods 1993-1997 and 1998-2002. The analysis shows also a significant decline in lethality between the 1983-1987 and 1993-1997 periods. Among women, lethality is systematically higher than in men in spite of the presence or the absence of antecedents of myocardial infarction.

CONCLUSIONS

Favourable evolutions in the attack rates of acute coronary events in the study population appear clearly on the 25-year period of observation. The whole lethality rates decreased during the first 15 years of the register; after that, it stabilized.

International differences in self-reported health measures in 33 major metropolitan areas in Europe

BACKGROUND

The increasing concentration of populations into large conurbations in recent decades has not been matched by international health assessments, which remain largely focused at the country level. We aimed to demonstrate the use of routine survey data to compare the health of large metropolitan centres across Europe and determine the extent to which differences are due to socio-economic factors.

METHODS

Multilevel modelling of health survey data on 126,853 individuals from 33 metropolitan areas in the UK, Republic of Ireland, Sweden, Norway, Finland, Spain, Belgium and Germany compared general health, longstanding illness, acute sickness, psychological distress and obesity with the average for all areas, accounting for education and social class.

RESULTS

We found some areas (Greater Glasgow; Greater Manchester, Cheshire and Merseyside; Northumberland, Tyne and Wear and South Yorkshire) had significantly higher levels of poor health. Other areas (West Flanders and Antwerp) had better than average health. Differences in individual socio-economic circumstances did not explain findings. With a few exceptions, acute sickness levels did not vary.

CONCLUSION

Health tended to be worse in metropolitan areas in the north and west of the UK and the central belt and south east of Germany, and more favourable in Sweden and north west Belgium, even accounting for socio-economic composition of local populations. This study demonstrated that combining national health survey data covering different areas is viable but not without technical difficulties. Future comparisons between European regions should be made using standardized sampling, recruitment and data collection protocols, allowing proper monitoring of health inequalities.

Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes

Socio-economic gradients in cardiovascular disease (CVD) and diabetes have been found throughout the developed world and there is some evidence to suggest that these gradients may be steeper for women. Research on social gradients in biological risk factors for CVD and diabetes has received less attention and we do not know the extent to which gradients in biomarkers vary for men and women. We examined the associations between two indicators of socio-economic position (education and household income) and biomarkers of diabetes and cardiovascular disease (CVD) for men and women in a national, population-based study of 11,247 Australian adults. Multi-level linear regression was used to assess associations between education and income and glucose tolerance, dyslipidaemia, blood pressure (BP) and waist circumference before and after adjustment for behaviours (diet, smoking, physical activity, TV viewing time, and alcohol use). Measures of glucose tolerance included fasting plasma glucose and insulin and the results of a glucose tolerance test (2 h glucose) with higher levels of each indicating poorer glucose tolerance. Triglycerides and High Density Lipoprotein (HDL) Cholesterol were used as measures of dyslipidaemia with higher levels of the former and lower levels of the later being associated with CVD risk. Lower education and low income were associated with higher levels of fasting insulin, triglycerides and waist circumference in women. Women with low education had higher systolic and diastolic BP and low income women had higher 2 h glucose and lower HDL cholesterol. With only one exception (low income and systolic BP), all of these estimates were reduced by more than 20% when behavioural risk factors were included. Men with lower education had higher fasting plasma glucose, 2 h glucose, waist circumference and systolic BP and, with the exception of waist circumference, all of these estimates were reduced when health behaviours were included in the models. While low income was associated with higher levels of 2-h glucose and triglycerides it was also associated with better biomarker profiles including lower insulin, waist circumference and diastolic BP. We conclude that low socio-economic position is more consistently associated with a worse profile of biomarkers for CVD and diabetes for women.

Smoking and atherosclerotic cardiovascular disease: part III: functional biomarkers influenced by smoking

Smoking cigarettes induces rapidly occurring and reversible functional changes in the cardiovascular system, which precede morphologic changes. These functional changes are also related to atherosclerotic disease development and thus may qualify as prognostic parameters in chronic smokers. As opposed to smoking-induced morphologic changes functional alterations occur and revert within minutes, thus, allowing for the detection of smoking-induced effects on the cardiovascular system within minutes following exposure to mainstream smoke. Some alterations represent 'direct' changes (e.g., endothelial function), others reflect changes in a different organ system (e.g., the autonomous nervous system influencing heart rate variability), while some represent the sum of alterations in many organs and systems (e.g., exercise performance influenced by the autonomous nervous and by endothelial and cardiac function). Since a specific functional parameter usually changes with at least one or several others, caution should be exercised when trying to establish a direct cause relationship between the alteration of a single parameter and a clinical outcome.