Search

We investigated the association between perceived occupational noise exposure and depressive symptoms in young Finnish adults and whether noise sensitivity moderates this association. This study was based on an ongoing longitudinal twin study. We included those who had been working daily (n = 521) or weekly (n = 245) during the past 12 months (mean age 22.4, SD 0.7, 53% female). We asked about occupational noise exposure at age 22 and assessed depressive symptoms using the General Behavior Inventory (GBI) at age 17 and 22. Noise sensitivity and covariates were used in linear regression models. Perceived daily occupational noise exposure was associated, as a statistically independent main effect with depressive symptoms at age 22 (beta 1.19; 95% CI 0.09, 2.29) among all, and separately for females (beta 2.22; 95% CI 0.34, 4.09) but not males (beta 0.22; 95% CI −1.08, 1.52). Noise sensitivity was independently associated with depressive symptoms among all (beta 1.35; 95% CI 0.54, 2.17), and separately for males (beta 1.96; 95% CI 0.68, 3.24) but not females (beta 1.05; 95 % CI −0.04, 2.13). Noise sensitivity was independent of perceived occupational noise exposure. Pre-existing depressive symptoms at age 17 were predictive of perceived occupational noise exposure, suggesting complex interactions of noise and depression.

The exposome represents the totality of environmental effects, but systematic evaluation between it and depressive symptoms is scant. We sought to comprehensively identify the association of the exposome with depressive symptoms in late adolescence and early adulthood and determine genetic and environmental covariances between them. Based on the population-based FinnTwin12 cohort (3025 participants in young adulthood and 4127 at age 17), the exposome-wide association study (ExWAS) design was used to identify significant exposures from 12 domains. Bivariate Cholesky twin models were fitted to an exposome score and depressive symptoms. In ExWASes, 29 and 46 exposures were significantly associated with depressive symptoms in young adulthood and at age 17, respectively, and family-related exposures were the most influential. Twin models indicated considerable genetic and environmental covariances between the exposome score and depressive symptoms. The findings underscore the systematic approach of the exposome and the consideration of relevant genetic effects.

Objective: To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. Method: In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. Results: The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Conclusion: Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success. Dr. Border has received support from the National Institutes of Health (NIH; MH100141 and MH016880 ). Dr. Peterson has received support from NIH (K01MH113848) and the Brain & Behavior Research Foundation Young Investigator Grant (28632). Drs. Sallis and Munafò have served as members of the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/7). Dr. Shabalin has received support from a NARSAD Young Investigator Award. Dr. Vilor-Tejedor has received support from a post-doctoral grant, Juan de la Cierva Programme (FJC2018-038085-I), Ministerio de Ciencia, Innovación y Universidades – Spanish State Research Agency. Her research has received additional support of “la Caixa” Foundation (LCF/PR/GN17/10300004) and the Health Department of the Catalan Government (Health Research and Innovation Strategic Plan (PERIS) 2016-2020 grant# SLT002/16/00201). She has acknowledged the support of the Spanish Ministry of Science, Innovation and Universities to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme/Generalitat de Catalunya. Dr. Alemany has received support from a Juan de la Cierva – Incorporación Postdoctoral Contract from Ministerio de Economía, Industria y Competitividad (IJCI-2017-34068). Dr. Corley has received support from NIH (DA011015, AG046938, and DA035804). Dr. Evans has received support from NIH (MH100141, DA044283, and AG046938). Dr. Havdahl has received support from the South-Eastern Norway Regional Health Authority (2018059) at Nic Waals Institute, Lovisenberg Diaconal Hospital. Dr. Rimfeld has received support from a Sir Henry Wellcome Postdoctoral Fellowship. Dr. Andreassen has served as a consultant to HealthLytix and has received funding from the Research Council of Norway (223273 and 273291) and KG Jebsen Stiftelsen. Dr. Boomsma has received support from the Royal Netherlands Academy of Science Professor Award (PAH/5535). Dr. Brown has received support from NIH (DA035804). Dr. Copeland has received support from NIH (MH117559 and HD093651). Dr. Dick has received support from a mid-career award NIH K02 AA018755, NIH R01 AA015416 (Finnish Twin Study), P50 AA022537 (Alcohol Research Center), R25 AA027402 (VCU GREAT), and U10 AA008401 (COGA) from the National Institute on Alcohol Abuse and Alcoholism. Drs. Hewitt, Krauter, Smolen, and Stallings have received support from NIH (DA011015 and DA035804). Dr. Hopfer has received support from NIH (DA042755, DA035804, and DA032555). Dr. Kaprio has received support from the Academy of Finland (grants 308248 and 312073). Dr. Larsson has served as a speaker for Evolan Pharma and Shire/Takeda and has received research grants from Shire/Takeda, all outside the submitted work. Dr. Lichtenstein has received support from the Swedish Research Council for Health, Working Life and Welfare (project 2012-1678) and the Swedish Research Council (2016-1989). Dr. Lundström has received support from the Swedish Research Council ( 2017-02552 ). Dr. Magnus has received support from the Research Council of Norway through its Centers of Excellence scheme, project no 262700. Dr. Njølstad has received support from the European Research Council (AdG #293574). Stiftelsen Kristian Gerhard Jebsen has received support from the Research Council of Norway (FRIPRO 240413), the Western Norway Regional Health Authority (Strategic Fund “Personalized Medicine for Children and Adults”) and the Novo Nordisk Foundation (54741). Dr. Plomin has received support from a MRC Professorship award (G19/2). Dr. Reichborn-Kjennerud has received support from the Research Council of Norway grant 274611 . Drs. Reynolds and Wadsworth have received support from NIH (AG046938). Dr. Rose has received support from a NIH Research Scientist Award (AA-000145). Dr. Wall has received support from NIH (DA03580 and DA021905). Dr. Whitehouse has received support from an Investigator Grant from the National Health and Medical Research Council. Dr. Ystrøm has received support from the Research Council of Norway (262177 and 288083). Dr. Nivard has received support from ZonMW (849200011), the Netherlands Organisation for Health Research and Development (531003014 937), a Jacobs Foundation Research Fellowship, and a VENI grant awarded by NWO (VI.Veni.191G.030). Dr. Bartels has received support from an ERC Consolidator Grant (WELL-BEING 771057). Ms. Hagenbeek and Mr. Ip have received support from the Aggression in Children: Unraveling gene-environment interplay to inform Treatment and Intervention strategies project (ACTION). ACTION received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no 602768

This review offers an update on research conducted with FinnTwin12 (FT12), the youngest of the three Finnish Twin Cohorts. FT12 was designed as a two-stage study. In the first stage, we conducted multiwave questionnaire research enrolling all eligible twins born in Finland during 1983–1987 along with their biological parents. In stage 2, we intensively studied a subset of these twins with in-school assessments at age 12 and semistructured poly-diagnostic interviews at age 14. At baseline, parents of intensively studied twins were administered the adult version of the interview. Laboratory studies with repeat interviews, neuropsychological tests, and collection of DNA were made of intensively studied twins during follow-up in early adulthood. The basic aim of the FT12 study design was to obtain information on individual, familial and school/neighborhood risks for substance use/abuse prior to the onset of regular tobacco and alcohol use and then track trajectories of use and abuse and their consequences into adulthood. But the longitudinal assessments were not narrowly limited to this basic aim, and with multiwave, multirater assessments from ages 11 to 12, the study has created a richly informative data set for analyses of gene–environment interactions of both candidate genes and genomewide measures with measured risk-relevant environments. Because 25 years have elapsed since the start of the study, we are planning a fifth-wave follow-up assessment.

Aggressive behavior in school is an ongoing concern. The current focus is on specific manifestations such as bullying, but the behavior is broad and heterogenous. Children spend a substantial amount of time in school, but their behaviors in the school setting tend to be less well characterized than at home. Because aggression may index multiple behavioral problems, we used three validated instruments to assess means, correlations and gender differences of teacher-rated aggressive behavior with co-occurring externalizing/internalizing problems and social behavior in 39,936 schoolchildren aged 7-14 from 4 population-based cohorts from Finland, the Netherlands, and the UK. Correlations of aggressive behavior were high with all other externalizing problems (r: 0.47-0.80) and lower with internalizing problems (r: 0.02-0.39). A negative association was observed with prosocial behavior (r: -0.33 to -0.54). Mean levels of aggressive behavior differed significantly by gender. Despite the higher mean levels of aggressive behavior in boys, the correlations were notably similar for boys and girls (e.g., aggressive-hyperactivity correlations: 0.51-0.75 boys, 0.47-0.70 girls) and did not vary greatly with respect to age, instrument or cohort. Thus, teacher-rated aggressive behavior rarely occurs in isolation; boys and girls with problems of aggressive behavior likely require help with other behavioral and emotional problems. Important to note, higher aggressive behavior is not only associated with higher amounts of other externalizing and internalizing problems but also with lower levels of prosocial behavior.

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range $$\left| {r_g} \right|$$ r g : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range $$\left| {r_g} \right|$$ r g : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. The pattern of genetic correlations suggests that childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalising symptoms over time and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Aggressive behavior in school is an ongoing concern, with the current focus mostly on specific manifestations such as bullying and extreme violence. Children spend a substantial amount of time in school, but their behaviors in the school setting tend to be less well characterized than in the home setting. Since aggression may index multiple behavioral problems, we assessed associations of teacher-rated aggressive behavior with co-occurring externalizing/internalizing problems and social behavior in 39,936 schoolchildren from 4 population-based cohorts from Finland, the Netherlands, and the UK. Mean levels of aggressive behavior differed significantly by gender. Correlations of aggressive behavior were high with all other externalizing problems (0.47-0.80) and lower with internalizing problems (0.02-0.39). A negative association was seen with prosocial behavior (−0.33 to −0.54). Despite the higher mean levels of aggressive behavior in boys, the correlations were notably similar for boys and girls (e.g., aggressive-hyperactivity correlations: 0.51-0.75 boys, 0.47-0.70 girls) and did not vary greatly with respect to age, instrument or cohort. Thus, aggressive behavior at school rarely occurs in isolation and children with problems of aggressive behavior likely require help with other behavioral and emotional problems. It is important to note that greater aggressive behavior is not only associated with greater amount of other externalizing and internalizing problems but also with lower levels of prosocial behavior.

Modestly prevalent in the general population (~ 4%), but highly prevalent in prison populations (> 40%), the diagnosis of antisocial personality disorder (ASPD) involves aggression as one of several possible criteria. Using multiple informants, we aimed to determine if general aggression, as well as direct and indirect subtypes, assessed in early adolescence (ages 12, 14) predict young adulthood ASPD in a population-based sample. Using data from a Finnish population-based longitudinal twin cohort study with psychiatric interviews available at age 22 (N = 1347), we obtained DSM-IV-based ASPD diagnoses. Aggression measures from ages 12 (parental and teacher ratings) and 14 (teacher, self, and co-twin ratings) were used to calculate odds ratios (OR) of ASPD from logistic regression models and the area under the curve (AUC) from receiver operating characteristic curve analysis. Analyses were adjusted for sex, age, and family structure. All informants’ aggression ratings were significant (p

Childhood aggression and its resulting consequences inflict a huge burden on affected children, their relatives, teachers, peers and society as a whole. Aggression during childhood rarely occurs in isolation and is correlated with other symptoms of childhood psychopathology. In this paper, we aim to describe and improve the understanding of the co-occurrence of aggression with other forms of childhood psychopathology. We focus on the co-occurrence of aggression and other childhood behavioural and emotional problems, including other externalising problems, attention problems and anxiety-depression. The data were brought together within the EU-ACTION (Aggression in Children: unravelling gene-environment interplay to inform Treatment and InterventiON strategies) project. We analysed the co-occurrence of aggression and other childhood behavioural and emotional problems as a function of the child's age (ages 3 through 16years), gender, the person rating the behaviour (father, mother or self) and assessment instrument. The data came from six large population-based European cohort studies from the Netherlands (2x), the UK, Finland and Sweden (2x). Multiple assessment instruments, including the Child Behaviour Checklist (CBCL), the Strengths and Difficulties Questionnaire (SDQ) and Multidimensional Peer Nomination Inventory (MPNI), were used. There was a good representation of boys and girls in each age category, with data for 30,523 3- to 4-year-olds (49.5% boys), 20,958 5- to 6-year-olds (49.6% boys), 18,291 7- to 8-year-olds (49.0% boys), 27,218 9- to 10-year-olds (49.4% boys), 18,543 12- to 13-year-olds (48.9% boys) and 10,088 15- to 16-year-olds (46.6% boys). We replicated the well-established gender differences in average aggression scores at most ages for parental ratings. The gender differences decreased with age and were not present for self-reports. Aggression co-occurred with the majority of other behavioural and social problems, from both externalising and internalising domains. At each age, the co-occurrence was particularly prevalent for aggression and oppositional and ADHD-related problems, with correlations of around 0.5 in general. Aggression also showed substantial associations with anxiety-depression and other internalizing symptoms (correlations around 0.4). Co-occurrence for self-reported problems was somewhat higher than for parental reports, but we found neither rater differences, nor differences across assessment instruments in co-occurrence patterns. There were large similarities in co-occurrence patterns across the different European countries. Finally, co-occurrence was generally stable across age and sex, and if any change was observed, it indicated stronger correlations when children grew older. We present an online tool to visualise these associations as a function of rater, gender, instrument and cohort. In addition, we present a description of the full EU-ACTION projects, its first results and the future perspectives.

This study examined whether acute cannabis use leads to an increased collision risk. Participants were 860 drivers presenting to emergency departments in Toronto and Halifax, Canada, with an injury from a traffic collision, between April 2009 and July 2011. Cannabis and other drug use were identified either through blood sample or self-report. A case-crossover design was employed with two control conditions: a fixed condition measuring substance use during last time driving, and whether the driver typically uses cannabis prior to driving. Collision risk was assessed through conditional fixed-effects logistic regression models. Results revealed that 98 (11 %; 95 % CI: 9.0–13.1) drivers reported using cannabis prior to the collision. Regression results measuring exposure with blood and self-report data indicated that cannabis use alone was associated with a fourfold increased (OR 4.11; 95 % CI: 1.98–8.52) odds of a collision; a regression relying on self-report measures only found no significant association. Main findings confirmed that cannabis use increases collision risk and reinforces existing policy and educational efforts, in many high-income countries, aimed at reducing driving under the influence of cannabis.

As it did in Healthy People 2000 and Healthy People 2010, disparity once again plays a prominent role in the recently released Healthy People 2020 (U.S. Department of Health and Human Services 1990, 2000, 2010), a decennial national road map for health in the United States. One of the road map's four overarching goals is to “achieve health equity, eliminate disparities, and improve the health of all groups.” Healthy People 2020 regards health disparity as an ethical concept of unfair health differences associated with certain characteristics or attributes (U.S. Department of Health and Human Services 2008, 2010). Reporting the progress (or lack of progress) on eliminating health disparities and comparing them across communities and over time is fundamental for this renewed call to succeed (Truman et al. 2011). Among the various challenges of reporting on health disparity, one that is often overlooked is how best to report health disparities associated with multiple attributes. In Healthy People 2020, the key attributes of health disparities are race/ethnicity, gender, sexual identity and orientation, disability status or special health care needs, and geography (U.S. Department of Health and Human Services 2010). While recognizing the multidimensionality of health disparity is important, measuring all aspects of health disparity, even when all the relevant data are available, is a formidable task for researchers, policymakers, and community partners. This is the focus of this article, and we propose a new approach that measures health disparities associated with multiple attributes at the same time and summarizes them as the overall health disparity in the population. Researchers and policymakers most commonly measure health disparities in a bivariate (Wolfson and Rowe 2001) or pair-wise (Truman et al. 2011) fashion, as a joint distribution of health and another attribute such as income, sex, or race/ethnicity. The degree of health disparity across groups within the chosen attribute (e.g., low-, middle-, and high-income groups) can be quantified by an index. An index can be simple (e.g., a range measure) or complex (e.g., the Concentration index) (Harper and Lynch 2005). A more sophisticated approach uses individual-level data rather than aggregate-level data to examine the level of health and the level of the attribute (e.g., income) of each individual rather than the average level of health of each group. Regardless of the unit of analysis (group or individual) or the disparity index used, the bivariate approach always measures health disparity in relation to one other attribute. The previous literature on this subject used this approach extensively (e.g., Cameron et al. 2009; Frohlich, Ross, and Richmond 2006; Harper et al. 2007; James et al. 2007; Mackenbach et al. 2003; Mustard and Etches 2003), and it has also influenced policymaking. For example, responding to the second goal of Healthy People 2010, “to eliminate health disparities among segments of the population, including differences that occur by gender, race or ethnicity, education or income, disability, geographic location, or sexual orientation” (U.S. Department of Health and Human Services 2000), analysts measured health disparities in a bivariate fashion (Hines et al. 2011). Measuring and reporting health disparities for Healthy People 2020, therefore, would most likely use a bivariate approach. While the bivariate measurement approach has created a wealth of information useful for many purposes and contexts, this vast array of information obscures the overall picture of health disparities. Suppose that state A has large health disparity due to race/ethnicity but small disparities associated with sex, income, and education and that state B has modest health disparities due to each of these attributes. Given the different degrees of health disparities associated with various attributes from state to state, it is difficult to determine which state has the largest health disparities. Such a determination presumes a systematic mechanism to compare multiple bivariate health disparity relationships (e.g., health/race/ethnicity, health/sex, health/income, and health/education) across states, which is an ambitious task given the current state of the literature. Easily accessible information about the overall picture of health disparities could enhance policymaking, particularly if this information included a set of relevant bivariate disparities, since policy often develops around target groups. To our knowledge, the only attempt to summarize multiple bivariate health disparities is the measure developed for the Health of Wisconsin Report Card 2007 (Booske et al. 2007, 2010). After measuring the health disparities associated with different attributes (gender, education, rurality, and race/ethnicity) in a bivariate fashion, the Wisconsin researchers calculated the Index of Disparity, a modified version of the coefficient of variation developed by Pearcy and Keppel for Healthy People 2010 (Pearcy and Keppel 2002; U.S. Department of Health and Human Services 2000), using all fourteen groups (two gender, three education, four rurality, and five race/ethnicity groups). Although the information about the overall picture of health disparities in the population offered by this measure shows significant progress, this measure needs improvement regarding the following two properties. First, this measure is insensitive to the group's population size, as it treats all groups as having equal importance. Second, it uses the healthiest group as the reference group, regardless of attributes, and compares its health with the average health of the other groups (e.g., as the reference, the highest-income group's health is compared with the health of the low-education group, that of women, and that of rural residents). Measuring overall health disparity in this way does not readily offer between-group disparities specific to attributes (e.g., health disparities across education groups). We expanded the Wisconsin researchers’ approach by proposing a new analytic approach to summarizing social disparities in health. Our approach derives from the bivariate health disparity measure developed by Gastwirth (Gastwirth 2007), which has a number of attractive policy-relevant features, including intuitive between-group comparisons within each attribute (e.g., income groups) and the minimum (i.e., group rather than individual level) data requirement. While maintaining these features, we extended the Gastwirth index, originally developed to measure health disparity associated with a single attribute, to a summary measure of health disparities, which simultaneously accounts for multiple attributes. Our approach, an extended Gastwirth index, improves these two properties of the Wisconsin measure by incorporating sensitivity to the group size and preserving bivariate health disparity information. As an empirical demonstration, we applied our approach to the 2009 American Community Survey (U.S. Census Bureau n.d.a) and ranked overall health disparity as well as health disparities by income, education, sex, and race/ethnicity across all fifty states plus the District of Columbia (Washington, DC).

Introduction Regular reporting of health inequalities is essential to monitoring progress of efforts to reduce health inequalities. While reporting of population health became increasingly common, reporting of a subpopulation group breakdown of each indicator of the health of the population is rarely a standard practice. This study reports education-, sex-, and race-related inequalities in four health outcomes in each of the selected 93 counties in the United States in a systematic and comparable manner. Methods This study is a cross-sectional analysis of large, publicly available data, 2008, 2009, and 2010 Behavioral Risk Factor Surveillance System (BRFSS) Selected Metropolitan/Micropolitan Area Risk Trends (SMART) and 2008, 2009, and 2010 United States Birth Records from the National Vital Statistics System. The study population is American adults older than 25 years of age residing in the selected 93 counties, representing about 30% of the US population, roughly equally covering all geographic regions of the country. Main outcome measures are: (1) Attribute (group characteristic)-specific inequality: education-, sex-, or race-specific inequality in each of the four health outcomes (poor or fair health, poor physical health days, poor mental health days, and low birthweight) in each county; (2) Overall inequality: the average of these three attribute-specific inequalities for each health outcome in each county; and (3) Summary inequality in total morbidity: the weighted average of the overall inequalities across the four health outcomes in each county. Results The range of inequality across the counties differed considerably by health outcome; inequality in poor or fair health had the widest range and the highest median among inequalities in all health outcomes. In more than 70% of the counties, education-specific inequality was the largest in all health outcomes except for low birthweight. Conclusions It is feasible to extend population health reporting to include reporting of a subpopulation group breakdown of each indicator of the health of the population at a small jurisdictional level using publicly available data. No single group characteristic or health outcome represents the whole picture of health inequalities in a population. Examining multiple group characteristics and outcomes in a comparable manner is essential in reporting health inequalities.