Isa Gender Neighborhood And Al 071310

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Presention on Neighborhood Effects on Allostatic Load at the International Sociological Association Conference, July 13th in Gothenburg, Sweden.

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  • Again, this all may be a disciplinary difference in presentation. I actually think you could delete this slide or combine concepts with slide 2. Alsok, nothing here sets up gender. I know that’s what you are getting at by individual characteristics, but for a poster, its not immediately obvious
  • Again, this all may be a disciplinary difference in presentation. I actually think you could delete this slide or combine concepts with slide 2. Alsok, nothing here sets up gender. I know that’s what you are getting at by individual characteristics, but for a poster, its not immediately obvious
  • We defined NSES using a previously validated index consisting of the following six census-tract level components. Median household income % of adults older than age 25 with less than a high school education % of households with income below the federal poverty line Households with children that are headed only by a female The NSES Index ranges from 0 to 100 where higher NSES means a neighborhood with better socioeconomic status. In the WHI data neighborhoods has NSES values ranging from from 11 to 99 with a mean of 75 (8.7)
  • I don’t see the term individual characteristics in the prior slide-assume this goes in SES characteristics, but either way should make it clear
  • I’d be tempted to take the last bullet and move it to a new slide that says Future Work—and talks about moving on to other neighborhood mechanisms
  • I’d be tempted to take the last bullet and move it to a new slide that says Future Work—and talks about moving on to other neighborhood mechanisms
  • Isa Gender Neighborhood And Al 071310

    1. 1. Gender Differences in the Relationship Between Neighborhood Socioeconomic Characteristics and Allostatic Load Chloe E. Bird, Tamara Dubowitz, Mary Ellen Slaughter, Patricia P. Rieker, and Beth Ann Griffin Supported by funding from the U.S. National Institutes of Health, National Heart, Lung, and Blood Institute (NHLBI)
    2. 2. What Do We Know? <ul><li>Research demonstrates the relationships between residential neighborhood context and health </li></ul><ul><li>A combination of individual and neighborhood factors structure individuals lives, thereby affecting physical and mental health and mortality risk </li></ul><ul><li>Studies on the independent effect of social environments laid the groundwork for research that considers neighborhood effects more comprehensively </li></ul>
    3. 3. <ul><li>Do neighborhood effects on health outcomes (e.g., development of diabetes or cardiovascular disease) simply reflect selection of less healthy individuals into disadvantaged neighborhoods? </li></ul><ul><li>A new research agenda aims at understanding </li></ul><ul><ul><li>How do neighborhoods “get under the skin” to affect health? </li></ul></ul><ul><ul><li>To what extent do the pathways and effects differ for men and women? </li></ul></ul>What Are the Gaps?
    4. 4. <ul><li>Understanding interactions between the environment and individual may help to explain and address disparities in health and in life expectancy among key population groups </li></ul><ul><ul><li>Such knowledge can inform interventions to address disparities </li></ul></ul><ul><ul><li>May support more aggressive medical screenings and treatment (secondary prevention) </li></ul></ul>What Are the Gaps? (continued)
    5. 5. <ul><li>Independent of individual characteristics: </li></ul><ul><ul><li>Do the relationships between neighborhood characteristics and biological markers of stress differ for men and women? </li></ul></ul>Research Question
    6. 6. Allostatic Load (AL) <ul><li>A multisystem measure of physiologic dysregulation across multiple biologic systems (e.g., cardiovascular, endocrine, metabolic) </li></ul><ul><li>Measures the cumulative impact of adaptive physiological responses that chronically exceed optimal operating ranges </li></ul><ul><li>Assessed using biomarkers (e.g., pulse, cholesterol) </li></ul>
    7. 7. <ul><li>Geocoded National Health and Nutrition Examination Survey (NHANES) data </li></ul><ul><ul><li>NHANES III (conducted 1988 – 1994) and NHANES 1999-2004 </li></ul></ul><ul><ul><li>No upper age limit </li></ul></ul><ul><ul><li>Black Americans and Mexican-Americans were over-sampled </li></ul></ul><ul><li>Merged with Census Data at the Census Tract level </li></ul>Individual-Level Data
    8. 8. <ul><li>Neighborhood Socioeconomic Status (NSES) at the census-tract level </li></ul><ul><li>Population density within the residential Census-tract </li></ul><ul><ul><li>US Census tracts vary from ~1500 to ~8000 with an average of 4000 residents </li></ul></ul>Census Tract-Level Data
    9. 9. Components of NSES Index <ul><li>Median household income </li></ul><ul><li>Adults ≤ high school education (%) </li></ul><ul><li>Male unemployment (%) </li></ul><ul><li>Households with income below poverty (%) </li></ul><ul><li>Households receiving public assistance (%) </li></ul><ul><li>Households headed by a single female (%) </li></ul>
    10. 10. Analytic Sample <ul><li>11,910 adults age 20+ </li></ul><ul><ul><li>From 83 counties and 1805 census tracts, </li></ul></ul><ul><ul><li>Who completed surveys and medical exams, </li></ul></ul><ul><ul><li>Were not missing on key components of outcome measures, </li></ul></ul><ul><ul><li>Could be geocoded to a census tract </li></ul></ul><ul><li>Sample Characteristics </li></ul><ul><ul><li>48% Male </li></ul></ul><ul><ul><li>35% white, 30% black, 30% Hispanic, 5% other </li></ul></ul><ul><ul><li>Age range: 19.5 to 90 (mean 48) </li></ul></ul><ul><ul><li>60% were married </li></ul></ul><ul><ul><li>74% were US born </li></ul></ul><ul><ul><li>69% had at least a high school education </li></ul></ul><ul><ul><li>Mean family income/poverty ratio: 2.41 </li></ul></ul>
    11. 11. <ul><li>Our three-level hierarchical models: </li></ul><ul><ul><li>Partition the variance between individual-level, census tract-level and county-level </li></ul></ul><ul><ul><li>Adjust for covariates at the individual and census tract level </li></ul></ul><ul><li>Advantages of Multi-Level Model </li></ul><ul><ul><li>Standard errors corrected for clustering at each level </li></ul></ul><ul><ul><li>T-Statistics are based on correct sample size for each level </li></ul></ul>Methods
    12. 12. <ul><li>Gender </li></ul><ul><li>Age </li></ul><ul><li>Race/ethnicity (white, black, Hispanic, other) </li></ul><ul><li>Education (college, some college, high school, less than high school) </li></ul><ul><li>Family needs ratio (family income/poverty line) </li></ul>Individual SES Characteristics
    13. 13. <ul><li>Summary score (range 0-9) based on clinical cut points for 9 indicators from 3 physiologic systems: </li></ul><ul><ul><li>metabolic (total cholesterol, HDL cholesterol, glycosylated hemoglobin, waist/hip ratio), </li></ul></ul><ul><ul><li>cardiac (systolic and diastolic blood pressure, pulse), and </li></ul></ul><ul><ul><li>inflammatory (c-reactive protein, serum albumin). </li></ul></ul>Outcome – Allostatic Load (AL)
    14. 14. Individual Sociodemographic Characteristics Were Independently Associated with AL age (<.0001) education (<.0001) income (0.016) age (<.0001) education (<.0001) income (0.029) Model 2: Individual-level characteristics + NSES + Population Density age (<.0001) education (<.0001) income (0.016) age (<.0001) education (0.015) income (0.080) Model 1: Individual-level characteristics + NSES Women Men Variable (p-value)
    15. 15. Neighborhood Characteristics Were Independently Associated with AL NSES (0.016) Population Density (<.0001) NSES (0.029) Population Density (<.0001) Model 2: Individual-level characteristics + NSES + Population Density NSES (0.0003) NSES (0.009) Model 1: Individual-level characteristics + NSES Women Men Variable (p-value)
    16. 16. <ul><li>After adjusting for individual characteristics, poorer neighborhood SES characteristics were associated with higher AL, but the relationships differed by gender. </li></ul><ul><li>For both men and women, living in a Census tract with </li></ul><ul><ul><li>higher NSES was protective (lower AL) </li></ul></ul><ul><ul><ul><li>the effect appeared stronger for women until population density was added to the model </li></ul></ul></ul><ul><ul><ul><li>the interquartile difference ~.314 </li></ul></ul></ul><ul><ul><li>higher population density was also protective, </li></ul></ul><ul><ul><ul><li>but the effect of population density was far stronger for men than for women (~30 fold) </li></ul></ul></ul><ul><ul><ul><li>for men, the interquartile difference ~.015 </li></ul></ul></ul>Relationship between Neighborhood SES Characteristics and AL Differed by Gender
    17. 17. <ul><li>The association of lower NSES with higher AL suggests a pathway through which low-SES neighborhoods may affect health and health disparities </li></ul><ul><li>The stronger protective effect of population density particularly for men warrants further research into how and why men benefit from living in more populated areas </li></ul><ul><li>Gender differences in the effect of population density on AL suggest that neighborhoods may influence the health of men and women differently </li></ul>Conclusion
    18. 18. Future Work <ul><li>By assessing potential pathways through which mortality risk and disparities may be generated, we hope to determine whether changing neighborhood features could improve health and longevity, and reduce disparities. </li></ul><ul><li>Planned analyses will examine additional potential neighborhood mechanisms including aspects of the built environment and the impact on mortality. </li></ul>
    19. 20. Individual Characteristics Were Independently Associated with AL income (0.029) education (<.0001) age (<.0001) income (0.201) education (0.014) age (<.0001) Model 4: Basic + NSES + Residential Stability + Population Density income (0.017) education (<.0001) age (<.0001) income (0.029) education (<.0001) age (<.0001) Model 5: Basic + NSES + Population Density income (0.028) education (<.0001) age (<.0001) income (0.160) education (0.014) age (<.0001) Model 3: Basic + NSES + Residential Stability income (0.016) education (<.0001) age (<.0001) income (0.080) education (0.015) age (<.0001) Model 2: Basic + NSES FEMALES MALES Variable (p-value)

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