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How Where You Live Affects Your Health
 

How Where You Live Affects Your Health

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מצגת מתוך הרצאה שקיימה פרופסור פרנסין ליידן בכנס סביבת המגורים ובריאותינו: האם הדשא של השכן בריא יותר? ...

מצגת מתוך הרצאה שקיימה פרופסור פרנסין ליידן בכנס סביבת המגורים ובריאותינו: האם הדשא של השכן בריא יותר? שהתקיים ב- 30 במאי, 2011

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    How Where You Live Affects Your Health How Where You Live Affects Your Health Presentation Transcript

    • Location, Location, Location How Where You Live in the US Affects Your Health Francine Laden, ScD Mark and Catherine Winkler Associate Professor of Environmental Epidemiology Harvard School of Public Health Channing Laboratory, Brigham and Women’s Hospital
    • Overview The study of environmental epidemiology – issues with exposure assessment Location as a “measure” of exposure  Aggregate data  Individual data Examples from my research group  Ultraviolet radiation  Air pollution  Built environment
    • Cohort Studies in the Examples The Nurses’ Health Study (NHS) The Harvard Six Cities Study The Trucking Industry Particle Study (TrIPS) The US Renal Data System (USRDS) The Nurses’ Health Study II (NHSII)
    • Environment is all thatsurrounds us, food we eat,soil we live on, buildings wedwell in, work we do, societywe are a part of.
    • Environmental Exposures My working definition Exposures that are outside of ourselves Experienced passively Natural and unnatural extras Common factors  Ubiquitous  Low levels with a tight range  Small effects
    • Exposure Assessment
    • Self-reports of Proxies of Exposure Where do you live now, and Do you spendAre you where smoky time in bars?exposed todust or fumes did you livein your job? then? Do you drink tap water?
    • Location, Location, Location
    • Defining andMeasuring Location
    • Aggregate Data Country, Region, State, County, City Exposure = Location
    • Breast Cancer Mortality Rates
    • Aggregate Data Country, Region, State, County, City Exposure = Location Exposure = Aggregate value of an environmental exposure  e.g. ultraviolet light, urbanicity, air pollution
    • Individual DataResidential Address
    • Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information But first of all, the exposure of interest has to have been  Measured and mapped  in the right space and  at the right time
    • Where You Livein the US AffectsYour Health
    • Region State CityResidence
    • Region State CityResidence
    • US Census Regions
    • The Nurses’ Health Study  121,700 women  Enrolled in 1976  Biennial follow-up  Information specific to location  Biennial mailing address  State at birth, age 15 and age 30
    • The NHS: Addresses 1986-2006
    • Breast Cancer Region Age-adjusted Multivariate*at baseline Cases HR (95%CI) HR (95% CI)South 222 reference referenceNortheast 1103 1.08 (0.93–1.24) 1.12 (0.97–1.30)Midwest 353 1.08 (0.91–1.27) 1.09 (0.92–1.29)California 327 1.24 (1.05–1.47) 1.18 (1.00–1.40)Postmenopausal breast cancers, *adjusted for known breast cancer riskfactors Laden et al. JNCI 1997;89:1373-8
    • Rheumatoid Arthritis Region Multivariate at baseline Cases HR (95% CI)West 121 referenceMidwest 161 1.33 (1.05-1.69)Mid-Atlantic 392 1.30 (1.05-1.60)New England 137 1.42 (1.10-1.82)Southeast 21 1.20 (0.75-1.91) Costenbader et al. Arch Intern Med. 2008;168(15):1664-70
    • Region State CityResidence
    • Ultraviolet Radiation Exposure a function of time of day, cloud cover, haze, ozone concentrations, latitude and altitude
    • Skin Cancer women living in the same state at birth, age 15, age 30Cancer UV rank HR (95% CI)Melanoma Low 1 Medium 1.26 (0.97-1.63) High 1.12 (0.72-1.72)SCC Low 1 Medium 1.61 (1.31-1.98) High 2.07 (1.55-2.77)BCC Low 1 Medium 1.24 (1.16-1.31) High 1.30 (1.18-1.43) Qureshi et al. Arch Intern Med 2008;168:501-7
    • Non-Hodgkin LymphomaTime point UV rank HR (95% CI) P for trendBirth Low 1 <0.01 Medium 1.21 (1.03-1.42) High 1.18 (0.97-1.43)Age 15 Low 1 <0.01 Medium 1.17 (1.00-1.38) High 1.21 (1.00-1.47)Baseline Low 1 0.02 Medium 1.01 (0.88-1.16) High 1.11 (0.95-1.29) Bertrand et al. in preparation
    • Region State CityResidence
    • Harvard 6 Cities Study Portage (Madison) Watertown # (Boston) # Steubenville # St. Louis Topeka # # # Kingston-Harriman (Knoxville)
    • Cities Defined By Various components of air pollution  Particles: total (TSP), inhalable (PM10), fine (PM2.5)  Sulfate particles  Aerosol acidity  Sulfur dioxide  Nitrogen dioxide  Ozone Measured at a central location Averaged over the study period
    • Total Mortality 1972-1991Dockery et al. NEJM 1993; 329: 1753-9
    • Continued Follow-up 1998 1.3 S 1.2 H LRate Ratio 1.1 H T W S 1 P T L 0.9 0.8 W Period 1 Period 2 0.7 0 5 10 15 20 25 30 35 PM2.5 mg/m3 Laden et al. AJRCCM 2006;173:667-72
    • PM Inhalation Lungs • Inflammation Heart • Oxidative stress Blood • Accelerated progression • Altered rheology • Altered cardiac autonomic function and exacerbation of COPD • Increased coagulability • Increased respiratory symptoms • Translocated particles • Increased dysrhythmic • Effected pulmonary reflexes • Peripheral thrombosis susceptibility • Reduced lung function • Reduced oxygen saturation • Altered cardiac repolarization •Increased myocardial ischemia Systemic Inflammation Oxidative Stress • Increased CRP • Proinflammatory mediators Vasculature • Leukocyte & platelet activation Brain • Atherosclerosis, accelerated progression of and • Increased cerebrovascular destabilization of plaques ischemia • Endothelial dysfunction • Vasoconstriction and HypertensionThere are multiple mechanistic pathways with complex interactions and interdependencies
    • The Built Environment
    • The Built Environment: IOM Definition Land-Use Patterns  Spatial distribution of human activities Transportation Systems  Physical infrastructure and services that provide the spatial links or connectivity among activities Design Features  Aesthetic, physical, and functional qualities of the built environment, such as the design of buildings and streetscapes, and relates to both land use patterns and the transportation system
    • Sprawl Development outpaces population growth Low density Rigidly separated homes, shops, and workplaces Roads marked by large blocks and poor access Lack of well-defined activity centers, such as downtowns Lack of transportation choices Relative uniformity of housing options
    • Street Conceptual model: connectivity Effects of the built Residential or population environment on physical Physical density activity Access to activity and obesity environment physical activity resources Physical Access, density, and diversity of activity Morbidity destinations Obesity / Mortality Supermarkets Access and grocery / stores density Dietary food retail Convenience intake Food storesenvironment Access Sit-down / restaurants density food Fast-food * Food retail and food service facilities could also service restaurants be physical activity destinations.
    • The County Sprawl Index Developed by the National Center for Smart Growth Incorporates 6 Census based measures of  Residential density  Street accessibility Calculated for the year 2000 Higher sprawl index = higher density  New York County, NY = 352.1  Jackson County, GA = 62.6
    • County Sprawl in the NHS Mean=109.5 SD=26.4 Range=62.6-352.1Higher sprawl = more compact county
    • : Sprawl Index and BMI/Physical Activity Cross-sectional analysis 2000 β (95% CI)Outcome 1 SD (25.7) ↑ in DensityWeight BMI (kg/m2) -0.08 (-0.14, -0.02)Physical Activity Total METS 0.30 (0.04, 0.57) Walking METS 0.23 (0.14, 0.33) Outdoor METS 0.34 (0.20, 0.47)Adjusted for age, smoking, race, and husbands education James et al. in preparation
    • Sprawl Index and Overweight/Obesity Survival analysis 1986-2006 Among the women who were not overweight (BMI 25-30) or obese (BMI ≥30) at baseline HR for each 1 SD ↑ in Density  Overweight: HR 0.96 (95% CI: 0.95, 0.98)  Obesity: HR 0.95 (95% CI: 0.94, 0.97)
    • Region State CityResidence
    • All-cause Cardiovascular Outcomes Mortality DiabetesRheumatoidArthritis Kidney Function Cognitive Decline
    • Distance to Major RoadCensus Road Classifications A1 (primary roads, typically interstates, with limited access) 15 m fr A2, A2 (primary major, non- 510 m fr A1 interstate roads) 163 m fr A2, A3 (smaller, secondary 645 m fr A1 roads, usually with more 85 m fr A2 than two lanes) 220 m fr A2
    • Rheumatoid ArthritisDistance to A1-A3 Person (meters) Cases yrs HR (95% CI) 0 to < 50 52 136,205 1.31 (0.98-1.74) ≥50 to < 200 67 271,200 0.84 (0.65-1.08) ≥200 568 1,976,600 1 Hart et al. EHP 2009;117: 1065-1069
    • EPA Air Quality System (AQS) Database of measurements of air pollutant concentrations throughout the US Criteria Air Pollutants  PM10, PM2.5, CO, NO2, SO2, O3, Pb Hazardous Air Pollutants (HAPS)  Organic compounds and toxic metals Dates of PM measurements:  PM10 – 1985 on  PM2.5 – 1999 on
    • EPA PM10 Monitors - 1985
    • EPA PM10 Monitors – 2000
    • Distribution of PM in the Airways EHP 2006
    • Spatio-temporal Models GIS techniques  Complex model including existing monitoring networks, weather, and  GIS covariates including distance to road, elevation, land-use, county level emissions, population density, point source emissions Annual average models PM10, NO2 and SO2 Monthly average models PM10 and PM2.5
    • Annual Modeling of PM10 and NO2 The Trucking Industry Particle Study Hart et al. EHP 2009 117:1690–6
    • Hart et al. EHP 2009 117:1690–6
    • Exposure to PM10, S02 and NO2 the Trucking Industry Particle Study PM10 (6 µg/m3) SO2 (4 ppb) NO2 (8 ppb)Cause of % Increase % Increase % IncreaseDeath Cases (95%CI) (95%CI) (95%CI)All Cause 2,816 9.7% 10.6% 14.9% (5.2%,14.5%) (4.6%,16.9%) (9.9%,20.2%)Lung 475 4.7% 9.1% 7.3%Cancer (-5.9%,16.5%) (-4.6%,24.8%) (-3.9%,19.9%)CVD 972 7.6% 9.6% 10.9% (-0.2%,16.0%) (-0.4%,20.7%) (2.7%,19.8%)Respiratory 184 8.2% 25.1% 20.1%Disease (-8.6%,28.0%) (2.0%,53.5%) (0.9%,42.8%)Non-truck driversControlling for job title Hart et al. AJRCCM 2011;183:73-78
    • End-stage Renal Disease
    • End-Stage Renal Disease Hypotheses:  Particularly vulnerable to adverse effects of PM  More severe anemia with higher exposures  Greater resistance to erythropoiesis stimulating agents (EPO) Preliminary results:  ↑ anemia with ↑ annual average PM10  ↑ EPO dose with ↑ annual PM10
    • PM10 PM2.5 PM10-2.5 Yanosky et al. Atmos Env 2008;42:4047-62; Yanosky et al. EHP 2008;117:22-9
    • Yanosky et al. Atmos Env 2008;42:4047-62
    • All-cause Mortality and PM10 Northeastern Region 1992-200416% increase 1.30with a 10 μg/m3↑ in 12-month 1.20 Hazard Ratioavg PM10 1.10 1.00 0.90 1 month avg 3 month avg 12 month avg 24 month avg 36 month avg 48 month avg Adjusted for age, year, season and state of residence Puett et al. AJE 2008: 168:1161–68
    • Mortality and Coronary Heart Disease – 10 μg/m3 ↑ Fine and Coarse PM HR (95% CI) Outcome PM2.5 PM10-2.5 1.29 0.96 All-cause mortality (1.03,1.62) (0.82,1.12) 1.10 1.01 First CHD (0.76,1.60) (0.78,1.31) 2.13 0.91 Fatal CHD (1.07,4.26) (0.56,1.48) 0.71 1.06 Non-fatal MI (0.44,1.13) (0.77,1.47) Adjusted for the other size fraction, age, state, year, season, smoking , BMI, risk factors for CHD, physical activity, neighborhood SES. Puett et al. EHP 2009: 117:1697–1701
    • Effect Modification BMI and Smoking Fatal CHD and PM10 Puett et al. AJE 2008: 168:1161–68
    • Diabetes Particulate Matter Distance to Road1 IQR ↑ HR (95% CI) meters HR (95% CI)PM2.5 0.99 (0.92,1.08) <50 1.14 (1.03,1.27)PM10-2.5 1.04 (0.98,1.11) 50-99 1.16(0.99,1.35) 100-199 0.97(0.88,1.08) 200+ 1Adjusted for age, season,year, state, smoking , BM,hypertension, alcohol intake,physical activity, and diet. Puett et al. 2010 EHP epub ahead of print
    • Cognitive Decline PM can access the brain via  Circulation  Intranasal route → direct translocation through olfactory bulb … where it may precipitate inflammatory response, injure BBB, increase amyloid beta Associations with CVD, stroke, and vascular risk factors
    • Cognitive Decline NHS participants ≥ 70 yrs old n= ~17,000 Cognitive assessment by telephone  Tests of working memory attention, global cognition, verbal memory/learning and verbal fluency  Baseline administered 1995-2001  2nd and 3rd approx 2 and 4 yrs later PM10, PM2.5, Distance to Road  Assessed different averaging periods
    • Long-term Exposure to PM10 in Relation to Cognitive Decline Ptrend = 0.003 Adjusted for age, education, husband’s education, long-term physical activity and long-term alcohol consumption Weuve et al. in preparation
    • Stronger Association with Measures of Long-term ExposureΔ in cognitive 0.010 score per 0.005 10 μg/m3 0.000 ↑ in PM10 -0.005 Past 5 yrs Since 1989 -0.010 -0.015 Past month -0.020 -0.025 Past yr Past 2 yrs -0.030 Weuve et al. in preparation
    • Personal Level Built Environment
    • Objective Measures By creating buffers around an address we can measure  Residential density  # housing units/area  Land use mix  Density of walking destinations  Diversity  Street connectivity  Intersection density  Pedestrian route directness
    • Land Use Mix Walking destinations: Counts of businesses within the buffers based on stores, facilities, and services from 2006 InfoUSA spatial database on businesses, which include grocery stores, restaurants, banks , etc.
    • Street Connectivity Intersection Count: Number of intersections within each buffer
    • Subjective Measures: yes/no questions Shops, stores and markets are within easy walking distance of my home My neighborhood has free or low cost recreation facilities, such as parks, walking trails, bike paths, recreation centers, playgrounds, public swimming pools, etc. There are sidewalks on most of the streets in my neighborhood The crime rate in my neighborhood makes it unsafe to go on walks at night.
    • Neighborhood Environment and Meeting Physical Activity Recommendations Attribute OR (95% CI)Crime-Unsafe to Walk at Night 0.80 (0.74, 0.87)Shops/Stores Easy Walking Distance 1.41 (1.36, 1.47)Sidewalks on Most Streets 1.22 (1.18, 1.27)Free/Low Cost Recreation Facilities 1.33 (1.28, 1.38) Adjusted for age, race, ethnicity, BMI categories, and husbands education Meeting physical activity recommendation by walking ≥ 500 MET-minutes/week. Troped et al. submitted
    • Conclusions
    • Location, Location, Location Knowing a person’s address, or better yet residential history, gives us the opportunity to estimate a multitude of environmental exposures Residential address allows relatively inexpensive assessment of exposures unknown to the participant
    • Location, Location, Location Meaningful environmental assessments can be made at the area and personal level  There are limitations and sources of error not discussed here Location data can be a powerful tool to incorporate assessment of environmental exposures into a variety of study designs
    • Acknowledgments:Kimberly Bertrand, M. Alan Brookhart, DouglasDockery, Mathilda Chiu, Karen Costenbader, MiguelCraig, Mary Davis, Chris Garcia, Eric Garshick, DianeGold, Sue Hankinson, Jaime Hart, David Hunter, ISEE,Elizabeth Karlson, Susan Korrick, Petros Koutrakis,Peter James, Steve Melly, Lucas Neas, Chris Paciorek,Robin Puett, Abrar Qureshi, Eric, Rimm, Joel Schwartz,Tom Smith, Frank Speizer, Donna Spiegelman, MeirStampfer, Sheila Stewart, Helen Suh, Philip Troped,Veronica Vieira, Scott Weiss, Jennifer Weuve, JeffYanosky, Barbara Zuckerman…Funding sources: US EPA, HEI, NIEHS, NCI, FAMRI, HarvardCatalyst Program