Modeling the Built Environment, Indoor Air Quality, and Health: A Systems Science Approach

Size: px

Start display at page:

Download "Modeling the Built Environment, Indoor Air Quality, and Health: A Systems Science Approach"

Lorena Robbins
6 years ago
Views:

1 Modeling the Built Environment, Indoor Air Quality, and Health: A Systems Science Approach M. Patricia Fabian Boston University School of Public Health National Academies of Sciences, Engineering, and Medicine Irvine, California October 2016

2 Questions about how changes in the built environment can Impact of home humidification on respiratory virus survival/transmission? Impact of interventions on indoor air quality and health? Integrated pest management HEPA filters Weatherization (energy retrofits) No smoking policies Humidifiers/de-humidifiers Impact cost tradeoffs between interventions and health? Challenging to answer with traditional field & epidemiology studies

3 Challenges come from The indoor environment is complex, with multiple tradeoffs and feedback loops Many diseases (e.g. asthma, cardiovascular disease) are complex and multi-factorial Some health-related health outcomes are rare but high cost (e.g. hospitalizations) Studies are rarely sufficiently powered to capture effects of indoor environment on health care utilization

4 Why is this important? Without evidence of changes in healthcare use, it is challenging to develop generalizable insights for policy analysis or to compare the benefits and costs of building interventions

5 Systems science a family of analytic approaches used to characterize complex systems in order to evaluate one or more system problems and identify optimal solutions Lich, K.H., et al. Prev Sci, 2013

6 Systems Science Building simulation Health modeling IAQ $ Built environment Indoor air quality Health outcomes Costs

7 Systems Science Building simulation Health modeling IAQ $ Built environment Indoor air quality Health outcomes Costs

airflow contaminant concentration Outputs for individual units are

8 Building simulation model Multi-zone indoor air quality and ventilation analysis computer program designed to help you determine airflow contaminant concentration Outputs for individual units are time-resolved ventilation rates pollutant concentrations temperature & relative humidity

9 Templates available 209 housing templates already developed Representative of US housing stock Limitations Housing age - types of housing Custom modifications are time-consuming Limited temperature modeling Recent work coupling CONTAM to TRNSYS for dynamic temperature modeling (Dols et al 2015)

10 Example 1: relative humidity and influenza virus survival 2-story detached residential building Indoor moisture sources: cooking, dishwashing, bathing, and occupants 13 (Myatt et al 2010) (Myatt et al 2010)

11 Example 1: relative humidity and influenza virus survival 14 (Myatt et al 2010)

12 Example 2: impact of hypothetical interventions on IAQ in Boston Public Housing Population: Boston Public Housing Multi-family apartment building #26 4 stories construction Naturally ventilated Square footage: 703 sf Selected 1 st and 4 th floor apartments B

13 Example 2: impact of hypothetical interventions on IAQ in Boston Public Housing apartment Percent change in pollutant concentrations A. NO 2 B. PM 2.5 B for each intervention scenario compared to baseline scenario. Fabian et al. (2014) JACI

14 17 Example 3: impact of weatherization (deep energy retrofits) in low income multi family housing Castle Square Apartments located in Boston, Mass. 500 units: 200 mid-rise, 300 townhouses Majority Asian population Privately owned affordable rental housing development

5 over 7 days generated using CONTAM to simulate a low-rise apartment at CSA

15 Example 3: impact of weatherization in low income multi family housing Figure. 24-h average concentrations of total indoor PM 2.5 over 7 days generated using CONTAM to simulate a low-rise apartment at CSA under different building conditions and occupant activity scenarios during heating and cooling seasons.

16 Systems Science Building simulation Health modeling IAQ $ Built environment Indoor air quality Health outcomes Costs

17 The Asthma Problem Asthma is a chronic lung disease that inflames and narrows the airways Causes recurring periods of wheezing, chest tightness, shortness of breath, and coughing NHLBI, 2015

18 Environmental risk factors of asthma exacerbations 90% of time spent indoors Biological Environmental tobacco smoke Outdoor air pollutants Combustion by-products

19 Systems science: discrete event simulation (DES) Housing characteristics Occupant behavior Meteorological conditions Outdoor pollutant concentrations Building Simulation Cockroach allergen, mold & other pollutants Air exchange rates IAQ parameters NO 2 PM 2.5 RH Temperature Individual characteristics, sociodemographics Lung function (FEV1% predicted) Fabian et al. (2012) Environ. Health Fabian et al. (2014) JACI Asthma history Asthma medication Asthma attacks Healthcare utilization ER visits Hospital stays Clinic visits Cumulative risk assessment

20 How to populate parameters in the DES Exiting building templates Build your own from architectural plans Housing characteristics Occupant behavior Meteorological conditions Outdoor pollutant concentrations Weather/monitoring stations Land use regression models Remote sensing data Surveys EPA CHAD Building Simulation Cockroach allergen, mold & other pollutants Asthma history Asthma medication Epidemiology studies Electronic health records Air exchange rates IAQ parameters NO 2 PM 2.5 RH Temperature Field studies Individual characteristics, sociodemographics Lung function (FEV1% predicted) Asthma attacks Healthcare utilization ER visits Hospital stays Clinic visits Surveys Electronic health records Epidemiology studies Electronic health records

21 DES example 1: impact of deep energy retrofits on asthmatic children Housing characteristics Occupant behavior Meteorological conditions Outdoor pollutant concentrations Castle Square apartments Townhouses Building Simulation Constructed from building plans Multifactorial modeling: Pre/post retrofit Smoking Window opening Cooking Kitchen and bath exhaust fan use Cockroach allergen, mold & other pollutants Asthma history Asthma medication Air exchange rates IAQ parameters NO 2 PM 2.5 RH Temperature Individual characteristics, sociodemographics Lung function (FEV1% predicted) Asthma attacks Healthcare utilization ER visits Hospital stays Clinic visits

22 Results: 100,000 asthmatics, 10 years Baseline: pre-retrofit, exhaust fans off Smoker Post-retrofit Exhaust fans OFF Exhaust Exhaust fans fans on ON None Light Heavy None Light Heavy % Change in Asthma outcomes Asthma outcomes

23 DES example 2: asthmatics living in Boston Public Housing Housing characteristics Occupant behavior Meteorological conditions Outdoor pollutant concentrations Boston Public housing Multifamily 1960 s construction Building Simulation Simulate 1,000,000 asthmatic children Time: 10 years Cockroach allergen, mold & other pollutants Asthma history Asthma medication Air exchange rates IAQ parameters NO 2 PM 2.5 RH Temperature Individual characteristics, sociodemographics Lung function (FEV1% predicted) Asthma attacks Healthcare utilization ER visits Hospital stays Clinic visits

24 Example 2: Boston Public Housing residents Occupant characteristics Simulated population values Gender 50% male Age 6-17, uniformly distributed Own a gas stove (Kattan et al. 1997) 89% Use the stove for supplemental heating in winter (Brugge et al. 2001) 38%, assuming that supplemental heat was turned on only on days when the 24-hour average outdoor temperature was below 32 F Below average housekeeping (vs. average or above 25% average housekeeping) Current smoker in the house (Digenis-Bury et al. 34% 2008) Among smokers, % heavy vs. light smoker 50% Housing characteristics Apartment level (upper 4 th floor/lower 1 st floor) 50% Leakiness category c I 20% (Zota et al. 2005) II 50% III 30% Functioning kitchen and bathroom fan 13% (Kattan et al. 1997) Houses with holes in walls/ceiling 73% (Peters et al. 2007)

25 Results. Changes in asthma outcomes Percent change in health outcomes for each intervention compared to the baseline scenario. A) asthma symptom days include days with any symptom, including wheeze, cough, night awakenings; B) severe attacks include asthma hospitalizations, emergency room visits and clinic visits. Asthma outcomes reflect changes in exposure to NO 2, PM 2.5, cockroach allergen, and damp moldy homes. Fabian et al. (2014) JACI A A B

26 Results. Changes in costs of healthcare utilization for each intervention compared to baseline averaged over all asthmatics Fabian et al. (2014) JACI

27 Results. Changes in costs of healthcare utilization for each intervention compared to baseline averaged over all asthmatics Cost of fan installation: $ Healthcare savings: $ 173/year Payback period: 2-3 years BB

28 Results. Changes in costs of healthcare utilization for each intervention compared to baseline averaged over all asthmatics Intervention cost: $ 6,500 Healthcare costs: $314/year Energy savings: $605/year Payback wo/health: 11 years Payback w/health: 22 years It s a model!

29 Conclusions Systems science tools are useful in evaluating complex exposure-outcome relationships across multiple pollutants (e.g. viruses, fungi, combustion by-products), social stressors & health outcomes Results from simulation models can be used to conduct cost-benefit and policy analyses

30 Acknowledgements BU Jonathan Levy Lindsay Underhill Megan Sandel SCF: Robert Putnam, Kadin Tseng, Daniel Kamalic CDC Green Housing Study Ginger Chew HSPH Gary Adamkiewicz Meryl Colton Marty Alvarez-Reeves Amelia Geggel Natasha Stout Urban Habitat Initiatives Kimberly Vermeer Funding: HUD Technical Grant MAHHU ; NIEHS award number R21ES017522; Boston University Initiative on Cities

31 M. Patricia Fabian Boston University School of Public Health

The Effects of Indoor Environmental Exposures on Pediatric Asthma: A Discrete Event Simulation Model

The Effects of Indoor Environmental Exposures on Pediatric Asthma: A Discrete Event Simulation Model The Harvard community has made this article openly available. Please share how this access benefits