Public Health Nursing Vol. 31 No. 6, pp. 484–491 0737-1209/© 2014 Wiley Periodicals, Inc. doi: 10.1111/phn.12164
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Identifying Populations at Risk: Interdisciplinary Environmental Climate Change Tracking Laura Anderko, PhD, RN,1 John Davies-Cole, PhD, MPH,2 and Andrew Strunk, BS, PhD Student3 1
School of Nursing and Health Studies, Georgetown University, Washington, District of Columbia; 2Center for Policy, Planning, and Evaluation, District of Columbia Department of Health, Washington, District of Columbia; and 3Boston University, Boston, Massachusetts Correspondence to: Laura Anderko, Robert and Kathleen Scanlon Endowed Chair, School of Nursing and Health Studies, Georgetown University, 3700 Reservoir Rd NW, Washington, DC 20057. E-mail: [email protected]
ABSTRACT Background: Climate change, experienced as extreme weather events such as heat waves can lead to poorer air quality and underscores the critical need to consider the consequences of these environmental changes on health. Changes are occurring at a rate that exceeds what the world has experienced over the last 650,000 years, yet little attention has been focused on the potentially catastrophic public health effects of climate change. Methods: This study instituted a two-phase approach. In building capacity for an Environmental Public Health Tracking Network, the District of Columbia Department of Health first examined the availability of climate change and health data. These data were then used to assess vulnerabilities and disease burden associated with heat, air quality, and hospitalizations for asthma (N = 5,921) and acute myocardial infarction (AMI) (N = 2,773) during 2007–2010. A Poisson regression analysis was applied to the time series of daily counts for hospitalizations for selected age, race, and gender groups. Results: Although no significant associations were found for PM2.5, PM10, or ozone with asthmarelated or AMI-related hospitalizations with seasonal changes, surveillance data found disparities in hospitalizations particularly in female, African American residents for both asthma and AMI. Conclusions: Tracking Networks are critical for assessing community environmental health vulnerabilities. Key words: air pollution, environmental health, health disparities.
Climate change poses a threat to health, including increases in asthma, respiratory allergies, airway, and cardiovascular diseases (Ebi, 2009; Ebi & McGregor, 2008; Frumkin, Hess, & Vindigni, 2009). Environmental factors such as ambient air pollutants and allergens can exacerbate cardiorespiratory diseases and are clearly impacted by climate changes related to carbon dioxide levels and heat (Anderson, Krall, & Bell, 2012; Balbus & Malina, 2009; Dominici et al., 2006; Luber et al., 2014; Mustafic et al., 2012; Smith et al., 2014). Rising temperatures can make smog pollution worse and is particularly dangerous for people with cardiovascular disease and asthma. It is anticipated that car-
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dio-respiratory disease will become more prevalent and severe because of increased exposure to pollen (due to altered growing seasons), molds (from extreme or more frequent precipitation), and air pollution as a result of climate change, namely extreme heat events (Ariano, Canonica, & Passalacqua, 2010; D’Amato, Cecchi, D’Amato, & Liccardi, 2010).
Background Air quality is strongly dependent on weather and climate (Jacob & Winner, 2009). Climate change alters concentrations of fine particulate matter (PM2.5) and ozone (O3), attributing to air pollu-
Anderko et al.: Environmental Public Health Tracking tion. Although only a few large-scale studies have assessed population exposures to high concentrations of air pollution and health, those conducted have consistently found poor air quality to increase the risk for hospitalization (Dominici et al., 2006) and premature mortality (Fang, Mauzerall, Liu, Fiore, & Horowitz, 2013). The Harvard Six Cities Study found that fine particulate air pollution contributed to excess mortality in some U.S. cities (Dockery et al., 1993) and more recently it has been reported that worldwide, approximately 100,000 deaths occur annually as a result of air pollution (Fang et al., 2013). One challenge to conducting large-scale studies is the lack of climate, environmental, and population health data for local regions that can be tracked over time. The importance of local public health departments establishing systems for collecting data and conducting surveillance on public health concerns that are impacted by extreme weather events and climate change is underscored by a recent report by the Council of State and Territorial Epidemiologists (Houghton, 2013). Local public health departments are increasingly involved in activities such as hazard mitigation planning and response that are impacted by climate change. Environmental Public Health Tracking can provide the data needed to respond appropriately (Hess, McDowell, & Luber, 2012; Maibach et al., 2008). Environmental Public Health Tracking (Tracking Network) is an ongoing surveillance system coordinated by the Centers for Disease Control and Prevention (CDC) that collect data that can be used to explore how environmental exposures can affect people’s health. This surveillance system integrates health, exposure, and hazard data from a variety of national, state, and city sources. CDC is currently funding health departments in 23 states and New York City to build local tracking networks (http://www.cdc.gov/nceh/tracking/pdfs/National_ FactSheet_508.pdf). The District of Columbia Department of Health (DC DOH), Center for Policy Planning and Evaluation (CPPE) has been building capacity in establishing a Tracking Network since 2009 with an overall goal to protect communities through planning, implementation, and evaluation of public health activities (Davies-Cole, 2011). A Technical Advisory Group (TAG) was created to support the development of the Tracking Network including
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data content, partnership building, and evaluation activities. Members also help identify common needs, promote resource and information sharing, and support associated public health actions. The TAG is composed of 12 members and includes DC DOH program managers, District of Columbia Department of the Environment (DDOE) program managers, an attorney, epidemiologists, public health nurses, university professors, data owners, environmental health professionals, and laboratorians. The TAG initiated the two-phase pilot study on climate and health outcomes. Consistent with CDC’s BRACE (Building Resilience Against Climate Effects) Framework recommendations (http://www.cdc.gov/climateandhealth/ brace.htm), CPPE conducted a two-phase pilot study that addressed two steps within the BRACE Framework: Step 1: Forecasting Climate Impacts and Assessing Vulnerabilities and Step 2: Projecting the Disease Burden. This two-phase pilot study was conducted after receiving approval from the Institutional Review Board at the DC DOH, in collaboration with an interdisciplinary team that included the TAG, a local School of Nursing & Health Studies, the DDOE, and the Maryland State Health Department to identify local-level environmental exposure and vulnerability indicators for climate change, then conducted vulnerability assessments for the region.
Methods Phase I: Environmental public health indicator pilot As part of its environmental public health tracking efforts, CPPE applied and received funding from the Council of State and Territorial Epidemiologists (CSTE) to collect and pilot test 24 Climate Change Indicators as part of a State Environmental Health Indicators Collaborative. This phase of the study was to answer the following research question: What climate change and health indicators are available for populating a Tracking Network in DC? CSTE developed the climate and health indicators (called Environmental Public Health Indicators [EPHI]) including instructions on how to calculate each indicator and a template to facilitate organizing and recording the data. Data for each indicator were collected and pilot-tested by organizations
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such as CPPE that were then used by CSTE to finalize a list of climate and health indicators and to generate multistate data for each EPHI. Data were collected from a variety of sources including federal (e.g., air quality data from the Environmental Protection Agency [EPA]) and regional (e.g., climate adaptation plans for DC). Final EPHI measures included environmental variables that can affect human health or can be used to project future health impacts based on changes in exposure. Categories and measures within each category include: (a) environmental (e.g., greenhouse gas emissions), (b) health outcome (e.g., rate of hospitalizations in the summer months), (c) mitigation (e.g., vehicle miles traveled), (d) adaptation (e.g., access to cooling centers), and (e) policy (e.g., development of a state climate action plan). The finalized set of EPHI includes a standardized set of measurement units, rationale, sources of data, and data limitations. The full list of indicators and criteria can be found at: http://www.cste.org/? page=EHIndicatorsClimate.
Phase II: Assessing vulnerabilities Once the EPHIs were finalized by CSTE, CPPE conducted the second phase of the pilot study to explore trends in hospitalizations for acute myocardial infarction (AMI) and asthma in relation to particulate matter (PM 2.5 and PM 10.0) and ozone concentrations over the period from 2007 to 2010. The research explored the impact of several air quality measures and two health outcomes: Is there a significant association between air quality concentrations (PM2.5, PM10, and ozone) with health outcomes (asthma or acute myocardial infarction)? Hospitalization data were available through the DC Hospital Discharge Data network and the air quality database was obtained from the “Air Now” web site sponsored by the EPA (2014). This phase of the study was funded through a Capacity Building Grant from The Association of State and Territorial Health Officers. Based on de-identified individual hospitalization data during the period of 2007–2010, daily counts of total asthma-related and total AMI hospitalizations were computed for nine different age groups (analyses for specific age groups were conducted to isolate additional associations based on age). Hospitalization data included (a) total admissions for asthma (International Classification of Dis-
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eases Ninth [ICD-9]: 493.00) by month and year, race, age, gender, and zip code/Ward (N = 5,921) and (b) total admissions for AMI (ICD-9: 410.00) by month and year, race, age, gender, and zip code/ Ward (N = 2,773). Daily and monthly PM2.5, PM10, and ozone concentrations for one monitoring site in the District of Columbia were obtained from the EPA online database for the same time period. Several Poisson regression models were performed to investigate the relationship between daily air quality concentrations and the selected hospitalization outcomes between 2007 and 2010. Separate regression models were run for each individual air quality indicator. To control for seasonal effects, the regression models included dummy variables for each month, accounting for the average change in hospitalizations resulting from seasonal fluctuations. A lag of 0 days was used for all regression models (i.e., daily hospitalization counts were matched to the same day of pollution measurement). Given that there were so few asthma/AMI hospitalizations per day, the analyses were conducted using raw daily counts (as opposed to stratifying by race, gender, age). This was done to increase the number of cases in each model, and in turn increase power.
Results Air quality Although the average PM2.5 concentration exceeded the EPA standard (15 lg/m3) five times over the course of 48 months, there is not an increasing trend over the time period studied. PM2.5 concentrations generally reached their peak during the summer months (June–August), reaching a maximum of 20.38 lg/m3 in August 2007. PM10 concentrations were well below the EPA standard throughout the 4 years time period (2007–2010). Monthly averages of Ozone concentration remained below the EPA standard even during the summer months, however, daily 8-hr Ozone concentration levels exceeded the EPA standard several times between 2007 and 2010. Hospitalizations Asthma. The mean number of monthly asthma admissions from 2007 to 2010 was 123.4 (with a standard deviation of 23.33) with a minimum of 78
Anderko et al.: Environmental Public Health Tracking in June 2007 and a maximum of 195 in September 2009. Data demonstrate that about half of the Wards in the Washington, DC area had total increases in the number of cases of people hospitalized for asthma attacks over the 4-year span, with the highest numbers occurring in the zip codes for Wards located in the Southeast which experience a higher rate of poverty. Monthly hospitalizations for asthma generally peaked during the months of September–November, as well as in March. Vulnerability assessments found disparities in race, age, and gender. Figures 1, 2, and 3 show that (a) African Americans accounted for approximately 90% of all asthma hospitalizations, (b) females accounted for almost 61.5% of all asthma-related hospitalizations, (c) females were more likely to be hospitalized than men over the 4-year period, (d) children 0–9 years of age were more likely to be hospitalized for asthma when compared to other age groups, and (e) adults within the 50–59 age group experience a 48.7% increase in the number of asthma admissions from 2007 to 2010. The number of asthmarelated admissions for African Americans rose 21.7% between 2007 and 2009, while hospitalizations generally declined for those identified as “White”, “Other”, and “Unknown”.
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with a minimum of 35 in January 2008 and a maximum of 76 in January 2009. Monthly hospitalizations for AMI generally peaked during the months of March through May. Vulnerability assessments found disparities in race, age, and gender. Figures 4, 5, and 6 show that: (a) African Americans accounted for approximately 74% of all hospitalizations for AMI, (b) Annual AMI hospitalizations for Whites fell approximately 31% between 2007 and 2010. African Americans experienced a decrease of 3% over the same period, (c) hospitalizations occurred most frequently in the 80+ age group and accounted for 25% of total hospitalizations during this period, and (d) between 2007 and 2010, women accounted for a slightly higher proportion of total hospitalizations for AMI than men (50.49% and 49.51% respectively).
Acute myocardial infarction. The mean number of monthly AMI admissions from 2007 to 2010 was 58 (with a standard deviation of 9.94) Figure 2. Asthma Hospitalizations in DC by Race, 2007–2010 (N = 5,921)
Figure 1. Asthma Hospitalizations in DC by Age, 2007–2010 (N = 5,921)
Figure 3. Asthma Hospitalizations in DC by Gender, 2007–2010 (N = 5,921)
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Figure 4. AMI Hospitalizations in DC by Age, 2007–2010 (N = 2,773)
Figure 5. AMI Hospitalizations in DC by Race, 2007–2010 (N = 2,773)
Association between daily hospitalizations and air quality indicators Descriptive statistics. Descriptive statistics were produced for daily asthma and AMI hospitalizations, as well as for each air quality indicator (daily mean PM2.5 concentration, daily mean PM10 concentration, daily max 8-hr ozone concentration). The mean number of asthma hospitalizations per day over the period of 2007–2010 was 4.1 (2.1), with a minimum of 0 and a maximum of 13. This maximum occurred once in September 2009 and again in September 2010. The mean number of AMI hospitalizations per day over the specified
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Figure 6. AMI Hospitalizations in DC by Gender, 2007–2010 (N = 2,773) time period was 1.9 (1.4), ranging from 0 to a maximum of 8 in February 2009 (see Table 1). PM2.5, PM10, and ozone concentrations rarely exceeded EPA daily standards during the observed time period. PM2.5 concentrations, for example, exceeded the EPA 24-hr fine particle standard of 35 lg/m3 a total of 10 times between 2007 and 2010, with violations occurring primarily during the months of June, July, and August. Daily maximum 8-hr ozone measurements surpassed the EPA standard of 0.075 ppm, 22 times over the same period, with these violations also occurring during the months of June, July, and August. PM10 concentrations did not exceed the EPA daily standard of 150 lg/m3 at any time between 2007 and 2010. Poisson regression models. The results of six Poisson regression models relating daily asthma and AMI hospitalizations to each environmental air pollutant are shown in Tables 2 and 3. Overall, no significant association was found between hospitalization outcome and any of the air quality indicators. The most statistically significant association (p = .144) was found between daily AMI hospitalizations and ozone concentration. On average, an increase of 0.01 ppm in the daily max-8 hr ozone concentration was associated with an increase in daily AMI hospitalizations by a factor of 1.025 (95% CI: 0.992, 1.058).
Discussion Monthly hospitalizations for asthma generally peaked during the months of September–November,
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TABLE 1. Descriptive statistics for daily hospitalizations and air quality measurements (2007–2010) Mean (SD) Asthma hospitalizations AMI hospitalizations PM10 (lg/m3)c PM2.5 (lg/m3)c O3 (ppm)d
4.1 1.9 20.9 11.8 0.038
Median
(2.1) (1.4)
4 2 18.5 10.5 0.035
(6.7) (0.017)
Minimum
Maximum
0 0 2.0 0 0.002
13 8 91.0 62.2 0.089
No. of daysa 1,461 1,461 238 1,386 1,408
EPA standardb N/A N/A 150 35 0.075
Note. aNo. of days with available data. b Daily (24-hr) standards. c Daily mean concentrations. d Daily max 8-hr ozone concentration.
TABLE 2. Association between daily asthma hospitalizations and air quality indicators (2007–2010)
Pollutant PM10 (lg/m3) PM2.5 (lg/m3) O3 (ppm)d
Multiplicative effect estimatea 0.997 0.997 0.988
95% CI (0.992, 1.003) (0.993, 1.001) (0.966, 1.010)
No. of daysb
No. of casesc
.385
238
965
.175
1,386
5,632
.284
1,408
5,707
pvalue
TABLE 3. Association between daily AMI hospitalizations and air quality indicators (2007–2010)
Pollutant PM10 (lg/m3) PM2.5 (lg/m3) O3 (ppm)d
Multiplicative effect estimatea 0.998 1.003 1.025
95% CI (0.992, 1.003) (0.993, 1.001) (0.966, 1.010)
No. of daysb
No. of casesc
.639
238
432
.339
1,386
2,646
.144
1,408
2,670
pvalue
Note. aValues 1 indicate an increase in daily hospitalizations per one unit increase in pollutant concentrations. b Days with available pollutant data. c No. of cases occurring on days for which pollutant data were available. d Effect estimate for ozone corresponds to a .01 ppm increase in ozone concentration.
as well as in March. However, particulate matter and ozone concentrations are typically highest during the summer months. This inconsistency is most likely the result of strong seasonal effects on asthma hospitalizations associated with other factors, such as respiratory infections occurring during early autumn. To control for these seasonal effects, the regression model used included dummy variables for each month, accounting for the average change in hospitalizations resulting from seasonal fluctuations. Disparities in asthma-related and AMI-related hospitalizations that spanned the 4-year study period were consistently noted for African Americans and for females. African Americans were more likely to be hospitalized for both asthma and myocardial infarction. Poisson regression analyses of time series of daily counts of asthma-related and AMI-related hospitalizations were conducted to explore associa-
tions with air quality indicators. The dataset covered the period from 2007 to 2010 for DC residents. During the study period, we found no statistically significant effect of PM2.5, PM10, or ozone on asthma-related or AMI-related hospitalizations. This result is likely related to the relatively low levels of air pollutants that rarely exceeded EPA standards over the course of the 4-year period, which is consistent with air quality levels found in DC in past research (Babin et al., 2007). Despite the lack of significance between air quality indicators and hospitalizations, the identification of vulnerable populations within the community for asthma-related hospitalizations prompted a follow-up study on African American children with asthma. Specifically, how families of African American children seek information on asthma risk reduction during high-risk days as a result of heat and poor air quality. This study will provide critical
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information on characterizing at-risk children and families exploring how social, environmental, and demographic factors intersect to increase risks for climate-related asthma exacerbations, as well as preferences for receiving public health information. These data will be used to design, disseminate, and evaluate risk communication messages by public health nurses that are culturally sensitive to reduce these risks, and will support goals of the EPA’s Plan EJ 2014 (2011) to protect health in communities over-burdened by pollution, and empower communities to take action to improve their health. Data were not adjusted for a variety of exposure factors including weather conditions (e.g., sunny vs. cloudy, relative humidity, precipitation) and may have resulted in different findings as ozone and PM 2.5 and PM10.0 concentrations can be impacted by these weather variables. Global warming can influence the allergenicity of certain plant aero-allergens such as ragweed, which can trigger asthma and hay fever (Emberlin, 1994; EPA, 2008; Reid & Gamble, 2009). In addition, higher temperatures are changing the timing and duration of the pollen season. The impact of aero-allergens was not included in the regression model. Adding pollen counts may have resulted in significant findings, particularly for asthma-related hospitalizations. The Poisson regression models we employed were limited by the fact that there were generally very few hospitalizations per day. This likely influenced our nonsignificant findings and prevented stratified analyses, which could result in more precise effect estimates. In addition, the acute effects of exposure to environmental air pollutants may take several days before ultimately resulting in a hospitalization. Our models, which matched hospitalization counts to the same day of pollution measurements, did not take this lag time into account. Incorporating different lag times into our models may prove to be a worthwhile strategy for future investigation. The role of the public health nurse in surveillance has long been advocated by professional organizations (Public Health Nursing Section, 2001). Surveillance data from a Tracking Network can lead public health nurses to a better understanding of population vulnerabilities to environmental exposures. As climate changes and health risks from environmental exposures increase, public health nurses can provide leadership in community adap-
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tation and mitigation interventions based on these surveillance data (Anderko, Chalupka, & Afzal, 2012). Examples of public health interventions include opening cooling centers during heat waves and educating community residents about risk reduction measures on ozone action days when high levels of air pollution exist (Frumkin, Hess, & Vindigni, 2009). Additional opportunities for public health nursing involvement include a variety of strategies to reduce risk including the following:
• Identify local community vulnerabilities through • •
tracking environmental changes and health threats, Promote education and public dialog regarding local vulnerabilities, Implement hazard mitigation plans,
○ ○ ○ ○
Increased use of mass transit, Improved pollution control policies, Increased use of alternative energy, Improved watershed protection policies,
• Develop state and regional climate-health adaptation plans,
○ Improved early warning systems, ○ Enhanced water systems and improved water systems engineering, ○ Enhanced insect control, ○ Establishment of accessible cooling centers, and ○ Vaccine development and improved protection for travelers Finally, public health nurses can protect the public’s health by advocating for strong environmental policies that require stronger controls on greenhouse gas emissions to meet current air quality standards and to avoid higher health risks associated with climate change (Fang et al., 2013). Reductions in exposure to air pollution can contribute to significant and measurable improvements in life expectancy in the United States (Pope, Ezzati, & Dockery, 2009). An online series of media modules entitled, Advancing Clean Air, Climate, & Health: Opportunities for Nurses is offered through the Alliance of Nurses for Healthy Environments (for 3 nursing continuing credits) to help inform nurses on the Clean Air Act, health and economic benefits associated with it, and the important role of advocacy
Anderko et al.: Environmental Public Health Tracking (http://envirn.org/pg/pages/view/82102/advancingclean-air-climate-amp-health-opportunities-fornurses). Increasing public health capacity is critical to address climate-health threats. Environmental Public Health Tracking is an important tool for increasing capacity, assessing community vulnerabilities and guiding public health nursing interventions and practice locally. Public health action by nurses is needed in surveillance and using these data to reduce risks of populations especially vulnerable to environmental exposures.
Acknowledgments This research funded through grants from the Association of State and Territorial Health Officers (ASTHO) and the Council of State and Territorial Epidemiologists (CSTE).
References Anderko, L., Chalupka, S., & Afzal, B. (2012). Climate change and health: Is there a role for the health care sector?. Washington: Catholic Health Association. Anderson, G. B., Krall, R. D., & Bell, M. L. (2012). Is the relation between ozone and mortality confounded by chemical components of particulate matter? Analysis of 7 components in 57 US communities. American Journal of Epidemiology, 176, 726–732. doi:10.1093/aje/kws188. Ariano, R., Canonica, G. W., & Passalacqua, G. (2010). Possible role of climate changes in variations in pollen seasons and allergic sensitizations during 27 years. Annals of Allergy, Asthma and Immunology, 104, 215–222. doi:10.1016/j.anai. 2009.12.005. Babin, S., Burkom, H., Holtry, R., Tabernero, N., Stokes, L., DaviesCole, J., et al. (2007). Pediatric patient asthma-related emergency department visits and admissions in Washington, DC, from 2001-2004, and associations with air quality, socio-economic status, and age group. Environmental Health, 6(9). doi:10.1186/1476-069X-6-9. Balbus, J. M., & Malina, C. (2009). Identifying vulnerable subpopulations for climate change health effects in the United States. Journal of Occupational and Environmental Medicine, 51(1), 33–37. D’Amato, G., Cecchi, L., D’Amato, M., & Liccardi, G. (2010). Urban air pollution and climate change as environmental risk factors of respiratory allergy: An update. Journal of Investigational Allergology Clinical Immunology, 20(2), 95–102. Davies-Cole, J. (2011). Environmental public health tracking: Phase II capacity building grant. Washington, DC: District of Columbia Department of Health. Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., et al. (1993). An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine, 329(24), 1753–1759. doi:10.1056/ NEJM199312093292401. Dominici, F., Peng, R. D., Bell, M. L., Pham, L., McDermott, A., & Zeger, J. M. (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. Journal of the American Medical Association, 295(10), 1127–1134.
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Ebi, K. L. (2009). Public health responses to the risks of climate variability and change in the United States. Journal of Occupational and Environmental Medicine, 51(1), 4–12. Ebi, K. L., & McGregor, G. (2008). Climate change, trophospheric ozone and particulate matter, and health impacts. Environmental Health Perspectives, 116(11), 1449–1455. Emberlin, J. (1994). The effect of patterns in climate and pollen. Allergy, 49(Suppl. 18), 15–20. Environmental Protection Agency [EPA]. (2008). A review of the impact of climate variability and change on aeroallergens and their associated effects (final report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-06/164F. Environmental Protection Agency [EPA]. (2011). Plan EJ 2014. Retrieved from http://www.epa.gov/environmentaljustice/ plan-ej/ Environmental Protection Agency [EPA]. (2014). Air now [data file]. Retrieved from http://www.epa.gov/airquality/airdata/ Fang, F., Mauzerall, D. L., Liu, J., Fiore, A. M., & Horowitz, L. W. (2013). Impacts of 21st century climate change on global air pollution-related premature mortality. Climatic Change, 121, 239–253. doi:10.1007/s10584-013-0847-8. Frumkin, H., Hess, J., & Vindigni, S. (2009). Energy and public health: The challenge of peak petroleum. Public Health Reports, 124(1), 5–19. Hess, J. J., McDowell, J. Z., & Luber, G. (2012). Integrating climate change adaptation into public health practice: Using adaptive management to increase adaptive capacity and build resilience. Environmental Health Perspectives, 120, 171– 179. doi:10.1289/ehp.1103515. Houghton, A. (2013). Developing climate change environmental public health indicators: Guidance for local health departments. Atlanta, GA: Council of State and Territorial Epidemiologists (CSTE). Retrieved from http://www.c.ymcdn.com Jacob, D. J., & Winner, D. A. (2009). Effect of climate change on air quality. Atmospheric Environment, 43(1), 51–63. Luber, G., Knowlton, K., Balbus, J., Frumkin, H., Hayden, M., Hess, J., et al. (2014). Ch. 9: Human health. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third national climate assessment (pp. 220– 256). Washington, DC: U.S. Global Change Research Program. Maibach, E., Chadwick, A., McBride, D., Chuk, M., Ebi, K. L., & Balbus, J. (2008). Climate change and local public health in the United States: Preparedness, programs, and perceptions of local public health department directors. PLoS ONE, 3(7), e2838. doi:10.1371/journal.pone.0002838. Mustafic, H., Jabre, P., Caussin, C., Murad, M. H., Escolano, S., Tafflet, M., et al. (2012). Main air pollutants and myocardial infarction: A systematic review and meta-analysis. Journal of the American Medical Association, 307(7), 713– 721. doi:10.1001/jama.2012.126. Pope, C. A., Ezzati, M., & Dockery, D. W. (2009). Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360(4), 376–386. Public Health Nursing Section (2001). Public health interventions: Applications for public health nursing practice. St. Paul: Minnesota Department of Health. Reid, C. E., & Gamble, J. L. (2009). Aeroallergens, allergic disease, and climate change: Impacts and adaptation. EcoHealth, 6 (3), 458–470. doi:10.1007/s10393-009-0261-x. Smith, K. R., Woodward, A., Campbell-Lendrum, D., Chadee, D. D., Honda, Y., Liu, Q., et al. (2014). Human health: Impacts, adaptation, and co-benefits. In C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, & L. L. White (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press.
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Identifying Populations at Risk: Interdisciplinary Environmental Climate Change Tracking Laura Anderko, PhD, RN,1 John Davies-Cole, PhD, MPH,2 and Andrew Strunk, BS, PhD Student3 1
School of Nursing and Health Studies, Georgetown University, Washington, District of Columbia; 2Center for Policy, Planning, and Evaluation, District of Columbia Department of Health, Washington, District of Columbia; and 3Boston University, Boston, Massachusetts Correspondence to: Laura Anderko, Robert and Kathleen Scanlon Endowed Chair, School of Nursing and Health Studies, Georgetown University, 3700 Reservoir Rd NW, Washington, DC 20057. E-mail: [email protected]
ABSTRACT Background: Climate change, experienced as extreme weather events such as heat waves can lead to poorer air quality and underscores the critical need to consider the consequences of these environmental changes on health. Changes are occurring at a rate that exceeds what the world has experienced over the last 650,000 years, yet little attention has been focused on the potentially catastrophic public health effects of climate change. Methods: This study instituted a two-phase approach. In building capacity for an Environmental Public Health Tracking Network, the District of Columbia Department of Health first examined the availability of climate change and health data. These data were then used to assess vulnerabilities and disease burden associated with heat, air quality, and hospitalizations for asthma (N = 5,921) and acute myocardial infarction (AMI) (N = 2,773) during 2007–2010. A Poisson regression analysis was applied to the time series of daily counts for hospitalizations for selected age, race, and gender groups. Results: Although no significant associations were found for PM2.5, PM10, or ozone with asthmarelated or AMI-related hospitalizations with seasonal changes, surveillance data found disparities in hospitalizations particularly in female, African American residents for both asthma and AMI. Conclusions: Tracking Networks are critical for assessing community environmental health vulnerabilities. Key words: air pollution, environmental health, health disparities.
Climate change poses a threat to health, including increases in asthma, respiratory allergies, airway, and cardiovascular diseases (Ebi, 2009; Ebi & McGregor, 2008; Frumkin, Hess, & Vindigni, 2009). Environmental factors such as ambient air pollutants and allergens can exacerbate cardiorespiratory diseases and are clearly impacted by climate changes related to carbon dioxide levels and heat (Anderson, Krall, & Bell, 2012; Balbus & Malina, 2009; Dominici et al., 2006; Luber et al., 2014; Mustafic et al., 2012; Smith et al., 2014). Rising temperatures can make smog pollution worse and is particularly dangerous for people with cardiovascular disease and asthma. It is anticipated that car-
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dio-respiratory disease will become more prevalent and severe because of increased exposure to pollen (due to altered growing seasons), molds (from extreme or more frequent precipitation), and air pollution as a result of climate change, namely extreme heat events (Ariano, Canonica, & Passalacqua, 2010; D’Amato, Cecchi, D’Amato, & Liccardi, 2010).
Background Air quality is strongly dependent on weather and climate (Jacob & Winner, 2009). Climate change alters concentrations of fine particulate matter (PM2.5) and ozone (O3), attributing to air pollu-
Anderko et al.: Environmental Public Health Tracking tion. Although only a few large-scale studies have assessed population exposures to high concentrations of air pollution and health, those conducted have consistently found poor air quality to increase the risk for hospitalization (Dominici et al., 2006) and premature mortality (Fang, Mauzerall, Liu, Fiore, & Horowitz, 2013). The Harvard Six Cities Study found that fine particulate air pollution contributed to excess mortality in some U.S. cities (Dockery et al., 1993) and more recently it has been reported that worldwide, approximately 100,000 deaths occur annually as a result of air pollution (Fang et al., 2013). One challenge to conducting large-scale studies is the lack of climate, environmental, and population health data for local regions that can be tracked over time. The importance of local public health departments establishing systems for collecting data and conducting surveillance on public health concerns that are impacted by extreme weather events and climate change is underscored by a recent report by the Council of State and Territorial Epidemiologists (Houghton, 2013). Local public health departments are increasingly involved in activities such as hazard mitigation planning and response that are impacted by climate change. Environmental Public Health Tracking can provide the data needed to respond appropriately (Hess, McDowell, & Luber, 2012; Maibach et al., 2008). Environmental Public Health Tracking (Tracking Network) is an ongoing surveillance system coordinated by the Centers for Disease Control and Prevention (CDC) that collect data that can be used to explore how environmental exposures can affect people’s health. This surveillance system integrates health, exposure, and hazard data from a variety of national, state, and city sources. CDC is currently funding health departments in 23 states and New York City to build local tracking networks (http://www.cdc.gov/nceh/tracking/pdfs/National_ FactSheet_508.pdf). The District of Columbia Department of Health (DC DOH), Center for Policy Planning and Evaluation (CPPE) has been building capacity in establishing a Tracking Network since 2009 with an overall goal to protect communities through planning, implementation, and evaluation of public health activities (Davies-Cole, 2011). A Technical Advisory Group (TAG) was created to support the development of the Tracking Network including
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data content, partnership building, and evaluation activities. Members also help identify common needs, promote resource and information sharing, and support associated public health actions. The TAG is composed of 12 members and includes DC DOH program managers, District of Columbia Department of the Environment (DDOE) program managers, an attorney, epidemiologists, public health nurses, university professors, data owners, environmental health professionals, and laboratorians. The TAG initiated the two-phase pilot study on climate and health outcomes. Consistent with CDC’s BRACE (Building Resilience Against Climate Effects) Framework recommendations (http://www.cdc.gov/climateandhealth/ brace.htm), CPPE conducted a two-phase pilot study that addressed two steps within the BRACE Framework: Step 1: Forecasting Climate Impacts and Assessing Vulnerabilities and Step 2: Projecting the Disease Burden. This two-phase pilot study was conducted after receiving approval from the Institutional Review Board at the DC DOH, in collaboration with an interdisciplinary team that included the TAG, a local School of Nursing & Health Studies, the DDOE, and the Maryland State Health Department to identify local-level environmental exposure and vulnerability indicators for climate change, then conducted vulnerability assessments for the region.
Methods Phase I: Environmental public health indicator pilot As part of its environmental public health tracking efforts, CPPE applied and received funding from the Council of State and Territorial Epidemiologists (CSTE) to collect and pilot test 24 Climate Change Indicators as part of a State Environmental Health Indicators Collaborative. This phase of the study was to answer the following research question: What climate change and health indicators are available for populating a Tracking Network in DC? CSTE developed the climate and health indicators (called Environmental Public Health Indicators [EPHI]) including instructions on how to calculate each indicator and a template to facilitate organizing and recording the data. Data for each indicator were collected and pilot-tested by organizations
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such as CPPE that were then used by CSTE to finalize a list of climate and health indicators and to generate multistate data for each EPHI. Data were collected from a variety of sources including federal (e.g., air quality data from the Environmental Protection Agency [EPA]) and regional (e.g., climate adaptation plans for DC). Final EPHI measures included environmental variables that can affect human health or can be used to project future health impacts based on changes in exposure. Categories and measures within each category include: (a) environmental (e.g., greenhouse gas emissions), (b) health outcome (e.g., rate of hospitalizations in the summer months), (c) mitigation (e.g., vehicle miles traveled), (d) adaptation (e.g., access to cooling centers), and (e) policy (e.g., development of a state climate action plan). The finalized set of EPHI includes a standardized set of measurement units, rationale, sources of data, and data limitations. The full list of indicators and criteria can be found at: http://www.cste.org/? page=EHIndicatorsClimate.
Phase II: Assessing vulnerabilities Once the EPHIs were finalized by CSTE, CPPE conducted the second phase of the pilot study to explore trends in hospitalizations for acute myocardial infarction (AMI) and asthma in relation to particulate matter (PM 2.5 and PM 10.0) and ozone concentrations over the period from 2007 to 2010. The research explored the impact of several air quality measures and two health outcomes: Is there a significant association between air quality concentrations (PM2.5, PM10, and ozone) with health outcomes (asthma or acute myocardial infarction)? Hospitalization data were available through the DC Hospital Discharge Data network and the air quality database was obtained from the “Air Now” web site sponsored by the EPA (2014). This phase of the study was funded through a Capacity Building Grant from The Association of State and Territorial Health Officers. Based on de-identified individual hospitalization data during the period of 2007–2010, daily counts of total asthma-related and total AMI hospitalizations were computed for nine different age groups (analyses for specific age groups were conducted to isolate additional associations based on age). Hospitalization data included (a) total admissions for asthma (International Classification of Dis-
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eases Ninth [ICD-9]: 493.00) by month and year, race, age, gender, and zip code/Ward (N = 5,921) and (b) total admissions for AMI (ICD-9: 410.00) by month and year, race, age, gender, and zip code/ Ward (N = 2,773). Daily and monthly PM2.5, PM10, and ozone concentrations for one monitoring site in the District of Columbia were obtained from the EPA online database for the same time period. Several Poisson regression models were performed to investigate the relationship between daily air quality concentrations and the selected hospitalization outcomes between 2007 and 2010. Separate regression models were run for each individual air quality indicator. To control for seasonal effects, the regression models included dummy variables for each month, accounting for the average change in hospitalizations resulting from seasonal fluctuations. A lag of 0 days was used for all regression models (i.e., daily hospitalization counts were matched to the same day of pollution measurement). Given that there were so few asthma/AMI hospitalizations per day, the analyses were conducted using raw daily counts (as opposed to stratifying by race, gender, age). This was done to increase the number of cases in each model, and in turn increase power.
Results Air quality Although the average PM2.5 concentration exceeded the EPA standard (15 lg/m3) five times over the course of 48 months, there is not an increasing trend over the time period studied. PM2.5 concentrations generally reached their peak during the summer months (June–August), reaching a maximum of 20.38 lg/m3 in August 2007. PM10 concentrations were well below the EPA standard throughout the 4 years time period (2007–2010). Monthly averages of Ozone concentration remained below the EPA standard even during the summer months, however, daily 8-hr Ozone concentration levels exceeded the EPA standard several times between 2007 and 2010. Hospitalizations Asthma. The mean number of monthly asthma admissions from 2007 to 2010 was 123.4 (with a standard deviation of 23.33) with a minimum of 78
Anderko et al.: Environmental Public Health Tracking in June 2007 and a maximum of 195 in September 2009. Data demonstrate that about half of the Wards in the Washington, DC area had total increases in the number of cases of people hospitalized for asthma attacks over the 4-year span, with the highest numbers occurring in the zip codes for Wards located in the Southeast which experience a higher rate of poverty. Monthly hospitalizations for asthma generally peaked during the months of September–November, as well as in March. Vulnerability assessments found disparities in race, age, and gender. Figures 1, 2, and 3 show that (a) African Americans accounted for approximately 90% of all asthma hospitalizations, (b) females accounted for almost 61.5% of all asthma-related hospitalizations, (c) females were more likely to be hospitalized than men over the 4-year period, (d) children 0–9 years of age were more likely to be hospitalized for asthma when compared to other age groups, and (e) adults within the 50–59 age group experience a 48.7% increase in the number of asthma admissions from 2007 to 2010. The number of asthmarelated admissions for African Americans rose 21.7% between 2007 and 2009, while hospitalizations generally declined for those identified as “White”, “Other”, and “Unknown”.
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with a minimum of 35 in January 2008 and a maximum of 76 in January 2009. Monthly hospitalizations for AMI generally peaked during the months of March through May. Vulnerability assessments found disparities in race, age, and gender. Figures 4, 5, and 6 show that: (a) African Americans accounted for approximately 74% of all hospitalizations for AMI, (b) Annual AMI hospitalizations for Whites fell approximately 31% between 2007 and 2010. African Americans experienced a decrease of 3% over the same period, (c) hospitalizations occurred most frequently in the 80+ age group and accounted for 25% of total hospitalizations during this period, and (d) between 2007 and 2010, women accounted for a slightly higher proportion of total hospitalizations for AMI than men (50.49% and 49.51% respectively).
Acute myocardial infarction. The mean number of monthly AMI admissions from 2007 to 2010 was 58 (with a standard deviation of 9.94) Figure 2. Asthma Hospitalizations in DC by Race, 2007–2010 (N = 5,921)
Figure 1. Asthma Hospitalizations in DC by Age, 2007–2010 (N = 5,921)
Figure 3. Asthma Hospitalizations in DC by Gender, 2007–2010 (N = 5,921)
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Figure 4. AMI Hospitalizations in DC by Age, 2007–2010 (N = 2,773)
Figure 5. AMI Hospitalizations in DC by Race, 2007–2010 (N = 2,773)
Association between daily hospitalizations and air quality indicators Descriptive statistics. Descriptive statistics were produced for daily asthma and AMI hospitalizations, as well as for each air quality indicator (daily mean PM2.5 concentration, daily mean PM10 concentration, daily max 8-hr ozone concentration). The mean number of asthma hospitalizations per day over the period of 2007–2010 was 4.1 (2.1), with a minimum of 0 and a maximum of 13. This maximum occurred once in September 2009 and again in September 2010. The mean number of AMI hospitalizations per day over the specified
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Figure 6. AMI Hospitalizations in DC by Gender, 2007–2010 (N = 2,773) time period was 1.9 (1.4), ranging from 0 to a maximum of 8 in February 2009 (see Table 1). PM2.5, PM10, and ozone concentrations rarely exceeded EPA daily standards during the observed time period. PM2.5 concentrations, for example, exceeded the EPA 24-hr fine particle standard of 35 lg/m3 a total of 10 times between 2007 and 2010, with violations occurring primarily during the months of June, July, and August. Daily maximum 8-hr ozone measurements surpassed the EPA standard of 0.075 ppm, 22 times over the same period, with these violations also occurring during the months of June, July, and August. PM10 concentrations did not exceed the EPA daily standard of 150 lg/m3 at any time between 2007 and 2010. Poisson regression models. The results of six Poisson regression models relating daily asthma and AMI hospitalizations to each environmental air pollutant are shown in Tables 2 and 3. Overall, no significant association was found between hospitalization outcome and any of the air quality indicators. The most statistically significant association (p = .144) was found between daily AMI hospitalizations and ozone concentration. On average, an increase of 0.01 ppm in the daily max-8 hr ozone concentration was associated with an increase in daily AMI hospitalizations by a factor of 1.025 (95% CI: 0.992, 1.058).
Discussion Monthly hospitalizations for asthma generally peaked during the months of September–November,
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TABLE 1. Descriptive statistics for daily hospitalizations and air quality measurements (2007–2010) Mean (SD) Asthma hospitalizations AMI hospitalizations PM10 (lg/m3)c PM2.5 (lg/m3)c O3 (ppm)d
4.1 1.9 20.9 11.8 0.038
Median
(2.1) (1.4)
4 2 18.5 10.5 0.035
(6.7) (0.017)
Minimum
Maximum
0 0 2.0 0 0.002
13 8 91.0 62.2 0.089
No. of daysa 1,461 1,461 238 1,386 1,408
EPA standardb N/A N/A 150 35 0.075
Note. aNo. of days with available data. b Daily (24-hr) standards. c Daily mean concentrations. d Daily max 8-hr ozone concentration.
TABLE 2. Association between daily asthma hospitalizations and air quality indicators (2007–2010)
Pollutant PM10 (lg/m3) PM2.5 (lg/m3) O3 (ppm)d
Multiplicative effect estimatea 0.997 0.997 0.988
95% CI (0.992, 1.003) (0.993, 1.001) (0.966, 1.010)
No. of daysb
No. of casesc
.385
238
965
.175
1,386
5,632
.284
1,408
5,707
pvalue
TABLE 3. Association between daily AMI hospitalizations and air quality indicators (2007–2010)
Pollutant PM10 (lg/m3) PM2.5 (lg/m3) O3 (ppm)d
Multiplicative effect estimatea 0.998 1.003 1.025
95% CI (0.992, 1.003) (0.993, 1.001) (0.966, 1.010)
No. of daysb
No. of casesc
.639
238
432
.339
1,386
2,646
.144
1,408
2,670
pvalue
Note. aValues 1 indicate an increase in daily hospitalizations per one unit increase in pollutant concentrations. b Days with available pollutant data. c No. of cases occurring on days for which pollutant data were available. d Effect estimate for ozone corresponds to a .01 ppm increase in ozone concentration.
as well as in March. However, particulate matter and ozone concentrations are typically highest during the summer months. This inconsistency is most likely the result of strong seasonal effects on asthma hospitalizations associated with other factors, such as respiratory infections occurring during early autumn. To control for these seasonal effects, the regression model used included dummy variables for each month, accounting for the average change in hospitalizations resulting from seasonal fluctuations. Disparities in asthma-related and AMI-related hospitalizations that spanned the 4-year study period were consistently noted for African Americans and for females. African Americans were more likely to be hospitalized for both asthma and myocardial infarction. Poisson regression analyses of time series of daily counts of asthma-related and AMI-related hospitalizations were conducted to explore associa-
tions with air quality indicators. The dataset covered the period from 2007 to 2010 for DC residents. During the study period, we found no statistically significant effect of PM2.5, PM10, or ozone on asthma-related or AMI-related hospitalizations. This result is likely related to the relatively low levels of air pollutants that rarely exceeded EPA standards over the course of the 4-year period, which is consistent with air quality levels found in DC in past research (Babin et al., 2007). Despite the lack of significance between air quality indicators and hospitalizations, the identification of vulnerable populations within the community for asthma-related hospitalizations prompted a follow-up study on African American children with asthma. Specifically, how families of African American children seek information on asthma risk reduction during high-risk days as a result of heat and poor air quality. This study will provide critical
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information on characterizing at-risk children and families exploring how social, environmental, and demographic factors intersect to increase risks for climate-related asthma exacerbations, as well as preferences for receiving public health information. These data will be used to design, disseminate, and evaluate risk communication messages by public health nurses that are culturally sensitive to reduce these risks, and will support goals of the EPA’s Plan EJ 2014 (2011) to protect health in communities over-burdened by pollution, and empower communities to take action to improve their health. Data were not adjusted for a variety of exposure factors including weather conditions (e.g., sunny vs. cloudy, relative humidity, precipitation) and may have resulted in different findings as ozone and PM 2.5 and PM10.0 concentrations can be impacted by these weather variables. Global warming can influence the allergenicity of certain plant aero-allergens such as ragweed, which can trigger asthma and hay fever (Emberlin, 1994; EPA, 2008; Reid & Gamble, 2009). In addition, higher temperatures are changing the timing and duration of the pollen season. The impact of aero-allergens was not included in the regression model. Adding pollen counts may have resulted in significant findings, particularly for asthma-related hospitalizations. The Poisson regression models we employed were limited by the fact that there were generally very few hospitalizations per day. This likely influenced our nonsignificant findings and prevented stratified analyses, which could result in more precise effect estimates. In addition, the acute effects of exposure to environmental air pollutants may take several days before ultimately resulting in a hospitalization. Our models, which matched hospitalization counts to the same day of pollution measurements, did not take this lag time into account. Incorporating different lag times into our models may prove to be a worthwhile strategy for future investigation. The role of the public health nurse in surveillance has long been advocated by professional organizations (Public Health Nursing Section, 2001). Surveillance data from a Tracking Network can lead public health nurses to a better understanding of population vulnerabilities to environmental exposures. As climate changes and health risks from environmental exposures increase, public health nurses can provide leadership in community adap-
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tation and mitigation interventions based on these surveillance data (Anderko, Chalupka, & Afzal, 2012). Examples of public health interventions include opening cooling centers during heat waves and educating community residents about risk reduction measures on ozone action days when high levels of air pollution exist (Frumkin, Hess, & Vindigni, 2009). Additional opportunities for public health nursing involvement include a variety of strategies to reduce risk including the following:
• Identify local community vulnerabilities through • •
tracking environmental changes and health threats, Promote education and public dialog regarding local vulnerabilities, Implement hazard mitigation plans,
○ ○ ○ ○
Increased use of mass transit, Improved pollution control policies, Increased use of alternative energy, Improved watershed protection policies,
• Develop state and regional climate-health adaptation plans,
○ Improved early warning systems, ○ Enhanced water systems and improved water systems engineering, ○ Enhanced insect control, ○ Establishment of accessible cooling centers, and ○ Vaccine development and improved protection for travelers Finally, public health nurses can protect the public’s health by advocating for strong environmental policies that require stronger controls on greenhouse gas emissions to meet current air quality standards and to avoid higher health risks associated with climate change (Fang et al., 2013). Reductions in exposure to air pollution can contribute to significant and measurable improvements in life expectancy in the United States (Pope, Ezzati, & Dockery, 2009). An online series of media modules entitled, Advancing Clean Air, Climate, & Health: Opportunities for Nurses is offered through the Alliance of Nurses for Healthy Environments (for 3 nursing continuing credits) to help inform nurses on the Clean Air Act, health and economic benefits associated with it, and the important role of advocacy
Anderko et al.: Environmental Public Health Tracking (http://envirn.org/pg/pages/view/82102/advancingclean-air-climate-amp-health-opportunities-fornurses). Increasing public health capacity is critical to address climate-health threats. Environmental Public Health Tracking is an important tool for increasing capacity, assessing community vulnerabilities and guiding public health nursing interventions and practice locally. Public health action by nurses is needed in surveillance and using these data to reduce risks of populations especially vulnerable to environmental exposures.
Acknowledgments This research funded through grants from the Association of State and Territorial Health Officers (ASTHO) and the Council of State and Territorial Epidemiologists (CSTE).
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