UCLA Heat Maps: A Tool for Building California’s Climate Resilience

Mapping communities suffering the most during extreme heat days

How to Use the Heat Maps

About UCLA Heat Maps

Do you want to know how many people from your zip code or county go to a hospital emergency room on an extreme heat day?


This interactive map of heat-related health outcomes in California shows the excess daily emergency room visits that occur on an extreme heat day compared to the usual, non-extreme heat day. It shows this excess by county and zip code.


By using the map, public health professionals, emergency service providers, urban planners, legislators, health and human services providers, non-governmental organizations, and communities themselves can find out which neighborhoods across the state are at greatest risk of harm during extreme heat events.


Uses


Knowing which neighborhoods are most at risk from extreme heat allows communities across California to:


Target heat mitigation programs such as those designed to increase shade.

Target extreme heat emergency programs to neighborhoods with high morbidity.

Provide a tool for non-governmental organizations to support their efforts to reduce harms in their communities.


Who We Are


UCLA Center for Healthy Climate Solutions (C-Solutions)

At C-Solutions we work alongside communities to turn public health research into actionable policies and practices. Our faculty are experts in climate change and the health implications of climate-induced crises including air pollution, wildfires, extreme heat, drought, and disasters. We focus on identifying public health co-benefits and building resilience through research, education, and collaboration with our community partners.


UCLA Center for Public Health and Disasters

The UCLA Center for Public Health and Disasters promotes interdisciplinary efforts to reduce the health impacts of domestic and international, natural and human-generated disasters. Our faculty and staff have diverse backgrounds that include emergency medicine, environmental health, urban planning, engineering, international health, health services, epidemiology, gerontology, sociology, and community health. The Center collaborates with state and local public health agencies, community-based organizations, schools, hospitals, and agencies in the public and private sector to address the critical issues that arise when disaster impacts a community.


Acknowledgements


UCLA Heat Maps are created by Dr. David Eisenman, Dr. Yu Yu, Dr. Diane Garcia-Gonzales, and Dr. Michael Jerrett. We thank the Conrad N. Hilton Foundation, the LA Urban Cooling Collaborative, and the UCLA Sustainable LA Grand Challenge for supporting the creation of UCLA Heat Maps.


Methods


We analyzed emergency room visits obtained from the California Department of Health Care Access and Information using the most recent years available at the time of the analysis, 2009-2018. Specifically, we used previously published methods and considered emergency room visits due to heat-related illnesses and all internal causes in any of up to 25 diagnoses for each emergency room record (Riley et al. 2018). The diagnoses in 2009-2016 data were coded according to ICD -9 code and 2016-2018 data was coded according to ICD-10 codes. In the 2009-2015 dataset, heat-related illness was defined as exposure to excessive natural heat (Code E900) or effects of heat and light (code 992). Emergency room visits attributed to all-internal causes were the following: electrolyte imbalance (Code 276); cardiovascular disease (Codes 390–398, 401–429, 430-438, 440–459); respiratory illness (Codes 460–519); acute kidney failure and chronic kidney disease (Code 584-586); disease of urinary system (Code 580-599); diabetes mellitus (Code 250); dehydration (Code 276.5) and disorders of fluid, electrolyte and acid-base balance (Code 276). For the visits during 2016-2018, heat-related illness was defined as those records with code X30 (exposure to excessive natural heat) or T67 (effects of heat and light). Emergency room visits due to all-internal causes were those whose diagnosis was listed as: cardiovascular (I00–I99); respiratory (J00–J99); acute kidney failure and chronic kidney disease (N17–N19); Disease of urinary system (N00–N39); diabetes (E08–E13); dehydration (E86); and disorders of fluid, electrolyte, and acid-base balance (E87).


Heat event days were defined using the spatial synoptic classification (SSC) system version 3 (http://sheridan.geog.kent.edu/ssc3.html), which has been applied in previous studies relating to climate and human health (Kalkstein et al. 2018; Riley et al. 2018). The SSC system is based on surface-based observations at an individual station, which includes four-times daily observations of temperature, dew point, wind, pressure, and cloud cover. The SSC has been used extensively in climate/human health analyses (Dixon et al. 2016; Hondula et al. 2014), including the development of “heat-health warning systems” around the world (Kalkstein et al. 2009), climate change-health analyses (Greene et al. 2011; Greene et al. 2016), and the development of a heat wave categorization system for vulnerable urban areas worldwide (Adrienne Arsht-Rockefeller Foundation Resilience Center 2020).


The SSC places each day into one of several air mass types: Dry Polar, Dry Moderate, Dry Tropical, Moise Polar, Moist Moderate, Moist Tropical and Transitional. An air mass is a volume of air defined by its homogeneous characteristics of temperature, atmospheric moisture, and other meteorological variables (Oliver 2008), and research suggests that humans respond to the simultaneous impacts of numerous meteorological elements, rather than just individual weather variables (Kalkstein et al. 2018). Thus, air masses present a comprehensive picture of how organisms respond to their meteorological environment. We focused on the weather types that are associated with statistically significantly higher daily mortality: Dry Tropical including Dry Tropical+ and Dry Tropical++, and Moist Tropical including Moist Tropical+ and Moist Tropical++.


There were 67 SSC stations in California. Since there are 126 zip codes that do not have an SSC station within 50 miles, 13 SSC stations in the neighboring states including Oregon, Nevada, and Arizona were included. For each zip code area, the heat event days were identified using the weather type information from the nearest SSC station that was assigned by ArcGIS software. The number of heat event days for each county was averaged by the number of heat event days identified at each zip code area belonging to the specific county.


We confined our analysis to meteorological summer, the period between May 1 and September 30, and we evaluated weather/emergency room visit relationships during this seasonal period for each calendar year. Considering that heat-health associations diminish after a lag period of three days, we added a lag three days to the end of each heat event to capture any deaths which may have been related to the heat event but did not occur during the heat event. To obtain the excess daily emergency room visit rate at zip code or county level, we first calculated daily emergency room visit rate during the heat event days and non-heat event days for each area separately. Both were age-adjusted (<5, 5-18, 19-65, 65+ years old) using the whole California population as the standardized population. By subtracting the daily emergency room visit rate on non-heat event days from that on heat event days, we obtained the excess daily emergency room visit rate, which is the number of excess emergency room visits per day per 10,000 persons in heat event days versus non-heat event days in that area. To calculate the excess number of emergency room visits per day, we multiplied the excess daily emergency room visit rate with the total population in that specific area and divided it by 10,000.


To our knowledge, this analysis is the first to produce maps displaying the distribution of excess morbidity due to extreme heat in California, using the most recently available 10 years of data. There are still some limitations that need to be noted. There are ~5% of zip code areas that do not have SSC stations within 50 miles to which we had to use weather data from SSC stations in the neighboring states, raising the possibility of extreme heat event days misclassification. Additionally, zip code attributed to some ER patients might also be incorrect. For example, 90095 is the zip code for the University of California, Los Angeles campus and its hospital. It is possible that this campus zip code might be used for homeless patients during their ER visits and thus the results need to be interpreted with caution.

References

Dixon PG, Allen M, Gosling SN, Hondula DM, Ingole V, Lukas R, et al. Perspectives on synoptic climate classification and its role in interdisciplinary research. Geogr Compass. 2016;10:147–164. https://doi.org/10.1111/gec3.12264.

Hondula DM, Vanos JK, Gosling SN. 2014. The SSC: A decade of climate-health research and future directions. Int J Biometeorol 58(2):109-120, PMID: 23371289, https://doi.org/10.1007/s00484-012-0619-6.

Greene JS, Kalkstein LS, Kim KR, Choi YJ, Lee DG. The application of the European heat wave of 2003 to Korean cities to analyze impacts on heat-related mortality. Int J Biometeorol. 2016;60:231–243. https://doi.org/10.1007/s00484-015-1020-z.

Greene S, Kalkstein LS, Mills DM, Samenow J. 2011. An examination of climate change on extreme heat events and climate–mortality relationships in large us cities. Weather, Climate, and Society 3(4):281-292.

Kalkstein LS, Sheridan SC, Kalkstein AJ. Biometeorology for adaptation to climate variability and change: research Frontiers and perspectives. Heidelberg: Springer-Verlag; 2009. Heat health warning systems: development, implementation, and intervention activities; pp. 33–48.

Kalkstein AJ, Kalkstein LS, Vanos JK, Eisenman DP, Grady Dixon P. 2018. Heat/mortality sensitivities in los angeles during winter: A unique phenomenon in the united states. Environ Health 17(1):45, PMID: 29724242, https://doi.org/10.1186/s12940-018-0389-7.

Oliver JE. 2008. Encyclopedia of world climatology:Springer Science & Business Media.

Riley K, Wilhalme H, Delp L, Eisenman DP. 2018. Mortality and morbidity during extreme heat events and prevalence of outdoor work: An analysis of community-level data from los angeles county, california. Int J Environ Res Public Health 15(4), PMID: 29570664, https://doi.org/10.3390/ijerph15040580.