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Characterization of heat index experienced by individuals residing in urban and rural settings


Heatwave warning systems rely on forecasts made for fixed-point weather stations (WS), which do not reflect variation in temperature and humidity experienced by individuals moving through indoor and outdoor locations. We examined whether neighborhood measurement improved the prediction of individually experienced heat index in addition to nearest WS in an urban and rural location. Participants (residents of Birmingham, Alabama [N = 89] and Wilcox County, Alabama [N = 88]) wore thermometers clipped to their shoe for 7 days. Shielded thermometers/hygrometers were placed outdoors within participant’s neighborhoods (N = 43). Nearest WS and neighborhood thermometers were matched to participant’s home address. Heat index (HI) was estimated from participant thermometer temperature and WS humidity per person-hour (HI[individual]), or WS temperature and humidity, or neighborhood temperature and humidity. We found that neighborhood HI improved the prediction of individually experienced HI in addition to WS HI in the rural location, and neighborhood heat index alone served as a better predictor in the urban location, after accounting for individual-level factors. Overall, a 1 °C increase in HI[neighborhood] was associated with 0.20 °C [95% CI (0.19, 0.21)] increase in HI[individual]. After adjusting for ambient condition differences, we found higher HI[individual] in the rural location, and increased HI[individual] during non-rest time (5 a.m. to midnight) and on weekdays.

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Fig. 1: Diurnal pattern of maximum and mean HI[individual] (yellow triangle) compared to HI[neighborhood] (gray dot) and HI[WS] (blue square) in rural residents (participant N = 88, neighborhood iButton N = 13, WS N = 4), urban residents (participant N = 57, neighborhood iButton N = 18, WS N = 2), and urban OutWor (participant N = 32, neighborhood iButton N = 11, WS N = 2).
Fig. 2: Date pattern of maximum and mean HI[individual] (yellow triangle) compared to the matched HI[neighborhood] (gray dot) and HI[WS] (blue square) in rural residents (participant N = 88, neighborhood iButton N = 13, WS N = 4), urban residents (participant N = 57, neighborhood iButton N = 18, WS N = 2), and urban OutWor participants (OutWor) (participant N = 32, neighborhood iButton N = 11, WS N = 2).
Fig. 3: Mean frequency % of risk classification based on heat index in rural residents (N = 88), urban residents (N = 57), and urban OutWor (N = 32).


  1. Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KL, Maycock TK, et al. Impacts, risks, and adaptation in the United States: Fourth National Climate Assessment, Volume II. Washington, DC: US Global Change Research Program; 2018.

  2. Gasparrini A, Guo Y, Hashizume M, Kinney PL, Petkova EP, Lavigne E, et al. Temporal variation in heat–mortality associations: a multicountry study. Environ health Perspect. 2015;123:1200–7.

    Article  Google Scholar 

  3. Bobb JF, Peng RD, Bell ML, Dominici F. Heat-related mortality and adaptation to heat in the United States. Environ health Perspect. 2014;122:811–6.

    Article  Google Scholar 

  4. NWS. National Weather Service Summary of Natural Hazard Statistics for 2018 in the United States. 2019. Accessed 21 Sep 2020.

  5. Berko J. Deaths attributed to heat, cold, and other weather events in the United States, 2006–2010, vol 76. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2014.

  6. CDC. QuickStats: number of heat-related deaths, by sex—National Vital Statistics System, United States, 1999–2010. MMWR Morb Mortal Wkly Rep. 2012;61:729.

    Google Scholar 

  7. Choudhary E, Vaidyanathan AJM. Heat stress illness hospitalizations—Environmental Public Health Tracking Program, 20 states, 2001–2010. J Morbidity Mortal Wkly Rep. 2014;63:1–10.

    Google Scholar 

  8. McGregor GR, Bessmoulin P, Ebi K, Menne B. Heatwaves and health: guidance on warning-system development. World Meteorological Organisation and World Health Organisation; 2015.

  9. Lowe D, Ebi KL, Forsberg B. Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves. Int J Environ Res Public Health. 2011;8:4623–48.

    Article  Google Scholar 

  10. NWS. Heat. National Weather Service. Accessed 17 Nov 2019.

  11. NWS. NWS experimental heat risk: identifying potential heat risks in the seven day forecast. Accessed 7 Sep 2020.

  12. CDC. Heat stress risk factors. 2017. Accessed 21 Nov 2019.

  13. Grundstein AJ, Ramseyer C, Zhao F, Pesses JL, Akers P, Qureshi A, et al. A retrospective analysis of American football hyperthermia deaths in the United States. Int J Biometeorol. 2012;56:11–20.

    Article  Google Scholar 

  14. Bernhard MC, Kent ST, Sloan ME, Evans MB, McClure LA, Gohlke JM. Measuring personal heat exposure in an urban and rural environment. Environ Res. 2015;137:410–8.

    Article  CAS  Google Scholar 

  15. Mac VVT, Hertzberg V, McCauley LA. Examining agricultural workplace micro and macroclimate data using decision tree analysis to determine heat illness risk. J Occup Environ Med. 2019;61:107–14.

    Article  PubMed  Google Scholar 

  16. Sugg MM, Fuhrmann CM, Runkle JD. Temporal and spatial variation in personal ambient temperatures for outdoor working populations in the southeastern USA. Int J Biometeorol. 2018;62:1521–34.

    Article  Google Scholar 

  17. Uejio CK, Morano LH, Jung J, Kintziger K, Jagger M, Chalmers J, et al. Occupational heat exposure among municipal workers. Int Arch Occup Environ Health. 2018;91:705–15.

    Article  Google Scholar 

  18. Kuras E, Hondula D, Brown-Saracino J. Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure. Int J Biometeorol. 2015;59:1363–72.

    Article  CAS  Google Scholar 

  19. Wang S, Richardson MB, Wu CY, Cholewa CD, Lungu CT, Zaitchik BF, et al. Estimating occupational heat exposure from personal sampling of public works employees in Birmingham, Alabama. J Occup Environ Med. 2019;61:518–24.

    Article  Google Scholar 

  20. Li Y, Odamne EA, Silver K, Zheng S. Comparing urban and rural vulnerability to heat-related mortality: a systematic review and meta-analysis. J Global Epidemiol Environ Health. 2017;1:9–15.

    Article  Google Scholar 

  21. Martiello MA, Giacchi MV. High temperatures and health outcomes: a review of the literature. J Scand J Public Health. 2010;38:826–37.

    Article  Google Scholar 

  22. Gabriel KM, Endlicher WR. Urban and rural mortality rates during heat waves in Berlin and Brandenburg, Germany. J Environ Pollut. 2011;159:2044–50.

    Article  CAS  Google Scholar 

  23. Runkle J, Kunkel K, Stevens L, Frankson R. 2017: Alabama State Climate Summary. NOAA technical report NESDIS 149-AL. NOAA; 2017. Accessed 2 Dec 2019.

  24. Maxim. DS1922L iButton temperature loggers with 8KB data-log memeory. 2018.

  25. Scott AA, Misiani H, Okoth J, Jordan A, Gohlke J, Ouma G, et al. Temperature and heat in informal settlements in Nairobi. PLoS ONE. 2017;12:e0187300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Scott AA, Zaitchik B, Waugh DW, O’Meara K. Intraurban temperature variability in Baltimore. J Appl Meteorol Climatol. 2017;56:159–71.

    Article  Google Scholar 

  27. Richardson MB, Chmielewski C, Wu CYH, Evans MB, McClure LA, Hosig KW, et al. The effect of time spent outdoors during summer on daily blood glucose and steps in women with type 2 diabetes. J Behav Med. 2020;43:783–90.

    Article  PubMed  Google Scholar 

  28. Anderson G, Peng R. weathermetrics: Functions to convert between weather metrics (R package). 2012.

  29. Tudor-Locke C, Bassett DR, Shipe MF, McClain JJ. Pedometry methods for assessing free-living adults. J Phys Act Health. 2011;8:445–53.

    Article  Google Scholar 

  30. Samara A, Sanna T, Algasser D, Alzahrani M, Aro AR. Pedometers as an effective tool for measuring physical activity in young females in Saudi Arabia. Int J Community Fam Med. 2017;2:133.

    Google Scholar 

  31. Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. 2004;33:261–304.

    Article  Google Scholar 

  32. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. 2014.

  33. United States Census Bureau. United States Census Bureau QuickFacts: Birmingham city, Alabama; Wilcox County, Alabama. United States Census Bureau.,wilcoxcountyalabama/PST045219. Accessed 13 Feb 2020.

  34. Kubota T, Chyee DTH, Ahmad S. The effects of night ventilation technique on indoor thermal environment for residential buildings in hot-humid climate of Malaysia. Energy Build. 2009;41:829–39.

    Article  Google Scholar 

  35. Reid CE, O’neill MS, Gronlund CJ, Brines SJ, Brown DG, Diez-Roux AV, et al. Mapping community determinants of heat vulnerability. J Environ Health Perspect. 2009;117:1730–6.

    Article  Google Scholar 

  36. Morabito M, Messeri A, Noti P, Casanueva A, Crisci A, Kotlarski S, et al. An occupational heat–health warning system for Europe: The HEAT-SHIELD Platform. Int J Environ Res Public Health. 2019;16:2890.

    Article  Google Scholar 

  37. Toloo G, FitzGerald G, Aitken P, Verrall K, Tong S. Evaluating the effectiveness of heat warning systems: systematic review of epidemiological evidence. Int J Public Health. 2013;58:667–81.

    Article  Google Scholar 

  38. Kalkstein AJ, Sheridan SC. The social impacts of the heat–health watch/warning system in Phoenix, Arizona: assessing the perceived risk and response of the public. Int J Biometeorol. 2007;52:43–55.

    Article  Google Scholar 

  39. Ebi KL, Schmier JK. A stitch in time: improving public health early warning systems for extreme weather events. Epidemiol Rev. 2005;27:115–21.

    Article  Google Scholar 

  40. NIOSH. NIOSH criteria for a recommended standard: occupational exposure to heat and hot environments. In: B J, WJ W, K M, A C, J-H K, N T, editors. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication; 2016.

  41. Liljegren JC, Carhart RA, Lawday P, Tschopp S, Sharp R. Modeling the wet bulb globe temperature using standard meteorological measurements. J Occup Environ Hyg. 2008;5:645–55.

    Article  PubMed  Google Scholar 

  42. Bassett DR, Ainsworth BE, Leggett SR, Mathien CA, Main JA, Hunter DC, et al. Accuracy of five electronic pedometers for measuring distance walked. Med Sci sports Exerc. 1996;28:1071–7.

    Article  Google Scholar 

  43. Schneider PL, Crouter SE, Lukajic O, Bassett DR Jr. Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Med Sci sports Exerc. 2003;35:1779–84.

    Article  Google Scholar 

  44. Melanson EL, Knoll JR, Bell ML, Donahoo WT, Hill J, Nysse LJ, et al. Commercially available pedometers: considerations for accurate step counting. Preventive Med. 2004;39:361–8.

    Article  Google Scholar 

  45. Washburn R, Chin MK, Montoye HJ. Accuracy of pedometer in walking and running. Res Q Exerc Sport. 1980;51:695–702.

    Article  CAS  Google Scholar 

  46. Le Masurier GC, Tudor-Locke C. Comparison of pedometer and accelerometer accuracy under controlled conditions. Med Sci sports Exerc. 2003;35:867–71.

    Article  Google Scholar 

  47. Foster RC, Lanningham-Foster LM, Manohar C, McCrady SK, Nysse LJ, Kaufman KR, et al. Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure. Preventive Med. 2005;41:778–83.

    Article  Google Scholar 

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We gratefully acknowledge collaboration with Sheila Tyson, Keisha Brown, and Nakeia Pullman (Friends of West End), and Sheryl Threadgill-Mathews and Ethel Johnson (West Central Alabama Community Health Improvement League), for their aid in recruitment and implementation of the research. Thanks to Mary Evans, Anna Scott, Michael Milazzo, Pranavi Ghugare, Kaya Bryant, and Claudiu Lungu for help with the data collection.


This project was funded through a grant from the National Institute of Environmental Health Sciences (R01ES023029).

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Correspondence to Julia M. Gohlke.

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Wang, S., Wu, C.Y.H., Richardson, M.B. et al. Characterization of heat index experienced by individuals residing in urban and rural settings. J Expo Sci Environ Epidemiol 31, 641–653 (2021).

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  • Exposure assessment
  • Exposure sensors
  • Environmental health policy

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