Real-time Surveillance of Heat-related Morbidity in NYC

1. Real-time Surveillance of Heat-related Morbidity in NYC Kristi Metzger, PhD NYC Dept of Health and Mental Hygiene Bureau of Environmental Surveillance & Policy June 3, 2008 Climate Information for Public Health Summer Institute 2008

2. Overview What is syndromic surveillance? 2006 NYC Heat Wave Heat Emergency Plan Surveillance for heat-related illness Emergency planning and response

3. What is Syndromic Surveillance? Timely, non-traditional surveillance system Monitors multiple non-specific health indicators Designed to detect unexpected clustering in space and time for communicable diseasesTimely, non-traditional surveillance system Monitors multiple non-specific health indicators Designed to detect unexpected clustering in space and time for communicable diseases

4. Time windows for response to a bioterrorism event

5. Syndromic Data Sources ED visits EMS dispatches OTC, prescription pharmacy sales School/work attendance Ambulatory care EMR (vital signs) Attributes Timely Electronic Simple Large numbers

6. Origins of NYC Syndromic Surveillance 1995: Sales of anti-diarrheal meds 1999: EMS ambulance dispatches Post-9/11: Emergency department visits CDC deployed dozens of epidemiologists for 24/7 data collection in selected NYC EDs Oct 2001 – ED transmission of triage logs by fax, email Nov 2001 – email, direct data transfer from EDs

7. NYC Syndromic Surveillance Today All data reported electronically Syndrome definitions, analyses refined Main analyses focus on ILI, GI illness Additional analyses of asthma, heat-related illness (Almost) automated daily analysis ED visits EMS dispatches OTC and pharmacy sales

8. Data analysis and cluster detection For outbreak detection (including bioterrorism) nonspecific prior hypothesis: If an event occurs, illness will clustered in space and time Multiple comparisons problem Use methods to detect most likely clusters, while adjusting for multiple comparisons Use ratio of syndrome-to-total to adjust for temporal patterns in ED/EMS/med use Tradeoff: false positive signal frequency vs. sensitivity to true outbreak

10. This graph shows the daily maximum temperature from the beginning of July through mid-August. Heat wave #1 – 4 days – July 16-19 Heat wave #2 – 10 days – July 27-Aug 5 This graph shows the daily maximum temperature from the beginning of July through mid-August. Heat wave #1 – 4 days – July 16-19 Heat wave #2 – 10 days – July 27-Aug 5

11. Heat-specific deaths or illness Hyperthermia/ failure of thermoregulation E.g., heat stroke, death due to naturally occurring excessive heat Classified as due to ‘external causes’ Individually attributable, can be counted, described and placed in space and time Small numbers and % of overall burden May be inconsistently diagnosed, especially for deaths

12. Heat-Stroke Deaths, by Date, 2006 These indicate that the first heat wave occurred from July 16-July 18... These indicate that the first heat wave occurred from July 16-July 18...

13. Heat Stroke Deaths, 2006

14. Heat Stroke Deaths, By Age Group, 2006 This chart shows the number of heat-stroke deaths by age group… with age group on the x-axis… and the number of heat stroke deaths during the summer of 2006 on the y-axis. It shows that the majority of heat-stroke deaths occurred among those aged 65 and older.This chart shows the number of heat-stroke deaths by age group… with age group on the x-axis… and the number of heat stroke deaths during the summer of 2006 on the y-axis. It shows that the majority of heat-stroke deaths occurred among those aged 65 and older.

15. Number of Medical Conditions AmongHeat Stroke Decedents This chart shows the number of medical conditions that were known to exist among those dying of heat-stroke throughout the city. It shows that the majority of heat-stroke decedents had known medical problems… with 2/3 having 2 or more known medical problems… as indicated in red… AND an additional 20% having one known medical problem… as shown in orange. This chart shows the number of medical conditions that were known to exist among those dying of heat-stroke throughout the city. It shows that the majority of heat-stroke decedents had known medical problems… with 2/3 having 2 or more known medical problems… as indicated in red… AND an additional 20% having one known medical problem… as shown in orange.

16. Household Conditions Among Heat Stroke Decedents This table shows the number of heat stroke decedents who lived alone… and the number who were known to have a working air conditioner… at the time of their deaths. This table shows the number of heat stroke decedents who lived alone… and the number who were known to have a working air conditioner… at the time of their deaths.

17. Excess natural-cause deaths, illness Normal body temperature – heat exposure leads to decompensation of chronic conditions or increased risk of acute event E.g., congestive heart failure, acute MI, emphysema Classified as due to ‘natural causes’ Estimated by comparing numbers of observed and expected events Larger numbers and % of overall burden Total mortality less subject to diagnosis and reporting artifacts Lagged effects can be estimated

18. This graph shows the daily maximum temperature from the beginning of July through mid-August. Heat wave #1 – 4 days – July 16-19 Heat wave #2 – 10 days – July 27-Aug 5 This graph shows the daily maximum temperature from the beginning of July through mid-August. Heat wave #1 – 4 days – July 16-19 Heat wave #2 – 10 days – July 27-Aug 5

21. N=46N=46

22. In the fall, once we were able to obtain daily mortality data from Vital Statistics, we statistically estimated an excess of 8% in natural-cause mortality per day, or about 100 extra deaths, during 2nd heatwave of the summer. **************************** Looking back, we saw that the peak in EMS calls and emergency department visits for heat-related illness preceded the reports of heat stroke deaths and the peak in natural-cause mortality by a couple of days. Given that we know how hot it is, and how hot its expected to be, our challenge was how to use this well-described, predictable relationship between extreme heat and morbidity as a tool to help prevent heat-related mortality. In the fall, once we were able to obtain daily mortality data from Vital Statistics, we statistically estimated an excess of 8% in natural-cause mortality per day, or about 100 extra deaths, during 2nd heatwave of the summer. **************************** Looking back, we saw that the peak in EMS calls and emergency department visits for heat-related illness preceded the reports of heat stroke deaths and the peak in natural-cause mortality by a couple of days. Given that we know how hot it is, and how hot its expected to be, our challenge was how to use this well-described, predictable relationship between extreme heat and morbidity as a tool to help prevent heat-related mortality.

24. NYC Heat Emergency Plan Plan activated when NWS issues heat advisory of heat index of =100° F within next 24 hours OEM convenes Heat Emergency Steering Committee call to coordinate response Special needs advance warning system Homeless outreach Cooling centers Spray cap program Excavation safety alert HESC includes city agencies and companies and organizations providing services to city residents. [OEM, NWS, NYPD, FDNY, DHS, Parks, MTA, DSNY, DFTA, DOHMH, ConEd/LIPA/KEYSPAN, DEP, Dept of Design and Construction, DoITT, DOT, Verizon, OCME] The response can be ratcheted up or down, depending on the forecast. [I’m not going to go through the heat response in detail, but it includes plans to give advance warning to special needs populations and increasing outreach services to the homeless, opening cooling centers and putting spray caps on neighborhood fire hydrants, as well as alerting contractors to excavate safely to ensure power and communications lines remain intact.] ***** … First I’ll explain how syndromic surveillance is typically used, and then I’ll talk about how we adapted it to look at heat related illness. HESC includes city agencies and companies and organizations providing services to city residents. [OEM, NWS, NYPD, FDNY, DHS, Parks, MTA, DSNY, DFTA, DOHMH, ConEd/LIPA/KEYSPAN, DEP, Dept of Design and Construction, DoITT, DOT, Verizon, OCME] The response can be ratcheted up or down, depending on the forecast. [I’m not going to go through the heat response in detail, but it includes plans to give advance warning to special needs populations and increasing outreach services to the homeless, opening cooling centers and putting spray caps on neighborhood fire hydrants, as well as alerting contractors to excavate safely to ensure power and communications lines remain intact.] ***** … First I’ll explain how syndromic surveillance is typically used, and then I’ll talk about how we adapted it to look at heat related illness.

25. NYC Heat Emergency Plan Department of Health & Mental Hygiene (DOHMH) roles Conduct ongoing surveillance for heat stroke deaths ? delay of days to weeks Conduct analyses of syndromic surveillance data (e.g., emergency room visits and EMS calls) for heat-related illness ? within 24-hours HESC includes city agencies and companies and organizations providing services to city residents. [OEM, NWS, NYPD, FDNY, DHS, Parks, MTA, DSNY, DFTA, DOHMH, ConEd/LIPA/KEYSPAN, DEP, Dept of Design and Construction, DoITT, DOT, Verizon, OCME] The response can be ratcheted up or down, depending on the forecast. [I’m not going to go through the heat response in detail, but it includes plans to give advance warning to special needs populations and increasing outreach services to the homeless, opening cooling centers and putting spray caps on neighborhood fire hydrants, as well as alerting contractors to excavate safely to ensure power and communications lines remain intact.] ***** … First I’ll explain how syndromic surveillance is typically used, and then I’ll talk about how we adapted it to look at heat related illness. HESC includes city agencies and companies and organizations providing services to city residents. [OEM, NWS, NYPD, FDNY, DHS, Parks, MTA, DSNY, DFTA, DOHMH, ConEd/LIPA/KEYSPAN, DEP, Dept of Design and Construction, DoITT, DOT, Verizon, OCME] The response can be ratcheted up or down, depending on the forecast. [I’m not going to go through the heat response in detail, but it includes plans to give advance warning to special needs populations and increasing outreach services to the homeless, opening cooling centers and putting spray caps on neighborhood fire hydrants, as well as alerting contractors to excavate safely to ensure power and communications lines remain intact.] ***** … First I’ll explain how syndromic surveillance is typically used, and then I’ll talk about how we adapted it to look at heat related illness.

26. Heat-related Illness Precedes Heat-related Death Heat-related deaths are preventable Surveillance for heat-related illness may provide early warning system Augment public health messages Guide citywide response

27. Syndromic surveillance during a heat emergency: how and when might it be useful?

28. Syndromic surveillance for heat related illness - analytic strategy Heat-related syndrome, small N, but best signal-to-noise ratio Prior is that illness is associated with heat Weather forecast is best predictor of risk Analysis to detect greater than expected increase in heat related illness, reflecting impact not being captured in meteorology ? Is illness leading indicator of mortality

29. Real-time Surveillance for Heat-Related Illness

30. Higher temperatures associated with more heat-related illness Meteorolgical variables included maximum temperature on the same day as the EMS calls or emergency department visits and an average over the previous several days. Maximum dew point temperature on the same day was also included to factor in the effect of humidity This graphic illustrates the correlation between the daily number of EMS calls for heat and maximum temperature. Around 90 F, the number of calls starts to increase rapidly.Meteorolgical variables included maximum temperature on the same day as the EMS calls or emergency department visits and an average over the previous several days. Maximum dew point temperature on the same day was also included to factor in the effect of humidity This graphic illustrates the correlation between the daily number of EMS calls for heat and maximum temperature. Around 90 F, the number of calls starts to increase rapidly.

31. ED visits, EMS calls influenced by other temporal patterns Temporal variables that were included in the retrospective model included: Linear, quadratic, periodic terms for time to account for seasonality, and Dummy variables for year, day-of-week, and holidays to account for trends in usage of emergency departments and calls to 911 This graphic illustrates the day-of week trend in EMS calls for heat-related illness – there were more calls mid-week than on weekends.Temporal variables that were included in the retrospective model included: Linear, quadratic, periodic terms for time to account for seasonality, and Dummy variables for year, day-of-week, and holidays to account for trends in usage of emergency departments and calls to 911 This graphic illustrates the day-of week trend in EMS calls for heat-related illness – there were more calls mid-week than on weekends.

32. Temporal Analysis of ED Visits, EMS calls Time-series analysis of daily counts ED visits for “heat” chief complaint EMS calls for “heat” call-type Two models used evaluate impact of heat Controlling for time only Are we seeing more heat-related illness than expected given the time of year? 2. Controlling for time and meteorology Are we seeing more heat-related illness than expected given the weather conditions? First we did a retrospective analysis of the syndromic surveillance data for heat-related illness. We used time-series models to evaluate the daily counts of emergency department visits for a chief complaint of “heat” and daily counts of EMS calls for the “heat” call type. We used all available data through 2005. We constructed 2 models to evaluate the impact of heat. The first controlled for temporal trends only – which could help us answer the question “Are we seeing more heat-related illness than expected given the time of year?” The second controlled for time and meteorological factors – this model would help us answer the question “Are we seeing more heat-related illness than expected given the current weather conditions?” First we did a retrospective analysis of the syndromic surveillance data for heat-related illness. We used time-series models to evaluate the daily counts of emergency department visits for a chief complaint of “heat” and daily counts of EMS calls for the “heat” call type. We used all available data through 2005. We constructed 2 models to evaluate the impact of heat. The first controlled for temporal trends only – which could help us answer the question “Are we seeing more heat-related illness than expected given the time of year?” The second controlled for time and meteorological factors – this model would help us answer the question “Are we seeing more heat-related illness than expected given the current weather conditions?”

33. Spatial Analysis of EMS Calls Calculate heat-to-total EMS calls by ZIP SaTScan analysis comparing heat wave days with non-heat wave days

34. Evaluating Impact of Heat

35. “Prospective” Analysis of 2006 Simulation of day-to-day analysis Warm season: May through September Signal = day with observed count statistically greater than expected count What would we have seen during heat wave days? Using the model we developed with data through 2005, we conducted a simulation of a prospective day-to-day analysis of the warm season in 2006 to evaluate what we would have seen during the heat wave days. We considered a signal to occur when the observed count of EMS calls or emergency department visits was statistically greater than the count we would have expected based on the results of the model.Using the model we developed with data through 2005, we conducted a simulation of a prospective day-to-day analysis of the warm season in 2006 to evaluate what we would have seen during the heat wave days. We considered a signal to occur when the observed count of EMS calls or emergency department visits was statistically greater than the count we would have expected based on the results of the model.

36. This is a busy graph so I’m going to point out the most important features. This is a graph the summer of 2006 -- of the daily MAXIMUM TEMPERATURE – the blue line – observed daily counts of EMS CALLS – the green crosses – the predicted daily counts of EMS calls based on the results of the EMS model controlling for time and meteorology – the black line. I’m only going to show this model and not the other EMS model or either emergency deparment visit models, because they all tell the same basic story. These orange lines indicate the two heat wave periods. These red dots signal the days during heat waves in which the observed number of EMS calls for heat were significantly higher than the number predicted by this model, indicating that there was an excess of heat-related illness beyond what we would have expected given the time of year and weather conditions. We saw signals on the 1st 2 days of the 1st heatwave and beginning on day 3 of the 2nd heatwave. In hindsight, had we had this system of formally assessing heat-related illness using the syndromic surveillance data in place in 2006, we would have been able to see some indication that this 2nd 10 day heat wave was a particularly bad one by day 3, especially in conjunction with the forecast that called for prolonged and very hot weather. We might have been able to mitigate some of the heat-related mortality attributed to that heat wave, by augment public health messages to include information about the excesses in heat-related illness that we were seeing wave and potentially ratchet up the public health response. Heat wave #1: July 16-19 EMS signals for both models began on day 1 -- time model signaled all 4 days -- time+met model signaled 1st 2 days only ED time only model signaled days 1-3 Heat wave #2: July 27-Aug 5 EMS signals for both models began on day 3 -- time only model signaled days 3-10 -- time+met model signaled 6 of 10 days ED signals for both models began on day 4 -- time only model signaled 5 of 10 – time+met model signaled 4 of 10 daysThis is a busy graph so I’m going to point out the most important features. This is a graph the summer of 2006 -- of the daily MAXIMUM TEMPERATURE – the blue line – observed daily counts of EMS CALLS – the green crosses – the predicted daily counts of EMS calls based on the results of the EMS model controlling for time and meteorology – the black line. I’m only going to show this model and not the other EMS model or either emergency deparment visit models, because they all tell the same basic story. These orange lines indicate the two heat wave periods. These red dots signal the days during heat waves in which the observed number of EMS calls for heat were significantly higher than the number predicted by this model, indicating that there was an excess of heat-related illness beyond what we would have expected given the time of year and weather conditions. We saw signals on the 1st 2 days of the 1st heatwave and beginning on day 3 of the 2nd heatwave. In hindsight, had we had this system of formally assessing heat-related illness using the syndromic surveillance data in place in 2006, we would have been able to see some indication that this 2nd 10 day heat wave was a particularly bad one by day 3, especially in conjunction with the forecast that called for prolonged and very hot weather. We might have been able to mitigate some of the heat-related mortality attributed to that heat wave, by augment public health messages to include information about the excesses in heat-related illness that we were seeing wave and potentially ratchet up the public health response. Heat wave #1: July 16-19 EMS signals for both models began on day 1 -- time model signaled all 4 days -- time+met model signaled 1st 2 days only ED time only model signaled days 1-3 Heat wave #2: July 27-Aug 5 EMS signals for both models began on day 3 -- time only model signaled days 3-10 -- time+met model signaled 6 of 10 days ED signals for both models began on day 4 -- time only model signaled 5 of 10 – time+met model signaled 4 of 10 days

38. Protocol for Summer Season Automated daily analysis Today’s results tell us about what happened yesterday On the 2nd day of extreme heat event, communicate results of analyses to OEM Interpretation of results can be used to augment messages, guide response Based on the 2006 simulation, we developed a protocol for summer 2007. We automated the daily analysis and had an e-mail sent each morningto the analytic team with the results from yesterdays data. The goal was to have information to communicate with OEM on the 2nd day of an extreme heat event. We would help with the interpretation of these results to augment public health messages and guide the city’s response.Based on the 2006 simulation, we developed a protocol for summer 2007. We automated the daily analysis and had an e-mail sent each morningto the analytic team with the results from yesterdays data. The goal was to have information to communicate with OEM on the 2nd day of an extreme heat event. We would help with the interpretation of these results to augment public health messages and guide the city’s response.

39. 2007 Heat Experience Mild season – no heat waves Heat Emergency Steering Committee convened twice July 9-11: Hazardous weather predicted, no heat advisories August 7-9: Heat advisories issued for 8/7, 8/8 only Fortunately for New York City, but unfortunately for evaluating the system, 2007 was a mild season with no heat waves. Fortunately for New York City, but unfortunately for evaluating the system, 2007 was a mild season with no heat waves.

40. 2008 Strategy

41. NYC Heat Emergency Planning and Response

42. Heat-related Deaths are Preventable Elderly, those with multiple medical and psychiatric conditions are vulnerable Air conditioning saves lives NYC Heat Plan interventions Opening cooling centers Outreach for special needs populations

43. Seniors are vulnerable during heat waves Less able regulate body temperature because of decreased circulation and perspiration. Chronic health conditions and some medications increase vulnerability. 18% of low income seniors don’t own an AC and of those that do 28% may not use them due to the energy costs. May be less able to get to a cool environment if they are socially isolated or have limited mobility. New Yorkers age 65 and older accounted for 53% of heat stroke deaths during a severe 2006 heat wave; only 12% of NYC population is 65+.

44. AC as vulnerability marker Data from Schwartz et al.

45. What about past severe heat waves in NYC? July 14-26, 1972 NYC heat wave 10 deaths certified as due to heat 2319 total deaths week ending July 28 1592, 1428 same week in 1970, 1971 Aug 28 – Sept 4 1973 heat wave 30 deaths certified as due to heat from June 11 – October 21 1910 deaths week ending 9/7/73, 25% more than expected

46. Air conditioning prevalence, NYC*

47. Proactive AC energy subsidy vs emergency response Most vulnerable may be least likely to benefit from cooling centers Most vulnerable may not call for help Cost low relative to ED visit or outreach response team visit Reduces ozone exposure But: ? Adds to existing electricity load Funding, installation, eligibility

48. A small start NYC DFTA distributed 1500 A/Cs using funding from NYS office of ageing This year, NYC HRA planning distribution of additional ACs using funding from LIHEAP