DISASTERS: BEFORE / DURING / AFTERINJURY MORTALITY & MORBIDITY SURVEILLANCE

SO…(FROM A SURVEILLANCE PERSPECTIVE…)

Jon Roesler, MSCSTE Disaster Epidemiology Workshop

May 9th, 2013Atlanta, GA

3 Examples…

Before / During / After

3 Examples

After / During / Before

3 Examples

After: Recalling Disasters in Minnesota

Example 1

Recalling Minnesota Disasters 1991 Halloween Blizzard37” of Snow in Duluth. Between the blizzard and the ice storm , 22 fatalities and over 100 people were injured.

1997 Red River FloodThe most severe flood of the river since 1826. Affected MN, ND, Canada. 50,000 evacuated in Grand Forks / E. Grand Forks alone, where record flooding occurred. $3.5 billion in damages. No fatalities.

1998 Comfrey – St. Peter Tornado Outbreak16 tornados in 4 hours including F2, F3, & F4. $235 million in damages. 2 fatalities.

2005 Red Lake Massacre – Murder/Suicide16 year-old shot grandfather & girlfriend, then 7 at high school, before killing self; 10 fatalities, 15 injured.

2007 I-35W Mississippi River Bridge CollapseDuring evening rush hour on August 1, 2007, it suddenly collapsed with 13 fatalities and injuring 145 people.

2009 Red River FloodRecord flooding in Fargo / Moorhead. 3 fatalities.

Recalling Minnesota Disasters 1991 Halloween Blizzard37” of Snow in Duluth. Between the blizzard and the ice storm , 22 fatalities and over 100 people were injured.

1997 Red River FloodThe most severe flood of the river since 1826. Affected MN, ND, Canada. 50,000 evacuated in Grand Forks / E. Grand Forks alone, where record flooding occurred. $3.5 billion in damages. No fatalities.

1998 Comfrey – St. Peter Tornado Outbreak16 tornados in 4 hours including F2, F3, & F4. $235 million in damages. 2 fatalities.

2005 Red Lake Massacre – Murder/Suicide16 year-old shot grandfather & girlfriend, then 7 at high school, before killing self; 10 fatalities, 15 injured.

2007 I-35W Mississippi River Bridge CollapseDuring evening rush hour on August 1, 2007, it suddenly collapsed with 13 fatalities and injuring 145 people.

2009 Red River FloodRecord flooding in Fargo / Moorhead. 3 fatalities.

TEEN LONER KILLS 9, SELF

Posted Tuesday, March 22nd 2005, 12:00 AM

A CRAZED TEENAGER murdered his grandparents and then went on a shooting rampage through his Minnesota school yesterday, killing a teacher, a security guard and five students before putting a bullet in his own head, authorities said.

The bloodbath that killed 10 people also injured as many as 15 other kids at Red Lake High School on the Chippewa Indian Reservation.

Timeline Beltrami County

March 21, 2005Red Lake Massacre - Murder/Suicide

Fall 2005Headwaters Alliance

Winter 2007MDH Contacted

Fall 2007MDH Report

Figure 2

SuicideAge Adjusted Rates

0

5

10

15

20

25

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Rat

e Pe

r 100

,000

U.S.

Minnesota

Beltrami County(5 yr floating)

Roesler J: Suicide and Nonfatal Self-inflicted Harm, Beltrami County, 1990-2006 . St. Paul, MN: Minnesota Department of Health, September 14, 2007

Figure 7

Nonfatal Self-inflicted Harm Hospital-treated, Age Adjusted Rate

0

50

100

150

200

250

1998 1999 2000 2001 2002 2003 2004 2005 2006

U.S.

Minnesota

Beltrami County

Roesler J: Suicide and Nonfatal Self-inflicted Harm, Beltrami County, 1990-2006 . St. Paul, MN: Minnesota Department of Health, September 14, 2007

Nonfatal Self-inflicted Harmby Age Group, 2005

472772

242879

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Minnesota Beltrami County

25+5 to 24

Roesler J: Suicide and Nonfatal Self-inflicted Harm, Beltrami County, 1990-2006 . St. Paul, MN: Minnesota Department of Health, September 14, 2007

Minn Med. 2009 Aug;92(8):53-5.

Nonfatal suicide attempts and other self-inflicted harm. Beltrami County youths, 2002-2006.

Roesler J, Petcoff M, Azam A, Westberg S, Kinde M, Crosby A.

Case Definition:Hospital-treated, Self-Inflicted Harm(SIH)

Forms: Self-Inflicted Injury Data Collection Form; Report of Injury (ROI)

Data Source(s): Select cases that meet sample criteria from UB discharge data

Purpose: Continued epidemiologic surveillance of self-inflicted harm in youth from Beltrami County.

Inclusion:Under 25 years of age ANDAll sequences AND All hospitals AND Year of discharge = 2002-2006 ANDBeltrami County residents only ANDInpatient or Outpatient ANDNon-fatal or Fatal ANDWith any of the following ICD-9-CM Cause codes: E950.0-E958.9 (self-inflicted injury), V62.84

(suicidal ideation) AND With any of the following ICD-9-CM Diagnostic codes: 800-904.99, 910-994.99, 995.5-995.59

or 995.80-995.85 AND Exclusion:

Any with the following ICD-9-CM Diagnostic codes: 909.3, 909.5, 905.0-909.99, 995.0-995.49, 995.6-995.79, 995.86, 995.89, 996-999.99 (late effects and certain adverse effects)

Abstraction2002-2006

All Hospitalized SIH, <25 years oldSample of ED-treated SIH, <25 years old

2 Main FindingsDouble the expected rate of prior attempts

Pump Handle Effect

Expected Prior Attempt Hx

23%Zahl DL, Hawton K: Repitition of deliberate self-harm and subsequent suicide risk: long term

follow-up study of 11,583 patients. British Journal of Psychiatry, 185:70-75, 2004.

20%Miranda R, Scott M, Hicks R, et al.: Suicide attempt characteristics, diagnoses, and future

attempts: comparing multiple attempters to single attempters and ideators. J Am Acad Child Adolesc Psychiatry, 47(1):32-40, January 2008.

26%Hawton K, Harriss L: Deliberate self-harm in young people: characteristics and subsequent

mortality in a 20-year cohort of patients presenting to hospital. J Clin Psychiatry, 68(10):1574-83, October 2007.

Prior HistorySelf-Inflicted Harm

53%42%

5%

Positive

Negative

UnknownUndocumented

Minn Med. 2009 Aug;92(8):53-5.Nonfatal suicide attempts and other self-inflicted harm. Beltrami County youths, 2002-2006.Roesler J, Petcoff M, Azam A, Westberg S, Kinde M, Crosby A

Beltrami County Timeline2004

Nonfatal SIH Peak: 96

3/21/2005Red Lake murder/suicide

Fall 2005Headwaters Task Force

2005Suicides Peak: 13

2006Nonfatal SIH Decline (2nd year in row): 61

Winter 2007MDH contacted

Fall 2007MDH Report

2007Suicides Decline (2nd year in row): 8

Removing the Handlefrom the Broad Street Pump: the epidemic may have already been in rapid decline!“…the attacks had so far diminished before the use of the water was stopped, that it is impossible to decide whether the well still contained the cholera poison…”-John Snow

So… (from a surveillance perspective…) Cars, Alcohol, Suicide Each kill many more…

I got my fatality numbers from Media, Wikipedia…

Real-time surveillance is hard…

Our role is in post-hoc analysis… Leading to prevention efforts

During: Syndromic Surveillance System For Heat-Related Illnesses & Deaths

Example 2

MDH 2012 Syndromic Surveillance System For Heat-Related Illnesses and Deaths

Syndromic Surveillance: Use of data from non-traditional sources (e.g., chief complaints from emergency department visits, absenteeism data, over-the-counter drug sales)

in order to detect public health events earlier than possible with other methods

(e.g., laboratory confirmed diagnosis, physician diagnosis).

MDH 2012 Syndromic Surveillance System For Heat-Related Illnesses and Deaths

Understand the health implications of extreme heat

Discuss current strategies to prevent heat-related morbidity and mortality

Understand available data related to morbidity and mortality related to heat

Begin to assess need for real-time data

MDH 2012 Syndromic Surveillance System For Heat-Related Illnesses and Deaths

“In Minnesota, the National Weather Service (NWS) provides weather forecasts and determines the issuance of heat advisories, watches or warnings.”

“All Minnesota jurisdictions involved in planning and implementing heat response plans should develop relationships with their local NWS stationto ensure: daily monitoring of weather conditions, and early detection and transfer of information regarding

the characteristics of the upcoming event”

So… (from a surveillance perspective…)

The NWS is the lead agency during a heat event

The role of MDH injury surveillance is probably at looking at heat-related illness/death post-hoc (after the fact)

Before: Using Surveillance Data to Predict Disaster

Example 3

Predicting Trauma Admissions:

The Effect of Weather, Weekday,and Other Variables

Kevin A. FriedeMarc C. Osborne, MDDarin J. Erickson, PhDJon S. Roesler, MSArsalan AzamJ. Kevin Croston, MDMichael D. McGonigal, MDArthur L. Ney, MD November 2009

One of the challenges all hospitals, especially designated trauma centers, face is how to make sure they have adequate staffing on various days of the week and at various times of the year.

A number of studies have explored whether factors such as weather, temporal variation, holidays, and events that draw mass gatherings may be useful for predicting patient volume.

This article looked at the effects of weather, mass gatherings, and calendar variables on daily trauma admissions at the three Level I trauma hospitals in the Minneapolis-St. Paul metropolitan area.

Minn Med. 2009 Nov;92(11):47-9.Predicting trauma admissions: the effect of weather, weekday, and other variables.Friede KA, Osborne MC, Erickson DJ, Roesler JS, Azam A, Croston JK, McGonigal MD, Ney AL.

Minn Med. 2009 Nov;92(11):47-9.Predicting trauma admissions: the effect of weather, weekday, and other variables.Friede KA, Osborne MC, Erickson DJ, Roesler JS, Azam A, Croston JK, McGonigal MD, Ney AL.

ARIMA: Autoregressive integrated moving average

In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model.

These models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting).

Using ARIMA statistical modeling, we found that:• weekends, • summer, • lack of rain• snowfall were predictive of daily trauma admissions

• holidays • mass gatherings such as sporting events were not predictive.

The forecasting model was successful in reflecting the pattern of trauma admissions.

Minn Med. 2009 Nov;92(11):47-9.Predicting trauma admissions: the effect of weather, weekday, and other variables.Friede KA, Osborne MC, Erickson DJ, Roesler JS, Azam A, Croston JK, McGonigal MD, Ney AL.

Minn Med. 2009 Nov;92(11):47-9.Predicting trauma admissions: the effect of weather, weekday, and other variables.Friede KA, Osborne MC, Erickson DJ, Roesler JS, Azam A, Croston JK, McGonigal MD, Ney AL

The forecasting model was successful in reflecting the pattern of trauma admissions.

However, it's usefulness was limited in that the predicted range of daily trauma admissions was much narrower than the observed number of admissions.

Nonetheless, the observed pattern of increased admission in the summer months and year-round on Saturdays should be helpful in resource planning.

Minn Med. 2009 Nov;92(11):47-9.Predicting trauma admissions: the effect of weather, weekday, and other variables.Friede KA, Osborne MC, Erickson DJ, Roesler JS, Azam A, Croston JK, McGonigal MD, Ney AL.

So… (from a surveillance perspective…)

So…(from a surveillance perspective…)

Before / During / After

So…(from a surveillance perspective…)

Before / During / After

Jon Roesler, MS                     Minnesota Department of Health  85 East Seventh Place, Suite 220PO Box 64882                    St. Paul, MN  55164-0882       

Voice:  651.201.5487                               E-mail:  jon.roesler@state.mn.us www.health.state.mn.us/injury