Assessing the Influence of Weather in 5 U.S. Cities During Wintertime High Mortality Days

ASSESSING THE INFLUENCE OF WEATHER IN 5 U.S. CITIES DURING WINTERTIME HIGH MORTALITY DAYS

A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Master of Arts

Michael James Allen

December, 2010

Thesis written by Michael James Allen B.S., California University of Pennsylvania, 2008 M.A., Kent State University, 2010

Approved by

______, Dr. Scott Sheridan, Advisor

______, Dr. Mandy Munro-Stasiuk, Chair, Department of Geography

______, Dr. Timothy Moerland, Dean, College of Arts and Sciences

TABLE OF CONTENTS

Page

LIST OF FIGURES...... vii

LIST OF TABLES...... ix

ACKNOWLEDGEMENTS...... xii

CHAPTER 1 INTRODUCTION...... 1

CHAPTER 2 BACKGROUND...... 5 2.1 Weather Mortality...... 5 2.1.1 Biological Causes...... 5 2.1.2 Socio-Economic, Demographic, and Behavioral Factors...... 7 2.1.3 The Lag Effect and Mortality Harvesting...... 9 2.2 Defining Winter Weather…...... 11 2.3 Winter Mortality Studies...... 12 2.3.1 Spatial Variability...... 12 2.3.2 Climate-Health Relationships...... 13 2.3.3. Indices and Models...... 15

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CHAPTER 3 DATA AND METHODOLODY...... 18 3.1 Data...... 18 3.1.1 Study Region and Demographics...... 18 3.1.2 Mortality Data...... 21 3.1.3 Atmospheric Data...... 22 3.1.4 Map Comparison...... 24 3.2 Methodology...... 24 3.2.1 Mortality Methods...... 24 3.3 Data Analysis...... 25 3.4 Methodology Summary...... 28

CHAPTER 4 RESULTS...... 30 4.1 Demographics...... 30 4.1.1 A Monthly Comparison of Mortality Spikes...... 30 4.1.2 Mortality Spikes: Causes of Death...... 31 4.1.3 Mortality Spikes: Age Categories...... 34 4.1.4 Mortality Spikes: Race Categories...... 36 4.1.5 Mortality Spikes: Gender Comparisons...... 38

4.2 Temperature (δT, δT1day , δT3day) ...... 40 4.2.1 Minneapolis-St. Paul...... 40 4.2.2 Pittsburgh...... 43 4.2.3 St. Louis...... 46 4.2.4 San Antonio...... 49 4.2.5 Miami...... 52

4.3 Pressure (δP, δP1day , Min/Max) ...... 55 4.3.1 Minneapolis-St. Paul...... 55

4.3.2 Pittsburgh...... 59 4.3.3 St. Louis...... 61 4.3.4 San Antonio...... 64 4.3.5 Miami...... 67 4.4 Precipitation and Snowfall...... 71 4.4.1 Minneapolis-St. Paul...... 71 4.4.2 Pittsburgh...... 73 4.4.3 St. Louis...... 75 4.4.4 San Antonio...... 77 4.4.5 Miami...... 79 4.5 Spatial Synoptic Classification...... 80 4.5.1 Minneapolis-St. Paul...... 80 4.5.2 Pittsburgh...... 82 4.5.3 St. Louis...... 84 4.5.4 San Antonio...... 86 4.5.5 Miami...... 89 4.6 Map Comparisons...... 92

CHAPTER 5 DISCUSSION...... 100 5.1 Synthesis of Results...... 100 5.2 Weather on Spike and Non-Spike Days...... 101 5.3 Cause of Death and Demographic Factors...... 104 5.4 Variability between Cities...... 105 5.5 Seasonality and Lag Effect...... 106 5.6 Limitations to Research...... 107

CHAPTER 6 CONCLUSION...... 109

BIBLIOGRAPHY...... 113

LIST OF FIGURES

Figure 1: Lag and Mortality Effect...... 10

Figure 2: Temperature-Mortality Relationship...... 14

Figure 3: MSA: Study Area...... 19

Figure 4: Temperature Graph: 1.5 SD Minneapolis-St. Paul...... 42

Figure 5: Temperature Graph: 2.0 SD Minneapolis- St. Paul...... 43

Figure 6: Temperature Graph: 1.5 SD Pittsburgh...... 45

Figure 7: Temperature Graph: 2.0 SD Pittsburgh...... 46

Figure 8: Temperature Graph: 1.5 SD St. Louis...... 48

Figure 9: Temperature Graph: 2.0 SD St. Louis...... 49

Figure 10: Temperature Graph: 1.5 SD San Antonio...... 51

Figure 11: Temperature Graph: 2.0 SD San Antonio...... 51

Figure 12: Temperature Graph: 1.5 SD Miami...... 53

Figure 13: Temperature Graph: 2.0 SD Miami...... 54

Figure 14: Pressure Graph: 1.5 SD Minneapolis-St. Paul...... 57

Figure 15: Pressure Graph: 2.0 SD Minneapolis- St. Paul...... 58

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Figure 16: Pressure Graph: 1.5 SD Pittsburgh...... 60

Figure 17: Pressure Graph: 2.0 SD Pittsburgh...... 61

Figure 18: Pressure Graph: 1.5 SD St. Louis...... 63

Figure 19: Pressure Graph: 2.0 SD St. Louis...... 64

Figure 20: Pressure Graph: 1.5 SD San Antonio...... 66

Figure 21: Pressure Graph: 2.0 SD San Antonio...... 67

Figure 22: Pressure Graph: 1.5 SD Miami...... 69

Figure 23: Pressure Graph: 2.0 SD Miami...... 70

Figure 24: Map Comparison: Pittsburgh Approaching Front ...... 93

Figure 25: Map Comparison: Pittsburgh High Pressure Return Flow...... 94

Figure 26: Map Comparison: Pittsburgh Elongated High...... 95

Figure 27: Map Comparison: Pittsburgh Other...... 96

Figure 28: Map Comparison: Miami Approaching Front...... 97

Figure 29: Map Comparison: Miami Frontal Passage...... 98

Figure 30: Map Comparison: Miami Other...... 99

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LIST OF TABLES

Table 1: Station Climatology...... 19

Table 2: MSA Demographics...... 20

Table 3: MSA Counties...... 21

Table 4: SSC Weather Types...... 23

Table 5: Weather Variables...... 27

Table 6: Equation 2 Explanation...... 29

Table 7: Morality Spike Days...... 31

Table 8: Demographics: Cause of Death...... 32

Table 9: Demographics: Age...... 35

Table 10: Demographics: Race...... 37

Table 11: Demographics: Gender...... 39

Table 12: Temperature: Minneapolis-St. Paul...... 41

Table 13: Temperature: Pittsburgh...... 44

Table 14: Temperature: St. Louis...... 47

Table 15: Temperature: San Antonio...... 50

Table 16: Temperature: Miami...... 52

Table 17: Pressure: Minneapolis-St. Paul...... 56

Table 18: Pressure: Pittsburgh...... 59

Table 19: Pressure: St. Louis...... 62

Table 20: Pressure: San Antonio...... 65

Table 21: Pressure: Miami...... 68

Table 22: Precipitation and Snowfall: 1.5 SD Minneapolis-St. Paul...... 72

Table 23: Precipitation and Snowfall: 2.0 SD Minneapolis-St. Paul...... 73

Table 24: Precipitation and Snowfall: 1.5 SD Pittsburgh...... 74

Table 25: Precipitation and Snowfall: 2.0 SD Pittsburgh...... 75

Table 26: Precipitation and Snowfall: 1.5 SD St. Louis...... 76

Table 27: Precipitation and Snowfall: 2.0 SD St. Louis...... 77

Table 28: Precipitation: 1.5 SD San Antonio...... 78

Table 29: Precipitation: 2.0 SD San Antonio...... 78

Table 30: Precipitation: 1.5 SD Miami...... 79

Table 31: Precipitation: 2.0 SD Miami...... 79

Table 32: Spatial Synoptic Classification: 1.5 SD Minneapolis-St. Paul...... 81

Table 33: Spatial Synoptic Classification: 2.0 SD Minneapolis-St. Paul...... 82

Table 34: Spatial Synoptic Classification: 1.5 SD Pittsburgh...... 83

Table 35: Spatial Synoptic Classification: 2.0 SD Pittsburgh...... 84

Table 36: Spatial Synoptic Classification: 1.5 SD St. Louis...... 85

Table 37: Spatial Synoptic Classification: 2.0 SD St. Louis...... 86

Table 38: Spatial Synoptic Classification: 1.5 SD San Antonio...... 87

Table 39: Spatial Synoptic Classification: 2.0 SD San Antonio...... 88

Table 40: Spatial Synoptic Classification: 1.5 SD Miami...... 90

Table 41: Spatial Synoptic Classification: 2.0 SD Miami...... 91

ACKNOWLEDGEMENTS

I would like to this opportunity to thank the countless family members, lifelong friends, and colleagues for their support and encouragement throughout the process of completing this thesis.

First, I would like to thank my advisor, Scott Sheridan, for his patience and assistance. Without your influence, advice, and words of encouragement, the possibility of finishing this research would have only been a dream. Whether in a coffee shop in

Akron or pizzeria halfway around the world, your willingness to discuss my thesis was unmatched and will forever be appreciated. Noroc!

In addition, thank you to my other committee members, Tom Schmidlin and

Debs Ghosh, for your recommendations throughout the last two years. Your perspectives added value to this work and challenged me to think about the larger picture.

My friends both near and far, have been invaluable and I could not thank you enough. A special thanks is owed to Chris Gilson and Megan Schwitzer. Both of you provided me with an ear to listen, voice of reason, and stern perspective on life when it was needed. To Blake Weber and Colt Kozisek, thank you for creating diversions for me

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in Ohio and making me think beyond academics. Without all of your friendships, my head would have spun around in circles.

In addition, without the constant encouragement of Jerry Mochan, this thesis would have never been completed. While being a role model, you have provided me with a strong desire to learn, a determined spirit to persevere, and helped me focus in order to continue along with this path of education. For that, I am eternally grateful.

Most of all, to my immediate and extended family, thank you for your love and support over the years. Whether in Pittsburgh, Boston, or beyond, your influence has provided me with the strong foundation required for success. Thank you mom and dad for always seeking the best for Michelle and I. Without your hard work, sacrifices, and love, our ambitious goals would only have been dreams. To my grandma, EhEh- thank you for always taking an interest in my endeavors, offering advice on life, and making me drink orange juice as a child. Pap is looking down from heaven. He surely would be proud.

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CHAPTER 1

INTRODUCTION

Weather plays an important role in our everyday lives- past and present. During

World War II, ice-jammed rivers prevented German troops from gaining access into the

British Isles and thus saved Allied forces. Weather was also directly attributed to the death of nearly 160,000 German lives during the war (Burroughs 1997). Economic stability is also influenced by climate. For example, aviation requires a knowledge and understanding of atmospheric relationships, and agriculture relies on moisture, temperature, and photosynthesis which are also driven by the atmosphere (Laing-

Morton et al. 2008). The connection between climate and human health displays a similar relationship. Hippocrates first noted this climate-health relationship when he suggested that an observed the increase in deaths and strokes was due to an abnormally cold spring season that followed a mild winter (McKee 1989). These first observations have been improved upon by researchers throughout the centuries as a way to explain the relationship between climate and health. Following the Scientific

1 2

Revolution, many studies within geography, atmospheric science, and more recently public health have attempted to better understand this multi-faceted relationship between climate and human health (Laing-Morton et al. 2008). For instance, excessively warm and cold days have been noted for increasing mortality rates (excess deaths), and the increase may be attributed to several factors including weather. This seasonality of mortality was first examined in the 1980s (Alderson 1985; Diaz et al.

2005). Persistent weather patterns such as prolonged heat events have also showed increases in mortality. One reason for the mortality increases is attributed to the consecutive days of oppressive weather types (e.g. Diaz et al. 2005, Keatinge and

Donaldson 2004).

While much research focusing on the impact of heat on human health exists, more deaths occur during the winter season (Curriero et al. 2002). One reason for the increased winter mortality is that winter conditions vary significantly more than any other season, both from day to day and from one winter to the next. (Burroughs 1997).

While winter mortality may be associated with weather, various other factors contribute to the high seasonal mortality such as health care accessibility, pre-existing health conditions, or other socio-economic factors (Basu and Samet 2002; Curriero et al.

2002). The physiological response to weather conditions also plays a role in winter mortality. Each individual responds to the environment differently (Borrell et al. 2006).

In addition, until recently, severe influenza outbreaks and viral epidemics were very common during the winter season (Analitis et al. 2008).

As a way to examine the impact winter weather has on human health, this thesis compared winter (October-March) weather conditions on days when mortality was high, a spike day, to weather conditions on non-spike days. Mortality spike days were defined as days in which daily mortality was significantly greater (at least 1.5 or 2 standard deviations) than previous days. Therefore, spike days represent the individual days during the winter season with the highest mortality.

Temperature, pressure, precipitation, and Spatial Synoptic Classification were used in the analysis which encompassed 1975-2004 mortality data. Using these criteria, the mortality response to atmospheric conditions was examined in order to address the following objectives:

 How do the weather conditions vary between mortality spike days and non-

spike days?

 How do demographic factors and causes of death change during winter spike

days?

 How does the winter weather-mortality relationship vary through space and

time? For example, do people in Minneapolis-St. Paul respond differently than

those living in Miami and how does this change throughout the winter season?

Many climate-health studies examine specific weather scenarios that are linked with increases in mortality. However, this thesis provides a unique perspective by examining the environmental response of mortality first and then relating it to the

weather scenario. While this environment-to-circulation approach is used in synoptic climatology, circulation-to-environment research is more commonly used. This is the first attempt to evaluate solely high mortality days and associated weather scenarios during the winter season in an attempt to understand the impact weather has on mortality. While mortality is clearly also impacted by non-climate factors, this research examines the weather on mortality spike days in an attempt to provide insight into the physiological response to individual weather variables and the atmosphere as a whole.

CHAPTER 2

BACKGROUND

Relating climate and human health, Armstrong (2006) stated that “ambient temperature is a major cause of fluctuations in mortality over time”. Ambient temperature refers to the current air temperature in a location. The climate has been considered a factor in winter mortality everywhere around the world excluding the tropics (Keatinge 2004). Despite this relationship, there are numerous other factors that contribute to the difficulties associated with the analysis of wintertime mortality.

2.1 Weather Mortality

2.1.1 Biological Causes

While there are several non-weather related factors that contribute to winter- related mortality, various diseases also contribute to the increase of mortality during the winter season. Illnesses are more common in the winter season and various pre-

existing health conditions play an important role in winter-related mortality. For example, 48% of Finland asthmatics reported a shortness of breath during the winter

(Raatikka et al. 2007). Hypothermia, the condition in which body temperatures drop below normal metabolic levels, may seem to be a common cause of winter mortality.

However, Goodwin (2007) showed that less than 1% of winter deaths are directly attributed to hypothermia. Influenza outbreaks have been shown to be an important factor in excess winter mortality (e.g. Curriero 2002; Dushoff et al. 2006). According to the World Health Organization (2010), influenza is easily spread through the air and is most common in children and older adults. Research indicates that influenza and other pathogen-based diseases may be the largest role in winter mortality (Dushoff et al.

2006). However, it has been noted that climate and other factors still may contribute to the precise mortality timing and the speed in which influenza outbreaks occur (Reichert et al. 2004).

Besides the influence of pathogens, the most problematic issue associated with winter weather is the thermoregulatory system. During the cold winter, red blood cells, blood pressure, and viscosity increase upwards of 20% (Gorjanc et al. 1999, McKee

1990). As a result, clotting becomes common and conservation of heat becomes a problem as blood rushes away from the peripheral organs (i.e. skin). With an internal temperature of 37°C, small changes in this core temperature can be deadly especially to those individuals already susceptible to illness (Azevedo et, al 1995, Nixdorf-Miller et al.

2006). Inadequate thermoregulatory systems place unneeded stress and strain on the

entire cardiovascular system. This can increase risk during both heat and cold waves with the elderly being highly vulnerable (Basu and Samet 2002).

Respiratory issues begin when a person breathes in cold air causing bronchial tubes to constrict. As a result, cilia efficiency is lowered and the person’s immune system becomes compromised (McKee 1990). Bacteria and infections can easily infiltrate one’s respiratory tract and increase the risk of disease. One-fifth of all winter hospitalizations may be attributed to such respiratory infections (Stewart et al. 2002)

Without sufficient regulation of diseases, a person may become susceptible to these diseases and eventually die. Similar to heat related mortality, it has been suggested that air quality may play a role in winter-related deaths (Cagle and Hubbard

2005). Up until 1976, influenza epidemics occurred often. However, with the improvement of medical technology, influenza rates have been declining (Keatinge

2002). While many of these diseases cannot be prevented fully, understanding the relationship they have with climate may be an important mitigation tool and a way to understand why large numbers of people die during the wintertime months.

2.1.2 Socio-Economic, Demographic, and Behavioral Factors

The relationship between mortality and winter weather varies across different demographic groups. While all people may have some susceptibility to cold weather negatively impacting their health, some are at higher risk. The older population may be

at the highest risk of cold wave mortality due to poor or declining heath (Aylin et al.

2001). This is consistent with heat wave mortality. In a United Kingdom study, mortality rates for people over the age of 65 increased when mean daily temperature exceeded 17°C (63°F). When temperatures extended below 5°C (41°F), the same population subset experienced an increased risk as well (Hajat et al. 2007). Research suggests that the proximity of people to each other during the cold season may substantiate an increase in mortality (Davis et al. 2004). This is not uncommon for older people who may live in nursing homes or stay inside in close quarters with others.

However, younger demographics may also be at a similar high risk. This may be attributed to outdoor activities and behaviors such as snow shoveling (Analitis et al.

2008, Gorjanc et al. 1999).

In addition to age, education and race have also been found to have an association with winter vulnerability (Barnett 2007). Those who are more highly educated may have a tendency to adequately prepare for winter weather whereas the homeless are more vulnerable to winter weather (Barnett 2007; Kysely et al. 2009).

Socio-economic status may also be important to consider when discussing winter mortality. The most vulnerable population in winter mortality would be the homeless.

However, the availability of such information is limited (Kysely et al. 2009). Other studies concluded that socioeconomics were less important with cold related mortality.

People lacking education, African-Americans, and southern locations were locations all

were more susceptible to cold-related mortality but the relationship was uncertain

(Anderson and Bell 2009).

As with heat related mortality, community infrastructure and individual response to weather are just some of the factors that may play a part in the changes in winter seasonal mortality (Braga et al. 2001). How people respond and the decisions they make are important factors in the winter season as well. Stewart et al. (2002) noted an increase in wintertime mortality and related it to alcohol consumption. Lifestyle decisions may play a role in understanding vulnerability during the winter season.

2.1.3 The Lag Effect and Mortality Harvesting

Within weather-related mortality research, a lag has been displayed (Figure 1).

This lagging refers to the delay time between exposure to atmospheric conditions and the body’s physiological response to it (i.e. death). While cardiovascular deaths account for the majority of wintertime deaths, many of these deaths occur within 3 days of a cold wave (Patz et al. 2000). Similarly, respiratory diseases increase following cold waves and are another common cause of death in the winter months. However, most of respiratory infections lag for upwards of 14 days after an event (Gorjanc et al. 1999;

Keatinge 2002). These two disease statistics may help explain the double peak (lag) nature of winter mortality (Diaz et al. 2005). In both cases, lag models have been used to account for this delayed response. A 14-day lag model is most appropriate for winter

mortality studies because it accounts for the long term changes (Armstrong 2006).

However, others suggest a shorter response such as a week (Diaz et al. 2005).

Figure 1. A comparison of lag in association with mortality risk during both heat and cold events (Anderson and Bell 2009).

The issue of mortality displacement or harvesting, a term referring to the group of individuals who may have died soon after regardless of climate circumstances, has been considered in various climate mortality studies (Basu and Ostro 2008; Schwartz 2000).

With regard to heat waves, mortality increases are generally observed for a few days.

Following this increase, mortality may actually decrease in the days afterwards.

Although excess mortality may increase for several weeks following a cold spell, there is no indication of a mortality displacement or harvesting (Huynen et al. 2001).

2.2 Defining Winter Weather

Another factor contributing to the difficulty of understanding winter mortality may be associated with the variety of definitions of winter weather. The National

Weather Service defines a cold wave as a rapidly changing temperature in a 24-hour period (AMS Glossary 2000). However, in the Netherlands for example, cold waves refer to three or more sustained days of -5°C (23°F) temperatures. European cold waves are thus longer in duration than the U.S. definition requires (Huyen et al. 2001).

Astronomically, winter season extends from the winter solstice to spring equinox, but others describe the period as the months of December-February (Analitis et al. 2008).

This creates difficulty when comparing regions temporally. Besides cold temperatures, snowfall conditions, variable weather patterns, and pressure changes have been utilized as ways to characterize winter weather (Gorjanc et al. 1999; Kassomeno et al. 2007;

Danet et al. 1999). During winter, weather conditions vary significantly more than any other season, both from day to day and from one winter to the next. One year may be extremely cold while the next may be extremely snowy and windy. This variation also makes it difficult in defining ‘winter’ (McGregor 1999, Burroughs 1997).

2.3 Winter Mortality Studies

2.3.1 Spatial Variability

Overall, winter has a negative effect on overall human health worldwide

(Keatinge and Donaldson 2004). Excess mortality patterns exist during the winter months and the increase in the prevalence of diseases such as the flu also contributes to higher winter mortality (Analitis et al. 2008). The human response to the winter season also varies based on geographic region and relativity, making regional perception of cold important for winter seasonal mortality (Keatinge 2002). Winter mortality is viewed as a major problem in warmer regions. The Eurowinter Group (1997) concluded that lower temperatures were related to increased heart disease mortality during the winter season. In surveys, The Eurowinter Group (1997) also showed that residents in relatively warmer climates do not generally prepare adequately for the cold (e.g. wear proper clothing). Similar research has shown this difference between warm and cold climate regions, so making comparisons between countries can be difficult due to differing infrastructures in place (Analitis et al. 2008, McKee 1990).

Being “cold” in Northern Siberia is not the same as being “cold” in Miami,

Florida. Cooler climates such as Siberia may also be more readily prepared for winter weather whereas milder climates such as Italy may not be equipped to deal with such elements. This preparation may include adequate housing infrastructure, availability of external heating sources, and personal acclimation to the environment (Davie et.al

2007).

2.3.2 Climate-Health Relationships

The relationship between ambient temperature and mortality has been widely used since the 1960’s. As shown in Figure 2, this relationship curve is a U, V, or J shape

(Armstrong 2006, Barnett 2007). At the base of the curve is the optimum temperature which is generally the most comfortable temperatures. For heat waves, the slope rises dramatically as temperature increases. However, with cold weather, the slope is more of a slow, steady increase in mortality away from the optimum temperature

(Kassomenos et al. 2007). This temperature varies based on geographic region. For example, Finland’s optimum temperature is 15.8°C whereas Athens’ is 24.2°C (Laaidi et al. 2006).

While ambient temperature has been used in some studies, various other methods have been attempted to find a link between weather and increasing mortality rates (e.g. Keatinge 2004; Jendritzky et al. 2001; Hajat et al. 2007) . In most, the minimum temperature (nighttime low) has had the largest impact on mortality rates in the winter (e.g. Basu et al. 2008; Bi et al. 2008). A more holistic approach to temperature-mortality relationships has also been used. Relating temperature and moisture, the apparent temperature is viewed as a comfort factor in heat wave research

(Analitis et al. 2008). During the winter months, the wind chill [factor] has been used more appropriately. By taking into account the relationship between wind speed and

Figure 2. Relationship between temperature and mortality

(Donaldson 2003).

heat transfer, wind chill is a method in which heat loss is measured through evaporation and convection. However, a Scottish study by Carder (2005) showed wind chill as being no better of an indicator of mortality than ambient temperature. This finding was supported by Kalkstein and Davis (1989).

Besides various temperature thresholds, winter-related mortality may also be related to snowfall accumulation, pressure changes, moisture, or measured weather variables (Hajat et al. 2007). According to Gorjanc et al. (1999), deaths increased 8% in

Pennsylvania following blizzards. Similar results were obtained by Glass and Zack (1979) which showed heart disease mortality increasing after blizzards. Many of the deaths may be attributed to snow shoveling as well as the biological response to the atmospheric elements (Glass and Zack 1979; Gorjanc et al. 1999).

2.3.3 Indices and Models

While some studies have examined weather variables independently, many others have conducted research by combining various metrics together. One example of this is the environmental stress index (ESI) which combines dry bulb temperature, relative humidity, and solar radiation. With instrumentation constantly being improved, this index was developed as an alternative, portable method by which environmental stress may be measured (Moran et al. 2001).

Although there are hundreds of thermal indices that attempt to describe the spatial and temporal variability of the physiological response to the environment, none take into account mechanisms of heat exchange. Therefore, these indices cannot be universally valid for every biometeorological task across time and space (Jendritzky et al.

2001). However, a Universal Thermal Climate Index (UTCI) used in all climates, regions,

and scales to relate the physiological response to the whole range of heat exchange would be more useful. The model takes into account the human thermoregulatory system, behavioral adaptation of clothing, and the reduction of insulation cause by movement and wind speed (Jendritzky et al. 2001).

The most common of these heat budget models is the German Klima-Michel-

Model (KMM). First described in the 1970’s, this heat budget model takes into account air temperature, humidity, cloud type, and wind along with personal behavior. The model is represented by Klima-Michel, a German man, who is 35 years old, 1.75 meters tall and weighs 75 kg. His work performance, 172.5 W, is also taken into account.

Depending on climate conditions, clothing is chosen in order to gain thermal comfort as fast as possible. The KMM began including to perceived and wind chill temperatures in

1995 (Jendritzky et al. 2001). The KMM is just one of the heat budget models being used in climate-health research which describes the body’s heat gain/loss in mathematical terms.

Despite the numerous ways in which people have studied the climate-health relationship, many of the methods described do not fully capture the nonlinear relationship between climate and health. Recent splining techniques such as that of

Anderson and Bell (2009) have attempted to use more complex equations to explain the relationship by using over-dispersed Poisson generalized additive models which take into account the day of the week, various temperature metrics, and expected mortality

rates. Using this method, it was confirmed that a longer exposure (lag) was more useful for cold-related mortality compared to that of heat-related mortality (Anderson and Bell

2009).

Other studies have examined the slope of the V-shape temperature-mortality curve (Armstrong 2006; Chestnut et al. 1998). However, since mortality is impacted by various nonlinear factors these linear models may not represent the actual temperature-mortality relationships (Anderson and Bell 2009). The slope of these curves varies spatially and temporally (Kassomenos 2007).

Many studies have examined the relationship between climate-health.

Atmospheric conditions including temperature and snowfall have been found to have relationship to winter mortality, and various weather indices have been created to examine the role climate and weather have on human health. In addition, other non- climate factors such as socioeconomics, health status, and community infrastructure have been associated with increased winter mortality. Using previous research as a framework, this thesis examines the seasonal and spatial variability of winter mortality by comparing the weather on specific mortality spike days with non-spike days. By incorporating various demographic categories, the research also addresses some of the uncertainties associated with winter mortality.

CHAPTER 3

DATA AND METHODOLOGY

3.1 Data

3.1.1 Study Region and Demographics

Five major metropolitan cities in the U.S. were chosen to account for a variety of winter climates (Figure 3 and Table 1). As previously discussed, the definition of cold weather varies throughout region. Residents of warmer climates do not perceive cold the same as individuals living in polar regions (Keatinge 2002). With this in mind, the winter season was defined as October – March. This accounted for both early and late winter seasonal mortality which has been noted by previous studies (e.g. Medina-

Ramón and Schwartz 2007).

It is expected that people in colder regions such as Minneapolis will respond differently than those in the southeast (Miami). The locations chosen, Minneapolis-St.

Paul, Pittsburgh, St. Louis, San Antonio, and Miami, were analyzed at the Metropolitan

Statistical Area (MSA) level, with several counties aggregated together to provide a

larger sample size for analysis. These locations also vary in population size and demographics, providing a small sampling of the general U.S. population (Table 2 and

Table 3).

Figure 3. The Metropolitan Statistical Areas for five cities used in this research.

Table 1. Each MSA was represented by a single weather station. Winter climatology data (October-March; 1971-2000) are presented for each of the locations. Weather Max T Min T Mean T Precipitation Snowfall Station (°C) (°C) (°C) (cm) (cm) Minneapolis-St. Paul KMSP 2.2 -7.2 -2.5 22.2 133.9 Pittsburgh KPIT 7.8 -1.6 3.1 41.6 99.3 St. Louis KSTL 10.2 0.2 5.2 44.1 55.6 San Antonio KSAT 21.2 8.1 14.6 34.8 2.0 Miami KMIA 26.6 18 22.3 46.5 0

Each of the five MSAs varies in population size as well as age, race, and gender

(Table 2). With large populations over the age of 65, the MSA of Pittsburgh (~17%) and

Miami (~14%) represent the oldest population percentage. Minneapolis- St. Paul is the youngest. There are three categories of race: white, black, and other. St. Louis and

Miami both represent the largest black population with around 20% each.

Table 2. The five Metropolitan Statistical Area’s demographic information: age, race, and gender categories.

AGE RACE GENDER TOTAL <65 65-74 75+ White Black Other Male Female Minneapolis 90.4% 4.9% 4.7% 86.1% 5.3% 8.6% 49.4% 50.6% 2,968,806 Pittsburgh 82.3% 8.8% 8.9% 89.5% 8.1% 2.4% 47.7% 52.3% 2,431,087 St. Louis 87.1% 6.7% 6.1% 78.3% 18.3% 3.4% 48.0% 52.0% 2,406,139 San Antonio 89.3% 5.8% 4.9% 70.6% 6.6% 22.8% 48.7% 51.3% 1,711,694 Miami 85.5% 7.2% 7.3% 70.1% 20.4% 9.5% 48.3% 51.7% 5,007,564

With 23% considered other race, San Antonio represents the largest population percentage while Pittsburgh has the smallest (2%). As with overall U.S. population statistics, females represent the majority of all five cities. Pittsburgh has the largest difference between male and female (nearly 5%) while Minneapolis-St. Paul varies by only one percent.

3.1.2 Mortality Data

The first type of data used in this research was the mortality data obtained from the National Center for Health Statistics (NCHS). These data included multiple pieces of information on every death within each MSA during the period of study. These deaths

Table 3. The five Metropolitan Statistical Areas (MSA) were chosen based upon the variety of climatic condition, population demographics, and overall size represented by various counties.

Metropolitan 2000 Specific Statistical Area Population Counties

Minneapolis- 2,968,806 Anoka, Carver, Chisago, Dakota, Hennepin, St. Paul Isanti, Ramsey, Scott, Sherburne, Washington, Wright (MN), Pierce, St. Croix (WI)

Pittsburgh 2,431,087 Allegheny, Armstrong, Beaver, Butler, Fayette, Washington, Westmoreland (PA)

2,406,139 Franklin, Jefferson, Lincoln, St. Charles, St. St. Louis Francois, St. Louis, Warren, Washington (MO), Bond, Calhoun, Clinton, Jersey, Macoupin, Madison, Monroe, St. Clair (IL)

San Antonio 1,711,694 Atascosa, Bandera, Bexar, Comal, Guadalupe, Kendall, Medina, Wilson (TX)

Miami 5,007,564 Miami-Dade, Broward, Palm Beach (FL)

were then aggregated to obtain total daily mortality counts for each MSA (Table 3); subsets of analysis included total deaths segregated by race (white, black, other), sex

(male, female), age (<65, 65-75, 75+), and primary cause of death (cardiovascular,

respiratory, other). The mortality data included the years 1975-2004, but only the winter season of October-March was examined.

3.1.3 Atmospheric Data

Each Metropolitan Statistical Area was represented by a single weather station for which atmospheric data were obtained (Table 1). Each of the stations was located at a major airport and was chosen in order to provide a reliable climate record (1975-

2004). While previous climate-health studies have used this approach, the large area of each MSA and the use of a single weather station may be a limiting factor in the results.

The Spatial Synoptic Classification (SSC; Sheridan 2002) was used to categorize the daily weather types for each study region and obtained from

(http://sheridan.geog.kent.edu/ssc/data/). Developed in the 1990’s, the SSC is a way to categorize weather types using only surface observations. Utilizing four time periods each day of 4, 10, 16, 22 Eastern Standard Time, the SSC uses a total of 24 daily surface observations to determine a weather type. Each time period records temperature, dew point, pressure, cloud cover, wind speed, and wind direction. Following initial manual classification, the SSC uses seed days to automatically classify each day of a station’s history into one of eight synoptic classifications (Table 4) based on observed atmospheric conditions. In heat wave research, these classifications have revealed certain prevalent patterns associated with high heat-related mortality events. Moist

Table 4. The 8 weather types determined by the Spatial Synoptic Classification.

Weather Synoptic Characteristics Type Dry Polar Cold and dry (DP) Dry Mild, dry conditions Moderate usually associated with (DM) zonal flow Dry Tropical Warm and dry (DT) Moist Polar Cloudy, humid, cool (MP) conditions Moist Approaching front Moderate possible with cooler (MM) conditions and more clouds than MP Moist Warmest and humid Tropical conditions (MT) Moist Extreme scenario of MT Tropical Plus weather type (MT+) Transitional Usually occurs on days (TR) with large shifts in pressure, dew point, or windy conditions.

Tropical (MT), Moist Tropical Plus (MT+), and Dry Tropical (DT) are common during mortality increases during the summer seasons. Regarding the winter season, limited usage of the SSC has been conducted.

The atmospheric variables examined included temperature, pressure, precipitation, snowfall, along with the SSC. Within each of these variables, subsets were

created in order to include a variety of definitions (Table 4). Daily temperature and pressure values were computed based upon the four times analyzed in the Spatial

Synoptic Classification. Daily total precipitation and snowfall data were obtained through the National Climatic Data Center (www.ncdc.noaa.gov). Daily precipitation and snowfall data were classified as binary (yes/no) variables as well as a secondary threshold of heavy precipitation and heavy snowfall. The SSC determined a single weather type for each of the days. All of the other variables were daily numerical values.

3.1.4 Map Comparison

A manual classification of surface synoptic weather maps was completed for a subset of the spike days as a way to qualitatively assess the frequency of patterns. This process was conducted for 2.0 SD results of Pittsburgh (December) and Miami (January) spike days. The maps were obtained through The NOAA Central Library U.S. Daily

Weather Maps Project and NCEP Operational Dataset

(http://docs.lib.noaa.gov/rescue/dwm/data_rescue_daily_weather_maps.html).

3.2 Methodology

3.2.1 Mortality Methods

Total daily mortality was separated based upon the three causes of death, age, gender, and race categories. Therefore, for each metro area, a total of four

spreadsheets were used for the analysis in Microsoft Excel ©. Using mortality data

(1975-2004), a mortality spike definition was created. This variable was used to compare the climate conditions on high mortality days (spike day) with non high- mortality days. Using the annual standard deviation (SD) of daily mortality (Equation 1), a mortality spike was defined as an increase in mortality greater than a specific standard deviation threshold (e.g. 2.0 SD) when compared to the previous subset of days. That is, mortality spikes were days in which the daily mortality was at least 2.0 standard deviations greater than the mean of the previous 14 days. Various definitions of a mortality spike were considered based upon 3, 7, and 14 day thresholds as well as variations of the standard deviation (1.0, 1.5, 2.0, 2.5). All definitions provided similar results, therefore, only the 14 day 1.5 SD and 14 day 2.0 SD results will be discussed further.

Equation 1. Standard deviation which takes into account the square root of the average value.

3.3 Data Analysis

Using the software program Microsoft Excel ©, the differences between spike and non- spike days was compared for each of the atmospheric variables across each month (e.g.

all Januaries) as well as the winter ‘season’ as a whole (i.e. October – March; 1975-

2004). The primary method in which atmospheric variables were determined to be significant was by using a two sample t-test. This statistic takes into account the sample mean, sample size, and population standard deviation.

Spike days were considered to be the sample in the study and varied based upon

MSA (Chapter 4). All days in the winter season were used to determine the population standard deviation. Using the one sample t-test, significant (p<.05) and near significant

(p<.10) thresholds were determined for each of the atmospheric variables.

A proportional t-test was used to compare the differences of precipitation, snowfall, and weather type (SSC) between spike and non-spike days (Equation 2 and

Table 6). This method was chosen to account for the changes in the likelihood of precipitation or weather types. For example, heavy precipitation may have been more likely on November mortality spike days than on November non-spike days. This statistic takes into account the percentage of days in which a particular outcome occurred (rainfall spike days) and compares it with the opposite outcome (rainfall on non-spike days). Using this proportional testing method, a p-value was determined and classified as significant (p<.05), near significant (p<.10), or not significant (p>0.10). The total percentage increase of specific variables is discussed in Chapter 4.

Table 5. Various definitions of atmospheric variables were used to account for the climate leading up to a mortality spike day. Each MSA’s weather data was based upon the single weather station for the region. Climate Definition Variable δT Difference between spike day and non-spike day average (Average temperature. Temperature) A positive value signifies temperatures are warmer on spike days.

δT1day Difference in temperature between spike day and (1 Day previous day. Temperature) Positive values signify spike days are warmer than the previous day.

δT3day Difference between mean temperature on spike day and (3 Day previous 3 days. Temperature) Positive value signifies spike day was warmer than the 3 previous days. δP Difference between average pressure on a spike say and (Average non-spike day. Pressure) A negative value signifies pressure is lower on a spike days.

δP1day Difference in pressure on spike day and the previous day. (1 Day Negative values signify pressure is lower on spike day than Pressure) the day prior. Min/Max Comparison of pressure range between spike and non- (Min. spike days: minimum pressure today and maximum Yesterday/Max pressure yesterday Today) A negative value signifies the pressure range on spike days is lower than non-spike days. Precipitation Binary comparison as to whether any precipitation occurred (x>0) Heavy Binary comparison as to whether any heavy precipitation Precipitation occurred (x>0.1”) Snowfall Binary comparison as to whether any snowfall occurred (x>0) Heavy Binary comparison as to whether and heavy snowfall Snowfall occurred (x>1.0”)

3.4 Methodology Summary

 Aggregation of county mortality data into single MSA

 Established criteria for spike day definition (1.5 and 2.0 SD)

 Creation of non-traditional atmospheric variables (δT, δP)

 Determined statistical significance of weather variables

 Compared conditions on spike days with non-spike days

 Demographic categories analyzed and described

Equation 2. Test statistic for two proportions.

Table 6. Explanation of Equation 2 variables.

Variable Definition

흅o The percentage of non-spike days resulted in an occurrence of a particular variable (precipitation, snowfall, SSC). 흅1 The percentage of spike days resulted in an occurrence of a particular variable (precipitation, snowfall, SSC). ŋo Total non-spike days over a given period ŋ1 Total spike days over a given period.

CHAPTER 4

RESULTS

4.1 Demographics

4.1.1 A Monthly Comparison of Mortality Spike Days

The number of mortality spikes varies spatially and temporally (Table 7). Overall, the 1.5 SD results show Minneapolis-St. Paul having the most spikes (402) during the winter season (1975-2004). Miami has the fewest spike days during the winter (226). In both the 1.5 SD and 2.0 SD spike days, December has the most spike days for all cities.

Besides two months, Minneapolis had the most spikes in all months. All locations also have more spike days in the first half of the winter season.

While San Antonio had the most 2.0 SD spike days (141), Minneapolis- St. Paul still had 138 total spike days. Pittsburgh was also the only city with more spike days in the second half of the 2.0 SD winter season. Again, December had the most spike days in all of the cities. While most of the cities have a decrease in the number of mortality spike days following the December maximum, San Antonio and Pittsburgh both showed a secondary maximum in March.

Table 7. Number of wintertime mortality spike days (1975-2004) using the results from 1.5 SD and 2.0 SD. 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Minneapolis-St. Paul 79 62 84 67 52 58 402 Pittsburgh 42 48 78 50 37 46 301 St. Louis 54 47 98 66 45 45 355 San Antonio 51 55 78 61 46 63 354 Miami 30 40 55 48 28 25 226 2.0 SD OCT NOV DEC JAN FEB MAR SEASON Minneapolis-St. Paul 23 20 29 25 22 19 138 Pittsburgh 13 15 25 22 14 24 113 St. Louis 13 17 25 18 16 15 104 San Antonio 17 22 35 20 18 29 141 Miami 7 7 20 8 10 9 61

4.1.2 Mortality Spikes: Causes of Death

By definition, overall mortality increases significantly during spikes; however not all causes of death change by the same level (Table 8). Respiratory deaths increased the most in the southern cities of San Antonio and Miami while cardiovascular deaths increased more in the other cities. San Antonio had the largest increases (41% for 1.5

SD; 47% for 2.0 SD) in mortality during the winter season while Miami and Pittsburgh both had the lowest (~25%; ~30%). Despite these variations, cardiovascular and respiratory deaths increased more than all other causes in all cities except Miami. By definition, the cause of death increases in 2.0 SD results was consistently higher than 1.5

SD percentages. However, these increases were also more variable due to the smaller sample size.

Table 8. Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon causation of death. MSP 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Cardio 30% 31% 35% 37% 32% 33% 33% Respiratory 16% 27% 25% 41% 44% 32% 32% Other 31% 32% 24% 25% 29% 28% 28% All 29% 31% 28% 31% 32% 30% 30% 2.0 SD Cardio 34% 39% 38% 44% 34% 40% 38% Respiratory 15% 5% 39% 41% 54% 39% 34% Other 36% 37% 34% 30% 38% 42% 36% All 34% 36% 36% 37% 38% 41% 37% PIT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Cardio 29% 30% 25% 31% 27% 29% 28% Respiratory 25% 27% 30% 24% 35% 28% 28% Other 23% 22% 25% 21% 24% 27% 24% All 26% 26% 25% 26% 27% 28% 26% 2.0 SD Cardio 34% 37% 26% 37% 38% 35% 35% Respiratory 61% 34% 49% 33% 35% 28% 39% Other 26% 21% 33% 25% 26% 29% 27% All 32% 30% 31% 31% 33% 32% 31% STL 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Cardio 34% 30% 32% 31% 32% 35% 32% Respiratory 23% 20% 39% 33% 31% 38% 31% Other 35% 25% 29% 25% 28% 34% 29% All 33% 27% 31% 28% 30% 35% 31% 2.0 SD Cardio 37% 38% 37% 40% 35% 44% 38% Respiratory 39% 35% 26% 41% 29% 61% 39% Other 35% 29% 27% 31% 31% 35% 31% All 36% 34% 32% 36% 33% 41% 35%

Table 8 (continued). Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon causation of death. SAT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Cardio 43% 36% 43% 42% 37% 47% 41% Respiratory 38% 31% 49% 64% 46% 32% 45% Other 53% 50% 35% 39% 30% 34% 40% All 48% 43% 39% 43% 35% 39% 41% 2.0 SD Cardio 48% 43% 45% 39% 42% 52% 45% Respiratory 33% 45% 71% 47% 69% 33% 51% Other 64% 61% 45% 44% 29% 44% 48% All 56% 53% 47% 42% 38% 46% 47% MIA 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Cardio 21% 22% 22% 22% 21% 23% 22% Respiratory 33% 22% 25% 15% 28% 53% 29% Other 29% 22% 24% 23% 27% 37% 27% All 26% 22% 23% 22% 24% 31% 25% 2.0 SD Cardio 24% 32% 24% 26% 28% 28% 27% Respiratory 27% 42% 26% 22% 62% 78% 44% Other 40% 24% 26% 25% 32% 40% 31% All 32% 29% 25% 25% 32% 37% 30%

4.1.3 Mortality Spikes: Age Categories

The increases in mortality during spike days also changed based on age categories (Table 9). For the northern cities, the 65-74 population showed the largest increases in mortality during 1.5 SD spike days. These cities also showed the <65 population with the second largest increase. Miami was the only city in which 1.5 SD mortality increases were highest within the 75+ population while San Antonio 1.5 SD results showed the largest increases in the <65 population. Miami also had the lowest percentage (~23% for 1.5 SD; ~27% for 2.0 SD) increases of all of the examined cities while San Antonio increases were consistently the largest (41% for 1.5 SD; ~45% for 2.0

SD). The transitional months, especially in southern cities, showed the largest mortality increases in the 75+ population. Despite the smaller sample size, the percentage increases were generally larger in 2.0 SD results.

Table 9. Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon three age categories.

MSP 1.5 SD OCT NOV DEC JAN FEB MAR SEASON <65 32% 30% 31% 27% 28% 29% 29% 65-74 31% 36% 26% 41% 35% 38% 34% 75+ 28% 30% 28% 30% 32% 29% 29% 2.0 SD <65 45% 28% 32% 38% 30% 32% 34% 65-74 47% 52% 25% 45% 41% 52% 44% 75+ 23% 34% 41% 34% 40% 41% 36% PIT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON <65 29% 19% 27% 20% 35% 37% 28% 65-74 27% 30% 24% 34% 23% 30% 28% 75+ 24% 28% 25% 25% 24% 24% 25% 2.0 SD <65 43% 20% 24% 25% 45% 49% 34% 65-74 29% 38% 28% 41% 39% 29% 34% 75+ 29% 31% 36% 30% 24% 25% 29% STL 1.5 SD OCT NOV DEC JAN FEB MAR SEASON <65 36% 28% 31% 26% 27% 39% 31% 65-74 38% 34% 33% 34% 39% 28% 34% 75+ 31% 24% 30% 27% 28% 36% 29% 2.0 SD <65 40% 27% 26% 39% 36% 33% 33% 65-74 47% 46% 45% 46% 33% 43% 43% 75+ 31% 32% 29% 31% 31% 44% 33% SAT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON <65 46% 47% 43% 47% 32% 40% 43% 65-74 49% 43% 41% 40% 37% 31% 40% 75+ 49% 40% 35% 42% 35% 43% 40% 2.0 SD <65 58% 64% 52% 55% 33% 54% 52% 65-74 41% 46% 52% 37% 36% 29% 40% 75+ 61% 47% 42% 36% 43% 49% 46%

Table 9 (continued). Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon three age categories.

MIA 1.5 SD OCT NOV DEC JAN FEB MAR SEASON <65 28% 21% 25% 15% 23% 30% 24% 65-74 24% 20% 18% 10% 15% 18% 17% 75+ 25% 23% 25% 29% 28% 37% 28% 2.0 SD <65 36% 24% 30% 21% 30% 25% 28% 65-74 33% 20% 22% 9% 26% 24% 22% 75+ 30% 34% 24% 33% 35% 48% 34%

4.1.4 Mortality Spikes: Race Categories

The mortality response during spike days was also variable based upon racial categories (Table 10). Some of the largest increases in 1.5 SD white mortality occur during the transitional months. Pittsburgh has the largest increase in the March (32%) as opposed to San Antonio where mortality increases the most among whites during the month of October (48%). With a large black population in St. Louis, the largest increases in mortality occurred during the month of February (37% for 1.5 SD; 52% for 2.0 SD).

Similarly, the other population in San Antonio had the largest mortality increases during

October. With black and other categories not equally represented compared to the white population, the small sample size is a factor to consider in the results.

Table 10. Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon three race categories.

MSP 1.5 SD OCT NOV DEC JAN FEB MAR SEASON White 29% 31% 28% 32% 31% 32% 31% Black 31% 34% 31% 26% 40% -9% 26% Other 35% 58% 44% 15% 31% -7% 29% 2.0 SD White 34% 36% 35% 37% 38% 43% 37% Black 52% 4% 56% 41% 37% -21% 29% Other 0% 59% 77% 26% 26% 5% 32% PIT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON White 26% 26% 25% 26% 27% 28% 26% Black 19% 28% 30% 29% 22% 27% 26% Other 66% 69% -15% 19% 19% 68% 37% 2.0 SD White 33% 30% 31% 31% 33% 33% 32% Black 30% 32% 35% 36% 26% 19% 30% Other 31% 31% 1% 129% -100% 114% 32% STL 1.5 SD OCT NOV DEC JAN FEB MAR SEASON White 34% 27% 31% 28% 29% 35% 31% Black 31% 30% 35% 31% 37% 32% 33% Other 38% 23% 31% 85% 166% 57% 65% 2.0 SD White 37% 33% 33% 36% 30% 41% 35% Black 28% 35% 18% 32% 53% 46% 35% Other -38% 27% 0% 101% 152% 135% 60% SAT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON White 48% 43% 39% 44% 35% 39% 41% Black 42% 48% 48% 20% 32% 47% 39% Other 102% 10% -13% 49% 44% 21% 35% 2.0 SD White 57% 52% 46% 42% 37% 46% 46% Black 30% 64% 65% 28% 51% 55% 49% Other 221% -32% -36% 230% 62% 4% 77%

Table 10 (continued). Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon three race categories.

MIA 1.5 SD OCT NOV DEC JAN FEB MAR SEASON White 24% 23% 23% 21% 24% 29% 24% Black 38% 15% 26% 26% 27% 51% 31% Other 18% -32% 21% 28% 39% 40% 19% 2.0 SD White 26% 32% 23% 24% 33% 35% 29% Black 75% 6% 38% 32% 25% 61% 39% Other 37% 75% 29% 32% 80% -30% 37%

4.1.5 Mortality Spikes: Gender Comparison

The increases in mortality were similar between male and female in both 1.5 SD and 2.0 SD results (Table 11). Three cities showed higher 1.5 SD mortality increases in the male population (Minneapolis-St. Paul, Pittsburgh, San Antonio). The mid-winter months had the largest mortality increases. However, in locations where the female population was higher, the transitional months had the largest increases.

Table 11. Percentage of spike day mortality compared to a non-spike day. Mortality is separated based upon male and female genders.

MSP 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Male 31% 30% 30% 34% 30% 28% 31% Female 28% 33% 27% 28% 33% 32% 30% 2.0 SD Male 37% 34% 36% 43% 33% 37% 37% Female 30% 37% 36% 31% 42% 44% 37% PIT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Male 28% 25% 24% 28% 28% 30% 27% Female 24% 27% 27% 24% 25% 26% 26% 2.0 SD Male 41% 31% 28% 33% 37% 32% 33% Female 23% 29% 35% 30% 28% 32% 29% STL 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Male 31% 23% 31% 27% 29% 34% 29% Female 35% 31% 31% 30% 31% 36% 32% 2.0 SD Male 34% 27% 36% 44% 31% 40% 36% Female 38% 40% 28% 28% 34% 42% 35% SAT 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Male 44% 42% 42% 45% 39% 40% 42% Female 52% 44% 36% 41% 30% 39% 40% 2.0 SD Male 47% 55% 51% 42% 39% 43% 46% Female 66% 50% 43% 43% 38% 50% 48% MIA 1.5 SD OCT NOV DEC JAN FEB MAR SEASON Male 25% 21% 22% 22% 20% 28% 23% Female 26% 23% 25% 22% 29% 36% 27% 2.0 SD Male 36% 29% 25% 33% 28% 30% 30% Female 28% 28% 26% 16% 37% 46% 30%

4.2 Temperature (δT, δT1day , δT3day)

For the five cities, the atmospheric variables of temperature, pressure, precipitation, snowfall, and Spatial Synoptic Classification were analyzed. Within each of these variables, various metrics were created. The two levels of statistical significance, hereafter significant (p<0.05) and near significant (p<0.1), will be discussed within the context of both high mortality (spike day) definitions, the lower (1.5 standard deviations, hereafter 1.5 SD) and the higher (2.0 SD) thresholds.

4.2.1 Minneapolis- St. Paul

In both 1.5 and 2.0 SD thresholds, the majority of temperature variables that were significant or near significant were positive indicating warmer temperatures on spike days than non-spike days (Table 12). January mean temperature (δT) was the largest statistically significant value in both of the spike definitions, with spike days

2.29°c and 3.45°c warmer than non-spike days, respectively. In 1.5 SD, no temperature parameters were significant between October and December, but the latter portion of winter season displayed four significant or near significant results. These mean temperatures were at least 0.7°C warmer on high mortality days.

Similarly, November’s spike-day temperature difference from the previous 3 days (δT3day) was the only significant value within the first half of 2.0 SD winter with a value of 1.54°C. The later winter months of January (δT) and March (δT3day) were also

significant with temperatures nearly 2.0°C warmer. The 2.0 SD temperatures were often warmer than those of 1.5 SD.

All February temperature values were negative, suggesting a mortality spike occurs on days in which temperatures are lower than preceding days. However, only the spike-day temperature difference from the previous day (δT1day) value was significant, the only instance that a significant negative result occurred in Minneapolis-

St. Paul.

Table 12. Temperature values for the winter season in Minneapolis- St. Paul. Bolded red (p<.05) are significant and bolded grey (p<.1) are near significant.

Minneapolis-St. Paul 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δT 0.65 0.18 -0.55 2.29 -0.40 -0.24 0.52

δT1day 0.31 0.36 -0.21 0.50 0.44 0.72 0.32

δT3day 0.45 0.64 0.18 0.84 1.08 0.88 0.63 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δT 0.63 0.93 0.73 3.45 -0.93 0.80 0.52

δT1day 0.58 0.99 0.36 0.77 -1.95 0.40 0.20

δT3day 0.34 1.54 0.60 1.05 -0.07 1.84 0.84

Over the entire winter season, temperatures on 1.5 SD spike days range from 0.4 to 2.2°C cooler all 5 days prior to a spike day (Figure 4). Individually, only during the month of December are conditions preceding the mortality spike warmer than the day of the spike itself. Although the mean response in temperatures diverge following Day

0, the majority of months display a decreasing value with January (-2.9°C) and February

(-1.3°C) suggesting the largest temperature fall. During the transitional month of March, temperatures actually begin to rise immediately and peak at 0.9°C.

The 2.0 SD temperatures are consistent with the previously discussed results

(Figure 5). Excluding February, on average all 5 days before a spike day were cooler.

Temperatures peaked at Day 0 (spike day) and decreased afterwards. While November and January temperatures fell by -2°C after the spike day, the months of October and

December were nearly constant.

2 MSP C) ° 1 1.5 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 4. Minneapolis-St. Paul mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is graph for the 1.5 SD results.

2 MSP C) ° 1 2.0 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 5. Minneapolis-St. Paul mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.2.2 Pittsburgh

At 1.5 SD, all at or near statistically significant temperature values were positive

(Table 13). All of these values were at least 0.6°C. For February and March, δT1day indicated temperatures were 1°C warmer on a high mortality day than the day prior.

This was the only two consecutive month period with statistically significant values. The largest significant value in 1.5 SD and 2.0 SD, March’s δT3day, showed temperatures of

2.83°C and 2.23°C respectively.

October δT1day and δT3day were positive and significant values in the 2.0 SD,

1.26:C and 1.89:C, respectively. These same two temperature parameters were also

positive and significant in the month of December, but the values were slightly higher,

2.06:C and 2.26:C, respectively. October, December, and March temperatures were consistently significant in both 1.5 SD and 2.0 SD whereas November and January were not.

Table 13. Temperature values for the winter season in Pittsburgh. Bolded red (p<.05) are significant and bolded grey (p<.1) are near significant.

Pittsburgh 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δT 0.62 0.12 -0.07 1.27 1.00 0.82 0.12

δT1day 0.25 0.30 0.87 0.39 1.00 0.91 0.64

δT3day 0.74 0.91 1.08 0.63 0.98 2.83 1.18 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δT 1.74 -1.59 0.24 -1.63 2.24 0.57 -0.56

δT1day 1.26 1.17 2.06 -0.76 -0.24 0.65 0.72

δT3day 1.89 1.29 2.26 -1.02 0.31 2.23 1.20

In all months, temperatures on the days preceding a high mortality day were cooler (Figure 6). The month of March had the largest rise in temperatures with values increasing by 3°C by the spike day. Over all months, temperatures were 1°C cooler on

Day -5 and fell to a minimum of -1.8°C by Day -2. While monthly averages diverge following a spike day, the seasonal trend still displays consistently decreasing temperatures.

2 PIT C) ° 1 1.5 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 6. Pittsburgh mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

Though not as consistent from month-to-month, 2.0 SD temperatures still generally increase towards a spike day, although in January and February temperatures actually decrease slightly (Figure 7). The transitional months of October (-2.2°) and

March (-3.1°) had the largest increases in temperature prior to a spike in mortality.

While November, December, and January exhibited warming temperatures following a spike day, the seasonal curve still shows the spike day as the warmest day of the 8 day time series.

2 PIT C) ° 1 2.0 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TempeatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 7. Pittsburgh mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 2.0 results.

4.2.3 St. Louis

The St. Louis 1.5 SD resulted in 8 significant positive values (Table 14).

Temperature differences from spike to non-spike day were as high as +2 to +3°C in

November and March; generally, the largest increase and greatest statistical significance are found during the transitional months of the study. Neither January nor February had any significant values.

As with other cities, the 2.0 SD temperatures were consistently warmer than 1.5

SD results. There were a total of 8 significant and 4 near significant results across the season. All variables in October and November were significant with values exceeding

2.0°C. The month of December was inconsistent with positive and negative results

while no January variables were significant. As with 1.5 SD, November’s δT and March’s

δT3day were the two largest, 2.95° and 3.43° respectively.

Table 14. Temperature values for the winter season in St. Louis. Bolded red (p<.05) are significant and bolded grey (p<.1) are near significant.

St. Louis 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δT 1.53 3.01 -0.29 -0.75 -0.99 1.64 -0.29

δT1day 0.70 1.46 0.35 0.37 0.86 2.00 0.83

δT3day 0.62 2.06 1.28 0.37 -0.39 2.48 1.05 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δT 2.65 2.95 -2.22 -2.08 3.12 0.61 -0.28

δT1day 2.19 1.89 -0.13 -0.23 1.78 2.33 1.12

δT3day 2.79 2.93 1.54 -0.48 1.19 3.43 1.79

Besides the month of February, all other months exhibited warming temperatures towards a high mortality day (Figure 8). November temperatures were lower both before and after a mortality spike day (-4.0°C and -2.8°C). While all months, excluding February again, resulted in decreasing temperatures after a spike day,

March’s rebounded by Day 3 to 1.5°C above the spike-day value.

2 STL C) ° 1 1.5 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 8. St. Louis mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

As with other cities, the 2.0 SD monthly time series displayed more variation

(Figure 9). However, the winter seasonal curve still displayed a warming trend in temperatures towards the spike day. Temperatures after a spike day cooled slightly, but began to rebound by Day 2. The transitional months of October and November had the largest increases in temperature (5.5°C) from Day -5.

2 STL C) ° 1 2.0 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 9. St. Louis mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.2.4 San Antonio

The San Antonio results show less statistical significance than previously discussed locations with 9 of 14 at or near statistically significant values being positive

(Table 15). There were two significant values and 5 near significant 1.5 SD San Antonio results. November and March exhibited similar results with both the δT1day and δT3day variables being significant or near significance. The δT1day in November was the largest value suggesting temperatures were 0.9°C warmer on a spike day than the day previous.

Only the 2.0 SD temperature of January’s δT1day was significant, and it was negative.

There was less agreement between the three temperature metrics within the 2.0SD mortality spikes.

Table 15. Temperature values for the winter season in San Antonio. Bolded red (p<.05) are significant and bolded grey (p<.1) are near significant.

San Antonio 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δT 0.21 0.48 -0.89 0.23 -1.11 -0.11 -0.48

δT1day -0.21 0.90 0.79 0.22 0.69 0.63 0.52

δT3day 0.17 0.88 0.76 0.83 0.57 0.71 0.67 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δT -0.23 0.19 -0.91 -0.04 -1.07 -1.12 -0.91

δT1day 0.02 0.12 0.44 -1.22 0.31 0.56 0.11

δT3day 0.28 -0.05 0.13 -0.04 0.97 0.19 0.22

Average temperature before a spike day was consistently lower though the range is much smaller than with the northern cities (Figure 10). Temperatures following a mortality day increased in all cases by Day 2. The seasonal trend of 2.0 SD temperatures displays warming temperatures towards the high mortality day (Figure

11). The month of January was the most variable with cooling temperatures on the day prior to a spike. This is consistent with Table 4 which showed all of January’s temperature variables as being negative.

2 SAT C) ° 1 1.5 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 10. San Antonio mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

2 SAT C) ° 1 2.0 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 11. San Antonio mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.2.5 Miami

Miami 1.5 SD temperatures show 10 significant results (Table 16). All of the results were positive indicating temperatures being warmest on a spike day. During the entire winter season, δT3day was significant with values peaking in December-February suggesting a seasonal pattern. Similarly, all of the δT1day values were significant except

November. December and February were once again the highest values (1.18°C and

1.38°C).

As with previous cities, the results of 2.0 SD were warmer than the results of 1.5

SD. December and February were at least near significant for all three temperature variables with February’s δT3day being the largest (4.21°C). Similar to the 1.5 SD results, the δT3day was significant or near significant from December through March. Three of these values were at least 2°C suggesting temperatures 3 days prior to a high mortality event are important to consider.

Table 16. Temperature values for the winter season in Miami. Bolded red (p<.05) are significant and bolded grey (p<.1) are near significant.

Miami 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δT 1.00 0.20 0.38 0.09 0.45 0.61 0.10

δT1day 0.22 0.22 1.18 0.68 1.38 0.46 0.72

δT3day 0.38 0.57 2.04 1.02 1.52 1.09 1.17 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δT 0.65 0.57 0.98 0.69 1.61 -0.02 0.43

δT1day 0.05 -0.48 1.34 0.99 2.92 0.20 1.03

δT3day 0.07 -0.30 2.61 1.97 4.21 1.30 1.97

The temperatures in 1.5 SD all increased towards a high mortality spike day

(Figure 12). The middle months of December, January, and February showed the largest rise in temperature. While temperatures initially remained stable on Day 1, they began to decrease by Day 2 as shown by the season curve.

2 MIA C) ° 1 1.5 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 12. Miami mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

While the seasonal curve of temperature with 2.0 SD indicates temperatures rising towards a spike day, there is considerable variability from month to month (Figure

13). Both December and February have temperatures rising more than 4.5°C from Day -

3 to the spike day. Many of the other months indicate cooling or stable temperatures 1 day prior to a spike. The two transition months of October and March show temperatures rising immediately after a spike day whereas the other months show decreasing temperatures.

2 MIA C) ° 1 2.0 SD 0 -1 Oct -2 Nov -3 Dec -4 Jan -5

TemperatureDeparture ( Feb -6 -7 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 13. Miami mean temperature on mortality spike days (Day 0) compared with the previous 5 days before and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.3 Pressure (δP, δP1day , Min/Max)

While the temperature results show that spike days are generally warmer than non-spike days, the pressure results clearly display that mortality spike days are also associated with atmospheric pressure values lower than previous days.

4.3.1 Minneapolis-St. Paul

All 1.5 SD pressure variables at or near statistical significance were negative

(Table 17). The average pressure decrease was largest in January (-3.5mb). Many of these significant and near significant variables had a value exceeding a 2mb decrease with the seasonal values decreasing by at least 1.5mb. The transition months of

October, November and March consistently also resulted in larger pressure falls on spike days. Overall, the entire winter season was statistically significant.

All values of 2.0 SD, at or near significant, were negative which was consistent with the 1.5 SD results. However, unlike the 1.5 SD results, the winter months of

November-January had higher statistical significance- with all variables significant or near significant except December’s Min/Max (magnitude of the pressure fall from the highest pressure on the day before the spike to the lowest pressure on the day of the spike). As with the temperature results, 2.0 SD results were larger values. The largest decrease, January’s δP (pressure on a mortality spike day compared to a non-spike day),

was -7 mb while November pressure values all exceeded -5.5 mb. Overall, the entire winter season was statistically significant.

Table 17. Pressure values for the winter season in Minneapolis-St. Paul. Bolded blue (p<.05) are significant and bolded grey (p<.1) are near significant.

Minneapolis-St. Paul 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δP -1.8 -2.9 -1.1 -3.5 -2.1 -1.8 -1.4

δP1day -2.2 -2.6 -0.4 -1.9 -2.0 -2.0 -1.3 Min/Max -3.0 -3.0 -0.7 -2.4 -1.6 -2.9 -1.6 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δP -1.6 -5.7 -2.9 -7.0 -2.3 -2.2 -2.1

δP1day -2.5 -6.5 -1.8 -3.1 -0.7 -1.6 -2.0 Min/Max -2.6 -6.6 -1.8 -3.2 -0.2 -2.6 -2.2

While the range is larger in October and November than December, all monthly

1.5 SD pressure values decrease towards the high mortality day (Figure 14). Pressure values immediately increase on Day 1 in all months excluding December and November, but both of these months increase by Day 3. The middle winter months of January and

February respectively increase by 3.9 mb and 3.3 mb by Day 2 while the other months take longer to reach this level.

MSP 6 1.5 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 14. Minneapolis-St. Paul monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

Shaped more like a V, 2.0 SD monthly pressure graph shows pressure falling sharply towards a spike day (Figure 15). The month of February displays pressure gradually changing between Day -1 and Day 0 whereas the months of October-

December more sharply decrease between 3-6 mb leading up to a mortality spike day.

These months also show a larger range in pressure values- quickly increasing pressure values by Day 2.

MSP 6 2.0 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 15. Minneapolis-St. Paul monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.3.2 Pittsburgh

Although the range was not as large as other cities, all of the 1.5 SD pressure variables that were at or near statistically significant were all negative (Table 18). Four of these values were significant with the majority occurring in February and March.

February results were consistently greater than a 2.0 mb decrease in pressure.

All at or near significant 2.0 SD results were negative, but there was less consistency. The month of December had a single significant and near significant variable (δP1day and Min/Max). Both February (δP) and March (Min/Max) also had a negative, near significant variable.

Table 18. Pressure values for the winter season in Pittsburgh. Bolded blue (p<.05) are significant and bolded grey (p<.1) are near significant.

Pittsburgh 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δP 0.5 -1.1 -1.3 0.0 -2.2 -1.7 -0.2

δP1day -0.7 -1.6 -1.2 -0.9 -2.9 -1.2 -1.3 Min/Max -0.7 -1.3 -1.2 -1.1 -2.6 -2.0 -1.7 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δP -0.9 -1.5 -0.1 2.1 -3.6 0.4 0.2

δP1day -1.9 -1.6 -3.0 1.0 -1.9 -0.4 -1.1 Min/Max -2.1 -2.1 -2.5 0.5 -2.5 -2.3 -2.1

The 1.5 SD monthly pressure graph shows higher pressure values on the days leading up to a high mortality day (Figure 16). The largest decrease occurs in March (-

4.1mb) while the smallest occurs in January (-0.9mb). The seasonal curve shows a decrease upwards of 2 days before a spike day.

PIT 6 1.5 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 16. Pittsburgh monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

The 2.0 SD pressure graph is similar to the previous 1.5 SD results (Figure 17). In all months except January, pressure is higher on the days prior to a high mortality event.

December, October, and, to a lesser extent, March are all 3mb higher on Day -2 than on a spike day. While most months exhibit minimal change on Day 1, the transitional months are more dramatic. For example, March’s pressure rises immediately following a spike day to a value of 3.7mb while October and November change ±1mb.

PIT 6 2.0 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 17. Pittsburgh monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.3.3 St. Louis

The majority of 1.5 SD pressure variables were statistically significant and negative (Table 19). The Min/Max variable was significant or near significant in all months except February. Most of these months show nearly a 2.0 mb decrease in pressure. All pressure variables were significant in November with values exceeding a -

2.0 mb change. Overall, all pressure variables were at least near significant for the winter season.

The 2.0 SD pressure values were larger than the 1.5 SD counterparts. The δP1day and Min/Max for October and November were both statistically significant with

negative values approaching -3.5 mb. Similarly, these variables were near significant for

January and February with negative values between 2 and 3mb. For the entire winter season, δP1day and Min/Max were both statistically significant.

Table 19. Pressure values for the winter season in St. Louis. Bolded blue (p<.05) are significant and bolded grey (p<.1) are near significant.

St. Louis 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δP -0.8 -2.0 0.1 -0.4 0.8 0.0 0.4

δP1day -1.7 -2.7 -0.6 -1.7 -1.5 -2.2 -1.1 Min/Max -1.9 -2.4 -1.1 -2.4 -1.7 -1.8 -1.5 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δP -1.8 -1.7 1.2 -1.5 -2.4 1.1 0.1

δP1day -3.4 -4.0 1.0 -2.1 -3.0 -2.7 -1.2 Min/Max -4.2 -3.3 0.5 -2.5 -3.3 -1.4 -1.6

Although the range of pressure values is less than 3mb, all 1.5 SD months exhibit decreasing pressure towards a high mortality day (Figure 18). December and January were both less than 1.5mb while November and March were closer to 3mb. While the seasonal trend suggests pressure continuing to fall after Day 0, the months of

November-January all continued to increase at different rates through Day 2. In

February and March, pressure continued to fall after the spike day, however.

STL 6 1.5 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 19. St. Louis monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

As with previous locations, 2.0 SD pressure values were more variable especially prior to Day -3 (Figure 19). All months excluding December had pressure values falling towards a spike day. All of the values in the transition months were greater than 3mb.

The fall in pressure continued through Day 1 in November and March while during

October and January pressure increased.

STL 6 2.0 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 19. St. Louis monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.3.4 San Antonio

While the pressure range was not as large as other cities, all of at or near significant 1.5 SD pressure values were negative (Table 20). All six near significant values showed pressure decreases of at least 1.0 mb. The δP1day and Min/Max metrics were both near significant in November and February. For the season, these two values were significant. While there were 8 significant or near significant values in 1.5 SD, only the February Min/Max variable was significant in 2.0 SD results.

Table 20. Pressure values for the winter season in San Antonio. Bolded blue (p<.05) are significant and bolded grey (p<.1) are near significant.

San Antonio 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δP -1.0 -0.4 0.5 0.3 0.3 -0.9 0.3

δP1day 0.0 -1.0 -0.8 -0.2 -1.1 -0.5 -0.4 Min/Max -0.2 -1.1 -1.0 0.0 -1.4 -0.4 -0.5 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δP -0.5 0.6 0.3 -0.8 -1.0 -0.1 0.4

δP1day 0.6 0.5 0.1 0.9 -1.6 -0.4 0.0 Min/Max 0.3 0.0 0.0 0.5 -2.5 0.2 -0.2

Despite the San Antonio 1.5 SD pressure values not varying as much as other cities, all months exhibited a decrease towards the spike day (Figure 20). January was the largest decrease in pressure, 1.5 mb, from Day -1 to Day 0. In all cases, except

January and October, pressure continued to fall beyond Day 0. October was the only month in which pressure stayed above the Day 0 threshold for all three days after a spike day.

SAT 6 1.5 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 20. San Antonio monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

The 2.0 SD pressure results showed considerable variability between months

(Figure 21). Unlike other results, pressure actually increased in all cases except

February. This is consistent with Table 9 which showed February as the only month with falling pressure values. March’s pressure values continued to descend to a minimum value of -3 mb by Day 2.

SAT 6 2.0 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 21. San Antonio monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.3.5 Miami

While there were 5 significant variables in the 1.5 SD results, only three of these were in the negative direction (Table 21). February was the only month in which all variables were consistently negative. The transitional months of November and March were the only months in which values at or near significant were positive. All of the other months showed negative significance indicating pressure was lower on spike days.

The 2.0 SD pressure variables were also inconsistent. All three of the pressure variables in the month of November were statistically significant but positive. All six of the at or near significant pressure variables were negative in December and February.

The month of February exhibited statistical significance with values all exceeding -

2.5mb.

Table 21. Pressure values for the winter season in Miami. Bolded blue (p<.05) are significant and bolded grey (p<.1) are near significant.

Miami 1.5 SD OCT NOV DEC JAN FEB MAR SEASON δP 0.0 0.9 0.2 0.1 -1.3 1.5 0.4

δP1day 0.1 0.3 -0.7 -0.4 -1.0 0.1 -0.2 Min/Max 0.0 0.4 -0.7 -0.2 -0.6 0.3 -0.3 2.0 SD OCT NOV DEC JAN FEB MAR SEASON δP 0.2 1.0 0.3 -0.1 -3.7 0.9 0.1

δP1day -0.1 1.6 -0.9 -0.9 -2.6 0.4 -0.5 Min/Max 0.0 1.8 -1.0 -1.2 -2.6 0.7 -0.6

Only during February and December did pressure fall towards a spike day (Figure

22). The pressure values of 1.5 SD rose in the transitional months of March and

November. Overall, however, the pressure remained constant until a high mortality day where it decreased slightly.

In 2.0 SD pressure results, a clear delineation between transitional and middle- of-winter months is displayed (Figure 23). Only during December-February did pressure fall towards a spike day. The largest decrease was in February with a pressure change of

-4.0mb. During the three other months, pressure actually rose towards a spike day.

MIA 6 1.5 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 22. Miami monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 1.5 SD results.

MIA 6 2.0 SD

3 Oct Nov 0 Dec Jan -3 PressureDeparture(mb) Feb

-6 Mar -5 -4 -3 -2 -1 0 1 2 3 Season Day

Figure 23. Miami monthly pressure values on a mortality spike day (0) compared to the previous 5 and 3 days after the mortality spike. This is the graph for the 2.0 SD results.

4.4 Precipitation and Snowfall

Binary comparisons were conducted to compare the likelihood of four precipitation categories on spike- and non-spike-days. Whether there was any measureable precipitation during the day of a mortality spike was the first category. A higher classification of precipitation only examined the likelihood of heavy precipitation, defined as a liquid equivalent of at least 0.10” over the course of the day. To consider the impact of snow, two thresholds were also used; first, whether any measureable snowfall occurred, and second, whether snow of at least 1.0” occurred (herein called

“heavy”)

4.4.1 Minneapolis-St. Paul

In all 1.5 SD winter months with at or near significant thresholds, the likelihood of precipitation and heavy precipitation was higher during a mortality spike day (Table

22). All months excluding October and December were at least near significant with the largest values in November and March, each associated with a 15 percentage point increase in precipitation likelihood on a high mortality day. A similar result was observed when discussing snowfall. While heavy precipitation was similar, snowfall may be a better proxy during the middle winter months. Besides December and January, all months were at least statistically significant when examining the snowfall variable suggesting more snowfall on mortality spike days. Similarly, heavy snowfall was also

significant in every month except December. Overall, the entire winter season was statistically significant for all of the precipitation and snowfall variables pointing towards a higher likelihood during high mortality days.

Table 22. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant. Minneapolis-St. Paul values for the 1.5 SD are shown.

Minneapolis-St. Paul 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 27% 29% 29% 31% 26% 31% 29% Precipitation 30% 44% 32% 40% 38% 50% 38% Heavy 13% 13% 8% 9% 7% 14% 11% Precipitation 19% 24% 8% 18% 23% 29% 19% 2% 17% 22% 26% 19% 16% 17% Snowfall 0% 26% 25% 33% 31% 28% 23% 0% 7% 8% 10% 7% 7% 6% Heavy Snowfall 0% 19% 7% 19% 19% 21% 13%

The 2.0 SD precipitation and snowfall results were similar to those of 1.5 SD, broadly suggesting that precipitation and snowfall are more likely on spike days (Table 23). High mortality days more often had precipitation between December and March. Most of these values were at least 20 percentage points more likely and statistically significant.

Only the months of November and January were near significant for heavy precipitation, but the winter season as a whole was still significant. Snowfall in November, January,

and March all exhibited statistical significance, with snow being at least 20 percentage points more likely a high mortality event.

Table 23. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant Minneapolis-St. Paul values for the 2.0 SD are shown.

Minneapolis-St. Paul 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 27% 30% 29% 31% 26% 31% 29% Precipitation 26% 40% 45% 56% 41% 68% 46% Heavy 13% 13% 8% 9% 8% 15% 11% Precipitation 17% 30% 10% 40% 14% 21% 22% 2% 17% 22% 26% 20% 16% 17% Snowfall 0% 35% 31% 44% 32% 42% 30% 0% 7% 8% 10% 7% 7% 7% Heavy Snowfall 0% 30% 10% 32% 14% 21% 17%

4.4.2 Pittsburgh

As a whole, 1.5 SD precipitation was more likely on a mortality spiked day than not (Table 24). While only the month of January displayed near statistical significance for heavy precipitation, the winter season still was significant. With a nearly 10 percentage point greater likelihood of heavy precipitation, snowfall, or heavy snowfall on a mortality spike day, January was the only consistent month. While February shows significance regarding snowfall and heavy snowfall, it was less likely to have these precipitation outcomes on a spike day. Although the range is smaller than Minneapolis-

St. Paul, the seasonal values are still significant suggesting a greater likelihood of precipitation and snowfall on high mortality days.

Table 24. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant. Pittsburgh values for the 1.5 SD are shown.

Pittsburgh 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 35% 43% 47% 53% 47% 47% 45% Precipitation 26% 46% 54% 62% 43% 54% 49% Heavy 19% 21% 21% 21% 19% 24% 21% Precipitation 21% 27% 21% 32% 27% 28% 26% 1% 11% 24% 37% 26% 19% 19% Snowfall 2% 13% 27% 48% 14% 17% 22% 0% 2% 6% 12% 8% 6% 6% Heavy Snowfall 0% 2% 8% 20% 3% 7% 7%

The precipitation results for 2.0 SD exhibited a dual peak (Table 25). November and February both were near significant with a value suggesting 25 percentage points greater likelihood of precipitation on a high mortality day. Overall, the entire 6-month time period was also significant. Only the month of February was significant for heavy precipitation. With the smaller sample size of high mortality events, 2.0 SD snowfall and heavy snowfall variables were not as consistent or statistically significant as other results.

Table 25. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant. Pittsburgh values for the 2.0 SD are shown.

Pittsburgh 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 35% 43% 48% 53% 46% 47% 45% Precipitation 31% 67% 56% 59% 64% 46% 54% Heavy 19% 21% 20% 22% 19% 25% 21% Precipitation 31% 33% 24% 23% 43% 17% 27% 1% 11% 24% 37% 26% 19% 19% Snowfall 0% 13% 24% 50% 14% 21% 23% 0% 2% 7% 12% 7% 6% 6% Heavy Snowfall 0% 0% 4% 27% 0% 4% 7%

4.4.3 St. Louis

While the month of February was near significant for precipitation, November and February both exhibited significance for 1.5 SD heavy precipitation (Table 26). Both values were greater than 10 percentage points more likely on a high mortality day.

While these months were important in the precipitation variables, the middle winter months of December and January showed more significance with snowfall variables.

While a smaller range than Minneapolis-St. Paul, the statistical significance still suggests a stronger possibility of snowfall or heavy snowfall on a high mortality day especially in the middle winter months.

Table 26. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant. St. Louis values for the 1.5 SD are shown.

St. Louis 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 29% 33% 28% 31% 28% 36% 31% Precipitation 22% 40% 32% 35% 40% 31% 33% Heavy 17% 20% 15% 13% 15% 21% 17% Precipitation 19% 34% 14% 15% 29% 16% 20% 0% 3% 9% 16% 10% 7% 7% Snowfall 0% 2% 16% 21% 24% 9% 13% 0% 1% 4% 5% 3% 2% 3% Heavy Snowfall 0% 2% 7% 12% 16% 7% 7%

A 2.0 SD comparison of high mortality day precipitation variables was not as robust as the 1.5 SD results (Table 27). The months of November and February were again significant or near significant for heavy precipitation. However, neither precipitation variable was significant for the winter season as a whole. While only

January was near significant in the snowfall variable, the season was statistically significant more likely on a high mortality days by 7 percentage points.

Table 27. A comparison between spike (bottom) and non-spike (top) days in which precipitation, heavy precipitation (x>1"), snowfall, or heavy snowfall (x>0.1") occurred. Bold colored boxes are significant and grey colored boxes are near significant. St. Louis values for the 2.0 SD are shown.

St. Louis 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 29% 33% 28% 31% 28% 36% 31% Precipitation 15% 41% 24% 44% 44% 33% 34% Heavy 18% 20% 15% 14% 16% 21% 17% Precipitation 15% 35% 8% 11% 38% 13% 19% 0% 3% 10% 16% 11% 7% 8% Snowfall 0% 6% 16% 33% 19% 13% 15% 0% 1% 5% 5% 4% 2% 3% Heavy Snowfall 0% 6% 0% 17% 13% 7% 7%

4.4.4 San Antonio

For 1.5 SD precipitation results, there was no greater likelihood on high mortality days except January (Table 28). While this value was 10 percentage points, many of the others months suggested less likelihood of precipitation or heavy precipitation on spike days. In contrast to other cities, with 2.0 SD precipitation results there was more consistency (Table 29). The months of January-March all showed the precipitation more likely on high mortality days. January and February were both at least near significant with a difference exceeding 20 percentage points. The same occurred for heavy precipitation with January and March both showing near significance.

Table 28. A comparison between spike (bottom) and non-spike (top) days in which precipitation or heavy precipitation (x>1"), occurred. Bold colored boxes are significant and grey colored boxes are near significant. San Antonio values for the 1.5 SD are shown.

San Antonio 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 23% 25% 24% 23% 26% 27% 23% Precipitation 22% 20% 21% 33% 26% 32% 23% Heavy 14% 12% 10% 12% 12% 13% 12% Precipitation 14% 5% 12% 16% 7% 17% 12%

Table 29. A comparison between spike (bottom) and non-spike (top) days in which precipitation or heavy precipitation (x>1"), occurred. Bold colored boxes are significant and grey colored boxes are near significant. San Antonio values for the 2.0 SD are shown.

San Antonio 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 23% 25% 24% 23% 25% 28% 23% Precipitation 18% 14% 20% 50% 44% 31% 26% Heavy 14% 12% 10% 12% 12% 13% 12% Precipitation 6% 0% 14% 25% 17% 24% 14%

4.4.5 Miami

Both 1.5 SD and 2.0 SD precipitation results suggested the likelihood of precipitation to be less on a high mortality day (Tables 30 and 31). For the entire winter season, the differences were also significant.

Table 30. A comparison between spike (bottom) and non-spike (top) days in which precipitation or heavy precipitation (x>1"), occurred. Bold colored boxes are significant and grey colored boxes are near significant. Miami values for the 1.5 SD are shown.

Miami 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 42% 31% 23% 23% 23% 21% 37% Precipitation 33% 20% 22% 13% 21% 12% 27% Heavy 25% 15% 11% 12% 13% 12% 23% Precipitation 30% 15% 4% 6% 11% 4% 17%

Table 31. A comparison between spike (bottom) and non-spike (top) days in which precipitation or heavy precipitation (x>1"), occurred. Bold colored boxes are significant and grey colored boxes are near significant. Miami values for the 2.0 SD are shown.

Miami 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 42% 30% 23% 23% 23% 21% 37% Precipitation 29% 43% 20% 25% 30% 11% 29% Heavy 25% 15% 11% 11% 13% 12% 22% Precipitation 29% 43% 0% 0% 20% 0% 16%

4.5 Spatial Synoptic Classification

4.5.1 Minneapolis-St. Paul

The Spatial Synoptic Classification (SSC) 1.5 SD results for Minneapolis-St. Paul showed Dry Polar (DP) weather types to be less likely on a high mortality day (Table 32).

This was statistically significant in November, January, and March as well as the entire winter season. Moist Moderate (MM) was also significant during January, and it was more likely to occur on spike days. In March, Transitional (TR) weather types were more likely to occur on high mortality days with a 7 percentage point increase in likelihood along with a significant decrease in DP.

Table 32. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Minneapolis-St. Paul 1.5 SD results are shown.

Minneapolis-St. Paul 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 33% 24% 18% 13% 17% 20% 21% DM 32% 19% 17% 15% 13% 16% 19% 21% 27% 33% 44% 38% 33% 33% DP 16% 19% 32% 24% 46% 21% 26% 5% 2% 0% 0% 0% 1% 1% DT 9% 8% 0% 0% 0% 0% 3% 10% 8% 8% 9% 14% 12% 10% MM 8% 8% 13% 16% 12% 17% 12% 14% 26% 27% 19% 20% 20% 21% MP 14% 32% 24% 24% 17% 21% 22% 3% 2% 0% 0% 1% 3% 1% MT 6% 2% 0% 1% 2% 2% 2% 12% 12% 13% 14% 11% 11% 12% TR 15% 11% 14% 19% 10% 24% 16% 0% 0% 0% 0% 0% 1% 0% MT+ 0% 0% 0% 0% 0% 0% 0%

Similar to the previous subset, 2.0 SD results show MM weather types to have a near significant increase in likelihood during spike days along with a significant decrease in DP (Table 33). Over the whole winter season, the increase in likelihood for MM was 4 percentage points whereas DP was 11 percentage points less likely. Transitional was less consistent with near significant values showing a greater likelihood on non-spike days in November and February.

Table 33. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Minneapolis-St. Paul 2.0 SD results are shown.

Minneapolis-St. Paul 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 33% 23% 18% 13% 17% 20% 21% DM 43% 25% 21% 12% 14% 11% 21% 21% 27% 34% 44% 38% 32% 32% DP 9% 15% 24% 8% 55% 16% 21% 6% 2% 0% 0% 0% 1% 1% DT 9% 5% 0% 0% 0% 0% 2% 10% 7% 8% 9% 14% 12% 10% MM 4% 15% 14% 16% 18% 21% 14% 14% 26% 27% 19% 20% 20% 21% MP 13% 35% 24% 44% 9% 21% 25% 3% 2% 0% 0% 1% 3% 2% MT 4% 0% 0% 0% 0% 5% 1% 13% 12% 13% 15% 11% 11% 12% TR 17% 5% 17% 20% 5% 26% 15% 0% 0% 0% 0% 0% 1% 0% MT+ 0% 0% 0% 0% 0% 0% 0%

4.5.2 Pittsburgh

More likely to occur during non-spike days, DP was statistically significant during

January and the overall winter season (Table 34). During February, there was a near significant increase in likelihood of MT weather type on spike days with a significant decrease of MP and TR weather types. Examining the whole season, there was a near

significant increase in the likelihood of MM weather type during spike days along with a statistically significant decrease in DP.

The 2.0 SD SSC results showed less likelihood of Dry Moderate (DM) during mortality spike events (Table 35). During both March and the season as a whole, there was a near statistically significant increase in the likelihood of TR weather type on spike days along with a significant decrease in DM.

Table 34. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Pittsburgh 1.5 SD results are shown.

Pittsburgh 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 35% 28% 19% 16% 17% 22% 23% DM 40% 21% 22% 16% 19% 17% 22% 20% 18% 25% 29% 27% 22% 23% DP 14% 19% 19% 14% 35% 15% 19% 3% 4% 2% 1% 4% 8% 4% DT 7% 4% 3% 0% 5% 11% 5% 12% 10% 11% 10% 15% 12% 11% MM 17% 17% 13% 12% 19% 11% 14% 15% 20% 27% 29% 23% 20% 22% MP 7% 21% 28% 34% 3% 17% 20% 8% 8% 4% 3% 3% 3% 5% MT 5% 8% 4% 2% 11% 11% 6% 7% 11% 10% 10% 9% 11% 10% TR 10% 10% 9% 18% 3% 13% 11% 0% 0% 1% 2% 1% 3% 1% MT+ 0% 0% 3% 4% 3% 4% 2%

Table 35. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Pittsburgh 2.0 SD results are shown.

Pittsburgh 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 35% 28% 20% 16% 17% 22% 23% DM 31% 13% 8% 14% 29% 8% 15% 19% 18% 25% 28% 28% 21% 23% DP 23% 13% 24% 27% 29% 25% 24% 3% 4% 2% 1% 4% 8% 4% DT 8% 0% 4% 0% 0% 17% 5% 12% 10% 11% 10% 15% 12% 12% MM 23% 20% 16% 0% 14% 0% 11% 15% 20% 27% 29% 22% 20% 22% MP 0% 33% 32% 32% 7% 13% 21% 8% 8% 4% 3% 3% 3% 5% MT 8% 7% 4% 0% 7% 8% 5% 7% 11% 10% 10% 9% 11% 10% TR 8% 13% 4% 23% 7% 25% 14% 0% 0% 1% 2% 1% 3% 1% MT+ 0% 0% 8% 5% 7% 4% 4%

4.5.3 St. Louis

January 1.5SD results showed a significant increase in the likelihood of TR on spike days along with a significant decrease in DM (Table 36). DM also had a significant or near significant increase in likelihood on spike days during the transitional months of October

and March. Only MP was near significant when examining the whole winter season, being more 3 percentage points more likely on non-spike days.

The 2.0 SD results showed DM to be more likely to occur during high mortality events during the early months of October and November (Table 37). During December, there was a near significant increase in the likelihood of DP weather type on spike days.

Table 36. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. St. Louis 1.5SD results are shown.

St. Louis 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 35% 25% 25% 20% 27% 28% 27% DM 48% 30% 22% 12% 16% 38% 26% 16% 20% 24% 27% 21% 15% 20% DP 9% 13% 28% 29% 31% 11% 21% 9% 8% 3% 3% 6% 9% 6% DT 11% 15% 4% 3% 7% 16% 8% 11% 9% 12% 12% 14% 13% 12% MM 13% 11% 12% 9% 11% 13% 12% 7% 17% 20% 26% 20% 15% 18% MP 4% 9% 15% 26% 20% 11% 15% 12% 8% 3% 2% 3% 5% 6% MT 6% 13% 5% 0% 9% 2% 5% 9% 11% 11% 9% 7% 12% 10% TR 7% 11% 12% 20% 7% 4% 11% 1% 1% 2% 1% 2% 4% 2% MT+ 2% 0% 1% 2% 0% 4% 1%

Table 37. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. St. Louis 2.0 SD results are shown.

St. Louis 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 35% 25% 25% 20% 27% 28% 27% DM 54% 47% 20% 17% 6% 33% 28% 15% 20% 24% 27% 22% 15% 20% DP 0% 6% 40% 33% 19% 13% 21% 9% 8% 4% 3% 6% 9% 6% DT 15% 18% 0% 0% 6% 20% 9% 11% 10% 11% 11% 14% 13% 12% MM 0% 6% 20% 17% 13% 13% 13% 7% 17% 19% 26% 20% 15% 17% MP 8% 6% 12% 17% 19% 13% 13% 12% 8% 4% 2% 3% 5% 6% MT 8% 12% 0% 0% 25% 0% 7% 9% 12% 11% 10% 7% 11% 10% TR 15% 6% 8% 17% 13% 7% 11% 1% 1% 2% 1% 2% 4% 2% MT+ 0% 0% 0% 0% 0% 0% 0%

4.5.4 San Antonio

In the 1.5 SD results, DT, during the months of January and March, was at least 6 percentage points more likely to occur during high mortality days (Table 38). These two months were near significant. In October, there was a near significant increase in the likelihood of TR weather type on spike days along with a decrease in MM. Both DT and

MP showed a near significant increase in likelihood on spike days during the month of

January.

In February, there was a near significant increase in the likelihood of 2.0 SD MP weather type on spike days with a decrease in MM (Table 39). DT was more likely on high mortality days during the month of October with a 15 percentage point increase in likelihood. For the season as a whole, there was a near significant increase in the likelihood of TR weather type on spike days along with a decrease in MT.

Table 38. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. San Antonio 1.5 SD results are shown.

San Antonio 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 31% 34% 38% 35% 30% 29% 33% DM 29% 35% 41% 30% 28% 30% 33% 2% 4% 5% 6% 4% 3% 4% DP 2% 4% 9% 0% 7% 2% 4% 9% 4% 6% 7% 11% 11% 8% DT 12% 2% 8% 13% 9% 19% 10% 10% 12% 12% 14% 14% 12% 12% MM 2% 11% 15% 15% 9% 14% 12% 3% 8% 10% 13% 14% 6% 9% MP 2% 5% 5% 20% 15% 6% 9% 31% 22% 13% 10% 12% 17% 18% MT 33% 33% 9% 7% 11% 11% 16% 7% 11% 9% 9% 7% 10% 9% TR 14% 11% 10% 11% 13% 10% 11% 7% 4% 6% 5% 8% 12% 7% MT+ 6% 0% 3% 5% 9% 8% 5%

Table 39. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. San Antonio 2.0 SD results are shown.

San Antonio 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 30% 35% 39% 35% 30% 29% 33% DM 29% 32% 37% 25% 22% 38% 32% 1% 4% 5% 6% 4% 3% 4% DP 6% 0% 6% 0% 0% 3% 3% 9% 4% 6% 7% 11% 11% 8% DT 24% 0% 9% 10% 6% 14% 10% 10% 12% 12% 14% 13% 12% 12% MM 6% 14% 17% 15% 6% 24% 15% 3% 8% 10% 13% 13% 6% 9% MP 0% 5% 9% 20% 28% 7% 11% 32% 22% 13% 10% 12% 17% 18% MT 24% 27% 9% 5% 11% 7% 13% 7% 11% 9% 9% 7% 10% 9% TR 12% 23% 14% 15% 17% 0% 13% 7% 4% 6% 5% 8% 11% 7% MT+ 0% 0% 0% 10% 11% 7% 4%

4.5.5 Miami

With fewer weather types in Miami, there were only a few SSC categories that were relevant (Table 40). In March, there was a significant increase in the likelihood of

MT weather type on spike days along with a decrease in DM. In both of these cases, these weather types were 15 percentage points more likely to occur. October also showed a statistically significant increase in the likelihood of MT+ weather type on spike days with a decrease in DM and TR.

With 2.0 SD results, only the October MT weather type was near significant being 23 percentage points more likely to occur during high mortality days (Table 41).

DM was significantly or near significantly less likely to occur on spike days in October and February.

Table 40. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Miami 1.5 SD results are shown.

Miami 1.5 SD OCT NOV DEC JAN FEB MAR SEASON 14% 19% 21% 26% 23% 25% 21% DM 0% 23% 29% 29% 29% 12% 22% 0% 0% 2% 3% 3% 3% 2% DP 0% 0% 0% 4% 4% 0% 1% 1% 1% 1% 1% 2% 1% 1% DT 0% 3% 0% 0% 4% 0% 1% 10% 9% 9% 11% 10% 7% 9% MM 3% 5% 5% 8% 7% 4% 6% 0% 0% 1% 1% 1% 1% 0% MP 0% 0% 0% 0% 0% 0% 0% 48% 42% 36% 27% 30% 34% 36% MT 50% 48% 38% 35% 21% 52% 40% 3% 6% 8% 9% 7% 8% 7% TR 0% 3% 7% 2% 7% 4% 4% 23% 24% 21% 21% 24% 21% 22% MT+ 47% 18% 20% 21% 29% 28% 25%

Table 41. Spatial Synoptic Classification weather types- a comparison between spike (bottom) and non-spike days (top). Bolded colored boxes are statistically significant and grey boxes are near significant. Values in which the frequency was not at least 4% on spike and non-spike days were discolored to improve reading ability. Miami 2.0 SD results are shown.

Miami 2.0 SD OCT NOV DEC JAN FEB MAR SEASON 14% 19% 22% 26% 23% 25% 22% DM 0% 14% 15% 38% 10% 22% 16% 0% 0% 2% 3% 3% 2% 2% DP 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 2% 1% 1% DT 0% 0% 0% 0% 0% 0% 0% 10% 9% 9% 11% 10% 7% 9% MM 0% 14% 5% 13% 10% 0% 7% 0% 0% 1% 1% 1% 1% 0% MP 0% 0% 0% 0% 0% 0% 0% 48% 42% 36% 28% 30% 34% 36% MT 71% 43% 45% 13% 30% 44% 41% 3% 5% 7% 9% 7% 8% 7% TR 0% 0% 10% 0% 20% 0% 7% 24% 23% 21% 21% 24% 21% 22% MT+ 29% 29% 25% 38% 30% 33% 30%

4.6 Map Comparison

Since the Spatial Synoptic Classification showed few significant increases of particular weather types during high winter mortality events, a qualitative examination and manual classification of surface synoptic weather maps was completed for a subset of the spike days. This process was conducted for 2.0 SD results of Pittsburgh

(December) and Miami (January) spike days.

Examining December spike days in Pittsburgh, four categories were created based upon visual similarities. Nearly half of December spike days show an approaching front in the Pittsburgh region (Figure 24). Most of these days show precipitation, windy conditions, and represent a synoptic change in the weather pattern. Five days were classified as high pressure return flow (Figure 25). With a high pressure centered east of Pittsburgh, these days represent the second most common weather type during

December mortality spike days in Pittsburgh. The weather varies slightly within this group, however, with rain on some of the days. The third category was represented by an elongated high centered over the Pittsburgh area (Figure 26). The four days in this category were determined based upon the spatial extent of the high pressure system.

The final five days of this visual analysis were considered other (Figure 27). During the other category, Pittsburgh was generally between synoptic systems and experienced variable weather patterns. These days did not show much visual similarity with any of the previous categories.

Figure 24. A synoptic pattern map for December 22.1983. This December pattern for Pittsburgh was classified as Approaching Front and also occurred on December 28, 1984, December 6, 1986, December 8, 1986, December 24, 1986, December 16, 1987, December 10, 1988, December 27, 1996, December 18, 1998, December 10, 2003, and December 10, 2004.

Figure 25. A synoptic pattern map for December 5, 2001. This December pattern for Pittsburgh was classified as High Pressure Return Flow and also occurred on December 8, 1989, December 29, 1993, December 15, 1996, and December 7, 2002.

Figure 26. A synoptic pattern map for December 22, 1980. This December pattern for Pittsburgh was classified as Elongated High and also occurred on December 27, 1977, December 27, 1980, and December 22, 1989.

Figure 27. A synoptic pattern map for December 29, 1999. This December pattern for Pittsburgh was classified as Other and also occurred on December 2, 1982, December 28, 1990, December 28, 2001, and December 6, 2003.

Miami’s eight January spike days also showed similar patterns, thus three categories were created. The first group was signified by an approaching front near the state of Florida (Figure 28). This pattern occurred three days and signified a changing weather scenario for the state of Florida. The second category represents a frontal passage and thus a change in the weather pattern for the Miami region (Figure 29). In these three days, precipitation was recorded and a shift in winds was experienced as the frontal system passed. The final two days are considered other (Figure 30). Without many similarities, these days showed entirely different synoptic patterns than the

previous categories. One of the days, Miami was under the influence of a large high while the other day was between synoptic systems.

Figure 28. A synoptic pattern map for January 8, 1999. This January pattern for Miami was classified as Approaching Front and also occurred on January 13, 1992 and January 18, 1993.

Figure 29. A synoptic pattern map for January 14, 1992. This January pattern for Miami was classified as Frontal Passage and also occurred on January 18, 1975 and January 20, 1991.

Figure 30. A synoptic pattern map for January 1, 1998. This January pattern for Miami was classified as Other and also occurred on January 1, 1981.

While this qualitative map classification shows similarities between some of the synoptic weather patterns, noting the need for further examination and statistical verification is necessary.

CHAPTER 5

DISCUSSION

This study examined the relationship between various atmospheric variables and mortality in five U.S. cities during the winter season (October-March; 1975-2004). This chapter’s goal is to synthesize the results within the context of the established objectives. The objectives of this research include determining the differences between the weather variables on spike and non-spike days, examining the temporal and spatial variability of winter mortality, and comparing the demographic characteristics of the defined mortality spikes.

5.1 Synthesis of Results

While variable through time and space, the main conclusion from this research is that there are significant differences between the weather conditions during mortality spike days compared to non-spike days. Warmer temperatures, lower atmospheric pressure, and an increased likelihood of precipitation were more common on mortality spike days. In northern locations, the largest pressure shifts occurred during

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transitional months, compared with December-February for southern locations. While this seasonal variability existed, there were also regional differences in the ways people were impacted during mortality spike days. There were more high mortality days in northern location, although in all cities, the month of December reported the most spike days. This variability confirmed previous literature suggesting some locations and individuals were more sensitive to winter weather (Keatinge 2002).

5.2 Weather on Spike and Non-Spike Days

Examining the relationship between atmospheric variables and mortality, many variables exhibited consistent results throughout the cities. Temperatures on spike days were generally warmer than non-spike days. Temperatures were also warmer on spike days compared to the preceding days (T1day and T3day). These temperature variations may suggest a transitional in weather pattern, and a result of warming temperatures may be atmospheric instability. It is well documented that other factors including changing air masses and overall unsettled atmospheric conditions also contribute to winter mortality (Kassomeno et al. 2007; McGregor 1999). However, this positive correlation between temperature and mortality is not entirely consistent with previous findings that suggest mortality increases the most during cold temperatures (Huynen et al. 2001; Analitis et al. 2008). The lag effect and any delay in physiological response may influence these results, however. The biological response may also play a role in

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the defined mortality spikes. It is well documented that cold exposure changes blood viscosity and cholesterol levels, but this physiological response may be delayed for several days adding to the lag effect (Armstrong 2006; Azevedo et al. 1995).

This thesis’ results are also supported by Keatinge (2002) who showed increases in cardiovascular diseases after cold spells. Warming temperatures could suggest a recent cold spell. Despite the interest in ambient temperatures, O’Neil et al. (2003) suggests the variance in temperature may cause increased respiratory deaths.

Therefore, the rising temperatures may be a factor in these deaths.

Similarly, pressure showed a consistent V-shaped pattern throughout the cities.

Daily pressure was lower on spike days compared to the previous subset of days (P1day and Min/Max). Besides pressure decreasing leading up to a mortality spike day, daily pressure was also lower on spike days than non-spike days. Danet et al. (1999) noted similar V-shaped relationships with winter mortality and pressure. This relationship was the strongest in the northern locations when compared to the southern locations, but the seasonal synoptic pattern may cause this spatial variability. Although decreasing pressure coupled with warming temperatures seems to suggest a changing synoptic pattern, with an approaching mid-latitude cyclone, little relationship with specific SSC weather types was evident.

While more temporally and spatially variable than the temperature or pressure results, precipitation and snowfall both consistently showed increased likelihood during

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mortality spike days. While it may be difficult to distinguish the impact of precipitation on mortality individually, Healy (2003) and McGregor (1999) suggest that precipitation and abrupt changes in weather patterns may be a factor in high winter mortality. With rainfall, respiratory agents such as pollen and dust change concentration. Heavy rainfall cleanses the atmosphere while light rains may enhance allergies and respiratory issues.

This may also explain the increase in respiratory mortality in southern locations such as

Miami. There have also been studies correlating snowfall with mortality. For example,

Gorjanc et al. (1999) showed increased mortality during snowfall events in Pennsylvania.

Cardiac arrest hospitalizations also increase during snowfall events (Medina-Ramón and

Schwartz 2007).

Temperature and pressure tendencies coupled with an increase in precipitation may signify a particular weather pattern. Despite all of this, the Spatial Synoptic

Classification (SSC) showed little significance or relationship with the defined mortality spike days. The transitional weather type showed some changes but they were not consistent throughout the cities or winter season.

A qualitative map comparison exercise was an attempt to holistically understand the synoptic patterns exhibited during mortality spike days. The two locations examined, Pittsburgh and Miami, both provided similar synoptic system patterns. In both locations, nearby or passing frontal systems were visually evident during many high mortality days. While these patterns would include warming temperatures, V-

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shape pressure tendencies, and increased precipitation, further validation may be needed to provide statistical significance to the qualitative comparison.

5.3 Cause of Death and Demographic Factors

In all of the locations, 1.5 SD results showed cardiovascular and respiratory mortality increasing the most. With increases in blood pressure during the winter season, cardiovascular diseases are more prevalent during the winter season (Curriero et al. 2002). One-fifth of all winter hospitalizations may be attributed to such respiratory infections (Stewart et al. 2002). Therefore, the results presented are consistent with previous research which show increases in cardiovascular and respiratory deaths during the winter season (Patz et al. 2000; Basu and Samet 2002).

The southern locations of San Antonio and Miami both had a greater increase in respiratory disease mortality, while in the northern cities cardiovascular deaths showed the greater increase. While respiratory issues are generally associated with heat waves, the close relationship between diseases may contribute to the observed increases in other disease categories (Huynen et al. 2001).

Concerning the age categories, the older population did not show any more increases in mortality than the younger subpopulations. Only the city of Miami showed the oldest population as being the most vulnerable during mortality spike days. The movement of people to southern locations during the winter may help explain this

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(Curriero et al. 2002). The vulnerability of the younger population is evident in San

Antonio. However, this city also represents one of the youngest overall populations.

This is consistent with previously discussed research that indicates mixed results associated with age and winter mortality vulnerability (Analitis et al. 2008, Gorjanc et al. 1999).

The city of St. Louis had one of the largest black populations and showed the largest percentage increases in black deaths. However, overall, inconsistent results associated with gender and race existed. This research also parallels previous completed work indicating inconsistent gender relationships associated with wintertime mortality (Wilkinson et al. 2004; Hajat et al. 2007; Davie et al. 2007).

5.4 Variability between Cities

While the climate relationships were similar throughout all of the cities, San

Antonio showed the weakest correlations. The results may have been impacted by being the only location classified as a humid subtropical. However, north-south variability existed between the cities with Miami exhibiting the largest temperature increases while Minneapolis-St. Paul showed the most significant pressure decreases.

While this variability verifies the regional differences associated with winter mortality discussed by Keatinge (2002), it also encompasses seasonal weather patterns associated with synoptic climatology. The movement of the jet stream and thus the progression of

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synoptic systems throughout the winter season may account for some of the north- south relationships shown in the results.

5.5 Seasonality and Lag Effect

Many of the locations and weather variables exhibited a seasonality factor. The more northern cities of Minneapolis-St. Paul and Pittsburgh showed significant pressure shifts in the transitional months, whereas Miami’s largest pressure changes occurred in middle winter months (December-February). With the exception of St. Louis, temperature relationships showed less of a seasonal component. Still, the northern locations displayed more temperature variation in the transitional months.

Precipitation patterns follow similar seasonal variations. The seasonal progression of the jet stream pattern may play a role in this cycle.

While the results of warming temperatures, decreasing pressure, and an increased likelihood of precipitation may be associated the warm sector of a mid-latitude cyclone, a lag effect may influence the analyzed high mortality days. Previous literature indicates a long term lag effect associated with winter (cold) mortality with cardiovascular-related deaths generally increasing 3 days after a cold wave while respiratory infections lag upward of 14 days (Patz et al. 2000; Gorjanc et al. 1999; Keatinge 2002). This delayed response may not be accounted for since this study only examined the weather on mortality spike days without direct inclusion of such a lag effect, although the trends in

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analyzed variables suggest what weather conditions typically preceded high mortality days.

5.6 Limitations to Research

One issue associated with winter mortality is that of snowbirds. This term refers to the movement of people, in this case, to more desirable locations during the winter months (e.g. warmer climates). While there are few statistics available, Curriero et al.

(2002) suggests the movement of people to southern locations plays a role in winter mortality. Miami, one of these locations, was the only location to show the oldest population was most vulnerable. All other locations showed the younger population, those 65-74, at more of a risk.

A physician’s ruling on direct and indirect cause of death may be a limiting factor. Barker and Mullooly (1981) claim specific causes of deaths may be underestimated (e.g. influenza); therefore accurately and precisely reporting cause of deaths is also an issue (Borrell et al. 2006; Dixon et al. 2005).

The data used in this research is also a limiting factor. Since the weather varies throughout each MSA, the use of a single weather station to represent the region may have impacted the results. Although numerous studies have examined weather conditions related to mortality, this research focused solely on mortality spike days which, by definition, represent days with dramatic increases in mortality and the days

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with the highest mortality during the winter season. As a result of comparing the weather on spike days with non-spike days, this research examines the environmental response of mortality to the weather scenarios. While other factors contribute to wintertime mortality, this research was the first to examine the weather on particular mortality spike days. Since the definition of spike day was defined upon a specific threshold, the definition of mortality spike was both a unique characteristic and possible limitation of this research. By choosing a different mortality spike definition, the results of this research may have been modified.

CHAPTER 6

CONCLUSION

This thesis expands upon previous research associated with the impact of climate and weather on human health. Many previous studies have examined the numerous factors contributing to high winter mortality including deprivation, health status, and physiological response to the environment (e. g. O’Neill et al. 2003; Analitis et al. 2008; Armstrong 2006). In addition, numerous studies exist relating weather conditions such as temperature and pressure to mortality (Raatikka et al. 2007;

Schwartz 2005; Danet et al. 1999). The relationship between climate-mortality has been further advanced by focusing solely on mortality spike days and the particular weather conditions on these days as well as days prior. Mortality spike days were days in which daily mortality was greater (at least 1.5 or 2 standard deviations) when compared to previous days. Therefore, spike days represented dramatic increases in mortality and the days with the highest winter (October-March) mortality.

Weather variables taken into account included temperature, pressure, precipitation, and the Spatial Synoptic Classification (SSC). In addition, by incorporating trends in variables, the weather on days prior to a mortality spike was also analyzed

(e.g. T3day). A comparison of weather conditions between spike and non-spike days was

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conducted through statistical tests. This method determined significant differences between the atmospheric conditions on high mortality spike days and non-spike days. A total of five metropolitan regions (i.e. Minneapolis-St. Paul, Pittsburgh, St. Louis, San

Antonio, Miami) were analyzed during the winter seasons (1975-2004). Increases in mortality stratified by cause of death, age, gender, and race subdivisions were also examined as a secondary focus of the thesis.

Although temporally variable, the atmospheric results of this thesis exhibited consistency between all cities. Temperatures were generally warmer on mortality spike days than non-spike days or the 3 days prior to a spike. A V-shaped pressure pattern was evident in all locations signifying a decrease in pressure leading up to a high mortality day. Precipitation and snowfall were both generally more likely on spike days.

The results of a pressure fall and a temperature rise on a spike day, along with greater likelihood of precipitation, suggest an approaching mid-latitude cyclone.

However, despite these relationships, the Spatial Synoptic Classification did not suggest any consistent differences in particular weather types between mortality spike days and non-spike days. As a result, a qualitative map examination was also conducted for

Pittsburgh (December) and Miami (January) spike days. By classifying map patterns based upon synoptic characteristics, similar patterns on some spike days were observed in both locations.

Many of these maps showed approaching frontal systems and generally unsettled weather conditions. This was consistent with previous literature suggesting

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abrupt weather changes have an effect on winter mortality (Healy 2003; McGregor

1999). The results of this thesis, warming temperatures, decreasing pressure, and increases in precipitation, suggest higher mortality associated with the warm sector of a mid-latitude cyclone. However, these results could be confounded by the lag effect and delayed physiological response noted in previous literature (Armstrong 2006; Basu and

Samet 2002).

Geographically, there were differences between all of the locations suggesting seasonality. In northern locations, the largest pressure shifts occurred during transitional months, compared with December-February for southern locations.

Cardiovascular mortality increased the most in northern locations while Miami showed the largest increases in respiratory mortality. These results are supported by previous research that shows individuals and locations are impacted differently by winter weather (Keatinge 2002). Both young and older demographics show vulnerability associated with winter weather which is supported by previous studies (Gorjanc et al.

1999; Analitis et al. 2008).

This thesis presented a unique methodological approach by solely examining specific mortality spike days and the weather scenarios associated with these days.

Within previous research, few studies examine the relationship between climate and mortality through an environment-to-circulation approach. The presented methodology provides confirmation of previous research associated with wintertime mortality. While the results show consistency regarding the weather leading up to a mortality spike day,

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it also raises important questions regarding the issue of lag during the winter season.

The discussed results may contribute to high mortality days, however, the lag effect is again showed to be a key factor associated with winter mortality. Public health stakeholders may use this methodology as another way to address high mortality days while continuing to investigating the issue of winter's lag effect. In addition, this study added to the field of geography by showing spatial and seasonal variability associated with the defined wintertime mortality spikes.

The future direction of this research encompasses various avenues. While the five cities analyzed provided unique insights into the human mortality response to environmental conditions, increasing the spatial resolution would be beneficial. By analyzing more cities and regions, the results of this research could be verified and expanded. While demographic results were mixed, further examination of subgroups such as age and particular causes of death would assist in vulnerability assessment.

Also, examining the differences between rural and urban settings would provide further insight into the issue of urbanization (Davie et al. 2007; Hajat et al. 2007). While the lag effect was not included, incorporating various multi-lag models would improve upon the results of this research by adding a delayed response factor. All of these future projects would improve our understanding of the impact winter weather has on human health.

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