More Related Content Similar to Dunlap Plan B (20) 2. Sara Dunlap June 11, 2009
MPH Thesis
Abstract
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Growing research has focused on morbidity and mortality during heat and air quality
emergencies amid concern over climate change. To help plan for public health responses during these
emergencies, researchers in the State Environmental Health Indicators Collaborative (SEHIC) developed
an indicator model to identify both elderly individuals who live alone and individuals who live below the
poverty line according to census tract. This study piloted the SEHIC template using 2000 census data for
a seven county metropolitan area surrounding the Twin Cities, Minnesota. A case study also examined
potential relationships between the vulnerable populations and Chronic Lower Respiratory Disease
(CLRD). Two key research questions were addressed in this paper. First, does the SEHIC indicator
accurately account for demographic and vulnerability inequities? Second, does the addition of CLRD
hospitalization data to the existing SEHIC template provide evidence of errors in the SEHIC calculation
methods?
Results of the piloted SEHIC template indicator showed an overrepresentation of below poverty
line populations among the vulnerable census tracts. The overrepresentation is a serious concern after
literature identified elderly to be more at risk during emergencies and to be outnumbered four times by
low income groups. Four areas of concern were identified and potential recommendations made to
adjust for errors. First, the Minnesota state averages were established as standard vulnerability to
evaluate each census tract against. Second, use of percentages for vulnerability scores showed to be
inaccurate. Recommendations include changing percentages to a standard error model and calculate
vulnerability score by Z‐Score. Additionally, calculating individual vulnerable population z‐scores then
creating a composite score resulted in a more equal representation of both vulnerable populations.
Third, no risk threshold had been established for choropleth maps. Recommendations include using Z‐
score increments in a five color gradient. Fourth, the existing template is not compatible with other
health data. To adjust, percentages were changed to rates of vulnerable individuals per 10,000. Results
of overlaid SEHIC and CLRD data also showed over‐representation in below poverty line populations.
When combined with the recommended SEHIC calculations a more equal distribution of vulnerable
individuals was identified.
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Table of Contents
Introduction: ..................................................................................................................................................4
Statement of the Problem: ........................................................................................................................4
Study Objectives:........................................................................................................................................5
Study Population Demographics:...............................................................................................................6
Literature Review:......................................................................................................................................6
Conceptual Framework: Public Health Geography..................................................................................17
Research Questions..................................................................................................................................19
Methodology:...............................................................................................................................................19
Study Design.............................................................................................................................................21
Data Collection:........................................................................................................................................22
Results:.........................................................................................................................................................23
Individual Vulnerable Population Analysis:.............................................................................................32
Recommendations’ to SEHIC: Adjusted SEHIC Indicator:.......................................................................35
Case Study: Chronic Lower Respiratory Disease: ....................................................................................38
Discussion:....................................................................................................................................................44
Study Limitations:.....................................................................................................................................44
Bias:..........................................................................................................................................................46
Conclusions: .................................................................................................................................................47
References....................................................................................................................................................50
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Introduction:
Statement of the Problem:
Climatologists and public health researchers agree that climate change causes serious health
effects in the population. Scientists predict a 70% increase in hazardous ozone concentration days and
heat waves extending from 3‐8 days in some cities (Ebi, 2006). Public health officials in urban areas fear
a combination of growing populations and increasing frequency and severity of air quality and heat
emergencies have the potential to be extreme public health hazards. To help plan for public health
responses during these emergencies, researchers have developed indicator models to help identify the
location of vulnerable populations in order to most effectively distribute education resources, staff, and
preventative care.
The Council of State and Territorial Epidemiologists (CSTE) is a collaborative of state and local
public health agency epidemiologists that encourage interdisciplinary research and project
development. As a subgroup of CSTE, the State Environmental Health Indicators Collaborative (SEHIC)
developed an indicator model to identify populations most likely to be effected by air quality and
elevated heat emergencies. The SEHIC indicator model identifies both elderly individuals who live alone
and individuals who live below the poverty line. These groups are at risk because of their reduced
abilities to alter behaviors to adapt to rapidly changing environments (Ebi, 2006), (Luber, 2006). The
SEHIC indicator model identifies at risk geographic areas by mapping vulnerable populations using
Geographic Information Technology (GIS). The SEHIC indicator template is currently in pilot stage. If the
indicator can be established as an accurate method, it could become a standard for inter‐state air
quality and heat emergency preparedness.
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Study Population Demographics:
Demographic expert’s project that the entire U.S. population will increase by 130% from 2005‐
2035; individually, Minnesota’s state’s population is expected to increase by 122% from 2005 to 2035
(U.S. Census Bureau, 2008). Of the targeted vulnerable populations, the 2000 census found 12.37% of
the U.S. population lived below poverty line (BPL). In Minnesota the 2000 census confirmed that 7.94%
of the population reported to be surviving below the poverty line, with 47% of all below poverty line
individuals in Minnesota living in the Twin Cities. By 2007 the number had expanded by 136% to nearly
244,000 individuals (Bureau, 2007).
In the U.S., elderly individuals living alone (ELA) accounted for 6.15% while Minnesota’s number
was slightly lower at 5.74% in 2000. The elderly population is projected to increase by 213% during
2005‐2035; 233% in the seven county Twin Cities study area alone. Combined, over 26,300 vulnerable
individuals were identified in 2000. This is expected to rise to nearly 350,000 by 2035.
Literature Review:
Review of the literature justifies SEHIC’s development of the indicator model to identify
vulnerable populations at risk during heat and air quality emergencies. Information gathered includes
the health outcomes related to climate change, air quality, heat waves, urban areas, and vulnerable
populations. An extensive review of current and relative literature defines study methods, research
gaps, statistically significant studies, and conceptual developments.
Included literature was identified using the University of Minnesota and Minnesota Department
of Health’s library systems, in addition to Pubmed, and Google Scholar internet search engines.
Database searches within the University of Minnesota’s Biomedical Library returned results of literature
based on set criteria. Limitations of data included those articles in English, published after 2000, and
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selected by keyword entries. Keyword search examples included “urban health,” “vulnerable elderly,”
“health indicator models,” “air quality and health outcomes,” and “heat related morbidity.” Literature
was excluded if the content was outside the scope of the project or did not have significant statistical
relevance to the topic.
Bias is acknowledged based on electronic availability of resources, journals the University of
Minnesota subscribes to, and those fitting within the set criteria. However, literature included covers a
wide breath of authors and viewpoints and should be considered representative of the extensive
literature available on the topics.
Climate Change and the Health of Vulnerable Populations:
Scientists show that climate change is being accelerated by decisions in the built environment. Research
shows an association between uncapped industrial productions, agricultural growth, unlimited urban
development, and resulting climate change symptoms (Control, Climate Change and Public Health, 2007).
Climate change symptoms include include coral reef beds bleaching, wildlife migration changes, rising sea
levels, changes in precipitation, frequency of severe weather, and reduced crop output (Administration
N. A.). Air quality and elevated heat both have drastic health outcomes with broad supporting
literature.
Air Quality and Health Outcomes
Reduced air quality and increasing temperatures are intimately related to Climate Change.
Greenhouse gasses (carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons) are a factor in
climate change. Scientists believe a combination of greenhouse gases are trapping the earth’s rays near
the surface, preventing them from disseminating back out into the atmosphere (Administration, 2008).
Although the largest hole in the ozone layer is located above Antarctic stratosphere, there is a rapid
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depletion of ozone in a band stretching across the United States. Scientist point to the U.S. extravagant
traffic use, industrial production, and lenient restrictions (Agency, 2009). Rising temperatures have
serious public health consequences. Extreme heat waves cause large populations to become ill straining
health care facilities.
Ozone is associated with the greenhouse effect and is a public health concern because it
increases the intensity of the sun and temperatures in urban areas. Multiple studies have compared
ozone levels and mortality rates using cities with stratified data sets. The stratified data defines
historical community mortality rates to compare against deaths during elevated ozone levels. Results
indicate cities with generally low ozone levels saw a greater spike in mortality during high ozone days
suggesting the greater variance in ozone, the greater probability for deaths in the vulnerable
populations (Ren, 2008), (Abelsohn, 2002).
Particulate Matter is a concern to public health officials because it aggravates existing respiratory
diseases and leads to greater health care needs. Air quality data is a surveillance tool used to link health
outcomes with environmental factors. The Environmental Protection Agency’s (EPA) National Ambient
Air Quality Standards (NAAQS) regulate air quality for six pollutants including Ozone (O3), Particulate
Matter (P.M. 10) and (P.M. 2.5), Carbon Monoxide (CO), Sulfur Dioxide (SO2), Nitrogen Dioxide (NOx), and
Lead (Pb). Standards are based on adverse health effects in vulnerable populations including asthmatics,
children, and the elderly (Agency M. P., 2003). According to the EPA, P.M.2.5 and ozone are the highest
concern in Minnesota (Agency M. P., 2003).
Heat and Health Outcomes
According to the National Oceanic and Atmospheric Administration, “heat is the number one
weather related killer” claiming over 1,500 lives each year and climate change experts predict death
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rates to rise with increases in temperature (Administration, 2008). A report by the EPA listed
Minneapolis as the 10th
highest city with heat related mortality. Researchers estimated a mortality rate
of 2.32 per 100,000 according to 1990 populations records (Agency, 2006). Models from a prospective
study of U.S. northeastern cities estimate summer temperatures to rise by 3 to 80
F degrees by 2050,
increasing the potential for deadly heat waves (O'Neill, 2009). Among them, Minneapolis is expected to
increase from 8 to 16 “Mortality Days, ” defined as days with a temperature over 1040
F and resulting in
an addition 35 heat related deaths in Minneapolis each summer (Agency, 2006). In the United Kingdom,
scientists project a 250% increase in heat related morbidity and mortalities by 2050 from increasing
average summer temperatures. The potential for more frequent and severe heat waves is a major
concern for urban health officials who need to coordinate education and public health response efforts
(Knowlton K. L., 2007).
Elevated heat emergencies or heat advisories are issued by the national weather service within
12 hours of the incoming weather conditions. Criteria for health alerts include a “heat index of at least
105°F but less than 115°F for less than three hours per day, or night time lows above 80° for two
consecutive days” and are broadcast by television and radio networks (Administration, 2008).
Epidemiological studies use data from past heat emergencies to project health effects for future
elevated temperature events. A time‐series analysis measures health outcomes before, during, and
after the heat event. In a specific study, Kovats reviewed time‐series data from multiple heat waves
concluding that temperature‐death relationships are usually U or V shaped. Kovats reported the highest
death rates occur during very low and very high temperatures during the year (Kovats, 2008). Given
Minnesota’s variety of temperatures, researchers may find an example of Kovat’s U shaped mortalities.
Furthermore, studies examined 11 eastern United States cities found that cities in generally
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cooler climates had higher mortalities during high heat events hypothesizing that individuals were not
accustomed to such drastic changes in weather (Curriero, 2000), (O'Neill, 2009), (Figure 2), (Agency
2006). In another time‐series study, Braga measured weather extremes in 12 U.S. cities using a 3‐week
‘lag time’ scale to examine respiratory conditions aggravated by heat. Braga hypothesized that death
rates would climb above the normal rate during the elevated heat days and drop to the normal death
rate afterwards. Braga’s study showed more deaths occurred than were expected to according to
baseline death rates. The study explained if those who had died during the high heat days were those
that were going to die within the next few days, there would have been a below average response in the
death rates following the heat event. However, because no dip below the average occurred researchers
concluded that excess in deaths occurred during the heat event (Braga, 2002).
Figure 2: “Estimated Excessive Heat Related Events‐ Attributed Mortality Rates” (Agency, 2006)
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Climate Change and Urban Areas
Degrading urban health is concerning on multiple levels, but population explostions in urban
areas pose a serious public health risk. In 2007, U.S. census identified nearly 30% of the U.S. population
residing in urban areas (Bureau, 2007). When 30% of the total population is subject to chronic
environmental health based on their urban living status, it is a major health problem.
Urban areas are the major site of industry and development and are growing in both population
and size to accommodate for population needs. Growing urban populations, unbridled industrial
production, traffic construction, and power generating facilities have spewed gases into the atmosphere
that are having a direct effect on the climate (Bellia, 2007), (Amann, 2006). Research has shown that
urban areas are both expelling the most pollution but also suffering the consequences with increasing
numbers of reduced air quality days and more frequent heat waves. In Minnesota, the Pollution Control
Agency’s (MPCA) Air Quality Index monitors identified 27 reduced air quality days across the state in
2003 and 35 alert days in 2005. 17 of the 35 low air quality days were located in the Twin Cities (Agency,
2005).
Urban areas are at particular risk from rising temperatures. The elements of the built
environment including parking lots, roofs, and reflective buildings that trap hot air and prevent heat
dissemination. Known as the “urban heat effect,” the phenomenon has been linked to poor health
outcomes (Shmaefsky, 2006), (Rosenzweig, 2006), (Figure 3), (Agency, 2006). McGeehin found
convincing data linking morbidity and mortalities to urban heat retention. The study specifically
recognized non‐ air‐conditioned buildings and low socioeconomic populations as having a great risk
during heat events (McGeehin, 2001), (Kovats, 2008).
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Figure 3: Impact of the urban heat island on ambient temperatures
(Agency, 2006)
Vulnerable Populations: Elderly and Below Poverty Line Individuals
The increasing number of vulnerable individuals in urban areas poses a public health challenge
during weather emergencies. Vulnerable populations during heat and air quality events include those
with pre‐existing health conditions, the elderly, children, and individuals living below the poverty line
(Ebersol, 2005), (Braga, 2002).
Vulnerable Elderly Populations:
Extensive evidence identifies the elderly populations as heavily at risk for morbidity and mortality
during weather emergencies. Science has shown that social and clinical vulnerabilities make elderly the
most concerning population during heat and air quality events.
Researcher Thomas used the “The Vulnerability Perspective” framework to identify social causes
of elderly vulnerabilities. He concluded that limited access to health care, reduced social networks,
reduced economic, political, social, and education resources, dangerous living locations, and lack of
disaster preparedness are the basis for elderly vulnerabilities during emergencies (Thomas, 2002).
These results agree with findings from a heat wave analysis in Lyon, France where factors of being
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confined to bed, not leaving the home daily, medical co‐morbidities, and having no access to cooling
centers were associated with increased deaths from heat stroke (Argaud, 2007). Multiple studies also
remarked on various risk factors including: being male, single marital status, living alone, lack of
transportation, smoking, being overweight, and having depressed moods (Vandentorren, 2006),
(O'Malley, 2007), (Perkins, 2004 ), (Ostro, 2006), (Halonen, 2008), (Youger, 2008), (Gilmour, 2006),
(Bellia, 2007).
The elderly population is also clinically more at risk during emergencies because of pre‐existing
health concerns. Studies identify pre‐existing medical conditions including Cardio Vascular Diseases
(CVD’s), chronic respiratory diseases and psychiatric diseases to be associated with more heat related
deaths (Luber, 2006). A study following a 2006 heat wave in California examined morbidity and
mortality in the elderly by geographic areas. The study correlated deaths with the heat index and
identified an excess of over 16,000 Emergency Room visits and over 1,000 excess hospitalizations
statewide in the elderly population (Knowlton, 2009). A study by O’Neill identified Hyperthermia as the
underlying cause of death in over 54% of deaths during 1999‐2003 during elevated heat events (O'Neill,
2009).
It may be difficult for individuals with chronic disease to distinguish between symptoms of their
chronic disease, side effects of their medications, or symptoms of heat or air quality distress.
Researchers also hypothesize that elderly individuals who experience broad‐spectrum somatic
symptoms of heat exhaustion and heat stroke may not recognize the severity of their health needs.
Some researchers also attribute deaths to unrecognized homeostatic changes caused by anti‐epileptic,
beta‐blockers, diuretics, and anti‐ cholinergic pharmaceutical drugs. These drug classes often have heat
dispersion side effects that could be intensified during a heat event (Conti, 2007), (Naughton, 2002).
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Additional factors may also discourage elderly from seeking help during emergencies. Elderly
individuals may resist seeking help during heat waves justifying that heat always comes in the summer
and there should be no reason to be concerned over a particularly hot day (O'Malley, 2007), (Conti,
2007), (Jelicic, 1997). As proof, the U.S. National Assessment on Climate Change indicated that only 46%
of elderly individuals modified their behaviors to adapt to changing temperatures after hearing a heat
warning since most individuals did not consider themselves to be ‘at risk’ and therefore not necessary to
take the necessary precautions (O'Neill, 2009). Changing behaviors for elderly individuals may be very
difficult, particularly for those living alone. Making decisions to go to a cooling shelter include finding
direction, navigating traffic, parking, and other major arrangements. Often staying inside may seem like
the best option. Unfortunately, elderly individuals may also resist using the air conditioner to save
money and may lack the strength to open windows and doors for ventilation. Considering these factors,
it is apparent that identifying pockets of elderly who are living alone before emergencies happen is
extremely important.
Low Social Economic Status
Social economic status is a solid predictor of health outcomes during emergencies. The U.S.
census identifies below poverty line (BPL) as those individuals with a yearly income under $8,959
(Bureau, 2008). During emergencies, individuals must react with the resources at hand and individuals
with limited funds often lack the avenues to change their behaviors for a limited time, especially if the
event is not considered an extreme danger.
Health and social economic status (SES) are highly related to the built environment. For instance,
an individual with limited funds will most likely live in urban areas to use public transit, access to
services, and low‐cost housing. Analysis in urban areas show low socioeconomic areas are generally
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close to high traffic areas resulting in high particuate matter exposure and causing greater health
outcomes (Gold, 2005). Low SES individuals may also not participate in preventative health care and
may be adversely affected by heat and air quality events because they are unaware of an underlying
respiratory condition or lack the medication to treat it.
Health risks are also associated with housing construction year and placement. Dwellings built
before 1975 have shown to be more susceptible to ventilation problems. Advances in airtight homes
have made windows and doors heavier and more difficult to open in emergencies (Thomas, 2002),
(Selgrade, 2006). Homes in the Midwest and northeast of the U.S. are built to retain heat during colder
months but are not adequately designed to disperse heat during summer (Miller, 2007). As a result,
occupants who lived on the top floor or had a bedroom directly under the roof are shown to have little
airflow and a higher risk of heat‐related death (Vandentorren, 2006).
Clinically, BPL and minority racial groups are overrepresented in morbidity and mortality rates
during past heat and air quality emergencies. Thomas examined mortalities in Chicago between 1990
and 1997 and found high correlations between socioeconomic status, racial group, and asthma
hospitalizations. The study found that the urban rate of asthma hospitalizations was more than twice
the national average, and low socio‐economic status individuals were overly represented compared to
the whole population. Thomas hypothesized that factors of living in urban environments, substance
abuse, and low use of preventative care was associated with elevated asthma rates (Thomas S. W.,
1999). Another cross‐sectional study of black and caucasian individuals indicate a lifetime prevalence of
asthma two times as high in black individuals compared to caucasian groups. Researcher Browning
point to socioeconomic factors including barriers to health care, racial discrimination, and differential
housing treatment (Browning, 2006).
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Case Study: Chronic Lower Respiratory Diseases (CLRD):
Chronic Lower Respiratory Diseases are associated with both vulnerable populations and urban
areas. Urban areas have a higher concentration of particulate matter and ozone that exacerbate
symptoms. Both elderly living alone and individuals below poverty line have shown to have a higher
morbidity and mortality during weather events because of limited resources and pre‐existing conditions.
Thus, CLRD data is used as a case study to examine any spatial overlap of the vulnerable populations
identified by the SEHIC indicator and CLRD data.
Chronic diseases account for 70% of all deaths in the U.S. and cause a tremendous financial and
workforce stress on the health care system. Nearly half of Americans were diagnosed with at least one
chronic condition in 2005. America’s chronic disease pushed medical expenditures to $2 trillion a year,
equivalent to over 75% of total U.S. health care costs. It is estimated that Chronic Obstructive
Pulmonary Disease (COPD) alone cost $30 billion in 2000, over $14 billion from direct care costs
(Control, Chronic Disease Overview, 2008). 2006 data from the Center for Disease Control (CDC) state
over 9.5 million adults were diagnosed with chronic bronchitis within the last year, 4.1 million adults
were diagnosed with emphysema, and 16.1 million adults had been reported to have asthma.
Combined, the CLRD’s were responsible for 130,933 deaths in 2006 (Control, 2008). Noting the
enormous number of people diagnosed with a chronic respiratory disease and the financial cost, treating
individuals with respiratory distress during emergencies alone would require a great deal of medical
resources and staff. The financial cost of treatment is unpredictable.
Chronic Lower Respiratory Diseases (CLRD) are a group of diseases that include asthma,
bronchitis, COPD, and emphysema. The CLRD’s are conditions that restrict or limit the exchange of air
into the lungs and cause shortness of breath, reduced lower limb function, skeletal muscle strength,
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balance, and basic physical actions (Eisner, 2008). Medication and respiratory interventions are
available for treatment of asthma, while chronic bronchitis, COPD, and emphysema are non‐repairable
conditions (Health, 2006).
Chronic Lower Respiratory Disease morbidities and mortalities are associated with high
temperatures and air quality events. CLRD’s are a concern in urban areas because symptoms are
exacerbated by particulate matter and high heat and humidity. Considering these factors, researchers
are watching CLRD’s as an indicator for climate change health outcomes. The respiratory system is
responsible for oxygen and carbon dioxide transfer through the body and is divided between the upper
and lower respiratory tracts. The upper respiratory tract uses the nose, tonsils, adenoids, and trachea to
eliminate and reduce the particulate matter and allergens pushed into the lungs during respiration. The
lower respiratory function is most commonly associated with symptoms of CLRD’s and involves the
bronchi, bronchioles, alveolar duct, and alveoli (Lewis, 2004). During air quality emergencies, heat
events, stressful situations, or acute illness, the respiratory tract becomes vital to maintain oxygen flow
to the body. A lowered oxygen exchange leads to shortness of breath, elevated heart rates, and
generalized weakness can instill anxiety or panic, and in severe cases impair cognitive function (Lewis,
2004). Additionally, public health scientists are concerned that climate change is predicted to intensify
the pollination cycles in plants and molds that trigger CLRD symptoms (Gilmour, 2006), (Peden, 2002),
(Perkins, 2004).
Conceptual Framework: Public Health Geography
Geographic Information Systems (GIS) tools use spatial data of geographic areas to form maps
representing a variety of topics. Maps can portray physical topographic characteristics, population
demographics, health information, or economic development. In public health, GIS is used to identify
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risks, assess hazards, and develop health responses. These actions are loosely grouped under the term
‘health geography.’ Health geography is a combination of social epidemiology, geography, and
cartography, and used to develop a more cohesive picture of health outcomes based on a set place and
population (Cutchin, 2007), (Roberts, 2008). A specific aspect of public health geography are health
indicators. Indicator models are a risk evaluation method used to calculate multiple aspects of health,
the social environment, and the physical environment. Indicator often combine many risk factors of the
target populations into one value that represents the overall health of the target population or area.
Multiple studies combine public health indicator techniques with GIS technology. For example,
the CDC’s Geospatial Research, Analysis, and Service Program (GRASP) used a composite indicator based
on four risk categories to calculate a ‘Human Vulnerability Assessment Index (HVA).’ The four categories
include a variety of 15 risk factors, loosely grouped by economic measurements, personal and household
assessments, housing and transit evaluations, and race and ethnicity factors. Mapping techniques used
four separate maps to represent the four major indicator groups for each study area (Keim, 2007). A
study by Knowlton used an indicator model to project hospital admissions associated with increasing
temperature (Knowlton K. L., 2007). The indicator model is significant to planners who would benefit
from knowing where hospital admissions raise the most. Although dated, a study by Dever in 1988 is an
excellent example of how a variety of social factors can be identified, classified, and measured. Dever
developed a social vulnerability index to rank health justice in Alabama using 13 variables. The variables
were converted to five distinct categories and using GIS technology mapped the five indexes including
social pathology, economic resources, education level, access to health care, and health status. The
total indexes were combined and mapped in a scale of ‘poor, fair, good, and excellent.’ Tangible results
from this study helped the Alabama Department of Health identify neighborhoods not reached by health
services (Dever, 1988).
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Research Questions
There are two key research questions addressed in this paper. First, does the SEHIC indicator
accurately account for demographic and vulnerability inequities?
Rational: First, the SEHIC indicator template calculations consider the vulnerability of both
populations to be equal. However, existing literature suggests that elderly living alone are more at risk
than below poverty line populations during weather events. Second, below poverty line groups far
outnumber the vulnerable elderly in the Twin Cities.
Second, does the addition of CLRD hospitalization data to the existing SEHIC template provide
evidence of errors in the SEHIC calculation methods?
Rational: The SEHIC template is not designed to be compatible with health data and therefore
difficult to relate to in Public Health significance. However, CLRD is related to both elderly living alone
and below poverty line populations. Using the 2000 CLRD hospitalization records as a case study, the
existing SEHIC template can be analyzed by overlapping the CLRD data on the indicator template.
Methodology:
As a health indicator, the SEHIC template is considered an observational epidemiological study.
In theory, an epidemiological study maybe designed to formulate hypotheses about relationships
between health outcomes and environmental exposures. Observational studies generally use
population data sets and can be used for hypothesis generating, testing, and tabulating data.
Specifically, the SEHIC model is an Ecologic study design. Ecological studies are used to identify
exposures in populations but data results are not transferable to individuals.
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Epidemiological studies use five measurements to verify the content of the study including
consistency, biological plausibility, gradient, temporality, and strength of association (Aschengrau,
2003). First, the consistency of the study is determined by the study’s stability during retesting. This
paper is primarily a challenge to the calculation methods used by the SEHIC indicator. Results of this
paper show inconsistencies and flaws in the indicator that need to be addressed before it be used on a
larger scale.
Second, biologic plausibility refers to how well the study adheres to biological agents and
resulting health outcomes. The literature base shows a positive correlation between temperature and
air quality emergencies resulting in higher morbidity and mortality rates among vulnerable populations.
Biologically, this can be explained by acknowledging that elderly living alone and populations below the
poverty line are at increased risk given their limited resources, reduced access to healthcare,
substandard housing, and depleted social support networks.
Third, the gradient response is a major theme behind the development of vulnerability indexing.
In this study the gradient response examined if higher temperatures or lower air quality result in higher
morbidity and mortalities. Although measuring the extent of morbidity and mortalities in the population
was outside the scope of this project, the literature has shown a significant correlation between weather
and health.
Fourth, temporality is used to confirm that the exposure precedes the health outcome. Specific
to this study, temporality first identifies the environmental exposure as heat and air events, and then
identifies the vulnerable population with increased morbidity or mortality as the result. The
environmental exposures examined in the study have previously been identified by the SEHIC group and
the exposures’ legitimacy confirmed with literature reviews.
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Fifth, the most statistically significant measure of causality in epidemiological studies is the
strength of the association. These measures calculate the association between exposures and health
outcomes. Although this study is an epidemiological study, it does not have individual health
information, thus the study cannot use strength of association measurements. Therefore the strength
of the study is based on the study design, results, and conceptual recommendations of the SEHIC
indicator.
Study Design
The SEHIC indicator analysis uses biostatistical calculations to examine possible bias in the
calculation methodology. The study involves a three‐step process, plus a case study (Figure 3). First, the
SEHIC vulnerability scores of the Twin Cities areas were calculated according to the published template
(Appendix 1). Second, to test the hypothesis of unequal representation, elderly living alone and below
poverty line groups are calculated and analyzed separately. Third, after identifying the vulnerable
populations separately, recommendations are made to adjust for demographic and vulnerability
inequalities. Fourth, after identifying each population separately and making recommendations to
SEHIC, information from Chronic Lower Respiratory Diseases is used as a case study. The purpose of the
case study was to examine the accuracy of recommendations to SEHIC.
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Figure 4: Information Collection Procedure
Data Collection:
Information for the SEHIC analysis was gained from the 2000 U.S. Census Bureau. The Census
Summary Files are sample of the population, roughly 1 in 6 complete the survey. Information in the
summary files includes detailed population data about the place of birth, employment, housing
information, education and ethnicities (Bureau, 2008). Based on the template, census date for all seven
counties was downloaded and interpreted using Microsoft Excel software.
CLRD data was accumulated through the Minnesota Hospital Discharge Database, purchased
yearly from the Minnesota Hospital Association. Cases of CLRD include Minnesotans aged 65 and older
in 2000 and hospitalized within the Twin Cities zip codes. Included cases where identified from billing
information from the physician diagnostic codes and records. Tabulated cases were identified by the
International Classification of Diseases‐9 codes of the four disease including 490 (COPD), 491.2
(bronchitis), 492 (emphysema), 493 (asthma), and 496 (chronic airway obstruction) (Organization,
Diseases of the Respiratory System, 2008).
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Results:
Description of the Original SEHIC Template Calculations:
SEHIC is a health indicator that calculates the vulnerability of two populations, elderly living alone
(ELA) and below poverty line population (BPL) in one measurement. SEHIC’s indicator template analysis
requires two steps. First, establishing a standard to measure each tract against. Second, calculating
each tract’s vulnerability against the set standard. The basic calculations includes:
SEHIC Total vulnerability score % = (MN BPL % ‐ Tract BPL %) + (MN ELA % ‐ Tract ELA %)
Issues of Concern 1: No Set Standard for Measuring Census Tract Vulnerability:
Although the template directs the users to measure the risk of each tract BPL and ELA the
average, the template does not identify which average to use. The significance of choosing an
appropriate average is to make the results as realistic as possible to aid public health planners in
preparing for heat and air quality emergencies. The user could complete vulnerability calculations using
averages from a single county, the seven county metropolitan areas, the state of Minnesota, the
Midwest region of the U.S., or the U.S. (Table 1). Changing the standard vulnerability changes the
amount of vulnerable areas seen in the study area. Differences could be skewed to portray either a
larger or less significant problem depending on the need. Additionally, identifying the level of
vulnerability in urban areas may have financial implications from grants and city allocations to combat
problems.
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Table 1: ELA and BPL Averages (Census, 2008)
Averages ELA % BPL %
U.S. 6.15% 12.37%
Minn. 5.75% 7.94%
7 County 4.35% 6.91%
Anoka Co. 3.04% 4.19%
Hennepin
Co.
4.93% 8.27
Solution: Minnesota Averages Set as Standard for Establishing Census Tract Vulnerability
Therefore, this study uses averages of below poverty line and elderly living alone individuals
based on the 2000 Minnesota averages. In 2000, the average number of below poverty line individuals
was 7.94% (Government, 2009). Elderly individuals living alone accounted for 5.75% of the population
(Government, 2009). Using the Minnesota averages, the extracted SEHIC equation is:
SEHIC Total vulnerability score %= ∑ 7.94 ‐ [(tract BPL population / total tract population) *100] + 5.75 ‐
[(tract ELA population / occupied housing units) *100]
Calculating Census Tract Vulnerability Scores Using the SEHIC Indicator:
To effectively demonstrate the calculations, each group is calculated and explained separately.
Below Poverty Line Calculations:
Census Tract BPL % = 7.94‐ [(tract BPL population / total tract population) x 100]
The measured below poverty line risk in each census tract equals the tract 1999 BPL population
per tract divided by the total tract population in 1999, multiplied by 100. The result is the census tract’s
percent of below poverty line individuals. The calculation numerator equals the number of Minnesotans
per census tract identified by the 2000 U.S. Census Bureau Summary File 3 data series P087002 as
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having an income below the poverty line in 1999. The denominator source is the total tract population
accounted for during the 2000 U.S. Census under data series number P087001 in Summary File 3.
Determining the specific vulnerability of each tract is determined by subtracting the census
tract’s BPL percentage from Minnesota’s BPL, 7.94 % (Government, 2009). Negative scores for the tract
equate to greater risk; a positive result equals a less vulnerable census tract during heat or air quality
emergencies.
Elderly Living Alone Calculations:
Census Tract ELA % = 5.75‐ [(tract ELA population / total occupied housing units) x 100]
Calculations to identify the percent of elderly living alone in each tract require the same two step
process as determining the risk associated with BPL in census tracts. First, the calculation identifies the
percentage of elderly living alone in every tract. Second, the tract percentage is subtracted from the
Minnesota average ELA percentage to determine if the tract is above or below the standard level of risk.
A negative calculated value indicates that the tract has more elderly living alone than the state average
equaling a greater risk area, a postive number indicates fewer numbers of elderly living alone and a
lower risk during weather emergencies.
The calculation specifically includes the number of elderly living alone in each census track
divided by the total number of occupied housing units in each census tract multiplied by 100. Elderly
living alone are defined as the number of Minnesotans identified in the 2000 U.S. Census Bureau
Summary File 3 series as male or female householders, who live alone, and are aged 65 or over. The
denominator source is the total occupied housing units during the 2000 U.S. Census, in Summary File 3,
data set H019001. Each tract percentage is subtracted from Minnesota’s percentage of elderly living
alone, roughly 5.75% (Government, 2009) to establish the risk in each tract.
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SEHIC Issue of Concern 2: Vulnerability Scores Calculated in Percentages
A composite score using percentages is an issue of concern because it does not account for
either demographic or vulnerability differences between the populations. An example from the
database analyzed according to the SEHIC calculations shows inequalities (Table 2). First, the 2000
census showed BPL individuals outnumbering ELA 4:1 (Bureau, 2008). The demographic inequalities
cannot be accounted for by using percentages, and shown in tract 1 (Table 2) where BPL out numbers
ELA by 700 individuals. Since the tract has more BPL that ELA, the composite score lists the census tract
as very high risk though ELA are considered at higher risk during emergencies.
Second, the percentage calculations do not adapt of the vulnerability differences in the
population. Examined literature shows ELA are at higher risk during heat and air quality emergencies
(Argaud, 2007) (Thomas, 2002) (Conti, 2007), (Naughton, 2002). In census tract 2 (Table 2), there are
many elderly living alone but the few number of BPL individuals gives the tract a low risk composite
score.
Table 2: Original SEHIC Calculations by Percentage
Census
Tract
Total
Pop
Total
BPL
Pop BPL %
Risk
Factor 1
Total
ELA
Total
Housing
Units ELA %
Risk Factor
2: Total Vuln.
Score
Total Vuln
Score %
BPL
pop/total
pop
7.94-BPL
%
ELA/House
Units 5.75-% ELA
R.F 1 + R.F
2
1 1721 703 40.85 -32.91 0 464 0 -5.75 -38.66
2 2008 109 5.43 2.51 95 1182 8.04 -2.29 0.22
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the SEHIC template by subtracting the tract vulnerability percentages for both groups from their
associated standards.
Second, to identify the mean of the deviated scores, sum the squared deviated scores to equal
the sum of variances:
Third, the standard error ( of the seven county population data is calculated by taking the
square root of the sum of variances: The standard error is the same for each census
tract since it a calculation involving all census tracts.
Fourth, to find the corresponding z‐score for each census tract, the equation equals the original
SEHIC score ( minus the mean (µ), divided by the standard error ( :
(Gravetter, 2000). An example of using the Z‐score calculation to determine the vulnerability level of
ELA in a tract includes: Z‐score = ( 5.75 ‐ tract ELA %) / (
The significance of the standard error models is that it relates the vulnerability of each tract
according to the SEHIC template.
Table 3: Original SEHIC Scores Standardized by Z‐Score
Census
Tract
Total
Pop
Total
BPL
Pop BPL %
Risk
Factor
1
Total
ELA
Total
Housing
Units ELA %
Risk
Factor
2: Total
Vuln.
Score
Total
Vuln
Score
Vul. Z-
Score
BPL
pop/total
pop
7.94-
BPL %
ELA/House
Units
5.75-%
ELA
R.F 1 +
R.F 2
SEHIC σ
= 9.52
x µ x µ (µ-x)/σ
1 1721 703 40.85 -32.91 0 464 0 -5.75 -38.66 -4.06092
2 2008 109 5.43 2.51 95 1182 8.04 -2.29 0.22 0.023109
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populations. The SEHIC map seems to overly represent areas with below poverty line individuals
regardless that the literature indicates elderly have a higher risk during emergencies.
In census tracks where both elderly living alone and below poverty line percents were above the
Minnesota average, the areas are considered at greatest risk. In these areas, the SEHIC indicator
identified 24,321 people (21%) in 43 of 686 tracts with extremely high potential for morbidity and
mortality. Within these small areas, below poverty line individuals account for 80% while elderly living
alone make up only 20%.
Table 4: Results from Original SEHIC Calculations (Row Totals)
Below Poverty Line Elderly Living Alone Total
# Tracts Identified
by SEHIC
163/198 (82%) of SEHIC
tracts
79/198 (39%) of SEHIC tracts 198 (29%) of 7 Co. tracts
SEHIC Population
Identified
102,949 (90%) of SEHIC
Pop
10,822 (10%) of SEHIC Pop 113,71 (51%) of all Vul.
Pop.
High Risk Areas
43/198 (22%) of
SEHIC Area
19,660 (80%) of High
Risk pop.
4,661 (20%) of High Risk pop. 24,321 (21%) of total
SEHIC
SEHIC Issue of Concern 4: SEHIC Template is Incompatible with Other Data
As stated, vulnerability scores in percents have a number of limitations. In addition to not
accounting for demographic inequalities, percentages are not compatible with other sources of data.
Although the SEHIC indicator is clearly a public health related measure, most health related analysis use
health outcome/population rates. This is especially relevant in epidemiological studies.
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Solution: Calculate Vulnerabilities Using Rate per 10,000 People
To make the SEHIC indicator more compatible with other health data, the calculation formula
has been altered to portray the number of vulnerable people per 10,000, expressed in a rate. The
benefit of using a rate is that it retains the original vulnerability of the tract, only represented as a rate.
Changing the calculation methods from using a percentage (1/100) to a rate of 1/10,000 does not
change the relative vulnerability.
Individual Vulnerable Population Analysis:
In order to investigate the inequalities in the SEHIC calculations, each vulnerable population is
mapped separately. Separating the maps into two individual analyses allows the researcher to analyze
the patterns of vulnerability.
Below Poverty Line Groups:
Tract BPL Z‐score = MN average (value 773.44 per 10,000) ‐ [(1999 BPL/total 1999 pop)*10,000] /
(value of 942.76)
According to the 2000 census, 179,316 were listed as living below the poverty line in the Twin
Cities. In addition, the number of at risk BPL individuals was changed from vulnerable population
percentages to a rate of the number of vulnerable people per 10,000 individuals. Additionally, the BPL
scores are changed into Z‐Scores for map scales and mapped by five color gradients associated with risk
levels (Figure 6).
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The Twin Cities metropolitan counties account for over 44,000 elderly individuals living alone,
24.9% of all those counted in Minnesota. Statistics from the 2000 census show the number of elderly
living alone in occupied housing in Minnesota to be 862.38 per 10,000, the standard for measuring at
risk areas.
In 2000, 10,822 vulnerable elderly were recognized in the original SEHIC template, measuring just
10% of the total vulnerable population in 39% of the census tracts. However, in the individual ELA
analysis, 139 (70%) of census tracts reported elevated rates of ELA.
The ELA analysis identified 18,635 ELA people, 7,800 more than the original calculations. The
100 tract difference and number of people identified in the separate analysis suggest a drastic
underrepresentation in the original template. Additionally, the spatial patterns show vulnerable elderly
groups residing in suburban areas and not highly associated with below poverty line areas but did not
show up on the original SEHIC indicator map.
Recommendations’ to SEHIC: Adjusted SEHIC Indicator:
Recommendations to SEHIC are based on the four major issues of concern found in the original
calculations that led to errors. To adjust for the bias, an “Adjusted SEHIC indicator” has been developed
to adapt for the demographic inequalities, promote data compatibility, and encourage accurate and
standardized mapping techniques.
The Adjusted indicator calculation equals the sum of the individually calculated BPL and ELA Z‐
scores. By adding the Z‐scores of each vulnerable group together to create an adjusted vulnerability
score, the score accommodates for the drastic overpowering of BPL to ELA people in the Twin Cities
(Table 3). Adjusted SEHIC tract Score Calculations:
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Figure 8: Adjusted SEHIC Vulnerable Areas
In a comparison between the original and adjusted SEHIC models, the adjusted calculation
method identified 128,211 at risk individuals. 3,117 more than the original template in an additional 38
census tracts (Figure 8). The original SEHIC map includes 88% BPL population and 12% ELA. The
adjusted SEHIC template changed the BPL population to 83% and ELA to 17%.
Table 6: Analysis of Original and Adjusted SEHIC Results (Row Totals)
Original SEHIC Below Poverty Line Elderly Living Alone Total
# of Tracts
Identified
163/198 (82%) of SEHIC
tracts
79/198 (39%) of SEHIC
tracts
198 (29%) of 7 Co. tracts
SEHIC Population 102,949 (90%) of SEHIC 10,822 (10%) of SEHIC 113,71 (51%) of all V.Pop
High Risk Areas
43/198 (22%)
19,660 (80%) of High Risk
pop.
4,661 (20%) of High Risk
pop.
24,321 (21%) of total
SEHIC
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Adjusted SEHIC
(A.S)
Below Poverty Line Elderly Living Alone Total
Tracts 142/235 (60%) of A.S. 139/235 (59%) of A.S 235 (34%) of 7 Co. tracts
Population 106,285 (83%) of A.S 21,926 (17%) of A.S 128,211 (57%) of all V.Pop
High Risk Areas
46/235 (20%)
20,708 (80%) of High Risk 5,058 (20%) of High Risk 25,766 (20%) of total A.S
In the spatial analysis, 235 census tracts were identified as vulnerable in the Adjusted SEHIC
analysis. Of those, 142 were BPL areas, a decrease of 11 from the original template. In contrast, elderly
living alone tracts accounted for 139 of the 235 tracts, an increase of 60 tracts from the original SEHIC
template. The nearly equal number of tracts associated with the vulnerable population is significant
because it equally represents the vulnerable areas and accommodates for the overwhelming burden of
below poverty line individuals against elderly individuals.
Associated with the change in tract distribution the population percentages saw a reduction in
the number of BPL individuals and an increase in ELA. Additionally, an additional 1,500 very high risk
individuals were identified, those who fit census tracts with elevated ELA and BPL scores. Although
more individuals were identified, the distribution did not change. This can be explained from the small
percentage of individuals relative to entire vulnerable population.
Case Study: Chronic Lower Respiratory Disease:
Chronic Lower Respiratory Diseases (CLRD) has historical excess morbidity and mortality levels
among low‐income elderly populations during heat and air quality emergencies. Analyzing CLRD data in
the Twin Cities accomplishes two tasks. First, to incorporate health data into the existing SEHIC
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study, to identify the vulnerable populations where diseases are currently overlapping to aid in
preventive public health planning.
Analyzing the Disease indicators requires two changes in spatial analysis. First, to use the rate
calculations. CLRD data is only available by rate, thus the SEHIC calculations must be based in rates to be
accurate. Second, CLRD data is based on Zip code, not by census tract. Therefore, the unit of spatial
analysis is called a ZIP Code Tabulation Areas (ZCTA’s) (Division, 2001) is used as the mapping base.
ZCTA’s are a sort of ‘best‐fit’ polygon of census tracts and ZIP codes, which have overlapping boundaries.
The change to ZCTA’s does not change the vulnerability index, only a slight appearance difference in the
map. The GIS method to combine the census tracts and Zip Codes into ZCTA’s eliminates the ability to
identify the number of people associated with outcomes. Additionally, the number of ZCTA’s drastically
increases to 1316, verses 686 census tracts.
Disease Indicator # 1: Original SEHIC Template and CLRD
Hospitalizations:
The Disease Indicator 1 (DI1) is the combination of the original SEHIC indicator in rates and the
CLRD data in rates.
ZCTA Vulnerability by Z‐Score = (Original SEHIC template scores) + (CLRD Scores)
Z‐scores are especially important to incorporate into this model because the average rate of
SEHIC vulnerability is 1348.60 people per 10,000 while the CLRD rate is only 8.14 people per 10,000.
Even the largest prevalence in CLRD rates would have little effect on a low SEHIC vulnerability rate.
In the spatial analysis, it is important to recognize that darker areas are considered vulnerable by
both SEHIC and CLRD measures. These areas indicate that the vulnerable populations may have
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indicator reduce its potential for interstate use as an accurate and consistent model. Limitations are
loosely grouped into two categories: data and population, and methods
First, limitations in the study related to data include the completion and accuracy of the census.
The populations focused on in this study can be considered relatively transient groups. Below poverty
line populations may be frequently moving to accommodate for jobs or better housing opportunities.
Elderly individuals are also a transient group, possibility moving from family homes to smaller homes or
apartments. Depending on health concerns, they may move to be with their children or into assisted
living centers for more advanced health care.
The census also does not distinguish living standards other than by yearly income. Regardless
that a person makes a below the poverty line income per year, they also may have a life savings that
allows them to live comfortably with excellent health care. In addition, the census also cannot calculate
the clinical age of an individual who may have extensive chronic diseases who would technically be of an
age under 65 though clinically fit descriptions of chronically ill and vulnerable populations.
Data related study limitations include the potential that hospitals could be under or over
reporting CLRD diagnostic codes. Because Chronic Lower Respiratory Diseases have similar symptoms,
doctors may diagnose more than one condition or consider it part of another systematic illnesses.
Additionally, it is impossible to tell if an individual was admitted to the hospital with CLRD more than
once, but the visit would be counted as a separate incident. For example, one person with excessive
CLRD symptoms could potentially skew the CLRD hospitalizations rates by being hospitalized multiple
times.
A methodological study limitation is that this study is a pilot of a template that is under‐
development. Because there is no existing data to compare the results against, it is difficult to weigh the
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effectiveness of the recommended measures other than using census tract analysis. Additionally,
because there is no individual health data, no strength of association calculations can be completed to
add statically significance to the findings. Therefore, the study must rely on the conceptual models to
provide a better health indicator.
Bias:
Study bias is defined as a systemic flaw resulting in a false association between the exposure and
health outcome (Aschengrau, 2003). Bias is difficult to remove once calculated into the data thus the
study design must reactively identify and account for the bias. Methods for reducing bias in
epidemiological studies include large participation numbers, multiple data collection methods, and using
multiple researchers to compare data analysis outcomes. Specific to the SEHIC study, bias types include
confounding and Ecological bias.
Confounding error is defined as an unknown factor that propels a false association between the
exposure and outcome (Gordis, 2004). Criteria for a confounding factor include being associated with
the exposure, being an independent cause of the disease, and the factors cannot lie in the causal
pathway of the exposure‐health outcome track. Confounding factors in quantitative studies are
measured using crude and adjusted relative risk calculations, but because the analysis of the SEHIC
indicator is an Ecological study the individual confounding errors cannot be quantitatively analyzed.
Specific to the SEHIC study, confounding factors may result from using census data, where
individuals’ responses to the census surveys may not explain the circumstances that might include or
exclude them from the vulnerable population. Confounding factors in CLRD calculations may include
misdiagnosis from physicians, or individuals who chose not to be treated for their symptoms due to
financial strain or personal beliefs.
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Ecological Fallacy are unique to Ecological studies and occur when the results of a population
based study are imposed on individuals. Specific to the SEHIC indicator, an ecological bias would suggest
that all individuals that are identified by the census as ‘vulnerable’ might have a greater chance of
morbidity or mortality during a heat or air quality event. In addition, when investigating CLRD rates
among the vulnerable populations, the study must not indicate that all patients with CLRD fit the criteria
of a ‘vulnerable population’ or that all vulnerable individuals will eventually develop CLRD.
Conclusions:
Fervent investigation of the SEHIC indicator reveals that the model identified two vulnerable
populations that need public health surveillance, assistance, education, and resources during air quality
and heat emergencies. Reviewed literature supports the associated between climate change, urban
health, and health outcomes in vulnerable populations. Results identified below both poverty line
populations and elderly living alone to have an excess risk during these events, though elderly
individuals have shown to have a greater risk considering their pre‐existing clinical conditions, and
reduced social and economic capacity.
The study first calculated the data according to the indicator template. From the results, four
issues of concerns were found stemming from the conceptual design of the calculation methodology.
First, the indicator model did not address what average to use if more than one county is analyzed. This
is significant because using a different standard of vulnerability will result in a different number of at‐risk
individuals being identified. The Minnesota state averages for both vulnerable populations were chosen
to serve as the standard to provide the most realistic results for the Twin Cities. Second, the original
SEHIC indicator template used vulnerability scores in percents to show at risk areas. Percentages were
shown to be inaccurate and difficult to map. Therefore, the standard error method was incorporated to
48. Sara Dunlap June 11, 2009
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use Z‐Scores to identify each piece of data in a standardized distribution. The change to Z‐scores made
analysis and mapping easier to complete. Third, the SEHIC template did not establish cut‐off points for
determining high risk individuals from low risk areas. Therefore, Z‐scores computed in the databases
were used as mapping scales and effectively identified at risk areas based on the distribution of the data.
Fourth, the original SEHIC data template is not compatible with other data sources. Therefore, the
vulnerability index was changed to using rates per 10,000 individuals to be well‐matched in other health
data analysis. This change was especially relevant to the CLRD case study.
After discovering the four issues and solutions identified, a new calculation methodology was
developed using the solutions. The new calculation method or ‘adjusted SEHIC indicator’ succeeded in
identifying more vulnerable census tracts, adjusted for demographic and vulnerability inequalities, and
highlighted very high risk areas more than the original SEHIC template.
To investigate the effectiveness of the adjusted SEHIC indicator, Chronic Lower Respiratory
Disease hospitalization was overlaid on the SEHIC template to create two “Disease Indicators.” The DI
that combined the adjusted SEHIC indicator with the CLRD data accomplished identifying more ZCTA
areas, the same proportion of CLRD percentages, and more SEHIC vulnerabilities areas.
Based on the testing of the original SEHIC model and development of an adjusted model, the
researcher feels confident submitting these results to the SEHIC indicator development team.
Additionally, the researcher hopes the team will consider these issues when designing future indicators.
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Appendix 1: SEHIC “How‐To‐Guide”
Indicator: Susceptible Populations to Health
Measure: Heat Vulnerability Index: Percent of Households of Elderly Living Alone and Percent Below the
Poverty Level at the Census Tract
How‐To‐Guide
1. Got to U.S. Census website, http://factfinder.census.gov/home/staff/main.html?_lang=en
2. Select “get data” under “Decennial Census”
3. Select “Census File 3”
4. Select “detailed tables:
5. Select “Census tract” as geographic type; your state and county of interest, and select “all census
tracts”
6. Click on the ‘add’ button. All census tracts in the selected country should appear. Then click
“next”
7. Select “P87 Poverty Status in 1999 by Age” and “H19 Tenure by Housing Type (including living
alone) by Age of Householder.” Click “add” to add each one. Click “show results.”
8. Table of results will appear. Click “print/download” at the top of the page.
9. Select “download” and “Microsoft Excel zip file”
10. Download zip file to a folder you specify on your hard drive.
11. Open the zip file. There will be two excel files. One has geographic information, including census
tract number, and the other file will have the population data. (In the population data file, the
census tract number is also embedded in a variable called “geographic identifier.”
12. In the population excel file, erase all columns except “geography identifier, P087001, P087002,
H019001, H019037, H019038, H019054, and H019055”.
13. Create a new column and calculate percent of population below poverty level by dividing
P087001 by P087002 and multiplying by 100.
14. Compute the mean for the percent of the population below poverty level.
15. Create a new column and center each value of the percent of the population below the poverty
level by census tract by subtracting each value from the mean
16. Add the total male householders living alone (65+) by adding columns H019037 and H019038.
Do the same for females (H019054 and H019055.) Sum the males and females by census tract.
17. Compute the percent of households with elderly living alone by dividing the total in #16 by
H019001.
18. Center the percentages as before in #15
19. Create a final heat vulnerability score by adding the centered values for the two variables.
20. Create a choropleth map by census tract using a standard GIS package. We will need to decide
on standard cut‐points.
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