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Transcript of Rady MBAs hackathon presentation
BUILDING A HEALTHIER, SAFER, THRIVING SAN DIEGO
Analysis and perspective from MBA students at the UCSD Rady School of Management
YOUR EXPERT TEAM
WHO WE ARE
ALLISON NOEL
SAPNA HEGDE
FENG JIANG
SABRINA QUTB
JASMINE REZAI
We’re a diverse team of analytics-focused MBA students from the UCSD Rady School of
Management, united with a common purpose of delivering actionable insights that
accelerate the growth of healthy, safe, and thriving communities in San Diego.
OUTLINE
Follow-up ideas
Important questions to ask next
Data limitations and assumptions
INTRO
Predictive model, estimating disease rates through the lens of race/ethnicity
10-yr projections
A look at the 3 factors: smoking, diet, and exercise
Additional factors
Reducing chronic disease
Where we see the highest rates of medical encounters
Who has the highest rates
Trends
THE SITUATION
slides 4 − 20
THE NEXT STEPSslides 21 − 26
PREDICTING WHAT WILL HAPPENslides 27 − 41
REVIEW AND FOLLOW UP IDEASslides 42 − 44slides 1 − 3
Q1 Q2 Q3 +4
Q1: THE SITUATION
An analysis of trends across disease, economics, and behavior data
Q1: THE SITUATION
EXECUTIVE SUMMARY: THE STRONGEST TRENDS
The incidence of disease varies significantly by city and health equity lens.
Certain sub-regional areas have higher rates of medical encounters than others.
Certain groups, as viewed through the five lenses of health equity, have higher rates of medical encounters than others.
The concept of “underserved,” as it relates to populations, can be viewed in different ways. We look at it from the perspective of sub-regional area as well as health equity group, looking at disparities in rates of medical encounters.
Underserved groups exhibit different behaviors and have different economic qualities, compared to well-served groups.
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): CANCER
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
Anza-Borrego Springs & Mountain Empire
Chula Vista
La Mesa
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): STROKE
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
Anza-Borrego Springs & Mountain Empire
Chula Vista
La Mesa
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): HEART DISEASE
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
Anza-Borrego Springs & Mountain Empire
Chula Vista
National City
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): DIABETES
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
National City
Chula Vista
Southeastern San Diego
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): LUNG DISEASE
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
Chula Vista
Harbison Crest/ El Cajon
National City
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
WHERE WE SEE THE HIGHEST RATES
MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): ASTHMA
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Total rate is the average number of annual encounters per sub-regional area (SRA) per 100,000 population from 2010-2013.
Highest-rate SRAs:
National City
Chula Vista
Southeastern San Diego
Under or equal to 100101 to 200201 to 300301 to 400401 to 500501 to 600601 to 700Over or equal to 700
Unlike for other diseases, East County is now among the lowest-rate areas
WHO HAS THE HIGHEST RATES
RISK OF MEDICAL ENCOUNTERS BY GENDER IS BALANCED
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Risk is measured as likelihood, relative to San Diego County as a whole, of having a medical encounter in 2012.
Male Female
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
Cancer CHD Stroke COPD Diabetes Asthma
Ris
k of
med
ical
enc
ount
er
WHO HAS THE HIGHEST RATES
BLACK INDIVIDUALS HAVE HIGHER RATES OF MEDICAL ENCOUNTERS
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Risk is measured as likelihood, relative to San Diego as a whole, of having a medical encounter in 2012.
White Black Hispanic Asian Pacific Islander
Other Race Ethnicity
-1
0
1
2
3
4
Cancer CHD Stroke COPD Diabetes Asthma
Ris
k of
med
ical
enc
ount
er
WHO HAS THE HIGHEST RATES
VERY URBAN AREAS HAVE HIGHER RATES OF MEDICAL ENCOUNTERS
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Risk is measured as likelihood, relative to San Diego as a whole, of having a medical encounter in 2012.
Rural Exurban Suburban Urban Very Urban
0.4
0.8
1.2
1.6
2
Cancer CHD Stroke COPD Diabetes Asthma
Ris
k of
med
ical
enc
ount
er
WHO HAS THE HIGHEST RATES
LOWER INCOME INDIVIDUALS HAVE HIGHER RATES OF MEDICAL ENCOUNTERS
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Risk is measured as likelihood, relative to San Diego as a whole, of having a medical encounter in 2012.
Lowest Low Moderately Low
Moderately High
High Highest
0.2
0.6
1
1.4
1.8
Cancer CHD Stroke COPD Diabetes Asthma
Ris
k of
med
ical
enc
ount
er
WHO HAS THE HIGHEST RATES
INDIVIDUALS AGES 65+ HAVE HIGHER RATES OF MEDICAL ENCOUNTERS
Notes: Medical encounters includes hospitalizations, deaths, and emergency department discharges. Risk is measured as likelihood, relative to San Diego as a whole, of having a medical encounter in 2012.
Age 0-14 Age 15-24 Age 25-44 Age 45-64 Age 65+
-1
0
1
2
3
4
5
6
7
Cancer CHD Stroke COPD Diabetes Asthma
Ris
k of
med
ical
enc
ount
er
TRENDS WE SEE
WHO ARE THE UNDERSERVED? TWO WAYS OF LOOKING AT IT:
• Chula Vista
• Harbison/El Cajon
• La Mesa
• National City
• South Bay
• Fallbrook
• Southeastern San Diego
• Anza-Borrego Springs
Individuals ages 65+
Those living in very urban areas
The lowest-to-low income earners
Black individuals
SRAs with highest disease rates Groups with disparities, as seen through the 5 health equity lenses
*See slide notes for more detailed description of how SRAs with highest disease rates were determined
TRENDS WE SEE
THE TWO METHODS OF DEFINING UNDERSERVED ARE SIMILAR
6% black
12.2% age 65+
$55,000 median income
38% very urban
underserved geographies
well-served geographies
4% black
12.3% age 65+
$74,000 median income
9% very urban
Underserved geographies, defined by rates of medical encounters, are also more likely home to underserved groups, as defined through the 5 lenses. For example:
TRENDS WE SEE: BEHAVIORS OF THE UNDERSERVED
COMPARED TO THE WELL-SERVED, UNDERSERVED GEOGRAPHIES ARE…
18% more likely to have smoked in the last 12 months
13% more likely to not exercise during a typical week
7% less likely to be presently controlling their diet and 22% less likely to buy foods labeled as natural/organic
11% less likely to have a savings account
21% less likely to have volunteered for a charitable organization in the last 12 months
17% less likely to have voted in a federal/state/local election in the last 12 months
TRENDS WE SEE: DEMOGRAPHICS & ECONOMICS OF THE UNDERSERVED
COMPARED TO THE WELL-SERVED, UNDERSERVED GEOGRAPHIES ARE…
79% less likely to have a bachelor's degree
19% more likely to have a disability
32% more likely to live below the federal poverty level
35% more likely to be unemployed
43% more likely to have the marital status “separated”
19% more likely to be widowed
Q2: THE NEXT STEPS
Identifying protective behaviors that may reduce the risk of disease
Q2: THE NEXT STEPS
EXECUTIVE SUMMARY: POTENTIALLY PROTECTIVE BEHAVIORS & FACTORS
The three behaviors identified by the Live Well program—smoking, lack of exercise, and poor diet– are correlated with total (combined) rates of medical encounters across chronic diseases, as well as with rates broken out by disease.
Additional factors are correlated with rates of medical encounters
A college education: Bachelors degrees are associated with lower rates of disease
Unemployment: More unemployment is associated with higher rates of disease
Household income: Higher incomes are associated with lower rates of disease
Sinus and headache medication: Higher rates of sinus and headache medication use are associated with higher rates of disease. This could be a proxy for stress.
Depression medication: Higher rates of depression medication use are associated with higher rates of disease. This could be a proxy for mental health.
Separation (as a marital status): Higher rates of separated people is associated with higher rates of disease; marriage tends to reduce rates of some diseases.
Q2: RELATED BEHAVIORS
SMOKING, INACTIVITY, AND POOR DIET ARE POSITIVELY CORRELATED WITH TOTAL RATE OF MEDICAL ENCOUNTERS
Smoking Lack of exercise Fast food spending ($50-100/week)
10000 15000 20000 25000 300000
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Rates of Smoking
Rate
of E
ncou
nter
s
Correlation: .22
20000 30000 40000 500000
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Rates of No Exercise
Rate
of E
ncou
nter
s
Correlation: .19
1200014000160001800020000220000
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Rates of Spending in RangeRa
tes
of E
ncou
nter
sCorrelation: .16
Q2: THE NEXT STEPS
EXAMPLE: DEPRESSION MEDICATIONS ARE CORRELATED WITH STROKE, HEART DISEASE, AND LUNG DISEASE
Correlation: .17
4000 5000 6000 7000 80000
100
200
300
400
500
600
Rate of Stroke
Use of Medication
Rate
of S
troke
Correlation: .18
4000 5000 6000 7000 80000
100
200
300
400
500
600
700
800Rate of CHD
Use of Medication
Rate
of C
HD
4000 5000 6000 7000 80000
100
200
300
400
500
600
700
800
900
1000Rate of COPD
Use of Medication
Rate
of C
OPD
Correlation: .14
Q2: THE NEXT STEPS
EXAMPLE: SINUS/HEADACHE MEDICATIONS (A PROXY FOR STRESS) ARE CORRELATED WITH STROKE, HEART DISEASE, AND LUNG DISEASE
0
100
200
300
400
500
600Rate of Stroke
Use of Medication
Rate
of s
troke
2000 3000 4000 5000 6000 70000
100
200
300
400
500
600
700
800Rate of CHD
Use of MedicationRa
te o
f CHD
2000 3000 4000 5000 6000 70000
200
400
600
800
1000
1200Rate of Cancer
Use of Medication
Rate
of C
ance
r
Correlation: .24 Correlation: .22 Correlation: .19
Q2: REDUCING RATES OF DISEASE
REDUCING RATES OF MEDICAL ENCOUNTERS
Based on our analysis, there are a number of potential ways to reduce the rate of medical encounters in underserved populations.
Reducing rates of smoking and increasing rates exercise and healthy eating are three obvious (though difficult!) ways to move the needle.
Other ways include:
Promoting education Reducing unemployment Encouraging volunteering Supporting healthy marriages Controlling depression and making clear any risks of medications
** Our regression analysis will shed further light on which of these factors most strongly drive rates of medical encounters for chronic diseases
Q3: PREDICTIONS
Projecting disease rates in under-served communities for the next ten years through the
health equity lens of race/ethnicity
Q3: PREDICTING WHAT WILL HAPPEN
EXECUTIVE SUMMARY: PREDICTING AND PROJECTING MEDICAL ENCOUNTERS FOR THE UNDERSERVED
As detailed on the following slides, a number of key factors emerged as important in determining medical encounters for chronic diseases for the underserved. They are:
Exercise—exercise decreases risk of disease
Diet–less fast food decreases risk of disease
Smoking—increases risk of disease
Being married—decreases risk of disease
Being educated (having a Bachelor’s degree)—decreases risk of disease
Volunteering—decreases risk of disease
Race/ethnicity—impacts risk of disease
*Note: in the following slides, underserved is defined by SRA, as discussed in Q1
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING DIABETES MEDICAL ENCOUNTERS IN UNDERSERVED GROUPS
Coefficient Std. error T-value P-value SignificanceIntercept 173.94 147.06 1.183 0.239
Year 0.79 6.07 0.13 0.897 Have a Bachelor's degree -1,415.69 322.33 -4.392 < 0.001 ***
Married -314.56 167.92 -1.873 0.063 Volunteered -1,283.48 434.96 -2.951 0.004 Eat fast food 2,927.98 888.51 3.295 0.001
Black -67.66 20.84 -3.246 0.001 **Hispanic 72.34 20.84 3.471 0.001 ***
Asia Pacific -100.69 20.84 -4.831 < 0.001 ***Other -112.66 20.84 -5.405 < 0.001 ***
R-squared: 0.536 Adjusted R-squared: 0.508 Number of observations: 160
Conclusion: Key drivers of diabetes include race/ethnicity, eating fast food (increases risk); volunteering, being married, and being educated, which is associated with socioeconomic status (decreases risk).
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF DIABETES THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
*Due to the positive (though insignificant) coefficient on Year, rates are increasing. Also, due to the insignificance of other predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
50
100
150
200
250
300
Projected diabetes rates for Chula Vista
White Black Hispanic Asian Other
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING HEART DISEASE MEDICAL ENCOUNTERS IN UNDERSERVED GROUPS
Coefficient Std. error T-value P-value Significance
Intercept 1.457 0.116 12.542 < 0.001 ***Year -0.025 0.052 -0.485 0.629
Have a bachelor's degree -0.601 0.163 -3.692 < 0.001 ***Volunteered -0.177 0.147 -1.202 0.231
Saved -0.029 0.114 -0.256 0.798 Exercises -0.549 0.226 -2.428 0.016 *
Eats fast food 1.126 0.365 3.082 0.002 **Black -1.86 0.164 -11.324 < 0.001 ***
Hispanic -1.631 0.164 -9.928 < 0.001 ***Asia Pacific -1.854 0.164 -11.288 < 0.001 ***
Other -1.938 0.164 -11.801 < 0.001 *** R-squared: .595 Adjusted R-squared: .568 Number of observations: 160
Conclusion: Key drivers of heart disease include race/ethnicity, eating fast food (increases risk); volunteering, exercising, saving, and being educated, which is associated with socioeconomic status (decreases risk).
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF HEART DISEASE THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
10
20
30
40
50
60
70
Projected heart disease rates for Chula Vista
Black Hispanic Asian Other
*Due to the negative (though insignificant) coefficient on Year, rates are falling. Also, due to the insignificance of other predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING LUNG DISEASE MEDICAL ENCOUNTERS IN UNDERSERVED GROUPS
Coefficient Std. error T-value P-value SignificanceIntercept 534.786 169.997 3.146 0.002 **
Year -2.617 8.594 -0.304 0.761 Have a bachelor's degree -981.402 344.78 -2.846 0.005 **
Married -383.063 289.367 -1.324 0.188 Volunteered -516.472 779.143 -0.663 0.508
Smoked in last year 680.947 642.199 1.06 0.291 Black -197.844 29.171 -6.782 < 0.001 ***
Hispanic -85.719 29.171 -2.939 0.004 **Asia Pacific -227.656 29.171 -7.804 0.001 ***
Other -240.156 29.171 -8.233 0.001 *** R-squared: 0.479 Adjusted R-squared: .448 R-squared: 0.448 Number of observations: 160
Conclusion: Key drivers of lung disease include race/ethnicity, smoking (increases risk); volunteering, being married, and being educated, which is associated with socioeconomic status (decreases risk).
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF LUNG DISEASE THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
*Due to the negative (though insignificant) coefficient on Year, rates are falling. Also, due to the insignificance of other predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
50
100
150
200
250
300
350
Projected lung disease rates for Chula Vista
White Black Hispanic Asian Other
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING ASTHMA MEDICAL ENCOUNTERS IN UNDERSERVED POPULATIONS
Conclusion: Key drivers of asthma include race/ethnicity, being on Medicaid (increases risk); volunteering, being married, and being educated, which is associated with socioeconomic status (decreases risk).
Coefficient Std. error T-value P-value SignificanceIntercept 0.174 0.127 1.368 0.173
Year -0.002 0.062 -0.038 0.97 Have a Bachelor's degree -0.216 0.088 -2.462 0.015 *
Married -0.105 0.149 -0.701 0.484 Volunteered -0.021 0.126 -0.166 0.869
Have Medicaid 0.233 0.136 1.711 0.089 .Black -0.205 0.18 -1.138 0.257
Hispanic 0.783 0.18 4.353 < .001 ***Asia Pacific -0.701 0.18 -3.901 < .001 ***
Other -0.746 0.18 -4.149 < .001 *** R-squared: 0.512 Adjusted R-squared: 0.483 Number of observations: 160
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF ASTHMA THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
*Due to the negative (though insignificant) coefficient on Year, rates are falling slightly. Also, due to the insignificance of other predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
50
100
150
200
250
300
350
Projected asthma rates for Chula Vista
White Black Hispanic Asian Other
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING STROKE MEDICAL ENCOUNTERS IN UNDERSERVED POPULATIONS
Conclusion: Key drivers of stroke include race/ethnicity, eating fast food (increases risk); volunteering, being married, and being educated, which is associated with socioeconomic status (decreases risk).
Coefficient Std. error T-value P-value SummaryIntercept 25.194 133.958 0.188 0.851 Year -1.312 5.477 0.24 0.811 Have a Bachelor's degree -1359.186 401.573 -3.385 0.001 ***Married -120.972 158.864 -0.761 0.448 Volunteered -842.026 569.895 -1.478 0.142 Exercise -1760.873 1889.549 -0.932 0.353 East fast food 4449.409 2269.079 1.961 0.052 .Black -142.094 18.793 -7.561 <0.001 ***Hispanic -51.125 18.793 -2.72 0.007 **Asia Pacific -136.594 18.793 -7.268 Other -156.875 18.793 -8.348 <0.001 *** R-squared: 0.479 Adjusted R-squared: .444 Number of observations: 160
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF STROKE THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
* Due to the negative (though insignificant) coefficient on Year, rates are falling. Also, due to the insignificance of many predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
50
100
150
200
250
Projected stroke rates for Chula Vista
White Black Hispanic Asian Other
Q3: PREDICTING WHAT WILL HAPPEN
MODEL FOR PREDICTING CANCER ENCOUNTERS IN UNDERSERVED POPULATIONS
Conclusion: Key drivers of cancer include race/ethnicity, eating fast food (increases risk); volunteering, being married, exercising, and being educated, which is associated with socioeconomic status (decreases risk).
Coefficient Std. error T-value P-value SignificanceIntercept -3.251 186.99 -0.017 0.986
Year -1.884 7.645 -0.246 0.806 Have Bachelor's degree -2026.8 560.551 -3.616 < .001 ***
Married -145.702 221.757 -0.657 0.512 Volunteered -1221.33 795.509 -1.535 0.127
Exercise -2670.471 2637.597 -1.012 0.313 Eat fast food 6802.637 3167.379 2.148 0.033 *
Black -210.562 26.233 -8.027 < .001 Hispanic -84.094 26.233 -3.206 0.002 **
Asia Pacific -204.719 26.233 -7.804 < .001 ***Other -229.281 26.233 -8.74 < .001
R-squared: 0.5 Adjusted R-squared: .467 Number of observations: 160
Q3: PREDICTING WHAT WILL HAPPEN
PREDICTING RATES OF CANCER THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA
*Due to the negative (though insignificant) coefficient on Year, rates are falling. Also, due to the insignificance of other predictors, we are unable to predict with confidence what would happen if protective behaviors that reduce risks of disease are adopted.
2014 2015 2016 2017 2018 2019 2020 2021 2022 20230
50
100
150
200
250
300
350
Projected cancer rates for Chula Vista
White Black Hispanic Asian Other
+4: SUMMARY & LIMITATIONS
Recommendations, next steps, and an outline of limitations and assumptions
+4: FOLLOW UP IDEAS
KEY FINDINGS & RECOMMENDATIONS
BEHAVIORSare notoriously
difficult to change
Study outcome of past initiatives to shed light on key
factors for success in changing
behavior
Potential partners: businesses,
including healthcare companies, and
academia
PREVENTIONIs key in building a healthier San Diego
Study impact of family member secondhand
smoke on children (within urbanicity and
socioeconomic frameworks)
Potential partners: schools. Identify and
analyze P.E., arts, and extracurricular
programs
ADDITIONAL ANALYSIS
to identify stress correlations
Study correlation between stress, the four diseases, and
the five lenses
Potential partners: cities. Does community
involvement impact stress levels?
+4: LIMITATIONS & ASSUMPTIONS
ANALYSES WERE LIMITED IN THE FOLLOWING WAYS:
The data are aggregated to the SRA level. This limits the types of analyses we can undertake. Having individual-level data would provide stronger results.
Due to the level of aggregation, the dataset became relatively small as it was cut different ways. This made it difficult to get statistical significance.
Data points with fewer than 5 components were dropped. We treated such observations as 0s. This lowered some of our numbers, relative to others.
Behavioral data were only available for 2013. We assumed these rates were constant from 2010-2013 for our analysis.
Some zip codes in San Diego (and therefore in the shape file downloaded to create the maps) were not present in the data; we assigned SRAs to these zip codes based on what similar cities were nearest the missing values. See notes for details.
THANK YOUSpecial thanks to BIOCOM, livegoode, the County of San Diego, Live Well San Diego, the San Diego Regional Library,
and all other partner organizations and volunteers for their generous support of this event