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


MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): STROKE


Highest-rate SRAs:


Chula Vista

La Mesa



MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): HEART DISEASE


Highest-rate SRAs:


Chula Vista

National City



MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): DIABETES


Highest-rate SRAs:

National City

Chula Vista

Southeastern San Diego



MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): LUNG DISEASE


Highest-rate SRAs:

Chula Vista

Harbison Crest/ El Cajon

National City



MEDICAL ENCOUNTERS BY SUB-REGIONAL AREA (SRA): ASTHMA


Highest-rate SRAs:

National City

Chula Vista

Southeastern San Diego


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

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1

1.1

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1.3

Cancer CHD Stroke COPD Diabetes Asthma

Ris

k of

med

ical

enc

ount

er


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


Ris

k of

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ical

enc

ount

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VERY URBAN AREAS HAVE HIGHER RATES OF MEDICAL ENCOUNTERS


Rural Exurban Suburban Urban Very Urban

0.4

0.8

1.2

1.6

2


Ris

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LOWER INCOME INDIVIDUALS HAVE HIGHER RATES OF MEDICAL ENCOUNTERS


Lowest Low Moderately Low

Moderately High

High Highest

0.2

0.6

1

1.4

1.8


Ris

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INDIVIDUALS AGES 65+ HAVE HIGHER RATES OF MEDICAL ENCOUNTERS


Age 0-14 Age 15-24 Age 25-44 Age 45-64 Age 65+

-1

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Ris

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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

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Rates of Smoking

Rate

of E

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Correlation: .22

20000 30000 40000 500000

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Rates of No Exercise

Rate

of E

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Correlation: .19

1200014000160001800020000220000

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1500

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Rates of Spending in RangeRa

tes

of E

ncou

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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

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Rate of Stroke

Use of Medication

Rate

of S

troke

Correlation: .18

4000 5000 6000 7000 80000

100

200

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500

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800Rate of CHD

Use of Medication

Rate

of C

HD

4000 5000 6000 7000 80000

100

200

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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

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800Rate of CHD

Use of MedicationRa

te o

f CHD

2000 3000 4000 5000 6000 70000

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400

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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


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).


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


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).


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.


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).


PREDICTING RATES OF LUNG DISEASE THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA


2014 2015 2016 2017 2018 2019 2020 2021 2022 20230

50

100

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250

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Projected lung disease rates for Chula Vista



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 *


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


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



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


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



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 ***


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


PREDICTING RATES OF CANCER THROUGH THE RACE/ETHNICITY LENS: ILLUSTRATION OF OUTPUT FROM MODEL FOR CHULA VISTA


2014 2015 2016 2017 2018 2019 2020 2021 2022 20230

50

100

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Projected cancer rates for Chula Vista


+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

rady mbas hackathon presentation

Healthcare