Data Mining for Racial, Gender, and Social Economic Status Disparities in Thyroid Cancer

Data Mining for Racial, Gender, and

Social Economic Status Disparities

in Thyroid CancerSylvia Le

April 29, 2010

Disparities in Health CareDisparities in health care have serious impact

on the quality of health care.

Identifying health disparities may seem difficult at times, but recognition is essential.

Data mining has been used to effectively recognize several disparities in different areas of health care.

The ThyroidThe thyroid is an endocrine gland

(hormone secreting and producing organ) located anterior to the trachea.

This organ secretes hormones thyroxine (T4) and triiodothyronnine (T3) which help regulate metabolism and growth.

Thyroid cancer can occur at any age, and there are over 20,000 new cases of thyroid cancer every year.

Purpose

Are there race, gender, and socioeconomic

disparities affecting treatment and survival for

thyroid cancer patients?

SEER for Data MiningSurveillance, Epidemiology and End Results

(SEER) program of the National Cancer Institute is the data and software source for this data mining process.

Software: SEER*Stat 6.5.2

Dataset: Incidence - SEER 17 Regs Limited-Use + Hurricane Katrina Impacted Louisiana Cases, Nov 2009 Sub (2000-2007) <Katrina/Rita Population Adjustment>

Data Selection Only Thyroid cancer patients

from Variable Set {Site and Morphology. Site rec with Kaposi and mesothelioma}

where there exists known data for Race - White, Black, American Indian/Alaska Native, Asian/Pacific Islander,

Non-white Hispanic Sex – Male, Female Age - 00 - 85+ years

To limit data size and ensure the more recent information is used, only patients diagnosed from 2000 - 2006 will be used.

Information about socioeconomic status (SES) is not directly available from the patients but rather this data is calculated from US Census data by County. This variable was formed into Quintiles based on Median Household Income.

Total number of patients = 47,278.

Data Mining for Survival Disparities

Compare variables to Survival time in 6-month intervals (000-083 months)

Used SEER*Stat Frequency Session to calculate Count and Frequency (Column%)

Exported tables to .txt and visualized using Excel graphs

Survival Time vs SexCount

Observe Sex Ratio in these two kinds of stacked bar graphs.

Females make up ~75% of the thyroid cancer patients.

612182430364248546066727883

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MaleFemale

Surv

ival

Tim

e (m

onth

s)

6

18

30

42

54

66

78

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

Number of Patients

Surv

ival

Tim

e (m

onth

s)

Survival Time vs SexFrequency

Observe that survival time has rapid decrease in the first year after diagnosis.

The two survival curves do not seem to indicate a disparity between male and female survival.

6 12 18 24 30 36 42 48 54 60 66 72 78 830.00%2.00%4.00%6.00%8.00%

10.00%12.00%14.00%16.00%18.00%

MaleFemale

Survival Time (months)

Freq

uenc

y (%

)

Survival Time vs RaceCount

Observe Rate Ratio in these two kinds of stacked bar graphs.

Notice whites make up ~70% of the patients.

6

18

30

42

54

66

78

0 2,000 4,000 6,000 8,000

WhiteBlackAmerIndAsia/PacHispan

Number of Patients

Surv

ival

Tim

e (m

onth

s)

6

18

30

42

54

66

78

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Surv

ival

Tim

e (m

onth

s)

Survival Time vs RaceFrequencyThe five survival curves do not seem to indicate a disparity

between races in survival.American Indian/Alaska Native have a few unusual blips

probably because their patient population is very low, making the curve susceptible to such increases/decreases.

6 12 18 24 30 36 42 48 54 60 66 72 78 830.00%2.00%4.00%6.00%8.00%

10.00%12.00%14.00%16.00%18.00%



Freq

uenc

y (%

)

Survival Time vs SESCountObserve Quintile Ratio in these two kinds of

stacked bar graphs.Notice Third and Fourth Quintile make up

~40% and ~55% of total number of patients.

6

18

30

42

54

66

78

0 10002000300040005000600070008000

First Quintile - LowestSecond QuintileThird QuintileFourth QuintileFifth Quintile - Highest

Number of Patients

Surv

ival

Tim

e (m

onth

s)

6

18

30

42

54

66

78

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Surv

ival

Tim

e (m

onth

s)

Survival vs SESFrequencyThe five survival curves do not seem to indicate a disparity

between socioeconomic status and survival. The Second Quintile has a few unusual blips for similar reasons as

Native American/Alaska Native did for previous frequency graph; the patient count for this groups is very low.

6 12 18 24 30 36 42 48 54 60 66 72 78 830%

2%

4%

6%

8%

10%

12%

14%

16%

18%



Freq

uenc

y (%

)

Treatment vs SexCountObserve Sex Ratio in these two kinds of

stacked bar graphs.Notice that the fraction of females is still

greater, but less clear for each treatment.

No Surgery, patient died before recommended surgery

Radiation before and after Surgery

Radiation before Surgery

No Surgery, Recommended but no perform for unknown reasons

Radiation after Surgery

0 10000 20000 30000

Number of Patients

Trea

tmen

t

0% 20%

40%

60%

80%

100%

MaleFemale

Trea

tmen

t

Treatment vs SexFrequencyBecause proportion of Radiation before and after

Treatment is so large, it was removed to form the graph on the right so view other treatment graphs.

Females appear to have less Intraoperative Radiation and Males tend to not have recommended surgeries.

No Surgery, patient died before recommended surgeryIntraoperative Radiation


No Surgery, Recommended but patient refused

Radiation before SurgeryNo Surgery, Constraindicated due to other conditions


No Surgery, Not recommended


0.00%

50.00%

100.00%

MaleFemale

Intraoperative Radiation




No Surgery, Constraindicated due to other conditions




0.00%

5.00%

10.00%

Treatment vs RaceCountObserve Race Ratios in these two kinds of

stacked bar graphs.Notice that the fraction of whites is still

greater, but less clear for each treatment.






0 10000 20000 30000


Number of Patients

Trea

tmen

t

0% 20% 40% 60% 80% 100%Tr

eatm

ent

Treatment vs RaceFrequency Because proportion of Radiation before and after Treatment is so

large, it was removed to form the graph on the right so view other treatment graphs.

Several disparities seem to appear: Blacks seem to have the most non-recommended surgeries. American Indian/Alaska Natives most frequently seem to have

no surgery due to patient refusal or other constraints..

Rad after SurgNo Surg, Not recommend

No Surg, Rec but no perform

No Surg, Constrain other

Rad before SurgNo Surg, Rec but Pt refuse

Rad before and after Surg

Intraoperative Rad

No Surg, Pt died before

0.00%

50.00%

100.00%


Intraoperative RadiationRadiation before and after Surgery






0.00%

5.00%

10.00%

Treatment vs SESCountObserve Quintile Ratio in these two kinds of stacked

bar graphs.Notice that Third and Fourth Quintile still make up

the majority of patients, but fraction is less clear that in Survival set.

Notice also that Radiation before and after Surgery treatment makes up ~90% of all treatment counts.






0 5000 10000 15000 20000 25000Number of Patients

Trea

tmen

t

0% 20% 40% 60% 80% 100%


Trea

tmen

t

Treatment vs SESFrequencyBecause proportion of Radiation before and after

Treatment is so large, it was removed to form the graph on the right so view other treatment graphs.

Radar graphs do not seem to indicate disparity in treatment based on socioeconomic status.

No Surgery, patient died before recommended surgeryIntraoperative Radiation







0%

50%

100%First Quintile - LowestSecond QuintileThird QuintileFourth QuintileFifth Quintile - Highest

Intraoperative RadiationRadiation before and after Surgery






0%

5%

10%

ConclusionsNone of Race, Gender or Socioeconomic

Status appear to have Survival disparities.

However, several disparities can be observed with patient Treatment for Race and Gender.

Treatment does not seem to have as significant of disparities for Socioeconomic Status.

Questions?

Documents

Data Mining for Racial, Gender, and Social Economic Status Disparities in Thyroid Cancer