do us thyroid cancer incidence rates increase with

For peer review only

Do US Thyroid Cancer Incidence Rates Increase with

Socioeconomic Status among People with Health Insurance?

Journal: BMJ Open

Manuscript ID: bmjopen-2015-009843

Article Type: Research

Date Submitted by the Author: 31-Aug-2015

Complete List of Authors: Altekruse, Sean; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences Das, Anita; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences Cho, Hyunsoon; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences; National Cancer Center, Division of Cancer Registration and Surveillance Petkov, Valentina; National Cancer Institute, Surveillance Research

Program, Division of Cancer Control and Population Sciences Yu, Mandi; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences

<b>Primary Subject Heading</b>:

Oncology

Secondary Subject Heading: Epidemiology

Keywords: Endocrine tumours < ONCOLOGY, Epidemiology < ONCOLOGY, PUBLIC HEALTH

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Do US Thyroid Cancer Incidence Rates Increase with Socioeconomic Status among People with Health

Insurance?

Sean Altekruse1, Anita Das1, Hyunsoon Cho1,2, Valentina Petkov1, Mandi Yu1

1. National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences

Rockville, Maryland, United States

2. National Cancer Center, Division of Cancer Registration and Surveillance

Goyang, Korea (the Republic of)

Abstract: 238 words

Text: 2 259 words

Tables: 3

Figures: 1

References: 34

Key words: Thyroid cancer; Incidence; Insurance; Socioeconomic Status; Overdiagnosis

Running header: Thyroid cancer, socioeconomic, and insurance status

Correspondence to: Sean F. Altekruse, Surveillance Research Program, Division of Cancer Control and Population

Sciences, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive 4E536, Rockville MD

20850

Telephone: 240.276.6933

Fax: 240.276.7908

E-mail: [email protected]

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Abstract

Objectives: To determine the extent to which access to health care and census tract socioeconomic status influence

incidence of thyroid cancer, and particularly papillary thyroid cancer (PTC).

Design: Relationships between thyroid cancer incidence, insurance and census-tract socioeconomic status (SES)

during 2007-2010 were examined in population-based cancer registries. Cases were stratified by tumor histology,

size and demography.

Setting: Surveillance, Epidemiology, and End Results (SEER) registries covering 25% of the US population.

Results: PTCs accounted for 88% of incident thyroid cancer cases. Small PTCs (≤2 cm) accounted for 60% of

cases. Unlike non-PTC cases the majority of those diagnosed with PTC were <50 years of age and had ≤2 cm

tumors. Rate ratios (RR) of PTC diagnoses increased monotonically with SES among fully insured cases. The effect

was strongest for small PTCs, high- versus low-SES quintile RR=2.7, 95% confidence interval (CI): 2.6–2.9, two-

sided trend test P < 0.0001. For small PTC cases with insurance the monotonic increase in incidence rates with

rising SES persisted among cases younger than 50 years of age (RR=3.3, 95% CI=3.0–3.5), women (RR=2.6, 95%

CI=2.5–2.8) and whites (RR=2.5, 95% CI=2.4–2.7). Among other than fully insured cases, RRs were typically less

than one in high versus the lowest SES strata.

Conclusion: The greater than 2.5 fold increase in risk of PTC diagnosis among insured individuals associated with

high SES may be informative with respect to the contemporary issue of papillary thyroid cancer overdiagnosis.

Strengths and limitations of this study

• The study included 41 072 incident thyroid cancer case during 2007-2010 across 25% of the US population.

• Rate ratios and confidence intervals were used to assess effects of SES and insurance on thyroid cancer incidence.

• Effects were also examined by patient attributes, tumor size, and histology.

• Potential misclassification of histology data from population-based pathology reports was a limitation of this study.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Competing Interests

The authors have no conflicts of interest to declare.

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Introduction

In Surveillance, Epidemiology, and End Results (SEER) registry data for the United States from 1975–2011,

thyroid cancer incidence rates tripled (1-3) while US thyroid cancer mortality trends were steady (1). These

discrepant trends are consistent with cancer overdiagnosis (2-4). Similar patterns are reported in many but not all

industrialized countries (3). Thyroid cancer incidence rates are higher among women than men (5) and among

whites than other major racial groups (6,7). Most of the rising incidence results from the increasing diagnosis of

small papillary thyroid cancers (PTCs) (2-4,8-10).

Access to health insurance contributes to the overdiagnosis of small PTCs (3,11-13) which carry relatively low

risk of death (3). Incidence rates of small PTCs are elevated in high-socioeconomic status (SES) counties (7,14,15)

and census tracts (11,12). Overdiagnosis and overtreatment of PTCs are associated with adverse effects, including

postsurgical complications, extended hospitalization, and lifelong hormone replacement therapy (2-4,16). In

contrast, several non-PTC types, including follicular, medullary, and anaplastic thyroid cancers carry progressively

worse prognoses (17). The present analysis, based on population-level cancer registry data covering approximately

25% of the United States, demonstrates the magnitude of combined effects of neighborhood socioeconomic status,

or SES (18, 19), and personal insurance status on the overdiagnosis of small PTCs including by age, gender, race

and ethnicity.

Materials and Methods

Data were obtained from 16 National Cancer Institute (NCI) SEER registries that cover approximately one-

quarter of the US population. The SEER November 2012 dataset was used for all analyses. Registries included in

analyses were Connecticut, Detroit, Hawaii, San Francisco-Oakland, Atlanta, Iowa, New Mexico, Seattle-Puget

Sound, Utah, Los Angeles, San Jose-Monterey, Rural Georgia, Greater California, Greater Georgia, Kentucky, and

New Jersey. Alaska Native cases were excluded because census-tract attributes were not available, and Louisiana

cases were excluded because of uncertainty about the population impact of Hurricane Katrina on census tract SES.

Case Attributes

Gender and age distributions (<50 years, 50–64 years, and ≥65 years of age at diagnosis) were examined. Race

and ethnicity were defined as Hispanic, non-Hispanic white, non-Hispanic black, and non-Hispanic Asian/Pacific

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Islander. Non-Hispanic American Indian/Alaska Natives and other and unknown race were combined as one group.

Tumors were classified as ≤2cm, >2cm, and unknown size. When ≤10 cases were observed data were suppressed.

Histologic Classification

Only malignant primary thyroid cancers were included in analyses. Thyroid cancer histologic classifications

were coded using the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) (20). A total of

283 cases with poorly specified cancer histologies (ICD-O-3 morphologies 8000–8005) were excluded. ICD-O-3

histology codes for papillary thyroid cancer were 8050,8052,8260,8340,8341,8342,8343,8344. , Other types were

follicular (8290,8330,8331,8332,8335), medullary (8290,8330,8331,8332,8335) and anaplastic thyroid cancer

(8012,8020-8021,8030-8032). Approximately 2% of cases were diagnosed with non-classical thyroid cancer

histologies including 284 cases (1%) with other epithelial neoplasms (8010,8013,8015,8022,8033,8035,8041,8046)

and 377 cases (1%) with other rare specified primary thyroid cancer histologies (8070-8072,8074,8082-

8083,8130,8140-8141,8190,8200-8201,8230,8240,8246,8255,8262-8263,8310,8323,8337,8347,8350,8430,8450,

8452,8460,8480-8481,8504,8507,8560,8570,8588-8589,8800,8802,8810,8830,8890,8980,9040-9041,9071,9080,

9120,9250,9364). These included adenocarcinomas and squamous cell carcinoma, which are referenced on the

SEER thyroid/histology validation list (21) and other specified histologies that are not found in the SEER validation

list but were abstracted from patients’ medical records.

Insurance

SEER data include the six category variable “Insurance Recode” for diagnosis year 2007 forward, derived from

the more detailed “Primary Payer at Diagnosis” variable. For the purpose of the present analysis, categories were

collapsed into “Fully Insured,” and “Other than Fully Insured.” “Fully Insured” is defined by the following seven

categories of health insurance: 1. Insured - Private Insurance: Fee-for-Service, 2. Private Insurance: Managed care,

3. HMO or PPO, 4. TRICARE, 5. Medicare - Administered through a Managed Care plan, 6. Medicare with private

supplement, 7. Medicare with supplement, NOS and 7. Military. “Other than Fully Insured,” included 1. Uninsured

cases; 2. Cases with Medicaid, Indian Health Service insurance and 3. Patients reported to be insured without further

information available. The 1,228 cases with missing data on insurance status were excluded from insurance-related

analysis.

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Census-Tract Level SES

SES is typically measured using county-level attributes as a proxy for individual data. However, county-level

SES attributes are less precise than smaller-area or individual SES attributes. To improve area-based SES estimates,

we used a year-dependent census-tract SES composite index (18) linked to SEER cases by the census tracts of

residence at time of diagnosis. The SES index was generated from seven variables (percent working class

population, percent adult unemployment, educational attainment, median household income, percent of population

living below 150% of national poverty line, median rent, and median home value) capturing three principal

components of SES (income, occupation, education) based on factor analysis (18). The output factor that explains

more than 90% of common variance among these variables is called the “SES index.” This index was constructed

separately for 2007, 2008, 2009, and 2010 using the 5-year estimates from the 2005–2009, 2006–2010, 2007–2011,

and 2008–2012 American Community Surveys, respectively. Census tracts were categorized into SES quintiles

according to this index, with equal populations in each quintile. The first quintile (Q1, lowest SES) is the 20th

percentile or less, and the fifth quintile (Q5, highest SES) corresponds to the 80th percentile or higher. The SES

index used in the present study has the advantage over specific area level SES measures of protecting against

disclosure of case identity through identification of a census tract with a unique permutation of multiple SES

attributes. The index is linked to the SEER data and is available for public use upon request from the authors. A total

of 642 cases were missing data on SES and were excluded from SES-related analysis.

Incidence by Insurance and SES

Age-adjusted incidence rates (22) were calculated by tumor size, insurance status, SES, and histology for cases

diagnosed during 2007–2010, years during which insurance data were available (SEER*Stat version 8.1.5, IMS,

Inc., Calverton, MD). Thyroid cancer incidence rates per 100 000 persons were directly age-adjusted to the 2000 US

population. Rate ratios (RR) and 95% confidence intervals (CIs) were calculated using the SES lowest quintile as

the reference. Regression models were used to estimate the percentage change in rate by SES (23) for PTC and non-

PTC histologies using the SAS PROC REG procedure (SAS 9.3, Cary, NC). The regression line slope was

considered to statistically differ from zero at a cutoff of p ≤ 0.05 based on a two-sided test.

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Results

Demographics

Characteristics of patients diagnosed with specified thyroid cancer types during 2007–2010 are summarized in

Table 1. The 41 072 cases were diagnosed with the following thyroid cancer types: 36 020 papillary (88%), 3 288

follicular (8%), 722 medullary (2%), 381 anaplastic (1%), 284 other epithelial (1%), and 377 other histologies (1%).

Half of cases (49%) were less than 50 years of age, including 51% of PTC cases. Most cases in other histologic

groups were 50 years of age or older. Women accounted for 76% of all cases, including 77% of all PTC cases.

Compared to the overall proportion of Asian/Pacific Islander thyroid cancer cases, this racial group accounted for a

high proportion of anaplastic tumors (13%), while blacks accounted for a high proportion of follicular, medullary

(9% each), and other epithelial tumors (10%). Among whites and Hispanics, tumor type distributions were more

similar to overall thyroid cancer case distributions. Most PTCs (69%) were ≤2 cm (small PTCs). Small PTCs

accounted for 24 745 of all 41 072 thyroid cancer cases (60%). Most follicular thyroid (69%) and anaplastic thyroid

cancer (76%) tumors were >2cm in size. PTC cases had the highest percentage of full insurance coverage (74%).

The percentage of full insurance coverage ranged from 60% for cases with anaplastic and other epithelial tumors to

72% for follicular tumors. Overall, 3% of cases had missing insurance data. The analysis of SES was restricted to 40

430 cases because of missing census tract data for 642 cases. The number of cases increased with SES for each type

of thyroid cancer, more than doubling for PTC—from quintile 1 to 5—and increased by 60% for follicular cancer,

31% for medullary, 49% for anaplastic, 29% for other epithelial, and 67% for other specified cancers.

Insurance and SES

Among fully insured cases RRs for PTC incidence increased monotonically with SES (Table 2). The RR in the

high compared to the low SES stratum was 2.6 (95% CI: 2.4–2.7), two-sided trend test of rate, P < 0.0001. For small

PTCs this RR reached 2.7 (95% CI: 2.6–2.9), two-sided trend test P < 0.0001. This monotonic effect persisted

among patients with small PTCs including females and whites (Table 3). The effect appeared to strengthen among

individuals younger than 50 years, with an RR in the high compared to the low SES stratum of 3.3 (95% CI: 3.0–

3.5), two-sided trend test of rate, P < 0.0003. Elevated RRs associated with high SES and insurance were also seen

among men and Asians (Figure 1). Among insured non-PTC cases RRs did not increase monotonically with SES.

Among other than fully insured, RRs were typically less than one in high SES strata compared to the referent group.

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Discussion

In this SEER study among fully insured cases, risk of PTC diagnosis increased monotonically with SES. This

association was driven diagnosis of small PTCs, with a 2.7-fold higher rate in the highest compared to the lowest

SES quintile. The effect persisted within several subgroups including persons less than 50 years of age, women, and

whites. These overlapping subgroups comprise a large proportion of all cases, with large numbers of subjects in each

SES stratum. Among the “other than fully insured,” a more heterogeneous group including Medicaid and uninsured

patients, thyroid cancer incidence did not increase with SES. The enumeration of risk among insured cases in high

SES compared to low SES areas may inform current discussions pertaining to PTC overdiagnosis.

The present study is a large SEER population-based study, covering a quarter of the population, with both

census-tract SES and individual insurance data. The results are more detailed than previous studies of thyroid cancer

incidence and SES (3,7,11-15,24) or health insurance coverage (12-15) with findings that differ from early studies

that found no association with SES or insurance (25,26). Our study and an earlier New Jersey study spanning 1979–

2006 (12) include census tract-level SES. The New Jersey study included county-level insurance data, while the

present study is based on individual-level insurance data. This is the first report of which we are aware to report a

monotonic increase in PTC incidence rates with rising SES among insured cases. Our findings indicate that this

effect is largely driven by small PTCs.

The rising incidence of small PTCs is described as an epidemic of diagnosis (2-4), disproportionately affecting

women (4,5,11,12) and patients under 50 years of age (5). Although small PTCs accounted for 60% of thyroid

cancer cases in this report, previous studies indicate PTCs account for less than 5% of thyroid cancer deaths and that

other thyroid cancer types carry far worse prognoses (17). The higher incidence of PTC among insured individuals

residing in high SES areas could be a consequence of higher paid individuals being “overinsured” relative to lower

paid “underinsured” workers (27). Overinsurance could increase access to imaging and biopsy for cancer screening

and evaluation of benign thyroid conditions. Such technologies may detect small, relatively low-risk thyroid nodules

(2,4). In one study, areas with high numbers of young physicians reported increased incidence rates of thyroid

cancer compared to areas with high concentrations of older physicians (28). The authors postulated that there is

greater use of ultrasound-guided biopsy by young physicians trained in these technologies. If these diagnostic

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resources are more likely to be found in high SES areas, it could contribute to associations between affluence and

PTC incidence.

Unless needed, post-diagnosis thyroidectomy and radiation place patients with small PTCs at risk for avoidable

surgical complications, lifetime thyroid replacement therapy, and perhaps second malignancies (2-4). The cost of

care for US patients diagnosed with well-differentiated thyroid cancer exceeded $1.6 billion in 2013 alone, including

more than $0.5 billion each for initial treatment and continued follow-up (29). American Thyroid Association

(ATA) Management guidelines (30) for patients with thyroid nodules encourage non-invasive management unless

patients have risk factors such as a family history of thyroid cancer, specific medical or environmental radiation

exposures, or rapid tumor growth with hoarseness. One recent study estimated that, in the United States,

approximately 82 000 men and women were diagnosed with papillary thyroid cancers during 1981-2011 that would

never cause symptoms (31). It has been proposed that some small PTCs be reclassified as non-cancers (2,3,10) or

that the most biologically indolent tumors be managed with watchful waiting (2-4). Reclassifying any PTCs as non-

cancerous would affect cancer survival statistics (32), because thyroid cancer survival is known to vary by stage,

age, gender and treatment (33). Biomarker research may also help to distinguish thyroid cancers including PTCs that

are likely to exhibit aggressive behavior from other, more indolent types (34).

Strengths of this study include availability of SEER variables for census-tract SES and individual insurance

status in registries covering 25% of the US population. Most studies of thyroid cancer incidence and SES (3,7,11,13-

15,24) have measured SES at the county or registry level. Study limitations include potential misclassification of

histology and availability of insurance data only for 4 recent years. In summary, compared to persons with insurance

living in low SES areas, those from high SES areas had more than 2.5 fold higher risk of being diagnosed with PTC.

The association was driven by small PTCs and persisted among persons younger than 50 years, women and whites.

Quantifying the risk of PTC associated with SES and insurance may inform efforts to prevent overdiagnosis.

Data Sharing

The SES index used in this report is linked to the SEER public research dataset and is available from the authors

upon review of request.

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Figure Legend

Figure 1. Thyroid cancer rate ratios by SES and insurance status for papillary and other histologies stratified by

tumor size, age, race, and gender -- SEER registries, 2007-2010*

*SEER 18 excluding Louisiana and Alaska cases and 283 additional cases with poorly specified histologies (ICD-O-

3 morphologies 8000–8005).

Author's contributions

Sean Altekruse: Design; acquisition, analysis, and interpretation of data. Drafting and revising for important

intellectual content. Final approval of the version to be published; and agreement to be accountable for the accuracy

and integrity of any part of the work.

Anita Das: Design; acquisition and interpretation of data. Final approval of the version to be published; and

agreement to be accountable for the accuracy and integrity of any part of the work.

Hyunsoon Cho: Design; analysis, and interpretation of data. Drafting and revising of content. Final approval of the

version to be published; and agreement to be accountable for the accuracy and integrity of any part of the work.

Valentina Petkov: Analysis, and interpretation of data. Drafting and revising of content. Final approval of the


Mandi Yu: Design, acquisition, analysis, and interpretation of data. Drafting and revising for important intellectual

content. Final approval of the version to be published; and agreement to be accountable for the accuracy and

integrity of any part of the work.

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Table 1. Characteristics of thyroid cancer cases by histologic group, SEER registries 2007–2010*

Histology groups

Papillary Follicular Medullary Anaplastic Other

epithelial

Other

specified

Total

No. % No. % No. % No. % No. % No. % No. %

Diagnosis age

<50 years 18 358 51% 1 333 41% 279 39% 20 5% 65 23% 136 36% 20 191 49%

50–64 years 11 603 32% 1 026 31% 238 33% 90 24% 67 24% 117 31% 13 141 32%

65+ years 6 059 17% 929 28% 205 28% 271 71% 152 54% 124 33% 7 740 19%

Sex

Female 27 906 77% 2 304 70% 445 62% 233 61% 186 65% 257 68% 31 331 76%

Male 8 114 23% 984 30% 277 38% 148 39% 98 35% 120 32% 9 741 24%

Race/ethnicity†

White† 24 285 67% 2 241 68% 494 68% 264 69% 182 64% 251 67% 27 717 67%

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Hispanic 5 540 15% 412 13% 108 15% 45 12% 34 12% 47 12% 6 186 15%

API† 3 684 10% 264 8% 35 5% 51 13% 32 11% 34 9% 4 100 10%

Black† 1 930 5% 312 9% 68 9% 18 5% 28 10% 34 9% 2 390 6%

Other 581 2% 59 2% 17 2% * * * * 11 3% 679 2%

Tumor size (cm)

≤2cm 24 745 69% 784 24% 347 48% * * * * 154 41% 26 083 64%

>2cm 9 907 28% 2 271 69% 326 45% 289 76% 106 37% 172 46% 13 071 32%

Unknown 1 368 4% 233 7% 49 7% 83 22% 134 47% 51 14% 1 918 5%

Insurance status

Fully insured 26 800 74% 2 366 72% 497 69% 227 60% 169 60% 265 70% 30 324 74%

Uninsured 847 2% 83 3% 27 4% * * * * * * 985 2%

Any Medicaid 2 621 7% 289 9% 80 11% 47 12% 40 14% 35 9% 3 112 8%

Insured, NOS 4 683 13% 466 14% 104 14% 74 19% 43 15% 53 14% 5 423 13%

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Unknown 1 069 3% 84 3% 14 2% 23 6% 24 8% 14 4% 1 228 3%

Census-tract SES

Q1 (Low) 4 672 13% 508 15% 124 17% 55 14% 49 17% 58 15% 5 466 13%

Q2 5 904 16% 579 18% 133 18% 73 19% 61 21% 74 20% 6 824 17%

Q3 7 063 20% 618 19% 143 20% 75 20% 55 19% 66 18% 8 020 20%

Q4 8 134 23% 721 22% 151 21% 88 23% 47 17% 79 21% 9 220 22%

Q5 (High) 9 685 27% 811 25% 162 22% 82 22% 63 22% 97 26% 10 900 27%

Unknown 562 2% 51 2% * * * * * * * * 642 2%

Total 36 020 88% 3 288 8% 722 2% 381 1% 284 1% 377 1% 41 072 100%

*SEER 18 registries excluding Louisiana and Alaska and 232 cases with poorly specified histologies. Case counts suppressed to conceal cells with 10 or fewer cases.

†White, black, and API exclude persons of Hispanic ethnicity.

Abbreviations: API = Asian/Pacific Islander; cm = centimeter; NOS = not otherwise specified; Q = quintile; SEER = Surveillance, Epidemiology, and End Results.

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Table 2. Papillary thyroid cancer incidence rates and rate ratios by SES and tumor size among fully insured cases,

SEER registries, 2007-2010*

All cases ≤2 cm tumors

SES No. Rate‡ RR 95% CI No. Rate RR 95% CI

26 392 18 539

Q1(Low) 2 664 4.6 1.0 (Referent) 1 778 3.1 1.0 (Referent)

Q2 4 045 6.5 1.4 (1.4–1.5) 2 722 4.4 1.4 (1.3–1.5)

Q3 5 248 8.1 1.8 (1.7–1.9) 3 643 5.6 1.8 (1.7–1.9)

Q4 6 425 9.6 2.1 (2.0–2.2) 4 573 6.8 2.2 (2.1–2.3)

Q5(High) 8 011 11.6 2.6 (2.4–2.7) 5 823 8.4 2.7 (2.6–2.9)

Trend for SES <.0001 <.0001

*SEER 18 excluding Alaska and Louisiana and cases with unknown SES.

†Fully Insured: Private Insurance; Fee-for-Service; Private Insurance: Managed care, HMO, or PPO; TRICARE; Medicare

Administered through a Managed Care plan; Medicare with private supplement; Medicare with supplement, NOS; and Military;

Other: Uninsured; Any Medicaid; Insured, No Specifics; and Insurance status unknown.

‡Age-adjusted rates adjusted to the 2000 US standard population.

Abbreviations: CI = confidence interval; cm = centimeter; Q = quintile; RR = rate ratio; SEER = Surveillance, Epidemiology,

and End Results; SES = census tract socioeconomic status.

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Table 3. Papillary thyroid cancer incidence rates and rate ratios by SES, age, race, and gender among fully insured

cases with ≤2 cm tumors, SEER registries, 2007-2010*

Attribute Group/Trend for SES SES Count Rate Rate Ratio 95% Confidence Interval

Age Group

00-49 Years

p=0.0003

03

Q1(Low) 850 2.0 1.0 (Ref)

Q2 1 298 3.0 1.5 (1.4, 1.6)

Q3 1 833 4.2 2.1 (1.9, 2.2)

Q4 2 289 5.1 2.5 (2.3, 2.7)

Q5(High) 2 955 6.7 3.3 (3.0, 3.5)

50-64 Years

p<0.0001 Q1(Low) 596 6.2 1.0 (Ref)

Q2 933 8.9 1.4 (1.3, 1.6)

Q3 1 229 10.7 1.7 (1.6, 1.9)

Q4 1 606 13.2 2.1 (2.0, 2.4)

Q5(High) 2 084 15.6 2.5 (2.3, 2.8)

65+ Years

p=0.0008 Q1(Low) 332 5.2 1.0 (Ref)

Q2 491 6.8 1.3 (1.1, 1.5)

Q3 581 7.5 1.5 (1.3, 1.7)

Q4 678 8.6 1.7 (1.5, 1.9)

Q5(High) 784 9.5 1.8 (1.6, 2.1)

Race

White

p<0.0001 Q1(Low) 1 416 3.5 1.0 (Ref)

Q2 2 245 4.7 1.3 (1.2, 1.4)

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Q3 3 096 6.0 1.7 (1.6, 1.8)

Q4 3 883 7.2 2.1 (1.9, 2.2)

Q5(High) 4891 8.8 2.5 (2.4, 2.7)

Black

p=0.0025 Q1(Low) 247 1.9 1.0 (Ref)

Q2 198 2.6 1.3 (1.1, 1.6)

Q3 181 3.0 1.5 (1.3, 1.9)

Q4 147 3.4 1.8 (1.4, 2.2)

Q5(High) 117 4.5 2.3 (1.8, 2.9)

Asian

p=0.0199 Q1(Low) 84 2.4 1.0 (Ref)

Q2 248 4.9 2.1 (1.6, 2.7)

Q3 312 4.8 2.0 (1.6, 2.6)

Q4 478 5.5 2.3 (1.8, 3.0)

Q5(High) 716 6.6 2.8 (2.2, 3.5)

Gender

Male

p= 0.0014 Q1(Low) 318 1.1 1.0 (Ref)

Q2 491 1.7 1.4 (1.2, 1.7)

Q3 689 2.2 1.9 (1.7, 2.2)

Q4 888 2.7 2.4 (2.1, 2.7)

Q5(High) 1 266 3.7 3.2 (2.8, 3.7)

Female

p<0.0001 Q1(Low) 1 460 4.9 1.0 (Ref)

Q2 2 231 7.0 1.4 (1.3, 1.5)

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Q3 2 954 8.9 1.8 (1.7, 1.9)

Q4 3 685 10.7 2.2 (2.1, 2.3)

Q5(High) 4 557 12.8 2.6 (2.5, 2.8)






Abbreviations: cm = centimeter; Q = quintile;; SEER = Surveillance, Epidemiology, and End Results; SES = census tract

socioeconomic status.

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Figure 1

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STROBE Research checklist of items included in this manuscript

Item

No Recommendation

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract

(b) Provide in the abstract an informative and balanced summary of what was done

and what was found

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported

Objectives 3 State specific objectives, including any prespecified hypotheses

Methods

Study design 4 Present key elements of study design early in the paper

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment,

exposure, follow-up, and data collection

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of

selection of participants. Describe methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of

case ascertainment and control selection. Give the rationale for the choice of cases

and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of

selection of participants

(b) Cohort study—For matched studies, give matching criteria and number of

exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of

controls per case

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect

modifiers. Give diagnostic criteria, if applicable

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of

assessment (measurement). Describe comparability of assessment methods if there

is more than one group

Bias 9 Describe any efforts to address potential sources of bias

Study size 10 Explain how the study size was arrived at

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable,

describe which groupings were chosen and why

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding

(b) Describe any methods used to examine subgroups and interactions

(c) Explain how missing data were addressed

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was

addressed

Cross-sectional study—If applicable, describe analytical methods taking account of

sampling strategy

(e) Describe any sensitivity analyses

Continued on next page

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Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible,

examined for eligibility, confirmed eligible, included in the study, completing follow-up, and

analysed

(b) Give reasons for non-participation at each stage

(c) Consider use of a flow diagram

Descriptive

data

14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information

on exposures and potential confounders

(b) Indicate number of participants with missing data for each variable of interest

(c) Cohort study—Summarise follow-up time (eg, average and total amount)

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time

Case-control study—Report numbers in each exposure category, or summary measures of

exposure

Cross-sectional study—Report numbers of outcome events or summary measures

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their

precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and

why they were included

(b) Report category boundaries when continuous variables were categorized

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful

time period

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity

analyses

Discussion

Key results 18 Summarise key results with reference to study objectives

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision.

Discuss both direction and magnitude of any potential bias

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity

of analyses, results from similar studies, and other relevant evidence

Generalisability 21 Discuss the generalisability (external validity) of the study results

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable,

for the original study on which the present article is based

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and

unexposed groups in cohort and cross-sectional studies.

I affirm on this 26th day of August, 2015 that the items included in the STROBE checklist have been addressed in the

manuscript submitted to BMJ Open, “Does US Thyroid Cancer Incidence Increase with Socioeconomic Status and

Health Insurance?”

Sean Altekruse, DVM, MPH, PhD

National Cancer Institute

Division of Cancer Control and Population Sciences

Surveillance Research Program

Rockville, MD

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Do US Thyroid Cancer Incidence Rates Increase with

Socioeconomic Status among People with Health Insurance?

An observational study using SEER population-based data

Journal: BMJ Open

Manuscript ID bmjopen-2015-009843.R1

Article Type: Research

Date Submitted by the Author: 21-Oct-2015

Complete List of Authors: Altekruse, Sean; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences Das, Anita; National Cancer Institute, Surveillance Research Program,

Division of Cancer Control and Population Sciences Cho, Hyunsoon; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences; National Cancer Center, Division of Cancer Registration and Surveillance Petkov, Valentina; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences Yu, Mandi; National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences

<b>Primary Subject Heading</b>:

Oncology

Secondary Subject Heading: Epidemiology

Keywords: Endocrine tumours < ONCOLOGY, Epidemiology < ONCOLOGY, PUBLIC

HEALTH


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Do US Thyroid Cancer Incidence Rates Increase with Socioeconomic Status among People with Health

Insurance? An observational study using SEER population-based data

Sean Altekruse1, Anita Das1, Hyunsoon Cho1,2, Valentina Petkov1, Mandi Yu1

1. National Cancer Institute, Surveillance Research Program, Division of Cancer Control and Population Sciences

Rockville, Maryland, United States

2. National Cancer Center, Division of Cancer Registration and Surveillance

Goyang, Korea (the Republic of)

Abstract: 238 words

Text: 2 259 words

Tables: 3

Figures: 1

References: 34

Running header: Thyroid cancer, socioeconomic, and insurance status

Correspondence to: Sean F. Altekruse, Surveillance Research Program, Division of Cancer Control and Population

Sciences, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive 4E536, Rockville MD

20850

Telephone: 240.276.6933

Fax: 240.276.7908

E-mail: [email protected]

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Abstract

Objectives: United States thyroid cancer incidence rates are rising while mortality remains stable. Trends are driven

by papillary thyroid cancer (PTC), the predominant cancer subtype which has a very good prognosis. We

hypothesized that health insurance and high census tract socioeconomic status (SES) are associated with PTC risk.

Design: Relationships between thyroid cancer incidence, insurance and census-tract socioeconomic status (SES)

during 2007-2010 were examined in population-based cancer registries. Cases were stratified by tumor histology,

size and demography.

Setting: Surveillance, Epidemiology, and End Results (SEER) registries covering 30% of the US population.

Results: PTCs accounted for 88% of incident thyroid cancer cases. Small PTCs (≤2 cm) accounted for 60% of

cases. Unlike non-PTC cases the majority of those diagnosed with PTC were <50 years of age and had ≤2 cm

tumors. Rate ratios (RR) of PTC diagnoses increased monotonically with SES among fully insured cases. The effect

was strongest for small PTCs, high- versus low-SES quintile RR=2.7, 95% confidence interval (CI): 2.6–2.9, two-

sided trend test P < 0.0001. For small PTC cases with insurance the monotonic increase in incidence rates with

rising SES persisted among cases younger than 50 years of age (RR=3.3, 95% CI=3.0–3.5), women (RR=2.6, 95%

CI=2.5–2.8) and whites (RR=2.5, 95% CI=2.4–2.7). Among the less than fully insured, rates generally decreased

with increasing SES.

Conclusion: The greater than 2.5 fold increase in risk of PTC diagnosis among insured individuals associated with

high SES may be informative with respect to the contemporary issue of papillary thyroid cancer overdiagnosis.

Strengths and limitations of this study

• The study included 41 072 incident thyroid cancer case during 2007-2010 across 30% of the US population.

• Rate ratios and confidence intervals were used to assess effects of SES and insurance on thyroid cancer incidence.

• Effects were also examined by patient attributes, tumor size, and histology.

• Potential misclassification of histology data from population-based pathology reports was a limitation of this study.

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Introduction

In Surveillance, Epidemiology, and End Results (SEER) registry areas of the United States from 1975–2011,

thyroid cancer incidence rates tripled (1-3) while US thyroid cancer mortality trends were steady (1). These

discrepant trends are consistent with cancer overdiagnosis (2-4). Similar patterns are reported in many but not all

industrialized countries (3). Thyroid cancer incidence rates are higher among women than men (5) and among

whites than other major racial groups (6,7). Most of the rising incidence results from the increasing diagnosis of

small papillary thyroid cancers (PTCs) (2-4,8-10).

Access to health insurance contributes to the overdiagnosis of small PTCs (3,11-13) which carry relatively low

risk of death (3). Incidence rates of small PTCs are elevated in high-socioeconomic status (SES) counties (7,14,15)

and census tracts (11,12). Overdiagnosis and overtreatment of PTCs are associated with adverse effects, including

postsurgical complications, extended hospitalization, and lifelong hormone replacement therapy (2-4,16). In

contrast, several non-PTC types, including follicular, medullary, and anaplastic thyroid cancers carry progressively

worse prognoses (17). The present analysis, based on population-level cancer registry data covering approximately

30% of the United States, demonstrates the magnitude of combined effects of neighborhood socioeconomic status,

or SES (18, 19), and personal insurance status on the overdiagnosis of small PTCs including by age, gender, race

and ethnicity.

Materials and Methods

Data were obtained from 16 National Cancer Institute (NCI) SEER registries that cover approximately 30% of

the US population. The SEER November 2012 dataset was used for all analyses. Registries included in analyses

were Connecticut, Detroit, Hawaii, San Francisco-Oakland, Atlanta, Iowa, New Mexico, Seattle-Puget Sound, Utah,

Los Angeles, San Jose-Monterey, Rural Georgia, Greater California, Greater Georgia, Kentucky, and New Jersey.

Alaska Native cases were excluded because census-tract attributes were not available, and Louisiana cases were

excluded because of uncertainty about the population impact of Hurricane Katrina on census tract SES.

Case Attributes

Gender and age distributions (<50 years, 50–64 years, and ≥65 years of age at diagnosis) were examined.

While 98% of cases were 20 years of age and older at time of diagnosis, the analysis included children less than 20

years of age for purpose of completeness. Race and ethnicity were defined as Hispanic, non-Hispanic white, non-

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Hispanic black, and non-Hispanic Asian/Pacific Islander. Non-Hispanic American Indian/Alaska Natives and other

and unknown race were combined as one group. Tumors were classified as ≤2cm, >2cm, and unknown size. When

≤10 cases were observed data were suppressed.

Histologic Classification

Only malignant primary thyroid cancers were included in analyses. Thyroid cancer histologic classifications

were coded using the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) (20). A total of

283 cases with poorly specified cancer histologies (ICD-O-3 morphologies 8000–8005) were excluded. ICD-O-3

histology codes for papillary thyroid cancer were 8050,8052,8260,8340,8341,8342,8343,8344. Other types were

follicular (8290,8330,8331,8332,8335), medullary (8290,8330,8331,8332,8335) and anaplastic thyroid cancer

(8012,8020-8021,8030-8032). Approximately 2% of cases were diagnosed with non-classical thyroid cancer

histologies including 284 cases (1%) with other epithelial neoplasms (8010,8013,8015,8022,8033,8035,8041,8046)

and 377 cases (1%) with other rare specified primary thyroid cancer histologies (8070-8072,8074,8082-

8083,8130,8140-8141,8190,8200-8201,8230,8240,8246,8255,8262-8263,8310,8323,8337,8347,8350,8430,8450,

8452,8460,8480-8481,8504,8507,8560,8570,8588-8589,8800,8802,8810,8830,8890,8980,9040-9041,9071,9080,

9120,9250,9364). These included adenocarcinomas and squamous cell carcinoma, which are referenced on the

SEER thyroid/histology validation list (21) and other specified histologies that are not found in the SEER validation

list but were abstracted from patients’ medical records.

Insurance

SEER data include the six category variable “Insurance Recode” for diagnosis year 2007 forward, derived from

the more detailed “Primary Payer at Diagnosis” variable. For the purpose of the present analysis, categories were

collapsed into “Fully Insured,” and “Other than Fully Insured.” “Fully Insured” is defined by the following seven

categories of health insurance: 1. Insured - Private Insurance: Fee-for-Service, 2. Private Insurance: Managed care,

3. HMO or PPO, 4. TRICARE, 5. Medicare - Administered through a Managed Care plan, 6. Medicare with private

supplement, 7. Medicare with supplement, NOS and 7. Military. “Other than Fully Insured,” included 1. Uninsured

cases; 2. Cases with Medicaid, Indian Health Service insurance and 3. Patients reported to be insured without further

information available. The 1,228 cases with missing data on insurance status were excluded from insurance-related

analyses.

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Census-Tract Level SES

SES is typically measured using county-level attributes as a proxy for individual data. However, county-level

SES attributes are less precise than smaller-area or individual SES attributes. To improve area-based SES estimates,

we used a year-dependent census-tract SES composite index (18) linked to SEER cases by the census tracts of

residence at time of diagnosis. The SES index was derived from seven variables (percent working class population,

percent adult unemployment, educational attainment, median household income, percent of population living below

150% of national poverty line, median rent, and median home value). Taken together these variables capture three

principal components of SES (income, occupation, education). The weight assigned to each variable was determined

based on factor analysis (18). Specifically, the output from factor analysis that explained more than 90% of common

variance among these variables was used as the “SES index.” This index was constructed separately for 2007, 2008,

2009, and 2010 using the 5-year estimates from the 2005–2009, 2006–2010, 2007–2011, and 2008–2012 American

Community Surveys, respectively. Census tracts were categorized into SES quintiles according to this index, with

equal populations in each quintile. The first quintile (Q1, lowest SES) is the 20th percentile or less, and the fifth

quintile (Q5, highest SES) corresponds to the 80th percentile or higher. The SES index used in the present study has

the advantage over specific area level SES measures of protecting against disclosure of case identity through

identification of any census tract with a unique permutation of multiple SES attributes. The index is linked to

standard SEER data and is available for public use upon request from the authors. A total of 642 cases were missing

data on SES and were excluded from SES-related analysis.

Incidence by Insurance and SES

Age-adjusted incidence rates (22) were calculated by tumor size, insurance status, SES, and histology for cases

diagnosed during 2007–2010, years during which insurance data were available (SEER*Stat version 8.1.5, IMS,

Inc., Calverton, MD). Thyroid cancer incidence rates per 100 000 persons were directly age-adjusted to the 2000 US

population. Rate ratios (RR) and 95% confidence intervals (CIs) were calculated using the SES lowest quintile as

the reference. Regression models were used to estimate the percentage change in rate by SES (23) for PTC and non-

PTC histologies using the SAS PROC REG procedure (SAS 9.3, Cary, NC). The regression line slope was

considered to be statistically different from zero at a cutoff of p ≤ 0.05 based on a two-sided test.

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Results

Demographics

Characteristics of patients diagnosed with thyroid cancer during 2007–2010 are shown in Table 1. The 41 072

cases were diagnosed with the following subtypes: 36 020 papillary (88%), 3 288 follicular (8%), 722 medullary

(2%), 381 anaplastic (1%), 284 other epithelial (1%), and 377 other histologies (1%). Half of cases (49%) were less

than 50 years of age, including 51% of PTC cases. Most cases in other histologic groups were 50 years of age or

older. Women accounted for 76% of cases and 77% of PTC cases. Asian/Pacific Islander accounted for a high

proportion of anaplastic tumors (13%), while blacks accounted for a high proportion of follicular, medullary (9%

each), and other epithelial tumors (10%). Among whites and Hispanics, subtype distributions were similar to overall

thyroid cancer case distributions. Most PTCs (69%) were ≤2 cm (small PTCs). Small PTCs accounted for 24 745 of

all 41 072 thyroid cancer cases (60%). Most follicular thyroid (69%) and most anaplastic thyroid cancer (76%)

tumors were >2cm in size. PTC cases had the highest percentage of full insurance coverage (74%). The percentage

of full insurance coverage ranged from 60% for anaplastic and other epithelial to 72% for follicular cancer cases.

Insurance data were missing for 3% of cases. Analysis of SES was based on 40 430 cases, with missing SES data for

642 cases. Case counts increased with SES for each subtype, doubling for PTC from quintile 1 to 5, increasing by

60% for follicular, 31% for medullary, 49% for anaplastic, 29% for other epithelial, and 67% for other specified

cancers.

Insurance and SES

Among fully insured cases RRs for PTC incidence increased monotonically with SES (Table 2). The RR in the

high compared to the low SES stratum was 2.6 (95% CI: 2.4–2.7), two-sided trend test of rate, P < 0.0001. For small

PTCs this RR reached 2.7 (95% CI: 2.6–2.9), two-sided trend test P < 0.0001. Incidence rates for larger PTCs were

lower than those for small PTCs and the dose response with rising SES was attenuated compared to that of small

PTCs. The monotonic increase in RR with increasing SES for PTC cases persisted for small PTCs including among

persons less than 50 years of age, women and whites (Table 3). RRs associated with high SES and insurance are

shown in red while those of less than fully insured cases are shown in blue in Figures 1 and 2. In addition to the

overall effect among insured cases (Figure 1) large increases in RRs with SES were seen among persons <50 years

of age, men, and Asians (Figure 2). Among insured non-PTC cases the increase in RRs with SES was less

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pronounced than among cases with PTCs, with follicular tumors accounting for 60% of non-PTCs. Among less than

fully insured cases rates did not increase with SES.

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Discussion

In this SEER study among fully insured cases, risk of PTC diagnosis increased monotonically with SES. This

association was driven by diagnosis of small PTCs, with a 2.7-fold higher rate in the highest compared to the lowest

SES quintile. The effect persisted within several subgroups including persons less than 50 years of age, women, and

whites. These overlapping subgroups comprise a large proportion of all cases, with large numbers of subjects in each

SES stratum. Among the “other than fully insured,” a more heterogeneous group including Medicaid and uninsured

patients, thyroid cancer incidence did not increase with SES. The enumeration of risk among insured cases in high

compared to low SES areas may inform current discussions pertaining to PTC overdiagnosis.

The present study is a large SEER population-based study, covering 30% of the population, with both census-

tract SES and individual insurance data. The results are more detailed than previous studies of thyroid cancer

incidence and SES (3,7,11-15,24) or health insurance coverage (12-15) with findings that differ from early studies

that found no association with SES or insurance (25,26). Our study and a New Jersey study spanning 1979–2006

(12) include census tract-level SES. The New Jersey study included county-level insurance data, while the present

study is based on individual-level insurance data. This is the first report of which we are aware to report a monotonic

increase in PTC incidence rates with rising SES among insured cases. Our findings further indicate that this effect is

largely driven by the diagnosis of small PTCs.

The rising incidence of small PTCs is described as an epidemic of diagnosis (2-4), disproportionately affecting

women (4,5,11,12) and patients under 50 years of age (5). Among insured cases the effect of SES on risk of small

PTC diagnosis was most pronounced among persons less than 50 years of age. There is a need to determine why

people in this age stratum and other at-risk groups including women and whites are more likely to undergo tests that

lead to the diagnosis of PTC. Although small PTCs accounted for 60% of thyroid cancer cases in this report,

previous studies indicate PTCs account for less than 5% of thyroid cancer deaths and that other thyroid cancer types

carry far worse prognoses (17). The higher incidence of PTC among insured individuals residing in high SES areas

could be a consequence of higher paid individuals being “overinsured” relative to lower paid “underinsured”

workers (27). Overinsurance could increase access to imaging and biopsy for cancer screening and evaluation of

benign thyroid conditions. Such technologies may detect small, relatively low-risk thyroid nodules (2,4). In one

study, areas with high numbers of young physicians reported increased incidence rates of thyroid cancer compared

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to areas with high concentrations of older physicians (28). The authors postulated that there is greater use of

ultrasound-guided biopsy by young physicians trained in this technology. If these diagnostic resources are more

likely to be found in high SES areas, it could contribute to associations between affluence and PTC incidence. Other

potential explanations include that income and insurance are proxy measures for education. To the extent that this is

the case, more educated individuals may seek early care and press their physicians for specific thyroid-related tests

and treatments.

Unless needed, post-diagnosis thyroidectomy and radiation place patients with small PTCs at risk for avoidable

surgical complications, lifetime thyroid replacement therapy, and perhaps second malignancies (2-4). The cost of

care for US patients diagnosed with well-differentiated thyroid cancer exceeded $1.6 billion in 2013 alone, including

more than $0.5 billion each for initial treatment and continued follow-up (29). American Thyroid Association

(ATA) Management guidelines (30) for patients with thyroid nodules encourage non-invasive management unless

patients have risk factors such as a family history of thyroid cancer, specific medical or environmental radiation

exposures, or rapid tumor growth with hoarseness. One recent study estimated that, in the United States,

approximately 82 000 men and women were diagnosed with papillary thyroid cancers during 1981-2011 that would

never cause symptoms (31). It has been proposed that some small PTCs be reclassified as non-cancers (2,3,10) or

that the most biologically indolent tumors be managed with watchful waiting (2-4). Reclassifying any PTCs as non-

cancerous would affect cancer survival statistics (32), because thyroid cancer survival is known to vary by stage,

age, gender and treatment (33). Biomarker research may also help to distinguish thyroid cancers including PTCs that

are likely to exhibit aggressive behavior from other, more indolent types (34).

Strengths of this study include availability of SEER variables for census-tract SES and individual insurance

status in registries covering 30% of the US population. Most studies of thyroid cancer incidence and SES (3,7,11,13-

15,24) have measured SES at the county or registry level. Study limitations include potential misclassification of

histology and availability of insurance data only for 4 recent years. In summary, compared to persons with insurance

living in low SES areas, those from high SES areas had more than a 2.5 fold higher risk of being diagnosed with

PTC. The association was driven by small PTCs and persisted among persons younger than 50 years, women and

whites. Quantifying the risk of PTC associated with SES and insurance may inform efforts to prevent overdiagnosis.

Data Sharing

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The SES index used in this report is linked to the SEER public research dataset and is available from the authors

upon review of request.

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Figure Legends

Figure 1. Overall and age-specific thyroid cancer rate ratios by SES and insurance status for papillary thyroid

cancers (PTC) and other histologies with stratification of PTCs <2 cm in size-- SEER registries, 2007-2010*

*SEER 18 excluding Louisiana and Alaska cases and 283 cases with unspecified histologies (ICD-O-3 8000–8005).

Figure 2. Gender- and race-specific thyroid cancer rate ratios by SES and insurance status for papillary thyroid

cancers (PTC) and other histologies with stratification of PTCs <2 cm in size-- SEER registries, 2007-2010*

*SEER 18 excluding Louisiana and Alaska cases and 283 cases with unspecified histologies (ICD-O-3 8000–8005).

API: Asian/Pacific Islander

Author's contributions

Sean Altekruse: Design; acquisition, analysis, and interpretation of data. Drafting and revising for important

intellectual content. Final approval of the version to be published; and agreement to be accountable for the accuracy

and integrity of any part of the work.

Anita Das: Design; acquisition and interpretation of data. Final approval of the version to be published; and

agreement to be accountable for the accuracy and integrity of any part of the work.

Hyunsoon Cho: Design; analysis, and interpretation of data. Drafting and revising of content. Final approval of the


Valentina Petkov: Analysis, and interpretation of data. Drafting and revising of content. Final approval of the


Mandi Yu: Design, acquisition, analysis, and interpretation of data. Drafting and revising for important intellectual

content. Final approval of the version to be published; and agreement to be accountable for the accuracy and

integrity of any part of the work.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Competing Interests

The authors have no conflicts of interest to declare.

Data sharing

No additional data available.

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Table 1. Characteristics of thyroid cancer cases by histologic group, SEER registries 2007–2010*

Histology groups

Papillary Follicular Medullary Anaplastic Other

epithelial

Other

specified

Total

No. % No. % No. % No. % No. % No. % No. %

Diagnosis age

<50 years 18 358 51% 1 333 41% 279 39% 20 5% 65 23% 136 36% 20 191 49%

50–64 years 11 603 32% 1 026 31% 238 33% 90 24% 67 24% 117 31% 13 141 32%

65+ years 6 059 17% 929 28% 205 28% 271 71% 152 54% 124 33% 7 740 19%

Sex

Female 27 906 77% 2 304 70% 445 62% 233 61% 186 65% 257 68% 31 331 76%

Male 8 114 23% 984 30% 277 38% 148 39% 98 35% 120 32% 9 741 24%

Race/ethnicity†

White† 24 285 67% 2 241 68% 494 68% 264 69% 182 64% 251 67% 27 717 67%

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Hispanic 5 540 15% 412 13% 108 15% 45 12% 34 12% 47 12% 6 186 15%

API† 3 684 10% 264 8% 35 5% 51 13% 32 11% 34 9% 4 100 10%

Black† 1 930 5% 312 9% 68 9% 18 5% 28 10% 34 9% 2 390 6%

Other 581 2% 59 2% 17 2% * * * * 11 3% 679 2%

Tumor size (cm)

≤2cm 24 745 69% 784 24% 347 48% * * * * 154 41% 26 083 64%

>2cm 9 907 28% 2 271 69% 326 45% 289 76% 106 37% 172 46% 13 071 32%

Unknown 1 368 4% 233 7% 49 7% 83 22% 134 47% 51 14% 1 918 5%

Insurance status

Fully insured 26 800 74% 2 366 72% 497 69% 227 60% 169 60% 265 70% 30 324 74%

Uninsured 847 2% 83 3% 27 4% * * * * * * 985 2%

Any Medicaid 2 621 7% 289 9% 80 11% 47 12% 40 14% 35 9% 3 112 8%

Insured, NOS 4 683 13% 466 14% 104 14% 74 19% 43 15% 53 14% 5 423 13%

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Unknown 1 069 3% 84 3% 14 2% 23 6% 24 8% 14 4% 1 228 3%

Census-tract SES

Q1 (Low) 4 672 13% 508 15% 124 17% 55 14% 49 17% 58 15% 5 466 13%

Q2 5 904 16% 579 18% 133 18% 73 19% 61 21% 74 20% 6 824 17%

Q3 7 063 20% 618 19% 143 20% 75 20% 55 19% 66 18% 8 020 20%

Q4 8 134 23% 721 22% 151 21% 88 23% 47 17% 79 21% 9 220 22%

Q5 (High) 9 685 27% 811 25% 162 22% 82 22% 63 22% 97 26% 10 900 27%

Unknown 562 2% 51 2% * * * * * * * * 642 2%

Total 36 020 88% 3 288 8% 722 2% 381 1% 284 1% 377 1% 41 072 100%

*SEER 18 registries excluding Louisiana and Alaska and 232 cases with poorly specified histologies. Case counts suppressed to conceal cells with 10 or fewer cases.

†White, black, and API exclude persons of Hispanic ethnicity.

Abbreviations: API = Asian/Pacific Islander; cm = centimeter; NOS = not otherwise specified; Q = quintile; SEER = Surveillance, Epidemiology, and End Results.

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Table 2. Papillary thyroid cancer incidence rates and rate ratios by SES and tumor size among fully insured cases, SEER registries, 2007-2010*

All cases ≤2 cm tumors >2 cm tumors

SES No. Rate‡ RR 95% CI No. Rate RR 95% CI No. Rate RR 95% CI

26 392 18 539 6 774

Q1(Low) 2 664 4.6 1.0 (Referent) 1 778 3.1 1.0 (Referent) 484 1.3 1.0 (Referent)

Q2 4 045 6.5 1.4 (1.4–1.5) 2 722 4.4 1.4 (1.3–1.5) 1 205 1.9 1.5 (1.3, 1.6)

Q3 5 248 8.1 1.8 (1.7–1.9) 3 643 5.6 1.8 (1.7–1.9) 1 457 2.3 1.7 (1.6, 1.9)

Q4 6 425 9.6 2.1 (2.0–2.2) 4 573 6.8 2.2 (2.1–2.3) 1 670 2.5 1.9 (1.7, 2.1)

Q5(High) 8 011 11.6 2.6 (2.4–2.7) 5 823 8.4 2.7 (2.6–2.9) 1 958 2.9 2.2 (2.0, 2.4)

Trend for SES <.0001 <.0001 0.002


†Fully Insured: Private Insurance; Fee-for-Service; Private Insurance: Managed care, HMO, or PPO; TRICARE; Medicare Administered through a Managed Care plan; Medicare

with private supplement; Medicare with supplement, NOS; and Military; Other: Uninsured; Any Medicaid; Insured, No Specifics; and Insurance status unknown.


Abbreviations: CI = confidence interval; cm = centimeter; Q = quintile; RR = rate ratio; SEER = Surveillance, Epidemiology, and End Results; SES = census tract socioeconomic

status.

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Table 3. Papillary thyroid cancer incidence rates and rate ratios by SES, age, race, and gender among fully insured

cases with ≤2 cm tumors, SEER registries, 2007-2010*

Attribute Group/Trend for SES SES Count Rate Rate Ratio 95% Confidence Interval

Age Group

00-49 Years

p=0.0003

03

Q1(Low) 850 2.0 1.0 (Ref)

Q2 1 298 3.0 1.5 (1.4, 1.6)

Q3 1 833 4.2 2.1 (1.9, 2.2)

Q4 2 289 5.1 2.5 (2.3, 2.7)

Q5(High) 2 955 6.7 3.3 (3.0, 3.5)

50-64 Years

p<0.0001 Q1(Low) 596 6.2 1.0 (Ref)

Q2 933 8.9 1.4 (1.3, 1.6)

Q3 1 229 10.7 1.7 (1.6, 1.9)

Q4 1 606 13.2 2.1 (2.0, 2.4)

Q5(High) 2 084 15.6 2.5 (2.3, 2.8)

65+ Years

p=0.0008 Q1(Low) 332 5.2 1.0 (Ref)

Q2 491 6.8 1.3 (1.1, 1.5)

Q3 581 7.5 1.5 (1.3, 1.7)

Q4 678 8.6 1.7 (1.5, 1.9)

Q5(High) 784 9.5 1.8 (1.6, 2.1)

Race

White

p<0.0001 Q1(Low) 1 416 3.5 1.0 (Ref)

Q2 2 245 4.7 1.3 (1.2, 1.4)

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Q3 3 096 6.0 1.7 (1.6, 1.8)

Q4 3 883 7.2 2.1 (1.9, 2.2)

Q5(High) 4891 8.8 2.5 (2.4, 2.7)

Black

p=0.0025 Q1(Low) 247 1.9 1.0 (Ref)

Q2 198 2.6 1.3 (1.1, 1.6)

Q3 181 3.0 1.5 (1.3, 1.9)

Q4 147 3.4 1.8 (1.4, 2.2)

Q5(High) 117 4.5 2.3 (1.8, 2.9)

Asian

p=0.0199 Q1(Low) 84 2.4 1.0 (Ref)

Q2 248 4.9 2.1 (1.6, 2.7)

Q3 312 4.8 2.0 (1.6, 2.6)

Q4 478 5.5 2.3 (1.8, 3.0)

Q5(High) 716 6.6 2.8 (2.2, 3.5)

Gender

Male

p= 0.0014 Q1(Low) 318 1.1 1.0 (Ref)

Q2 491 1.7 1.4 (1.2, 1.7)

Q3 689 2.2 1.9 (1.7, 2.2)

Q4 888 2.7 2.4 (2.1, 2.7)

Q5(High) 1 266 3.7 3.2 (2.8, 3.7)

Female

p<0.0001 Q1(Low) 1 460 4.9 1.0 (Ref)

Q2 2 231 7.0 1.4 (1.3, 1.5)

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Q3 2 954 8.9 1.8 (1.7, 1.9)

Q4 3 685 10.7 2.2 (2.1, 2.3)

Q5(High) 4 557 12.8 2.6 (2.5, 2.8)






Abbreviations: cm = centimeter; Q = quintile;; SEER = Surveillance, Epidemiology, and End Results; SES = census tract

socioeconomic status.

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Figure 1. Overall and age-specific thyroid cancer rate ratios by SES and insurance status for papillary thyroid cancers (PTC) and other histologies with stratification of PTCs <2 cm in size-- SEER registries, 2007-2010* *SEER 18 excluding Louisiana and Alaska cases and 283 cases with unspecified histologies (ICD-O-3 8000–

8005). 235x119mm (300 x 300 DPI)

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Figure 2. Gender- and race-specific thyroid cancer rate ratios by SES and insurance status for papillary thyroid cancers (PTC) and other histologies with stratification of PTCs <2 cm in size-- SEER registries, 2007-

2010* *SEER 18 excluding Louisiana and Alaska cases and 283 cases with unspecified histologies (ICD-O-3 8000–

8005). API: Asian/Pacific Islander 235x119mm (300 x 300 DPI)

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Manuscript ID bmjopen-2015-009843, entitled ‘Do US Thyroid Cancer Incidence Rates Increasewith Socioeconomic Status among People with Health Insurance?”STROBE Statement—checklist of items that should be included in reports of observational studies

ItemNo Recommendation

Title and abstract (a) Indicate the study’s design with a commonly used term in the title or the abstract

(b) Provide in the abstract an informative and balanced summary of what was done

and what was found

Introduction

Background/rationale Explain the scientific background and rationale for the investigation being reported

Objectives State specific objectives, including any prespecified hypotheses

Methods

Study design f-i’resent key elements of study design early in the paper

Setting ‘‘escribe the setting, locations, and relevant dates, including periods of recruitment,

exposure, follow-up, and data collection

Participants —‘‘(a) Cohort study—Give the eligibility criteria, and the sources and methods of

selection of participants. Describe methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of

case ascertainment and control selection. Give the rationale for the choice of cases

and controls

.__t’rosssectional study—Give the eligibility criteria, and the sources and methods of

selection of participants

(b) Cohort study—For matched studies, give matching criteria and number of

exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of

controls per case

Variables Clearly define all outcomes, exposures, predictors, potential confounders, and effect

modifiers. Give diagnostic criteria, if applicable

Data Sources! For each variable of interest, give sources of data and details of methods of

measurement assessment (measurement). Describe comparability of assessment methods if there

7 is more than one group

Bias -, Describe any efforts to address potential sources of bias

Study size ‘IJY’ Explain how the study size was arrived at

Quantitative variables Explain how quantitative variables were handled in the analyses. If applicable,

describe which groupings were chosen and why

Statistical methods . (a) Describe all statistical methods, including those used to control for confounding

(b) Describe any methods used to examine subgroups and interactions

(c) Explain how missing data were addressed

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was

addressed

Cross-sectional study—If applicable, describe analytical methods taking account of

sampling strategy

(e) Describe any sensitivity analyses

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-

22-”Give the source of funding and the role of the funders for the present study and, if applicable,

for the original study on which the present article is based

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and

unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and

published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely

available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com!). Information on the STROBE Initiative is

available at www.strobe-statement.org.

Results

Participants (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible,

examined for eligibility, confirmed eligible, included in the study, completing follow-up, and

analysed

(b) Give reasons for non-participation at each stage

cc) Consider use of a flow diagram

Descriptive 4,—’ia) Give characteristics of study participants (eg demographic, clinical, social) and information

data on exposures and potential confounders

(b) Indicate number of participants with missing data for each variable of interest

c) Cohort study—Summarise follow-up time (eg, average and total amount)

Outcome data l.57’Cohort study—Report numbers of outcome events or summary measures over time

Case-control study—Report numbers in each exposure category, or summary measures of

exposure

9ross-sectional study—Report numbers of outcome events or summary measures

Main results ,fr”(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their

precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and

why they were included

(b) Report category boundaries when continuous variables were categorized

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful

/time period

Other analyses U1’ Report other analyses done—eg analyses of subgroups and interactions, and sensitivity

analyses

Discussion

Key results i,” unirnarise key results with reference to study objectives

Limitations j,9” Discuss limitations of the study, taking into account sources of potential bias or imprecision.

,Eiscuss both direction and magnitude of any potential bias

Interpretation Give a cautious overall interpretation of results considering objectives, limitations, multiplicity

1of analyses, results from similar studies, and other relevant evidence

Generalisability 31” Discuss the generalisability (external validity) of the study results

Other information

Funding

2

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