Effects of Social Determinants of Health Care on Pediatric Thyroid Cancer Outcomes in the United States

Abstract

Objective

To identify social determinants of health care that are associated with poorer pediatric well-differentiated thyroid cancer (WDTC) outcomes and increased stage at presentation.

Study Design

Using the SEER database (Surveillance, Epidemiology, and End Results), we retrospectively gathered data on pediatric WDTC across the United States between 1973 and 2015.

Setting

All patients between 0 and 19 years old with a diagnosis of WDTC were included.

Methods

Patient variables were analyzed for relationships to AJCC stage at presentation (American Joint Committee on Cancer), overall survival, and disease-specific survival.

Results

Among 3913 patients with pediatric thyroid cancer, 3185 were female (81.4%), 3366 had papillary thyroid cancer (85.3%), and 367 had follicular thyroid cancer (9.4%). Two- and 5-year overall and disease-specific survival approached 100%. However, when outcomes were analyzed by specific populations, male sex, non-Caucasian race, poverty, and language isolation were linked to worse overall survival. Male sex and poverty were associated with poorer disease-specific survival. Regarding overall AJCC stage at presentation, male sex and Black race were related to higher overall presenting AJCC stage. Later AJCC T stage at presentation was seen in male, Hispanic, Asian, and Black patients. There were no variables significantly related to following through with recommended surgery.

Conclusion

Pediatric WDTC continues to carry an excellent prognosis in the United States. However, when we consider specific populations, the social determinants of health care affect survival and disease burden at presentation: male sex, poverty, language isolation, and race affected survival and/or AJCC stage at presentation in pediatric WDTC.

Keywords

SEER health disparities thyroid cancer pediatric oncology pediatric otolaryngology pediatric endocrinology social determinants of health

Thyroid cancer is the most common pediatric endocrine malignancy in the United States.¹ Certain populations have seen a steady rise in incidence by 9.6% annually for the past decade.² The prognosis varies widely among histopathologic types. Well-differentiated thyroid cancer (WDTC), including follicular and papillary thyroid carcinoma, has a 10-year overall survival (OS) rate nearing 100%.^3-6 Undifferentiated thyroid cancer, including medullary thyroid carcinoma, is associated with significantly lower survival rates and a 10-year OS ranging from 65% to 21%.^7-9 Additionally, worsened WDTC outcomes have been reported in patients who present with advanced-stage disease.¹⁰

Irrespective of survival rates, the negative sequelae of thyroid cancer treatment can have lasting ramifications through adulthood; pediatric survivors are at increased risk of developing temperature dysregulation, chronic headaches, physical disabilities, and increased mental fatigue.¹¹ Furthermore, increasing treatment exposure has been linked to worsened survivor quality of life.¹¹

To date, there has been limited characterization on the role of health disparities in pediatric thyroid malignancies. Social determinants of health care (SDHs) are defined as “differences in the incidence, prevalence, mortality, and burden of diseases that exist among specific population groups in the United States.”¹² Several state and nationwide retrospective studies on thyroid malignancy have linked lower socioeconomic status and race to poorer survival rates and follow-up care among adults.^13-15 Patients of higher socioeconomic status tend to present with lower-stage thyroid cancer.¹⁶ In the pediatric population, lower survival rates have been indicated in males,¹⁷ and delayed time to treatment was noted in children lacking insurance coverage or coming from low-income households.^14-18 However, a more complete assessment of the relationship between SDHs and outcomes has not been studied.

The Surveillance, Epidemiology and End Results (SEER) program is a cancer population database. Funded by the National Cancer Institute of the National Institutes of Health, the SEER program “is an authoritative source of information on cancer incidence and survival in the United States. SEER currently collects and publishes cancer incidence and survival data from population-based cancer registries covering approximately 35 percent of the US population” (http://seer.cancer.gov/). SEER combines high-quality clinical, treatment, and outcomes data with that of SDHs. The sheer volume of data provided by the SEER database also permits for the detection of significant differences in population outcomes due to SDHs.

Recognizing which SDHs affect outcomes will facilitate targeted care, improving patient experience and prognosis. Herein, we present the first study to comprehensively describe health disparities in pediatric patients with WDTC on a population level. Our first objective is to identify SDHs that affect OS and disease-specific survival (DSS) in pediatric WDTC. In addition, we seek to identify SDHs that are associated with higher-stage disease at presentation.

Materials and Methods

The SEER database was queried for cases of WDTC from 1973 to 2015. This data set is a direct sample of the pediatric WDTC population, as all data are equally available (and all were used in this study). Given that the database does not include patient-protected health information and is accessible to the public, this study was exempt from approval by the Mayo Clinic institutional review board. Patients <20 years old were identified with ICD-O-3 site code for the thyroid gland (C73.9) and histologic codes for papillary thyroid carcinoma (8050, 8260, 8340-8344, 8350, 8450-8460) and follicular thyroid carcinoma (8290, 8330-8335; International Classification of Disease for Oncology, Third Edition). SEER*Stat 8.3.6 software (National Cancer Institute) was used to collect data on patient demographics, tumor characteristics, survival, and socioeconomic variables per county. We selected for primary tumors and eliminated secondary ones due to confounding variables. The demographics sex, race, and marital status were self-assigned by the patient or caregiver. Socioeconomic variables were percentage county without a high school diploma (less than high school graduate), percentage county language isolation, percentage county below poverty line, and county urban population. The county characteristic of language isolation is defined as the percentage of households within the county without an English-speaking individual of at least 14 years old, as calculated from the US census data. Poverty in the SEER database is defined per the US poverty federal guidelines. Each variable was modeled as a continuum.

Variables with too many levels were redefined and collapsed into fewer levels, as done in previous SEER analyses.¹⁹ Mean and standard deviation were used to summarize descriptive statistics and continuous features. Percentage and frequency count were utilized for summaries of categorical features.

Disparities in Survival

OS and DSS were summarized with the Kaplan-Meier method. Relationships between predictors of OS and DSS were analyzed via univariate and multivariable Cox models. The assumption of linearity was checked via splines of predictors in fits, while the assumption of proportionality was checked through Schoenfeld residuals. A P value <.05 was used for statistical significance. All statistical analyses were performed in R version 3.4.2 (R Foundation for Statistical Computing).

Hazard ratios (HRs) were used to analyze the effects of sex on mortality. This was calculated per the following steps:

Hazard was defined as probability of an event (death) at a specific time:

Hazard = probability (death at time t) / probability (survival until time t) .

HR was then calculated by comparing one population’s hazard with another’s. However, when probability of death at a certain time point is small (as in this study), the HR is approximately the ratio of the event incidences:

HR (male vs female) = incidence of death in males / incidence of death in females .

Disparities by Stage at Presentation

The relationships of tumor burden at presentation to socioeconomic status and patient demographics were assessed through T, N, M, and overall AJCC staging (American Joint Committee on Cancer). N and M stages were categorized binomially: a value of 1 was assigned if there was any nodal (N) or distant (M) metastasis and 0 if there was none. Overall AJCC stage and T stage were analyzed via ordinal logistic regression or binomial logistic regression when multiple categories or binary stages were present, respectively. Univariate modeling across each predictor precluded multivariable modeling in all cases.

Disparities in Following Through on a Recommendation for Surgery

Our group also queried the relationship between disparities and whether a recommended procedure was received. Patients were included in this analysis only if surgery was recommended.

Results

Baseline Patient and County Characteristics

In the SEER database, 3733 cases of pediatric thyroid cancer were identified between 1973 and 2015. Baseline county, socioeconomic, and patient data are recorded in Tables 1 and 2 .

Table 1.

Patient Characteristics.^a

	Follicular		Papillary		Total		P value
Patients	367	9.8	3366	90.2	3733
Pathology							<.001
Grade I	103	28.1	578	17.2	681	18.2
Grade II	22	6.0	134	4.0	156	4.1
Grade III	5	1.4	36	1.1	41	1.1
Anaplastic	2	0.5	3	0.1	5	0.1
Unknown	235	64.0	2615	77.7	2850	76.4
Age, y							<.001
0-9	11	3.0	127	3.8	138	3.7
10-14	90	24.5	651	19.3	741	19.9
15-19	266	72.5	2588	76.9	2854	76.5
Sex							<.001
Female	301	82.0	2783	82.7	3084	82.6
Male	66	18.0	583	17.3	649	17.4
Race							<.001
Hispanic	63	17.2	732	21.8	795	21.3
Non-Hispanic Asian / Alaska Native / Pacific Islander / American Indian	37	10.1	330	9.8	367	9.8
Non-Hispanic Black	37	10.1	139	4.1	176	4.7
White	224	61.0	2118	62.9	2342	62.7
Marital status							.818
Single	352	95.9	3277	97.4	3629	97.2
Not single	15	4.1	89	2.6	104	2.8
Percentage county
Less than high school graduate	13.5 (6.0)		13.4 (5.9)		13.4 (5.9)		.599
Language isolation	6.0 (4.1)		5.9 (4.1)		5.9 (4.1)		.923
Below poverty line	14.6 (5.7)		14.4 (5.2)		14.4 (5.3)		.727
County urban population							.025
<250,000	25	6.8	221	6.6	246	6.6
250,000-1,000,000	81	22.1	701	20.8	782	21.0
>1,000,000	222	60.5	2080	61.8	2302	61.7
Percentage county urban	88.6 (19.7)		88.2 (19.7)		88.2 (19.8)		.504
Surgery							.124
Performed	360	98.1	3299	98.0	3839	102.8
Not performed	6	1.6	28	0.8	34	0.9
Not recommended	1	0.3	30	0.9	31	0.8
Status at last follow-up							<.001
Alive	358	97.6	3297	98.0	3817	102.3
Cancer death	3	0.8	16	0.5	29	0.8
Other death	5	1.4	44	1.3	56	1.5
Unknown	1	0.3	9	0.3	11	0.3

Values are presented as No. and percentage or mean (SD).

Table 2.

Patient Cancer Stage.^a

AJCC	Follicular		Papillary		Total		P value
Overall							<.001
I	197	53.7	1940	57.6	2137	57.3
II	2	0.5	59	1.8	61	1.6
III	0	0.0	0	0.0	0	0.0
IVA	2	0.5	0	0.0	2	0.1
IVB	0	0.0	1	0.0	1	0.0
IVC	0	0.0	0	0.0	0	0.0
T stage							<.001
T0	0	0.0	3	0.1	3	0.1
T1	39	10.6	833	24.8	872	23.4
T2	92	25.1	477	14.2	569	15.2
T3	65	17.7	572	17.0	637	17.1
T4a	2	0.5	58	1.7	60	1.6
T4b	0	0.0	21	0.6	21	0.6
Tx	4	1.1	86	2.6	90	2.4
N stage							<.001
N0	191	52.0	976	29.0	1237	33.1
N1a	5	1.4	505	15.0	522	14.0
N1b	1	0.3	425	12.6	433	11.6
N1 NOS	0	0.0	108	3.2	112	3.0
Nx	5	1.4	36	1.1	41	1.1
M stage							.007
M0	199	54.2	1941	57.7	2231	59.8
M1	2	0.5	59	1.8	63	1.7
Mx	1	0.3	50	1.5	51	1.4

Abbreviation: AJCC, American Joint Committee on Cancer.

Values are presented as No. and percentage.

Overall and Disease-Specific Survival

Two- and 5-year OS was 99.6% and 99.0% while DSS was 99.8% and 99.6%, respectively ( Figure 1a ). There were 78 deaths.

Figure 1.

Kaplan-Meier plots of overall and disease-specific survival. (a) The solid line represents the survival curve while the dashed lines are 95% CIs: (b) by sex (P < .10) and (c) by race (P < .10).

Following multivariate analysis, mean percentage of a county below the poverty line (HR, 1.04; 95% CI, 1-1.08; P = .041), mean percentage of a county language isolated (HR, 1.09; 95% CI, 1.03-1.15; P = .002), and male sex (HR, 2.06; 95% CI, 1.3-3.26; P = .002; Figure 1b ) were linked to worsened OS. Only male sex (HR, 3.19; 95% CI, 1.46-6.97; P = .004) and mean percentage county poverty (HR, 1.09; 95% CI, 1.02-1.17; P = .007) were associated with poorer DSS ( Table 3 ). The HR for males was 2.06; the incidence of death in males was slightly larger than twice that in females ( Table 4 ). Race ( Figure 1c ) and county education (high school) were not statistically associated with decreased OS or DSS following multivariable analysis. However, in terms of overall mortality risk per 100 person-years, non-Caucasian race had an HR of 1.72 (95% CI, 1.13-2.62; P = .011; Table 5 ). When event occurrence is rare, as in this study, we often look at the number of events (NOE) per 100 person-years. NOE is calculated as the number of deaths per cumulative patient time elapsed. For example, if 1 patient died after 10 years and another after 5 years, the NOE would be 2/15, or 0.133 deaths per year. Then, multiplying the numerator and the denominator by 100 would give 13.3 NOE per 100 person-years (reported as 13.3).

Table 3.

Multivariate Analysis of Overall and Disease-Specific Survival.

	Hazard ratio (95% CI)	P value
Overall survival
Below poverty line	1.04 (1-1.08)	.041
Language isolation	1.09 (1.03-1.15)	.002
Male	2.06 (1.3-3.26)	.002
Disease-specific survival
Below poverty line	1.09 (1.02-1.17)	.007
Male	3.19 (1.46-6.97)	.004

Table 4.

Multivariable Analysis for Incidence of Death Following Diagnosis of Well-Differentiated Thyroid Cancer.

Years	Female, %	Male, %
5	0.7	2.5
10	1.1	3.2
20	2.1	7.2
30	5.6	10.1

Table 5.

Overall and Disease-Specific Deaths per 100 Person-Years.

	Caucasian	Non-Caucasian	Hazard ratio	95% CI (P value)
Overall	5.52	8.23	1.72	1.13-2.62 (.011)
Disease specific	1.78	2.22	1.19	0.55-2.57 (.665)

Based on Schoenfeld residuals, decade-specific all-cause and cancer-specific mortality between sexes and races was compared (Supplemental Data, available online). The data suggest no significant decrease in the effects of SDHs on survival over time:

SDH: Improved mortality with time P value

Sex: overall mortality .250

Sex: cancer-specific mortality .090

White race: overall mortality .100

White race: cancer-specific mortality .550

AJCC Staging at Presentation

Following multivariate analysis, male sex (odds ratio [OR], 1.87; 95% CI, 1.15-2.96; P = .009) and non-Hispanic Black race (OR, 2.61; 95% CI, 1.22-5.12; P = .008) were related to higher presenting AJCC stage ( Table 6 ). Patients who were male (OR, 1.43; 95% CI, 1.17-1.74; P < .001), Hispanic (OR, 1.74; 95% CI, 1.46-2.09; P < .001), non-Hispanic Asian (OR, 1.36; 95% CI, 1.04-1.79; P = .026), and non-Hispanic Black (OR, 1.88; 95% CI, 1.32-2.66; P < .001) had increased odds of presenting at later AJCC T stage. There were no patient or demographic variables associated with increased AJCC N or M stage at presentation.

Table 6.

Multivariate Analysis of AJCC Stage at Presentation.

	Odds ratio (95% CI)	P value
Overall
Male	1.87 (1.15-2.96)	.009
Non-Hispanic Black	2.61 (1.22-5.12)	.008
T stage
Male	1.43 (1.17-1.74)	<.001
Hispanic	1.74 (1.46-2.09)	<.001
Non-Hispanic Asian or Pacific Islander	1.36 (1.04-1.79)	.026
Non-Hispanic Black	1.88 (1.32-2.66)	<.001

Abbreviation: AJCC, American Joint Committee on Cancer.

Surgery If Recommended

Following multivariate analysis, there were no variables significantly related to declining or proceeding with recommended surgery ( Table 7 ).

Table 7.

Multivariable Analysis of Surgery If Recommended.

	Odds ratio (95% CI)	P value
Male	1.01 (1-1.01)	.141
Race
Hispanic	1 (1-1.01)	.368
Non-Hispanic Asian or Pacific Islander	1 (0.99-1.01)	.504
Non-Hispanic Black	1.01 (1-1.02)	.182

Discussion

To our knowledge, this is the first study to comprehensively characterize SDHs in pediatric patients with WDTC. While the observed statistical effect sizes in this article are small, their implications are much larger, as the entire populations of these patients are examined.

SDHs and Survival

Our study revealed that children with WDTC have an excellent prognosis, nearing 100% 10-year OS and DSS, similar to adult rates.^8,9 Despite the low mortality, certain SDHs still significantly affected survival. This is indicative of a well-powered study, capable of detecting disparities that affect the larger population.

We found that children living in poor or language-isolated counties had reduced OS, which has not been reported in pediatric WDTC. To investigate a potential cause of this finding, we hypothesized an association with higher disease stage at presentation, which was refuted. However, Garner et al noted that time from diagnosis to commencing treatment was delayed in poorer populations.¹⁸ Taken together, these findings support the need for improved health care access among children in these vulnerable groups. Including social work early and often may help dispel financial barriers to treatment. Additionally, ensuring cultural diversity among medical staff might further physician relatability and patient concordance.²⁰

Although OS of WDTC was affected by poverty and language isolation, DSS was unchanged. This finding would suggest that these SDHs have a more global impact on children with WDTC. For example, a delay in time to treatment, as mentioned earlier, may lead to prolonged treatment course or increased therapy burden requirements. This may result in secondary ailments that reduce life span by causes that are otherwise not specified.

Our finding that male sex was associated with decreased OS and DSS in pediatric thyroid cancer is consistent with previous literature.¹⁷ It has been hypothesized that tumor biology and pharmacogenetics contribute to worsened survival in males.^17,21,22 Additionally, delayed presentation in male children, leading to higher overall AJCC and T stage at presentation, may result in poorer survival rates. We agree with Williams and Spector that there is a need for further molecular-level and epigenetic research on the apparent survival discrepancy between sexes in pediatric thyroid malignancy.^17,23-26

Numerous studies have cited the effects of race and county education level on cancer survival rates.^27-32 Although non-Caucasian race had increased overall mortality risk per person-year ( Table 5 ), this did not carry over to disease-specific outcomes, where a statistically significant association was not seen. Following multivariable analysis, our study did not find a statistical relationship between mortality or county education and OS and DSS. The covariates of race and county education level may require a more lethal disease to become statistically significant prognostic factors in survival outcomes.

Finally, although we hypothesized that discrepancies in survival between sexes and races have been dissipating over time, we did not find this to be statistically significant (figures in Supplemental Data, available online).

SDH and Disease Burden at Presentation

Our results indicate greater overall AJCC presentation in non-Hispanic Black patients. Additionally, later AJCC T stage was associated with Hispanic, non-Hispanic Black, and Asian children. Several studies have linked SDHs to more advanced thyroid cancer stage at presentation in adults.^16,33 To our knowledge, this study is the first to examine the relationship between SDHs and stage at presentation in pediatric thyroid malignancy.

Increased disease burden at presentation may be due to distrust in the health care system among minority groups, leading to delayed medical presentation.^34-36 As reported by Arnett et al,³⁷ significant medical mistrust among African Americans warrants additional consideration in certain minority groups to improve outcomes and time to medical intervention. Provider implicit bias may also play a role in diminishing initial concerns of patients from minority groups, further delaying treatment.^38,39 Finally, access to specialty care, as is required in the treatment of pediatric thyroid cancer, has been poorer among minority races.⁴⁰ This has led to delayed and worsened disease burden at medical presentation among minority groups.^41,42

Although not unique to malignancy diagnosis, the aforementioned data emphasize racial influences on patient care and outcomes. This might be remedied by enhancing self-awareness to racial biases and increasing intervention efforts among vulnerable minority groups. Treatment of WDTC also requires collaboration among medical specialists (including but not limited to surgeons, endocrinologists, oncologists, radiologists, and geneticists). While SDHs are thought to impede a family’s ability to successfully navigate interdisciplinary care,^43,44 implementation of well-coordinated multidisciplinary teams may abate the negative health care effects of SDHs on “at risk” communities.^43,45,46 For this reason, children with WDTC may be best served by a dedicated team consisting of pediatric specialists from various relevant medical fields.

Male sex was associated with higher overall AJCC and T stage thyroid cancer at initial presentation. Male sex has been associated with more aggressive disease at presentation, defined as testing positive for specific genetic mutations, such as BRAF V600E, which carry poorer prognoses.⁴⁷ However, to our knowledge, worsened disease stage at presentation has yet to be reported in pediatric thyroid malignancy. Harvard Men’s Health Watch⁴⁸ indicated that women visit their physicians approximately 3 times more often than men. Female children and adolescents are also more affected by societal pressures of expected body mass index and physical appearance.⁴⁹ The anxiety among female children to meet these expectations is self-induced and from their peer groups.⁵⁰ With evidence of greater societal pressures on female physique and aesthetic appearance,^51-54 it is possible that female children and their guardians are more sensitive to physical changes. This may lead to the earlier discovery of thyroid pathology or reporting of thyroid dysfunction sequelae. Finally, several studies on elementary school-aged children have cited a greater increase in somatic and pain complaints with age in girls than in boys.^55-62 Although pubertal hormonal changes likely elicit gender differences in how pain is experienced and perceived, societal expectations for boys may delay help-seeking behavior.^41,63,64 It has been noted that boys are less willing to talk about their disease and accept their diagnoses.⁴¹ Future studies are needed to directly support the hypothesis that rearing boys with an emphasis on resilience may lead to embarrassment or denial of an ailment, thereby delaying diagnosis.

Following Through on Recommended Surgical Intervention

This study did not find predictors affecting patient follow-through on recommended surgery, a finding seen in other diseases.^19,65-67 At our institution, race/ethnicity is not associated with increased costs of surgical otolaryngology care.⁶⁸

Limitations

The SEER database provides AJCC staging and not the American Thyroid Association’s Pediatric Risk Classification, the latter being more appropriate for this study. Per AJCC and the age group analyzed, the highest stage possible in this population was stage II. Additionally, disease stage at presentation was used as a surrogate for time to presentation, as the database records the former only. Finally, SEER does not provide insight into the patient’s perspective, and exact reasoning for refusing surgery when recommended could not be elicited. We hypothesize that it may be due to the distrust in health care providers by the relevant minority groups, a relationship that has been appreciated in medicine and medical research.^69-71

There is a statistical rule of thumb to have 10 events for each degree of freedom; for example, with 96 total deaths, one could confidently assess OS by comparing and controlling for 9 variables. We acknowledge that in the case of cause-specific death, there are not enough patients for a full multivariable model, which is why variable selection was done. Overall, it is important to recall that power is related to making a type II error or concluding that a variable is not statistically significant when it is supposed to be. Given that there were variables that showed statistically significant differences in survival, even in the case of a small event number, it can be assumed that the variables in question would have an even lower P value in the case of a higher event count.

Conclusion

Pediatric WDTC continues to carry an excellent prognosis in the United States. However, several SDHs remain associated with worsened outcomes and increased disease burden at initial presentation. OS was decreased in males, those below the poverty line, or those with language isolation. DSS was decreased in males and in children below the poverty line. Race and county education level were not statistically associated with worsened prognosis. However, race did affect the total time that a patient was at risk, as seen by the increased event rate per person-year. Regarding disease burden at presentation, higher AJCC stage was seen in males and non-Hispanic Black patients, and AJCC T stage increase was associated with males and children of Hispanic, non-Hispanic Black, or Asian race. Awareness of SDHs is imperative to the early diagnosis and prognosis of pediatric thyroid cancer in vulnerable populations. With increasing awareness of implicit bias and SDHs, future studies could focus on what interventions have been effective at eliminating the effects of SDHs and where limited resources should be allocated.

Supplemental Material

sj-docx-1-oto-10.1177_01945998211032901 – Supplemental material for Effects of Social Determinants of Health Care on Pediatric Thyroid Cancer Outcomes in the United States

Supplemental material, sj-docx-1-oto-10.1177_01945998211032901 for Effects of Social Determinants of Health Care on Pediatric Thyroid Cancer Outcomes in the United States by Nelson R. Gruszczynski, Christopher M. Low, Garret Choby, Kara D. Meister, Byron H. Smith and Karthik Balakrishnan in Otolaryngology–Head and Neck Surgery

Footnotes

Author Contributions

Nelson R. Gruszczynski, design and interpretation of data, drafted the article, final approval, and agree to be accountable for all aspects of the work; Christopher M. Low, design, acquisition of data and interpretation of data, revised article critically, final approval, and agree to be accountable for all aspects of the work; Garrett Choby, analysis and interpretation of data, revised article critically, final approval, and agree to be accountable for all aspects of the work; Kara D. Meister, analysis and interpretation of data, revised article critically, final approval, and agree to be accountable for all aspects of the work; Byron H. Smith, acquisition, analysis, and interpretation of data, revised article critically, final approval, and agree to be accountable for all aspects of the work; Karthik Balakrishnan, conception and interpretation of data, revised article critically, final approval, and agree to be accountable for all aspects of the work.

Disclosures

Competing interests: None.

Sponsorships: None.

Funding source: Internal departmental funding was utilized without commercial sponsorship or support.

Supplemental Material

Additional supporting information is available in the online version of the article.

References

Dinauer

Breuer

Rivkees

. Differentiated thyroid cancer in children: diagnosis and management. Curr Opin Oncol. 2008;20(1):59-65.

Qian

Jin

Meister

, et al. Pediatric thyroid cancer incidence and mortality trends in the United States, 1973-2013. JAMA Otolaryngol Head Neck Surg. 2019;145(7):617-623.

Klein Hesselink

Nies

Bocca

, et al. Pediatric differentiated thyroid carcinoma in the Netherlands: a nationwide follow-up study. J Clin Endocrinol Metab. 2016;101(5):2031-2039.

Schmidt Jensen

Grønhøj

Mirian

, et al. Incidence and survival of thyroid cancer in children, adolescents, and young adults in Denmark: a nationwide study from 1980 to 2014. Thyroid. 2018;28(9):1128-1133.

Hogan

Zhuge

Perez

Koniaris

Lew

Sola

. Pediatric thyroid carcinoma: incidence and outcomes in 1753 patients. J Surg Res. 2009;156(1):167-172.

Hay

Gonzalez-Losada

Reinalda

Honetschlager

Richards

Thompson

. Long-term outcome in 215 children and adolescents with papillary thyroid cancer treated during 1940 through 2008. World J Surg. 2010;34(6):1192-1202.

Wang

Palmer

, et al. Postoperative nomogram for predicting cancer-specific mortality in medullary thyroid cancer. Ann Surg Oncol. 2015;22(8):2700-2706.

Wells

Jr Asa

Dralle

, et al. Revised American Thyroid Association guidelines for the management of medullary thyroid carcinoma. Thyroid. 2015;25(6):567-610.

Modigliani

Cohen

Campos

, et al; GETC Study Group (Groupe d’etude des tumeurs a calcitonine). Prognostic factors for survival and for biochemical cure in medullary thyroid carcinoma: results in 899 patients. Clin Endocrinol (Oxf). 1998;48(3):265-273.

10.

Lim

Devesa

Sosa

, et al. Trends in thyroid cancer incidence and mortality in the United States, 1974-2013. JAMA. 2017;317(13):1338-1348.

11.

Nies

Hesselink

MSK

Huizinga

, et al. Long-term quality of life in adult survivors of pediatric differentiated thyroid carcinoma. J Clin Endocrinol Metab. 2017;102(4):1218-1226.

12.

National Institutes of Health. NIH Strategic Research Plan and Budget to Reduce and Ultimately Eliminate Health Disparities: Fiscal Years 2002-2006. Vol 1. National Institutes of Health; 2006.

13.

Roche

Fedewa

Chen

. Association of socioeconomic status and race/ethnicity with treatment and survival in patients with medullary thyroid cancer. JAMA Otolaryngol Head Neck Surg. 2016;142(8):763-771.

14.

Reitzel

Nguyen

Regan

Sturgis

. Trends in thyroid cancer incidence in Texas from 1995 to 2008 by socioeconomic status and race/ethnicity. Thyroid. 2014;24(3):556-567.

15.

Keegan

Raymon Grogan

Parsons

, et al. Sociodemographic disparities in differentiated thyroid cancer survival among adolescents and young adults in California. Thyroid. 2015;25(6):635-648.

16.

Siu

McDonald

Rajaraman

, et al. Is lower socioeconomic status associated with more advanced thyroid cancer stage at presentation? A study in two Canadian centers. Thyroid. 2014;24(3):545-551.

17.

Williams

Spector

. Survival differences between males and females diagnosed with childhood cancer. JNCI Cancer Spectr. 2019;3(2):pkz032.

18.

Garner

Maizlin

Dellinger

, et al. Effects of socioeconomic status on children with well-differentiated thyroid cancer. Surgery. 2017;162(3):662-669.

19.

Mehta

Lenzner

Argiris

. Race and health disparities in patient refusal of surgery for early-stage non-small cell lung cancer: a SEER cohort study. Ann Surg Oncol. 2012;19(3):722-727.

20.

Traylor

Schmittdiel

Uratsu

Mangione

Subramanian

. The predictors of patient-physician race and ethnic concordance: a medical facility fixed-effects approach. Health Serv Res. 2010;45(3):792-805.

21.

Anthony

Berg

. Biologic and molecular mechanisms for sex differences in pharmacokinetics, pharmacodynamics, and pharmacogenetics: part II. J Womens Health Gend Based Med. 2002;11(7):617-629.

22.

Anthony

Berg

. Biologic and molecular mechanisms for sex differences in pharmacokinetics, pharmacodynamics, and pharmacogenetics: part I. J Womens Health Gend Based Med. 2002;11(7):601-615.

23.

Notterman

Mitchell

. Epigenetics and understanding the impact of social determinants of health. Pediatr Clin North Am. 2015;62(5):1227-1240.

24.

Sharma

Kelly

Jones

. Epigenetics in cancer. Carcinogenesis. 2010;31(1):27-36.

25.

Roberti

Valdes

Torrecillas

Fraga

Fernandez

. Epigenetics in cancer therapy and nanomedicine. Clin Epigenetics. 2019;11(1):81.

26.

Brehar

Bulgar

Dumitrache

. Genetic and epigenetic alterations in differentiated thyroid carcinoma. J Med Life. 2013;6(4):403-408.

27.

Miller

Smith

Ryerson

Tucker

Allemani

. Disparities in breast cancer survival in the United States (2001-2009): findings from the CONCORD-2 study. Cancer. 2017;123(suppl 24):5100-5118.

28.

Luzzago

Palumbo

Rosiello

, et al. Racial and ethnic differences in survival in contemporary metastatic renal cell carcinoma patients, according to alternative treatment modalities. Cancer Causes Control. 2020;31(3):263-272.

29.

Nelson

Bostanci

Jones

, et al. Insurance status predicts survival in women with breast cancer: results of breast and cervical cancer treatment program in California. Ann Surg Oncol. 2020;27(7):2177-2187.

30.

Rana

Gosain

Lemini

, et al. Socio-demographic disparities in gastric adenocarcinoma: a population-based study. Cancers (Basel). 2020;12(1):157.

31.

Kollman

Sobotka

. Poverty and cancer disparities in Ohio. Prev Chronic Dis. 2018;15:E152.

32.

Coughlin

. Social determinants of breast cancer risk, stage, and survival. Breast Cancer Res Treat. 2019;177(3):537-548.

33.

Ghori

Gutterman-Litofsky

Jamal

Yeung

SCJ

Arem

Sherman

. Socioeconomic factors and the presentation, management, and outcome of patients with differentiated thyroid carcinoma. Thyroid. 2002;12(11):1009-1016.

34.

Benkert

Cuevas

Thompson

Dove-Meadows

Knuckles

. Ubiquitous yet unclear: a systematic review of medical mistrust. Behav Med. 2019;45(2):86-101.

35.

Safran

Taira

Rogers

Kosinski

Ware

Tarlov

. Linking primary care performance to outcomes of care. J Fam Pract. 1998;47(3):213-220.

36.

Kaiser Family Foundation. Race, Ethnicity and Medical Care: A Survey of Public Perceptions and Experiences; Improving Access in a Diverse Society. Kaiser Family Foundation; 1999.

37.

Arnett

Thorpe

Jr Gaskin

Bowie

LaVeist

. Race, medical mistrust, and segregation in primary care as usual source of care: findings from the Exploring Health Disparities in Integrated Communities Study. J Urban Health. 2016;93(3):456-467.

38.

Green

Carney

Pallin

, et al. Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 2007;22(9):1231-1238.

39.

Cooper

Roter

Carson

, et al. The associations of clinicians’ implicit attitudes about race with medical visit communication and patient ratings of interpersonal care. Am J Public Health. 2012;102(5):979-987.

40.

Institute of Medicine. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. National Academies Press; 2003.

41.

Haugland

Wold

Stevenson

Aaroe

Woynarowska

. Subjective health complaints in adolescence: a cross-national comparison of prevalence and dimensionality. Eur J Public Health. 2001;11(1):4-10.

42.

National Academies of Sciences, Engineering, and Medicine. Cost Benefit Analysis of Providing Non-emergency Medical Transportation. Transportation Research Board; 2005.

43.

Vanderbilt

Dail

Jaberi

. Reducing health disparities in underserved communities via interprofessional collaboration across health care professions. J Multidiscip Healthc. 2015;8:205-208.

44.

Mullins

Blatt

Gbarayor

Keri Yang

HWK

Baquet

. Health disparities: a barrier to high-quality care. Am J Health Syst Pharm. 2005;62(18):1873-1882.

45.

Vanderwielen

Vanderbilt

Dumke

, et al. Improving public health through student-led interprofessional extracurricular education and collaboration: a conceptual framework. J Multidiscip Healthc. 2014;7:105-110.

46.

Centers for Disease Control and Prevention. Strategies for Reducing Health Disparities. Centers for Disease Control and Prevention; 2016.

47.

Rahbari

Zhang

Kebebew

. Thyroid cancer gender disparity. Future Oncol. 2010;6(11):1771-1779.

48.

Mars vs venus: the gender gap in health. Harv Mens Health Watch. 2010;14(6):1-5.

49.

Helfert

Warschburger

. The face of appearance-related social pressure: gender, age and body mass variations in peer and parental pressure during adolescence. Child Adolesc Psychiatry Ment Health. 2013;7(1):16.

50.

Carlson Jones

. Body image among adolescent girls and boys: a longitudinal study. Dev Psychol. 2004;40(5):823-835.

51.

McKnight

. Risk factors for the onset of eating disorders in adolescent girls: results of the McKnight longitudinal risk factor study. Am J Psychiatry. 2003;160(2):248-254.

52.

Micali

Hagberg

Petersen

, et al. The incidence of eating disorders in the UK in 2000-2009: findings from the General Practice Research Database. BMJ Open. 2013;3(5):e002646.

53.

Pedersen

Mors

Bertelsen

, et al. A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. JAMA Psychiatry. 2014;71(5):573-581.

54.

Whale

Gillison

Smith

. “Are you still on that stupid diet?” Women’s experiences of societal pressure and support regarding weight loss, and attitudes towards health policy intervention. J Health Psychol. 2014;19(12):1536-1546.

55.

Brattberg

. Do pain problems in young school children persist into early adulthood? A 13-year follow-up. Eur J Pain. 2004;8(3):187-199.

56.

McGrath

Speechley

Seifert

, et al. A survey of children’s acute, recurrent, and chronic pain: validation of the pain experience interview. Pain. 2000;87(1):59-73.

57.

Haugland

Wold

. Subjective health complaints in adolescence—reliability and validity of survey methods. J Adolesc. 2001;24(5):611-624.

58.

Fichtel

Larsson

. Psychosocial impact of headache and comorbidity with other pains among Swedish school adolescents. Headache. 2002;42(8):766-775.

59.

Hakala

Rimpelä

Salminen

Virtanen

Rimpelä

. Back, neck, and shoulder pain in Finnish adolescents: national cross sectional surveys. BMJ. 2002;325(7367):743.

60.

Perquin

Hazebroek-Kampschreur

Hunfeld

, et al. Pain in children and adolescents: a common experience. Pain. 2000;87(1):51-58.

61.

Petersen

Bergstrom

Brulin

. High prevalence of tiredness and pain in young schoolchildren. Scand J Public Health. 2003;31(5):367-374.

62.

Roth-Isigkeit

Thyen

Raspe

Stöven

Schmucker

. Reports of pain among German children and adolescents: an epidemiological study. Acta Paediatr. 2004;93(2):258-263.

63.

Vingilis

Wade

Adlaf

. What factors predict student self-rated physical health? J Adolesc. 1998;21(1):83-97.

64.

Brun Sundblad

Saartok

Engstrom

. Prevalence and co-occurrence of self-rated pain and perceived health in school-children: age and gender differences. Eur J Pain. 2007;11(2):171-180.

65.

Jacobs

Kelley

Rosson

Detrani

Chang

. Disparities in urban and rural mastectomy populations: the effects of patient and county-level factors on likelihood of receipt of mastectomy. Ann Surg Oncol. 2008;15(10):2644-2652.

66.

George

Duran

Norris

. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health. 2014;104(2):e16-e31.

67.

Kennedy

Mathis

Woods

. African Americans and their distrust of the health care system: healthcare for diverse populations. J Cult Divers. 2007;14(2):56-60.

68.

Balakrishnan

Moriarty

Rosedahl

, et al. Predictors of high costs of care among otolaryngology patients. Otolaryngol Head Neck Surg. 2019;161(2):271-277.

69.

Corbie-Smith

Thomas

St George

. Distrust, race, and research. Arch Intern Med. 2002;162(21):2458-63. doi:10.1001/archinte.162.21.2458

70.

Kennedy

Mathis

Woods

. African Americans and their distrust of the health care system: healthcare for diverse populations. J Cult Divers. 2007;14(2):56-60.

71.

Gamble

. Under the shadow of Tuskegee: African Americans and health care. Am J Public Health. 1997;87(11):1773-1778. doi: 10.2105/ajph.87.11.1773.

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