Lung Cancer Screening Penetration in an Urban Underserved County

doi:10.21203/rs.3.rs-2231475/v1

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Lung Cancer Screening Penetration in an Urban Underserved County

https://doi.org/10.21203/rs.3.rs-2231475/v1

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Journal Publication

published 09 Mar, 2023

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Purpose

To compare distributions of residential geography, sex, socioeconomic status (SES), and ethnicity of patients who underwent screening at Montefiore’s Lung Cancer Screening Program with those of patients diagnosed with lung cancer, assessing whether screening efforts are appropriately focused.

Methods

This is a retrospective cohort study involving patients within our multisite urban academic medical center who underwent lung cancer screening or were diagnosed with lung cancer from January 1, 2015 to December 31, 2019. Inclusion criteria consisted of residence within seven neighborhoods in the Bronx, NY and age between 55 and 80 years. Institutional review board approval was obtained. Data were analyzed using the Wilcoxon two-sample t-test and chi-square.

Results

The cohort comprised 1568 (50.1%) women and 1551 (49.1%) men (mean age 65.6 ± 6.16). The Southeast Bronx had the highest rates of diagnosed lung cancers (423, 29.96%) and lung cancer screenings (533, 31.22%). However, overall geographic distribution between cohorts differed significantly (p = 0.0015). Sex between cohorts did not significantly differ (p = 0.0534). Both cancer and screening cohorts were from impoverished neighborhoods with mean SES of -3.11 ± 2.78 and -3.44 ± 2.80, respectively (p < 0.01). Both cohorts included a majority of Hispanic patients, although ethnicity differed significantly (p = 0.009).

Conclusion

Though statistically significant differences were found between cohorts, likely due to sample size, few clinically meaningful differences were found, implying our lung cancer screening program was effective in reaching the desired population. Demographics-based screening programs should be considered in global efforts efficaciously screen vulnerable populations.

Of all cancers, lung cancer demonstrates the highest mortality worldwide, necessitating early screening methods. It is essential that these screens not exclude any demographic, especially those underserved or vulnerable to disease. Lung cancer risk has been shown to vary among race and ethnicity as well as sex and socioeconomic status [1-3]. There is also evidence that geographic neighborhoods and their associated area-based measures of socioeconomic position (SEP) serve as indicators of lung cancer risk, even while controlling for cigarette smoking exposure [4]. Adie et al utilized area deprivation index (ADI) to demonstrate that higher levels of neighborhood deprivation were associated with higher risks of lung cancer in a cohort of smokers, suggesting that geographic location, which encompasses multiple subtle social determinants of health, should be considered in lung cancer risk models along with other demographics [4].

In meta-analyses, lower socioeconomic status (SES) has been associated with poor coverage of lung cancer screening [5, 6]. Furthermore, disparities in cancer screening can be attributed to race and sex. Access is an important factor in screening uptake for lung, breast, and colorectal cancers [7]. Recent attention to identifying screening deficits focused on mapping geographic areas of the United States with high lung cancer incidences or low access to screens [8]. On a smaller scale, it has also proved productive for hospital systems to map their own geographic coverage areas to assess their efficacies in reaching all demographics [9].

Montefiore Medical Center has similarly endeavored to address demographic-based disparities for several disease processes, including atrial fibrillation and ST-elevation myocardial infarctions (STEMIs) [10, 11]. Specifically, Montefiore-based studies have identified SES as a risk factor for high mortality in atrial fibrillation and breast cancer [12, 13]. Montefiore provides care for the majority of the population in the Bronx, an area particularly susceptible to poor health outcomes and with the highest level of poverty of all 62 counties in New York State [14]. The Bronx serves as a prototypic predominantly impoverished, underserved urban population, identified as a high need with marked delays in treatment of head and neck cancers [15]. Moreover, the Bronx has a high population of current smokers and experiences disadvantages with respect to standardized medical care due to its high prevalence of ethnicities traditionally underrepresented in wide studies and patients with multiple comorbidities [16]. According to the New York State Department of Health, the Bronx reports between 600 and 700 new cases of lung cancer every year [17].

Purpose

The primary aim of the present study was to compare residential geography, sex, socioeconomic status (SES), and ethnicity of the population who underwent screening at Montefiore’s Lung Cancer Screening Program in comparison to the population diagnosed with lung cancer. Understanding this relationship will help to assess whether screening efforts are appropriately focused and to identify unmet needs.

Study Design

A retrospective population study was conducted spanning from January 1, 2015 to December 31, 2019. Two patient cohorts were identified, one consisting of patients who underwent lung cancer screening and the other consisting of patients diagnosed with lung cancer. Patient information was obtained using the electronic medical records at Montefiore Medical Center, a large urban multisite academic medical center situated in the Bronx, New York (NY) and the Montefiore Cancer Registry. The cancer screening population consisted of patients who had undergone an order type labelled, CT chest for lung cancer screening. The cancer diagnosis group consisted of patients with diagnosed with lung cancer and included in the cancer registry.

Inclusion criteria consisted of self-reported residence at one of twenty-five zip codes within the Bronx and age between 55 and 80 years. The twenty five Bronx zip codes situated patients in one of seven distinct neighborhoods in the Bronx as outlined by the New York State Department of Health [18]. Age criteria corresponded with the 2013 United States Preventive Services Task Force (USPSTF) criteria for lung cancer screening, which recommends screening with computed tomography (CT) chest for those within the age group who have a 30 pack-year smoking history and currently smoke or have quit within the past 15 years [20]. Ethnicity and race were self-reported in the medical record and consolidated into one of three categories: non-Hispanic black, non-Hispanic white, and Hispanic. As few patients self-reported as Asian, American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, these categories were excluded as outliers. Patients who self-reported black or white race were categorized as non-Hispanic black, non-Hispanic white, or Hispanic based on their reported ethnicities.

IRB Approval

Institutional Review Board (IRB) and Health Insurance Portability and Accountability (HIPAA) approval were obtained. Informed consent was waived via IRB.

Cohort Population

The cohort population consisted of patients residing in any of seven geographic neighborhoods in the Bronx: Central Bronx, Bronx Park and Fordham, High Bridge and Morrisania, Hunts Point and Mott Haven, Kingsbridge and Riverdale, Northeast Bronx, and Southeast Bronx. The Bronx is an urban area with a traditionally underserved population demonstrating high poverty rates and low rates of higher education [21].

SES Variable

The SES was calculated within the EMR using factor analysis. A summary score was generated accounting for six variables indicative of wealth and income including: (1) log of the median household income, (2) log of the median value of housing units, (3) the percentage of households receiving interest, dividend, or net rental income, (4) the percentage of adults 25 years or older who had completed high school, (5) the percentage of adults 25 years of age or older who had completed college, (6) the percentage of employed persons 16 years of age or older in executive, managerial, or professional specialty occupations. A z-score was generated for each variable, reflecting the deviation of the value from the mean of the population in New York State. For example, a score of 1.0 for the log of median household income would equate a value two standard deviations (SDs) above the overall mean. The SES score then consisted of the sum of the six z-scores per variable, with a score closer to 0 consistent to the mean SES status in New York State. As a caveat, patients admitted from nursing facilities were excluded from scoring as their addresses no longer represented their SES of origin. This standard scoring is consistent with previous literature [6].

Statistical Analysis

Descriptive analysis was conducted using Statistical Analysis Software (SAS). Descriptive data were analyzed using the Wilcoxon two-sample t test for dichotomous data and chi square for other data. Statistical significance was defined by p <0.05.

The final cohort consisted of 3,119 ethnically diverse patients who met inclusion criteria, with 1568 (50.1%) women and 1551 (49.1%) men, mean age of 65.6 ± 6.16 years. The cancer diagnosis cohort (64.7 ±6.48) was slightly younger than the screening cohort (66.4 ±5.79) (p < 0.01) (Table 1). The sex distribution between the cancer diagnosis and screening cohorts was not significantly different (p = 0.0534). Both cohorts comprised predominantly ethnic minorities with more Hispanic participants than non-Hispanic blacks and non-Hispanic whites, although ethnicity differed significantly (p = 0.009) between the two cohorts.

Both the cancer diagnosis and screening cohorts were from impoverished neighborhoods with mean SES of -3.11 ± 2.78 and -3.44 ± 2.80, respectively (p < 0.01), (Table 1).

Table 1. Basic characteristics of cancer diagnosis and screening groups

	Total (N = 3119)	Cancer Diagnosis (N = 1412)	Cancer Screen (N = 1707)	p-value
Age Mean (SD)	65.6 (6.16)	64.7 (6.48)	66.4 (5.79)	<0.01
SES Mean (SD)	-3.29 (2.77)	-3.11 (2.78)	-3.44 (2.80)	<0.01
Female	1568 (50.1%)	603 (48.4%)	885 (51.9%)	0.053
Hispanic	1091 (42.4%)	429 (38.9%)	662 (44.9%)	0.009

Table 2 demonstrates the distribution of ethnicity and geographic locations of the patients with cancer and those who underwent screening. A plurality of patients identified as Hispanic, 38.93% in the cancer diagnosis cohort and 44.91% in the screening cohort (p = 0.009). Patients were least likely to identify as non-Hispanic white, with 23.60% in the cancer cohort and 20.76% in the screening cohort (p = 0.009).

In terms of neighborhood, the Southeast Bronx had both the highest rate of diagnosed lung cancers (423, 29.96%) and lung cancer screenings (533, 31.22%), (Table 2). However, over the seven neighborhoods, there was overall difference in geographic distribution of lung cancer diagnoses and lung cancer screens (p = 0.0015).

Table 2. Frequency of ethnicities and neighborhoods in diagnosis and screening cohorts

To account for the differences between the neighborhoods, the frequencies of each ethnicity, sex, SES, and age with respective to individual neighborhoods were also studied. For both the lung cancer and screening cohorts, ethnicity varied significantly with Bronx neighborhoods (p = < 0.0001 and p = <0.0001, respectively) (Table 3). In the cancer cohort, the sex distribution did not vary by neighborhood (p = 0.3489), nor did it vary in the screening cohort (p = 0.7869) (Table 4). Furthermore, in the cancer cohort, SES varied by neighborhood (p < 0.0001) as did age (p = 0.0359), (Table 5). The neighborhood with the lowest SES was Highbridge and Morrisania, and the neighborhood with the highest SES was Kingsbridge and Riverdale. Similarly, in the screening cohort, both SES and age varied by neighborhood (p < 0.0001 and p = 0.0002, respectively), (Table 5). The neighborhood with the lowest SES was Highbridge and Morrisania, and the neighborhood with the highest SES was Kingsbridge and Riverdale.

Table 3. Frequency of ethnicity by neighborhood

	Cancer Diagnosis				Cancer Screening
Frequency %	Non-Hispanic Black	Non-Hispanic White	Hispanic	p-value for Chi-Sq test	Non-Hispanic Black	Non-Hispanic White	Hispanic	p-value for Chi-Sq test
Central Bronx	40(35.4%)	6(5.31%)	67(59.29%)	<0.0001	55(40.44%)	4(2.94%)	77(56.62%)	<0.0001
Bronx Park and Fordham	87(38.84%)	40(17.86%)	97(43.3%)		102(30.91%)	57(17.27%)	171(51.82%)
High Bridge and Morrisania	45(45.92%)	5(5.1%)	48(48.98%)		59(40.14%)	9(6.12%)	79(53.74%)
Hunts Point and Mott Haven	18(40.91%)	2(4.55%)	24(54.55%)		25(34.25%)	6(8.22%)	42(57.53%)
Kingsbridge and Riverdale	15(21.43%)	29(41.43%)	26(37.14%)		8(13.56%)	26(44.07%)	25(42.37%)
Northeast Bronx	134(57.26%)	47(20.09%)	53(22.65%)		115(43.73%)	50(19.01%)	98(37.26%)
Southeast Bronx	74(23.2%)	131(41.07%)	114(35.74%)		142(30.47%)	154(33.05%)	170(36.48%)

Table 4. Frequency of sex by neighborhood

	Cancer Diagnosis			Cancer Screening
Frequency %	Female	Male	p-value for Chi-Sq test	Female	Male	p-value for Chi-Sq test
Central Bronx	80(54.42%)	67(45.58%)	0.3489	89(54.94%)	73(45.06%)	0.7869
Bronx Park and Fordham	127(45.04%)	155(54.96%)		193(48.86%)	202(51.14%)
High Bridge and Morrisania	65(55.08%)	53(44.92%)		94(55.29%)	76(44.71%)
Hunts Point and Mott Haven	28(49.12%)	29(50.88%)		42(51.22%)	40(48.78%)
Kingsbridge and Riverdale	42(46.15%)	49(53.85%)		38(55.07%)	31(44.93%)
Northeast Bronx	134(45.58%)	160(54.42%)		152(51.35%)	144(48.65%)
Southeast Bronx	207(48.94%)	216(51.06%)		277(51.97%)	256(48.03%)

Table 5. Frequency of SES and age by neighborhood

	Cancer Diagnosis				Cancer Screening
Frequency %	SES	p-value for Chi-Sq test	Age	p-value for Chi-Sq test	SES	p-value for Chi-Sq test	Age	p-value for Chi-Sq test
Central Bronx	-5.171±2.05	<0.0001	64±6.639	0.0359	-5.418±2.214	<0.0001	66.87±5.75	0.0002
Bronx Park and Fordham	-4.344±2.431		64.23±6.397		-4.411±2.373		65.668±5.654
High Bridge and Morrisania	-5.752±2.029		64.347±6.456		-5.77±2.364		66.018±5.952
Hunts Point and Mott Haven	-4.867±2.141		63.526±6.885		-5.241±2.229		65.744±5.524
Kingsbridge and Riverdale	-1.312±3.652		66.341±6.08		-1.456±3.083		68.507±5.428
Northeast Bronx	-1.645±1.906		64.782±6.562		-2.097±2.291		67.291±6.013
Southeast Bronx	-1.97±2.181		65.163±6.383		-2.043±2.157		66.137±5.711

As efforts are made worldwide to improve the early diagnosis of deadly cancers in order to improve mortality, it is essential to identify discrepancies between populations who undergo screening compared with those diagnosed with cancer. This effort provides screening programs with the opportunity to focus on outreach to underrepresented populations. In the present study, we evaluated the reach of lung cancer screening compared with lung cancer diagnoses at Montefiore Medical Center, an urban institution that provides medical care to the majority of patients in the Bronx, NY. Our study demonstrated that lung cancer diagnosis and screening rates were both highest in the Southeast Bronx. There were no differences in sex between the cohorts, which is particularly significant, as women have traditionally been underrepresented in cancer screens, and concerted efforts must be made to bridge the gap [22]. Overall, we found statistically significant but probably not clinically meaningful differences in neighborhood distribution between the lung cancer diagnosis and screening cohorts, age (64.7 ±6.48 years versus 66.4 ±5.79 years), and SES (-3.11 versus -3.44). The results for SES were especially important to consider, as low SES has been shown to negatively impact patient care in breast cancer, making patients less likely to adequately communicate with their clinical teams and to follow up on guideline-related treatments [23]. It is vital to identify deficits in cancer screening in low SES populations, as there exist strategies to attenuate certain treatment-related risks in these patients by providing health care literacy resources such as pictorial health information [23].

Though both lung cancer and screening cohorts demonstrated the ethnic diversity characteristic of the Bronx, there were statistical discrepancies in the breakdown of ethnicity between the diagnosis and screening cohorts, of uncertain clinical significance. As the Bronx has a high predominance of Hispanic patients, it concurs that Hispanic patients would be in the majority for both the diagnosis and screening groups. Although Hispanic populations in the United States demonstrate lower overall incidences of all cancers, they are more prone to health care disparities related to sociodemographic and cultural factors, with one third of the demographic lacking health insurance in 2013 [24]. Recent institution-based studies have identified Hispanic ethnicity as risk factors for multiple morbidities, including high-mortality STEMIs, incidence of acute critical illness, and metabolic syndrome [11, 25, 26]. As such, the predominance of Hispanics in both lung cancer and screening groups suggests that our hospital system is reaching this traditionally underserved population.

Although there was a significant difference in the breakdown of neighborhood between the cancer diagnosis and screening cohorts, both cohorts demonstrated the highest number of patients from the Southeast Bronx. The highest rates of lung cancer screening were in the Southeast Bronx and Bronx Park and Fordham, the neighborhoods where Montefiore Medical Center has it two major campuses. Our analysis identified the Northeast Bronx, the neighborhood demonstrating the second highest caseload of lung cancer in the borough, as a geographic area towards which more concerted screening efforts should be made. As recognizing a geographic area in high need of screening has allowed institutions the opportunity to distribute patient-facing resources and clinician-led education as well as bolster efforts to facilitate primary care referrals [27]. If the number of clinics proves to be a limiting factor, efforts towards establishing mobile clinics should be considered, as mobile low-dose whole body CT screening has recently emerged as a screening mechanism with a comparable detection rate to that described in the National Lung Screening Trial [28].

The Bronx neighborhoods themselves were heterogeneous in terms of distribution of ethnicity and SES. Interestingly, the data demonstrated a variation in SES based on neighborhood. For example, the in Kingsbridge and Riverdale, which demonstrated the highest average SES, the standard deviation was 3.652, whereas in the Northeast Bronx, the standard deviation in SES was 1.906. Future avenues of study to investigate whether living in a neighborhood with a relatively high average SES confers health benefits or even risks despite falling into a low SES bracket. Previous literature has identified a smaller incidence of non-small cell lung cancer in geographic areas with high average income [29]. Moreover, lung cancer incidence is positively associated with exposure to tobacco, including exposure from secondhand smoke [30]. As individuals within higher SES brackets tend to be less likely to smoke [31], residence within neighborhoods with higher average SES where tobacco exposure is more likely to be minimal may confer a protective effect in terms of carcinogenic exposure.

Demographics-based personalization of lung cancer screening programs should be considered in all global efforts to achieve higher screening rates in eligible populations. Future study expanding on the role of sex in screening should consider the role of gender identity in lung cancer screening, as non-gender conforming individuals have been demonstrated as having significantly lower rates of disease detection, especially when individuals are situated in underserved areas [32]. These studies will take place in a continued effort to address psychosocial barriers to health and maintain equal access to care among all.

Limitations

Patients were not stratified by smoking behavior when SES was evaluated in discrete neighborhoods within the cancer diagnosis cohort; smoking has previously been identified as a confounding variable in the relationship between SES and lung cancer. That said, recent evidence has limited the significance of this confounding relationship, suggesting that controlling for smoking history attenuates but does not eliminate the significant indirect relationship between SES and lung cancer incidence [5]. There were additional limitations in the method by which the EMR calculated SES, which did not include variables such as the total number of domestic occupants supported per household. Some selection bias may also have been introduced, as the study cohorts consisted of patients who willingly reported to the hospital. Furthermore, demographics were not stratified by age, which was shown to affect the risk profile of ethnicity and medical comorbidities for STEMI patients at Montefiore [11].

Overall, the demographics of the lung cancer screening and cancer diagnosis populations at our urban academic medical center were highly similar, indicating that our lung cancer screening program is effective in reaching the most at-risk populations. Ongoing efforts to address the underserved socioeconomic and ethnic populations in the Bronx have been effective in serving the appropriate population and overall neighborhoods for lung cancer screening, although additional outreach to the Northeast neighborhood of the Bronx is warranted.

Acknowledgement: The authors have no grants or funding to disclose.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. All authors contributed to the study conception and design. Material preparation and data collection were performed by Kristen Lee, MD, Edward Mardakhaev, MD, and Arash Gohari, MD. Data analysis was performed by Kenny Ye, PhD and Juan Lin, PhD. The first draft of the manuscript was written by Kristen Lee, MD, and all authors commented on versions of the manuscript. All authors read and approved the final manuscript.

Institutional Review Board (IRB) and Health Insurance Portability and Accountability (HIPAA) approval for this study were obtained. Informed consent was waived via IRB.

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No competing interests reported.

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You are reading this latest preprint version

Lung Cancer Screening Penetration in an Urban Underserved County

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Abstract

Introduction

Materials And Methods

Results

Discussion

Conclusion

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References

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