Individual and Co-Occurring Social Needs,and Breast and Cervical Cancer Screening Among Patients in Texas Federally Qualified Health Centers

Abstract

Introduction

While prior research has examined individual health-related social needs (HRSNs) in relation to preventive care, few studies have investigated how cumulative social needs affect cancer screening behaviors. This study examines how individual and combined HRSNs affect breast and cervical cancer screening completion.

Methods

Participants included 1,964 female patients overdue for breast or cervical cancer screening, receiving care at Federally Qualified Health Centers. The dependent variable was the completion of the appropriate screening test within 90 days of SMS text message reminders. Adjusted logistic regression models assessed associations between individual and combined HRSNs and screening completion.

Results

Food insecurity (aOR = 0.54) was the only significant predictor of breast cancer screening completion and was also associated with reduced odds of cervical cancer screening. Housing insecurity (aOR = 0.79), living at or below 100%FPL (aOR = 0.82), and having family history of cancer (aOR = 0.62) were associated with lower odds of cervical cancer screening, while completing an annual wellness visit in the previous year was associated with higher odds of cervical cancer screening (aOR = 1.25). In the combined models, each additional HRSN reduced likelihood of completing breast (OR=0.72) and cervical cancer screening (OR=0.77).

Conclusions

Addressing HRSN is essential for improving screening completion in safety-net populations. Interventions targeting food insecurity and housing instability can support access to preventive cancer care.

Keywords

cancer screening health related social needs (HRSN)underserved text-message reminders preventive care

Introduction

Cancer is the second leading cause of death in the United States,¹ and the number of new cancer cases is projected to reach 1.96 million by 2030.² Among females, breast and cervical cancer remain especially consequential, accounting for 316,950 new cases and 42,170 deaths (breast cancer) and 13,360 new cases and 4320 deaths (cervical cancer) in 2025 only,¹ underscoring their significant public health impact. Although there are national declines in mortality for many cancers, late-stage diagnosis continues to affect women in medically underserved urban and rural communities.

The U.S. Preventive Services Task Force recommends mammography and clinical breast examination as the primary modalities for breast cancer screening, while cervical cancer screening is guided by cytology (Pap smear) and high-risk human papillomavirus (HPV) testing.³ Prior research has shown that early screening for both types of cancer decreases mortality rates yet screening gaps persists.⁴ There is a cascade of consequences when early screening is not completed, including later stage diagnoses, intensive treatment with reduced survival, and greater financial burden – a domino effect that increases disparities and worsens outcomes in vulnerable populations.^1,2

Texas trails national averages in both breast and cervical cancer screening. For breast cancer screening, 76.4% of Texas women ages 50–74 reported having a mammogram within the past two years, slightly below the national rate of 78.5%.⁵ Cervical cancer screening shows a wider gap: only 46.9% of Texas women ages 25–44 received screening consistent with USPSTF guidelines, compared with a national rate of 51.6%.⁶ These lower screening rates underscore persistent access barriers in Texas, where socioeconomic factors have been shown to disproportionately impact cancer screening uptake in underserved populations.^7-9

Federally Qualified Health Centers (FQHCs), which provide primary care services to 32.4 million low-income individuals across the U.S,¹⁰ play a critical role in delivering preventive services to populations in medically underserved urban and rural communities that experience elevated poverty and health risks.¹¹ Many FQHC patients experience unmet health-related social needs (HRSNs), which can impede their ability to engage in routine preventive care, making timely cancer screening especially important within these settings.

HRSNs encompass socioeconomic factors such as food insecurity, housing instability, social isolation, and transportation barriers,¹² each of which has been linked to adverse health outcomes. When these needs are unmet, individuals may encounter barriers that compromise their health and well-being, often resulting in delayed or missed preventive services.¹³ Assessing how these HRSNs relate to closing cancer screening gaps is essential to reducing the burden of late-stage cancer diagnoses that arise from inadequate screening participation. Although prior research has examined individual HRSNs in relation to preventive health behaviors,^14-18 many patients experience multiple, overlapping needs, and few studies have explored this relationship. For instance, a multi-site study of adults aged 50–75 from nine U.S. cancer centers found that food insecurity was associated with 40% lower odds of being up-to-date with breast cancer screening even after adjusting for socioeconomic factors.¹⁹ Using a nationally representative dataset, Pohl et al. found that transportation insecurity was associated with a 41% lower likelihood of breast cancer screening, while work by Misha et al., found that neighborhood vehicle access and distance to clinics are linked to reduced screening rates.²⁰ Other studies have used text message reminders successfully to increase screening for breast and cervical cancer.^21-25

Despite these significant efforts in addressing social needs, the majority of these studies evaluate HRSNs in isolation, without exploring their combined effects. Highlighting the combination of these HRSNs and their role in cancer screening among women is essential to designing more equitable and integrated approaches to cancer prevention and early screening.²⁶ This study examines how individual and combined HRSNs—social isolation, food insecurity, housing instability, and transportation challenges—affect breast and cervical cancer screening completion.

Method

Data Source

Secondary data, encompassing 2023-2024 records, were obtained from a large FQHC network comprising 56 clinics across Texas. For the purpose of this study, all records were for females, 25 years and older, who were empaneled at the clinic for the past 12 months, and who were overdue for breast or cervical cancer screening at the time the data were extracted. This study was reviewed and approved by the University of Houston Institutional Review Board (IRB Study #STUDY00004081).

Study Sample

As part of routine primary care, the FQHC administers the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE) tool to systematically collect information on social determinants of health.²⁷ This standardized instrument assesses a comprehensive range of factors, including housing stability, food security, transportation access, employment, education, social support, stress, and safety. In addition, PRAPARE captures key demographic and enabling characteristics such as language, income, insurance coverage, and household composition, thereby providing a detailed picture of each patient’s social context to inform care planning and linkage to resources.

A total of 4,116 patients were eligible for this study; however, only about 48% had complete HRSN information, and thus, the analytic sample was limited to 1,964 patients who had complete information. Figure 1 shows the data processing flowchart.

Figure 1.

Data processing and analysis flowchart

The study population was divided into two subgroups: breast cancer screening group (mammogram; n = 695) and a cervical cancer screening group (HPV/Pap; n = 1,269). We also examined the overlap of patient IDs between the two groups. Approximately 5% (n = 101) of patients were included in both cancer screening test groups.

Measurement

This study is a secondary analysis of data previously collected from patients who received an SMS reminder encouraging cancer screening completion. The SMS quality improvement intervention involved a standardized text-message outreach over a 3-month period to patients empaneled to the clinics for at least 12 months prior to the intervention, and who were overdue for recommended cancer screenings. Because all clinics were part of the same FQHC network, message content was consistent across clinics and included brief reminders, encouragement to schedule screening, and instructions for contacting the clinic for assistance. Patients received at least three reminder messages during the outreach period, spaced at regular intervals. While the core protocol was standardized, clinics implemented the messaging within their existing workflows. Additional details regarding the intervention design and implementation are described in our earlier work.²⁸

There were two separate dependent variables: a) completion of a mammogram within 90 days of reminder SMS notification, and b) completion of an HPV/Pap smear test within 90 days of reminder SMS notification. The key independent variables were binary indicators of HRSNs, including food insecurity, housing instability, transportation limitation, and social isolation. Additional independent variables captured demographic and enabling characteristics such as age, race and ethnicity, poverty status, education level, and employment status. Additional covariates included whether the patient had an annual well visit in the prior year (No, Yes), whether they reported a family history of cancer (No, Yes), and the presence of comorbidities, including heart disease, diabetes mellitus, or depression. The combined social needs variable was defined as the sum of three binary indicators: transportation challenges, housing instability, and food insecurity, corresponding to material social needs.²⁹ The combined social needs variable ranges from 0 to 3, with higher scores indicating a greater number of reported material social needs. Social isolation was retained as a separate variable because it reflects a distinct psychosocial domain and may influence outcomes through different mechanisms.

Statistical Analysis

Descriptive statistical analysis was used to summarize the characteristics of the two cancer screening test subgroups. Next, we performed two adjusted multivariate logistic regression analyses for each cancer screening test subgroup to examine a) the relationship between individual social needs and cancer screening completion, and b) the relationship between combined social needs and cancer screening completion. All analyses were conducted using SAS 9.4, with statistical significance set at p < 0.05. Results are reported as adjusted odds ratios (aORs) with corresponding 95% confidence intervals (CIs).

Results

Table 1 presents the descriptive results and the multivariate logistic regression models examining the relationship between social needs and the breast cancer screening completion. Overall, 15.0% of participants were aged 45–54 years, 54.0% were aged 55–64 years, and 31.1% were aged 65 years or older. Among these individuals, 90.1% had a family income at or below 100% of the federal poverty level. In terms of race and ethnicity, 43.98% were Hispanic, 13.7% were Black or African American, 37.6% were non-Hispanic White, and 4.9% were categorized as Other. With respect to education, 54.0% had a high school education or less, and 55.0% were unemployed. Overall, 14.9% of participants reported feelings of social isolation, 10.4% experienced food insecurity, 14.0% faced housing insecurity, and 12.1% had transportation challenges. Twenty-three percent had an annual wellness visit in the prior year, 70.5% had heart disease; 68.4% had hypertension; 12.5% had a family history of cancer; and 39.3% had diabetes.

Table 1.

Statistical Descriptions and Regression Model Results for Breast Cancer Screening Test

Variable	Frequency (%)	Individual social needs variables model		Combined social needs variables model
Variable	Frequency (%)	OR (SE)	95%CI	OR (SE)	95%CI
Age group
45 – 54	104 (14.96)	0.85 (0.17)	[0.60, 1.19]	0.85 (0.17)	[0.60, 1.19]
55 – 64	375 (53.96)	1.12 (0.12)	[0.88, 1.43]	1.12 (0.12)	[0.88, 1.43]
Over 65+	216 (31.08)	REF		REF
Family FPL income
100% FPL and below	626 (90.07)	1.12 (0.14)	[0.93, 1.61]	1.19 (0.14)	[0.91, 1.56]
Over 100% FPL	69 (9.93)	REF		REF
Race/Ethnicity
Black/African American	95 (13.67)	0.80 (0.22)	[0.52, 1.24]	0.79 (0.22)	[0.51, 1.22]
Non-Hispanic White	261 (37.55)	REF		REF
Hispanic	305 (43.88)	1.30 (0.30)	[0.73, 2.31]	1.33 (0.29)	[0.75, 2.37]
Others	34 (4.89)	1.00 (0.16)	[0.73, 1.36]	0.99 (0.16)	[0.73, 1.35]
Education
High school degree or less	468 (67.34)	0.92 (0.10)	[0.76, 1.11]	0.92 (0.10)	[0.77, 1.12]
More than high school degree	227 (32.66)	REF		REF
Employment status
Unemployed	382 (54.96)	1.04 (0.09)	[0.87, 1.25]	1.05 (0.09)	[0.88, 1.25]
Full time/part time job	313 (45.04)	REF		REF
2022 annual wellness visit
Yes	159 (22.88)	0.90 (0.11)	[0.73, 1.11]	0.91 (0.11)	[0.74, 1.11]
No	536 (77.12)	REF		REF
Heart disease
Yes	490 (70.50)	1.13 (0.28)	[0.65, 1.94]	1.15 (0.28)	[0.67, 1.98]
No	205 (29.50)	REF		REF
Hypertension
Yes	475 (68.35)	0.54 (0.55)	[0.18, 1.60]	0.52 (0.55)	[0.18, 1.52]
No	220 (31.65)	REF		REF
Family history of cancer
Yes	87 (12.52)	1.09 (0.13)	[0.84, 1.42]	1.08 (0.13)	[0.84, 1.40]
No	608 (87.48)	REF		REF
Diabetes
Yes	273 (39.28)	0.92 (0.10)	[0.76, 1.12]	0.904 (0.10)	[0.77, 1.11]
No	422 (60.72)	REF		REF
Social isolation
Yes	103 (14.82)	0.95 (0.14)	[0.73, 1.24]	0.94 (0.13)	[0.72, 1.21]
No	592 (85.18)	REF		REF
Food insecurity
Yes	72 (10.36)	0.54 (0.21)**	[0.37, 0.83]	--	--
No	623 (89.64)	REF
Housing insecurity
Yes	97 (13.96)	1.12 (0.14)	[0.85, 1.47]	--	--
No	598 (86.04)	REF
Transportation limitation
Yes	84 (12.09)	0.86 (0.17)	[0.63, 1.16]	--	--
No	611 (87.91)	REF
	Mean (SD)	OR	95%CI	OR	95%CI
Combined social needs	0.36 (0.70)	--	--	0.72 (0.14)*	[0.53, 0.94]
Total sample size	695

Note. federal poverty level = FPL; Other includes Asian, Native American, two more races, others. The value inside the brackets is the standard error. OR = odds ratio; SE: Standard Errors. REF = reference group; CI = confidence interval; SD = Standard deviation. The combined social needs variable was calculated as the sum of food insecurity, housing insecurity, and transportation limitation. *p < 0.05; **p < 0.01; and ***p < 0.001.

Results of the breast cancer regression models are also shown in Table 1. In the individual social needs breast cancer model, food insecurity emerged as the only statistically significant predictor of breast cancer screening completion (aOR = 0.54; 95%CI: 0.36, 0.82) and was associated with lower odds of breast cancer screening compared to patients without food insecurity. In the combined social needs model, each additional social need was associated with a 28% decrease in the likelihood of completing a mammogram (aOR=0.72; 95%CI: 0.53, 0.94).

Table 2 presents the descriptive statistics and results of the multivariate logistic regression models examining the relationship between social needs and cervical cancer screening completion. Overall, 33.0% of participants were aged 25–34 years, 26.3% were aged 35–44 years, 21.4% were aged 45–54 years, 17.2% were aged 55–64 years, and 2.2% were aged 65 years or older. Among these individuals, 82.3% had a family income at or below 100% of the federal poverty level. In terms of race and ethnicity, 55.8% were Hispanic, 11.3% were Black or African American, 27.0% were non-Hispanic White, and 5.9% were categorized as Other. 68.1% had a high school education or less, and 42.0% were unemployed. Regarding social needs, 14.3% of participants reported feelings of social isolation, 6.4% experienced food insecurity, 11.0% faced housing insecurity, and 10.6% had transportation challenges. About 15% had an annual wellness visit in the prior year, 33.4% had heart disease; 14.7% had a family history of cancer; and 21.6% had diabetes.

Table 2.

Statistical Descriptions and Regression Model Results for Cervical Cancer Screening Test

Variable	Frequency (%)	Individual social needs variables model		Combined social needs variables model
Variable	Frequency (%)	OR (SE)	95%CI	OR (SE)	95%CI
Age group
25 – 34	419 (33.02)	1.18 (0.15)	[0.88, 1.57]	1.19 (0.15)	[0.89, 1.58]
35 – 44	333 (26.24)	0.97 (0.14)	[0.73, 1.29]	0.99 (0.14)	[0.74, 1.30]
45 – 54	271 (21.36)	0.93 (0.15)	[0.69, 1.25]	0.92 (0.15)	[0.68, 1.23]
55 – 64	218 (17.18)	1.20 (0.16)	[0.88, 1.64]	1.19 (0.16)	[0.87, 1.63]
Over 65+	28 (2.21)	REF		REF
Family FPL income
100% FPL and below	1,044 (82.27)	0.82 (0.09)*	[0.69, 0.97]	0.82 (0.09)*	[0.69, 0.97]
Over 100% FPL	225 (17.73)	REF		REF
Race/Ethnicity
Black/African American	143 (11.27)	1.07 (0.16)	[0.78, 1.46]	1.07 (0.16)	[0.78, 1.45]
Non-Hispanic White	343 (27.03)	REF		REF
Hispanic	708 (55.79)	1.07 (0.20)	[0.73, 1.58]	1.08 (0.20)	[0.74, 1.59]
Others	75 (5.91)	1.07 (0.11)	[0.87, 1.33]	1.08 (0.11)	[0.88, 1.34]
Education
High school degree or less	864 (68.09)	1.10 (0.07)	[0.96, 1.26]	1.10 (0.07)	[0.96, 1.27]
More than high school degree	405 (31.91)	REF		REF
Employment status
Unemployed	533 (42.00)	REF		REF
Full time/part time job	736 (58.00)	1.01 (0.07)	[0.89, 1.15]	1.01 (0.07)	[0.89, 1.15]
2022 annual wellness visit
Yes	187 (14.74)	1.25 (0.09)*	[1.06, 1.48]	1.24 (0.13)*	[1.05, 1.47]
No	1,082 (85.26)	REF		REF
Heart disease
Yes	437 (33.44)	1.08 (0.23)	[0.69, 1.71]	1.09 (0.23)	[0.69, 1.70]
No	832 (65.56)	REF		REF
Hypertension
Yes	419 (33.02)	0.71 (0.47)	[0.29, 1.77]	0.71 (0.46)	[0.29, 1.78]
No	850 (66.98)	REF		REF
Family history of cancer
Yes	121 (14.74)	0.62 (0.13)***	[0.48, 0.81]	0.63 (0.13)***	[0.48, 0.82]
No	1,082 (85.26)	REF		REF
Diabetes
Yes	274 (21.59)	1.03 (0.08)	[0.87, 1.22]	1.03 (0.08)	[0.88, 1.22]
No	995 (78.41)	REF		REF
Social isolation
Yes	182 (14.34)	1.13 (0.09)	[0.94, 1.34]	1.14 (0.09)	[0.61, 1.36]
No	1,087 (85.66)	REF		REF
Food insecurity
Yes	81 (6.38)	0.87 (0.15)	[0.65, 1.18]	--	--
No	1,188 (93.62)	REF
Housing insecurity
Yes	140 (11.03)	0.79 (0.12)*	[0.63, 0.99]	--	--
No	1,129 (88.97)	REF
Transportation limitation
Yes	134 (10.56)	0.99 (0.11)	[0.80, 1.23]	--	--
No	1,135 (89.44)	REF
	Mean (SD)	OR	95%CI	OR	95%CI
Combined social needs	0.28 (0.61)	--	--	0.77 (0.11)*	[0.61, 0.96]
Total sample size	1,269

Note. federal poverty level = FPL; Other includes Asian, Native American, two more races, others. The value inside the brackets is the standard error. OR = odds ratio; SE: Standard Errors; REF = reference group; CI = confidence interval; SD = Standard deviation. The combined social needs variable was calculated as the sum of food insecurity, housing insecurity, and transportation limitation. *p < 0.05; **p < 0.01; and ***p < 0.001.

Results of the cervical cancer regression models are also shown in Table 2. Those experiencing housing instability had lower odds (aOR = 0.79; 95%CI: 0.63, 0.99) of completing a cervical cancer screening test, compared to female patients who did not report housing issues. Those with family incomes at or below 100% FPL had significantly lower odds (aOR = 0.82; 95%CI: 0.69, 0.97) of completing a cervical cancer screening compared to those with higher income levels. Female participants who had an annual wellness visit in the prior year had higher odds (aOR = 1.25; 95%CI: 1.06, 1.48) of completing a cervical cancer screening test. Conversely, those with a family history of cancer had lower odds (aOR = 0.62; 95%CI: 0.48, 0.81) of completing a cervical cancer screening test. In the combined social needs model, each additional social need was associated with a 23% decrease in the lower likelihood of completing a HPV/Pap screening (aOR=0.77; 95%CI: 0.61, 0.96).

Discussion

In this study examining social factors and cancer screening completion rates within a large FQHC network in Texas, we found that females reporting food insecurity had an almost fifty percent lower odds of completing their breast cancer screening, while those reporting housing insecurity had a twenty percent lower likelihood of completing their cervical cancer screening. The effect of multiple social needs was especially concerning, as each additional need was associated with a 20-30% reduced likelihood of completing the cancer screenings. While other variables including family income, prior annual wellness visits, and family cancer history were associated with cervical cancer screening rates, food insecurity emerged as the single correlate associated with reduced breast cancer screening completion. These findings emphasize that social needs are associated with key healthcare process measures and underscore the importance of sustained investment in interventions that address these social needs.

Our results align with prior findings examining social factors influencing cancer screening rates.³⁰ Pavlicic et al, using data from the New York State Behavioral Risk Factor Surveillance System (BRFSS) reported a decreased likelihood of being up to date on colorectal cancer screening among individuals with food insecurity.³¹ Mendoza and colleagues reported a 40% lower odds of breast cancer screening among individuals who screened positive for food insecurity.¹⁹ Prior research has documented how individuals experiencing food insecurity often have limited access to nutrient-dense foods, relying more on processed/calorie-dense options. Often, those experiencing food insecurity reside in food and medical deserts, and this geographic pattern may hinder access to healthy foods and engagement in preventive care.^16,32-39

Additionally, individuals experiencing food insecurity often face broader socioeconomic stressors, which may take precedence over preventive health screenings.^14,15 In this context, food insecurity may function as a proxy indicator of acute life stress and resource scarcity, which can limit individuals’ ability to engage in routine, usual care.^14,40 Addressing food insecurity may therefore be an important strategy for improving cancer screening uptake in underserved populations.

Prior research supports the observed relationship between housing insecurity and cancer screening.⁴¹ Decker et al, in a matched cohort study, reported that unhoused veterans were less commonly screened for cancer, and gaining housing increased screening rates.⁴² Because housing insecurity is often associated with frequent moves, it can interfere directly with scheduling, completing, or following up on recommended cancer screenings. These trends reinforce the need for interventions that address housing-related barriers among medically vulnerable populations.

The significant negative association between family history of cancer and the likelihood of completing a screening deserves particular attention. This relationship has been reported in other studies that included predominantly African American and Hispanic populations. For example, prior work suggests that in some African American and Hispanic communities, discussing or planning for cancer is often considered taboo, as it may be perceived as inviting or “speaking” the disease into existence.^43,44 We posit that a “you become what you confess” belief can potentially discourage open conversations about family health history and preventive screening. Given that safety-net populations in this study are predominantly Hispanic and African American, these findings align with cultural narratives that may inadvertently contribute to delayed screening and later-stage diagnoses. Accordingly, there is an urgent need for culturally responsive interventions in these subpopulations, to boost cancer screening completion rates. Recent work provides evidence of CHW involvement in cancer screening, training, education, and prevention in medically underserved populations.⁴⁵

Our results support prior work that suggests that having a prior annual wellness visit was strongly associated with increased rates of cervical cancer screening completion. For example, Hamer et al found that having an annual wellness visit was associated with more than a 20-percentage point increase in the mammogram completion within six months.⁴⁶ Annual visits serve as opportunities for preventive care, allowing providers to review screening eligibility, update family and medical histories, and place orders for age-appropriate cancer screenings such as mammography, Pap tests, and colorectal cancer tests. By integrating screening reminders and care coordination into these visits, health systems can leverage the annual wellness visits as a low-cost, high-impact strategy to improve population-level cancer screening rates and reduce disparities in preventive care.

Finally, these findings have important implications for access to care in Texas—which has the highest uninsured population in the US (21.6% in 2023)and remains a Medicaid non-Expansion state.^47,48 Both uninsurance and Medicaid non-expansion have been closely tied to limited access to routine preventive care.^49,50 Additionally, in March 2025, the Centers for Medicare & Medicaid Services (CMS) formally rescinded federal guidance encouraging Medicaid authorities to cover and integrate supports for HRSNs,⁵¹ signaling a shift in policy emphasis that may make it more difficult for states and providers to secure sustainable coverage for programs addressing upstream determinants. Together, high uninsurance rates and diminished federal encouragement for systematized social needs coverage⁵² may reduce opportunities for patients experiencing food or housing insecurity to engage in preventive care, even within safety-net clinical settings.

This study is not without limitations. First, the findings are based on data from a large FQHC network located within a single state (Texas), which may limit the generalizability of the results to other regions or healthcare systems. Additionally, we did not formally compare the demographic characteristics of the study sample with those of the broader catchment area served by the network. Therefore, it is unclear whether the study population fully represents the overall population within the service area. The analysis was limited to patients without missing social needs data. Because more than 50% of patients had missing social needs information, those individuals were excluded rather than imputed, which may introduce selection bias. Data are from FQHC clinics; hence, findings may not be fully generalizable to other healthcare environments with different patient demographics. Due to the dynamic nature of social needs, a patient may have one need resolved, but a new need may arise months later that can impact the likelihood of screening. These cyclical changes in social needs are not captured in the secondary data, and hence, unaccounted for in this study. In addition, because clinic-level identifiers were not available, we were unable to account for potential clustering across the 56 clinics within the FQHC network, which may have led to underestimated standard errors. Finally, social needs information are self-reported, and as with all self-reports, there is a likelihood of bias due to factors such as recall inaccuracies, social desirability, or misunderstanding of the questions. Despite these limitations, this study adds to the limited body of work on cancer screening in Texas, where screening rates have long been among the lowest in the country.

Future research should examine whether these findings are consistent across other health systems and geographic settings. Replication studies in larger and more diverse populations will help determine the generalizability of these associations and strengthen the evidence base on the impact of social needs on cancer screening uptake.

Conclusion

Addressing health-related social needs is vital for improving screening completion in safety-net populations. Targeted interventions that address multiple social risks, including food insecurity and housing instability, are particularly important in promoting preventive care screenings.

Footnotes

Acknowledgements

We acknowledge the teams at the FQHC for data acquisition support.

ORCID iDs

Omolola E. Adepoju

Credo DjeDje

Ethical Considerations

This study was reviewed and approved by the University of Houston Institutional Review Board (STUDY00004081).

Author Contributions

OEA and TA conceptualized the study; OEA and TX were responsible for data analysis; OEA, TX and GRP wrote initial drafts of the manuscript; CD, CE, RM and LW contributed to interpreting finals and edited multiple drafts of the manuscript. All authors approved the final version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Prior Presentation

No prior presentations applicable to this work.

References

Siegel

Kratzer

Giaquinto

Sung

Jemal

. Cancer statistics, 2025. Ca. 2025;75(1):10-45. doi:10.3322/caac.21871.

Siegel

Miller

Wagle

Jemal

. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763.

US Preventive Services Task Force Nicholson

Silverstein

, et al. Screening for Breast Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2024;331(22):1918-1930. doi:10.1001/jama.2024.5534.

Yamashita

Takahashi

Ishizaki

Imura

Nakayama

. Associations of multimorbidity with breast, cervical, and colorectal cancer screening delivery: a cross-sectional study of a nationally representative Japanese sample. Cancer Epidemiol. 2020;69:101798. doi:10.1016/j.canep.2020.101798.

National Cancer Institute . State Cancer Profiles > Screening and Risk Factors Table. State Cancer Profiles Dynamic views of cancer statistics for prioritizing cancer control efforts across the nation. 2024. https://statecancerprofiles.cancer.gov/risk/index.php?stateFIPS=00&topic=women&risk=v05&race=00&type=risk. Accessed March 3, 2026.

Explore Cervical Cancer Screening in Texas | AHR. https://www.americashealthrankings.org/explore/measures/cervical_cancer_screen_women/TX. Accessed March 3, 2026.

Gupta

Sussman

Doubeni

, et al. Challenges and possible solutions to colorectal cancer screening for the underserved. J Natl Cancer Inst. 2014;106(4):dju032. doi:10.1093/jnci/dju032.

Jensen

Khan

Layeequr

. Sociodemographic Determinants in Cervical Cancer Screening Among the Underserved West Texas Women. Womens Health Rep. 2023;4(1):191-201. doi:10.1089/whr.2022.0050.

Falk

Cubbin

Jones

Carrillo-Kappus

Crocker

Rice

. Increasing Breast and Cervical Cancer Screening in Rural and Border Texas with Friend to Friend Plus Patient Navigation. J Cancer Educ Off J Am Assoc Cancer Educ. 2018;33(4):798-805. doi:10.1007/s13187-016-1147-6.

10.

Impact of the Health Center Program . Bureau of Primary Health Care. https://bphc.hrsa.gov/about-health-center-program/impact-health-center-program. Accessed October 29, 2025.

11.

Adepoju

Chae

Ojinnaka

Shetty

Angelocci

. Utilization Gaps During the COVID-19 Pandemic: Racial and Ethnic Disparities in Telemedicine Uptake in Federally Qualified Health Center Clinics. J Gen Intern Med. 2022;37(5):1191-1197. doi:10.1007/s11606-021-07304-4.

12.

Kreuter

Thompson

McQueen

Garg

. Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health. Annu Rev Public Health. 2021;42:329-344. doi:10.1146/annurev-publhealth-090419-102204.

13.

Beachy

Rising

Hass

, et al. Validation of the Jefferson health-related social needs screener. Front Health Serv. 2025;5:1658661. doi:10.3389/frhs.2025.1658661.

14.

Jia

Fung

Meigs

Thorndike

. Food Insecurity, Dietary Quality, and Health Care Utilization in Lower-Income Adults: A Cross-Sectional Study. J Acad Nutr Diet. 2021;121(11):2177-2186.e3. doi:10.1016/j.jand.2021.06.001.

15.

O’Connor

Webber

Martin

Henninger

Eder

Lin

. Preventive Services for Food Insecurity: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2025;333(15):1340-1351. doi:10.1001/jama.2024.22805.

16.

Gundersen

Ziliak

. Food Insecurity And Health Outcomes. Health Aff (Millwood). 2015;34(11):1830-1839. doi:10.1377/hlthaff.2015.0645.

17.

Lee

KMN

Hunleth

Rolf

, et al. Distance and Transportation Barriers to Colorectal Cancer Screening in a Rural Community. J Prim Care Community Health. 2023;14:21501319221147126. doi:10.1177/21501319221147126.

18.

Martin

Liaw

Bazemore

Jetty

Petterson

Kushel

. Adults with Housing Insecurity Have Worse Access to Primary and Preventive Care. J Am Board Fam Med JABFM. 2019;32(4):521-530. doi:10.3122/jabfm.2019.04.180374.

19.

Mendoza

Miller

Martin

, et al. Examining the Association of Food Insecurity and Being Up-To-Date for Breast and Colorectal Cancer Screenings. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2022;31(5):1017-1025. doi:10.1158/1055-9965.EPI-21-1116.

20.

Pohl

Aderonmu

Grab

Cohen-Tigor

Morris

. Transportation Insecurity, Social Support, and Adherence to Cancer Screening. JAMA Netw Open. 2025;8(1):e2457336. doi:10.1001/jamanetworkopen.2024.57336.

21.

Lopez

Trinh-Shevrin

Kwon

Sherman

Liang

. Text Messaging Interventions on Cancer Screening Rates: A Systematic Review. J Med Internet Res. 2017;19(8):e296. doi:10.2196/jmir.7893.

22.

Molokwu

Dwivedi

Alomari

Guzman

Shokar

. Effect of Text Message Reminders on Attendance at Cervical Cancer Screening Appointments in a Predominantly Hispanic Population. Hisp Health Care Int. 2023;21(3):121-128. doi:10.1177/15404153221098950.

23.

Kerrison

Shukla

Cunningham

Oyebode

Friedman

. Text-message reminders increase uptake of routine breast screening appointments: a randomised controlled trial in a hard-to-reach population. Br J Cancer. 2015;112(6):1005-1010. doi:10.1038/bjc.2015.36.

24.

Lee

Koopmeiners

Rhee

Raveis

Ahluwalia

. Mobile Phone Text Messaging Intervention for Cervical Cancer Screening: Changes in Knowledge and Behavior Pre-Post Intervention. J Med Internet Res. 2014;16(8):e196. doi:10.2196/jmir.3576.

25.

Korn

Walsh-Bailey

Correa-Mendez

, et al. Social determinants of health and U.S. cancer screening interventions: a systematic review. CA Cancer J Clin. 2023;73(5):461-479. doi:10.3322/caac.21801.

26.

Gautam

Goel

Jahagirdar

, et al. Impact of Neighborhood-Level Transportation Insecurity on Colorectal Cancer Screening in the United States. ACG 2024 Annual Scientific Meeting Abstracts. 2024. https://acgposters2024.eventscribe.net/fsPopup.asp?PosterID=683206&mode=posterInfo. Accessed March 3, 2026.

27.

The PRAPARE® Screening Tool | PRAPARE. https://prapare.org/the-prapare-screening-tool/. Accessed November 16, 2025.

28.

Omolola

Angelocci

. From SMS Text Messaging to Screening Completion: Understanding How SMS Responses Influence Cancer Screening Uptake in Federally Qualified Health Center Clinics. Health Services Research. (in press).

29.

Berkowitz

Meigs

DeWalt

, et al. Material need insecurities, diabetes control, and care utilization: Results from the Measuring Economic iNsecurity in Diabetes (MEND) study. JAMA Intern Med. 2015;175(2):257-265. doi:10.1001/jamainternmed.2014.6888.

30.

Mahmood

Kedia

Dillon

Kim

Arshad

Ray

. Food security status and breast cancer screening among women in the United States: Evidence from the Health and Retirement Study and Health Care and Nutrition Study. Cancer Causes Control CCC. 2023;34(4):321-335. doi:10.1007/s10552-023-01667-1.

31.

Ivic-Pavlicic

Sly

Tuminello

, et al. The association between food insecurity and colorectal cancer screening: 2018–2021 New York State Behavioral Risk Factor Surveillance System (BRFSS). Cancer Causes Control. 2024;35(12):1565-1572. doi:10.1007/s10552-024-01915-y.

32.

Laraia

. Food Insecurity and Chronic Disease. Adv Nutr. 2013;4(2):203-212. doi:10.3945/an.112.003277.

33.

Household Food Security in the United States in 2020 | Economic Research Service. https://www.ers.usda.gov/publications/pub-details?pubid=102075. Accessed November 16, 2025.

34.

Frongillo

Nguyen

Smith

Coleman-Jensen

. Food Insecurity Is Associated with Subjective Well-Being among Individuals from 138 Countries in the 2014 Gallup World Poll1234. J Nutr. 2017;147(4):680-687. doi:10.3945/jn.116.243642.

35.

Butler

Johnson

Hardison-Moody

Bowen

. Food Insecurity Associated with Higher Stress, Depressive Symptoms, and Lower Diet Quality among Women Caregivers in North Carolina. Nutrients. 2024;16(15):2491. doi:10.3390/nu16152491.

36.

Tapper

Mehta

Leung

. Food Insecurity is associated with chronic liver disease among US adults. J Clin Gastroenterol. 2023;57(7):737-742. doi:10.1097/MCG.0000000000001741.

37.

Bergmans

Palta

Robert

Berger

Ehrenthal

Malecki

. Associations between food security status and dietary inflammatory potential within lower-income adults from the United States National Health and Nutrition Examination Survey (NHANES), cycles 2007 to 2014. J Acad Nutr Diet. 2018;118(6):994-1005. doi:10.1016/j.jand.2017.12.003.

38.

Ostrer

Seligman

. Food Insecurity, Health, and Health Care in the US. JAMA. 2025;333(15):1352-1353. doi:10.1001/jama.2024.26784.

39.

Cain

Meyer

Cummer

, et al. Association of Food Insecurity with Mental Health Outcomes in Parents and Children. Acad Pediatr. 2022;22(7):1105-1114. doi:10.1016/j.acap.2022.04.010.

40.

Pruitt

Leonard

Xuan

, et al. Who Is Food Insecure? Implications for Targeted Recruitment and Outreach, National Health and Nutrition Examination Survey, 2005–2010. Prev Chronic Dis. 2016;13:E143. doi:10.5888/pcd13.160103.

41.

Fan

Keene

Banegas

, et al. Housing Insecurity Among Patients With Cancer. JNCI J Natl Cancer Inst. 2022;114(12):1584-1592. doi:10.1093/jnci/djac136.

42.

Decker

Graham

Titan

, et al.

Is Gaining Housing Associated With Higher Rates of Cancer Screening Among US Veterans Experiencing Homelessness?

Ann Fam Med. 2025;23(4):330-336. doi:10.1370/afm.240520.

43.

Adegboyega

Wiggins

Obielodan

Dignan

Schoenberg

. Beliefs associated with cancer screening behaviors among African Americans and Sub-Saharan African immigrant adults: a cross-sectional study. BMC Public Health. 2022;22(1):2219. doi:10.1186/s12889-022-14591-x.

44.

Carrion

Neelamegam

Lewinson

Polonio

Bonano

. Words of wisdom from older immigrant Latino/as with cancer. Humanit Soc Sci Commun. 2022;9(1):357. doi:10.1057/s41599-022-01375-2.

45.

Brandford

Sanchez

Pitones

Bolin

. From the Ground Up: Building and Implementing a Successful CHW/Promotor(a) Program for Cancer Screening, Training, Education, and Prevention. Health Promot Pract. 2025;15248399241308198. doi:10.1177/15248399241308198. Published online January 6, 2025.

46.

Hamer

Bradley

Lindrooth

Perraillon

. The effect of Medicare Annual Wellness Visits on breast cancer screening and diagnosis. Med Care. 2024;62(8):530-537. doi:10.1097/MLR.0000000000002023.

47.

Kaiser Family Foundation . Data on Health Coverage & Uninsured. In: KFF State Health Facts. KFF. https://www.kff.org/state-category/health-coverage-uninsured/. Accessed March 3, 2026.

48.

Keiser Family Foundation . Status of State Medicaid Expansion Decisions. KFF; 2026. https://www.kff.org/medicaid/status-of-state-medicaid-expansion-decisions/. Accessed March 3, 2026.

49.

Monnette

Stoecker

Nauman

Shi

. The impact of Medicaid expansion on access to care and preventive care for adults with diabetes and depression. J Diabetes Complications. 2020;34(10):107663. doi:10.1016/j.jdiacomp.2020.107663.

50.

Sommers

Blendon

Orav

Epstein

. Changes in Utilization and Health Among Low-Income Adults After Medicaid Expansion or Expanded Private Insurance. JAMA Intern Med. 2016;176(10):1501-1509. doi:10.1001/jamainternmed.2016.4419.

51.

Drew Snyder . Rescission of Guidance on Health-Related Social Needs. Centers for Medicare & Medicaid Services; 2025. https://www.medicaid.gov/federal-policy-guidance/downloads/cib03042025.pdf

52.

Adepoju

. Rethinking Social Needs and Health Care Utilization—Treating the Symptom, Not the Cause. JAMA Netw Open. 2025;8(12):e2547963. doi:10.1001/jamanetworkopen.2025.47963.