Cancer Screening Behaviors among People with HIV in the United States and the Role of Multi-Level Social and Economic Factors: A Systematic Review

Abstract

Purpose of Review

In the US, people with HIV (PWH) are more likely to be diagnosed with cancer at advanced stages, contributing to higher cancer-specific mortality rates. As such, the leading cause of non-AIDS death among PWH is cancer, including cancers with guideline recommended screening as a part of preventative care. Nonmedical drivers of health contribute to poor access to timely cancer screening among PWH. Thus, the purpose of this systematic review is to summarize the available evidence on the relationship between social determinants of health (SDoH) and cancer screening behaviors among PWH in the US.

Recent Findings

Following PRISMA guidelines, we conducted a systematic search for peer-reviewed US-specific studies published between 2010–2024 using PubMed, Embase, CINAHL, and Web of Science. Our search strategy included key terms that fell under four key concepts: 1. SDoH, 2. HIV, 3. Cancers of interest, and 4. Outcomes of interest. Of the 2334 articles identified, 1146 underwent title/abstract screening and 86 full-length articles were reviewed for eligibility. Overall, 40 articles met eligibility criteria for inclusion in our data synthesis. Over two-thirds (68%, n =27) of articles focused on screening for anal and cervical cancer, both HPV-associated cancers, and less than one-quarter (22%) of studies provided information on screening for cancers without an infectious etiology (lung [i = 3], prostate [n =1], colorectal [n =4], breast cancers [n =1]). Importantly, the main SDoH reported by studies in this review were individual-level factors related to economic stability (i.e., annual income or employment status) or educational attainment. With respect to healthcare access and quality, the most reported indicator was health insurance coverage with a few studies reporting on provider or facility type, and number of visits per year. Finally, for neighborhood and built environment factors, only two studies reported on county-level social vulnerability and area-level poverty.

Summary

Although cancers without an infectious etiology, such as lung cancer, are major causes of cancer deaths among PWH, most existing work focuses on cervical and anal cancer prevention with limited SDoH measures. Measurement of SDoH in cancer prevention research among PWH should be prioritized and broadened to include structural factors.

Background

In the United States (US), widespread availability of antiretroviral therapy (ART) has improved the life expectancy of people with HIV (PWH), leading to an overall aging of the population [1]. In fact, the proportion of adult PWH in the US who are aged 65 years or older is projected to increase from 8.5% in 2010 to 21.4% in 2030 [2]. This shift in the age distribution among PWH is largely attributed to a reduction in risk of death due to AIDS and AIDS-related illnesses, including AIDS-defining cancers such as Kaposi Sarcoma [3]. Importantly, the combined effects of aging due to increased life expectancy and HIV have led to an increased risk of cancer in this population with cancer now emerging as a leading cause of morbidity and mortality in this population [2, 4, 5]. Recent studies have shown that the risk of cancer is significantly higher among PWH compared to those without HIV, across multiple cancer types, including cancers without an infectious etiology such as lung, colorectal, prostate and breast cancer. For example, the risk of lung cancer among PWH is 59% higher than in the general population and this trend has not significantly changed over the past decade [6, 7]. In addition to higher cancer incidence, cancer-specific mortality is higher among PWH compared to those without HIV and contemporary trends demonstrate that cancer is the leading cause of non-AIDS death among PWH in the US [8–11]. One major contributor to elevated mortality among PWH in the US is stage at cancer diagnosis, with PWH up to two times more likely to be diagnosed at advanced stages of cancer compared to those without HIV [12–14]. For example, PWH diagnosed with lung cancer are mostly diagnosed at late stage, with 15% presenting at the local, resectable stage, leading to median survival times of 3.5 (late) and 6.3 months (local) [15]. Timely, high-quality early detection methods via guideline-concordant cancer screening are therefore essential for improving long-term cancer outcomes among PWH. Currently, PWH are advised to follow national cancer screening guidelines similar to those for the general population, except for cervical and anal cancers, which have tailored guidelines for PWH [16]. Understanding trends in uptake of national recommendations and subsequent cancer outcomes requires consideration of the underlying demographic and socioeconomic distribution of the US HIV epidemic, which disproportionately affects minoritized populations and those facing persistent barriers to care. For instance, in 2019, Black adults accounted for 41% of new HIV diagnoses despite representing only 13% of the US population, while non-Latinx White adults made up 25% of new cases but 60% of the population [17]. These racial and ethnic disparities in HIV diagnoses also reflect the critical role of upstream social factors on health outcomes, especially cancer screening, and emphasize the importance of social determinants of health (SDoH) in shaping access to and use of preventive care services. The intersection of living with HIV, minoritized race and ethnicity, sexual orientation and gender identity, geography, and structural disadvantage suggests a compounding effect that limits access to timely cancer screening. Therefore, it is critical to identify and address the underlying and intervenable social, economic and environmental conditions and mechanisms driving inequities in cancer screening behaviors among PWH to develop tailored, effective interventions aimed at improving cancer outcomes in this population.

SDoH—including non-medical factors such as economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context—significantly influence health behaviors and outcomes of individuals [18]. In the context of cancer screening among PWH, SDoH likely influence not only whether individuals undergo screening but importantly the timeliness and quality of the preventative care they receive [19, 20]. This systematic review will synthesize the existing evidence on how SDoH are measured in cancer screening behavioral research among PWH in the US. By identifying the extent to which SDoH measures are incorporated into these studies, this review will provide information on the types of measures as well as consistency of measures used across studies to understand the impact of SDoH on cancer screening outcomes. In addition, this review will synthesize the methodological strengths and biases of the measures employed in these studies to provide insights on how to systematically collect SDoH information as a standard practice in cancer prevention research. Ultimately, this integration is essential for informing the development of tailored screening interventions to reduce health inequities in this growing and understudied cancer patient population.

Methods

Search Strategy and Data Sources

To identify relevant studies for inclusion in this review, a comprehensive literature search was conducted of the following four online databases: Medline via PubMed^®, CINAHL^® via Ebsco, Embase^® via Ovid, and Web of Science^® Core Collection. Four key search strings or concepts were built to identify relevant publications: 1. social determinants of health, 2. HIV, 3. cancer sites of interest (breast, colorectal, lung, prostate, cervical, and anal), and 4. outcomes of interest (i.e. screening behaviors). The search strategy implemented these four broader concepts by employing relevant and appropriate combinations of keywords and database-specific subject headings reflecting each concept. A detailed description of all keywords used for each concept for all search strategies is provided in Supplementary Tables 1–4. Of the articles identified, the reference lists articles selected for inclusion were then reviewed for additional studies. The search strategy was developed by the health sciences librarian (HL) on the research team, and the keyword list was refined collaboratively. To refine our search strategy, a pilot set of 50 random results from PubMed were screened to test for relevance before running full searches in each database. Duplicates and out of scope publication types, which included review articles, meeting abstracts, editorials, commentaries, retractions, guidelines, and case reports were manually filtered prior to screening. The remaining reports were uploaded to Covidence online software [21] for screening (n =1146; Fig. 1). Each study was screened independently by two reviewers on the research team. To review each study, first titles and abstracts were screened to remove publications that were out of scope or irrelevant (n =1060). Conflicts were resolved through discussion among the project leads to ensure consensus was attained. Finally, the full-texts of the remaining articles were reviewed to ensure they met eligibility criteria (n =86).

Fig. 1.

PRISM flowchart summarizing review of cancer prevention and social determinants of health research among people with HIV in the US, 2010–2024. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71.

Study Inclusion Criteria

To be included in this review, studies had to: (1) include a population of PWH, (2) focus on cancer screening behaviors, (3) be published in English, (4) focus on a US based population, and (5) be published after 2010. The final search was conducted on July 16th, 2024. The search was limited to publications after 2010 given the dramatic shift in age expectancy of PWH over the past two decades and the growing burden of chronic comorbidities within this aging population [4, 22]. Screening behaviors of interest were any of the following outcomes: (1) screening uptake (i.e., prevalence), (2) adherence to follow-up after an abnormal finding, and (3) longitudinal adherence to screening recommendation (i.e., second instance of screening among a screened population). We focused on the six types of cancers with recommended screening guidelines, which include anal, breast, cervical, colorectal, lung, and prostate cancers. In addition to the above five criteria, eligible studies needed to include assessment of at least one SDoH. We defined SDoH following the Healthy People 2030 framework [23], which states “SDoH are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” We focused exclusively on the US due to its unique historical and social context, particularly the downstream effects of structural racism that shape SDoH and influence access to care [24]. Studies not meeting these criteria or including relevant information were excluded (see Fig. 1).

Data Extraction and Risk of Bias Evaluation

Data from each included study were extracted in Covidence and categorized in data matrix tables by cancer screening type and SDoH domain (Tables 1, 2 and 3). First, we extracted information on author name, publication year, study design, setting, period of data collection, sample size (overall and for PWH), sample characteristics (age distribution, sexual behavior and/or orientation, sex, and gender identity). Next, screening behavior information included cancer screening type, screening outcome, and outcome assessment method. Finally, following the Healthy People 2030 framework, data on SDoH domains were extracted and grouped into the following five domains (1) economic stability, (2) education access and quality, (3) health care access and quality, (4) neighborhood and built environment, and (5) social and community context.

Table 1.

Summary of studies grouped by cancer screening type and characteristics of study participants included in each study of cancer prevention among people with HIV in the United States, 2010–2024

Author (Publication year)	Study design	Study setting	Study years	Sample size		Participant characteristics
				Total sample size*	People with HIV n	Age	Sexual behavior and/or orientation	Sex	Gender identity	Race and ethnicity
Anal cancer screening
Apaydin (2018)	Mixed-methods	Clinic-based	2016	14	6	≥ 18 years Mean age: 49 years	Gay: 93% Bisexual: 7% MSM: 100%	NR	Male: 100%	White: 93% More than one race: 7%
Cachay (2018)	Retrospective cohort	Clinic, EHR	2001–2012	X	2804	≥ 18 years Median age: 40 years	MSM: 80%	Male: 88% Female:11%	NR	NH-Black: 13% Hispanic: 17% NH-White: 62% NH-Other: 8%
Cachay (2023)	Retrospective cohort	Clinic, EHR	2007–2020	X	8239	≥ 18 years Median age: 44 years	MSM: 62%	Male: 86% Female: 14%	Cisgender Persons: 98% Transgender Persons: 1.4%	Black: 15% White: 62% Others: 23% Hispanic Ethnicity: 27%
Cruz (2023)	Cross-sectional	Community-based	2020–2021	X	202	≥ 26 years Median age: 54 years	Heterosexual: 55% Homosexual: 38% Bisexual: 8% MSM: 44%	Male: 68% Female: 32%	Men: 67% Women: 32% Transgender persons: 1%	Hispanic Ethnicity: 100% Puerto Rican
D'Souza (2013)	Prospective Cohort	Community and Clinic-based	2010–2011	1742	820	≥ 18 years Median age: 55 years	MSM: 100%	Male: 100%	NR	NH-Black: 20% NH-White 67% Other: 13%
Hernandez (2013)	Prospective Cohort	Community-based	1998–2005	X	318	≥ 18 years Mean age: 42.5 years	MSM: 100%	Male: 100%	NR	Asian: 2% Black: 2% Hispanic: 7% NH-White: 88% Other: 1%
Hernandez (2024)	Prospective Cohort	Community-based	2018–2022	238	129	≥ 50 years	MSM: 100%	Male: 97%	Cisgender: 97% Transgender: 3%	Asian/AI/AN: 7% Black: 10% NH-White: 64% Hispanic White: 5% Other/Mixed: 13%
Junkins (2024)	Retrospective Cohort	Clinic-based	2018	X	1,114	20–79 years Median: 48 years	Homosexual: 62% Heterosexual: 21% Bisexual: 11% Other: 6%	Men: 100%	NR	Black: 55% White: 42% Other: 2%
Kutner (2024)	Sequential Explanatory Mixed-Methods	Clinic-based	2022–2023	X	13	≥ 35 years Median age: 47 years	Gay: 62% Bisexual: 23% Queer: 8% Heterosexual: 8%	NR	Cisgender: 85% Transgender: 16%	Latinx: 31% Asian: 15% Black: 31% White (non-Latinx): 23% Multiracial: 8%
Nyitray (2023)	Cross-sectional	Community-based	2020–2022	241	65	≥ 25 years Median age: 46 years	Gay: 82% Bisexual: 12% Other: 2%	NR	Men: 94% Transgender, nonbinary or other: 6%	NH-Black: 19% Hispanic: 13% NH-White: 66% Other: 2%
Rim (2024)	Cross-sectional	National Survey	2019	X	4,100	≥ 18 years	Lesbian or Gay: 42% Heterosexual: 46% Bisexual: 9% Other: 3%	NR	Cisgender males: 75% Cisgender females: 23% Transgender females: 2%	NH-Black: 42% Hispanic/Latino: 22% NH-White: 29% Other: 7%
Schwartz (2013)	Cross-sectional	Clinic-based	2009–2010	X	305	≥ 18 years Median age: 53 years	MSM: 100%	Male: 100%	NR	White: 90% Non-White: 10% Non-Hispanic: 88% Hispanic: 12%
Wells (2018)	Retrospective Cohort	Clinic, EHR	2010–2013	X	200	≥ 18 years Mean age: 49.7 years	MSM: 21% Non-MSM: 80%	Male: 51% Female: 49%	NR	White: 5% Black: 74% Hispanic: 5% Other: 16.5%
Wells (2022)	Cross-sectional	Clinic-based	NR	X	149	≥ 21 years Median age: 46 years	Heterosexual/ Straight: 14% Bisexual/ Other: 18% Lesbian/gay: 68%	Born male: 89% Born female: 11%	NR	Black: 79% White: 16% Hispanic/Latino/Other: 5%
Ye (2021)	Retrospective cohort	Clinic, EHR	2006–2018	X	4482	≥ 18 years Median age: 46 years	Heterosexual: 41% MSM: 55% Other:0.2%	NR	Men: 76% Women: 23% Transgender persons: 0.5%	Black: 60% White: 36% Other: 4%
Breast cancer screening
Weinstein (2016)	Retrospective cohort	Clinic, EHR	2003–2008	X	292	≥ 40 years	NR	Female: 100%	NR	Black: 70% Hispanic: 11% White: 18% Other: 1%
Cervical cancer screening
Baranoski (2011)	Retrospective cohort	Clinic, EHR	2003–2008	X	549	18–60 years Mean age: 39.3 years	NR	Female:100%	NR	Black: 71% Hispanic: 14% White: 15% Other: 1%
Baranoski (2012)	Retrospective cohort	Clinic, EHR	2003–2007	X	177	≥ 18 years Mean: 37.3 years	NR	Female:100%	NR	Black/African: 72% Hispanic: 15% White: 13%
Barnes (2018)	Retrospective cohort	Clinic, EHR	2010–2014	X	1490	18–64 years	NR	Female: 100%	NR	White/Other: 13% Black: 72% Hispanic: 15%
Bynum (2016)	Cross-sectional	Clinic-based	2011–2012	X	145	≥ 18 years Mean: 46 years	NR	Female:100%	NR	Black: 90% AI/AN: 1% White: 6% Other: 2%
Dailey Garnes (2015)	Cross sectional	Clinic, EHR	2007	X	498	≥ 34 years Median: 43 years	NR	Female: 100%	NR	Black: 73% White: 7% Hispanic: 19% Other: 1%
Fletcher (2014)	Qualitative Focus Groups	Clinic-based	2012	X	33	≥ 18 years Median: 51 years	NR	NR	Women: 100%	Black: 70% White: 12% Hispanic: 18%
Fletcher (2014)	Cross-sectional	Clinic-based	2007–2009	X	138	≥ 18 years Mean: 45.4 years	NR	Female: 100%	NR	White: 12% Black: 79% Latino/Hispanic: 6% Other: 3%
Frazier (2016)	Cross-sectional	National Survey	2009–2010	X	2270	≥ 18 years	NR	Female: 100%	NR	Black-NH: 62% Hispanic/Latino: 17% White-NH: 17% Other: 4%
Logan (2010)	Retrospective Cohort	Clinic, EHR	2000–2006	X	200	≥ 18 years Mean: 38 years	NR	Female: 100%	NR	White, NH: 20% African American: 57% Hispanic: 23%
Peprah (2018)	Retrospective cohort	Clinic, EHR	2005–2014	X	554	≥ 18 years Median: 41 years	NR	Female: 100%	NR	Black: 79% White: 17% Other: 4%
Soto-Salgado (2024)	Cross-sectional	National Survey	2018–2021	X	3,871	≥ 18 years	NR	Female: 100%	NR	Hispanic/Puerto Rican: 100%
Tello (2010)	Mixed-methods	Clinic-based	2008	X	200	≥ 18 years Mean: 46 years	NR	Female:100%	NR	Caucasian: 11% African American: 85% Other: 4%
Colorectal cancer screening
Burkholder (2015)	Retrospective cohort	Clinic, EHR	2003–2010	X	265	≥ 50 years	MSM: 59% Heterosexual men: 19%	NR	Men: 79% Women: 21%	White: 60% Black/African American: 40%
Kelly (2021)	Cross-sectional	Clinic-based	NR	X	270	≥ 40 years Mean: 55 years	NR	Male: 100%	NR	Black or African American: 100%
Lam (2019)	Retrospective cohort	Clinic, EHR	2005–2016	32, 396	3,177	50–75 years Mean: 53 years	NR	Male: 91%	NR	White, NH: 59% Hispanic: 15% Black, NH: 14% Asian, NH: 4% Other: 7%
Momplaisir (2012)	Cross-sectional	Clinic. EHR	2010	X	115	50–81 years	NR	Male: 71% Female: 29%	NR	White: 26% Black and/or Hispanic: 74%
Lung cancer screening
Islam (2023)	Retrospective cohort	Clinic, EHR	2012–2021	365	73	50–80 years	NR	Male: 55% Female: 45%	NR	White, NH: 65% Black, NH: 28% Hispanic: 2%
Lopez (2022)	Retrospective cohort	Clinic, EHR	2016–2018	X	256	55–80 years Mean: 60 years	MSM: 63% Heterosexual: 57%	Male: 75% Female: 23%	Transgender Female: 1%	NH-White: 67% NH-Black: 27% Other: 7%
Triplette (2023)	Mixed-methods	Clinic-based	2021–2022	X	64	≥ 18 years Median: 59 years	NR	NR	Female: 16% Male: 81% Other: 3%	AI/AN: 11% Asian: 5% Black: 11% White: 69% Other: 6%
Prostate cancer screening
Leapman (2022)	Cohort	Clinic, EHR	2000–2015	123,472	37,819	≥ 45 years Median: 52 years	NR	Male: 100%	NR	White: 40% Black: 48% Hispanic: 8% Other: 4%
Multiple cancer screening types
Momplaisir (2014)	Cross-sectional	Clinic-based	2010–2011	762	401	≥ 50 years (M) ≥ 40 years (W) Mean: 54 years	NR	NR	Male: 54% Female: 45% Transgender persons: 5%	White, NH: 29% Black, NH: 45% Other: 5%
Rahangdale (2010)	Retrospective Cohort	Clinic, EHR	2002–2006	X	60	≥ 18 years	NR	Female: 100%	NR	Black: 43% Hispanic: 28% White: 25% Other: 4%
Short (2019)	Cross-sectional	National Survey, linked with EHR	2013–2014	X	2766	≥ 18 years	NR	Female: 100%	NR	NH-Black: 62% Hispanic/Latina: 20% NH-White: 15% Other: 3%
Simonsen (2014)	Retrospective cohort	Clinic, EHR	2009	X	192	≥ 18 years	NR	Female: 100%	NR	Caucasian: 68% African: 24% Other: 6%

Abbreviations: NR Not reported, EHR electronic health record, NH Non-Hispanic, MSM Men who have sex with men

^*Total denotes total sample size, including those without HIV. X indicates that the study only includes people with HIV, therefore the total is reported in the People with HIV “n” column

Table 2.

Social determinants of health measured and cancer screening outcomes by cancer screening type in each study of cancer prevention among people with HIV in the United States, 2010–2024

	Cancer screening measures			Social determinant of health domains based on healthy people 2030, quantitative measures
Author (year)	Cancer screening type assessed	Cancer screening outcome	Outcome assessment method	Economic stability	Education access and quality	Healthcare access and quality	Neighborhood and built environment	Social and community context
Anal cancer screening
Apaydin (2018)	HRA	HRA Follow-up within 6 months: 64% Transitions from HSIL to < HSIL	EHR abstracted	Annual household income ($20–29,999, $30–49,999, $50–79,000, $80–120,000, > $120,000)	Educational attainment (high school graduate, college graduate, post-graduate)	Health insurance (private, public, Medicare) Healthcare system inefficiencies Patient-provider relationship	X	Gender identity (male) Sexual orientation (gay, bisexual) Relationship status (single, in a relationship with a male partner, living with a male partner, married to a male partner) Internalized stigma Societal stigma
Cachay (2018)	Anal cytology	HSIL Prevalence: 14%	EHR abstracted	X	X	X	X	Sexual behavior (MSM, heterosexual) Drug user (Injection drug use, yes/no)
Cachay (2023)	Anal cytology	Anal Cytology Uptake: 49.8% HSIL Prevalence: 12%	EHR abstracted	X	X	X	X	Gender identity (cisgender, transgender) Sexual behavior/orientation (MSM, heterosexual) Drug user (injection drug use yes,no)
Cruz (2023)	Anal cytology HRA	Anal Cytology Uptake: 51% HRA Uptake: 19%	Self-reported via survey	Annual income (< $15,000 vs. > $15,000)	Educational attainment (more than high school, high school or less)	Insurance coverage (public, private)	X	Sexual orientation (heterosexual, homosexual, bisexual) Sexual behavior (MSM, MSW) Gender Identity (man, women, transgender) Relationship Status (married/living together, single/divorced/separated/ widowed)
D'Souza (2013)	Anal cytology	Anal cytology uptake: 85%	In-clinic assessment by study staff	Annual income (> $40,000 vs. $40,000/Unknown)	Educational attainment (College degree or higher, < College degree, Unknown)	X	X	Sexual behavior (MSM)
Hernandez (2013)	Anal swab for HPV	HPV Infection Prevalence: 92% Oncogenic HPV Infection: 80%	In-clinic testing	X	Educational attainment (Did not complete college, Completed college, Completed graduate school)	X	X	Injection drug use (Ever) Sexual behavior (MSM)
Hernandez (2024)	HPV testing HRA	Anal HSIL: 42%	In-clinic testing	Employment status (Employed full time, Employed part time, Retired, Other) Annual income ($0-47,999, $48,000-$83,999, $84,000 +)	Educational attainment (Less than college, Some college/technical school, Bachelor’s degree, Graduate or professional degree)	X	X	Gender Identity (male or transperson)
Junkins (2024)	Anal cytology	Anal screening prevalence: 52%	EHR abstracted	X	X	X	County Social Vulnerability (Lowest vulnerability, Low to moderate vulnerability, Moderate to high vulnerability, Highest vulnerability score)	Sexual orientation (homosexual, heterosexual, bisexual, other)
Kutner (2024)	HRA follow-up	Overdue for HRA by 2 years or more: 30.8%	EHR confirmed	Annual income (Below $29,999, $30,000–$59,999, $60,000-or more)	Educational attainment (< 2-year College Degree, 4-year College Degree, Masters or Doctoral Degree)	X	X	Gender Identity (cisgender male/female, transgender male/female, nonbinary, transgender) Sexual orientation (gay, bisexual, queer, heterosexual) Relationship status (single, casual dating, boyfriend/girlfriend/partner/lover)
Nyitray (2023)	DARE	Received DARE in the past year: 14%	Self-reported via survey	X	X	Insurance coverage (Yes, No)	X	Gender identity (man, transgender/non-binary, other) Sexual orientation (gay, bisexual, other)
Rim (2024)	Anal cytology	Anal cytology prevalence in past year: 5%	EHR abstracted	Employment status (Employed (for wages or self-employed), Unemployed or unable to work, Other employment status) Household income in past 12 months (Above federal poverty level, At or below federal poverty level)	Educational attainment (Less than high school, High school or equivalent, More than high school)	Insurance coverage (Any private insurance, Public only, Ryan White HIV/AIDS Program coverage only, Uninsured) Healthcare facility type (does not provide HRA, provides HRA, provides HRA through outside referral)	Region (Western Medical Monitoring Project or MMP states, Midwestern MMP states, Northeastern MMP states, Southern MMP states, Puerto Rico)	Gender Identity (cisgender male, cisgender female, transgender female) Sexual orientation (lesbian, gay, heterosexual, bisexual, other) Sexual behavior (MSM)
Schwartz (2013)	Anal cytology Histology HPV HRA	HR-HPV Positive: 50% (< AIN2) AIN2 + : 29%	In clinic testing	X	Educational attainment (High school, College, Graduate school)	X	X	Sexual behavior (MSM)
Wells (2018)	Anal cytology Anoscopy	Received Anal Cytology: 29% Abnormal cytology result: 69% Received Anoscopy: 35%	EHR abstracted	X	X	Insurance coverage (None, Medicaid, Medicare, Private, Other)	X	Sexual Behavior (MSM, non-MSM) Relationship Status (single/divorced/ widowed, married/live in partner, not documented)
Wells (2022)	Anal Cytology HRA	HSIL: 6% HRA within 6 months: 24% HRA within 12 months: 57%	EHR abstracted	Current Employment status (No, Yes) Average Household Monthly income (< $1000, $1001–2500, $2501–6250, > $6250) Housing stability (Own home/apartment, Someone else’s home/apartment/ shelter, Homeless/correctional/ treatment facility)	Educational attainment (High school diploma, High school diploma/ GED, Attended/ completed college, Attended/completed graduate school)	Health Insurance (Government (any), No coverage/self-pay, Other, Private health insurance)	X	Sexual orientation (heterosexual/straight, bisexual/other, lesbian/gay) Relationship (single/never married, married/ living together, divorced/married/ separated) HIV/AIDS-related stigma score Social support score
Ye (2021)	Anal cytology	Received anal cytology: 37%	EHR abstracted	Employment status (Unemployed, Disabled, Employed, Retired, Student)	X	X	X	Sexual orientation/behavior (Heterosexual, MSM) Gender identity (men, women, transgender people) Relationship Status (married/life partner, divorced/separated, singled, widowed)
Breast cancer screening
Weinstein (2016)	Mammography	Adherence to mammography within 2 years of first screening: 50%	EHR abstracted	Employment status (Employed, In school)	Educational attainment (Less than high school education)	X	X	Nativity (Foreign born, US born) First language (Non-English speaking) Relationship status (Single) Drug use (never/ever)
Cervical cancer screening
Baranoski (2011)	Cervical cytology	Pap test prevalence within 18 months: 53%	EHR abstracted	Employment Status (Unemployed/Disabled) Social service use (Healthcare for the Homeless program enrollee, yes/no)	Educational attainment (Did not complete high school)	Distance to care (Home distance from hospital, miles (< 2, 2- < 5, 5- < 10, ≥ 10); Home distances from hospital, range) Type of provider (ID trained, yes/ no) Number of visits with a male provider (> 75% of visits)	X	Nativity (US born) First language (English speaking) Relationship status (married) Drug use (yes/no)
Baranoski (2012)	Colposcopy	Colposcopy within 6 months: 59% HSIL Prevalence: 12%	EHR abstracted	Employment status (Unemployed, yes/no)	Educational attainment (High school graduate or higher education)	Insurance coverage (Uninsured, Medicaid, Medicare, private) Distance to care (Household 5 or more miles from hospital) Clinic facility type (gynecology clinic, HIV NP, other)	X	Nativity (US born) First language (English speaking) Relationship status (married) Drug use (yes/no)
Barnes (2018)	Cervical cytology HPV Colposcopy	Baseline screening prevalence: 44.3% Screening prevalence at 15 months: 58% Colposcopy prevalence: 60%	EHR abstracted	X	X	Insurance coverage (Payor Program (uninsured, fed-state government, Medicaid, Medicare, commercial))	X	X
Bynum (2016)	Cervical cytology	Pap test/ screening uptake within 1 year: 81% Two or more pap tests within first year of HIV diagnosis: 36% Having pap tests after HIV diagnosis: 35%	Self-reported via survey	Employment status (full time/part time, unemployed) Annual income (< $10 k/year, ≥ $10 k/year),	Educational attainment (no high school/GED, high school diploma/ GED, ≥ some college)	Type of provider (Personal healthcare provider, yes/no) Healthcare access (low access, high access) Transportation or getting to medical appointments (hard, easy)	X	Relationship status (married/partner, single) Perceived discrimination Perceived HIV Stigma (Stigma, no stigma)
Dailey-Garnes (2015)	Cervical cytology	Pap test/ screening within the past year: 52%	EHR abstracted	X	X	Number of primary care appointments (continuous measure) Type of care setting (specialty care, family practice)	Per capita income by zip code (< $12,000, 12–24,000, > $24,000)	Sexual orientation (heterosexual, yes/no) Drug use (intravenous drug use, yes/no)
Fletcher (2014)	Cervical cytology	Pap test/ screening uptake within the last year: 79%	Self-reported via survey	Employment status (Not working due to health, Not working for other reasons, Unable to find work) Annual Income (< $10,000 vs. ≥ $10,000)	Educational attainment (Less than high school, High school diploma/ General education development, Technical/vocational degree, Some college or two-year degree)	Insurance coverage (Medicare and/or Medicaid, No insurance) Patient-provider relationship Care coordination quality/referral process Transportation access Wait times	X	Drug use (injection drug use, yes/no) Relationship status (single, married) Religion (Christian, Muslim, other)
Fletcher (2014)	Cervical cytology	Pap test/ screening uptake within one year: 46%	EHR abstracted	Employment status (Working full or part time, Not working due to health, Unable to find work, Not working for other reasons)	Educational attainment (Less than high school, High school or equivalent, More than high school)	X	X	Drug use (injection drug use, yes/no; illicit drug use within the past 30 days, yes/no) Relationship status (married or living with partner, yes/no) Social support (ISEL) Perceived discrimination (Williams and colleagues' 9 item discrimination scale)
Frazier (2016)	Cervical cytology	Pap test/ screening uptake within the past year: 78%	Self-reported via survey	Poverty level (Above poverty level, At or below poverty level, Unknown) Homelessness (Yes/no)	Education attainment (< High school, High school diploma or GED, > High school)	Insurance coverage (Uninsured or lapse in insurance, yes/no) Patient-provider relationship (had 1:1 conversation with a health care professional, yes/ no)	X	Drug use (injection drug use, yes/no; any drug use, yes/no) Nativity (country of birth other than US or PR, yes/no) Incarceration (Yes/no)
Logan (2010)	Cervical cytology	Pap test/ screening uptake within one year: 83%	EHR abstracted	Annual income (Mean annual income)	X	Insurance coverage (Private, Need-based county plan, Medicaid, Medicare, Medicare/Medicaid, None (Ryan White only))	X	Sexual orientation (Heterosexual contact, yes/no) Drug use (injection drug use, yes/no) History of Incarceration (yes/no)
Peprah (2018)	Cervical cytology	Prevalence of pap testing/ screening: 79% Abnormal pap test results: 40%	EHR abstracted	X	X	Insurance coverage (Public (Medicaid/Medicare), Private, Ryan White, Uninsured/ Out of pocket, Unknown)	X	Drug use (injection drug user, yes/no)
Soto-Salgado (2024)	Cervical cytology	Prevalence of pap test/ screening within the past 3 years: 92%	Self-reported via survey	Employment status (Currently employed, Other status, including women who are homemakers, retired, students, unemployed, and unable to work) Annual income (< $20,000, ≥ $20,000)	Educational attainment (< High school, High school diploma or GED, > High school)	Insurance coverage (Any private, Dual coverage Medicaid/Medicare or Medicare only, Medicaid or other public insurance, Uninsured including RWHAP only) Regular provider (has regular HIV provider, yes/no)	Region (Puerto Rico versus 22 other Medical Monitoring or MMP states)	HIV Stigma Discrimination (Experienced any healthcare discrimination scale)
Tello (2010)	Cervical cytology	No pap smear in the past year: 22%	EHR abstracted	Employment status (Full-time or part-time, Not working, Other or disability)	Educational attainment (< High school, High school/GED +)	X	X	Caretaker role of children (yes/no) Drug use (substance use, yes/no) Social Support (Very low-low, Medium, High)
Colorectal cancer screening
Burkholder (2015)	Time to colorectal cancer screening at age 50 (defined as colonoscopy, flexible sigmoidoscopy, ACBE, or FOBT)	Prevalence of any colorectal cancer screening: 30% Median time to screening from age 50: 1.5 years	EHR abstracted			Insurance status (none, private, public) Regular external Primary provider (yes/no)		Sexual behavior (MSM, yes/no) Drug use (substance use, yes/no)
Kelly (2021)	Colorectal cancer screening	Prevalence of any colorectal cancer screening in the past 6 months: 30%	Self-reported via survey	X	Educational attainment (No formal schooling, Less than a high school diploma, A high school diploma or GED, Some college or a 2-year degree, 4-year college degree, Post-graduate work)	X	X	Relationship status (Married, yes/no)
Lam (2019)	Fecal test Sigmoidoscopy Colonoscopy	Prevalence of colorectal cancer screening in 1 year: 42% of PWH Prevalence in 5 years: 86% of PWH and 79% of those without HIV Prevalence of adenoma: 20% of PWH	EHR abstracted	X	X	Engagement in care (number of outpatient visits in year before start of follow-up, None to 3 or more)	X	X
Momplaisir (2012)	Colonoscopy Sigmoidoscopy Fecal occult blood test (FOBT)	Up to date with CRC screening: 47% Types of colorectal screening: 75% had colonoscopy, 14% had FOBT, and 10% had sigmoidoscopy	EHR abstracted	Poverty level (SES: Low SES-Yes, Low SES -No, Missing SES)	X	Insurance coverage (Medicaid, Other) Type of provider (Physician, Nurse practitioner or physician assistant) Clinic size (50–300 patients, > 300 patients) Facility type (Ryan white funded clinic, yes/no)	X	Substance use (yes/no)
Lung cancer screening
Islam (2023)	LDCT	LDCT follow-up within 1 year among PWH: 12%	EHR abstracted	X	X	Insurance type (Private insurance, Medicare, Others) Engagement in care (number of outpatient visits)	Area Level Poverty (< 10%, 10.1%-19.9%, ≥ 20.0%, Unknown) Rurality (Urban residence, Rural residence)	X
Lopez (2022)	LDCT	LDCT referrals of those eligible: 9% LDCT Screening referral order completed: 55% LDCT uptake: 5%	EHR abstracted	Poverty level (Less than 100% of FPL, 100–200% of FPL, Over 200% FPL)	X	Insurance (Private, Medicare/Medicaid/VA, Ryan White/Pending/ None) Engagement in care (number of HIV care visits)	X	Sexual behavior (MSM, yes/no) Gender identity (transgender, yes/no) Drug use (illicit drug use, yes/no)
Triplette (2023)	LDCT	Prior LDCT/LCS exam: 28%	Self-reported via survey	Employment status (Fulltime, Part-time, Retired, Unemployed, Disabled, Other) Annual income (< $5,000, $5,000 – 15,000, $15,001 – 30,000, $30,001 – 50,000, $50,001 – 75,000, > $75,000)	Educational attainment (Less than high school graduate, High school or GED, Some college, College degree, Graduate degree or professional school)	Insurance coverage (Private health insurance/ HMO, Medicare, Medicaid, Charity care/subsidized, Self-pay)	X	X
Prostate cancer screening
Leapman (2022)	PSA Prostate biopsy	Ever PSA Testing prevalence: 76% of PWH	EHR abstracted	X	X	X	US Region (Northeast, West, Midwest, South)	Substance Use (yes/no)
Multiple cancer screening types
Momplaisir (2014)	Colorectal cancer screening Mammography	Prevalence of guideline-concordant CRC screening: 54% of PWH Prevalence of guideline concordant mammogram in last year: 24% of PWH Prevalence of guideline concordant mammogram in five years: 42% of PWH	Self-reported via survey	Annual income (Low income, Yes/No/Missing income)	Educational attainment (0-11th grade, High school or GED, College or above)	Yearly visits to PCP (0, 1-3, > 3) Integrated care clinic vs. nonintegrated care clinic	X	Relationship status (married/living with a partner, never married, divorced, other) Gender (male, female, transgender)
Rahangdale (2010)	Cervical Cytology Mammography	Prevalence of at least one pap test/ screening: 78% Prevalence of abnormal pap smears: 33% Prevalence of mammography: 65% Prevalence of abnormal mammography: 0%	EHR abstracted	X	X	Type of provider (primary care physicians, gynecologists)	X	Primary language (English, Spanish)
Short (2019)	Cervical cytology Mammography	Prevalence of pap test/ screening in past 2 years: 44% Prevalence of mammography in past two years: 28%	EHR abstracted	Poverty level: Living at or below the poverty level (No, Yes) Homelessness (No, Yes)	Educational attainment (< High school, High school diploma or GED, > High school)	Insurance coverage (Any private insurance, Public insurance only, Ryan White coverage or uninsured)	X	X
Simonsen (2014)	Cervical cytology Mammography Colorectal cancer screening	Cervical cancer screening prevalence: 57% Mammography prevalence: 65% Colorectal cancer screening: 10%	EHR abstracted	Housing stability (Stable, Homeless/transient/ subsidized)	X	Insurance coverage (Private insurance, Medicaid/ Medicare, Primary Care Alliance (Ryan White Part C Program), Other)	X	Immigrant status (US citizen, Other) Primary language (English, Spanish, other)

Abbreviations: AIN anal intraepithelial neoplasia, CRC colorectal cancer, DARE digital anal rectal examination, EHR electronic health records, FPL federal poverty level, GED General educational development, HMO Health management organization, HRA high resolution anoscopy, HSIL high-grade intraepithelial lesions, HPV human papillomavirus, LDCT low-dose computed tomography, LCS Lung cancer screening, MSM men who have sex with men, MSW men who have sex with women, NP nurse provider, PR Puerto Rico, RWHAP Ryan White HIV/AIDS Program, PSA prostate-specific antigen, PCP primary care physician, PWH People with HIV

X indicates there was no SDOH measure within the column's category included in the study

Table 3.

Summary of major findings by social determinants of health and type of cancer screening in U.S. studies on cancer prevention among people with HIV, 2010–2024

	Social determinant of health domains based on healthy people 2030
	Economic stability	Education access and quality	Healthcare access and quality	Neighborhood and built environment	Social and community context
Anal cancer screening
Apaydin (2018)	Income level was not specifically discussed as a barrier to receiving HRA. However, participants discussed job flexibility and resources to address abnormal findings after HRA was cited as barriers to adherent follow-up. Resources included ability to schedule time away from work and transportation to HRA clinic	Educational level was not specifically discussed as a factor impacting HRA follow-up. However, knowledge and beliefs about HPV-related diseases or HRA was a topic of discussion. Beliefs of HRA were positive and thus a facilitator of follow-up. Participants reported that while HRA is painful and awkward, it is necessary. Fear of HRA results was a barrier to follow-up	Patient-provider communication was an important factor. Lack of provider knowledge and expertise to facilitate HRA follow-up is a barrier to care. Participants described facilitators including, positive provider skill and knowledge to offer anti-anxiety medication before the procedure. Strong provider communication skills and trust between the patient and provider were facilitators to follow-up Several participants described healthcare system inefficiencies, including barriers to using the scheduling system		Sexual behaviors were not specifically discussed as a barrier to HRA follow-up or relationship status Internalized stigma was a commonly cited barrier to HRA follow-up. Participants reported they avoided seeking support in their social circles as they felt embarrassed about having HPV infection Societal stigma was identified by almost all participants as a barrier to HRA follow-up. After disclosing their HPV anal disease to others, participants described difficulty gaining social support due to the stigma associated with the infection
Cachay (2018)					MSM HIV transmission risk factor was associated with a substantially increased rate from < HSIL to HSIL (HR: 3.30; 95% CI: 1.78–5.30) and HSIL regression (HR: 1.90; 95% CI: 1.07–2.90)
Cachay (2023)					Overall prevalence of anal cytology was 49.8%. The prevalence among transgender participants was (46.9%), with no significant differences compared to cisgender adults. Prevalence was: 57.5% among MSM only, 52.4% among MSM who were injection drug users, and 33% among injection drug users. Compared to MSM only with no history of injection drug use, all other cisgender men had 45% lower odds of anal HSIL at initial screening
Cruz (2023)	Annual income was not associated with self-reported anal pap uptake or HRA uptake	The prevalence of self-reported anal pap uptake was 42% among those with an educational attainment of high school or less and 59% with more than high school education (p = 0.018). Educational attainment was not associated with HRA uptake	Anal pap uptake or HRA uptake based on self-report did not differ by insurance type		The prevalence of anal pap uptake was 60% among heterosexuals, 32% among homosexual participants, and 53% among bisexual participants (p = 0.001). HRA uptake did not differ by sexual orientation Compared to men who have sex with women, men who have sex with men (aOR: 3.04; 95% CI: 1.79–5.19) and women (aOR: 3.00; 95% CI: 1.72–5.20) were more likely to self-report anal pap uptake. No associations were observed with HRA uptake
D'Souza (2013)	Prevalence of accepting anal pap testing was 90% among those with an income higher than $40,000 and 81%% among those with a lower income (p < 0.001). No significant associations were observed on multivariable analysis of gross income with declining anal pap testing	Prevalence of accepting pap testing was 88% among those with a college degree or higher and 78% among those with less than a college degree (p < 0.001). No significant association was observed between educational attainment with declining anal pap testing
Hernandez (2013)		Compared to those who did not complete college, those who completed college (aRR: 0.67; 95% CI: 0.48–0.93) and those who completed graduate school (aRR: 0.70; 95% CI: 0.49–1.00) had lower risk of HPV 16 infection			Compared to those without, those with a history of injection drug use had higher risk of prevalent HPV infection (aRR: 1.5; 95% CI: 1.1–1.9)
Hernandez (2024)	Annual income was not associated with biopsy-confirmed Anal HSIL	Compared to those with less than a graduate/professional degree, those with a graduate or professional degree had higher odds of biopsy confirmed anal HSIL (aOR: 2.63; 95% CI: 1.26–5.50)			Gender identity was not evaluated in multivariable models
Junkins (2024)				Prevalence of anal cancer screening was 63% among those living in the least socially vulnerable counties, 49% in low to moderate, 51% in moderate to high, and 60% in the most socially vulnerable counties. Social vulnerability was not evaluated in multivariable models	Prevalence of screening was 64% among homosexual participants, 8% among heterosexual participants, 66% among bisexual participants. Sexual identity was not associated with anal cancer screening uptake in multivariable models
Kutner (2024)	69% of participants reported financial incentives would improve HRA retention by mitigating opportunity costs of a visit (e.g., taking time off from work)	69% of patients reported that understanding anal cancer prevention facilitated their return for monitoring visits 62% of patients reported that more patient-facing educational materials about HRA would help them stay engaged with HRA	46% of participants thought that after care pain management could be improved by consistently providing: transportation service when bleeding excessively, information about how to manage pain and products to alleviate symptoms Clinic social environment was reported as a barrier to HRA among 62% of patients, such as exposure to different medical assistants, the presence of sex discordant providers, too many people in the exam room, lack of privacy and discretion of front desk staff, losing familiar HRA providers that left the clinic. Patients were also deterred by social interactions in the clinic, such as providers laughing about a joke that the patient does not understand Provider communication skills were important with 69% said providers' confidence, use of a gentle demeanor and ability to ease patient anxiety with small talk as facilitators. Discussion of potential pain was a facilitator to high quality HRA. 54% reported communication about HRA can be a barrier to retention About half of patients reported scheduling as a barrier to HRA. And 38% reported clinic disorganization, strict late policy, limited insurance options and delays in after care assistance were barriers to HRA		54% of patients recommended social support to motivate return visits 62% of patients reported internalized stigma or anticipated stigma alongside shame and embarrassment affected HRA retention
Nyitray (2023)			Among those uninsured, 14% received DARE in the past year. Among those with insurance 14% also received DARE in the past year. Insurance status was not evaluated in multivariable analyses		Among men, 14% received DARE in the past year and 7% of transgender adults received DARE in the past year 15% of gay participants, 10% of bisexual participants received DARE in the past year. Sexual orientation was not associated with receiving DARE in the past year on multivariable analyses
Rim (2024)	Prevalence of receiving anal cytology in the past year was 5.1% among those employed, 3.7% among those unemployed or unable to work. There was no significant difference in prevalence between the two groups Prevalence of receiving anal cytology in the past year was 4.9% among those above the poverty level and 4.3% at or below the poverty level. There was no significant difference between the two groups	Prevalence of receiving anal cytology in the past 12 months was 2.4% among those with less than high school education, 3.5% with a high school degree or equivalent and 6% among those with more than a high school degree. Compared to those with more than a high school degree, the prevalence difference was lower among those with less than a high school degree (aPD: −3.6; 95% CI: −5.2 to −2.1) and high school degree or equivalent (aPD: −2.5; 95% CI: −3.9 to −1.1)	Prevalence of anal cytology was 5.4% among those with private insurance, 4.4% among those with public insurance only, 5.1% among those with Ryan White coverage, and 2.4% among the uninsured. There was no significant difference in prevalence by insurance type Among those who received anal cytology, 32% received care at a facility not known to provide HRA, 22% received at a facility with HRA on site, and 45% received care at a facility with known outside referral relationships	Prevalence of anal cytology was 9% in Western states, 1.4% in Midwestern states, 5.2% in Northeastern states, 3.0% in Southern states, and 9.7% in Puerto Rico. Compared to Western states, those in Southern states had a lower prevalence of anal cytology (aPD: −6.0; 95% CI: −10.5 to −1.6)	Prevalence of anal cytology was 5% among gay, bisexual, and other MSM as well as transgender women over 35 years of age and other people with HIV aged 45 years or above; 7.7% among gay, bisexual, and other MSM as well as transgender women aged 35 years or above; 1.9% among other people with HIV aged 35 years or above, and 3.9% among those with HIV not in any high risk group. Compared to people with HIV not in a high risk group, gay, bisexual, other MSM, and transgender women aged 35 years or more had higher prevalence of anal cytology (aPD: 3.8; 95% CI: 1.1 to 6.5); and people with HIV aged 45 or above had a lower prevalence (aPD: −2.0; 95% CI: −3.3 to −0.7) Prevalence of anal cytology was 5.4% among cisgender males, 2.7% among cisgender females, and 6.6% among transgender women. Prevalence differences were not estimated for these groups
Schwartz (2013)		No significant associations were observed between educational attainment with AIN2 + positivity or any cytology outcome combination
Wells (2018)			Health insurance type was not correlated with anal pap test or receiving anoscopy		Relationship status was not correlated with anal pap test or receiving anoscopy Identifying as a man who has sex with men was correlated with both receiving anal pap test (Pearson's r correlation =0.434; p = 0.00) and receiving anoscopy (Pearson's r correlation =0.300; p = 0.00). On multivariable analyses, compared to non-MSM, men who had sex men had higher odds of receiving anal pap testing (aOR: 3.70; 95% CI: 1.24–10.97) and anoscopy (aOR: 6.88; 95% CI:2.02–23.52)
Wells (2022)	HRA prevalence within 6 months did not differ by monthly household income, housing status/stability, or employment status	HRA Prevalence within 6 months of abnormal anal cytology did not differ by educational attainment	HRA Prevalence within 6 months of an abnormal anal cytology did not differ by insurance type		HRA Prevalence within 6 months of an abnormal anal cytology did not differ by sexual orientation or relationship status HIV-related stigma score median did not differ by those who received HRA within six months or after six months of an abnormal anal cytology. On multivariable analyses, higher HIV/ AIDS related stigma score was associated with lower odds of follow-up within six months (aOR: 0.54; 95% CI: 0.33–0.90). Stigma was not associated with follow-up within 12 months Higher social support score was associated with higher odds of HRA follow up within six months (aOR: 1.03; 95% CI: 1.00–1.07). Social support was not associated with follow-up within 12 months
Ye (2021)	Prevalence of anal HSIL + was 3% among the unemployed, 5% among the disabled, 2.7% among the employed, 2.8% among the retired, and 6% among students				Prevalence of anal HSIL + among heterosexual adults was 0.7% and 4.3% among men who have sex with men. Prevalence of ever receiving anal pap was 62% among men who have sex with men, 8.4% among heterosexual men, and 1.5% heterosexual women Prevalence of anal HSIL + was 1.9% among those who were married with a life partner, 1.25% among those divorced/separated, 3.2% of single participants, and 1.6% among widowed participants Prevalence of anal HSIL + was 3.5% among men, 0.3% among women, and 0% among transgender individuals. Among men prevalence of ever receiving anal pap tests was 47%, 1.5% among women, and 87.5% among transgender adults
Breast cancer screening
Weinstein (2016)	The overall prevalence of adherence to mammography within 2 years was 50%. The prevalence was 64% among those who were employed or in school. Compared to their counterparts, those employed or in school had higher odds of adherence. (OR: 2.03; 95% CI: 1.10–3.77)	Among those with less than a high school education, prevalence of adherence to follow-up mammography within 2 years was 57%. Compared to their counterparts, those with less than a high school education had higher odds of adherence (aOR: 1.77; 95% CI: 1.06–2.95)			Among those who were foreign born the prevalence of follow-up was 62%. The prevalence was 63% among non-English speakers, 52% among single adults, and 51% among drug users. The odds of receiving follow-up mammography within 2 years of first screen was higher among those who were foreign born (aOR: 2.65; 95% CI: 1.52-4.64) and non-English speakers (OR: 1.96; 95% CI: 1.11–3.45) compared to their counterparts. Associations were not observed among those who were single or those with a history of drug use
Cervical cancer screening
Baranoski (2011)	77% of the unemployed or disabled, had no pap test done within 18 months of follow-up. Compared to the employed, the unemployed or disabled had higher odds of not receiving a pap test within 18 months (OR: 1.7; 95% CI: 1.3-.2.3). When stratified by nativity status, no association was observed among the non-US born but among US both the odds of not receiving a pap test within 18 months was higher among the unemployed (OR: 2.5; 95% CI: 1.4–4.7) 3% of those who received non-timely pap testing were enrolled in the Healthcare for the Homeless program,. No association was observed on multivariable analyses overall or by nativity status	About half of those with less than a high school education had no pap testing history within 18 months of follow-up. No association was observed on multivariable analyses or when stratified by nativity status	Among those who did not receive a pap test within 18 months, 24% lived within 2 miles of the hospital, 27.3% lived within 2 to < 5 miles, 18.0% lived within 5 to < 10 and 30.6% lived within ten miles away. No association was observed on multivariable analysis or when stratified by nativity status for most groups. Among those who were US born, those residing 5 to less than ten miles had higher odds of no pap testing within 18 months compared to those living within 2 miles of the hospital (OR: 2.0; 95% CI: 1.1–3.5) 80% of those who did not have timely pap had a ID trained provider but this provider characteristic was not associated with pap test receipt overall or by nativity status 31% of those who did not receive timely pap had over 75% of care visits with a male provider and this was associated with timely pap receipt (OR: 1.3; 95% CI: 1.0–1.7). This association was no longer observed after stratification by nativity status		17% of those who did not receiving timely pap were non-English speakers. No association was observed in multivariable analyses overall and by nativity status 58% were US born women. Compared to non-US born women, US born women had higher odds of not receiving timely pap (aOR: 1.5; 95% CI: 1.0–2.3) 16.5% married women but was not associated with receipt of timely pap testing on multivariable analyses overall or by nativity status 45% of those who did not receive a timely pap had a history of drug use. And compared to those without a drug use history, those with a drug use history had higher odds of no pap testing within 18 months (aOR: 4.6; 95% CI: 2.2-9.9). This association was consistent among US born women (OR: 1.7; 95% CI: 1.2–2.5) and non-US born women (aOR: 5.8; 95% CI: 2.5–13.9)
Baranoski (2012)	Among both those who received colposcopy within 6 months and those who did not, 79% were unemployed. On multivariable analyses, unemployment was not associated with decreased time to follow-up	51% of those who received colposcopy within six months and 34% of those who did not were a high school graduate or higher (p = 0.04). Compared to those with less than a high school education, having a high school education or higher educational level was associated with decreased time to follow-up (aHR: 1.7; 95% CI: 1.2–2.6)	Among those who received colposcopy within six months, 33% were uninsured, 49% had Medicaid, 12% had Medicare and 7% had private insurance. Among those who did not receive colposcopy within 6 months, 29% were uninsured, 59% had Medicaid, 11% had Medicare and 1% had private insurance. Insurance status was not associated with time to follow-up 53% of those who received colposcopy within 6 months lived within 5 miles of the hospital, whereas among those who did not receive timely colposcopy 40% lived within that distance. No associations were observed on multivariable analyses Among those who received timely colposcopy, 33% received care at a gynecology clinic, 48% with an HIV nurse practitioner, and 19% other type of provider. Among those who did not receive timely colposcopy, 42% received care at gynecology clinic, 33% with an HIV NP, and 25% with another type of provider. Compared to those who received care at a gynecology clinic, receiving care with an HIV NP was associated with decreased time to colposcopy (aHR: 1.7; 95% CI:1.1–2.7)		Among those who received timely colposcopy, 84% were an English speaker and among those who did not 77% were an English speaker. Primary English language speaker was not associated with time to follow-up Among those who received timely colposcopy, 41% were US born and among those who did not 45% were US born. Nativity status was not associated with time to follow-up 18% and 4% were married among those who did and did not receive timely colposcopy, respectively. Compared to those who were not married, being married was associated with decreased time to colposcopy (aHR: 3.5; 95% CI: 1.9–9.6) 24% and 33% reported illicit drug use among those who did and did not timely colposcopy within six months. Illicit drug use was not associated with time to colposcopy
Barnes (2018)			The prevalence of being under screened was 59.7% among the uninsured or on county medical assistance, 58.6% among fed-state government insurance, 47.9% among those on Medicaid, 52.9% among those on Medicare, and 53.3% on commercial insurance. Compared to those who were uninsured or on county medical assistance, the odds of being under screened was significantly lower among Medicaid insured women (aOR: 0.63; 95% CI: 0.41–0.96). Insurance status was not associated with odds of abnormal cytology
Bynum (2016)	Among those who received a pap test within less than a year, 25% were employed full time, 64% were unemployed. Among those who received two pap tests or more within first year of HIV diagnosis, 23% were employed full time, 67% were unemployed. Among those who received a pap test within less than a year, 59% had an income less than 10 k and 31% had an income of 10 k or more. Among those who received two pap tests or more within first year, 67% had an income of less than 10 k and 27% had an income of 10 k or more Employment status and Income were not associated with pap test receipt within less than a year, receiving two pap tests or more since HIV diagnosis, or with more frequent pap tests since HIV diagnosis	Among those who received a pap test within less than a year, 20% had no high school degree, 33% had a high school degree, 46% had some college or above. Among those who received two pap tests or more within first year, 31% did not have a high school degree, 35% graduated high school of had a GED, 35% had some college or more. Compared to those with some college or above, those with a high school degree or equivalent were less likely to have at least two pap tests after their HIV diagnosis (aOR: 0.29; 95% CI: 0.10–0.84). No other associations were observed	Compared to those with high healthcare access (scale), those with low access had higher odds of receiving a pap test within the past year (aOR: 3.80; 95% CI: 1.34–10.78). No associations were observed with receipt of two paps within first year of HIV diagnosis or change in pap test frequency No associations were observed between ease of getting to medical appointments (hard/easy) with any pap test screening behavior outcome No associations were observed between having a personal healthcare provider with any screening behavioral outcome		75% of participants who reported a pap within the past year and 80% of participants who reported two or more paps within their first year of HIV diagnosis were single. Compared to those who were married, those who were single were more likely to have two or more pap tests within first year of HIV diagnosis (aOR: 2.89; 95% CI: 1.07–7.81) Perceived HIV stigma and perceived discrimination were not associated with any cervical cancer screening behavioral outcome
Dailey-Garnes (2015)			Among those who were not screened for cervical cancer, 60% had less than four primary care visits in the year and 40% had four or more. Compared to those who had fewer than four primary care visits, those who had more than four had a higher prevalence of screening uptake (aPR: 1.21; 95% CI: 1.02–1.44) Among those who did not screen, 49% received care in a specialty care clinic setting and 45% at a family practice. Type of clinic was not associated with screening uptake		HIV risk factors were evaluated. Compared to those with heterosexual contact, no association was observed between injection drug use history with screening uptake
Fletcher (2014)		Participants reported knowledge that their HIV infection increased their risk of cervical cancer and higher awareness of cervical cancer as a preventable disease were facilitators of pap testing	Strong relationships with their provider was a facilitating factor to cervical cancer screening among women with HIV Transportation issues were a common barrier to attending cervical cancer screening, particularly for those with a longer commute to the clinic. Many relied on medical transportation assistance system Extensive wait times at the clinic were a barrier to screening Referral processes for gynecological care services were unclear. Several women thought that they needed a referral from their HIV care provider to receive screening
Fletcher (2014)	Among those who worked full or part time, prevalence of pap smear non-adherence was 52%, 56% among those who do not work due to health, 29% among those unable to work, 52% among those not working for other reasons	Prevalence of non-adherence to pap smear receipt was 63% among those with less than a high school education, 44% among those with a high school degree or equivalent, and 48% among those with more than a high school degree			62% of married participants were non-adherent to pap smears 65% of those with a history of injection drug use were non-adherent to pap smears 66% of those with a history of illicit drug use within the past 30 days were not adherent to pap smears Mean perceived stress score was 43.5 among those who were adherent to pap smear recommendations and 47.6 among those who were not Mean perceived discrimination score was 8.8 among those who were adherent to pap smears and 10.1 among those who were not Social support scale score mean was 35.6 among those who were adherent and 35.0% among those who were not adherent
Frazier (2016)	Among those above poverty level, the prevalence of pap testing was 75.2% and among those at or below poverty level the prevalence was 80.1%. Compared to those at or below the poverty level, the prevalence of having a pap test in the last 12 months was 6% lower (aPR: 0.94; 95% CI: 0.90–0.99) The prevalence of homelessness was 8% among those who received a pap test within the past year	Educational attainment was not evaluated in multivariable analyses	Insurance status and patient-provider communication was not evaluated in multivariable analyses		Compared to those without a drug history, those with a history of any drug use had a 9% lower prevalence of pap testing within the past 12 months (aPR: 0.91; 95% CI: 0.83–1.00) No association was observed with nativity status
Logan (2010)	Income was not associated with receipt of pap smears		Receipt of pap testing was associated with type of health insurance (p = 0.02). Patients who did not receive pap testing was more likely to be uninsured (64.7%) and receive care solely through Ryan White programs compared to other types like Medicaid or Medicare		HIV risk behavior was associated with receipt pap smear (p = 0.06) with injection drug users being more likely to have no pap smears History of incarceration was not associated with pap smear receipt
Peprah (2018)			Compared to those with public health insurance, those who were uninsured or paid out of pocket were less likely to utilize pap testing over a ten year period (aHR: 0.87; 95% CI: 0.76–0.99). No other associations were observed by insurance type		Compared to those who did not have a history of drug use, those with an injection drug use history were less likely to receive pap testing over a ten year period (aHR: 0.80; 95% CI: 0.70–0.93)
Soto-Salgado (2024)	Women in Puerto Rico with HIV who received cervical Pap screening were around 39% more likely to have a household annual income below $20,000 compared to WLWH in the other 22 MMP jurisdictions who received cervical Pap screening (PR: 1.39, 95% CI: 1.29–1.49)		Of those who received cervical Pap screening, the prevalence of those in Puerto Rico with Medicaid or other public insurance was 76% higher than those in the other 22 U.S. MMP jurisdictions (PR: 1.76, 95% CI: 1.56–2.00) No significant associations were observed for those with a regular HIV provider	Women in Puerto Rico were more likely than those in the 22 other U.S. MMP jurisdictions to undergo cytology (aPR: 1.08; 95% CI: 1.03–1.13)	The percentage of those who reported higher than the median HIV stigma score or experiences with HIV health care discrimination did not differ between Puerto Rico and the other 22 MMP jurisdictions
Tello (2010)	Employment status was not associated with missed gynecological appointments or a missed pap smear within the past year	Compared to those without a high school degree, women with a high school degree or equivalent (i.e., GED) were less likely to have no documented pap smear within the past year based on EHR data. (aOR: 0.3; 95% CI: 0.1–0.6)			Substance use was associated with higher odds of missing a gynecology appointment [aOR: 2.3 (95% CI: 1.0–5.3)] in the past year Caring for children and social support showed no associations with missing gynecological appointments or missed pap smears
Colorectal cancer screening
Burkholder (2015)			21% of those without insurance, 35% of those with private insurance, and 29% of those with public insurance received CRC screening. No significant associations were observed by insurance status 35% of those with an external primary care provider received CRC. No significant association was observed		23% of women, 27% of heterosexual men, and 33% of men who have sex with men received colorectal cancer screening. Compared to women, men who report to have sex with men were more likely to receive colorectal cancer screening (aHR: 2.03; 95% CI: 1.04–3.99) 15% of those with a history of substance use received CRC screening. No significant association was observed
Kelly (2021)		Educational status was significantly associated with receipt of CRC screening (aOR: 0.69; 95% CI: 0.52–0.91), although comparison groups are unclear			Relationship status was not associated with receipt of CRC screening
Lam (2019)			Among both those with and without HIV, three or more outpatient visits was associated with higher likelihood of receiving CRC screening; PWH: aHR: 1.51; 95% CI: 1.23–1.86 and People without HIV: aHR: 1.54; 95% CI: 1.49–1.60. No association was observed when evaluating detection of adenoma or invasive colorectal cancer as the outcome
Momplaisir (2012)	52% of those with low SES were screened		41% of those with Medicaid insurance and 51% with Other insurance were screened for CRC 51% of those who received care from a physician were screened for CRC and 36% of those who received care from a nurse practitioner or physician’s assistant Among those who received care with a clinic size of 50–300 patients during the observation period, 44% received CRC screening, compared to 48% of those at higher volume clinics with more than 300 patients 45% of those who received care at a Ryan White clinic received CRC screening		17% of those with active substance use, 42% of those with a history of substance use, and 56% with no history received CRC screening
Lung cancer screening
Islam (2023)			Compared to those on Medicare, those with Medicaid, charity care or other Government insurance were less likely to be adherent to lung cancer screening follow-up (aOR: 0.28; 95% CI: 0.09–0.89) No association was observed with increasing number of care visits during the observation period	No associations between LDCT adherence were observed with area-level poverty or rurality
Lopez (2022)	The prevalence of LDCT referral was 12% among those living at less than 100% of the federal poverty line, 12% among those 100–200% of the FPL, and 7% among those over 200% of the FPL. The prevalence of LDCT completion among those with a referral was 60%, 50%, and 33% respectively		8% of those with private insurance, 9% of those with Medicare/Medicaid/VA insurance, and 0% of those with other insurance or uninsured received a LDCT referral. Among those with a referral, 36% of those with private insurance and 73% with Medicare/Medicaid/VA insurance completed screening The average number of care visits during the observation window was 12 among those with a referral and 9 among those without a referral and was 13 among those who completed screening and 11 among those who did not		11% of those who identify as MSM received a referral and 63% completed screening. 11% of those with a history of illicit drug use received a referral and 30% completed screening. 0% of transgender participants received a referral
Triplette (2023)	Financial barriers and issues of cost were discussed by patients with HIV as a barrier to lung cancer screening. Income and employment status were not specifically discussed,	Educational level was not discussed. However, knowledge of LCS was low among PWH	Related to insurance, cost was discussed as a barrier. Also, healthcare provider recommendations and communication were facilitators to screening with 100% of patients stating they somewhat or strongly agree with the statement "If my provider recommended lung cancer screening, I would get it. "
Prostate cancer screening
Leapman (2022)				Among those with HIV, compared to those living in the Northeast, the rates of PSA testing were higher in the South (aIRR: 1.18; 95% CI: 1.16–1.20). Among those without HIV, compared to those living in the Northeast, the rates of PSA testing were lower among those in the West (aIRR: 0.91; 95% CI: 0.90–0.92), and higher in the South (aIRR: 1.12; 95% CI: 1.11–1.13)	A history of substance abuse was associated with lower rates of PSA testing among those with HIV (aIRR: 0.90; 95% CI: 0.89–0.91) and those without HIV (aIRR: 0.87; 95% CI: 0.86–0.88)
Multiple cancer screening types
Momplaisir (2014)	No significant associations were observed between annual income category with screening behaviors	No significant associations were observed between educational attainment and breast cancer screening behaviors Compared to those with a college education or above, those with a high school education or GED (aOR: 0.5; 95% CI: 0.3–0.8) and less than a high school education (aOR: 0.4; 95% CI: 0.2–0.6) were less likely to receive CRC screening	58% of those who received care at an integrated care clinic and 51% of those at a nonintegrated care clinic received age-appropriate CRC screening 18% of those at an integrated clinic and 29% of those at a non-integrated care clinic received a mammogram in the last year; 29% and 51% respectively received a mammogram within the past 5 years Compared to those with zero yearly visits to their primary care provider, those with 1–3 (aOR: 3.3; 95% CI: 1.5–7.5) or > 3 (aOR: 4.7; 95% CI: 2.0–11.0) were more likely to get CRC screening. The same trends were observed among those who received breast cancer screenings within the past year and within the past 5 years		No associations with gender were observed Marital status was not associated with breast cancer screening behaviors. Compared to those who were married or living with a partner, those who were never married (aOR: 0.6; 95% CI: 0.3–0.8), divorced (aOR:0.5; 95% CI: 0.3–0.8) or other marital status (aOR: 0.5; 95% CI: 0.3–0.8) were less likely to get CRC screening
Rahangdale (2010)			Of the pap smears conducted, 68 (45.9%) were performed by primary-care physicians, and 80 (54.1%) were performed by gynecologists		Primary language was not associated with pap smear uptake or receiving an abnormal pap result In the context of breast cancer screening, primary the language was important, with 100% of women who spoke primarily Spanish receiving mammograms, but only 58% of primarily English-speakers receiving mammogram
Short (2019)	Living at or below the poverty level was not associated with receipt of pap smears or mammography within the past two years among those eligible Homelessness was associated with pap smear receipt. Compared to those who reported being homeless, those who did not have a higher prevalence of mammography receipt (aPR: 1.71; 95% CI: 1.21–2.41)	Educational attainment was not associated with receipt of pap smears or mammography within the past two years among those eligible	Insurance type was not associated with mammography receipt. Compared to those with public insurance only, those with any private insurance were less likely to receive a pap smear within the past 2 years (aPR: 0.81; 95% CI: 0.69–0.94). Living with Ryan White program coverage or being uninsured was not associated with pap smear receipt
Simonsen (2014)	Receipt of pap testing and mammography did not differ significantly by housing stability		Those with private insurance had lower prevalence of pap testing compared to all other insurance types (46% vs. 64%, p = 0.025). Mammography receipt did not differ by insurance type		Prevalence of pap testing and mammography did not differ significantly by immigration status or primary language

Abbreviations: AIN anal intraepithelial neoplasia, aRR adjusted risk ratio, aIR adjusted incidence rate, aPD adjusted prevalence difference, aOR adjusted odds ratio, CI confidence intervals, CRC colorectal cancer, DARE digital anal rectal examination, EHR electronic health records, FPL: federal poverty level, GED General educational development, HMO Health management organization, HR hazards ratio, HRA high resolution anoscopy, HSIL high-grade intraepithelial lesions, HPV human papillomavirus, LDCT low-dose computed tomography, LCS Lung cancer screening, MMP Medical Monitoring Project, MSM men who have sex with men, MSW men who have sex with women, NP nurse provider, PR Puerto Rico, RWHAP Ryan White HIV/AIDS Program, PSA prostate-specific antigen, PCP primary care physician, PWH People with HIV

To reduce the potential for bias, two independent reviewers assessed each study. Any discrepancies between reviewers were resolved through discussion until consensus was reached. The risk of bias (ROB) was systematically assessed using standardized forms informed by critical appraisal tools developed by the Joanna Briggs Institute (JBI), an international organization dedicated to advancing evidence-based healthcare [25]. Summary risk ratings were tailored to each study design (e.g., cross-sectional, cohort, prevalence, and qualitative) based on the number of items included in the corresponding JBI appraisal checklist (Supplementary Tables 5–7). For each study, a summary risk rating was calculated by counting the number of items rated “yes” on the appraisal tool. For example, in cross-sectional studies with 8 appraisal items, scores of 0–2 indicated high risk of bias, 3–4 indicated moderate risk, and 5–8 indicated low risk.

Results

Study Characteristics

Among the n =40 studies (n =36, 90.0% quantitative studies and n =4, 10.0% qualitative or mixed methods investigations) identified for inclusion in this systematic review, n =16 (40.0%) included only women, n =17 (42.5%) included both men and women, and n =7 (17.5%) included only men (Table 1). Twenty-five percent (n =10) included transgender people. Measurement of sex and gender varied across studies with most studies focused on biological sex (n =27; 69%) and more recent studies (mostly since 2021) as well as studies focused on anal cancer screening (n =7 out of 15) including more thoughtful assessments of gender and biological sex. About 20% (n =8) of studies included PWH as well as people without HIV. In terms of inclusion of people from minoritized racial and ethnic backgrounds, n =2 (5.1%) studies focused specifically on Hispanic/Latino people, 8 (20.5%) on Black and white people (including an Other category) and 1 (2.5%) on only Black people. The remaining 28 (71.7%) studies included people of multiple minoritized and non-minoritized racial and ethnic groups.

Most studies (n =35) focused on screening for one cancer site whereas 10% (n =4) of studies examined screening for multiple cancers. Fifteen (33%) examined anal cancer screening (n =9 anal cytology/anal pap only, n =1 digital rectal exam [DRE], and n =3 assessed anal HPV) (Table 2)[26–40]. Six studies included high-grade resolution anoscopy [HRA], which in standard practice is used as a follow-up measure following abnormal screening results via anal pap or HPV tests. Overall, n =12 (30.8%) examined cervical cancer screening only and 100% of these studies focused on women only [41–52]. The cervical cancer screening modalities assessed included pap testing or cytology (n =11) and HPV testing (n =1). And two studies included colposcopy in the screening assessment. Only one study evaluated breast cancer screening via mammography adherence among those with a history of screening using electronic health record (EHR) data [53]. Three studies focused on colorectal cancer screening [54–56], of which only one provided specific estimates by screening modality, which included fecal tests, colonoscopy, and sigmoidoscopy. Three studies focused on lung cancer screening [57–59], specifically low-dose computed tomography (LDCT). Of these, one focused on LDCT adherence or follow-up within one year among those with a prior LDCT based on EHR data, Tripelette et. al [58]. focused on self-reported LDCT via survey, and Lopez et. al [59]. also focused on LDCT referrals, LDCT orders completed based on referrals and LDCT uptake. One study focused on prostate cancer screening trends, specifically PSA testing, via EHR data collected through the Veterans Aging Cohort Study [60]. Studies examining screening for multiple sites included cervical and breast cancer (n =2) [61, 62], breast and colon cancer (n =1) [63], and breast, cervical, prostate, and colon (n =1) [64]. Three of these four multi-site cancer screening studies used EHR data [61, 62, 64].

Economic Stability

This systematic review found that among all included 40 studies, 60.0% (n =24) studies included some measure of economic stability. The most used measures of economic stability across these 24 studies included measures of income level (n =13) and employment status (n =13). Despite this, a lack of consistent or standard approach to measuring employment status was evident across these studies. For example, Wells et. al. (2022) [38] presents information on current employment status, which includes both part- and full-time status, dichotomously as yes/no. Similarly, Baranoski et al. (2011) [41] presents information on employment with a dichotomous measure (yes/no) presented as unemployment. In a later paper, Baranoski et. al. (2012) [42] combines disability with unemployment status. In contrast to these three studies, the remaining 21 studies assess and present employment status data with more refined categories that distinguish full- versus part-time employment as well as disaggregate unemployment due to disability status, retirement, student/in school-status, etc. Importantly, Fletcher et. al. [47] disaggregates unemployment and includes information on ‘not working due to health’, ‘unable to find work’ and ‘not working for other reasons’. Important systemic economic stability factors such as food insecurity, access to healthy food options or food pantry, or financial hardship due to medical bills were unexplored.

As with assessment of employment status, there was no clear or consistent method for assessment or presentation of information on income. Categorization of income levels varied widely across each study. For example, Wells et. al. (2022) [38] presents information on disaggregated categories of monthly income, while multiple studies provide information on annual income either dichotomized (e.g., less than $15,000 vs. more than $15,000 annual income) or categorized, but all using a diverse range of values. Finally, eight (20.0%) of the included studies presented information on economic stability as measure of poverty level in three distinct ways, including poverty level [36, 48, 56, 59, 62], use of social services to navigate homelessness [41], and housing instability or self-reported homelessness [38, 48, 62, 64]. To define poverty level, the most commonly applied approach was to categorize people who lived either above the federal poverty level (FPL) to those living at or below the FPL. To define poverty level, Momplaisir et. al. [56] employed a CDC algorithm that encompasses income, family size and educational attainment to understand whether individuals are from low vs. high SES backgrounds. In general, studies that evaluated household income found that there was either no association between measures of income with cancer screening behaviors or that lower household income was associated with a lower prevalence of cancer screening behaviors (Table 3). Similarly, employment status was not frequently associated with cancer screening receipt.

Educational Access and Quality

Educational attainment is a critical determinant in designing effective cancer prevention interventions because it shapes how individuals access, understand, and act on health information, as well as how they navigate the healthcare system. A total of n =22 studies in this review measured educational attainment; however, none of the 40 studies included here present information on educational access or quality, such as health literacy. Of the studies that did collect and present information on educational attainment, a high school degree or GED equivalent was a baseline. However, 14 studies provide information on a ‘less than high school’ level of educational attainment [31, 36, 41, 44, 46–48, 50, 52–54, 58, 62, 63] and Kelly et. al. (2021) [54] include assessment of ‘no formal schooling’ as well as ‘less than a high school diploma’. Whereas Cruz et al. (2023) [29] combine high school and less than high school educational level. Finally, four studies examined educational attainment based on some college/college degree as the benchmark [30–32, 34]. While knowledge of risks associated with cancer and cancer screening recommendations frequently facilitated cancer screening behaviors, educational attainment was not consistently associated with behavioral outcomes (Table 3).

Healthcare Access and Quality

Across the n =26 studies that included measures of healthcare access and quality, the majority (20/26, 76%) described healthcare access in terms of health insurance coverage, albeit using a variety of measurements, of which 10 only reported insurance coverage. While only 2 studies identified insurance status dichotomously, as yes/no, the remaining studies provided some level of disaggregated information on private vs. public insurance, Ryan White HIV/AIDS Program (RWHAP) coverage, VA benefits, self-pay, or ‘other’ benefits. Further, only 11 of these 20 studies provided information on Medicare, Medicaid or dual Medicare/Medicaid coverage. With respect to healthcare quality, three studies [44, 56, 61], provided information on the type of healthcare provider (physician, nurse practitioner or physician assistant) participants received their care. Type of healthcare facility was also assessed by five different studies, which was measured based on the type of screening modality [36, 41, 44, 55, 60]. For example, one study indicated quality of the healthcare facility by indicating whether they offered HRA, which is the highest quality follow-up modality for those with abnormal anal cytology [36]. And as a measure of engagement in care, four studies presented data on different metrics of visits to different care sources: number of yearly visits to a primary care provider [45, 63], number of HIV care visits in the past year [59], and number of outpatient visits in the year prior [55]. Last, two different studies provided information on distance between home and hospital [41, 42] and ability to access transportation to get to medical appointments [44, 46]. Three studies were able to gain insights into the role of patient-provider relationships in healthcare quality [26, 46, 48]. Using qualitative approaches, one study was able to evaluate healthcare system inefficiencies and another [26, 46]) was able to discuss multiple important topics such as care coordination or referral processes and care wait times. Despite its major role in providing care to PWH, Ryan White service provision or clinic status was assessed in only eight studies [36, 49–51, 56, 59, 62, 64].

Neighborhood and Built Environment

Studies providing data on area-level factors were few, in fact, only six of the 40 studies included in this review provided such information [33, 36, 45, 50, 57, 60]. Among those that did, Islam et al. presented information on area-level poverty, disaggregated as < 10%, 10.1%–19.9%, ≥ 20.0%, or unavailable. Four studies presented measures of US regionality, including US census region (Northeast, West, Midwest, South), US state, or rurality [36, 50, 57, 60]. For example, Soto-Salgado and Rim et al. each included stratification by certain states or US territories that participated in the Medical Monitoring Project., such as Puerto Rico. In addition, Junkins et. al. [33] leveraged the CDCs Social Vulnerability Index (SVI) to characterize counties of residence by level of SVI, ranging from lowest to highest vulnerability. Given multiple neighborhood and built environment measures are publicly available based on a person’s census tract, zip code, county, or even state, the lack of studies under this SDoH domain is a significant missed opportunity.

Social and Community Context

Overall, 33 (82.5%) studies included a measure related to social and community context, however, the majority of these were measured at the individual-level (n =26/32; 81.2%). Measures related to sexual behavior (i.e., whether they identify as a man who has sex with men) were included in 12 studies (30.0%) and sexual orientation (e.g., gay or bisexual) was measured in 12 studies (30.0%). Only nine studies explicitly measured gender identity. Twenty-six studies assessed drug use, including injection drug use (IDU), largely due to the fact that IDU status is categorized as a high-risk HIV group. To understand social support or support networks, we evaluated relationship status measurements, which was included in 13 studies (32.5%). Of these, four studies included a dichotomous measure of married (yes/no), which misses those who may not be married but living with a partner. Three studies explicitly measured social support using validated scale measures [38, 47, 52]. And five studies assessed whether the participant’s first language was English [41, 42, 53, 61, 64] Four studies measured a form of stigma, these ranged from internalized or societal stigma [26] HIV stigma overall [38, 50], and perceived HIV stigma [44]. Two studies, both which focused on cervical cancer screening, included a measure of history of incarceration [48, 49]. Three studies, which all also focused on cervical cancer screening, included a measure of discrimination, which ranged from perceived discrimination [44, 47] and discrimination experienced in a healthcare setting [50].

Important community-level factors such as social integration (e.g., % Black or % Hispanic/Latino or % immigrants living in a person’s community), support systems (e.g., access to social services or peer navigators), community engagement, and stress were not measured in any included study. While incarceration history was measured in two studies [48, 49], exposure to violence/trauma, which could be measured by capturing police violence or adverse childhood experiences, were also not measured.

Discussion

In the US, cancer is a leading cause of death among PWH [65]. Several of the most common causes of cancer-related deaths among PWH, such as lung cancer, are potentially preventable through routine screening and timely follow-up [10]. Although non-infection-related cancers represent a growing burden in this population, our review found that few studies have evaluated the uptake of recommended screenings or follow-up care for breast, colorectal, lung, and prostate cancers in PWH. However, it is critical to understand cancer screening trends among PWH given timely cancer screening is critical. HIV-related immunosuppression may obscure early cancer symptoms [66–68] and PWH are often diagnosed with cancer at younger ages compared to the general population [69], underscoring the urgency of timely cancer screening initiation. Additionally, it is well established that PWH in the US experience systemic challenges—including limited access to routine preventive services [70, 71] and persistent stigma [72]—which may further contribute to late cancer detection. These delays in accessing preventive services can lead to more advanced disease at diagnosis with fewer effective treatment options [73]. To our knowledge, this is the first systematic review to synthesize evidence on cancer screening behaviors among PWH in the US and examine the influence of social determinants of health on screening uptake and follow-up. Our review shows that most measurements of SDoH, based on the Healthy People 2030 framework, are conducted at the individual level and often overlook key structural factors that influence access to healthcare systems in the US. Importantly, the key social factors that disproportionately affect PWH—including stigma and discrimination—were examined in fewer than 10% of the studies we reviewed, and only in those focused on anal and cervical cancer screening behaviors [26, 38, 44, 47, 50]. The most common SDoH evaluated included annual household income, educational attainment, insurance status, and risk factors associated with HIV infection such as sexual behaviors or injection drug use history. Importantly, given the significant differences in category definitions for these SDoH factors, inconsistent associations were observed across each cancer screening behavior evaluated. Integrating high-quality measures of SDoH into cancer prevention research among PWH is critical to improving access to preventive services among this aging population by facilitating measurement of access barriers and supporting intervention development. By incorporating SDoH into cancer screening research, we can better tailor interventions to the lived realities of diverse populations, design policies that address upstream barriers, and move toward more high-quality cancer prevention strategies.

Despite its significant role in the quality of healthcare that PWH receive, HIV-related stigma was understudied and only included in four studies [26, 38, 44, 50]. Stigma has played a damaging role in perpetuating the US HIV epidemic. Stigma is defined as a process in which classes of people are identified as socially undesirable and negatively stereotyped by others with greater power and influence [74, 75]. Labeling and stereotyping of classes of people can result in enacted stigma, active discrimination against members of stigmatized groups, and perceived, anticipated, or internalized stigma. The stigmatization of living with HIV impedes every step along the HIV continuum of care, particularly care engagement and retention [76]. Patients’ experiences with enacted stigma can occur within healthcare systems during interactions with their providers and/or staff leading to poorer retention in care. Stigmatization within healthcare contexts that PWH may face can range from providers who take extreme precautionary measures during routine examinations to the use of stigmatizing language or denial of services and treatment [77]. Not only is HIV a stigmatized condition, but cancer is also and both conditions require life-long engagement with the healthcare system to ensure high-quality survivorship care. However, the mechanisms through which compounding disease stigmatization may impact people living with HIV when accessing cancer prevention services are poorly understood. For example, lung cancer is a highly stigmatized condition due to its robust association with smoking and the perception of the disease as self-inflicted [78, 79]. It is plausible that PWH may experience unique barriers to lung cancer screening due to the added stigma associated with living with HIV. In addition to disease-related stigma that PWH with cancer may experience, there is a compounding stigmatization effect on marginalized populations due to, among other things, their race and ethnicity, gender, sexual orientation, gender identity, or drug use [80]. An intersectional lens and methodological approach will be critical to moving the needle on cancer prevention research among PWH [81, 82]. Importantly, only nine of 40 included studies that evaluated SDoH focused on breast (n =1), colorectal (n =4), lung (n =3), and prostate cancer (n =1) cancer screenings. To make progress in reducing deaths due to cancer without an infectious etiology among PWH in the US, we must keep the underlying drivers of the HIV epidemic at the forefront of our research questions [83]. The US HIV epidemic is fueled by social inequalities leading to higher rates of HIV transmission within certain communities [24, 84]. For example, while effective prevention tools like pre-exposure prophylaxis (PrEP), antiretroviral therapy (ART), and HIV testing are widely available, persistent disparities in access and uptake continue to drive new infections underscoring the urgency for systemic solutions [85]. PWH face numerous, multi-level SDoH such as low SES, housing instability, lack of employment, lack of health insurance coverage, barriers to transportation access, and experiences of stigma and discrimination within the healthcare setting that significantly influence their access to healthcare across the prevention and treatment continuum [85–88]. Populations at the highest risk of developing HIV include adults residing in the Southeast (mostly non-Medicaid expanded states like Florida [89]), Black and Hispanic/Latinx adults, men who have sex with men, and low-income adults with limited resources, such as Medicaid-insured adults. Structural factors—such as systemic racism, HIV-related stigma, homophobia, and policies that restrict access to care [90, 91]—intersect with these individual-level determinants to create barriers that can delay diagnosis, disrupt continuity of HIV care, and reduce engagement in preventive services, including cancer screening. Additionally, limited access to culturally competent care and fragmented health systems can further marginalize PWH, especially those from racial and ethnic sexual minority groups or rural communities [88, 92]. Addressing these interconnected social and structural barriers is essential for improving health equity and ensuring timely, high-quality care for PWH. To achieve health equity, the first step is measurement of the problem. Our review underscores the urgent need for further research in non-infectious cancer prevention among PWH and a focus on systemic issues within healthcare settings.

In our systematic review, we found that only six studies [33, 36, 45, 50, 57, 60] assessed measures within the “Neighborhood and Built Environment” domain of the Healthy People 2030 SDoH framework. Of these, three relied on US Census region as a proxy—a notably weak measure. Census regions encompass large, heterogeneous areas, lack geographic precision, and offer limited utility for designing targeted interventions. Future research should incorporate more granular geographic units, such as census tract, and direct measures of neighborhood and environmental context to better capture the complex ways in which place influences health. Understanding the role of the neighborhood and built environment—a key domain of SDoH—is critical for advancing cancer prevention efforts among PWH. In our review, one study (Junkins et. al [33]) included the county-level SVI measure, which incorporates four key themes into a composite measure, including socioeconomic status, household composition and disability, minority status and language, and housing type and transportation [93]. In addition to the SVI, future research should consider incorporating structural features such as residential segregation, neighborhood deprivation, housing instability, limited transportation access, and proximity to healthcare facilities influence health behaviors, access to preventive services, and engagement in care. For PWH, these factors may compound existing vulnerabilities related to HIV-related stigma and systemic marginalization, leading to delayed cancer screening, reduced uptake of preventive interventions, and poorer cancer outcomes. Despite their relevance, neighborhood-level determinants are often underrepresented in cancer prevention research involving PWH, as we have observed in our review. Incorporating spatially linked data and validated measures of neighborhood context can enhance our understanding of how place-based inequities shape cancer risk and preventive behaviors in this population. Doing so not only strengthens the methodological rigor of epidemiologic studies but also informs multi-level interventions and policies that address upstream drivers of cancer disparities among PWH.

When evaluating studies for inclusion in this review, we found about twenty studies that only evaluated race and ethnicity as a social factor. While these studies were ultimately excluded, this finding highlights the reliance on race and ethnicity as proxy measures for underlying disparities in care access. Race and ethnicity are frequently misclassified as SDoH in public health research; however, they are not causal determinants in and of themselves [94]. Rather, they are socially constructed categories, developed for administrative and demographic purposes—most notably by the US Census Bureau—that reflect historical and political processes rather than biological or genetic realities. When used uncritically in research, these categories risk reifying essentialist notions of difference and obscuring the structural forces that produce health inequities. Race and ethnicity often serve as proxies for the lived consequences of structural racism, xenophobia, and other intersecting systems of oppression that systematically shape access to health-promoting resources and opportunities [24]. Researchers must therefore interrogate the rationale for including measures of race and ethnicity in their analyses and clarify what these variables are intended to signify. For example, if researchers are interested in a measure of how individuals are treated due to their ancestry, they may be more interested in measuring observed race or what the interviewer or study staff believe the race of the participant is, also known as “street race [95],” or the color of their skin to understand the impacts of colorism [96]. Rather than interpreting racial and ethnic disparities in health outcomes as inherent to group membership, it is imperative to examine how structural racism, institutionalized discrimination, policy marginalization, and sociopolitical exclusion contribute to these inequities. Conceptual clarity and methodological rigor demand that we distinguish between markers of social stratification and the underlying mechanisms that drive health disparities. Centering analyses on systems of power and structural injustice, rather than on socially assigned identities alone, is essential for producing research that meaningfully advances health equity.

Conclusion

In conclusion, our review highlighted significant gaps in the HIV and cancer prevention literature. Key social drivers of health inequities—including HIV-related stigma, discrimination, and housing instability—were rarely assessed and were typically limited to studies focused on anal or cervical cancer screening. A systems-level perspective that centers SDoH is essential to understanding and addressing disparities in cancer prevention among people with HIV (PWH), who often face intersecting forms of marginalization. SDoH shape every stage of the cancer prevention continuum—from access to early detection and informed decision-making to navigating complex healthcare systems. Yet without accounting for these contextual factors, interventions risk failing to reach those most in need and may inadvertently reinforce existing disparities. Integrating robust, multidimensional SDoH measures into cancer prevention research is therefore not only a methodological priority but also a public health imperative. Doing so will ensure that future screening strategies are equitable, responsive to lived experiences, and capable of improving outcomes for underserved populations like PWH.

Supplementary Material

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s40471-025-00378-2.

Data Availability

No datasets were generated or analysed during the current study.