Latent class and time-to-event analyses of social determinants of health and COVID-19 vaccine uptake among Black women living with HIV.

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Published in final edited form as: Vaccine. 2025 Sep 6;64:127649. doi: 10.1016/j.vaccine.2025.127649 Search in PMC Search in PubMed View in NLM Catalog Add to search Latent class and time-to-event analyses of social determinants of health and COVID-19 vaccine uptake among Black women living with HIV Devina J Boga Devina J Boga a Department of Psychology, University of Miami, Coral Gables, FL, USA b Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Find articles by Devina J Boga a, b , Michael Robinson Michael Robinson a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Michael Robinson a , Manasa Tirupathi Manasa Tirupathi a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Manasa Tirupathi a , Reyanna St Juste Reyanna St Juste a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Reyanna St Juste a , Kimberly Lazarus Kimberly Lazarus a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Kimberly Lazarus a , Kayla Etienne Kayla Etienne a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Kayla Etienne a , Mya Wright Mya Wright a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Mya Wright a , Rachelle Reid Rachelle Reid a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Rachelle Reid a , Naysha Shahid Naysha Shahid a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Naysha Shahid a , C Mindy Nelson C Mindy Nelson b Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Find articles by C Mindy Nelson b , Tulay Koru-Sengul Tulay Koru-Sengul b Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Find articles by Tulay Koru-Sengul b , Steven A Safren Steven A Safren a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Steven A Safren a , Gail Ironson Gail Ironson a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Gail Ironson a , Allan Rodriguez Allan Rodriguez c Clinical Immunology – Infectious Diseases University of Miami Miller School of Medicine, USA Find articles by Allan Rodriguez c , Ian Wright Ian Wright d Department of Economics, University of Miami School of Business, Miami, FL, USA Find articles by Ian Wright d , Daniel Feaster Daniel Feaster b Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA Find articles by Daniel Feaster b , Sannisha K Dale Sannisha K Dale a Department of Psychology, University of Miami, Coral Gables, FL, USA Find articles by Sannisha K Dale a, * Author information Article notes Copyright and License information a Department of Psychology, University of Miami, Coral Gables, FL, USA b Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA c Clinical Immunology – Infectious Diseases University of Miami Miller School of Medicine, USA d Department of Economics, University of Miami School of Business, Miami, FL, USA Author’s contribution All persons listed as an author have participated adequately in this work and take responsibility for the content of this manuscript. S. K. Dale, the study’s principal investigator, developed the parent study hypotheses and procedures for data collection, oversaw the study team, provided the conceptual underpinning to inform analyses and interpret findings, provided iterative feedback and edits to the article, and worked closely with D. Boga. D. Boga co-developed the hypotheses for the current analyses, ran all analyses, and drafted the article. M. Robinson helped with drafting, revising, and formatting some sections of the article. M. Tirupathi, R. St. Juste, K. Lazarus, K. Etienne, M. Wright, R. Reid, and N. Shahid contributed to study coordination, data collection, and data management. C.M. Nelson contributed to data management. T. Koru-Sengul providing insights regarding analyses and feedback on drafts. A. Rodriguez provided medical consultations throughout the course of the project and data collection. S. Safren, G. Ironson, I. Wright, D. Feaster are study co-investigators. All co-authors read and provided feedback on the manuscript. Artificial Intelligence (AI) was not utilized in any stages of the hypothesis, data collection, data evaluation, analysis, manuscript preparation. * Corresponding author: Department of Psychology, University of Miami, 5665 Ponce de Leon Blvd, Miami, FL 33146, USA. [email protected] (S.K. Dale). Issue date 2025 Oct 3. PMC Copyright notice PMCID: PMC13069493  NIHMSID: NIHMS2145333  PMID: 40913818 The publisher's version of this article is available at Vaccine Abstract Existing inequities are exacerbated during the COVID-19 pandemic. Similar to HIV, COVID-19 disproportionately affects Black and other communities of color. Among Black women living with HIV (BWLWH) this study examined the relationship between community level and individual level social determinants of health and time to COVID-19 vaccine uptake. Utilizing longitudinal data collected between February 2021 and May 2023, pre-pandemic experiences of microaggressions, economic stability, health behaviors, resilience, self-efficacy and trauma/violence were modeled using latent class (LCA) and time-to-event (TTE) analyses methods to identify which classes emerge and how these determinants may influence COVID-19 vaccination uptake. Among 152 BWLWH, 75 % were vaccinated by 232 days (95 %CI: 202–441 days) after vaccine availability. A total of 25 women did not get vaccinated or were lost to follow up by the end of the study period. The primary reason for not getting a vaccine (endorsed by 76 % of unvaccinated women) was lack of trust in the government and institutions developing the vaccine. The LCA resulted in a 2-class solution. Class 1 exhibited lower scores for microaggressions and trauma/violence while reporting slightly higher economic stability, higher self-efficacy, resilience, self-care, and medication adherence. Women in Class 2 had higher probabilities for experiencing more microaggressions, trauma/violence, slightly less economic stability, and less resilience, self-efficacy, self-care, and medication adherence. The TTE analysis included 134 women, no significant difference was found between classes for COVID-19 vaccine uptake, but a pattern was observed with Class 2 taking longer to get the vaccine. Efforts to promote vaccine uptake must take into account and address community level and individual level social determinants of health. Keywords: Black women, HIV, COVID-19, Vaccine, Health equity 1. Introduction The relative HIV disparities in percentages continue to impact Black/African American persons at higher rates across the board in HIV-related incidence, prevalence and mortality 1 . Black women accounted for over 50 % of new HIV diagnoses through heterosexual contact among women in the United States (US) in 2019 1 . According to the 2019 HIV surveillance report, Florida was among the top three states with the highest HIV diagnosis rates (20.4 per 100,000 population). Furthermore, an epidemiology report looking at years 2017–2021 in Florida specifically, showed that across the 5-year span when compared to cisgender White women (heterosexual contact), there were approximately 4 times higher diagnoses of HIV, 5 times more persons living with HIV, and 4 times more HIV-related death among cis-gendered Black women (heterosexual contact) 2 . The prevention, treatment, and care related to HIV in Black or African American communities is profoundly impacted by a number of enduring barriers such as racism, housing stability, social and economic marginalization, and systemic inequities [ 3 , 4 ]. Intersectionality theory [ 5 , 6 ] details how the burden of long withstanding systemic inequities, racism, gender inequality, traumatic exposures, government failure to action, financial and economic marginalization, and healthcare barriers work together in a constant state of motion and impact Black women living with HIV (BWLWH) on various levels including physical health, mental health, and quality of life [ 3 , 4 , 7 – 11 ]. These existing structural and health inequities were exacerbated during the crisis of the COVID-19 pandemic. Similar to HIV, COVID-19 disproportionately affects Black and other communities of color. From an economic standpoint, in a recent study, substantial financial hardship was associated with 35–44 % lower adjusted odds of COVID-19 vaccination, socioeconomic factors were stably associated with vaccination explaining a large portion of variance [ 12 ]. Compared to non-Hispanic White individuals, COVID cases, hospitalizations and death were highest among minoritized populations (ranging between 0.7× to 2.4× higher) [ 13 ]. With impact on future generations, the COVID-19 pandemic reduced Black/African American life expectancy at birth by an estimated 2.1 years [ 14 ]. In the duration of the pandemic, we as a society learned about the uncertainty regarding the COVID-19 virus at rapid pace, however it was clear early on that immunocompromised individuals would be at a greater risk for more severe infection, hospitalization or death compared to their counterparts due to overlapping characteristics such as comorbidities and psychosocial burdens that are known risk factors [ 15 , 16 ]. In the state of New York, one study found among people living with HIV ( n = 101,205) 63.5 % of the sample had received either both doses of Pfizer-BioNTech or Moderna or one dose of the Janssen vaccine, but they found vaccinations were lowest among non-Hispanic Black and American Indian/Alaska Native individuals. For individuals that were not virally suppressed the vaccination rate was much lower [ 17 ]. Between March 1, 2020 and June 3, 2023 in the state of Florida only 42 % of non-Hispanic Black individuals (>6 months old) had been vaccinated [ 18 ]. There is limited information regarding COVID-19 vaccination rates among Black individuals living with HIV, studies include a majority of men and use of cross-sectional data [ 19 , 20 ]. There is a dearth of information pertaining to COVID-19 vaccine uptake among BWLWH in Florida and specifically, South Florida where the vaccination rates have been the lowest. Existing research recognizes the critical role played by traumatic experiences from exposure to unhealthy relationships, environments, and systemic oppression in the wellbeing and health behaviors in individuals [ 21 ]. One study found a higher likelihood for risk behaviors including not having a check up with a doctor in the past year and unsafe sexual practices with individuals who were experiencing trauma [ 22 ]. Specifically understanding trauma exposure with HIV medication adherence, a recent study with 1237 participants in Miami found trauma exposure (especially high levels) was significantly associated with worse adherence to antiretroviral therapy (ART) adherence, other factors of unstable housing, depression and substance use were also uniquely associated with worse adherence [ 23 ]. The COVID-19 pandemic quarantine had many negative impacts, with the loss of jobs, childcare and unsafe environments where abuse victims were forced to shelter in unsafe places [ 24 ]. Increased vaccine hesitancy has been found to be associated with increased adverse childhood experiences [ 25 ]. One of the major ways that health outcomes are impacted in BWLWH is through the experiences of discrimination and microaggressions. While discrimination measures may capture more obvious malicious indignities through behaviors and/or comments, microaggressions are known to be subtle and more frequent, both can be based on many things including race, sexual orientation, gender, and health status. Thus far, several studies have examined the effects of microaggressions on the health of minoritized individuals. A systematic review of 138 studies linked racial microaggressions to low self-esteem, stress, anxiety, depressive symptoms, and suicidal ideation [ 26 ]. Specifically in BWLWH, gendered-racial microaggressions and HIV-related discrimination were found to significantly predict higher total barriers to care [ 27 ]. Whereas we have highlighted how negative factors impact healthcare and wellness behaviors it is important to consider the resilience and resistance that intersectionality framework demands. 64 Minoritized communities historically have exhibited resilience and found ways to resist in the face of oppression. Resilience factors and behaviors (such as self-efficacy, medication adherence, self-care, and resilience) positively influence health. At the interpersonal level social support was found to be associated with medication adherence and clinic attendance. And at the individual level, resources such as conscientiousness and optimism were associated with viral suppression, CD4 cell count, and ART medication adherence [ 28 , 29 ]. Self-efficacy, often described as an attitude, was first conceptualized in Bandura’s social cognitive theory [ 30 ], which describes how an individual judges their own capabilities to perform activities to attain their goals based on preceding social experiences. For example, if self-efficacy is high it can act as a motivator, but low self-efficacy has been linked to higher stress and anxiety [ 31 , 32 ]. Studies have looked at self-efficacy in the context of influenza and HPV vaccinations in the past [ 33 , 34 ]. One longitudinal study looking at influenza vaccination found that autonomous motivation and self-efficacy were positively related to vaccination intentions [ 35 ]. One of the most prominent predictors of future health behaviors is past health behaviors in a various populations 36 – 39 . It can be postulated that high self-care behaviors, resilience, and medication adherence may lead to preventative health measures such as taking the COVID-19 vaccine in BWLWH. Authors Adkins-Jackson, Turner-Musa, and Chester found that self-care was predicted by resilience and self-care acted as mediator between stress and health conditions/diseases in Black women [ 40 ]. Individuals that reported getting a flu shot in the previous 8 months were more likely to indicate intention of getting the COVID-19 vaccination [ 41 ]. The current study utilized longitudinal data to understand the relationship between pre-pandemic experiences of trauma, violence, microaggressions, economic stability, medication adherence, self-care, and generalized self-efficacy with vaccine uptake patterns among BWLWH. To characterize our sample, we used mixture modeling, specifically a 3-step latent class analysis, a widely used technique that considers the uniqueness and differences of and between groups simultaneously [ 42 – 46 ]. To understand the outcome, time-to-event or survival analysis techniques were utilized as they allowed us to study the transition from not vaccinated to vaccinated as well as the length of the time-to-vaccination. The aims of this study were (a) to look at the patterns of vaccine uptake and to understand when vaccine uptake occurs in the sample overall and by latent class (b) to understand reasons for vaccine hesitancy in those individuals that did not get the vaccine in the duration of the study period. It was hypothesized that classes with higher exposure to violence/trauma, microaggressions, lower health behavior scores and lower resilience scores would predict lower/slower vaccine uptake compared to the other classes. A secondary hypothesis was that classes with higher scores for resilience and health behaviors will uptake the vaccine earlier than their counterparts. 2. Materials and methods The present study analyzed data of BWLWH ( n = 152) that were enrolled in the Monitoring Microaggressions and Adversities to Generate Interventions for Change (MMAGIC) study [ 47 ]. MMAGIC is an on-going longitudinal study assessing how daily microaggressions, discriminations, and adversities are related to HIV viral suppression and how the relationships are mediated by mental health symptoms and health behaviors among BWLWH. The parent study collects repeated measures every three months and daily brief microaggression assessments via text message. Women were recruited through six different avenues, that include calling women from a registry of participants from previous studies, various community outreach efforts through weekly visits to clinics and organizations, invited talks/presentations, and engagement with a community advisory board. If a potential participant expressed interest, a follow-up telephone or face-to-face meeting was conducted, and dependent upon eligibility a baseline visit was scheduled. Participants were considered eligible if they were (a) at least 18 years old, (b) a cisgender woman, (c) identified as Black or African American, (d) owned a cell phone with texting and internet capabilities, (e) spoke English and were able to fully comprehend the informed consent process and study procedures. If a woman was experiencing mental health symptoms such as active psychosis that would significantly interfere with functioning, they were excluded from the study. The baseline visit includes several self-report questionnaires through REDCap, a clinician interview, blood draw and medical record data information request. The parent study started data collection in October 2019, the current study will analyze baseline (visit 1) measures for all 152 women that were enrolled between October 2019 and January 2020 for the indicators in the Latent Class analysis and the outcome of interest COVID-19 vaccine uptake through visits 2–13 (varying dates per participant, April 2023). All participants were financially compensated $75 for the baseline assessment, and up to $75 for follow-up assessments. On an as-needed basis, participants were offered assistance and reimbursement for transportation. Participants were consented for screening and completed written informed consent procedures at the baseline and verbally consented at subsequent visits. The University of Miami Institutional Review Board approved all the study procedures and materials. 2.1. Measures 2.1.1. Socio-demographic characteristics A measure was included in the study that captured information pertaining to age, education level, religion, sexual orientation, relationship status, employment, and HIV viral load. 2.1.2. Economic stability Housing status and income were representative of economic circumstances. Collected from the demographic form two items with categorical responses were asked. Housing status had 9 options to choose from which were then further subcategorized into stable and unstable housing using the guidance from various references [ 48 , 49 ]. Three response options of renting a home or apartment, living in a home or apartment owned by you or someone else in the household, and publicly subsidized housing (like section 8) were considered stable housing, the remaining five options of residential drug, alcohol, or other treatment facility, friend or relative’s home (paying little to no rent), temporary/transitional housing, homeless (shelter or street) were categorized as unstable. A question about annual income before taxes was asked with the following 7 response options/levels (< $5000, $5000–$11,999, $12,000–$15,000, $16,000–$24,999, $25,000–$34,999, $35,000–$49,999, and ≥ $50,000), these levels were further dichotomized into unstable (<$35,000) vs stable (≥ $35,000) income based on living wages calculated specifically for living in Miami [ 50 ]. 2.1.3. Trauma and violence The Life Events Checklist for DSM-5 (LEC-5) gathers self-reported information on 16 types of traumatic events that can occur in a participant’s lifetime and one item for any other very stressful experience unlisted. Sample items include “assault with a weapon”, “sudden accidental death”, and “natural disaster”. Responses range from whether it happened to the participant, if they witnessed it, learned about it, if it was part of their job, not sure or doesn’t apply. For the current study, if the participant endorsed personal experience, learning about it, witnessing the event or part of job it was considered as score of 1, for not sure or doesn’t apply it was scored as 0. The sum score was taken for the 16 events. In our study, this measure showed a high internal consistency (α = 0.90), the LEC-5 has been validated and used in clinical and international populations [ 51 , 52 ]. The Woman Abuse Screening Tool (WAST) is an 8-item measure that screens for women that are experiencing physical and/or emotional abuse by their partner (Cronbach’s α = 0.91 for this sample) [ 53 ]. This tool has been psychometrically assessed and validated with diverse large samples [ 54 – 57 ]. Sample items include “Do arguments ever result in hitting, kicking or pushing?” and “Has your partner ever abused you emotionally?”. Responses can range from “(1) never” to “(3) often” with higher scores indicative of experiencing abuse. 2.1.4. Microaggressions Two measures were used to look at microaggressions experienced in relation to gendered racism and to living with HIV. The Gendered Racial Microaggressions Scale for Black Women (GRMS) [ 58 ] includes items such as “Someone accused me of being angry when I was speaking in a calm manner” and “I have been assumed to be a strong Black woman”. The GRMS includes 26-items that look at both frequency and stress appraisal with Likert-type scale responses ranging from “never” to “once a week or more” and “not stressful at all” to “extremely stressful”, respectively. For the current study frequency will be used to calculate a total average score for microaggression experience with higher scores indicating higher frequency. The internal consistency for this sample is α = 0.92, several studies have reported high reliability in studies with Black women [ 59 – 62 ]. The HIV Microaggressions Scale consists of 14 items (Cronbach’s alpha in this sample α = 0.87) with response options ranging from “never” to “often”. Sample items include “Someone assumed you must be depressed because of your HIV status” and “You have heard someone say, ‘but he or she doesn’t look HIV positive.’” The scale was developed using data with Black men living with HIV and shown good validity in studies including BWLWH [ 63 – 65 ]. 2.1.5. Health behaviors In efforts to describe the women by their health behaviors three different measures were assessed. The General Self Efficacy scale (GSE) describes how confident an individual is in their coping and despite challenges and consists of 10 statements such as “I can always manage to solve difficult problems if I try hard enough” and “I can remain calm when facing difficulties because I can rely on my coping abilities”. Participant responses were obtained on a Likert-type scale ranging from “Not true at all” to “Exactly true”, higher average scores were indicative of higher self-efficacy. In this sample this scale displayed high internal consistency α = 0.93. The other two questionnaires on health behaviors included the medication adherence and self-care, specifically asking “Thinking about the past 4 weeks, on average how would you rate your ability to take all your HIV antiretroviral medications as your doctor prescribed?” and “Thinking about the past 4 weeks, on average how would you rate your ability to practice self-care activities? (e.g., intentional actions you take to care for your physical, mental, and emotional health)”, respectively. Responses are rated from “very poor” to “excellent” in 6 options for both medication adherence and self-care activities. 2.1.6. Resilience The Connor Davidson Resilience Scale [ 66 ] (CD-RISC-10 item) gauges an individual’s ability to strive in the face of adversity (Cronbach’s alpha in this sample α = 0.92). Sample items include “I tend to bounce back after illness, injury, or other hardships” and “I try to see the humorous side of things when I am faced with problems”, with responses rated with 5 options between 0-not true at all and 4-true nearly all the time. This abridged version has been psychometrically tested in various populations including people living with HIV and shows good construct validity [ 67 – 71 ]. 2.1.7. COVID-19 vaccine uptake Two factors were taken into consideration for the outcome variable, (a) question availability and (b) vaccine availability. The parent study started in October 2019, however with the unfolding of the pandemic the measure was adapted to collect relevant data as it was appropriate. Therefore, the question used for analysis in this study “Have you gotten the COVID-19 vaccine?” (referring to the primary shot) with the response option of yes or no, was added on March 2nd, 2021, to the measure. Any participants that did not have visit data after March 2nd were not included in the outcome and were systematically removed. In Florida, on February 25th, 2021, the COVID-19 vaccine was released to only high risk/people living with comorbidities. Keeping this in mind, difference in times in days from February 25th, 2021, to May 30th, 2023, was calculated for each participant that was vaccinated, for those that were not vaccinated at the end of the study analysis period (May 30th, 2023), they were considered censored either due to loss of follow up or not getting vaccinated. For the individuals that did not get the vaccine by the study end period, another question was looked at to understand the reasons. A question asking, “Will you get the COVID-19 vaccine” allows for responses of “yes”, “no” or “I am unsure”. The “no” or “I am unsure” responses branched to the next question with a check all that apply 11-response set varying with reasons why such as “I am concerned about side effects”, “I am afraid of injections”, “I already had COVID-19” and “I do not trust the government or the people who are developing the vaccines”. 2.2. Statistical analysis Two analysis techniques were employed to test our hypotheses, Latent Class Analysis (LCA) and time-to-event (or survival) analysis [ 72 ]. First, a three-step LCA approach [ 45 ] was conducted to explore the heterogeneity within the sample based on indicators that are related to health outcomes and vaccine uptake. Economic stability (income and housing), trauma and violence (lifetime traumatic events and interpersonal violence), health behaviors (medication adherence and self-care), self-efficacy, and resilience indicators were added into the model and the best model was iteratively fit and compared. The most optimal model was selected on a number of criteria including having lower Bayesian Information Criterion (BIC), lower Sample-size adjusted Bayesian Information Criterion (SSBIC), higher entropy (indicating a greater degree of separation between classes), a significant Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT) p -value (which signifies rejection of the null hypothesis that the k-1 model is better than the alternative model), and class size (at least 5 % of the distribution) [ 73 ]. To test the assumption of local independence of our indicators in the LCA we looked at the variance inflation factor assessing multicollinearity. Descriptive statistics (means, standard deviations, frequencies, and percentages) were calculated for the socio-demographic variables for the overall sample and specific to the classes resulting from the LCA. A Student’s t -test (for the continuous variable), chi-square tests of variance with Monte-Carlo methods for exact test statistics to test for class were used. The next step included an unadjusted time-to-event analysis with the overall group using a Kaplan Meier (KM) survival curve. After the best fit model was chosen, classes were modeled with the continuous time-to-event outcome of vaccine uptake. To estimate the effects of latent classes on vaccine uptake, steps included checking the proportionality assumption of the classes by visually assessing a KM plot with groups, if the assumption was not violated, a Cox Proportional Hazard Regression model was fit to compare the length of time till vaccination and vaccine uptake (binary). The time-to-event analyses excluded participants with missing data on the indicators or outcome variables. The LCA model estimation and initial survival analysis were conducted using Mplusv8.6 [ 74 ]. Fixed classes based on posterior probabilities were imported into SAS ® v9.4 for Windows (SAS Institute, Cary, NC) to estimate summary statistics, display (KM plots) and hazard rates for vaccine uptake. Estimates included 95 % confidence interval (95 %CI). A p-value < .05 was considered statistically significant. 3. Results A total of 152 women were included in the descriptive and latent class analysis. The time-to-event analysis included 134 women due to missing data for either the indicator or outcome variables. Table 1 shows the means, standard deviations, frequencies, and difference tests by class for socio-demographic characteristics ( n = 152). The mean age was 52.9 years with a standard deviation of 10.4 years (min = 21, max = 69). The most practiced religion was Baptist (51.3 %), Christianity (26.6 %), and none (8.4 %). Many women graduated from high school or earned a GED (38.8 %), and over half of the women indicated they were on disability (65.1 %) when asked about employment. About 80 % of the women identified as heterosexual (80.5 %) followed by bisexual (7.9 %), with almost half reporting being single (46.7 %). About 71 % of the women self-reported an undetectable viral load at baseline. Table 1. Sociodemographic Characteristics of Overall group and Latent Classes. Characteristic Test Statistic Name (row%) OVERALL n = 152 Class 1 n = 105 (69.1) Class 2 n = 47 (30.5) Age a (years) 52.9(10.4) 55.1(9.6) 47.9(10.6) t(150) = 4.15 * Mean (standard deviation) min, max 21,69 25,69 21,65 Education b n(col%) χ 2 (7)=7.3 ≤8th grade 6(3.9) 2(1.9) 4(8.5) Some HS 48(31.6) 34(32.4) 14(29.8) HS graduate/GED 59(38.8) 42(40.0) 17(36.2) Some college 24(15.8) 16(15.2) 8(17.0) College graduate 9(5.9) 5(4.8) 4(8.5) Some graduate school 2(1.3) 2(1.9) – Graduate school degree 3(2.0) 3(2.9) – Choose not to answer 1(0.7) 1(0.9) – Work/School b n(col %) Full time or part time work 26(17.1) 20(19.0) 6(12.8) χ 2 (6)=7.4 Full time or part time school 1(0.7) 1(0.9) – Neither work nor school 15(9.9) 9(8.6) 6(12.8) On disability 99(65.1) 70(66.7) 29(61.7) Other 4(2.6) 3(2.9) 1(2.1) I choose not to answer 7(4.6) 2(1.9) 5(10.6) Relationship Status b n (col%) Married 18(11.8) 15(14.3) 3(6.4) χ 2 (6)=6.8 Not married, living with partner 23(15.1) 15(14.3) 8(17.0) Non-cohabitating relationship 13(8.5) 7(6.7) 6(12.8) Single 71(46.7) 46(43.8) 25(53.2) Divorced/separated 11(7.2) 8(7.6) 3(6.4) Loss of long-term partner/widowed 13(8.5) 11(10.5) 2(4.3) Missing 3(2.0) 3(2.9) – Religion b n(col%) Christian 41(27.0) 27(25.7) 14(29.8) χ 2 (7)=13.2 Catholic 6(3.9) 5(4.8) 1(2.1) Baptist 77(50.7) 58(55.2) 19(40.4) Protestant 2(1.3) 1(0.9) 1(2.1) Jewish 1(0.6) – 1(2.1) Islamic – – – Other 5(3.3) 5(4.8) – None 13(8.5) 5(4.8) 8(17.0) Choose not to answer 7(4.6) 4(3.8) 3(6.4) Sexual Orientation b n (col%) Heterosexual 122(80.3) 89(84.8) 33(70.2) χ 2 (5)=11.5 * Same gender loving (gay or lesbian) 4(2.6) 2(1.9) 2(4.3) Bisexual 12(7.9) 4(3.8) 8(17.0) Queer – – – Pansexual – – – Asexual 4(2.6) 3(2.9) 1(2.1) Unsure/Questioning/Exploring – – – Not listed/Other 3(2.0) 1(0.9) 2(4.3) Missing 7(4.6) 6(5.7) 1(2.1) Self-Reported Viral load Detectable viral load 37(24.3) 24(22.9) 13(27.6) χ 2 (2) = 2.5 Undetectable viral load 109(71.7) 75(71.4) 34(72.3) Unknown viral load 5(3.3) 5(4.8) – Open in a new tab a Student’s t-test; b χ 2 * Exact p values <.05 considered statistically significant, degrees of freedom vary according to missingness 3.1. Latent class analysis results Latent classes were iteratively estimated, Table 2 depicts the fit statistics of each standardized unconditional model. The 2-class solution showed the best fit as it had the lowest BIC, higher entropy, and best class distribution. The LMR-LRT result indicated that the 2-model solution had better fit than the 1 class model. Figs. 1a and b separately depict the 2-class standardized solution of the unconditional model based on continuous and categorical indicators for visual clarity. Fig. 3 illustrates all 10 indicators in one visual for full characterization of the classes. To assess multicollinearity, the variance inflation factor was estimated, results showed low to moderate correlation, therefore no corrections were needed. The following socio-demographic characteristics were found to be significantly different between class 1 and class 2: age [t-value = 4.15, p < .0001], religion [χ 2 (5) =13.6, p < .05], and sexual orientation [χ 2 (5) =11.8, p < .05]. Table 2. Latent Class Analysis Fit Statistics for Latent classes (n = 152). # of classes BIC SSABIC Entropy LMR-LRT (p-value) Class percentages (%) 1 3972.721 3912.581 – – 2 3929.710 3806.266 0.826 148.0 (< 0.05) 69.5, 30.5 3 3939.461 3765.373 0.762 70.0 (0.64) 35.5, 24.5, 40.0 4 3956.957 3732.225 0.831 62.2(1.0) 4.6 , 38.1, 36.2, 21.1 Open in a new tab BIC Bayesian Information Criteria, SSABIC Sample size adjusted BIC, LMR-LRT Lo-Mendell-Rubin likelihood ratio rest. Fig. 1. Open in a new tab (a) Unconditional Latent Class Solution of Adversity, Resilience, Self-efficacy and Health Behaviors. GRMS = Gendered Racial Microaggressions Scale; LEC = Lifetime Events Checklist; WAST = Women Abuse Screening Tool; GSE = General Self Efficacy; CDRS = Connor- Davidson Resilience Scale; MEDADH = Medication Adherence. (b) Economic Stability Indicators of Unconditional Model. Fig. 3. Open in a new tab Kaplan-Meier time-to-event (COVID-19 vaccine) plot with 95 % confidence intervals. 3.2. Class 1: Lower adversity, higher resilience, higher health behaviors and self-efficacy, higher economic stability The first class had 69.1 % ( n = 105) of the sample; the participants in this class reported lower scores of HIV-related microaggression, gendered-racial microaggressions, lifetime traumatic events, and interpersonal violence. They also reported higher self-efficacy, resilience, self-care over the past month and high medication adherence during the past month. Economic stability as measured by income and housing was slightly more stable in class 1 compared to class 2. The full 7 levels of income were entered in the model, as shown in Fig. 1b , there was a higher probability of having an income in the higher income thresholds or within stable income relative to class 2. The majority of participants were renting an apartment or house, followed by subsidized housing, and living in a house owned or rented by themselves or someone else in the household. 3.2.1. Socio-demographic characteristics of class 1 The mean age was 55.1 years (SD = 9.6), about a quarter of the class had some college or higher education. About 65 % of the women were living on disability followed by working full-time or part-time (19.0 %). The most prevalent religion was Baptist (55.2 %) and most of the women were single (43.8 %), not married and living with a partner (14.3 %), and widowed/loss of long-term partner (10.5 %). Similar to the overall group almost three quarters of the women had an undetectable viral load (71.4 %).), Women who were not aware of their viral load were also present in this class (4.8 %). 3.3. Class 2: Higher adversity, lower resilience, lower health behaviors, lower self-efficacy, lower economic stability The second class consisted of 30.5 % ( n = 47) participants. The magnitude of HIV-related and gendered-racial microaggressions were much higher compared to class 1. The average number of lifetime traumatic events and scores for interpersonal violence were relatively higher for class 2. Reported generalized self-efficacy, resilience, self-care, medication adherence was lower in this class. The highest probability for income was at the less than $5000 income, with the lowest possible probabilities for having an income in the stable category. There was a high probability of having stable housing in this class, however it was slightly lower (but not statistically significantly lower) than class 1 as shown in Fig. 2 . Fig. 2. Open in a new tab Depiction of All Indicators Included in the Unconditional Latent Class Solution. GRMS = gendered-racial microaggressions; LEC = lifetime trauma checklist; WAST = woman abuse screening tool; GSE = general self-efficacy; CDRS = resilience; MEDAHD = ART medication adherence; Probabilities in the y-axis refer only to Income and Housing instability. 3.3.1. Socio-demographic characteristics of class 2 The average age was younger compared to class 1 at 47.9 years (SD = 10.6), all the women in this class had at most a college degree. Sixty-one percent of the women were living on disability. The most prevalent religion was also Baptist (40.4 %), more women in this class did not practice a religion (17.0 %). Over half of the women were single (53.2 %), however 36.2 % of the women were in a committed relationship. A higher proportion of women identified as bisexual (17.0 %), but about 70 % of the women identified as straight/heterosexual. The highest proportion of women reported an undetectable viral load (72.3 %), the remaining women (27.7 %) reported a detectable viral load. 3.4. Time-to-event analysis 3.4.1. Overall sample A total of 134 women were included in the time-to-event analysis where the event was getting the COVID-19 vaccine. As shown in Fig. 3 , the Kaplan-Meier curve of time to vaccination, 109 women were vaccinated and 25 were right-censored (either unvaccinated or lost to follow up) by the largest event time (782 days). The median unvaccinated time is 118 days (95 % CI:105–151 days), 50 % of women are expected to be vaccinated by then. Seventy-five percent of the women were estimated to be vaccinated by 232 days (95 % CI: 202–441 days). A total of 25 women did not get vaccinated or were lost to follow up by the end of the study period which was approximately 35.5 months or 3 years from the start date (February 2021). A question asking reasons for not vaccinating was assessed. In Fig. 4 , the reasons are listed and each endorsement by the participants is depicted by a bar graph. As the response format was select all that apply, women could choose as many reasons as they wished. The topmost endorsed reason for not getting vaccinated was “I do not trust the government or the people who are developing the vaccines” which was endorsed 19 times, followed by “I am concerned by side effects” (14 times), and 12 endorsements of “The vaccine was developed quickly so I worry about the quality”. The maximum number of reasons endorsed was 9, the average number of reasons endorsed was 3.88. Fig. 4. Open in a new tab Frequencies of endorsed reasons for not getting vaccinated ( n = 25). 3.4.2. LCA with time-to-event In Fig. 5 , the Kaplan-Meier curves of vaccine uptake is depicted by comparing class 1 and class 2, a statistically significant difference between vaccination uptake in the two classes was not found as indicated by the log-rank test (χ 2 = 1.72, df = 1, p = .188). As shown in Table 3 , the median number of days to receive a COVID-19 vaccination was 118 days (95 % CI:103–147 days) for class 1 that is described as lower adversity, higher health behaviors, higher resilience and self-efficacy and high economic stability. The median number of days till vaccine uptake in Class 2 was 125 days (95 % CI: 102–230 days). The estimated probability of being unvaccinated 2 months after availability in class 1 was 79 % (95 % CI: 0.69–0.85) and 94 % (95 % CI: 0.80–0.98) for class 2. The Cox Proportional Hazards regression model results for each class are also shown in Table 3 , hazard ratio (HR) of 1.33 (95 % CI: 0.87,2.03) for class 1 and HR = 0.75 (95 % CI: 0.49,1.15) for class 2, suggests that class one had faster adoption of the vaccine however this was not a statistically significant difference in vaccination rate between classes estimated from the LCA. Fig. 5. Open in a new tab Kaplan Meier Curves for time to vaccination uptake by latent class 1 and class 2 with log-rank test results. Table 3. Survival Rate Estimates and Median Survival along with 95 % Confidence Interval (95 %CI) by Class ( n = 152). Class Time list Time (in days) Number at Risk Observed Events Survival Survival Standard Error 95 % CI for Survival Estimates Median Survival Time (95 %CI) Hazard Rate (95 %CI): time to vaccination Class 1 0 0 96 2 1.000 0 . 118.0 (103.0, 147.0) Class 1 vs class 2 1.330 (0.869,2.035) 60 60 75 1 0.789 0.042 (0.693, 0.859) 120 120 44 1 0.468 0.052 (0.364, 0.565) 180 172 31 0 0.346 0.050 (0.251, 0.443) 240 225 20 1 0.231 0.044 (0.150, 0.322) 300 269 18 1 0.208 0.043 (0.131,0.270) 360 355 16 0 0.185 0.041 (0.112,0.271) 420 377 14 1 0.172 0.040 (0.102,0.258) 480 441 13 1 0.160 0.039 (0.092,0.244) % Censored 16.7 540 509 12 1 0.148 0.038 (0.083,0.230) 600 551 11 0 0.135 0.037 (0.073,0.216) 660 551 7 0 0.135 0.037 (0.073,0.216) 720 691 3 0 0.102 0.040 (0.040,0.196) 780 691 2 0 0.102 0.040 (0.040,0.196) 840 691 1 0 0.102 0.040 (0.040,0.196) 1080 Class 2 0 0 38 1 1.000 0 . 125.0 (102.0, 230.0) Class 2 vs class 1 0.752 (0.491,1.151) 60 43 36 0 0.947 0.036 (0.805,0.986) 120 117 19 1 0.514 0.082 (0.345,0.660) 180 180 17 1 0.433 0.081 (0.272,0.583) 240 232 10 1 0.290 0.076 (0.154,0.441) 300 242 9 1 0.261 0.073 (0.132,0.410) 360 242 9 1 0.261 0.073 (0.132,0.410) 420 375 8 1 0.232 0.071 (0.111,0.379) 480 441 7 0 0.203 0.068 (0.090,0.347) % Censored 23.7 540 441 7 0 0.203 0.068 (0.090,0.347) 600 441 7 0 0.203 0.068 (0.090,0.347) 660 441 3 0 0.203 0.068 (0.090,0.347) 720 441 1 0 0203 0.068 (0.090,0.347) 780 840 Open in a new tab 4. Discussion In this study we sought to understand COVID-19 vaccine uptake between February 2021 and May 2023 among BWLWH living in South Florida. Our analysis aimed to fill in the knowledge gap in the literature regarding COVID-19 vaccine uptake among BWLWH, for whom there is scarce data. To our knowledge this is the first study among cisgender BWLWH using latent class and time-to-event analysis to model economic stability, intersectional adversities, resilience, health behaviors, and self-efficacy with COVID-19 vaccine uptake. We found novel findings including a high rate of vaccination, two distinct latent classes, and reasons for hesitation or refusal (e.g., lack of trust in the government and institutions) to take COVID-19 vaccine. Our results show that about 80 % of the women in our sample received the COVID-19 vaccine by the end of the analysis study duration, as this study is ongoing it is possible that the remaining 25 women may get vaccinated. This contrasts the literature showing low vaccine uptake among the Black community in general and highlights the need to understand how vaccine uptake may differ for subgroups [ 18 ]. For BWLWH, their experience living with HIV, engaging with healthcare regularly, and fear about the potential impact of COVID-19 given immunocompromisation may be motivating factors for vaccine uptake. Our latent class analysis revealed two distinct classes. The first class included women who were likely to report lower adversity (HIV and gendered-racial microaggressions, trauma, interpersonal violence, economic instability) scores and higher scores for positive factors (self-efficacy, resilience, self-care, and medication adherence). Women in the second class reported the inverse of class one. Although the difference among our two classes did not yield statistically significance, a pattern could be observed, those in class 2 with less economic stability, higher experiences of trauma, interpersonal violence, lower resilience, lower self-efficacy, lower self-care and medication adherence took longer to get the vaccine which echoes what is found in the literature [ 75 ]. This highlights the need to address economic barriers and psychosocial factors in order to optimize the uptake of vaccines. Several reasons for hesitation or refusal to take the vaccine were endorsed by the women in our study. The topmost reason of lack of trust in the government or the institutions developing the vaccine speak to the medical mistrust that is a result of unethical medical practices [ 76 ]. One qualitative study focused conceptualizing trust and other factors related to the COVID-19 vaccine prior to vaccine development in a sample of older Black/African American individuals highlighted the importance of addressing the systemic institutionalized neglect, uncertainty, and ensuring cultural relevance in disseminating information [ 77 ]. Trust in health providers and government was found to be associated with uptake of the COVID-19 vaccine [ 78 ]. Another study found that trust archetypes were stronger predictors for vaccine hesitancy compared to sociodemographic characteristics and political attitudes [ 79 ]. Many studies have highlighted the impact of medical mistrust on the health outcomes for individuals living with HIV [ 80 , 81 ]. Our findings exhibit the relationship and impact of unethical medical practices on BWLWH receiving the COVID-19 vaccine by direct expression as lack of trust was the highest endorsed response. Our overall sample of BWLWH may differ from the general community in that they are more likely in frequent care and therefore may have better relationships with health providers and sometimes find them as a source of support [ 82 ]. It is also important to consider the burdens of chronic health conditions on BWLWH and how that may play role in decisions on when and whether to get the vaccine. Our findings showed that self-care and medication adherence was reported relatively lower in class 2 compared to class 1. Where one individual may decide to take the vaccine due to fears of getting more severely sick without it, we see where the fear of side effects also plays a role in the decision to abstain from the vaccine. 4.1. Strengths and limitations This study offers insight into the disparities in COVID-19 vaccine uptake that have been found in the literature for minoritized communities. To gain a better understanding of the vaccination it is important to account for the uptake longitudinally, the large period of time used for the outcome in this study was vital in understanding vaccine uptake. It is also important to consider the structural level factors that influence health outside of the common individualistic view of health behaviors. A limitation of the study is that the impact of the pandemic and highlighting of violence against the Black community (specifically the Black Lives Matter movement) are not captured in the baseline due to temporality, these could potentially impact vaccination behaviors that are not captured in the current analysis. Also, the income and housing do not capture the economic destabilization that occurred during this period. Further, a majority of the women in the study reported undetectable viral loads which are often an indication of adherence to medication and of medical visits. This could describe the nature of our participants in regard to how their established healthcare behaviors impact vaccination or new healthcare needs. Another limitation is that the sample size is relatively small for complex analyses, and comes from a parent study thus limiting generalizability, larger sample may reveal more in terms of how the intersectional adversities play a role in vaccination uptake as has been found in the literature. 5. Conclusions In conclusion, our study used latent class and time-to-event analyses to explore disparities in COVID-19 vaccine uptake with a longitudinal survey among BWLWH and provide novel findings. A major takeaway is that institutions need to rebuild trust in Black/African American communities so that health related decisions can be informed and empower the community. Further, studies should acknowledge the role and presence of intersectional adversities, economic stability, health behaviors, self-efficacy, and resilience in healthcare outcomes so that future interventions can be better informed. Acknowledgements We would like to express our utmost gratitude to the women who participated in this study, research staff members, and community stakeholders. The research reported in this publication and the principal investigator (Dr. Sannisha Dale) was funded by R56MH121194 and R01MH121194 from the National Institute of Mental Health. Dr. Devina Boga and Naysha Shahid were funded by T32 MH126772 from NIMH. Mya Wright was funded by R01MH121194-03S1. Drs. Safren, Dale, Ironson, Rodriguez, and Feaster were also supported by P30MH133399. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding The research reported in this publication and the principal investigator was funded by R56MH121194 and R01MH121194 from the National Institute of Mental Health. Mya Wright was funded by R01MH121194-03S1. Devina Boga and Naysha Shahid were funded by T32 MH126772 from NIMH. Footnotes CRediT authorship contribution statement Devina J. Boga: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Michael Robinson: Writing – review & editing, Writing – original draft, Data curation. Manasa Tirupathi: Writing – review & editing, Project administration, Data curation. Reyanna St Juste: Writing – review & editing, Project administration, Data curation. Kimberly Lazarus: Writing – review & editing, Project administration, Data curation. Kayla Etienne: Writing – review & editing, Project administration, Data curation. Mya Wright: Writing – review & editing, Project administration, Data curation. Rachelle Reid: Writing – review & editing, Project administration, Data curation. Naysha Shahid: Writing – review & editing, Project administration, Data curation. C. Mindy Nelson: Writing – review & editing, Data curation. Tulay Koru-Sengul: Writing – review & editing. Steven A. Safren: Writing – review & editing, Investigation. Gail Ironson: Writing – review & editing, Investigation. Allan Rodriguez: Writing – review & editing, Investigation. Ian Wright: Writing – review & editing, Investigation, Funding acquisition. Daniel Feaster: Writing – review & editing, Investigation. Sannisha K. Dale: Writing – review & editing, Writing – original draft, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication Research participants have provided informed consent for the publication of the research findings in a peer-reviewed journal. Ethics approval This study was performed in accordance with the principles of the Declaration of Helsinki. All study procedures and materials were approved by the Institutional Review Board at the University of Miami (5/8/2017, No. 20170281). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The data that has been used is confidential. References [1]. CDC. Centers for Disease Control and Prevention https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-report-2020-updated-vol-33.pdf. [ Google Scholar ] [2]. 2021 Florida Epidemiologic Profiles, https://www.floridahealth.gov/diseases-and-conditions/aids/surveillance/epi-profiles/index.html . 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