S. Prabhu1,2, B. Oyaro1,3, G. Wanje1,3, F.M. Aunon4,5, N. Gomez Juarez1, B.P. Flaherty1, W. McCormick1, M.K. Andrew6, W. Jaoko3, R.S. McClelland1,3, S.M. Graham1
1. University of Washington, Seattle, WA, USA; 2. Brigham and Women’s Hospital, Boston, MA, USA; 3. University of Nairobi, Nairobi, Kenya; 4. Yale School of Medicine, New Haven, CT, USA; 5. Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA; 6. Department of Medicine (Geriatrics), Dalhousie University, Halifax, Canada
Corresponding Author: Sandeep Prabhu, MD, MPH, MS, Brigham and Women’s Hospital, 800 Huntington Ave, Boston, MA, 02115. Telephone 650-245-7276. Email: sprabhu4@bwh.harvard.edu; Alternate author: Susan M Graham, MD, MPH, PhD, University of Washington, 325 Ninth Avenue, Box 359909, Seattle, WA, 98104. Telephone: 206-351-0414. Email: grahamsm@uw.edu
J Frailty Aging 2024;in press
Published online September 18, 2024, http://dx.doi.org/10.14283/jfa.2024.71
Abstract
BACKGROUND: Social vulnerability reflects deficits in social resources that may disproportionally impact older women with HIV (WWH) in Africa.
OBJECTIVE: To examine the relationship between scores on an adapted Social Vulnerability Index (SVI) and measures of physical frailty and disability.
DESIGN: Cross-sectional study.
PARTICIPANTS: 293 women (156 HIV-positive, 137 HIV-negative) aged >40 years in Mombasa, Kenya who were recruited from the Mombasa Cohort of women with a history of transactional sex and the general community.
MEASUREMENTS: Assessments including an SVI adapted for the Kenyan context (SVI-Kenya), the Clinical Frailty Scale (CFS) and the World Health Organization Disability Assessment (WHODAS) were compared by HIV status. Linear regression was used to determine the relationship between SVI-Kenya score and both CFS and WHODAS, after adjustment for potential confounders. An exploratory analysis identified factors associated with SVI-Kenya score. An age-by-HIV-status interaction term was tested and retained if significant in unadjusted analyses.
RESULTS: Mean SVI-Kenya score was 34.1 (SD, 12.9) and did not differ by HIV status (p=0.49). In adjusted analyses, each increment in SVI-Kenya score was associated with a 1.10-point higher WHODAS score (95%CI:0. 21, 1.99), but not with CFS. In exploratory analysis, factors associated with higher SVI-Kenya score included WHODAS score (adjusted beta=0.20; 95%CI: 0.05,0.35) and Mombasa Cohort recruitment (adjusted beta=5.91; 95%CI: 2.07,9.75). Being married, separated/divorced, or widowed predicted lower SVI-Kenya scores (by 5.52-9.09 points) compared to being single. Age did not predict SVI-Kenya score.
CONCLUSION: Social vulnerability as measured by the SVI-Kenya score was associated with greater disability but not physical frailty. Social vulnerability was also associated with prior sex work and never having married. Our findings suggest that social vulnerability is a distinct construct from physical frailty among older Kenyan women and not related to HIV status.
Key words: HIV, social vulnerability, clinical frailty, disability, older women, Kenya.
Abbreviations: WWH: Women with HIV; ART: antiretroviral therapy; HAALSI: Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community; SVI: Social Vulnerability Index; CFS: Clinical Frailty Scale; WHODAS: World Health Organization Disability Assessment Schedule 2.0; SD: standard deviation; CI: confidence interval.
Background
Older women with HIV (WWH) in sub-Saharan Africa constitute an understudied group that may be more socially vulnerable than their HIV-negative peers due to stigma and loss of family members to HIV. In the early years of the HIV pandemic, Kenyan women experienced a marked decline in life expectancy, from 61 years in 1988 to just 53 years in 2000 (1). More recently, the widespread availability of antiretroviral therapy (ART) has increased life expectancy, which is now 62 years for Kenyan women (2). A demographic transition with declining death rates is expected to lead to a doubling of HIV prevalence among individuals aged 50 years and older in sub-Saharan Africa by 2040 (2, 3). As early as 2012, the highest HIV prevalence in Kenya was reported among individuals aged 45-54 years, in sharp contrast to prior decades when HIV prevalence was highest among younger adults (4).
Prior studies in African settings have found that older women report more chronic health problems and have increased physical frailty compared to older men (5, 6). For example, the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community (HAALSI) cohort in rural South Africa has followed adults aged 40 years and over to understand correlates of healthy aging in this population. In the HAALSI cohort, physical frailty was associated with worse subjective well-being, worse self-reported health, and increased mortality over an average of 17 months in follow-up; these results have motivated an important research focus on social determinants of healthy aging (6). Information on social determinants from other African countries is urgently needed to prepare for increasing demand for health services and optimize health outcomes.
In Kenya, formal social services for chronically ill or older adults are limited, so these women often rely on families and social contacts for emotional and instrumental support (7–10). Consequently, we sought to study social vulnerability, defined as the “continuum of being at risk for losing, or having lost, resources that are important to fulfilling one or more basic social needs during the life span” (11), among older WWH, compared to their HIV-negative peers. The concept of social vulnerability has been studied in high-income settings using a Social Vulnerability Index (SVI) developed by Andrew et al. in 2008 in Canada (12). Scores on this instrument have been associated with subsequent poor health outcomes, including cognitive decline, disability, and mortality, in Canada, the United States, Mexico, Tanzania, and several Western European countries (5, 13–16, 27). A major advantage of using an index or inventory such as the SVI instead of individual measures to assess social vulnerability is the ability to capture multiple domains of social circumstances, including socioeconomic status, living situation, and different aspects of social support and engagement.
The relationship between HIV and social vulnerability is an understudied but emerging concept. In addition, the relationships between social vulnerability, physical frailty, and disability are important to understand. If socially vulnerable individuals are more likely to be physically frail or disabled, social support interventions may be needed in addition to health care and functional supports to promote healthy aging and prevent adverse outcomes. We recently adapted the original SVI items (2008) for the Kenyan context, based on qualitative analyses of focus group transcripts and a Delphi process involving expert input from individuals with lived experience caring for older women in Mombasa, Kenya (17). In this paper, we describe results of the SVI-Kenya as administered to a cross-sectional sample of 300 older women, about half of whom were living with HIV. In addition, we assess the relationships between SVI-Kenya scores and a measure of physical frailty, the Clinical Frailty Scale (CFS) developed by Rockwood et al. (18), and a measure of disability, World Health Organization Disability Assessment Schedule 2.0 (WHODAS) (19). Because the SVI has been associated with worse clinical outcomes in prospective studies (5, 6), we hypothesized that after controlling for potential confounders such as age and HIV status, SVI-Kenya scores would be positively correlated with both CFS and WHODAS scores.
Methods
Study design and setting
The Social Vulnerability Study was a cross-sectional study of women aged 40 years or older living in or around Mombasa, Kenya between November 2020 and November 2021. This age group is consistent with that used in the HAALSI cohort (6). This work was conducted at the Ganjoni Dispensary, where the research team and its local partners have worked on women’s health since 1993. The site has been home to a long-running cohort (the “Mombasa Cohort”) following women who report currently engaging in transactional sex at enrollment. Many of the participants continue follow-up despite a reduction in sexual risk behaviors as they have gotten older (20).
Recruitment and study population
Participants for the current study were initially recruited from the Mombasa Cohort during COVID research restrictions that limited community outreach, then expanded to the general population as these restrictions eased. To recruit community participants, research staff distributed a poster approved by our ethical review board in outpatient clinics frequented by general-population women. Posters were displayed prominently in each facility and healthcare providers distributed copies to potential participants. Health talks were also conducted by research staff at targeted clinics to explain the study’s purpose. Additionally, staff held outreach meetings in markets, churches, and social groups for women, while adhering to COVID-19 guidelines for social distancing and mask use. We also used word-of-mouth and snowballing, asking women who had enrolled in the study to refer peers who met the age requirement. All interested women from these avenues were referred to the research clinic for eligibility screening. Recruitment was conducted within age strata to ensure approximately equal age distributions across HIV status groups. Three hundred participants were enrolled (150 with self-reported HIV-positive status and 150 with self-reported HIV-negative status), of whom 7 declined blood testing to confirm HIV status. These 7 women were dropped from the main analysis but included in sensitivity analyses. Of the remaining 293 older women, 156 were living with HIV (6 of whom were newly diagnosed) and 137 were HIV-negative.
Measures
The SVI-Kenya consists of 16 self-reported items (for example, “Do you feel safe at home?”) covering a range of social factors (17). All item responses had scores ranging from 0 to 1, with a higher score indicating the presence of a deficit. Supplemental Table 1 contains the scoring for each component of the SVI-Kenya. Each item score was summed, then this sum was divided by the number of items and multiplied by 100, to yield an SVI-Kenya score ranging from 0 to 100. Higher SVI-Kenya scores indicate higher levels of social vulnerability. The CFS is a clinician-administered 9-point continuous scale ranging from 1 to 9, with higher scores representing increasing levels of physical frailty (18). Descriptors for each level are: very fit (1), well (2), managing well (3), vulnerable (4), mildly frail (5), moderately frail (6), severely frail (7), very severely frail (8), and terminally ill (9). The WHODAS 2.0 is an instrument designed to measure various aspects of health and disability and includes self-reported disability ratings in 6 domains: cognition, mobility, self-care, getting along, life activities, and participation (19). Each item in the 12-item questionnaire is scored from 1 to 5, with 1 representing no difficulty and 5 representing extreme difficulty or inability. A composite WHODAS score was created by adding the scores for each question, dividing by the number of questions, and multiplying by 100 to yield a WHODAS score ranging from 0 to 100. Higher WHODAS scores indicate higher levels of disability.
Statistical analysis
Chi squared or Fisher’s exact tests were used to compare categorical variables between the two HIV status groups. Independent t-tests were used for comparing means of continuous variables.
A directed acyclic graph was created based on our hypotheses and the available literature (Supplemental Figure 1). Linear regression was used to examine the relationship between SVI-Kenya score as the predictor and both CFS and WHODAS score as outcome variables. Non-linear relationships were tested by including SVI-Kenya score squared in models and comparing the adjusted R2 values and root mean squared error across models. Age and HIV status were included in modeling as a priori confounders. An interaction between age and HIV status was also tested. Other potential confounders (income source and recruitment source) were included if they were associated with the outcome of interest at p<0.20. Finally, margins plots were created to graphically depict the predicted values of CFS and WHODAS by age (in 10-year increments) and HIV status for the final multivariable models.
An exploratory analysis was carried out to identify factors associated with the SVI-Kenya score. Factors that were tested in bivariable analysis included variables that significantly differed between the HIV status groups, a priori confounders (i.e., HIV status and age), WHODAS, CFS, and the interaction term between age and HIV status, to evaluate whether the association between age and SVI-Kenya score differed by HIV status. Variables other than the a priori confounders were carried forward into multivariable models if associated with the outcome at p<0.20. Sensitivity analyses were conducted by including the 7 women who declined blood tests as being HIV-negative and assessing if the results changed. All statistical analyses were performed using Stata version 15.1 software (StataCorp, College Station, Texas).
Ethical considerations
This study was approved by the University of Washington Human Subjects Division (STUDY00006985) and the Kenyatta National Hospital- University of Nairobi Ethical Review Committee (P732/08/2019). All participants provided written informed consent.
Cronbach’s alpha of the WHODAS scale was 0.93, indicating excellent internal consistency.
Results
293 participants were included in the analysis, of whom 156 were WWH and 137 were HIV-negative women. Table 1 presents sociodemographic characteristics of the study participants, overall and by HIV status, followed by three scores of interest (i.e., SVI-Kenya, CFS, and WHODAS). Mean participant age was 52.4 years (standard deviation [SD], 8.3 years, range 40 —80 years) and did not differ by HIV status (p=0.56). WWH were more likely to be single, more likely to be Christian, and were more often recruited from the Mombasa Cohort (details in Table 1). There was no significant difference between groups in the number of sexual partners in the past year or highest education level attained. The mean SVI-Kenya score was 34.1 and did not differ by HIV status (33.3 among WWH and 34.7 among HIV-negative women; p=0.49). The CFS was significantly higher among WWH than among HIV-negative women (2.8 vs. 2.4; p-value<0.01). In contrast, the WHODAS score did not differ by HIV-status (35.0 among WWH vs. 38.6 among HIV-negative women; p=0.33). Figure 1 presents bar graphs of the distribution of each measure (i.e., CFS, WHODAS score, and SVI-Kenya) by HIV status.
Abbreviations: ART: Antiretroviral therapy, CFS: Clinical frailty score; SD: standard deviation, SVI: Social vulnerability index, WHODAS: World Health Organization Disability Assessment Schedule 2.0. 1. Possible range 0-100, with higher score indicating a higher degree of social frailty. Actual range was 5.21-85.42 in this dataset. 2. Possible range 0-9, with higher score indicating a higher degree of physical frailty. Actual range was 1-6 in this dataset. 3. Possible range 0-100, with higher score indicating a higher degree of disability. Actual range was 25-83.3 in this dataset.
Bar graphs of outcomes by HIV status, presenting differences in (A) SVI-Kenya score quartiles, (B) CFS, and (C) WHODAS score quartiles.
Supplemental Table 2 presents the scores for each item in the SVI-Kenya stratified by HIV status. Fewer WWH reported not feeling safe at home compared to HIV-negative women (15.4% versus 26.3%), while WWH were less likely to see family members at least monthly than HIV-negative women (details in Supplemental Table 2). Neither difference was statistically significant after adjustment for family-wise error (21, 22). Most older women, regardless of HIV status, reported they had no medical insurance (85.7%) or had run out of money for basic needs many times in the past 12 months (74.0%). In addition, just over half (53.2%) did not participate in any social groups, regardless of HIV status. Supplemental Table 3 presents characteristics of the 293 participants by recruitment source.
Table 2 shows results of the linear regression between SVI-Kenya and both physical frailty and disability. The SVI-Kenya score was not significantly associated with the CFS in either unadjusted or adjusted analysis, but older age was associated with higher CFS, with an adjusted beta of 0.40 (95% confidence interval [CI]: 0.28, 0.53). Being HIV-positive was also associated with higher CFS (adjusted beta= 1.47; 95% CI: 0.57, 2.38). The age-by-HIV-status interaction term was significant, such that the change in CFS for each decade of age for HIV-positive women was lower by 0.21 (95% CI: -0.38, -0.04) than the change in CFS for each decade of age for HIV-negative women, at 0.19 instead of 0.40. Inclusion of an SVI squared term did not improve model fit. In the linear regression with WHODAS score as the outcome, higher SVI-Kenya score was associated higher WHODAS score, with an adjusted beta of 1.10 (95% CI: 0.21, 1.99). Older age was also associated with a higher WHODAS score, with an adjusted beta of 6.98 (95% CI: 5.41, 8.55), whereas recruitment from the Mombasa Cohort was associated with a lower WHODAS score, with an adjusted beta of -9.14 (95% CI: -11.89, -6.40). HIV status was not associated with WHODAS score in the adjusted analysis. Again, inclusion of an SVI squared term did not improve model fit. Figure 2 presents margin plots of the predicted values of physical frailty and disability by decade of age and HIV status, showing how these measures increased with each decade of participant age.
1. Wald tests were used to calculate p-values for categorical variables using Stata’s testparm command.
Margin plots of (A) predicted CFS score at each decade of age by HIV status, based on a model including SVI-Kenya score, age, recruitment source, HIV status, and the interaction of age and HIV status; (B) predicted WHODAS score at each decade of age by HIV status, based on a model including SVI-Kenya score, age, income source, recruitment source, and HIV status.
Finally, in the exploratory analysis evaluating potential predictors of the SVI-Kenya score (Table 3), being married, separated/divorced, or widowed was associated with a statistically significantly lower SVI score than being single (i.e., never married), whereas being a Mombasa Cohort participant and having higher WHODAS score were both associated with higher SVI, with adjusted beta 5.91 (95% CI: 2.07, 9.75) and adjusted beta 0.20 (95% CI: 0.05, 0.35), respectively. The age-by-HIV status interaction term was not statistically significant in the unadjusted analysis.
1. Wald tests were used to calculate p values for categorical variables using Stata’s testparm command.
There was no difference in the sensitivity analysis results of the three regression models after including the 7 women who self-reported as HIV-negative but declined blood testing in the HIV-negative status group.
Discussion
In this study of women aged 40 years and older living in Mombasa, Kenya, we found that most women, regardless of HIV status, had inadequate medical coverage and faced financial insecurity. SVI-Kenya scores did not differ significantly by HIV status. While we found significant differences in CFS between HIV status groups, the small difference (i.e., a 0.4-point higher mean CFS among WWH) may not be clinically meaningful. The SVI-Kenya was not associated with age but was positively associated with WHODAS score, never being married, and a history of sex work.
While we hypothesized that older WWH would have increased social vulnerability compared to HIV-negative women, the SVI-Kenya scores and most responses were very similar in the two groups. Although WWH reported that they were less likely to interact with family members regularly, this difference was not statistically significant after p-values were adjusted for false discovery rate. The social isolation that some older individuals with HIV face, even from family members, is well documented in the literature (23, 24), although many studies include no control group in contrast to this analysis. The relationship between social determinants and HIV status in African settings is not clearcut, with at least one publication demonstrating an inverse relationship between poverty and HIV status (25). It is possible that a social inventory such as the SVI-Kenya may not capture the social determinants of health that correlate most closely with prevalent HIV in older adults. Of interest, we excluded marital status from the SVI-Kenya, despite it being included in the original SVI, because married women often live separately from their husbands in Kenya for economic reasons. Instead, we asked whether women lived alone (17). We found in our exploratory analysis that women who had never married (i.e., single women) had significantly higher SVI-Kenya scores than women who had ever married (i.e., married, separated, divorced, or widowed women). Because WWH were more likely to have never married, including marital status in our SVI-Kenya score could potentially have increased the disparity in SVI-Kenya scores between WWH and their HIV-negative counterparts.
The association we found between SVI-Kenya and WHODAS may be in part due to some overlap in concepts between SVI-Kenya items and three domains of the WHODAS— namely participation, life activities and getting along. However, while the SVI-Kenya focuses on an individual’s social situation in general, the WHODAS specifically asks individuals to reflect upon difficulties they have experienced in the past 30 days due to health conditions, including “diseases or illnesses, other health problems that may be short or long lasting, injuries, mental or emotional problems, and problems with alcohol or drugs.” For example, while the SVI questions ask, “Do you participate in any social groups, such as ‘merry-go-rounds’?” (i.e., a social group that helps members save money), and “How often do you attend religious services?” without reference to physical health, the WHODAS asks whether an individual has had difficulty performing activities such as maintaining friendships or joining in community activities as a result of their health conditions. In addition, the SVI-Kenya captures information on aspects of social support, perceived safety and financial security that are unrelated to items in the WHODAS. These two measures focus on different constructs and were only associated in our regression analyses after adjustment for potential confounders.
While both older age and higher SVI-Kenya score were associated with higher WHODAS score, it was interesting that women recruited from the Mombasa Cohort had lower WHODAS scores, after adjustment for age and SVI-Kenya score. This may reflect selection bias, with women who continue participating in the Mombasa Cohort being generally healthier than the women we recruited from the general population, who were also older on average and required more assistance with transportation to the clinic. Alternatively, the availability of regular outpatient care to members of the Mombasa Cohort at no cost may contribute to their lower disability scores. During the initial phase of COVID-19 lockdowns in Kenya, the Mombasa Cohort was permitted to continue visits, since it was seen as an important source of healthcare for its participants. As COVID restrictions relaxed, we recruited women from the community using outreach at local health centers and through word-of-mouth. This change in recruitment method or the difference in timing since onset of the COVID pandemic may have also contributed to the enrolment of women with higher mean WHODAS scores (45.2 for the general population vs. 30.7 for the Mombasa Cohort) on average.
We found no relationship between the CFS, a clinician-assessed measure of the physical frailty phenotype, and the SVI-Kenya, which uses a deficit accumulation approach to assess social vulnerability (15). This may be a function of conducting this study in an outpatient rather than a hospital setting and including many women who were still in their 40s and 50s (77.8% of the study population) and relatively healthy. While the CFS scores in this study ranged from 1 (“very fit”) to 6 (“moderately frail”), only 5.1% of the sample had CFS scores over the median, which was 3 (“managing well”). That said, SVI-Kenya scores in the most frail women were similar to those in the least frail (mean score 31.4 if CFS = 1 versus 28.8 if CFS = 6). A recent paper by Amieva et al. posits that social vulnerability may be more related to stably poor social status and unfavorable social conditions rather than to an ongoing process of decline (26), which supports our finding that social vulnerability is a distinct concept from physical frailty.
We did find that HIV, age, and their interaction were all associated with physical frailty in this sample. Of note, the CFS was assigned by clinicians who may have been aware of participants’ HIV status; in addition, women who have taken ART often have visible stigmata of lipoatrophy and lipodystrophy, which may represent side effects of earlier medication regimens or consequences of HIV itself, leading to higher scores. Consequently, the significant age-by-HIV-status interaction term as a predictor of CFS and the extrapolation of predicted CFS scores above the median in our graph should be interpreted with caution due to the paucity of CFS values in this range.
In the exploratory analysis of predictors of SVI score, we found that being single was associated with a higher SVI-Kenya score in this study population. Single women in this age group may have fewer social contacts or more constrained social support than women who have ever been married, regardless of whether their spouse is still with them, either due to having fewer children or to differences in social status in the community related to never having married (23). The higher SVI-Kenya scores among participants recruited from the Mombasa Cohort may reflect sex work stigma and be related to the increased risk of violence and HIV acquisition that current and former women who engage in sex work in Kenya face (27, 28). Although women recruited from the Mombasa Cohort were nearly a decade younger, on average, than women recruited from in the general population, the SVI-Kenya score was not associated with age in this study. A more detailed analysis of egocentric social networks among this study population is underway and may further elucidate differences in social vulnerability between women with different HIV status, marital status and sex work histories.
The question of whether the SVI-Kenya also holds prognostic value in predicting future adverse health outcomes is one that our current cross-sectional study design was not designed to answer. Prior work using the original SVI inventory in a Honolulu-based cohort of older Japanese American men showed that higher SVI scores predicted cognitive decline and mortality, even among individuals who scored low on physical frailty measures (13, 29). Similar findings have been noted in work done among older individuals in several European nations (13, 15, 29, 30). Applying the SVI-Kenya in prospective cohort studies of older adults in Kenya and similar settings in sub-Saharan Africa could help determine how social vulnerability predicts health outcomes in resource-limited settings. Interestingly, a recent study in Tanzania derived an SVI for the Tanzanian context from existing cohort data for 235 individuals with a mean age of 75.2 years (14). In contrast to the Honolulu-based study’s findings, the association between the Tanzanian SVI and mortality lost significance after controlling for a frailty index that included measures of physical frailty (14). The adapted SVI used in the Tanzanian study differs from the SVI-Kenya in several ways. First, demographic factors (e.g., education, marital status) are included in the adapted SVI used in Tanzania, whereas we chose to capture these factors as sociodemographic variables rather include them in the SVI-Kenya index. Second, the SVI-Kenya captures elements of personal safety and social interactions in a more detailed manner than in the adapted SVI used in Tanzania. Consequently, further prospective work on our index is needed to assess how it will perform with regards to longitudinal outcomes and whether it or the adapted SVI used in Tanzania performs better.
Our study supports the SVI-Kenya as a measure that captures information on social vulnerability that is distinct from measures of physical frailty and from age. In fact, the importance of social factors in Kenyan society, such as attending social groups and having children available for support as one ages, arose in our qualitative work as something that older women often lack, regardless of HIV status (17). While we were unable to assess longitudinal outcomes in this cross-sectional study, the factors associated with increased social vulnerability that could be a focus of targeted interventions to decrease social vulnerability, including for women who never married and those with a history of sex work. Such interventions to increase the strength and depth of social support may be most effective when delivered before physical frailty or significant disability occurs, at an age closer to those of the women in our study.
We acknowledge several limitations of this study. First, recruitment was initially constrained to Mombasa Cohort participants early in the COVID pandemic, a group that was likely skewed towards healthy women since they were able to attend a research clinic at that time. When we broadened recruitment from the cohort to the general population, our community outreach likely reached women who were more socially connected. These sources of bias limit the generalizability of our findings. Future work to reach older women with greater degrees of social vulnerability may require different recruitment approaches, such as using lists of elders known to live in a given community or doing house-to-house surveys. Second, the study is cross-sectional and as such, cannot identify temporal sequence or causal relationships. Finally, there were significant differences in participants recruited from the Mombasa Cohort and those recruited from the general population. Although we attempted to mitigate these differences by controlling for source of recruitment in our models, unmeasured differences may result in residual confounding of the relationships found.
Conclusion
In this cross-sectional study, social vulnerability measured by the SVI-Kenya score did not differ by HIV status and was associated with greater disability but not physical frailty. In exploratory analysis, social vulnerability was associated with disability, history of sex work, and never having married. While prospective cohorts are needed to elucidate the relationship between SVI-Kenya score and clinical outcomes, these findings suggest that social vulnerability is a distinct construct from clinical frailty among older Kenyan women.
Acknowledgements: We thank the staff at the Ganjoni Dispensary for their assistance with recruitment for this study.
Funding: Research reported in this paper was supported by the National Institutes of Aging of the National Institutes of Health under award number 5R21AG063602 and by the University of Washington Behavioral Research Center for HIV (BIRCH), a NIMH-funded program (P30 MH123248). First author received GO Health Award from University of Washington to support travel to Kenya. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or University of Washington.
Conflict of interest: The authors declare no conflict of interest.
Compliance with Ethical Standards: This study was approved by the University of Washington Human Subjects Division (STUDY00006985) and the Kenyatta National Hospital- University of Nairobi Ethical Review Committee (P732/08/2019). All participants provided written informed consent at study enrollment. All methods were carried out in accordance with relevant guidelines and regulations.
Contributions: Funding acquisition: SMG. Study design: SP, SMG. Study implementation: BO, GW, RSM, SMG. Data analysis: SP, SMG. Writing the main manuscript text and preparing fig-ures: SP, SMG. All authors contributed to editing. All authors reviewed and approved the final manuscript.
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