Abstract
Background
Oral diseases are prevalent and linked to systemic health outcomes. People with HIV may face elevated oral disease risk, yet data on dental disease in this population remain limited.
Methods
In this cross-sectional study, we analyzed oral health data collected from 2927 participants in the MACS/WIHS Combined Cohort Study (968 women with HIV [WWH], 450 women without HIV [WWoH], 941 men with HIV [MWH], 568 men without HIV [MWoH]) who had intraoral photographs collected and evaluated by dentist-researchers. We used log-binomial regression to examine associations between demographic and clinical characteristics and two outcomes: missing teeth and untreated caries or residual roots, stratified by sex.
Results
Among 2927 participants (median [interquartile range, IQR] age: WWH, 55.3 years [48.3–61.6]; WWoH, 53.3 [44.8–60.3]; MWH, 55.1 [42.6–62.9]; MWoH, 62.9 [50.1–69.5]), women experienced a higher prevalence of tooth loss and untreated decay than men. Among participants aged 65 years or older, 15% to 21% of women were edentulous (compared with 2% to 3% of men), and 30% to 31% were missing at least a full arch of teeth (compared with 4% to 6% of men). In multivariable analyses, age was a dominant predictor of missing teeth among men (age ≥ 65 vs. <45 years: adjusted prevalence ratio [aPR], 1.49; 95% confidence interval [CI], 1.29–1.73). Income was the strongest predictor of untreated decay among women, more strongly predictive than age (highest vs. lowest income: aPR, 0.11; 95% CI, 0.03–0.43). Disparities by race/ethnicity persisted among men but were absent among women, who experienced extreme poverty and poor outcomes regardless of race/ethnicity. HIV serostatus was not independently associated with either outcome.
Conclusion
Dental disease burden in this population reflected socioeconomic disparities rather than HIV infection. Racial disparities were absent among women, who had uniformly low incomes and poor oral health outcomes across all groups, highlighting substantial barriers to dental care access that warrant policy attention.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12903-026-08822-5.
Keywords: HIV/AIDS, Oral health, Dental health care, Tooth loss, Dental caries, DMF index
Background
Oral diseases—including dental caries and periodontal disease—are the most prevalent health conditions globally, affecting 3.69 billion people worldwide [1]. The United States (US) has not been spared from this burden. According to surveillance data from the National Health and Nutrition Examination Survey (NHANES), approximately half of US adults aged 30 and older have periodontitis, and 1 in 5 adults (21%) aged 20 to 64 has at least one untreated carious lesion [2, 3].
In 2000, the first-ever Surgeon General’s Report on Oral Health in America issued a stark warning, concluding that “the mouth is the center of vital tissues and functions that are critical to total health and well-being across the lifespan” [4]. In the two decades since, the links between oral health and systemic disease have become increasingly clear. Oral disease is associated with dyslipidemia [5]; increased risks of cardiovascular diseases and cerebrovascular events [6–8]; premature onset, progression, and severity of neuroinflammatory diseases, such as dementia [9, 10]; and all-cause mortality [11].
In the context of human immunodeficiency virus (HIV) infection, oral manifestations can serve as early indicators of acute HIV infection and progression to acquired immunodeficiency syndrome (AIDS) [12]. The advent of antiretroviral therapy (ART) has increased life expectancy and decreased the incidence of AIDS-defining oral disease; nevertheless, people with HIV (PWH) continue to face elevated oral disease risks due to factors associated with HIV infection, including medication-related xerostomia [13] and higher prevalence of modifiable oral disease risk factors, such as smoking and alcohol consumption [14, 15]. Persistent structural barriers to dental care, including limited insurance coverage and low income, further compound oral disease risk in this population [16, 17]. The elevated oral disease burden among PWH contributes to morbidity and impaired quality of life [13, 18].
Oral health continues to be under-recognized in HIV care and research, and data documenting the extent of oral disease burden in the modern ART era remain limited. To address this gap, we used data from the MACS/WIHS Combined Cohort Study (MWCCS)—a long-running observational cohort—to describe the prevalence of dental disease and examine correlates of poor oral health outcomes in PWH and people without HIV (PWoH).
Methods
Study population
The MWCCS, which integrates the historic Multicenter AIDS Cohort Study (MACS) and Women’s Interagency HIV Study (WIHS), is a geographically diverse multicenter study of PWH alongside an age- and demographically similar group of PWoH [19]. In addition to 1577 existing MACS and 1916 WIHS participants who continued in the cohort, 2167 new participants enrolled in the MWCCS. MWCCS participants attend annual visits at 13 US sites that include physical examinations, specimen collection, laboratory testing, and structured interviews to collect demographic, clinical, and psychosocial data [19]. Institutional review boards approve study protocols at each site.
From October 2022 to September 2024, trained staff used a high-resolution dental camera, the Shofu EyeSpecial (Shofu Inc., Kyoto, Japan), to photograph the upper and lower arches to quantify dental disease. This analysis includes cross-sectional data from 2927 MWCCS participants who had complete photographic documentation of the oral cavity during this period. Participants with oral health assessments were similar to the overall MWCCS cohort attending visits during this period with respect to age, race/ethnicity, and income (Additional file 1: Table 1).
Table 1.
Characteristics of MWCCS Participants with Oral Health Assessments, by Sex and HIV Serostatus
| Characteristic a | Participants, No. (%) | |||
|---|---|---|---|---|
| WWH (n = 968) |
WWoH (n = 450) |
MWH (n = 941) |
MWoH (n = 568) |
|
| Cohort observation time, median (IQR), y b | 10.0 (1.1–22.3) | 10.4 (1.1–21.5) | 1.8 (0.0–21.0) | 21.1 (1.1–38.8) |
| Age at assessment, median (IQR), y | 55.3 (48.3–61.6) | 53.3 (44.8–60.3) | 55.1 (42.6–62.9) | 62.9 (50.1–69.5) |
| Age group, y | ||||
| < 45 years | 147 (15.9) | 118 (26.2) | 279 (29.6) | 108 (19.0) |
| 45 to < 65 years | 677 (69.9) | 275 (61.1) | 487 (51.8) | 217 (38.2) |
| ≥ 65 years | 144 (14.9) | 57 (12.7) | 175 (18.6) | 243 (42.8) |
| Race/ethnicity | ||||
| Black, non-Hispanic | 622 (64.3) | 275 (61.2) | 337 (35.9) | 125 (22.0) |
| White, non-Hispanic | 77 (8.0) | 31 (6.9) | 285 (30.3) | 321 (56.5) |
| Other, non-Hispanic | 147 (15.2) | 83 (18.5) | 115 (12.2) | 52 (9.2) |
| Hispanic | 122 (12.6) | 60 (13.4) | 203 (21.6) | 70 (12.3) |
| College graduate | 147 (15.3) | 72 (16.1) | 369 (39.4) | 320 (56.5) |
| Current annual income, $ | ||||
| ≤ 18,000 | 534 (56.2) | 235 (53.7) | 318 (34.4) | 96 (17.2) |
| 18,001 to 36,000 | 246 (25.9) | 102 (23.3) | 195 (21.1) | 100 (18.0) |
| 36,001 to 75,000 | 128 (13.5) | 82 (18.7) | 234 (25.3) | 169 (30.3) |
| ≥ 75,001 | 42 (4.4) | 19 (4.3) | 178 (19.2) | 192 (34.5) |
| Annualized income during cohort observation, median (IQR), $ | 15,445 (10,077–30,000) | 15,782 (9,750–33,654) | 31,538 (15,000–60,717) | 54,734 (25,978–87298) |
| Health insurance | ||||
| Current health insurance | 907 (93.7) | 420 (93.5) | 842 (89.6) | 530 (93.5) |
| Proportion of observed visits with health insurance, median (IQR) | 100.0 (93.9–100.0) | 97.5 (77.5–100.0) | 100.0 (92.7–100.0) | 98.0 (94.4–100.0) |
| Dental insurance | ||||
| Current dental insurance | 532 (56.2) | 250 (58.5) | 553 (63.0) | 370 (67.2) |
| Proportion of observed visits with dental insurance, median (IQR) | 39.6 (15.4–66.7) | 36.8 (14.3–66.0) | 59.4 (25.0–97.7) | 75.0 (33.3–96.6) |
| Biennial dental care | 370 (38.3) | 233 (52.1) | 618 (66.5) | 412 (73.0) |
| Cigarette use | ||||
| Ever (during lifetime) | 586 (60.5) | 312 (69.5) | 594 (63.1) | 363 (64.0) |
| Smoking pack-years, median (IQR) | 7.6 (2.8–15.0) | 9.2 (2.8–19.0) | 7.1 (1.8–19.2) | 6.0 (0.3–19.5) |
| Cannabis use | ||||
| Ever (during cohort observation) | 460 (47.7) | 275 (61.4) | 583 (62.6) | 392 (69.4) |
| Current | 264 (27.4) | 178 (39.7) | 396 (42.5) | 214 (37.9) |
| Proportion of observed visits with reported cannabis use, median (IQR) | 0.0 (0.0–40.0) | 15.9 (0.0–75.0) | 25.0 (0.0–100.0) | 15.2 (0.0–83.3) |
| Illicit substance use c | ||||
| Ever (during cohort observation) | 444 (46.0) | 273 (60.9) | 581 (62.4) | 402 (71.2) |
| Current | 111 (11.5) | 113 (25.2) | 337 (36.2) | 190 (33.6) |
| Proportion of observed visits with reported illicit drug use, median (IQR) | 0.0 (0.0–28.6) | 0.0 (14.9–69.7) | 22.6 (0.0–82.4) | 19.2 (0.0–68.9) |
| Alcohol use | ||||
| Ever (during cohort observation) | 795 (82.7) | 386 (86.2) | 810 (87.1) | 511 (90.8) |
| Current | 572 (59.5) | 323 (72.1) | 688 (74.0) | 441 (78.3) |
| Drinks/week during cohort observation, median (IQR) | 0.5 (0.1–2.9) | 1.8 (0.2–5.9) | 1.4 (0.2–5.1) | 2.4 (0.5–6.8) |
| HIV-related indicators | ||||
| Viral load suppressed for ≥ 80% of study visits | 449 (46.6) | NA | 478 (51.7) | NA |
| Proportion of observed visits with undetectable viral load, median (IQR) | 66.7 (47.1–83.3) | NA | 65.8 (44.8–82.5) | NA |
| Nadir CD4, median (IQR), cells/mm3 | 253 (119–409) | NA | 280 (134–442) | NA |
| Proportion of observed visits with CD4 < 200 cells/mm3 median (IQR) | 0.0 (0.0–5.9) | NA | 0.0 (0.0–3.7) | NA |
| ≥ 3 non-AIDS comorbidities d | 697 (72.0) | 278 (61.8) | 526 (55.9) | 395 (69.5) |
| Medication burden | ||||
| Current number of medications, median (IQR) | 5 (3–9) | 4 (1–7) | 4 (2–8) | 4 (1–8) |
| Number of medications during study observation, median (IQR) | 3.6 (2.1–6.0) | 2.3 (1.0–4.5) | 3.8 (2.3–6.0) | 2.6 (1.4–4.1) |
a Percentages calculated among participants with non-missing data. Missing data were less than 4% for all variables and are not shown. Percentages may not sum to 100% due to rounding
b Differences in years of cohort observation reflect targeted recruitment strategies, with enrollment waves from 2020–2025 prioritizing men with HIV
c Self-reported use of cocaine, crack, heroin, methamphetamine, injection drug use, non-medical use of prescription drugs, or other illicit substances (e.g., poppers, hallucinogens, PCP)
d Non-AIDS comorbidity burden was assessed as a binary variable indicating the presence of three or more of the following five conditions during study follow-up: hypertension [receipt of anti-hypertensive medication with self-reported diagnosis of hypertension or elevated blood pressure at any study visit (systolic ≥ 130 and/or diastolic ≥ 80)], diabetes [meeting at least one of the following criteria within a 2.5-year period: [1] concurrent anti-diabetic medication use with self-reported diagnosis at two distinct timepoints; [2] fasting glucose ≥ 126 mg/dL at two or more visits with no intermediate fasting glucose < 126 mg/dL; [3] concurrent anti-diabetic medication use with self-reported diagnosis plus fasting glucose ≥ 126 mg/dL at two distinct timepoints (at least one component must be present at each timepoint); [4] fasting glucose ≥ 126 mg/dL plus HbA1c ≥ 6.5% at the same visit or within 2.5 years; or [5] concurrent anti-diabetic medication use with self-reported diagnosis plus HbA1c ≥ 6.5% at two distinct timepoints (at least one component must be present at each timepoint)], dyslipidemia (receipt of lipid-lowering medication with prior self-report of diagnosis, or total cholesterol ≥ 200 mg/dL, or fasting LDL ≥ 130 mg/dL, or HDL < 40 mg/dL, or fasting triglycerides ≥ 50 mg/dL), chronic kidney disease (race-free eGFR < 60 mL/min/1.73 m²), elevated liver fibrosis markers (FIB-4 ≥ 1.45 or APRI ≥ 0.5), and high depressive symptoms [Center for Epidemiologic Studies Depression Scale (CES-D) score ≥ 16 when the 20-item version was administered or ≥ 10 when the 10-item version was administered]
Photographic evaluation of dentition
Images were reviewed by a team of two dentist-researchers at a centralized reading center. Each tooth was evaluated and marked as present, missing, restored (filled), affected by caries, or exhibiting residual roots. Calibration, using 20 sets of images, achieved high inter-rater agreement (Cohen’s Kappa = 0.81). The Decayed, Missing, and Filled Teeth (DMFT) index was calculated per the World Health Organization diagnostic standard [20]. The DMFT index represents lifetime caries experience: teeth currently affected by decay (D), defined as the presence of dental caries or residual roots; missing teeth (M); and filled teeth, representing restorations from prior caries (F).
Demographic and clinical covariates
Demographic and clinical variables were collected by the MWCCS, as described elsewhere [19]. For time-varying covariates, such as annualized income and laboratory results, we used values closest to the date of dental image collection. High non-AIDS comorbidity (NACM) burden was defined as ≥ 3 of the following: hypertension, diabetes, dyslipidemia, chronic kidney disease, elevated liver fibrosis markers, or high depressive symptoms (complete definitions in Table 1, footnote d). Additionally, for annualized income, health and dental insurance coverage, substance use, and selected clinical indicators, we calculated person-specific means (for income) or proportions (for other variables) over each participant’s entire MWCCS follow-up period, then reported the median and interquartile range of these individual-level summaries. Because income was collected in categorical bands, we assigned participants to the midpoint of their reported income category at each visit to calculate mean values.
Statistical analysis
We used log-binomial regression models to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for the associations between demographic and clinical characteristics—including HIV serostatus, age, race/ethnicity, annualized income, smoking pack-years, proportion of cohort visits with cannabis use, and presence of ≥ 3 NACMs—and two binary outcomes reflecting untreated dental disease: [1] any missing teeth, and [2] untreated caries or residual roots. Covariates were selected a priori based on known associations with oral health outcomes in the literature. Bivariate analyses examined the association between each independent variable and the outcomes. Multivariable models included the same covariates. When log-binomial models failed to converge, we used modified Poisson regression with robust error variances [21]. Analyses were stratified by sex to account for demographic and socioeconomic differences between men and women in the MWCCS, including marked differences in annualized income and race/ethnicity. All analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC).
Results
Study cohort
A total of 2927 MWCCS participants had oral photographs collected between October 2022 and September 2024: 968 women with HIV (WWH), 450 women without HIV (WWoH), 941 men with HIV (MWH), and 568 men without HIV (MWoH).
Demographic and clinical characteristics at the time of dental photography, by sex and HIV serostatus, are summarized in Table 1. Among women, there were no substantive differences by HIV serostatus in age, race/ethnicity, or current or cumulative income. Compared to MWoH, MWH were younger (median age 55.1 vs. 62.9 years), had lower annualized income over MWCCS observation ($31,538 vs. $54,734), were less likely to identify as White, non-Hispanic (30% vs. 56%), and had fewer years of cohort observation (1.8 vs. 21.1), reflecting recent targeted recruitment of MWH. Among men, socioeconomic profiles differed by race and ethnicity (Additional file 2: Table 1), with White, non-Hispanic men having higher annualized income than their non-White or Hispanic counterparts (MWH: $65,988 vs. $21,000; MWoH: $70,967 vs. $27,000).
Current health and dental insurance coverage was similar between MWH and MWoH. However, MWoH maintained dental insurance at a higher proportion of visits (75% vs. 59%). WWH and WWoH had comparably high health insurance coverage (94%) and similarly low dental insurance coverage (62% and 58%). Both MWoH and WWoH were more likely than their HIV-seropositive counterparts to report attending biennial dental visits (73% vs. 66% and 52% vs. 38%, respectively).
Substance use patterns differed by sex, HIV status and race/ethnicity. Among men, lifetime prevalence of smoking, cannabis use, and illicit substance use was similar by HIV status. WWoH had higher prevalence of cannabis use (61% vs. 48%), illicit substance use (61% vs. 46%), and greater cumulative pack-years (9.2 vs. 7.7) compared to WWH.
Median nadir CD4 counts were similar between WWH and MWH (253 vs. 280 cells/mm3) and both groups had HIV RNA levels below the limit of detection at two-thirds of study visits after 1996, when ART became available. NACM burden was higher among women, with WWH experiencing the highest burden (WWH: 72%, WWoH: 62%, MWH: 56%, MWoH: 70%).
Oral health indicators
Oral health indicators, by sex, HIV serostatus, and age group, are summarized in Table 2. Overall, the cohort demonstrated considerable dental disease burden across demographic groups, with the notable exception of the oldest men (≥ 65 years) who showed relatively preserved oral health. DMFT values, which measure the extent and severity of dental caries, were higher for women than for men in all subgroups. Among the oldest group (≥ 65 years), DMFT values for women were more than one-third higher than those of their male counterparts (WWH: 18 vs. MWH: 12, WWoH: 16 vs. MWoH: 12). As expected, DMFT values increased with age, but the underlying components driving these increases differed by sex (Fig. 1). In men, age-related increases in DMFT were primarily attributed to higher numbers of filled teeth (MWH: 1 to 9, MWoH: 1 to 10 filled teeth from youngest to oldest age groups), while women showed disproportionate increases in missing teeth with advancing age (WWH: 2 to 11, WWoH: 1 to 10 missing teeth from youngest to oldest age groups). Among women ≥ 65 years, nearly one-fifth were completely edentulous (WWH: 15%, WWoH: 21%) and approximately one-third were missing at least a full arch (WWH: 31%, WWoH: 30%), compared to much lower prevalence among men (edentulous: MWH: 3%, MWoH: 2%; missing full arch: MWH: 6%, MWoH: 4%).
Table 2.
Oral Health Indicators, by Sex, Age Group, and HIV Serostatus
| Women | ||||||
|---|---|---|---|---|---|---|
| < 45 years | 45 to < 65 years | ≥ 65 years | ||||
| WWH (n = 147) |
WWoH (n = 118) |
WWH (n = 677) |
WWoH (n = 275) |
WWH (n = 144) |
WWoH (n = 57) |
|
| DMFT index, median (IQR)a | 5 (2–13) | 5 (5–10) | 12 (7–16) | 12 (7–18) | 18 (11–24) | 16 (12–28) |
| Decayed | 0 (0–1) | 0 (0–0) | 0 (0–0) | 0 (0–1) | 0 (0–0) | 0 (0–0) |
| Missing | 2 (0–4) | 1 (0–4) | 4 (1–10) | 5 (2–11) | 11 (4–21) | 10 (4–22) |
| Filled | 2 (0–5) | 1 (0–5) | 4 (1–7) | 3 (0–6) | 3 (0–6.5) | 3 (0–8) |
| Sound | 23 (15–26) | 23 (18–26) | 16 (12–21) | 16 (10–21) | 10 (4–17) | 12 (0–16) |
| Any decay, No. (%)b | 41 (28) | 24 (20) | 151 (23) | 83 (32) | 26 (21) | 13 (29) |
| ≥ 1 residual root, No. (%) | 25 (17) | 13 (11) | 91 (14) | 56 (22) | 15 (12) | 6 (13) |
| ≥ 1 caries, No. (%) | 30 (20) | 16 (14) | 95 (15) | 44 (17) | 16 (13) | 10 (22) |
| Edentulous, No. (%) | 1 (1) | 0 (0) | 32 (5) | 16 (6) | 22 (15) | 12 (21) |
| Missing full arch, No. (%) | 3 (2) | 2 (2) | 77 (11) | 44 (16) | 45 (31) | 17 (30) |
| Men | ||||||
|---|---|---|---|---|---|---|
| < 45 years | 45 to < 65 years | ≥ 65 years | ||||
| MWH (n= 279) |
MWoH (n=108) |
MWH (n = 487) |
MWoH (n= 217) |
MWH (n = 175) |
MWoH (n = 243) |
|
| DMFT index, median (IQR)a | 4 (1–8) | 2.5 (1–8) | 10 (6–15) | 9 (4–13) | 12 (8–16) | 12 (9–15) |
| Decayed | 0 (0–0) | 0 (0–0) | 0 (0–0) | 0 (0–0) | 0 (0–0) | 0 (0–0) |
| Missing | 1 (0–2) | 0 (0–2) | 3 (0–6) | 1 (0–4) | 2 (0–4) | 1 (0–4) |
| Filled | 1 (0–4) | 1 (0–4) | 4 (1–8) | 4 (1–8) | 9 (4–12) | 10 (5–13) |
| Sound | 24 (20–27) | 26 (20–27) | 18 (13–22) | 19 (15–24) | 16 (12–20) | 16 (13–19) |
| Any decay, No. (%)b | 69 (25) | 21 (19) | 98 (21) | 36 (17) | 17 (10) | 13 (5) |
| ≥ 1 residual root, No. (%) | 45 (16) | 19 (18) | 74 (16) | 23 (11) | 10 (6) | 9 (4) |
| ≥ 1 caries, No. (%) | 41 (15) | 8 (7) | 50 (11) | 19 (9) | 10 (6) | 7 (3) |
| Edentulous, No. (%) | 2 (1) | 0 (0) | 18 (4) | 8 (4) | 5 (3) | 6 (2) |
| Missing full arch, No. (%) | 5 (2) | 0 (0) | 36 (7) | 14 (6) | 10 (6) | 9 (4) |
a Decayed, Missing, and Filled Teeth (DMFT) index: D = teeth with untreated decay or residual roots; M = missing teeth (any cause); F = teeth with restorations (filled). Range 0 – 28; higher values indicate greater cumulative caries experience
b Any decay, ≥ 1 residual root, and ≥ 1 caries, excludes edentulous participants; all other measures include all participants
Fig. 1.
Median Decayed, Missing, Filled and Sound Teeth, by Sex, Age Group and HIV Serostatus. A Participants < 45 years . B Participants 45 to < 65 years. C Participants ≥ 65 years. Box plots show the median (center line) and interquartile range (shaded area) for each group. WWH, women with HIV; WWoH, women without HIV; MWH, men with HIV; MWoH, men without HIV
Analyses stratified by race/ethnicity are provided in Additional file 3 (Tables 1, 2, 3 and 4). Among women, the prevalence of untreated caries and residual roots was similar across racial/ethnic groups, with more than a quarter of women having visible decay, including caries and residual roots. Untreated dental disease was more common among non-White or Hispanic men.
Correlates of oral health outcomes
Bivariate and multivariable relationships between demographic and clinical characteristics and any missing teeth or any untreated caries/residual roots are presented in Tables 3 and 4.
Table 3.
Bivariate and Multivariable Analysis of Factors Associated with Missing Teeth
| Women | Men | |||
|---|---|---|---|---|
| Bivariate (PR, 95% CI) |
Multivariablea (aPR, 95% CI) |
Bivariate (PR, 95% CI) |
Multivariablea (aPR, 95% CI) |
|
| HIV serostatus | ||||
| Seronegative | 0.98 (0.93–1.04) | 1.02 (0.96–1.07) | 0.87 (0.80–0.95) | 0.92 (0.84–1.01) |
| Seropositive | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Age group, y | ||||
| ≥ 65 | 1.39 (1.27–1.54) | 1.25 (1.13–1.38) | 1.19 (1.05–1.35) | 1.49 (1.29–1.73) |
| 45 to < 65 | 1.26 (1.15–1.38) | 1.19 (1.08–1.30) | 1.30 (1.17–1.45) | 1.30 (1.15–1.47) |
| < 45 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Race/ethnicity | ||||
| Hispanic | 0.96 (0.89–1.05) | 0.99 (0.91–1.07) | 0.83 (0.75–0.93) | 0.89 (0.80–0.99) |
| Other, non-Hispanic | 1.03 (0.97–1.10) | 1.01 (0.95–1.08) | 0.94 (0.84–1.05) | 0.97 (0.86–1.09) |
| White, non-Hispanic | 0.90 (0.79–1.01) | 0.98 (0.88–1.09) | 0.71 (0.64–0.78) | 0.76 (0.68–0.86) |
| Black, non-Hispanic | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Annualized income during cohort observation, $ | ||||
| ≥ 75,001 | 0.45 (0.34–0.61) | 0.47 (0.35–0.64) | 0.56 (0.50–0.63) | 0.63 (0.55–0.73) |
| 36,001 to 75,000 | 0.75 (0.68–0.83) | 0.78 (0.71–0.86) | 0.72 (0.66–0.79) | 0.80 (0.72–0.89) |
| 18,001 to 36,000 | 0.83 (0.78–0.89) | 0.86 (0.80–0.92) | 0.75 (0.68–0.83) | 0.80 (0.73–0.89) |
| ≤ 18,000 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Smoking pack-years | ||||
| ≥ 10 | 1.29 (1.21–1.37) | 1.17 (1.10–1.25) | 1.39 (1.27–1.52) | 1.23 (1.12–1.35) |
| 5 to < 10 | 1.20 (1.11–1.30) | 1.14 (1.05–1.23) | 1.25 (1.09–1.43) | 1.08 (0.95–1.24) |
| > 0 to < 5 | 1.13 (1.04–1.22) | 1.12 (1.03–1.20) | 1.11 (0.99–1.25) | 1.05 (0.94–1.18) |
| 0 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Proportion of observed visits with cannabis use | ||||
| ≥ 50 | 1.01 (0.95–1.07) | 0.94 (0.88—1.00) | 0.99 (0.91–1.09) | 1.02 (0.93–1.11) |
| > 0 to < 50 | 1.04 (0.98–1.10) | 0.96 (0.90–1.02) | 0.95 (0.86–1.05) | 0.99 (0.89–1.11) |
| 0 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| ≥ 3 non-AIDS comorbidities | ||||
| Yes | 1.15 (1.08–1.23) | 1.03 (0.96–1.10) | 1.09 (1.00—1.18) | 1.06 (0.97–1.17) |
| No | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
a Multivariable models used modified Poisson regression
Table 4.
Bivariate and Multivariable Analysis of Factors Associated with Visible Untreated Caries or Residual Roots
| Women | Men | |||
|---|---|---|---|---|
| Bivariate (PR, 95% CI) |
Multivariable (aPR, 95% CI) |
Bivariate (PR, 95% CI) |
Multivariable (aPR, 95% CI) |
|
| HIV serostatus | ||||
| Seronegative | 1.19 (0.98–1.44) | 1.22 (1.00—1.49) | 0.63 (0.49–0.81) | 0.95 (0.73–1.24) |
| Seropositive | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Age group, y | ||||
| ≥ 65 | 0.95 (0.67–1.34) | 0.89 (0.61–1.30) | 0.32 (0.21–0.47) | 0.61 (0.38–0.97) |
| 45 to < 65 | 1.05 (0.83–1.33) | 1.02 (0.79–1.32) | 0.85 (0.67–1.07) | 0.85 (0.65–1.12) |
| < 45 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Race/ethnicity | ||||
| Hispanic | 0.80 (0.59–1.10) | 0.84 (0.61–1.16) | 0.49 (0.35–0.66) | 0.57 (0.41–0.80) |
| Other, non-Hispanic | 1.04 (0.81–1.34) | 1.07 (0.83–1.39) | 0.66 (0.47–0.91) | 0.76 (0.54–1.07) |
| White, non-Hispanic | 0.95 (0.66–1.35) | 1.24 (0.86–1.78) | 0.21 (0.15–0.29) | 0.60 (0.40–0.89) |
| Black, non-Hispanic | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Annualized income during cohort observation, $ | ||||
| ≥75,001 | 0.11 (0.03–0.42) | 0.11 (0.03–0.43) | 0.11 (0.06–0.18) | 0.18 (0.10–0.33) |
| 36,001 to 75,000 | 0.52 (0.37–0.73) | 0.48 (0.34–0.68) | 0.26 (0.18–0.36) | 0.36 (0.25–0.54) |
| 18,001 to 36,000 | 0.73 (0.58–0.92) | 0.72 (0.57–0.92) | 0.55 (0.42–0.72) | 0.62 (0.47–0.84) |
| ≤ 18,000 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Smoking pack-years | ||||
| ≥ 10 | 1.38 (1.09–1.74) | 1.12 (0.87–1.45) | 1.72 (1.27–2.33) | 1.39 (1.03–1.89) |
| 5 to < 10 | 1.04 (0.75–1.43) | 0.96 (0.69–1.34) | 2.30 (1.61–3.29) | 1.37 (0.96–1.96) |
| > 0 to < 5 | 1.19 (0.92–1.54) | 1.10 (0.84–1.45) | 1.58 (1.14–2.20) | 1.24 (0.90–1.71) |
| 0 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Proportion of observed visits with cannabis use | ||||
| ≥ 50 | 1.22 (0.99–1.52) | 1.05 (0.82–1.34) | 1.11 (0.87–1.41) | 0.95 (0.73–1.22) |
| > 0 to < 50 | 1.04 (0.82–1.31) | 0.98 (0.76–1.25) | 0.53 (0.37–0.75) | 0.82 (0.57–1.18) |
| 0 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| ≥ 3 non-AIDS comorbidities | ||||
| Yes | 1.11 (0.90–1.36) | 1.01 (0.81–1.27) | 0.75 (0.60–0.94) | 1.10 (0.85–1.43) |
| No | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
Factors associated with visible untreated caries or residual roots were assessed only among non-edentulous participants
Relative to MWH, MWoH had a lower prevalence of missing teeth (prevalence ratio [PR]: 0.87, 95% CI: 0.80–0.95), although this association was attenuated after adjusting for demographic and clinical factors (adjusted prevalence ratio [aPR]: 0.92, 95% CI: 0.84–1.01). Advanced age was strongly associated with missing teeth in both sexes, with the effect being particularly pronounced in men ≥ 65 years (aPR: 1.49, 95% CI: 1.29–1.73). Income demonstrated protective effects across sexes, with the highest income group showing substantial reductions in the prevalence of missing teeth compared to the lowest income group (women: aPR 0.47, 95% CI 0.35–0.64; men: aPR 0.63, 95% CI 0.55–0.73). Heavy smoking (≥ 10 pack-years) was associated with increased prevalence of missing teeth in both women (aPR: 1.17, 95% CI: 1.10–1.25) and men (aPR: 1.23, 95% CI: 1.12–1.35). Race/ethnicity—while not associated with missing teeth among women—remained predictive for men, with White, non-Hispanic men showing lower prevalence (aPR: 0.76, 95% CI: 0.68–0.86) compared to Black, non-Hispanic men. Cannabis use and high NACM burden were not associated with missing teeth in multivariable models. Income emerged as the most predictive factor for women, while age was a stronger predictor for men.
Among women, being HIV-seronegative was marginally associated with untreated caries or residual roots (aPR: 1.22, 95% CI: 1.00—1.49). This pattern was reversed among men, for whom being HIV-seronegative showed weaker associations in bivariate analysis (PR: 0.63, 95% CI: 0.49–0.81), though this association was attenuated in the multivariable model. Among men, being in the oldest age group (≥ 65 years) was associated with a lower likelihood of untreated disease (aPR: 0.61, 95% CI: 0.38–0.97). Racial/ethnic disparities were pronounced among men, with White, non-Hispanic men less likely to have untreated disease (aPR: 0.60, 95% CI: 0.40–0.89) than Black, non-Hispanic men. Income demonstrated the strongest protective associations with decay in both sexes, with the highest income group showing reduced prevalence of untreated caries or residual roots (women: aPR: 0.11, 95% CI: 0.03–0.43; men: aPR: 0.18, 95% CI: 0.10–0.33). In multivariable analyses, heavy smoking remained associated with untreated disease among men (aPR: 1.39, 95% CI: 1.03–1.89) but not women.
Discussion
In this large, multi-site cohort of adults with and without HIV, we documented substantial dental disease burden with marked differences by demographic characteristics rather than HIV serostatus. The burden of dental disease was concentrated among women, who experienced higher prevalence of untreated decay, residual roots, and tooth loss than men across all age groups. Racial disparities were pronounced among men, with non-White or Hispanic men bearing higher prevalence of untreated disease than their White non-Hispanic counterparts. In multivariable analyses, age was a dominant predictor of missing teeth among men, while annualized income emerged as the strongest predictor for women for both outcomes, with income effects exceeding age effects in magnitude. HIV serostatus was not independently associated with either outcome after adjusting for demographic and socioeconomic factors.
Few studies have examined the relationship between HIV serostatus and dental disease in the modern ART era, with most available comparative studies finding no association—consistent with our results [22–24]. One exception is Wadhwa et al., who found higher prevalence of tooth loss in WWH compared to WWoH [25]. However, this study differed from ours in population characteristics: their participants were limited to virologically suppressed women with lower smoking prevalence (29%, vs. > 60% in our cohort) and included higher proportions of White and Hispanic women. These demographic differences may help explain the observed HIV-associated differences in tooth loss, rather than HIV infection itself.
While direct sex comparisons were not feasible due to the substantial demographic and socioeconomic differences between men and women in the MWCCS, dental disease burden was particularly concentrated among women, even when compared to national averages. According to the Centers for Disease Control and Prevention’s 2024 Oral Health Surveillance Report, the general US population aged 65 and over has, on average, 6.4 missing teeth [3]. In contrast, women aged 65 years and older in our cohort experienced greater tooth loss, with a median of 11 (WWH) and 10 (WWoH) missing teeth, representing approximately 60% more tooth loss than national estimates. By contrast, MWCCS men in this same age group had a median of 2 (MWH) and 1 (MWoH) missing teeth, which is five times fewer missing teeth than similarly aged women in our cohort and three times less tooth loss than national averages.
In multivariable models, race/ethnicity was independently associated with oral health outcomes among men but not among women—a finding that contradicts well-documented oral health disparities in the general population [3, 26]. White non-Hispanic men demonstrated 24% lower prevalence of tooth loss and 40% lower prevalence of untreated caries or residual roots compared to Black non-Hispanic men. This effect was partially attenuated after adjusting for income and other sociodemographic factors, reflecting the lower incomes among Black non-Hispanic men, though race/ethnicity remained independently protective. In contrast, race/ethnicity showed no association with either outcome among women, even in bivariate analyses. The absence of racial/ethnic disparities among women likely reflects the concentration of extreme poverty across all racial and ethnic groups, creating a “floor effect” where economic barriers dominate all other risk factors. In multivariable analyses, income demonstrated the strongest protective associations for women, with the highest income group showing a 53% reduction in prevalence of any missing teeth—an association even stronger than that of age. Similar patterns emerged for untreated caries or residual roots, where income again demonstrated protective effects. These findings align with research documenting the impacts of socioeconomic inequalities on oral health [27, 28], with our work demonstrating that economic factors can supersede even biological aging and race/ethnicity as determinants of dental disease among women facing extreme poverty.
Smoking is a well-established risk factor for oral disease [29]. In our cohort, any amount of smoking increased tooth loss risk among women, while men showed significant associations only with heavy smoking (≥ 10 pack-years). Heavy smoking remained predictive of untreated caries or residual roots among men but not women. This sex difference may reflect the extensive tooth loss already present among women—particularly older women, nearly half of whom were already missing at least a full arch of teeth—leaving fewer teeth at risk of decay. Smoking remains a significant modifiable risk factor for dental disease in this population. Despite observed declines in cigarette use among people with HIV over time, smoking rates remain high [30]. Dental care settings represent an underutilized opportunity for tobacco cessation interventions, as dental providers routinely observe smoking-related oral pathologies and are well-positioned to provide counseling and referrals to cessation programs [31].
While having ≥ 3 NACMs was associated with tooth loss in bivariate analyses, these associations were attenuated in multivariable models, likely reflecting the strong correlations between comorbidity burden and age, race/ethnicity, and income [32]. As people with HIV continue to age, understanding the complex relationships between oral health, chronic disease, and socioeconomic factors will be necessary, particularly given evidence linking oral disease to cardiovascular and metabolic outcomes [7, 33–35].
Dental care utilization differed by sex. Women were less likely to report regular dental care and dental insurance than men—both currently and across MWCCS follow-up—despite women in the general population demonstrating higher rates of dental care utilization [36]. While most participants had health insurance, adult dental benefits are typically limited under standard coverage. This gap in dental coverage, combined with women’s severe economic constraints, likely contributed to reduced receipt of dental care. MWoH demonstrated the highest dental care utilization. Consistent with these utilization patterns, men had fewer missing teeth and nearly three times as many filled teeth as women, with White, non-Hispanic men having approximately twice as many filled teeth as non-White or Hispanic men—differences that likely reflect socioeconomic disparities in access to dental insurance and preventive services. Among both sexes, PWH reported less dental care visits than PWoH. Structural factors specific to PWH—including HIV stigma in dental settings—may further limit access beyond economic constraints [37, 38].
Several limitations should be acknowledged. First, we relied on high-resolution intraoral photography rather than in-person examinations and radiographs, which may have resulted in underdetection of non-cavitated carious lesions or other conditions not readily visible in photographs. However, photographic assessment has been validated in previous studies [39, 40], and all images underwent rigorous quality control procedures conducted by trained dentist-researchers. Second, the cross-sectional design limits our ability to assess patterns of dental disease progression or to establish temporality between risk factors and outcomes. Despite these limitations, this study provides the first assessment of dental disease burden using intraoral photography in the largest and longest-running cohort of adults with, and at increased vulnerability to, HIV in the US. Ongoing collection of oral photographs will enable future longitudinal analyses.
Conclusions
Our findings strongly suggest that dental disease among adults with and without HIV reflects structural inequalities rather than HIV infection itself. Among women, extreme poverty was concentrated across all racial/ethnic groups and was the dominant predictor of poor outcomes. Among men, income varied by race/ethnicity, and both economic and racial/ethnic disparities in outcomes emerged. These patterns underscore the need to address underlying economic constraints that limit access to preventive oral disease management and restorative dental care.
Supplementary Information
Supplementary Material 1: Additional file 1.docx. Title: Demographic Characteristics of MWCCS Cohort and Participants with Oral Health Assessments. Description: Comparison of demographic characteristics between the overall MWCCS cohort and the subset of participants with oral health assessments, stratified by sex and HIV serostatus. Includes women with HIV (WWH), women without HIV (WWoH), men with HIV (MWH), and men without HIV (MWoH).
Supplementary Material 2: Additional file 2.docx. Title: Selected Characteristics of Male MWCCS Participants by Race/Ethnicity and HIV Serostatus. Description: Selected demographic and socioeconomic characteristics of male MWCCS participants with oral health assessments, stratified by race/ethnicity and HIV serostatus. Includes men with HIV (MWH) and men without HIV (MWoH).
Supplementary Material 3: Additional file 3.docx. Title: Oral Health Indicators by Race/Ethnicity, Sex, Age, and HIV Serostatus. Description: Four tables presenting oral health indicators stratified by race/ethnicity, sex, age group, and HIV serostatus. Each table includes DMFT index and its components, prevalence of untreated caries, residual roots, edentulism, and missing full arch. Table 1: Oral health indicators among White, non-Hispanic women; Table 2: Oral health indicators among non-White or Hispanic women; Table 3: Oral health indicators among White, non-Hispanic men; and Table 4: Oral health indicators among non-White or Hispanic men.
Acknowledgements
The authors gratefully acknowledge the contributions of the study participants and dedication of the staff at the MWCCS sites.
Abbreviations
- aPR
Adjusted Prevalence Ratio
- AIDS
Acquired Immunodeficiency Syndrome
- ART
Antiretroviral Therapy
- CD4
Cluster of Differentiation 4 (T-cell count)
- CES-D
Center for Epidemiologic Studies Depression Scale
- CI
Confidence Interval
- DMFT
Decayed, Missing, and Filled Teeth (index)
- eGFR
Estimated Glomerular Filtration Rate
- FIB-4
Fibrosis-4 (liver fibrosis marker)
- HbA1c
Hemoglobin A1c
- HDL
High-Density Lipoprotein
- HIV
Human Immunodeficiency Virus
- IQR
Interquartile Range
- LDL
Low-Density Lipoprotein
- MACS
Multicenter AIDS Cohort Study
- MWCCS
MACS/WIHS Combined Cohort Study
- MWH
Men with HIV
- MWoH
Men without HIV
- NACM
Non-AIDS Comorbidity
- NHANES
National Health and Nutrition Examination Survey
- PR
Prevalence Ratio
- PWH
People with HIV
- PWoH
People without HIV
- RNA
Ribonucleic Acid
- sIRB
Single Institutional Review Board
- US
United States
- WIHS
Women’s Interagency HIV Study
- WWH
Women with HIV
- WWoH
Women without HIV
Authors’ contributions
CR and AE contributed to conception and design, data acquisition, analysis and interpretation, drafted the manuscript, and critically revised the manuscript. CP contributed to conception and design, data interpretation, and critically revised the manuscript. MV, BB, BA, TTB, KMW, ANS, GD, DM, DLJ, MLA, VS, KWC, DAJ, MB, DG, JL, DW, SAS, MAFM, and MBD contributed to data acquisition and interpretation, and critically revised the manuscript. AAR contributed to conception and design, data acquisition, analysis and interpretation, drafted the manuscript, and critically revised the manuscript. All authors read and approved the final manuscript.
Funding
The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), National Institute on Aging (NIA), National Institute of Dental & Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Institute of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Cecile Lehiri, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos, David Hanna, and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D’Souza, Stephen Gange and Elizabeth Topper), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen, Audrey French, and Ryan Ross), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky, Frank Palella, and Valentina Stosor), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Deborah Jones, and Claudia Martinez), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, James B. Brock, Emily Levitan, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (M. Bradley Drummond and Michelle Floris-Moore), U01-HL146194. MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), P30-AI-124414 (ERC-CFAR), P30-MH-116867 (Miami CHARM), UL1-TR001409 (DC CTSA), KL2-TR001432 (DC CTSA), and TL1-TR001431 (DC CTSA). This material is also the result of work supported with resources and the use of facilities at the Boise VA Medical Center.
Data availability
Data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS)/Women’s Interagency HIV Study (WIHS) Combined Cohort Study (MWCCS). MWCCS data are available to the scientific community through a standard application process. Investigators interested in accessing MWCCS data should visit https://statepi.jhsph.edu/mwccs/ for information on data access procedures.
Declarations
Ethics approval and consent to participate
Written informed consent was obtained from all MWCCS participants. This study was conducted in accordance with the Declaration of Helsinki. Under the single Institutional Review Board (sIRB) waiver granted by the U.S. Department of Health and Human Services, the study protocol received approval from the institutional review board at each participating data collection site. This analysis was approved by the Office of Human Research Ethics (OHRE) at the University of North Carolina at Chapel Hill.
Consent for publication
Not applicable.
Competing interests
CP, ANS, GD, DLJ, DG, VS, DW, MBD, and AAR received research grant funding from the National Institutes of Health during the conduct of the study. TTB received grant funding from the National Institutes of Health during the conduct of the study and has served as a consultant to ViiV Healthcare, Merck, EMD-Serono, and Janssen. KMW serves as trustee for the Hektoen Institute of Medicine. MLA received grant funding from the National Institutes of Health during the conduct of the study, has served on an advisory board for Gilead Sciences, and received research funding from AbbVie. MAFM received grant funding from the National Institutes of Health during the conduct of the study and has served as a consultant and on an advisory board for ViiV Healthcare. CR, AE, MV, BB, BA, DM, KWC, DAJ, MB, JL, and SAS declare they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Catalina Ramirez and Andrew Edmonds contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Additional file 1.docx. Title: Demographic Characteristics of MWCCS Cohort and Participants with Oral Health Assessments. Description: Comparison of demographic characteristics between the overall MWCCS cohort and the subset of participants with oral health assessments, stratified by sex and HIV serostatus. Includes women with HIV (WWH), women without HIV (WWoH), men with HIV (MWH), and men without HIV (MWoH).
Supplementary Material 2: Additional file 2.docx. Title: Selected Characteristics of Male MWCCS Participants by Race/Ethnicity and HIV Serostatus. Description: Selected demographic and socioeconomic characteristics of male MWCCS participants with oral health assessments, stratified by race/ethnicity and HIV serostatus. Includes men with HIV (MWH) and men without HIV (MWoH).
Supplementary Material 3: Additional file 3.docx. Title: Oral Health Indicators by Race/Ethnicity, Sex, Age, and HIV Serostatus. Description: Four tables presenting oral health indicators stratified by race/ethnicity, sex, age group, and HIV serostatus. Each table includes DMFT index and its components, prevalence of untreated caries, residual roots, edentulism, and missing full arch. Table 1: Oral health indicators among White, non-Hispanic women; Table 2: Oral health indicators among non-White or Hispanic women; Table 3: Oral health indicators among White, non-Hispanic men; and Table 4: Oral health indicators among non-White or Hispanic men.
Data Availability Statement
Data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS)/Women’s Interagency HIV Study (WIHS) Combined Cohort Study (MWCCS). MWCCS data are available to the scientific community through a standard application process. Investigators interested in accessing MWCCS data should visit https://statepi.jhsph.edu/mwccs/ for information on data access procedures.