Abstract
We (1) estimated the prevalence of not getting patient navigation despite feeling a need for the service (unmet subjective need) or despite having unsuppressed viral load (unmet objective need) among people with HIV (PWH), (2) determined reasons why PWH did not use the service, and (3) determined factors associated with unmet need for patient navigation. We used combined data from the 2015 to 2017 cycles of the Medical Monitoring Project (MMP), an HIV surveillance system designed to produce nationally representative estimates of the characteristics of adults with diagnosed HIV infection in the United States. Six percent reported unmet subjective need and 28% had unmet objective need for patient navigation. When needs were combined, more than a third had unmet need for the service. Among PWH with unmet subjective need for patient navigation, 77% reported lack of knowledge about patient navigation as a reason for non-use. Younger age, female gender, racial/ethnic minority status, limited health literacy, homelessness, incarceration history, lack of health insurance/coverage, non-injection drug use, depression, and recent HIV diagnosis were associated with unmet subjective or objective need for patient navigation. One in three PWH did not use patient navigation despite needing the service. Lack of knowledge about patient navigation was a barrier to use, calling for increased availability and promotion of such services. PWH with social and economic vulnerabilities were less likely to get patient navigation when needed. It is important to address the question of how to make this service available to everyone who needs it.
Keywords: HIV patient navigation, unmet subjective need, unmet objective need
INTRODUCTION
Patient navigation is a patient-centered healthcare delivery model wherein patients manage their healthcare needs with the assistance of patient navigators, who help resolve barriers to care. Patient navigation was initially developed to reduce barriers to cancer care and was adapted for people with HIV (PWH), who often have multiple and complex healthcare needs. Although there is no standard definition or standard duties of a patient navigator in HIV care settings1, patient navigators help PWH link to and remain retained in care by working individually with patients to reduce structural, financial, and personal barriers to care2. For example, patient navigators might schedule medical appointments, send appointment reminders, accompany PWH to appointments, assist with health insurance or transportation, or make referrals to social services3–6. Patient navigators may improve PWH’s knowledge and skills required to engage in care and adhere to antiretroviral therapy (ART) by promoting self-efficacy, positive health expectations, and goal-setting behaviors4. In addition to removing structural and process barriers which providers tend to focus on7, patient navigators, specifically peer navigators, can also address relational concerns (e.g., fear, stigma, a lack of social support) of PWH7,8.
A systematic review1 showed that patient navigation can enhance linkage to and retention in care and viral suppression among PWH. Patient navigation is a promising strategy, particularly for PWH with a history of incarceration4,5,9,10. For PWH leaving jail, patient navigation offers support that allows them to maintain levels of HIV care engagement achieved while incarcerated. Common features of such programs include accompaniment to the initial primary care visit upon release from jail and activities that address post-release issues, such as lack of transportation, housing, employment, stigma, and discrimination. Additionally, patient navigation can improve engagement in care and viral suppression of PWH experiencing homelessness by transitioning them to more stable housing11. Thus, patient navigation can optimize the health of PWH—specifically PWH with subsistence needs who are out of care or at risk for falling out of care — and ultimately prevent onward HIV transmission.
As evidence for patient navigation accumulates, it is important to improve our understanding of the population of PWH in the United States (US) who may benefit from this strategy. Unfortunately, there is a gap in the literature; we do not know how many PWH have an unmet need for patient navigation, who they are, and why they are not getting the service. Thus, our objectives are to (1) estimate the prevalence of PWH in the US who needed patient navigation but did not get it (i.e., had unmet need for patient navigation), using two measures of need, namely, subjective and objective need, (2) determine reasons why PWH who had a subjective need for patient navigation did not use the service, and (3) determine demographic, socioeconomic, and clinical factors associated with unmet need for patient navigation.
METHODS
MMP design and data collection
The Medical Monitoring Project (MMP) is an annual cross-sectional survey designed to produce nationally representative estimates of behavioral and clinical characteristics of adults with diagnosed HIV in the United States12. We analyzed combined data from the 2015–2017 cycles of MMP.
Briefly, MMP used a two-stage sampling method. During the first stage, 23 project areas were sampled from all states in the United States, the District of Columbia, and Puerto Rico. During the second stage, simple random samples of persons with diagnosed HIV aged 18 years and older were drawn for each participating state/territory from the National HIV Surveillance System (NHSS), a census of persons with diagnosed HIV in the United States. Data were collected via phone or face-to-face interviews and medical record abstractions during June 2015–May 2018.
All sampled states and one territory participated in MMP. Response rates for adults with diagnosed HIV ranged from 40%–46%. Data were weighted based on known probabilities of selection at state or territory and person levels. Additionally, data were weighted to adjust for person nonresponse and post-stratified to NHSS population totals13. MMP methods have been described in detail elsewhere14.
Ethics statement
MMP data collection is a part of routine public health surveillance, and thus, determined to be non-research15. Participating states or territories obtained local institutional review board approval to collect data, when required. Informed consent was obtained from all participants.
Measures
Outcome measures
We used three outcomes to capture different types of unmet need for patient navigation―subjective need, objective need, and either subjective or objective need. Patient navigation was defined in the questionnaire as follows: “The next question is about patient navigation services. This is someone whose job is to help you get the best medical care for you. For example, a patient navigator might help you talk to your doctor about your care or go with you to your medical appointments.” Participants were asked: “During the past 12 months, did you get patient navigation services?” (yes/no). Those who responded ‘yes’ were considered to have had their needs met (i.e., having unmet subjective need=0 [no], having unmet objective need=0 [no]), while those who responded ‘no’ were asked “During the past 12 months, have you needed patient navigation services?” Those who responded ‘yes’ were coded as having unmet subjective need for patient navigation (1 [yes], and 0 [no] otherwise, irrespective of their viral load status [suppressed or unsuppressed]). Those who responded “no” or “I don’t know” and whose medical records indicated that any of their viral load tests were detectable (>200 copies/mL) or had no viral load tests in the past 12 months were coded as having unmet objective need for patient navigation=1 (yes), and 0 (no) otherwise. Given that patient navigation interventions are often found to be positively associated with viral suppression1, we used unsuppressed viral load among those who did not receive navigation services as an objective indicator of need for the service. Lastly, we created a variable that combined unmet subjective need and unmet objective need for patient navigation.
Reasons for not using patient navigation among those who reported subjective need
Participants who reported unmet subjective need for patient navigation were asked if they were not able to get this service for any of the reasons listed in Table 2 (yes/no). Participants could report multiple reasons.
Table 2.
Estimated prevalence of reasons* for not using patient navigation services among people with HIV with an unmet subjective need** for the service, United States, Medical Monitoring Project, 2015–2017
| n | Column%*** (95% CI) | |
|---|---|---|
| Total | 625 | 100 |
| Because I didn’t have enough money or insurance | 132 | 24.7 (18.8–30.6) |
| Because I couldn’t find the information I needed to get the service or didn’t know it existed | 504 | 77.4 (72.4–82.4) |
| Because the service didn’t meet my needs or I wasn’t eligible for it | 110 | 18.0 (13.4–22.7) |
| Because of personal reasons (I was afraid or embarrassed to get the service or I had other things going on…that made it difficult to get it) | 135 | 24.0 (19.1–29.0) |
Participants were able to report multiple reasons.
Unmet subjective need for patient navigation was defined as not getting patient navigation services despite reporting need for the service in the past 12 months, irrespective of viral load status (suppressed or unsuppressed).
Percentages are weighted percentages.
Potential correlates
We assessed age in years, gender, race/ethnicity, foreign-born, English proficiency (based on responses to “How well do you speak English?”), health literacy (based on responses to “How confident are you filling medical forms by yourself?”), education, and poverty. Household income, homelessness, and health insurance or coverage were measured in the past 12 months. Participants also reported whether they had engaged in binge drinking in the past 30 days (had ≥5 alcoholic beverages in a single sitting [≥4 for women]), whether they had engaged in alcohol use before or during sex, non-injection drug use, non-injection drug use before or during sex, and high-risk sex (defined as having engaged in condomless vaginal or anal sex with an HIV-negative or unknown status partner while not sustainably virally suppressed nor partner protected by pre-exposure prophylaxis [PrEP]) in the past 12 months.) Participants were also categorized into four sexual risk groups based on self-reported sexual behaviors in the past 12 months or self-identified sexual orientation. (See Table 3 for specific categories.)
Table 3.
Associations between demographic, socioeconomic and clinical characteristics and unmet need for patient navigation services among people with HIV, United States, Medical Monitoring Project, 2015–2017
| Unmet subjective need for patient navigation* | Unmet objective need for patient navigation** | Unmet subjective or objective need for patient navigation | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | %*** (95% CI#) | p-value | APR# (95% CI) | p-value | n | % (95% CI) | p-value | APR (95% CI) | p-value | n | % (95% CI) | p-value | APR (95% CI) | p-value | |
| TOTAL (Row%) [out of 7692] | 625 | 6.1 (5.3–6.9) | 2950 | 28.2 (26.9–29.5) | 3575 | 34.4 (32.9–35.9) | |||||||||
| Demographics | |||||||||||||||
| Age at the time of interview (years) | 0.061 | 0.577 | <.0001 | <0.001 | <.0001 | <0.001 | |||||||||
| 18–24 | 12 | 9.8 (4.3–15.2) | 1.49 (0.82–2.70) | 108 | 43.0 (34.7–51.2) | 1.60 (1.25–2.06) | 120 | 52.7 (45.0–60.3) | 1.55 (1.28–1.88) | ||||||
| 25–34 | 90 | 7.4 (5.1–9.7) | 1.17 (0.83–1.66) | 515 | 35.0 (31.8–38.1) | 1.38 (1.22–1.55) | 605 | 42.4 (38.8–45.9) | 1.31 (1.18–1.46) | ||||||
| 35–44 | 122 | 6.5 (5.1–7.9) | 1.03 (0.77–1.37) | 638 | 32.5 (29.6–35.4) | 1.31 (1.17–1.46) | 760 | 39.0 (36.3–41.7) | 1.23 (1.13–1.35) | ||||||
| 45–54 | 212 | 6.2 (5.0–7.4) | 1.12 (0.89–1.41) | 906 | 26.1 (24.3–27.8) | 1.08 (0.97–1.20) | 1118 | 32.3 (30.2–34.4) | 1.07 (0.98–1.17) | ||||||
| >=55 | 189 | 4.9 (3.6–6.2) | Reference | 783 | 23.8 (21.8–25.7) | Reference | 972 | 28.7 (26.5–30.9) | Reference | ||||||
| Gender | 0.006 | 0.001 | 0.836 | 0.801 | 0.166 | 0.061 | |||||||||
| Male | 422 | 5.8 (4.9–6.7) | Reference | 2096 | 28.1 (26.6–29.6) | Reference | 2518 | 33.9 (32.2–35.5) | Reference | ||||||
| Female | 196 | 7.4 (6.2–8.6) | 1.29 (1.06–1.56) | 810 | 28.7 (26.9–30.5) | 0.99 (0.91–1.07) | 1006 | 36.2 (34.0–38.3) | 1.04 (0.97–1.12) | ||||||
| Transgender | 7 | 2.8 (0.1–5.6)+^ | 0.36 (0.15–0.85) | 42 | 29.1 (20.7–37.6) | 0.91 (0.68–1.22) | 49 | 32.0 (22.8–41.1) | 0.79 (0.60–1.04) | ||||||
| Race/Ethnicity | 0.002 | 0.027 | <.0001 | 0.002 | <.0001 | <0.001 | |||||||||
| White, non-Hispanic | 139 | 4.5 (3.4–5.6) | Reference | 729 | 24.6 (21.9–27.4) | Reference | 868 | 29.2 (26.4–32.0) | Reference | ||||||
| Black, non-Hispanic | 222 | 5.4 (4.5–6.4) | 1.01 (0.77–1.32) | 1450 | 33.0 (31.3–34.8) | 1.30 (1.15–1.48) | 1672 | 38.5 (36.8–40.2) | 1.25 (1.12–1.39) | ||||||
| Hispanic/Latino | 201 | 8.5 (5.2–11.7) | 1.69 (1.07–2.66) | 582 | 24.6 (22.0–27.1) | 1.04 (0.89–1.22) | 783 | 33.1 (30.1–36.0) | 1.15 (1.03–1.29) | ||||||
| Other | 63 | 9.2 (6.6–11.9) | 1.67 (1.07–2.61) | 189 | 27.1 (22.3–32.0) | 1.05 (0.88–1.26) | 252 | 36.3 (31.0–41.6) | 1.15 (0.99–1.34) | ||||||
| Foreign born | 0.028 | --- | --- | <.0001 | --- | <.0001 | --- | ||||||||
| No | 545 | 6.3 (5.4–7.2) | --- | 2602 | 29.2 (27.8–30.6) | --- | 3147 | 35.5 (34.0–37.1) | --- | ||||||
| Yes | 74 | 4.7 (3.5–5.9) | --- | 336 | 22.3 (19.9–24.8) | --- | 410 | 27.0 (24.4–29.6) | --- | ||||||
| English proficiency | 0.817 | --- | --- | <.0001 | 0.014 | 0.002 | <0001 | ||||||||
| Not proficient | 165 | 6.4 (3.0–9.9) | --- | 538 | 23.0 (20.4–25.6) | 0.87 (0.77–0.97) | 703 | 29.5 (26.0–32.9) | 0.89 (0.80–0.99) | ||||||
| Proficient | 459 | 6.0 (5.3–6.7) | --- | 2412 | 29.5 (28.0–31.0) | Reference | 2871 | 35.6 (34.0–37.2) | Reference | ||||||
| Health literacy | <.0001 | 0.005 | 0.041 | --- | 0.155 | --- | |||||||||
| Limited health literacy | 227 | 9.7 (7.7–11.6) | 1.43 (1.11–1.84) | 614 | 26.1 (23.6–28.6) | --- | 841 | 35.8 (33.0–38.7) | --- | ||||||
| Adequate health literacy | 395 | 5.0 (4.3–5.8) | Reference | 2330 | 28.8 (27.5–30.2) | --- | 2725 | 33.9 (32.4–35.4) | --- | ||||||
| Socioeconomic | |||||||||||||||
| Education | 0.070 | --- | --- | 0.131 | --- | 0.157 | --- | ||||||||
| Less than High school, no diploma | 134 | 7.4 (6.0–8.8) | --- | 525 | 26.5 (24.2–28.8) | --- | 659 | 34.0 (31.4–36.6) | --- | ||||||
| High school diploma or GED | 160 | 6.6 (4.9–8.3) | --- | 811 | 29.6 (27.3–32.0) | --- | 971 | 36.2 (33.5–38.9) | --- | ||||||
| More than high school | 331 | 5.5 (4.5–6.4) | --- | 1610 | 28.1 (26.6–29.6) | --- | 1941 | 33.6 (31.9–35.4) | --- | ||||||
| Household income | <.0001 | --- | --- | 0.809 | --- | 0.190 | --- | ||||||||
| 0 – 19,999 | 388 | 7.6 (6.3–8.9) | --- | 1530 | 27.5 (26.0–29.0) | --- | 1918 | 35.1 (33.4–36.9) | --- | ||||||
| 20,000 – 39,999 | 128 | 6.0 (4.6–7.3) | --- | 571 | 27.5 (24.6–30.4) | --- | 699 | 33.5 (30.3–36.6) | --- | ||||||
| 40,000 – 74,999 | 42 | 3.3 (1.9–4.7) | --- | 369 | 28.4 (24.7–32.1) | --- | 411 | 31.7 (27.8–35.6) | --- | ||||||
| >= 75,000 | 29 | 2.3 (1.3–3.3) | --- | 262 | 29.4 (25.9–32.8) | --- | 291 | 31.7 (28.2–35.2) | --- | ||||||
| Poverty | <.0001 | --- | --- | 0.402 | --- | 0.002 | --- | ||||||||
| Above poverty level | 265 | 4.7 (3.9–5.6) | --- | 1437 | 27.3 (25.3–29.3) | --- | 1702 | 32.1 (30.0–34.1) | --- | ||||||
| At or below poverty level | 322 | 7.8 (6.5–9.2) | --- | 1295 | 28.5 (26.7–30.3) | --- | 1617 | 36.4 (34.5–38.3) | --- | ||||||
| Homelessness | <.0001 | 0.006 | 0.002 | --- | <.0001 | 0.001 | |||||||||
| No | 515 | 5.5 (4.7–6.4) | Reference | 2617 | 27.7 (26.4–29.1) | 3132 | 33.3 (31.7–34.8) | Reference | |||||||
| Yes | 110 | 12.4 (9.0–15.7) | 1.59 (1.14–2.21) | 332 | 33.5 (29.8–37.2) | 442 | 45.9 (41.5–50.3) | 1.18 (1.07–1.30) | |||||||
| Incarceration history | 0.004 | --- | <.0001 | 0.007 | <.0001 | 0.004 | |||||||||
| No | 576 | 5.9 (5.0–6.8) | --- | 2727 | 27.7 (26.3–29.0) | Reference | 3303 | 33.6 (32.0–35.1) | Reference | ||||||
| Yes | 48 | 9.7 (6.8–12.7) | --- | 221 | 38.4 (33.8–43.0) | 1.22 (1.06–1.40) | 269 | 48.2 (43.0–53.4) | 1.21 (1.07–1.36) | ||||||
| Health insurance or coverage | <.0001 | <0.001 | <.0001 | 0.005 | <.0001 | <0.001 | |||||||||
| Private insurance | 158 | 4.3 (3.2–5.5) | 0.24 (0.15–0.37) | 961 | 27.9 (25.5–30.3) | 0.66 (0.51–0.84) | 1119 | 32.3 (29.7–34.8) | 0.49 (0.42–0.57) | ||||||
| Public insurance (excluding RW/ADAP# only) | 395 | 6.4 (5.4–7.5) | 0.26 (0.18–0.36) | 1701 | 28.3 (26.7–29.9) | 0.66 (0.51–0.84) | 2096 | 34.8 (33.0–36.5) | 0.49 (0.42–0.57) | ||||||
| RW/ADAP Only | 39 | 6.1 (3.8–8.5) | 0.24 (0.14–0.42) | 209 | 25.3 (21.6–29.0) | 0.55 (0.40–0.74) | 248 | 31.5 (27.9–35.2) | 0.42 (0.34–0.50) | ||||||
| No insurance coverage | 26 | 25.7 (16.2–35.2) | Reference | 52 | 49.1 (38.0–60.3) | Reference | 78 | 74.7 (65.2–84.3) | Reference | ||||||
| Risk behavior | |||||||||||||||
| Binge drinker | 0.084 | --- | 0.306 | --- | 0.059 | --- | |||||||||
| No | 523 | 5.9 (5.0–6.8) | --- | 2446 | 28.0 (26.6–29.3) | --- | 2969 | 33.9 (32.3–35.4) | --- | ||||||
| Yes | 100 | 7.4 (5.7–9.1) | --- | 484 | 29.4 (26.7–32.2) | --- | 584 | 36.8 (33.7–40.0) | --- | ||||||
| Alcohol use before or during sex | 0.230 | --- | 0.371 | --- | 0.171 | --- | |||||||||
| No | 202 | 5.7 (4.5–7.0) | --- | 1049 | 28.0 (25.7–30.3) | --- | 1251 | 33.8 (31.3–36.3) | --- | ||||||
| Yes | 147 | 6.7 (5.4–8.0) | --- | 717 | 29.4 (26.9–31.9) | --- | 864 | 36.1 (33.4–38.8) | --- | ||||||
| Non-injection drug use | 0.002 | 0.001 | <.0001 | 0.001 | <.0001 | <0.001 | |||||||||
| No | 389 | 5.2 (4.1–6.3) | Reference | 1934 | 26.5 (25.1–27.9) | Reference | 2323 | 31.7 (30.0–33.4) | Reference | ||||||
| Yes | 234 | 8.4 (6.8–9.9) | 1.48 (1.17–1.86) | 1006 | 32.1 (29.9–34.4) | 1.15 (1.06–1.25) | 1240 | 40.5 (37.9–43.2) | 1.20 (1.13–1.29) | ||||||
| Non-injection drug use before or during sex | 0.000 | --- | 0.000 | --- | <.0001 | --- | |||||||||
| No | 213 | 5.0 (3.9–6.1) | --- | 1216 | 27.1 (25.0–29.2) | --- | 1429 | 32.1 (29.8–34.3) | --- | ||||||
| Yes | 133 | 9.0 (6.9–11.1) | --- | 556 | 32.8 (30.0–35.6) | --- | 689 | 41.8 (38.7–44.9) | --- | ||||||
| Sexual risk behavior | 0.485 | --- | 0.408 | --- | 0.660 | --- | |||||||||
| No | 276 | 6.4 (5.3–7.5) | --- | 1150 | 27.6 (25.8–29.4) | --- | 1426 | 34.0 (32.0–36.0) | --- | ||||||
| Yes | 341 | 5.9 (4.9–6.9) | --- | 1748 | 28.6 (26.7–30.5) | --- | 2089 | 34.6 (32.6–36.6) | --- | ||||||
| Same/opposite gender sexual behavior/orientation | 0.034 | --- | 0.389 | --- | 0.126 | --- | |||||||||
| MSM | 269 | 5.7 (4.7–6.7) | --- | 1363 | 27.3 (25.6–29.1) | --- | 1632 | 33.0 (31.3–34.8) | --- | ||||||
| MSW only | 142 | 5.9 (4.6–7.1) | --- | 694 | 29.5 (26.9–32.0) | --- | 836 | 35.4 (32.6–38.2) | --- | ||||||
| WSM | 190 | 7.4 (6.2–8.7) | --- | 790 | 28.7 (26.8–30.6) | --- | 980 | 36.2 (33.9–38.5) | --- | ||||||
| Others | 24 | 5.0 (2.7–7.3) | --- | 103 | 29.5 (23.6–35.3) | --- | 127 | 34.5 (28.0–40.9) | --- | ||||||
| Clinical | |||||||||||||||
| Depression | <.0001 | <0.001 | 0.494 | --- | <.0001 | <0.001 | |||||||||
| No depression | 352 | 4.3 (3.5–5.2) | Reference | 2284 | 28.1 (26.5–29.7) | --- | 2636 | 32.4 (30.8–34.1) | Reference | ||||||
| Other depression | 99 | 8.8 (6.3–11.2) | 1.67 (1.29–2.16) | 316 | 27.6 (24.2–30.9) | --- | 415 | 36.4 (33.0–39.8) | 1.05 (0.96–1.16) | ||||||
| Major depression | 166 | 16.1 (13.3–18.9) | 2.95 (2.18–3.99) | 333 | 30.2 (26.8–33.6) | --- | 499 | 46.4 (42.7–50.1) | 1.35 (1.23–1.48) | ||||||
| Recently diagnosed with HIV (<1 year) | 0.329 | --- | 0.003 | --- | <.0001 | 0.014 | |||||||||
| No | 621 | 6.1 (5.2–6.9) | --- | 2909 | 28.1 (26.8–29.4) | --- | 3530 | 34.2 (32.7–35.7) | Reference | ||||||
| Yes | 4 | 10.6 (0.0–22.4)+^ | --- | 41 | 46.1 (33.2–59.0) | --- | 45 | 56.7 (45.4–68.1) | 1.39 (1.10–1.76) | ||||||
| Ryan White HIV/AIDS program-funded facility | 0.047 | --- | 0.984 | --- | 0.379 | --- | |||||||||
| No | 146 | 4.6 (3.7–5.6) | --- | 725 | 25.6 (22.7–28.6) | --- | 871 | 30.3 (27.1–33.5) | --- | ||||||
| Yes | 430 | 6.3 (5.1–7.4) | --- | 1855 | 25.7 (24.5–26.9) | --- | 2285 | 32.0 (30.4–33.5) | --- | ||||||
Unmet subjective need for patient navigation was defined as not getting patient navigation service despite reporting need for the service in the past 12 months, irrespective of viral load status (suppressed or unsuppressed).
Unmet objective need for patient navigation was defined as not getting patient navigation, not reporting need for the service, but did not have sustained viral suppression over the past 12 months
Percentages are weighted percentages.
CI = Confidence Interval; APR = Adjusted Prevalence Ratio; RW = Ryan White HIV/AIDS Program; ADAP = AIDS Drug Assistance Program
Coefficient of Variation (CV)>=0.30, indicating that the estimate is not reliable with high variance.
Values with a denominator sample size <30, values with an absolute CI width of between 0.05 and 0.30, and a relative CI width 130% are marked with # and should be interpreted with caution
Depression in the past 2 weeks (no depression, other depression, major depression) was measured using the Patient Health Questionnaire (PHQ-8)16, an 8-item scale measuring frequency of depressed mood. Participants were categorized into recently (i.e., within 12 months) vs. not recently diagnosed with HIV, based on self-reported date of first HIV positive test. When self-reported date of diagnosis was missing, records from the NHSS were used instead. We also captured whether participants sought care from a Ryan White-funded facility or not.
Analytic Methods
Statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, North Carolina) and SAS-callable SUDAAN 10.0.1 (RTI International, Research Triangle Park, North Carolina) and accounted for clustering, unequal selection probabilities, and non-response. We calculated frequencies, weighted percentages, and associated 95% confidence intervals (CIs) for each outcome measure. Bivariate associations between each outcome measure and selected characteristics were evaluated using unadjusted prevalence ratios (PR) and 95% confidence intervals. We constructed multivariable logistic regression models to compute the adjusted prevalence ratios (APR). Collinearity among the independent variables was assessed. Characteristics with p-value less than 0.1 at bivariate analysis were entered in the initial multivariable model. Next, manual backward stepwise model selection was performed, with a p-value of 0.05 criterion for retention of variables in the final model. We retained age, gender, and race/ethnicity in the final model regardless of p-value to control for basic demographic characteristics. Models were compared through the Akaike’s information criterion (AIC). Model adequacy was evaluated using Hosmer and Lemeshow goodness-of-fit test. Variances of the regression parameters and prevalence ratios were computed by the Taylor linearization method, assuming a with-replacement (WR) design.
RESULTS
Approximately 6% of PWH reported unmet subjective need for patient navigation. The percentage was much higher (28%) for unmet objective need. More than a third of PWH did not use patient navigation despite having either a subjective or an objective need for the service (Table 1). Among PWH who reported unmet subjective need for patient navigation, more than three-quarters (77%) reported lack of information to access the service or knowledge that the service existed as a reason for not using the service. About a quarter cited lack of money or insurance for not using the service. (Table 2)
Table 1.
Estimated prevalence of unmet subjective or objective need for patient navigation among people with HIV, United States, Medical Monitoring Project, 2015–2017
| n | %* (95% CI) | |
|---|---|---|
| Total | 13254 | 100 |
| Unmet subjective need** for patient navigation | 625 | 6.1 (5.3–6.9) |
| Unmet objective need*** for patient navigation | 2950 | 28.2 (26.9–29.5) |
| Unmet subjective or objective need for patient navigation | 3575 | 34.4 (32.9–35.9) |
Percentages are weighted percentages.
Unmet subjective need for patient navigation was defined as not getting patient navigation services despite reporting need for the service in the past 12 months, irrespective of viral load status (suppressed or unsuppressed).
Unmet objective need for patient navigation was defined as not getting patient navigation, not reporting need for the service, but did not have sustained viral suppression over the past 12 months.
Table 3 shows multivariable correlates of unmet subjective, objective, and subjective or objective need for patient navigation. For subjective need for patient navigation, women were more likely and transgender persons were less likely than men to report unmet need. Hispanics and persons of “other races/ethnicities” were more likely than non-Hispanic whites to report unmet need. Persons with limited health literacy (vs. adequate health literacy) and who experienced homelessness in the past 12 months (vs. not), used non-injection drugs (vs. not) were more likely to report unmet need. PWH with health insurance/coverage (private, public, Ryan White/ADAP only) were less likely than those with no health insurance to report unmet need. An additional analysis (results not shown in Table 3) found no differences in the prevalence of unmet subjective need for patient navigation among PWH with different types of health insurance coverage.
For objective need for patient navigation, younger age groups (18–44) were more likely than PWH who were 55+ years old, and non-Hispanic blacks were more likely than non-Hispanic whites to have unmet need. PWH with limited English proficiency (vs. English proficient) were less likely to have unmet need. PWH who were incarcerated in the past 12 months (vs. not), used non-injection drugs in the past 12 months (vs. did not) were more likely to have unmet need. PWH with health insurance/coverage (private, public, Ryan White/ADAP only) were less likely than those with no health insurance to report unmet need. An additional analysis (results not shown in Table 3) found no differences in the prevalence of unmet objective need for patient navigation among PWH with different types of health insurance coverage.
Multivariable correlates of unmet subjective or objective need for patient navigation were age, race/ethnicity, English proficiency, homelessness, incarceration history, health insurance/coverage, non-injection drug use, depression, and recent HIV diagnosis. PWH who had been diagnosed with HIV within 12 months (vs. longer) were more likely to report unmet need. Directions of associations for other variables were the same as those reported for the other two outcomes.
DISCUSSION
We found that approximately one in three PWH did not use patient navigation despite needing the service either subjectively or objectively. Unmet need for patient navigation may be underestimated when only subjective need is considered. Patient navigation is often found to be positively associated with viral suppression1. Unmet need increased more than four-fold (6% to 28%) when using unsuppressed viral load in the past 12 months as an indicator of objective need for patient navigation, illustrating the importance of considering multiple measures of need.
Most PWH with an unmet subjective need for patient navigation cited not knowing how to access the service or that such service existed as a reason for non-use. This lack of knowledge calls for increased availability of patient navigation and its visibility by social marketing, social media, word of mouth, and other means of communication.
Multivariable correlates of patient navigation service non-utilization varied by whether the need for the service was subjective or objective. Regardless of the type of need, lack of health insurance or coverage and non-injection drug use consistently predicted unmet need. Although patient navigation is designed to optimize the health of PWH with subsistence needs, our findings suggest that PWH who could benefit most from the service are not receiving it. Across the outcomes, we found that PWH with social/economic vulnerabilities (e.g., younger age, racial/ethnic minority status, limited health literacy, homelessness, incarceration history, depression) were less likely to receive patient navigation when they needed it, and this was true even when health insurance/coverage status was statistically controlled. It is particularly unfortunate that PWH with a history of incarceration had an unmet need for patient navigation because research shows that this strategy can improve their health outcomes,5,9,10. Similarly, homeless PWH, also more likely to report an unmet need for patient navigation, could improve their health by transitioning into more stable housing with the help of patient navigators11. Based on these findings, programs might prioritize PHW with social/economic vulnerabilities to receive patient navigation upon being diagnosed with HIV or at a clinic appointment.
A quarter of PWH who did not use patient navigation but felt the need for the service cited lack of money or insurance as a reason for non-use. As noted above, lack of health insurance/coverage was a consistent predictor of unmet need for patient navigation. There were no differences in unmet subjective and objective needs among PWH with different types of health insurance/ coverage (e.g., public vs. private), suggesting that PWH are less likely to have an unmet need for patient navigation if they have some form of health coverage. The New York City Care Coordination Program17,18 is an evidence-informed intervention that includes patient navigation as one of the primary components. This program was funded through Ryan White Part A (under the “medical case management” service category), and clients were able to access patient navigation services for free if they were enrolled in that program. Given that there is no standard definition of HIV patient navigation1, it is unlikely that the service per se would be directly covered by private or public health insurance plans. It is possible that these plans could link PWH to clinics that provide patient navigation as part of their comprehensive HIV care coordination services. Given the promise of patient navigation for improving patient outcomes, it is important to explore how to best pay for the cost of patient navigation to implement this strategy at a larger scale, making it available to everyone who needs it.
One unexpected finding was that PWH with limited English proficiency were less likely than those who were proficient to have unmet objective need for patient navigation. This finding was contrary to the finding for unmet subjective need, for which English proficiency was not associated with the outcome, and bivariate analysis showing that the prevalence of unmet subjective need was slightly higher among PWH with limited English proficiency compared with those who were proficient. Given how we defined unmet objective need for patient navigation, some of the findings on unmet objective need may be explained by the fact that certain groups of PWH are more likely not to have sustained viral suppression. It is still unknown why PWH with English proficiency may be more likely than PWH with limited English proficiency to not to have sustained viral suppression, which is a topic for further investigation.
This paper is subject to the following limitations. First, the data are cross-sectional, thus no causality can be established. Second, not all PWH who were sampled participated in MMP. However, results were adjusted for nonresponse using standard methodology19. Even with suboptimal response rates, there is value in results that employ unbiased sampling methods20,21. Third, except for the clinical indicators (e.g., viral loads) obtained from medical records, data were self-reported and thus subject to social desirability and recall biases. Fourth, the definition of patient navigation provided in the questionnaire may not have comprehensively captured the range of tasks that a patient navigator performs, which may have led to under-reporting of service utilization or the extent to which participants subjectively felt the need for the service. Further, given that there are no standard duties of a patient navigator in HIV care settings1, the term “patient navigation” may have been interpreted differently by the study participants. Fifth, MMP does not collect information about the healthcare setting for PWH who are in care nor information on healthcare settings in which patient navigators are retained. Information like this would help estimate the proportion of PWH receiving care in settings that are most likely to provide patient navigation. Additionally, MMP does not provide information on how patient navigation services are provided thus no assessment can be made for how this strategy is implemented across the United States. Lastly, because of the way we defined objective need, the findings on unmet objective need may be driven by differences between groups in sustained viral suppression. Multivariable correlates that were unique to unmet objective need, such as younger age and black racial status may be interpreted with caution, as these variables have been consistently reported as factors associated with unsuppressed viral load22–26.
Despite evidence that patient navigation can improve the HIV care continuum outcomes1, one in three PWH did not use the service despite needing it. Lack of knowledge about how to access this service or that such service existed was a barrier to use, calling for increased availability and promotion of patient navigation. PWH with social and economic vulnerabilities were less likely to get patient navigation when needed, suggesting that the service is not reaching those who could benefit the most. It is important to address the question of how to make this service available to everyone who needs it.
Acknowledgements:
We thank participating Medical Monitoring Project patients, facilities, project areas, and Provider and Community Advisory Board members. We also acknowledge the contributions of the Clinical Outcomes Team and Behavioral and Clinical Surveillance Branch at CDC and the MMP 2015–2017 project areas (http://www.cdc.gov/hiv/statistics/systems/mmp/resources.html)
Source of Funding:
Funding for the Medical Monitoring Project is provided by a cooperative agreement (PS09–937) from the Centers for Disease Control and Prevention.
Footnotes
Conflicts of Interest
All the authors declare no conflict of interest.
Disclaimer
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.
REFERENCES
- 1).Mizuno Y, Higa DH, Leighton CA, Roland KB, Deluca JB, Koenig LJ. Is HIV patient navigation associated with HIV care continuum outcomes? AIDS 2018; 32(17):2557–2571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2).Bradford JB, Coleman S, Cunningham W. HIV System Navigation: an emerging model to improve HIV care access. AIDS Patient Care STDS 2007; 21 Suppl 1: S49–S58. [DOI] [PubMed] [Google Scholar]
- 3).Cunningham WE, Weiss RE, Nakazono T, et al. Effectiveness of a Peer Navigation Intervention to Sustain Viral Suppression Among HIV-Positive Men and Transgender Women Released from Jail: The LINK LA Randomized Clinical Trial. JAMA Intern Med 2018; 178:542–553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4).Hemmy Asamsama O, Squires L, Tessema A, et al. HIV Nurse Navigation: Charting the Course to Improve Engagement in Care and HIV Virologic Suppression. J Int Assoc Provid AIDS Care 2017; 16(6):603–607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5).Myers JJ, Kang Dufour MS, Koester et al. The Effect of Patient Navigation on the Likelihood of Engagement in Clinical Care for HIV-Infected Individuals Leaving Jail. Am J Public Health 2018, 108(3);385–392. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6).Schumann CL, Westergaard RP, Meier AE, Ruetten ML, Vergeront JM. Developing a Patient Navigation Program to Improve Engagement in HIV Medical Care and Viral Suppression: A Demonstration Project Protocol. AIDS Behav 2019; 23 Suppl 1: S5–S13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7).Taylor BS, Fornos L, Tarbutton J, et al. Improving HIV Care Engagement in the South from the Patient and Provider Perspective: The Role of Stigma, Social Support, and Shared Decision-Making. AIDS Patient Care STDS. 2018. September 1; 32(9): 368–378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8).Roland KB, Higa DH, Leighton CA, Mizuno Y, DeLuca JB, Koenig LJ. 2019. HIV navigation from the client perspective: a meta-synthesis of qualitative studies. Health Promotion Practice. DOI: 10.1177/1524839919875727 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9).Jordan AO, Cohen LR, Harriman G, Teixeira PA, Cruzado-Quinones J, Venters H. Transitional care coordination in New York City jails: facilitating linkages to care for people with HIV returning home from Rikers Island. AIDS Behav 2013; 17 Suppl 2: S212–219. [DOI] [PubMed] [Google Scholar]
- 10).Teixeira PA, Jordan AO, Zaller N, Shah D, Venters H. Health outcomes for HIV-infected persons released from the New York City jail system with a transitional care-coordination plan. Am J Public Health 2015;105(2):351–357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11).Rajabiun S, Tryon J, Feaster M, et al. The Influence of Housing Status on the HIV Continuum of Care: Results from a Multisite Study of Patient Navigation Models to Build a Medical Home for People Living with HIV Experiencing Homelessness. Am J Public Health 2018; 108 Suppl 7: S539–S545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12).Beer L, Johnson CH, Fagan JL, et al. A National Behavioral and Clinical Surveillance System of Adults with Diagnosed HIV (The Medical Monitoring Project): Protocol for an Annual Cross-Sectional Interview and Medical Record Abstraction Survey. JMIR Res Protoc 2019;8(11): e15453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13).Heeinga SG, West BT, Berflund PA. Applied Survey Data Analysis. 2010. Chapman and Hall, London [Google Scholar]
- 14).Centers for Disease Control and Prevention. Behavioral and Clinical Characteristics of Persons with Diagnosed HIV Infection—Medical Monitoring Project, United States, 2017 Cycle (June 2017–May 2018). HIV Surveillance Special Report 23. https://www.cdc.gov/hiv/library/reports/hiv-surveillance.html. Published September 2019. Accessed October 17, 2019.
- 15).Centers for Disease Control and Prevention. Distinguishing public health research and public health nonresearch. http://www.cdc.gov/od/science/integrity/docs/cdc-policy-distinguishing-public-health-research-nonresearch.pdf. Published July 29, 2010
- 16).Kroenke K, Strine TW, Spitzer RL, et al. The PHQ-8 as a measure of current depression in the general population. J Affect Disord 2009;114(1–3):163–173. [DOI] [PubMed] [Google Scholar]
- 17).Irvine MK, Chamberlin SA, Robbins RS, et al. Improvements in HIV care engagement and viral load suppression following enrollment in a comprehensive HIV care coordination program. Clin Infect Dis 2015; 60(2):298–310. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18).Centers for Disease Control and Prevention. HIV/AIDS Prevention Research Synthesis Project. Compendium of Evidence-Based Interventions and Best Practices for HIV Prevention. https://www.cdc.gov/hiv/research/interventionresearch/compendium/index.html. https://www.cdc.gov/hiv/pdf/research/interventionresearch/compendium/lrc/cdc-hiv-lrc-hiv-care-coordination-program.pdf. Accessed November 25, 2019.
- 19).Iachan R, Johnson CH, Harding RL, et al. Design and Weighting methods for a nationally representative sample of HIV-infected Adults receiving medical care in the United States - Medical Monitoring Project. The Open AIDS Journal 2016; 10, 164–181. doi: 10.2174/1874613601610010164 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20).Groves RM. Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly 2006;70(5), 646–675. [Google Scholar]
- 21).Beer L, Johnson CH, Fagan JL, et al. A National Behavioral and Clinical Surveillance System of Adults With Diagnosed HIV (The Medical Monitoring Project): Protocol for an Annual Cross-Sectional Interview and Medical Record Abstraction Survey. JMIR Res Protoc 2019;8(11):e15453. DOI: 10.2196/15453 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22).Beer L, Mattson CL, Bradley H, Skarbinski J. Understanding cross-sectional racial, ethnic, and gender disparities in antiretroviral use and viral suppression among HIV patients in the United States. Medicine 2016;95(13):e3171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23).Crepaz N, Tang T, Marks G, Mugavero MJ, Espinoza L, Hall HI. Durable viral suppression and transmission risk potential among persons with diagnosed HIV infection: United States, 2012–2013. CID 2016;63;976–983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24).Marks G, Patel U, Stirratt MJ, et al. Single viral load measurements overestimate stable viral suppression among HIV patients in care: Clinical and public health implications. J Acquir Immune Defic Syndr 2016;73(2): 205–212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25).Crepaz N, Tang T, Marks G, Hall HI. Changes in viral suppression status among US HIV-infected patients receiving care. AIDS 2017;31:2421–2425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26).Crepaz N, Dong Z, Wang X, Hernandez AL, Hall HI. Racial and ethnic disparities in sustained viral suppression and transmission risk potential among persons receiving HIV Care – United States, 2014. MMWR 2018;67(4):113–118. [DOI] [PMC free article] [PubMed] [Google Scholar]