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. 2026 Aug 25;69(1):276–285. doi: 10.1159/ore/aedag001

Patient and Neighborhood Factors Influencing Participation in a Personalized Glaucoma Coaching Program

Stephanie Lee a, Patrice M Hicks b,c, Justus N Dobson b, Jamie Mitchell d, Deborah Darnley-Fisch e, Michele Heisler f,g, Nauman R Imami e, Paula Anne Newman-Casey b,c,✉,✉
PMCID: PMC13688346  PMID: 42640865

Abstract

Introduction

The aim of this study was to assess barriers and facilitators to program engagement for participants in the Support, Educate, Empower (SEE) Personalized Glaucoma Coaching Pilot Program and identify differences by demographics or neighborhood area deprivation.

Methods

This mixed-methods study analyzed SEE glaucoma coaching program participants. Patient characteristics included age, sex, race, and neighborhood socioeconomic status (area deprivation index [ADI]). Interview transcripts were coded using the adapted Penchansky and Thomas framework, which defines healthcare access through access dimensions: availability (supply of services), accessibility (location), accommodation (organization of services), affordability (cost), awareness (communication/information), and acceptability (attitudes and comfort), to categorize barriers and facilitators. Thematic frequencies were calculated and stratified by participant characteristics.

Results

The 44 participants in the SEE pilot program were 52% ≥ 65 years old, 55% male, 46% White, and 34% lived in high ADI neighborhoods. Awareness (100%) and acceptability (66%) were the most cited themes. Low-ADI participants experienced more acceptability (79% vs. 39% high ADI) and awareness barriers (79% vs. 43% mid ADI). Mid-ADI participants had more accessibility barriers (71% vs. 13% low ADI) and accommodation facilitators (43% vs. 8% low ADI). High-ADI participants reported more patient-level facilitators (44% vs. 14% mid ADI). No differences emerged by other demographics.

Conclusion

Among SEE pilot program participants, acceptability barriers and awareness facilitators were the most frequently cited. There were ADI differences for barriers and facilitators. Neighborhood factors should be considered when implementing glaucoma coaching programs, as program interaction varies by these characteristics. The SEE pilot program facilitated glaucoma medication adherence through educational resources and adherence monitoring tools.

Keywords: Glaucoma, Neighborhood, Area deprivation, Access, Barriers, Facilitators, Health coaching

Introduction

Glaucoma is a leading cause of irreversible blindness, affecting millions in the USA [1]. Despite the availability of effective intraocular pressure-lowering therapies, suboptimal medication adherence remains a critical barrier to preventing disease progression [2–7]. At least half of the patients fail to take their prescribed medications regularly 1 year after diagnosis [8]. The clinical implications are significant, as poor adherence is associated with faster visual field loss and reduced quality of life [9–11]. Multiple factors contribute to medication non-adherence in glaucoma, including patient-level issues like low self-efficacy and limited understanding of the condition or its treatment, as well as healthcare system factors such as provider communication and ease of accessing appointments [3, 12–16]. Thus, effective interventions that address these barriers are essential to improve long-term treatment outcomes.

Patient coaching programs have become a promising strategy for improving chronic disease management in conditions such as diabetes, hypertension, and heart failure [17–21]. Trained healthcare professionals or peer coaches offer ongoing support, education, and motivation to help patients build self-management skills and improve treatment adherence. Studies have explored a range of facilitators and barriers, such as phone-based or digital platforms for accessibility, personalized and structured coaching sessions, differences in cost-effectiveness, and technological limitations [22–25]. However, the application of coaching interventions specifically for glaucoma management remains limited, with few studies examining the unique barriers and facilitators that influence program engagement in this population [26].

The Support, Educate, Empower (SEE) Personalized Glaucoma Coaching Program was developed to address these gaps by providing tailored education and motivational interviewing-based coaching, delivered by trained non-physician interventionists [2, 4, 26]. Early studies demonstrate that such interventions can significantly improve medication adherence and patient-centered outcomes, particularly among those with the greatest psychosocial and socioeconomic barriers [2, 4, 27–30]. Research has identified factors that influence participation in healthcare interventions. The Penchansky and Thomas framework offers a systematic way to understand healthcare access. For glaucoma coaching programs, which support medication adherence and disease management, this framework helps categorize barriers and facilitators to participation among different patient groups.

This study sought to understand the facilitators and barriers to participation in the SEE Program Pilot Study. By systematically examining participant experiences through the established Penchansky and Thomas framework and stratifying results by key demographic characteristics, including age, sex, race, and area deprivation, this research aims to provide actionable insights for optimizing glaucoma coaching interventions.

Methods

Participants and Sample Selection

This study was approved by the University of Michigan Medical School IRB (HUM# HUM00112614). The methodology of the SEE program has been documented prior [31]. Interview data were derived from participants enrolled in the SEE Glaucoma Pilot Program. Eligibility requirements included individuals diagnosed with glaucoma, glaucoma suspect status, or ocular hypertension who were ≥40 years old and prescribed ≥one glaucoma medication(s). In total, 147 individuals met the low-adherence criteria and were invited to participate in a 3-month electronic medication monitoring phase designed to capture objective adherence measurements. Formal written consent was obtained, and baseline questionnaires were administered. Electronic monitoring data identified 48 participants as eligible for SEE coaching intervention based on objectively measured adherence rates of ≤80% for prescribed medication doses taken according to schedule. Of these eligible participants, 44 individuals completed the qualitative interview component that forms the basis of this analysis.

Framework

This study employed an expanded version of the Penchansky and Thomas access framework, incorporating subsequent modifications by Saurman and Hicks et al. The original Penchansky and Thomas model identifies five key dimensions that affect healthcare access: affordability, availability, accessibility, accommodation, and acceptability. Building on this foundation, Saurman contributed “awareness” as an additional sixth dimension, while Hicks and colleagues further extended the model by including “patient-level factors” as a seventh component (online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/ore/aedag001). The application of the framework is detailed in the study codebook (online suppl. Table 2).

Mixed-Methods Analysis

The quantitative analysis examined demographic characteristics at the patient level, categorizing participants by age groups (40–64 years versus ≥65 years), sex (female versus male), race (White, Black), and Area Deprivation Index (ADI) scores classified as low (1–3), moderate (4–6), and high (7 or above), with results presented as frequencies and percentages. Participant addresses were converted to 12-digit Federal Information Processing Standards (FIPS) codes and linked to Michigan state-level ADI scores. The qualitative analysis employed a mixed-methods approach centered on interview data interpretation guided by the modified Penchansky and Thomas framework. Two coders (P.M.H. and J.N.D.) reviewed ten transcripts to develop themes categorized as barriers or facilitators within framework dimensions. A third researcher (P.A.N.C.) helped finalize the codebook. Both coders then independently coded five transcripts to ensure consistency before analyzing remaining interviews. Discrepancies were resolved by the third coder (P.A.N.C). The data analysis counted barrier and facilitator frequencies and participant reporting rates, linking responses to demographic characteristics. Coding used Dedoose 8.3.17 (Los Angeles, CA, USA), with frequency calculations performed in Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Demographic differences were considered significant when exceeding 30% between groups.

Results

Overall

The cohort was previously characterized [31]. The cohort comprised 20 females, with 23 (52%) participants aged 65 or older, and 20 (46%) participants reported Black race. Of the 44 participants, 22 (50%) participants lived in low deprivation areas (ADI 1–3), while 15 (34%) participants resided in high-deprivation areas (ADI 7–10), and 6 (14%) participants lived in mid-level deprivation areas (ADI 4–6). ADI data were missing for one participant (Table 1). All of the modified Penchansky and Thomas framework dimensions were mentioned as either a barrier or a facilitator (online suppl. Table 3).

Table 1.

Participant demographic characteristics

Characteristic N %
Age, mean
 65+ years old 23 52
 40–64 years old 21 48
Biological sex
 Female 20 45
 Male 24 55
Race/ethnicity, n (%)
 White 20 46
 Black/African American 20 46
 American Indian/Alaska Native 1 1
 Asian 3 7
 Other 1 2
ADI
 Missing ADI 1 2
 Low (1–3) 22 50
 Mid (4–6) 6 14
 High (7–10) 15 34
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Awareness

Awareness – defined as whether the glaucoma coaching program enhances participants’ understanding of glaucoma, the importance of medication adherence, the risks of non-adherence, and available support resources – was mentioned by all participants (participants: 100%; citations: 577; Fig. 1), for a total of 52 times (64%) as a barrier and 525 times (100%) as a facilitator. Twenty-eight participants (64%) identified awareness as a barrier. The most common barrier was having no reason to revisit the informational website (23 participants, 52%), followed by lacking education about glaucoma (11 participants, 25%), and insufficient instruction on medication use (8 participants, 18%). Neighborhood-level differences revealed that mid-level ADI participants reported fewer awareness barriers than those in low-level ADI neighborhoods (43% vs. 79%) (Fig. 2).

Fig. 1.

Bar chart comparing the percentages of participants who reported each dimension overall, as a barrier, and as a facilitator. For each dimension, three percentages are shown: the overall percentage and the percentages identifying it as a barrier or facilitator. Awareness was the most reported overall and as a facilitator.

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Overall percentage of participants reporting each dimension. This figure displays the percentage of participants who reported each dimension overall and who identified each dimension as a barrier or facilitator.

Fig. 2.

Four-panel figure showing the percentages of participants reporting barriers across the seven dimensions of the modified Penchansky and Thomas framework, stratified by demographic characteristics. Panel A compares male participants (n = 24) with female participants (n = 20). Panel B compares participants aged 40–64 years (n = 21) with those aged 65 years or older (n = 23). Panel C compares Black participants (n = 20) with White participants (n = 20). Panel D compares participants living in areas with low ADI scores (1–3; n = 22), middle ADI scores (4–6; n = 6), and high ADI scores (7–10; n = 15). Higher percentages indicate that a larger proportion of participants in that demographic group reported a barrier in the specified dimension.

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Percentage of participants reporting barriers by dimension. This figure displays the percentage of participants reporting barriers across the seven dimensions of the modified Penchansky and Thomas framework, stratified by demographic characteristics. a Compares barriers reported by males versus females (n = 24 males, n = 20 females). b Compares barriers reported by participants aged 40–64 years versus those 65 years and older (n = 21 aged 40–64, n = 23 aged ≥65). c Compares barriers reported by Black versus White participants (n = 20 Black, n = 20 White). d Compares barriers reported by participants across area deprivation index (ADI) levels: low-ADI (1–3, n = 22), mid-ADI (4–6, n = 6), and high-ADI (7–10, n = 15).

All participants cited awareness as a facilitator to program participation. The most mentioned facilitator was increased understanding of glaucoma through educational resources (43 participants, 98%). Drop reminders using bottle devices were noted by 40 participants (91%), and knowledge of adherence numbers by 36 participants (82%). Visualizing potential loss of vision was cited by 25 participants (57%), whilst establishing a daily routine for administering eye drops was mentioned by 19 participants (43%). Reminders from coaches to take eye drops were cited by 12 (27%) participants. No demographic differences were observed in reported facilitators for this dimension (Fig. 3).

Fig. 3.

Four-panel figure showing the percentages of participants reporting facilitators across the seven dimensions of the modified Penchansky and Thomas framework, stratified by demographic characteristics. Panel A compares male participants (n = 24) with female participants (n = 20). Panel B compares participants aged 40–64 years (n = 21) with those aged 65 years or older (n = 23). Panel C compares Black participants (n = 20) with White participants (n = 20). Panel D compares participants living in areas with low ADI scores (1–3; n = 22), middle ADI scores (4–6; n = 6), and high ADI scores (7–10; n = 15). Higher percentages indicate that a larger proportion of participants in that demographic group reported a facilitator in the specified dimension.

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Percentage of participants reporting facilitators by dimensions. This figure displays the percentage of participants reporting facilitators across the seven dimensions of the modified Penchansky and Thomas framework, stratified by demographic characteristics. a Compares facilitators reported by males versus females (n = 24 males, n = 20 females). b Compares facilitators reported by participants aged 40–64 years versus those 65 years and older (n = 21 aged 40–64, n = 23 aged ≥65). c Compares facilitators reported by Black versus White participants (n = 20 Black, n = 20 White). d Compares facilitators reported by participants across area deprivation index (ADI) levels: low-ADI (1–3, n = 22), mid-ADI (4–6, n = 6), and high-ADI (7–10, n = 15).

Acceptability

Acceptability – defined as participant satisfaction with the glaucoma coaching program, and the effectiveness and cultural sensitivity of program communication – was mentioned by 40 participants (participants: 91%; citations: 152; Fig. 1), for a total of 65 times (66%) as a barrier and 87 (75%) times as a facilitator. Twenty-nine (66%) participants identified acceptability as a barrier, most commonly due to a preference for face-to-face communication (29 participants, 66%). Participants in high ADI neighborhoods reported fewer acceptability barriers than those in low- and mid-ADI neighborhoods (39% vs. 79% and 71%, respectively; Fig. 2). As a facilitator, acceptability was mentioned by 33 (75%) participants. The most common facilitator was positive patient-coach relationships (22 participants, 50%), followed by healthcare navigation support (19 participants, 43%). No demographic differences were observed in reported facilitators for this dimension (Fig. 3).

Availability

Availability – defined as the program’s capacity to provide sufficient services and resources to meet participant needs – was referenced by 27 participants (participants: 61%; citations: 77; Fig. 1), cited 23 times (30%) as a barrier and 54 (52%) times as a facilitator. Thirteen (30%) participants identified availability as a barrier, with limited appointment scheduling being the most common barrier (mentioned by 7 participants, 16%). Participants in mid-ADI neighborhoods reported more availability barriers than those in high-ADI areas (43% vs. 17%; Fig. 2).

As a facilitator, 23 (52%) participants mentioned availability. The most frequently reported facilitator was flexible appointment scheduling (14 participants, 32%), followed by coordinating coaching sessions with regular clinic appointments (12 participants, 27%). Participants from low-ADI neighborhoods reported more availability facilitators than those from high-ADI areas (67% vs. 17%; Fig. 3).

Patient-Level Factors

Patient-level factors – defined as circumstances influencing participants’ ability to engage with the glaucoma coaching program outside of direct interactions with the healthcare system – were mentioned by 25 participants (participants: 57%; citations: 71; Fig. 1), cited 37 times (41%) as barriers and 34 times (39%) as facilitators. Overall, 18 (41%) participants identified patient-level factors as barriers. The most common barrier was comorbid medical conditions, reported by 8 (18%) participants. Another notable barrier was the location of the eye drop bottle within the home, mentioned by 7 (16%) participants. No demographic differences were observed for this dimension regarding reported barriers (Fig. 2).

In total, 17 (39%) participants mentioned patient-level factors as facilitators to the glaucoma coaching program. Positive social support was noted by 11 (25%) participants, while strategic placement of eye drops was cited by 10 (23%) participants. Participants living in high-ADI neighborhoods reported more patient-level facilitators compared to those in mid ADI neighborhoods (44% versus 14%) (Fig. 3).

Accommodation/Adequacy

Accommodation/adequacy – defined as whether the glaucoma coaching program ensured participants could effectively use its services – was mentioned by 16 participants (participants: 36%; citations: 32; Fig. 1), cited 17 times (23%) as a barrier and 15 times (16%) as a facilitator. Ten (23%) participants identified accommodation/adequacy as a barrier, with the most common issue being appointment length (reported by 5 participants, 11%). Being unaware of relevant phone numbers for contact was cited by 4 (9%) participants. No demographic differences were observed in reported barriers for this dimension (Fig. 2).

Seven (16%) participants mentioned accommodation/adequacy as a facilitator. The main facilitator was clinic staff assisting with medication refills, cited by all seven (16%) participants. Participants residing in mid-ADI neighborhoods reported more accommodation/adequacy facilitators than those in low- and high-ADI neighborhoods (43% versus 8% for low ADI and 6% for high ADI) (Fig. 3).

Accessibility

Accessibility – defined as participants’ ease of access to the glaucoma coaching program—was noted by 18 participants (participants: 41%; citations: 37; Fig. 1), cited 30 times as a barrier (36%) and 7 times as a facilitator (11%). Sixteen participants (36%) mentioned accessibility as a barrier to the glaucoma coaching program. Weather was the most cited barrier, mentioned by 8 participants (18%). Participants residing in mid-level ADI neighborhoods reported more accessibility barriers than participants residing in low ADI neighborhoods (71% vs. 13%) (Fig. 2). Five (11%) participants mentioned accessibility as a facilitator. The key accessibility facilitator was transportation services provided by the study/public transportation to appointments for five participants (11%). No demographic differences were observed for this dimension for reported facilitators (Fig. 3).

Affordability

Affordability – whether the glaucoma coaching program is financially accessible to participants, including any costs associated with participation – was mentioned by five participants (participants: 11%; citations: 9; Fig. 1), cited three times (5%) as a barrier and six times (9%) as a facilitator. Two (5%) participants identified affordability as a barrier, mainly due to concerns about potential co-payments for coaching programs. Four (9%) participants mentioned affordability as a facilitator, with the only facilitator being the gift cards received for participating. No demographic differences were observed among participants reporting affordability as either a barrier or facilitator (Fig. 2, 3).

Discussion

This study examined barriers and facilitators to the SEE glaucoma coaching program using the modified Penchansky and Thomas framework. The awareness dimension was the most cited facilitator, indicating the importance of the educational components for program interaction and engagement. The most frequently reported facilitator included understanding of glaucoma through educational resources. Acceptability was the most cited barrier, with the preference for face-to-face as the most frequently reported barrier. Significant differences across dimensions were found only by ADI: participants from less deprived areas reported more acceptability and awareness barriers; those from moderately deprived areas reported more accessibility barriers and accommodation facilitators; and those from highly deprived areas reported more patient-level facilitators. These results suggest that glaucoma coaching programs may be more effectively implemented when they address both individual-level and structural barriers.

Awareness emerged as a key facilitator and barrier, emphasizing the role of patient education in glaucoma coaching. In total, 98% of participants mentioned understanding glaucoma through education as a facilitator. Our results align with studies demonstrating the effectiveness of patient coaching interventions in chronic disease management through enhanced education and improved health outcomes. In a randomized clinical trial of patients that were previously non-adherent to their glaucoma medication, Newman-Casey et al. [32] reported that patients who received personalized glaucoma coaching participants that received a 6-month motivational interviewing-based glaucoma health-coaching program had significantly better glaucoma medication adherence compared to participants that received standard written glaucoma education (difference, 19.7%; 95% CI: 13.7–25.6; p < 0.001). In another randomized controlled trial, by Thom and colleagues [33], patients with poorly controlled diabetes, hypertension, and hyperlipidemia who received 12 months of individualized health coaching by medical assistants showed significantly higher medication adherence (difference in change, 10%; p = 0.05) and more fully adherent days (difference in change, 1.08 days; p < 0.001) compared to usual care. Similarly, in a collaborative program between pharmacists and health coaches described by Hurst et al. [21], a protocolized education curriculum was developed that combined medication education, lifestyle modification, self-monitoring, and goal-setting with motivational interviewing. This approach led to statistically significant 1-year improvements in glycemic control (mean reduction: 1.51%, p < 0.001), blood pressure (mean reduction: 13.6 mm Hg, p < 0.001), and lipids (mean reduction: 14.7 mg/dL, p = 0.014). Health-coaching studies have demonstrated improvements in patient knowledge and self-efficacy compared to usual care. Chen et al. [34] developed an eHealth coaching program for chronic kidney disease (CKD) patients that incorporated targeted CKD-specific health literacy and knowledge domains, demonstrating improved overall health literacy (coefficient = 2.51, p = 0.02) and CKD knowledge (coefficient = 1.66, p = 0.001) compared to the standard education. These studies, together with our findings, show that patients may benefit from the educational elements of coaching interventions by gaining knowledge they can apply to themselves and improving personalized chronic disease self-management. These findings suggest that comprehensive education may be an important core element of personalized glaucoma coaching programs.

The study found that preference for face-to-face interactions (66%) was the most commonly reported barrier to glaucoma coaching program engagement. This aligns with previous research, including a scoping review of mHealth and health-coaching interventions, which showed patients consistently prefer in-person communication over telehealth, reporting higher satisfaction and engagement with physical coaching [35]. Being physically present during health-coaching sessions helps build trust and a stronger relationship between coaches and patients. McQueen et al. [36] interviewed 140 adults in a health-coaching program and revealed that patients preferred incorporating in-patient interactions, associating them with greater empathy, motivation, and accountability. In-person sessions may also provide a dedicated environment free from home or workplace distractions. Johnson et al. [37] found that patient-coach visits allowed for focused engagement, with fewer external distractions compared to remote or home-based interactions, facilitating more effective coaching. Furthermore, attending scheduled face-to-face sessions is associated with increased motivation, accountability, and a stronger sense of commitment to health goals. Henry et al. [24] demonstrated that personalized, in-person coaching sessions for a primary care health-coaching program led to significant improvements in post-intervention patient activation (Patient Activation Measure score 57.1 vs. 70.8, p < 0.00001) and self-management behaviors. Questionnaire feedback showed that attending these sessions fostered ownership and commitment. These results suggest that including or retaining in-person visits may add value in glaucoma coaching programs.

Notable variations emerged across neighborhood deprivation levels. Participants residing in moderately deprived neighborhoods reported substantially more accessibility barriers compared to neighborhoods that face less deprivation (71% vs. 13%). This difference aligns with research showing that residents of moderate-ADI neighborhoods experience greater transportation challenges, such as limited vehicle ownership and less reliable public transport, along with technology barriers like reduced internet access and lower digital literacy, compared to those in low-ADI areas [38–41]. Conversely, participants from high ADI neighborhoods reported greater patient-level facilitators, particularly positive social support (44% vs. 14% in mid-ADI areas). Research by Kearns et al. [42] found that weak social networks and limited community engagement in high-deprivation areas contribute to worse health and well-being outcomes. However, Mohan and Barlow found that active involvement in social groups was associated with better mental health outcomes even in high-deprivation areas, suggesting that strong social support and community engagement can partially mitigate the adverse effects of neighborhood deprivation on health [43]. These studies support our findings, indicating that neighborhood deprivation could affect patient engagement and healthcare outcomes. This underscores the importance of considering neighborhood-level factors when implementing glaucoma coaching programs.

The study has several limitations that should be considered. First, the sample size, though suitable for qualitative analysis, limits demographic generalizability. Second, while Black and White participants were evenly represented, other racial and ethnic groups were underrepresented which restricts applicability to more diverse populations. All participants had insurance, possibly explaining the low affordability barriers observed. Third, focusing only on those who completed interviews could introduce selection bias, as less engaged individuals may have different barriers and facilitators. The study took place at a tertiary medical center, which may not reflect experiences in other healthcare settings. In addition, data were collected between 2016 and 2018, before recent changes in telehealth and digital health technologies. Finally, though the constructs of agency and affect were not included in our current framework, they are very relevant constructs that have been included in some expansions of Thomas and Penchansky’s framework [44] Taking a global look at participant comments using the construct of agency, the coaching program was instrumental in facilitating agency and self-efficacy by focusing on the importance of rapport and relationship building between the coach and the patient. Specifically, one participant mentioned “(they) certainly took time and made sure that it wasn’t just you know, like I have to do this, I have to check in. But she wanted to give me time to express anything and any question(s) that I (had) and I really appreciated that.” In terms of affect, participants expressed how having a coach and having good rapport with the coach helped them to become more empowered and less anxious about their glaucoma diagnosis. Participants in the program mentioned “I feel very confident, you know, now (I can handle), my situation you know for this, this drug… And thank you, thank you very much, you know, what you guys (are) doing with us because people (when) they don’t have (the) knowledge to know what’s going on and how to treat, (or) how to handle these situation with the glaucoma (they) make up mistakes … because they don’t really know how to handle their situation” and “I almost feel like this is a life-saving program because I feel like you guys have helped save my sight. Because I’m missing not quite 25% of my vision in my right eye and had I not started upping my compliance, there's a chance I could have lost more vision in either eye. And that would greatly affect my quality of life and that's something I want. So, this really has been good for me.”

In conclusion, this study identified awareness facilitators as an important influencer to participation in the SEE program, while acceptability was the most prevalent barrier. The study also identified that barriers and facilitators differed by participant area deprivation. The results suggest that tailoring glaucoma self-management programs to account for differences associated with neighborhood deprivation may help improve their effectiveness.

Statement of Ethics

This study was approved by the University of Michigan Medical School IRB (HUM# HUM00112614). The study followed the Tenants of Helsinki. Participants provided written informed consent.

Conflict of Interest Statement

The authors report no conflicts of interest related to this research.

Funding Sources

The authors have no proprietary or commercial interest in any materials discussed in this article. This research was supported by The Glaucoma Foundation, sponsored by Patricia Hill, through the Research to Prevent Blindness Glaucoma Fellowship (Hicks), and by the National Institutes of Health: NEI R01EY031337-01 (Newman-Casey, Mitchell, Darnley-Fisch, Heisler), NEI R01EY031337-03S1 (Hicks), NIGMS K12GM111725 (Hicks), and P30 EY007003 (Hicks). The funding sources had no role in the study design, execution and analysis, manuscript conception, planning, writing and decision to publish.

Author Contributions

S.L.: formal analysis, investigation, writing (original), writing (edits), and visualization. P.M.H.: funding, formal analysis, writing (original), writing (edits), visualization, conceptualization, investigation, project administration, methodology, data curation, and supervision. J.N.D.: data curation, investigation, and writing (edits). J.M.: writing – review and editing. D.D.F.: conceptualization, Writing – review and editing. M.H.: Conceptualization, Writing – review and editing. N.R.I.: Writing – review and editing. P.A.N.C.: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing – review and editing.

Funding Statement

The authors have no proprietary or commercial interest in any materials discussed in this article. This research was supported by The Glaucoma Foundation, sponsored by Patricia Hill, through the Research to Prevent Blindness Glaucoma Fellowship (Hicks), and by the National Institutes of Health: NEI R01EY031337-01 (Newman-Casey, Mitchell, Darnley-Fisch, Heisler), NEI R01EY031337-03S1 (Hicks), NIGMS K12GM111725 (Hicks), and P30 EY007003 (Hicks). The funding sources had no role in the study design, execution and analysis, manuscript conception, planning, writing and decision to publish.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy and ethical considerations, the data are not publicly accessible.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy and ethical considerations, the data are not publicly accessible.

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