ABSTRACT
Background
Tele‐behavioral health (TBH) expanded significantly during the COVID‐19 pandemic, but the extent to which this addressed or reinforced existing rural–urban disparities remains unclear, especially in states with diverse and underserved populations. This study investigates the role of rurality and broadband access in TBH utilization among adult Medicaid beneficiaries in New Mexico—a highly rural and racially diverse state.
Methods
We analyzed New Mexico Medicaid claims data from March 2019 to March 2021 for outpatient behavioral health visits. Using logistic regression models, we assessed differences in TBH use by rurality, race/ethnicity, and broadband connectivity across pre‐ and post‐public health emergency (PHE) periods. Visits were categorized by modality (in‐person, audio‐only, video) and linked with county‐level data on broadband access and provider availability.
Results
Our study included 2,934,451 behavioral health visits among 216,898 unique Medicaid members. Total patient visits for BH increased by 19.2% from 1,326,473 (72.7% urban, 27.3% rural) to 1,607,978 (72.3% urban, 27.7% rural) across the study period. Urban patients had greater odds of TBH use (specifically audio‐only visits) compared to rural patients, even in rural counties with high broadband access. Modeling results demonstrated that urban patients had greater odds of a TBH visit as compared to rural patients, which was more pronounced in high versus low connectivity counties (OR 2.20; 95% CI 2.21, 2.27 vs. OR 1.53; 95% CI 1.51, 1.56). Native American patients had the greatest odds of TBH visits, which may reflect established telehealth infrastructure within the Indian Health Service.
Conclusions
TBH expanded behavioral health access for Medicaid beneficiaries, but rural disparities persist and are not mitigated by broadband access alone. Rather, existing telehealth infrastructure appears to be related to improved rural TBH access. These findings underscore the need for policy efforts beyond broadband expansion to achieve equitable TBH access across diverse and rural populations.
Keywords: behavioral health, Medicaid, rural health, tele‐behavioral health, telehealth
1. Introduction
Tele‐behavioral health (TBH), or telemedicine treatment for mental illness and/or substance use disorders (SUD), is an effective but underutilized solution to reduce disparities in access to behavioral health in rural communities [1, 2, 3]. In January 2025, the Centers for Medicare and Medicaid Services (CMS) made permanent the pandemic‐related TBH flexibilities for Medicare enrollees, allowing patients to access TBH services in any setting, including their home, regardless of their geographic location (urban or rural). Many states have similarly made TBH services permanent for Medicaid members. Early research demonstrates that expansion of telehealth during the COVID‐19 pandemic experienced uneven uptake, and may in fact reinforce rather than mitigate rural–urban disparities in health care access [4, 5, 6, 7]. While urban areas outpaced telehealth access as compared to rural areas, disparities in access were present in urban settings, with affluent and White patients gaining improved access to telehealth as compared to lower‐income and minority patients [8, 9, 10, 11]. The differences in telehealth growth by geography are complicated, and raise concerns about whether telehealth, despite its promise, is closing equity gaps or inadvertently widening them.
Telehealth services are promoted as a solution to rural care disparities, but rural communities face the persistent problem of limited access to broadband and to healthcare [10, 12]—particularly BH clinics and providers [2, 13]. Additionally, rural communities are often low‐income, racially and ethnically diverse. Thus, improving broadband is critical, but other factors likely contribute to rural versus urban TBH disparities. This study examines whether broadband access affects urban and rural communities differently, which may provide insights for policymakers on prioritizing broadband or focusing on other, potentially equally impactful, factors.
New Mexico is an important setting in which to study this question. It consistently ranks fifth in suicide rates [14] and sixth in drug overdose deaths [15], and faces significant behavioral health treatment shortages [16]. It is a highly rural state, with 1 in 3 New Mexicans living in a rural area, and 26 of 33 counties are considered rural or frontier [17, 18] with variable broadband access within urban and rural regions [19]. While urban communities often have adequate broadband infrastructure, urban “digital divides” exist due to affordability and digital literacy, with lower‐income individuals relying on 5G cellular networks. The state's Medicaid program provides coverage to approximately 40% of the state population, which is 51% Hispanic and 13% Native American [18]. New Mexico has legislation supporting payment parity for telehealth coverage across payors [20], and with TBH coverage parity in Medicaid, including audio‐only TBH services. Further, the Northwest corner of the state is home to the Navajo Nation, which is served by the Indian Health Services (IHS). IHS is a health care system that provides medical care to individuals enrolled in Native American tribal communities, and approximately 30% of these individuals also qualify for Medicaid. IHS was an early adopter of telehealth as an important part of its care delivery model to fulfill its mission of service provision to remote and rural regions and to address specialty workforce shortages [21, 22, 23]. Given the existing telehealth infrastructure, supportive state policies, and the diverse landscape of racial–ethnic and urban–rural differences in New Mexico, measuring the uptake and differential access to Medicaid TBH services can generate important findings and questions that may extend to other states.
Medicaid claims are a detailed source of health care utilization data, particularly with respect to racial–ethnic and urban–rural differences. Thus, we examined: (1) whether TBH use increased more among beneficiaries in counties with greater access to broadband, and (2) whether TBH access differed by rural versus urban residence, and across racial–ethnic groups. Answering these questions provides critical insights into how telehealth expansion interacts with both structural inequities and technology infrastructure, shaping access to TBH services in a highly rural and racially and ethnically diverse state.
2. Methods
2.1. Study Design
We examined in‐person BH and TBH visits from March 1, 2019, to March 31, 2021, using New Mexico Medicaid claims data for outpatient behavioral health treatment visits. New Mexico declared a public health emergency (PHE) effective March 1, 2020, after which all clinical services were required to be telehealth‐based. We thus defined our pre‐PHE period as March 1, 2019–February 29, 2020, and post‐PHE period as April 1, 2020–March 31, 2021, with, March 2020 serving as a washout period. Using logistic regression models, we examined the extent to which sociodemographic county‐level factors, broadband access, and patient characteristics were associated with rural TBH access. We defined urban versus rural at the county‐level, in accordance with 2020 U.S. Census Bureau definitions. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. The University of New Mexico's Health Science Center's Institutional Review Board approved this study. This study is an analysis of secondary data (claims), and therefore, patient consent was not required. It is not a clinical trial, and there is no clinical study registration number.
2.2. Study Population
Our study was restricted to beneficiaries aged 18 and older with claims during March 1, 2019–March 31, 2021. We developed claims criteria to define outpatient visits for behavioral health using a combination of ICD‐10 codes, claim type codes, and place of service codes. To be included, claims had to have a first position diagnosis of a BH condition and meet the definitions for an outpatient visit, regardless of provider type (e.g., primary care, behavioral health, and hospital or health system). We defined audio‐only and video‐based outpatient encounters using claim type codes, procedure codes, procedure code modifiers, place of service codes, and revenue codes (Appendix A). As a significant proportion of BH care in New Mexico is provided by non‐BH providers, we did not limit BH care by clinician type or service provided (e.g., it was not restricted to psychotherapy).
2.3. Variables
Visit‐level variables included patient age, gender, race/ethnicity, county of residence, and primary ICD‐10 diagnosis codes. We used patients’ county of residence (per encounter) and the U.S. Census Bureau definitions to assign urban or rural location according to the U.S. Census Bureau [24]. Rurality was a binary, county‐level classification for counties that were designated as fully eligible rural counties in the HRSA Federal Office of Rural Health Policy (FORHP) Rural Health Grants Eligibility File as of September 1, 2021. Counties that were not designated as fully eligible rural were classified as urban. We identified mental illness and/or substance use disorders using primary ICD‐10 codes (Appendix A). We defined substance use disorder (SUD) as a visit with an ICD‐10 diagnosis of F10‐19. For each visit, we calculated an Elixhauser comorbidity score from a 12‐month look‐back of the claims preceding the analyzed visit. We also calculated a variable reflecting patient‐level outpatient visit utilization, allowing for encounters to be adjusted by the patient's propensity to use outpatient visits across the study period.
To consider county‐level factors, we assigned each visit two additional variables: (1) county population per behavioral health provider and (2) county Medicaid population per behavioral health provider, using the 2021 New Mexico Workforce Data Report to estimate total primary care and behavioral health providers per county. County‐level physician supply closely reflects the number of Medicaid‐serving providers, as over 93% of physicians in New Mexico accept Medicaid [25]. For broadband connectivity, we used the Federal Communications Commission's (FCC) internet services report, which includes information about county‐level subscriptions to home broadband internet, measured as the number of connections per 1000 households per county [26]. We categorized each claim as occurring in a low/medium/high broadband county. The cut‐points included every county with the following categorizations: low connectivity (< 200 connections with > 25 Mbps connectivity), medium connectivity (200–499 connections with > 25 Mbps connectivity), and high connectivity (≥ 500 connections with > 25 Mbps connectivity) per 1000 households in each county and with respect to rurality according to the U.S. Census designations for 2020 (Appendix B).
2.4. Analysis
The unit of analysis was the patient encounter. Encounters were categorized as rural versus urban, in accordance with the clients’ county of residence. Using descriptive statistics, we measured in‐person BH and TBH (audio‐only and video) visits for pre‐ and post‐PHE periods. We compared proportions of in‐person BH and TBH visits in the pre‐ versus post‐PHE periods, comparing rurality in Table 1. To improve proportional comparison interpretation, we adjusted these proportions by county‐level enrolled Medicaid population, with caveat that we did not consider person‐time. Finally, we examined encounters across behavioral health conditions, such as substance use, schizophrenia, or mood disorders (ICD‐10 F‐codes), comparing ratios of in‐person to TBH between rural versus urban counties.
TABLE 1.
Changes in behavioral health visits, comparing urban and rural in the pre‐ and post‐pandemic periods.
| Urban | Rural | |||||
|---|---|---|---|---|---|---|
| Pre‐PHE | Post‐PHE | % Change | Pre‐PHE | Post‐PHE | %Change | |
| Total | 964,114 | 1,162,991 | 20.6% | 362,359 | 444,987 | 22.8% |
| In‐person | 948,367 (98.4%) | 720,312 (61.9%) | −24.1% | 351,130 (96.9%) | 309,488 (69.5%) | −11.9% |
| Total TBH | 15,747 (1.6%) | 442,679 (38.1%) | 2711.2% | 11,229 (3.1%) | 135,499 (30.5%) | 1106.7% |
| Audio‐only | 5401 (0.6%) | 260,799 (22.4%) | 4728.7% | 3042 (0.84%) | 64,565 (14.5%) | 2022.5% |
| Video | 10,706 (1.1%) | 181,880 (15.6%) | 1598.9% | 8187 (2.3%) | 70,934 (15.9%) | 766.4% |
To determine whether the change in TBH encounters was associated with racial and ethnic characteristics and broadband access, we conducted a multivariable logistic regression analysis of visits across the entire study period, controlling for Medicaid enrollment at the county level, county behavioral health workforce, county poverty status, and patient comorbidities as described previously. For poverty status, we created a binary variable based on whether at least half of the population in the county was below poverty level or not. To facilitate equal comparisons, we combined low and medium connectivity counties (category: lower) and compared them to higher connectivity counties (see Appendix B). Models were stratified on connectivity to estimate the effect of rurality on TBH utilization among low/medium connectivity patients and separately among high connectivity patients. This stratified model between lower connectivity and higher connectivity counties examined the overall outcome (urban vs. rural TBH), and by patient characteristics. We report outcomes with odds ratios (ORs) and 95% confidence intervals (CIs).
3. Results
3.1. Sample Characteristics
Our study sample included 2,934,451 BH visits among 216,898 unique beneficiaries (Table 2). TBH visit counts were similar for urban and rural counties from March 2019 through February 2020 (Figure 1, Panel A). Beginning in March 2020, there was a sharp increase in telehealth visits among urban counties, while rural counties experienced a smaller increase. Gender, age, and race/ethnicity were similar between rural versus urban visits. There were more visits for rural versus urban patients with serious mental illness (SMI) (28.9% rural, 23.9% urban), and SUD (41.6% rural, 36.5% urban), and fewer visits for patients in optimal broadband regions (17% rural, 88% urban).
TABLE 2.
Characteristics of New Mexico Medicaid visits with a primary diagnosis of a mental health condition for telehealth and in‐person visits, by urban and rural counties, from March 2019 to March 2021.
| Urban (2,127,105) | Rural (807,346) | |||
|---|---|---|---|---|
| Telehealth | In‐Person | Telehealth | In‐person | |
| No. visits, count | 458,426 | 1,668,679 | 146,728 | 660,618 |
| Age, mean (SD) | 36.80 (12.12) | 38.36 (12.15) | 38.66 (12.60) | 39.24 (12.62) |
| Female, count (%) | 285,652 (62.3%) | 835,857 (50.1%) | 94,484 (64.4%) | 362,315 (54.8%) |
| Race/ethnicity, count (%) | ||||
| White/NH | 179,131 (39.0) | 584,258 (35.0) | 61,507 (41.9) | 248,970 (37.7) |
| White/Hispanic | 197,266 (43.0) | 806,494 (48.3) | 59,611 (40.6) | 306,205 (46.4) |
| AI/AN/NH | 39,547 (8.6) | 140,020 (8.4) | 14,767 (10.1) | 58,332 (8.8) |
| AI/AN/Hispanic | 3333 (0.7) | 12,394 (0.7) | 1495 (1.0) | 4777 (0.7) |
| Black/NH | 10,347 (2.2) | 40,015 (2.4) | 2624 (1.8) | 13,836 (2.1) |
| Black/Hispanic | 1727 (0.3) | 4134 (0.2) | 294 (0.2) | 1158 (0.2) |
| Multiracial/ethnic | 27,075 (5.9) | 81,364 (4.9) | 6430 (4.4) | 27,340 (4.1) |
| SMI, count (%) | 126,003 (27.4) | 381,549 (22.8) | 47,474 (32.3) | 185,936 (28.1) |
| SUD, count (%) | 106,122 (23.1) | 778,266 (46.6) | 32,278 (21.9) | 262,697 (39.7) |
| Connectivity, count (%) | ||||
| Low | 0 (0) | 0 (0) | 65,370 (44.6) | 327,781 (49.6) |
| Medium | 56,821 (12.4) | 190,365 (11.4) | 58,918 (40.1) | 218,472 (33.1) |
| High | 401,605 (87.6) | 1,478,314 (88.6) | 22,440 (15.3) | 114,365 (17.3) |
Note: These characteristics reflect visit‐level demographics, not beneficiary‐level. Each race is stratified by Hispanic and NH = non‐Hispanic, in accordance with Medicaid enrollment file data. Serious mental illness (SMI) is defined as F‐code F20‐39 (e.g., schizophrenia, psychosis, mood disorder). Substance use disorder (SUD) is defined as F10‐19. Connectivity was defined by FCC data, representing median connections per 1000 households with at least 25 Mbps internet speed within each county, categorized by tertiles (low, medium, high) from 2021.
FIGURE 1.
Unadjusted monthly tele‐behavioral health visits and percentages by urban and rural counties over the study period. (A) Monthly count of TBH visits by county classification (urban vs. rural). (B) Monthly percentage distribution of TBH visits by county classification. Percentages were calculated using all TBH visits in a given month as the denominator; thus, the proportions for urban and rural counties sum to 100% for each month.
3.2. Changes in Overall BH (In‐Person and Telehealth) Visits
Total patient visits for BH increased from 1,326,473 (27.3% rural, 72.7% urban) to 1,607,978 (27.7% rural, 72.3% urban) across the study period, a rise of 19.2% (Table 1). In the urban setting, the total BH visits increased by 20% (964,114–116,299) with a 3% expansion of beneficiaries served (73,875–76,050). For rural settings, visits increased by 22.8% (362,359–444,987) with a 1.6% expansion of beneficiaries served (37,283–37,838).
3.3. Changes in TBH Visits
During the pre‐PHE period, TBH visits in urban counties comprised a smaller proportion of total visits (e.g., in‐person and TBH; 1.6%) than in rural counties (3.1%), which reversed post‐PHE (38.1% urban, 30.5% rural) (Table 1). When examining the distribution of visits (Figure 1, Panel B), rural counties initially accounted for a slightly higher proportion of telehealth visits in March 2019 (53.43% rural vs. 46.57% urban). By June 2019, however, the proportion shifted toward urban counties (44.98% rural vs. 55.02% urban). The gap between urban and rural visit proportions widened rapidly in March 2020 and over time, reaching 23.32% rural and 76.68% urban by March 2021. Despite an overall larger increase from pre‐ to post‐PHE in the number of visits in urban counties, rural counties experienced a greater percentage increase (22.8%) compared to urban counties (20.6%) (Table 1). When adjusting for county population, we found similar results to our main findings, with overall visits rising from 2.1 visits per urban and 1.0 visits per rural Medicaid beneficiary to 2.2 for urban and 1.4 for rural beneficiaries.
3.4. Changes by TBH Visit Modality
Urban patients experienced a greater gain in audio‐only TBH (22.4% urban vs. 14.5% rural) and similar gains in video TBH (15.6% urban vs. 15.9% rural) visits (Table 1). When we examined this by BH diagnosis category, we found that only substance use disorder and schizophrenia maintained a greater proportion of in‐person visits, with no urban versus rural differences.
3.5. Model Results
Results from statistical modeling demonstrated that urban patients had greater odds of a TBH visit as compared to rural patients, which was more pronounced in high versus low connectivity counties (high: OR 2.20; 95% CI 2.21, 2.27 vs. low: OR 1.53; 95% CI 1.51, 1.56) (Table 3). Racial–ethnic differences demonstrated that American Indian/Alaskan Native (AI/AN) visits in lower connectivity counties had the greatest odds (OR 1.58 [1.55, 1.61]) of TBH visits, while White/Hispanic (OR 0.88 [0.86, 0.89]) and Black/non‐Hispanic [OR (0.79, 0.89]) individuals had lower odds of using TBH (Table 3). Those with SUD and SMI also had lower odds of TBH visits, regardless of connectivity.
TABLE 3.
TBH access and utilization between low/medium versus high connectivity counties.
| Lower connectivity | Higher connectivity | |||
|---|---|---|---|---|
| Characteristics | OR (95% CI) | p value | OR (95% CI) | p value |
| Urban (ref: rural) | 1.53 (1.51, 1.56) | < 0.001 | 2.20 (2.21, 2.27) | < 0.001 |
| Visit count > 5 (ref: < 5) | 1.49 (1.45, 1.53) | < 0.001 | 1.48 (1.45, 1.51) | < 0.001 |
| Race/ethnicity (ref: White/NHS) | ||||
| White/HS | 0.88 (0.86, 0.89) | < 0.001 | 0.95 (0.94, 096) | < 0.001 |
| AI/AN | 1.58 (1.55, 1.61) | < 0.001 | 1.03 (1.01, 1.05) | 0.003 |
| AI/AN/HS | 1.17 (1.10, 1.25) | < 0.001 | 1.00 (1.05, 0.92) | 0.924 |
| Black | 0.84 (0.79, 0.89) | < 0.001 | 0.77 (0.76, 0.79) | < 0.001 |
| Black/HS | 1.30 (1.13, 1.50) | < 0.001 | 1.13 (1.06, 1.21) | 0.001 |
| Multiracial/ethnic | ||||
| SUD | 0.38 (0.37, 0.38) | < 0.001 | 0.20 (0.19, 0.20) | < 0.001 |
| SMI | 0.89 (0.87, 0.90) | < 0.001 | 0.70 (0.70, 0.71) | < 0.001 |
Note: Multivariable logistic regression model was adjusted for poverty (below 50% poverty line, yes or no, by county), visit‐measured Elixhauser comorbidity score, and county population per healthcare provider and Medicaid population per healthcare provider. Healthcare providers were defined as behavioral health and primary care physicians, from the New Mexico Workforce Report, using the 2021 annual report. Race/ethnicity categories are presented with White, non‐Hispanic (White/NHS) as the reference group. HS denotes Hispanic, and NHS denotes non‐Hispanic. AI/AN denotes American Indian or Alaska Native. Categories include White Hispanic (White/HS), American Indian or Alaska Native non‐Hispanic (AI/AN), American Indian or Alaska Native Hispanic (AI/AN/HS), Black non‐Hispanic (Black), Black Hispanic (Black/HS), and Multiracial/ethnic. Poverty was made dichotomous, as counties with < 50% of population with incomes below the federal poverty line (wealthier) versus ≥ 50% (poorer counties). We defined serious mental illness (SMI) as primary (first position in the claim diagnosis) diagnosis code of F20‐39 and substance use disorder (SUD) as a primary diagnosis code of F10‐19.
4. Discussion
In this analysis, we observed an overall increase in behavioral health visits (both in‐person and telehealth) for Medicaid enrollees in New Mexico. TBH increased more in the urban versus rural settings, primarily driven by audio‐only visits, with video‐visits expanding similarly across both settings. Urban uptake of TBH was greater, as compared to rural, in both lower connectivity counties (OR 1.53 [1.51, 1.56]) and in higher connectivity counties (OR 2.20 [2.14, 2.27]). This finding that rural disparities are worsened in high connectivity counties indicates the presence of unmeasured factors beyond broadband, which may include use of 5G cellular service instead of broadband (e.g., being able to conduct audio‐visits despite low broadband) and telehealth system maturity, which in turn influences patient comfort and provider use of telehealth. A clue to unmeasured factors may be found in our results, where we found that visits for American Indian/Alaskan Native (AI/AN) patients had the greatest odds of TBH encounters, particularly in lower connectivity counties. This may reflect telehealth infrastructure as an unmeasured factor, as the IHS in the Navajo Nation (generally rural and low connectivity) has a mature telehealth delivery system, which may reduce rural TBH access disparities.
Our finding that TBH increases access to BH services overall is reassuring. Existing literature supports non‐inferiority of general BH treatment through phone and video modalities, as compared to in‐person visits [27, 28, 29, 30, 31]. Patients and providers find TBH acceptable, with some preference toward integrating audio‐ and video‐visits with in‐person visits [32, 33, 34]. Our study's finding that audio‐only visits increased in urban versus rural settings was unexpected, as we anticipated more utilization of video‐visits within urban settings and potentially more audio in rural due to poor connectivity. The limited audio‐only expansion in rural counties may be explained by rural New Mexico's struggles with reliable access to cellular service [35]. While patient and provider preferences may play a role in TBH disparities, rural patients express similar willingness to use telehealth services despite less access [36] and rural providers and patients are generally favorable toward telehealth, despite technology barriers [37]. As the expansion of TBH services continues, the balance of audio‐only, video, and in‐person visits will evolve, and close attention should be paid to narrowing the rural–urban gap in access.
A unique aspect of New Mexico Medicaid is that it provides insurance to a large proportion of American Indian/Alaska Native (AI/AN) and Hispanic individuals, allowing for detailed analysis of TBH uptake across these racial and ethnic communities, which have been understudied. In our study, AI/AN visits experienced the greatest increase in access to TBH, particularly in low/medium connectivity counties. While there is limited literature on AI/AN access or utilization of telehealth since the pandemic, one national study of Veterans Affairs data demonstrated fewer telemedicine visits for AI/AN veterans, particularly for urban versus rural areas, as compared to other races and ethnicities [38]. Our contrasting findings may reflect a regional investment in telemedicine services by the IHS (the agency responsible for healthcare provision to tribally‐enrolled Americans) in the Navajo Area, which spans Arizona and New Mexico, which is documented as an early adopter of telemedicine [21, 39].
The IHS established telehealth services in 1973 to achieve its mission of providing access to care for AI/AN living in rural and frontier areas [40]. Despite serving highly rural and poor broadband regions, the IHS has developed region‐specific telehealth systems which leverage community connections and local clinics, and are designed to meet the needs of the community. For example, for the last 30 years, the IHS has provided telehealth services across Alaska to tribal communities via the Alaskan Tribal Health System, with telehealth platforms installed in over 250 clinics in towns as small as 300 people [21, 39]. Similarly, the IHS Tele‐Behavioral Health Center of Excellence (TBHCE) was established in 2008 to provide behavioral health services across the country through real‐time (synchronous) televideo connections [41, 42]. This is particularly well developed in the Southwestern United States, including New Mexico and the Navajo Nation. Despite poor broadband access across the region, IHS utilized tribal health facilities and small clinics, serving as loci of telehealth. During the pandemic, the IHS further invested in updated facility infrastructure and increased broadband access [22, 42].
Similar to existing literature, we found lower odds of TBH visits for Black and Hispanic Medicaid beneficiaries compared to White/non‐Hispanic beneficiaries, which is consistent with literature on racial–ethnic telemedicine disparities since the pandemic [43, 44]. Notably, there are no specific policy initiatives (unlike within the Navajo IHS) that target those communities for TBH access which may play a role in these nuanced racial–ethnic findings. Thus, when the pandemic abruptly required a switch from in‐person to telehealth services, the IHS was likely able to leverage existing investment and infrastructure to transition larger numbers of rural and remote patients to telehealth services.
Broadband expansion is a critical policy initiative, and national studies note a positive relationship between video‐visits and optimal broadband [45, 46]. Yet, our study suggests that broadband access alone is insufficient to reduce the urban–rural divide, as one would expect to see an increase in TBH in rural communities with high broadband connectivity. In fact, our study demonstrates the opposite—that living in a high connectivity county reduces the odds of TBH visit for rural versus urban. This means factors beyond connectivity should be addressed. These may include provider availability and distribution, which is a persistent problem in rural mental health care [13], or perhaps less awareness of TBH services or slower adoption by rural general and behavioral health providers. Further research should examine more recent periods examining TBH provision by provider‐type and location, to address these questions.
4.1. Limitations
We have several limitations. First, our study concluded in 2021, and trends continue to evolve in TBH delivery across New Mexico. However, this serves as a baseline examination of early post‐pandemic, TBH utilization, and the demographics of New Mexico allow for a large rural sample size and a detailed racial–ethnic breakdown, with a particularly large Native American/American Indian and Hispanic population. Subsequent research can focus on how these disparities have evolved, particularly in recent years and with new federal policies supporting TBH. Second, this is a visit‐level analysis, where many beneficiaries have multiple visits. To adjust for this, we included a covariate of patient‐level outpatient visit utilization. Thus, each patient visit is adjusted by the patients propensity to use outpatient visits across the study period. Third, our urban/rural county designation is broad; while more granular geographic information (ZIP code) is preferable, these data were not available. By measuring broadband at the county level, our findings are at the population level and may not reflect the broadband experienced by the individual seeking treatment. Fourth, we restricted our dataset to primary diagnoses only, which may miss claims from PCPs where mental conditions were addressed, but a medical reason was the initial reason for the visit. Fifth, the relatively small proportion of pre‐PHE TBH claims compared with post‐PHE TBH claims limited the feasibility of temporally stratified regression models, as sparse data in several strata resulted in unstable and unreliable parameter estimates. Finally, we did not consider health care delivery locations (e.g., clinic, hospital, etc.) and are unable to account for facility‐level factors that may affect the availability and accessibility of telehealth.
5. Conclusion
Using Medicaid claims data in New Mexico, our analysis suggests that TBH utilization increased behavioral health service uptake/utilization among Medicaid enrollees. This occurred in both rural and urban settings, with a greater increase in TBH encounters in urban versus rural counties. Unresolved questions include why there are fewer rural visits per population even in regions with adequate broadband connectivity, and to explore the hypothesis that established telehealth delivery infrastructure is associated with improved access (and reduces traditional racial disparities). It will also be important to determine the balance between audio‐only, video‐visits, and in‐person services. It is particularly important to determine if traditionally disadvantaged groups are more likely to receive an audio‐only visit and the impact audio‐only visits have on care quality.
Funding
This research was supported by the Agency for Healthcare Research and Quality (K08HS029038).
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors have nothing to report.
1. Claims Criteria: Outpatient Behavioral Health Visit
ICD‐10 Diagnoses: Initial identification of claims was conducted by identifying any with a first‐position ICD‐10 diagnoses of a mental health condition, as below.
| Diagnoses name | ICD‐10 codes |
|---|---|
| Mental disorders due to known physiological conditions | F01‐09 |
| Mental and behavioral disorders due to psychoactive substance use | F10‐19 |
| Schizophrenia, schizotypal, delusional, and other non‐mood psychotic disorders | F20‐29 |
| Mood [affective] disorders | F30‐39 |
| Anxiety, dissociative, stress‐related, somatoform, and other nonpsychotic mental disorders | F40‐48 |
| Behavioral syndromes associated with physiological disturbances and physical factors | F50‐59 |
| Disorders of adult personality and behavior | F60‐69 |
| Intellectual disabilities | F70‐79 |
| Pervasive and specific developmental disorders | F80‐89 |
| Behavioral and emotional disorders with onset usually occurring in childhood and adolescence | F90‐98 |
| Unspecified mental disorder | F99 |
Criteria to further classify outpatient behavioral health visit: We used a combination of Claim Type Codes, Place of Service codes, CPT/HCPCS codes, Revenue Center codes, and procedure modifier codes to classify visits as tele‐behavioral health (video and audio‐only) or in person.
| Visit type | Claim Type O | Claim Type P |
|---|---|---|
| Video |
Criteria #1 Line_Proc_Code_Mod1‐4: GQ, GT, 95 AND Proc_Cd: list of CPT/HCPCS or Revenue Center Codes (see the Code Type table below) OR Criteria #2 Proc_Cd = Q3014 |
Criteria #1 (Line_Proc_Code_Mod1‐4: GQ, GT, 95 AND Proc_Cd: list of CPT/HCPCS or Revenue Center Codes (see the Code Type table below) AND Place_of_Svc_Cd: 3, 5–8, 11–14, 16–19, 22, 49, 50, 53, 57, 58, 62, 71, 72 OR Criteria #2 Proc_Cd = Q3014 AND Place_of_Svc_Cd: 3, 5–8, 11–14, 16–19, 22, 49, 50, 53, 57, 58, 62, 71, 72 |
| Audio‐only | N/A |
Criteria #1 Line_Proc_Code_Mod1‐4: Cannot be GQ AND Place of Service Code: 02 AND Proc_Cd: list of CPT/HCPCS or Revenue Center Codes (see the Code Type table below) OR Criteria #2 Line_Proc_Code_Mod1‐4: Cannot be GQ AND Place of Service Code: 02 AND Proc_Cd = Q3014 |
| All tele‐behavioral health | Any of the conditions as identifier for video and audio‐only, based on outpatient claim Type O or claim Type P | |
| In‐person visit |
CPT/HCPCS codes and Revenue Center Codes per Code Type Table without Modifier Codes or Place of Service Codes to Qualify as Video or Audio‐only above: video = 0 AND audio‐only = 0 |
CPT/HCPCS codes and Revenue Center Codes per Code Type Table without Modifier Codes or Place of Service Codes to Qualify as Video or Audio‐only above: video = 0 AND audio‐only = 0 AND Place of Service Codes: 3, 5–8, 11–14, 16–19, 22, 49, 50, 53, 57, 58, 62, 71, 72 |
| Code type | Codes for Behavioral Health Services—Current Procedural Terminology (CPT) and Healthcare Common Procedure Coding System (HCPCS) |
|---|---|
| Current Procedural Terminology (CPT) or Healthcare Common Procedure Coding System (HCPCS) | 907859079190792908329083390834 908369083790838 908399084090846 90847 9084990853908639920199202 992039920499205992119921299213 99214 99215 99217 992189921999220 992419924299243992449924599406 99407G0175G0406 G0407G0408G0443 G0444H0015H0025 H0031H0038H0039 H0049 H0050H2000H2010 H2011H2014 H2015H2017H2033S0201S0220T1001 T1007T10260362T 9715197152 97153 9715497155971569715797158 |
| Revenue center codes | 05130912 |
2. Classification of New Mexico Counties by Connectivity and Rurality
| Connectivity | Urban/rural | TBH visits as a % of all visits |
|---|---|---|
| High (n = 8) | ||
| Bernalillo | Urban | 22.2% |
| Curry | Rural | 16.4% |
| Dona Ana | Urban | 18.0% |
| Eddy | Rural | 19.1% |
| Lea | Rural | 13.6% |
| Los Alamos | Rural | 25.2% |
| Sandoval | Urban | 24.1% |
| Santa Fe | Urban | 20.5% |
| Average = 19.9% | ||
| Medium (n = 11) | ||
| Chaves | Rural | 20.1% |
| De Baca | Rural | 13.7% |
| Grant | Rural | 21.9% |
| Guadalupe | Rural | 24.5% |
| Lincoln | Rural | 18.0% |
| Otero | Urban | 18.6% |
| San Juan | Rural | 22.5% |
| Sierra | Rural | 10.5% |
| Taos | Rural | 28.3% |
| Union | Rural | 18.4% |
| Valencia | Urban | 23.6% |
| Average = 20.0% | ||
| Low (n = 14) | ||
| Catron | Rural | 6.1% |
| Cibola | Rural | 20.3% |
| Colfax | Rural | 27.6% |
| Harding | Rural | 16.9% |
| Hidalgo | Rural | 17.3% |
| Luna | Rural | 12.2% |
| McKinley | Rural | 24.1% |
| Mora | Rural | 20.9% |
| Quay | Rural | 14.4% |
| Rio Arriba | Rural | 12.3% |
| Roosevelt | Rural | 11.0% |
| San Miguel | Rural | 18.5% |
| Socorro | Rural | 8.3% |
| Torrance | Rural | 22.6% |
| Average = 16.6% |
Note: Counties are categorized into tertiles according to the number of connections per 1000 households with residential fixed broadband connections of at least 25 Mbps. Urban/rural defined by the US Census Bureau data, 2020, with rural as counties with > 50% rural population.
References
- 1. Larson A. E., Zahnd W. E., Davis M. M., et al., “Before and During Pandemic Telemedicine Use: An Analysis of Rural and Urban Safety‐Net Clinics,” American Journal of Preventive Medicine 63, no. 6 (2022): 1031–1036, 10.1016/j.amepre.2022.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Watanabe J., Teraura H., Nakamura A., and Kotani K., “Telemental Health in Rural Areas: A Systematic Review,” Journal of Rural Medicine 18, no. 2 (2023): 50–54, 10.2185/jrm.2022-059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Shaver J., “The State of Telehealth Before and After the COVID‐19 Pandemic,” Primary Care 49, no. 4 (2022): 517–530, 10.1016/j.pop.2022.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Zhu J. M., Myers R., McConnell K. J., Levander X., and Lin S. C., “Trends in Outpatient Mental Health Services Use Before and During the COVID‐19 Pandemic,” Health Affairs (Project Hope) 41, no. 4 (2022): 573–580, 10.1377/hlthaff.2021.01297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. McBain R. K., Cantor J., Pera M. F., Breslau J., Bravata D. M., and Whaley C. M., “Mental Health Service Utilization Rates Among Commercially Insured Adults in the US During the First Year of the COVID‐19 Pandemic,” JAMA Health Forum 4, no. 1 (2023): e224936, 10.1001/jamahealthforum.2022.4936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Patel S. Y. and Mehrotra A., “The Surge of Telehealth During the Pandemic Is Exacerbating Urban‐Rural Disparities in Access to Mental Health Care,” Health Affairs Forefront 2021, 10.1377/forefront.20211004.155145. [DOI] [Google Scholar]
- 7. Talbot J. A., Burgess A. R., Jonk Y. C., and O'Connor H., “Federal Telehealth Policy Changes During the COVID‐19 Public Health Emergency: Associations With Telemental Health Use Among Rural and Urban Medicare Beneficiaries,” Journal of Rural Health 40, no. 1 (2024): 5–15, 10.1111/jrh.12776. [DOI] [PubMed] [Google Scholar]
- 8. Eberly L. A., Kallan M. J., Julien H. M., et al., “Patient Characteristics Associated With Telemedicine Access for Primary and Specialty Ambulatory Care During the COVID‐19 Pandemic,” JAMA Network Open 3, no. 12 (2020): e2031640, 10.1001/jamanetworkopen.2020.31640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Roberts E. T. and Mehrotra A., “Assessment of Disparities in Digital Access Among Medicare Beneficiaries and Implications for Telemedicine,” JAMA Internal Medicine 180, no. 10 (2020): 1386–1389, 10.1001/jamainternmed.2020.2666. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Cummins M. R., Wong B., Wan N., et al., “Social Vulnerability, Lower Broadband Internet Access, and Rurality Associated With Lower Telemedicine Use in U.S. Counties,” JAMIA Open 8, no. 4 (2025): ooaf056, 10.1093/jamiaopen/ooaf056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Saeed S. A. and Masters R. M., “Disparities in Health Care and the Digital Divide,” Current Psychiatry Reports 23, no. 9 (2021): 61, 10.1007/s11920-021-01274-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Horstman C. and Shah A., The State of Rural Primary Care in the United States (Commenwealth Fund, 2025) https://www.commonwealthfund.org/publications/issue‐briefs/2025/nov/state‐rural‐primary‐care‐united‐states. [Google Scholar]
- 13. Morales D. A., Barksdale C. L., and Beckel‐Mitchener A. C., “A Call to Action to Address Rural Mental Health Disparities,” Journal of Clinical and Translational Sciences 4, no. 5 (2020): 463–467, 10.1017/cts.2020.42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.“Suicide Rates by State,” Centers for Disease Control Suicide Prevention , accessed March 15, 2026, https://www.cdc.gov/suicide/facts/rates‐by‐state.html.
- 15.“Drug Overdose Mortality,” Centers for Disease Control Stats of the States , accessed March 15, 2026, https://www.cdc.gov/nchs/state‐stats/deaths/drug‐overdose.html.
- 16. Altschul D. B., Bonham C. A., Faulkner M. J., et al., “State Legislative Approach to Enumerating Behavioral Health Workforce Shortages: Lessons Learned in New Mexico,” American Journal of Preventive Medicine 54, no. 6 (2018): S220–S229, 10.1016/j.amepre.2018.02.005. [DOI] [PubMed] [Google Scholar]
- 17.“Rural Health for New Mexico Overview,” Rural Health Information Hub , accessed March 15, 2026, https://www.ruralhealthinfo.org/states/new‐mexico.
- 18.“QuickFacts: New Mexico,” U.S. Census Bureau , accessed March 19, 2025, https://www.census.gov/quickfacts/fact/table/NM/PST045223.
- 19.“State of New Mexico Office of Broadband Access & Expansion,” Connect New Mexico , State Digital Equity Plan, accessed March 15, 2026, https://broadbandexpanded.com/files/iija_plans/NM%20‐%20Digital%20Equity%20Plan%20‐%20Final.pdf.
- 20.“New Mexico Telehealth Act,” New Mexico Legislature , accessed March 15, 2026, https://www.nmlegis.gov/sessions/04%20Regular/final/HB0581.html.
- 21. Hays H., Carroll M., Ferguson S., Fore C., and Horton M., “The Success of Telehealth Care in the Indian Health Service,” AMA Journal of Ethics 16, no. 12 (2014): 986–996, 10.1001/virtualmentor.2014.16.12.stas1-1412. [DOI] [PubMed] [Google Scholar]
- 22.“Telehealth Services Key to Accessing and Providing Continuity of Care During the COVID‐19 Pandemic | July 2021 Blogs,” Indian Health Services , accessed July 25, 2024, https://www.ihs.gov/newsroom/ihs‐blog/july2021/telehealth‐services‐key‐to‐accessing‐and‐providing‐continuity‐of‐care‐during‐the‐covid‐19‐pandemic/.
- 23. Mathews D., Abernethy A., Butte A. J., et al., Telehealth and Mobile Health: Case Study for Understanding and Anticipating Emerging Science and Technology (National Academy of Medicine, 2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.“Urban and Rural,” United State Census Bureau , accessed March 19, 2025, https://www.census.gov/programs‐surveys/geography/guidance/geo‐areas/urban‐rural.html.
- 25. Hest R., “Assessing Physician Acceptance of Medicaid Patients Using State Health Compare,” State Health Access Data Assistance Center , accessed March 19, 2025, https://datawrapper.dwcdn.net/IHVit/4/.
- 26.“Internet Access Services Reports,” Federal Communications Commission , accessed March 19, 2025, https://www.fcc.gov/internet‐access‐services‐reports.
- 27. Chen P. V., Helm A., Caloudas S. G., et al., “Evidence of Phone vs Video‐Conferencing for Mental Health Treatments: A Review of the Literature,” Current Psychiatry Reports 24, no. 10 (2022): 529–539, 10.1007/s11920-022-01359-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Hughes H. K., Hasselfeld B. W., and Greene J. A., “Health Care Access on the Line—Audio‐Only Visits and Digitally Inclusive Care,” New England Journal of Medicine 387, no. 20 (2022): 1823–1826, 10.1056/NEJMp2118292. [DOI] [PubMed] [Google Scholar]
- 29. Mohr D. C., Ho J., Duffecy J., et al., “Effect of Telephone‐Administered vs Face‐to‐face Cognitive Behavioral Therapy on Adherence to Therapy and Depression Outcomes Among Primary Care Patients: A Randomized Trial,” JAMA 307, no. 21 (2012): 2278–2285, 10.1001/jama.2012.5588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Acierno R., Knapp R., Tuerk P., et al., “A Non‐Inferiority Trial of Prolonged Exposure for Posttraumatic Stress Disorder: In Person Versus Home‐Based Telehealth,” Behaviour Research and Therapy 89 (2017): 57–65, 10.1016/j.brat.2016.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Liu L., Thorp S. R., Moreno L., et al., “Videoconferencing Psychotherapy for Veterans With PTSD: Results From a Randomized Controlled Non‐Inferiority Trial,” Journal of Telemedicine and Telecare 26, no. 9 (2020): 507–519, 10.1177/1357633X1985394. [DOI] [PubMed] [Google Scholar]
- 32. Kruis R., Brown E. A., Johnson J., Simpson K. N., McElligott J., and Harvey J., “Patient Perceptions of Audio‐Only Versus Video Telehealth Visits: A Qualitative Study Among Patients in an Academic Medical Center Setting,” Telemedicine Reports 5, no. 1 (2024): 89–98, 10.1089/tmr.2023.0065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Ganguli I., Orav E. J., Hailu R., et al., “Patient Characteristics Associated with Being Offered or Choosing Telephone vs Video Virtual Visits Among Medicare Beneficiaries,” JAMA Network Open 6, no. 3 (2023): e235242, 10.1001/jamanetworkopen.2023.5242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Yu J., Civelek Y., Casalino L. P., et al., “Audio‐Only Telehealth Use Among Traditional Medicare Beneficiaries,” JAMA Health Forum 5, no. 5 (2024): e240442, 10.1001/jamahealthforum.2024.0442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. MacNeil S., “Digital Mediocrity,” Santa Fe Reporter, accessed August 7, 2025, https://sfreporter.com/news/2017/11/01/digital‐mediocrity/. [Google Scholar]
- 36. Ko J. S., El‐Toukhy S., Quintero S. M., et al., “Disparities in Telehealth Access, Not Willingness to Use Services, Likely Explain Rural Telehealth Disparities,” Journal of Rural Health 39, no. 3 (2023): 617–624, 10.1111/jrh.12759. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Klee D., Pyne D., Kroll J., James W., and Hirko K. A., “Rural Patient and Provider Perceptions of Telehealth Implemented During the COVID‐19 Pandemic,” BMC Health Services Research 23, no. 1 (2023): 981, 10.1186/s12913-023-09994-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Kusters I. S., Amspoker A. B., Frosio K., et al., “Rural‐Urban Disparities in Video Telehealth Use During Rapid Mental Health Care Virtualization Among American Indian/Alaska Native Veterans,” JAMA Psychiatry 80, no. 10 (2023): 1055–1060, 10.1001/jamapsychiatry.2023.2285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Kruse C. S., Bouffard S., Dougherty M., and Parro J. S., “Telemedicine Use in Rural Native American Communities in the Era of the ACA: A Systematic Literature Review,” Journal of Medical Systems 40 (2016): 145, 10.1007/s10916-016-0503-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.“IHS Telehealth Programs,” Indian Health Service (IHS) , accessed March 17, 2026, https://www.ihs.gov/telehealth/telehealthprograms/.
- 41.“Telebehavioral Health,” Indian Health Service (IHS) , accessed March 17, 2026, https://www.ihs.gov/telebehavioral/.
- 42. Novak S., “Bridging Rural America's Digital Divide in Health Care,” Journal of Medical Internet Research 27 (2025): e88833, 10.2196/88833. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Adepoju O. E., Chae M., Ojinnaka C. O., Shetty S., and Angelocci T., “Utilization Gaps During the COVID‐19 Pandemic: Racial and Ethnic Disparities in Telemedicine Uptake in Federally Qualified Health Center Clinics,” Journal of General Internal Medicine 37, no. 5 (2022): 1191–1197, 10.1007/s11606-021-07304-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Marcondes F. O., Normand S. L. T., Le Cook B., et al., “Racial and Ethnic Differences in Telemedicine Use,” JAMA Health Forum 5, no. 3 (2024): e240131, 10.1001/jamahealthforum.2024.0131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Leung L. B., Yoo C., Chu K., et al., “Rates of Primary Care and Integrated Mental Health Telemedicine Visits Between Rural and Urban Veterans Affairs Beneficiaries Before and After the Onset of the COVID‐19 Pandemic,” JAMA Network Open 6, no. 3 (2023): e231864, 10.1001/jamanetworkopen.2023.1864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Pandit A. A., Mahashabde R. V., Brown C. C., et al., “Association Between Broadband Capacity and Telehealth Utilization Among Medicare Fee‐for‐Service Beneficiaries During the COVID‐19 Pandemic,” Journal of Telemedicine and Telecare 31, no. 1 (2025): 41–48, 10.1177/1357633x231166026. [DOI] [PMC free article] [PubMed] [Google Scholar]