Mental Health Emergency Hotlines in the United States: A Scoping Review (2012–2021)

More than 10 million Americans each year require acute care for a mental health emergency (1). However, the response system to meet patient needs varies significantly by locality. In many parts of the United States, the de facto mechanism for care is for patients to self-present at emergency departments (EDs); on average, one of every 10 ED visits is related to mental health (2). ED boarding volume for patients with mental health conditions has overwhelmed hospital systems in settings ranging from urban areas in the Northeast to rural Alaska (3, 4). In some parts of the United States, a broader continuum of facilities is available to those with mental health emergencies, including community-based residential treatment facilities and mental health urgent care centers that are open 24/7 (5). Although these facilities represent entry points to care that may be more clinically appropriate for many patients, they are relatively rare (6).

Before entering facility-based care, many individuals may interface with an emergency hotline—the most widely known of which is 911. However, 911’s deployment of law enforcement in response to mental health emergencies can result in an array of adverse outcomes (7, 8). An extensive literature has cataloged high rates of incarceration among those experiencing mental health emergencies (9, 10), particularly those with serious mental illness such as schizophrenia or bipolar disorder (11). Furthermore, individuals with psychiatric disorders are disproportionately injured or killed by law enforcement: in 2015, one in four individuals fatally shot by a police officer had a mental health condition (12, 13).

As an alternative to 911, in 2005 the Substance Abuse and Mental Health Services Administration launched the National Suicide Prevention Lifeline (Lifeline)—now known as the 988 Suicide & Crisis Lifeline (14). Since 2005, the Lifeline has received >20 million calls throughout the United States (15). Trained responders provide an array of services, including emotional support, suicide risk assessments, safety planning, transfers to emergency services, and referrals to treatment (16, 17). Since launching, the Lifeline has expanded to include >180 local and regional Lifeline network hotlines, allowing responders to be more familiar with local resources. In several of these, such as the “Arizona model” for crisis care, hotline responders can deploy mobile crisis response teams that include mental health professionals (18). These hotlines also have the potential to connect individuals with available inpatient services in their communities and to integrate with the 911 system to divert 911 calls to teams that offer more appropriate responses for mental health crisis events (19).

In September 2020, U.S. Congress passed legislation allowing for the creation of a new mental health emergency hotline number: 988 (20). Beginning on July 16, 2022, calls to 988 are directed to the existing and lengthier Lifeline number (1-800-273-8255). In 2022 alone, $282 million has been allocated for 988 to strengthen both operations of the Lifeline network and the capacity of local call centers (21). Whether the transition to 988 will catalyze systematic changes in local mental health emergency response systems remains an open question and is dependent on mobilization of resources and coordination across many agencies.

A critical aspect of this transition phase is for city, county, and state policy makers to have a comprehensive understanding of the evidence base regarding mental health emergency hotlines in the United States. We anticipate that policy makers, administrators, and researchers will be interested in the following questions: Whom do hotlines tend to reach and not to reach in the United States? What does typical caller volume and engagement look like? What are the barriers to and facilitators of successful implementation? What are the most common responses and outcomes observed from calls and other hotline-related interventions? The answers to these questions should enable administrators to make informed decisions on building and strengthening hotlines within and outside the Lifeline network, and they also should inform researchers of where the evidence base is weakest and in need of further scientific analysis.

To describe what is currently known, identify deficiencies, and develop recommendations for future efforts to bolster the network of mental health emergency hotlines, we conducted a scoping review to evaluate current evidence on these hotlines in the United States by looking at peer-reviewed literature published over the past 10 years (i.e., 2012–2021). We analyzed studies that conducted and discussed research on the composition, implementation, and effects of mental health emergency hotlines.

Methods

Data Sources and Search

We reviewed the literature from January 1, 2012, through December 1, 2021. We followed PRISMA guidance for reporting our results (22). (See the online supplement to this review for a literature flow diagram.)

Although the term “hotline” is traditionally understood to include point-to-point communication that serves a specific function—in this case, to provide mental health support and resources (23)—over the past decade, the modes of communication have expanded significantly alongside technological innovations (24, 25). Therefore, we conceptualized mental health hotlines to include telephone, text message, chat, and other Internet-based communication systems that offer timely support and resources for individuals in a mental health emergency. That is, we included hotlines to which individuals expressed an acute need pertaining to mental health concerns, such as depressive or anxiety symptoms or psychological distress.

We were specifically interested in U.S.-based hotlines, given our focus on understanding the characteristics and outcomes associated with U.S. hotlines along with the rollout of 988 in the United States. We did not focus on hotlines that extended beyond the purview of mental health concerns (e.g., substance use disorders) nor on hotlines primarily intended for use in nonemergency situations (e.g., informational service lines).

We conducted a literature search on December 14, 2021, using a search strategy that accounted for the diversity of terminology that describes mental health emergency hotlines in the literature. To narrow the search for this scoping review, studies needed to include at least one of the terms “mental health,” “behavioral health,” and “suicide” in the title or abstract. We did not include separate diagnoses, such as depression or anxiety, because mental health hotlines are rarely limited to a single diagnosis (26). (The full search strategy can be found in the online supplement.)

We searched PubMed, PsycINFO, Embase, and Scopus databases for studies published in January 1, 2012–December 1, 2021. As supplementary strategies, we searched Google Scholar for articles that met study selection criteria as well as scrutinized bibliographies from each of the articles identified for inclusion.

Study Selection

We sought to limit results to peer-reviewed articles published in the aforementioned period, written in English, and focused on hotlines in the United States. If an article discussed multiple hotlines, one or more of which focused on the United States, the article was included. If a hotline was an incidental feature of an article, meaning that the article focused on a set of mental health services for which the hotline component was an ancillary part, the article was excluded.

Titles and abstracts of search results were screened independently by two team members (J.H.C., R.K.M.) for agreement on inclusion or exclusion. In the event of a disagreement, a third team member served as tiebreaker. For situations in which an article’s inclusion was indeterminable from the title and abstract, both team members reviewed the full article. Studies deemed to meet inclusion criteria were marked for data abstraction. Studies published in the above period and meeting the criteria described in the following and corresponding to the PICOTS framework (27) were eligible for inclusion. Eligible populations comprised professional and volunteer hotline responders as well as callers to a mental health emergency hotline in the United States who were adults; children or adolescents; active duty military or veterans; or lesbian, gay, bisexual, transgender, queer, or other (LGBTQ+) individuals. Included interventions involved the use of a U.S.-based mental or behavioral health emergency hotline via telephone, online chat, or text/SMS. Outcome indicators corresponded to at least one of the four research questions stated above. These indicators included descriptive characteristics of callers and user volume, uptake and engagement of hotline services, and the effects of hotline use on callers, such as self-reported improvements in symptoms.

Data Abstraction

Articles that met the inclusion criteria were independently entered into a data abstraction form by three members of the research team (S.M., J.H.C., R.K.M.); the form comprised data elements that would aid readers in understanding the publication results. Articles were assigned a primary and secondary data abstracter, with the secondary abstracter being responsible for reviewing the work of the primary abstracter. If a disagreement arose, the third team member served as a tiebreaker.

We abstracted the following study characteristics: article title, year, journal, authorship team, study classification, study design, sample size, data source(s), name of hotline, hotline modality examined (i.e., telephone, text, or chat), special populations that were the focus of the article (e.g., active duty military, veterans, or youths), and main outcomes reported. We implemented the following guidelines for coding study classification. First, a study was classified as descriptive if it identified characteristics of the call or caller that predicted a call to a hotline or the number of calls to a hotline. Second, a study was classified as an intervention study if an exposure was assigned to a treatment group and outcomes were subsequently assessed. Most commonly, this exposure was to a hotline or public health campaign. Third, a study was classified as an implementation study if the authors described the development and implementation of a hotline. Studies that were not classified in any of the above categories, such as systematic reviews and perspective pieces, were classified as “other.”

Data Synthesis and Analysis

For each study, we documented outcome measures according to two conventions. First, we reviewed the methods section to determine whether authors identified key outcomes. If so, these outcomes were cataloged and collated according to whether they answered one or more of our four research questions. If not, we drew from the key outcomes listed in the abstract of the article, which we believed indicated that the authors viewed these outcomes as the main study findings. Again, these outcomes were collated according to whether they answered one or more of our four research questions. When describing the findings of the study, we prioritized the reporting of ORs, prevalence estimates, sample sizes, and p values to provide indication of statistical significance and strength of evidence. Given the breadth of findings reported in each article, we were unable to present an exhaustive list of findings but rather focused on key outcome measures from each study.

To provide additional specificity of outcomes, we classified each outcome in terms of whether the outcome pertained to the caller, the responder, or a third party (“other”). The other category primarily discussed qualitative implementation findings. In these instances, we again prioritized those findings reported by the authorship team in the study abstract.

We organized studies according to their primary study classification, which included descriptive, intervention, implementation, and other studies. Our rationale was to group studies according to the types of questions they sought to answer. For example, descriptive studies tended to answer the questions, Whom do hotlines tend to reach and not to reach in the United States? and What does typical caller volume and engagement look like? By contrast, implementation studies tended to answer the question, What are barriers to and facilitators of successful implementation? Last, intervention studies commonly answered the question, What are the most common responses and outcomes observed from calls and other hotline-related interventions?

Results

Overview

Of the initial 1,049 (N=723 deduplicated) articles identified through database searches and bibliography reviews, 96 met our initial criteria for full-text review. Full-text reviews resulted in the exclusion of an additional 43 studies. Among these, 24 were not focused on the United States, 14 were not topically relevant (i.e., the hotline was incidental and not the focus of the study or the study outcomes were not hotline related), two did not involve a hotline, and three were not peer reviewed. (The online supplement displays a literature flow diagram of our search.)

A table in the online supplement presents characteristics of the 53 included studies (28–80), organized by primary study classification. These classifications were descriptive studies (N=25), intervention studies (N=20), implementation studies (N=6), and other (i.e., a systematic review [N=1] and a perspective article [N=1]). Although some studies included aspects of more than one classification, we reached internal consensus as to the study’s primary focus. Most articles (28–42, 45–51, 53, 56–67, 69–73, 76–78) used quantitative methods only, five (43, 44, 68, 74, 75) used qualitative methods only, and three (52, 54, 55) used mixed methods.

Table 1 displays study counts by classification, hotline name, modality, and location. The included studies evaluated 13 different hotlines. The Lifeline was the most frequently evaluated hotline (N=19), followed by the Veterans Crisis Line (VCL; N=12) and the Crisis Text Line (CTL; N=4). When reported, studies most often evaluated hotlines at a national level (N=37). Three studies (40, 48, 51) used nationally representative survey samples. Four studies were analyzed at the state level: three in California (30, 45, 78) and one in Colorado (73). Ten studies (28, 31, 33, 37, 38, 58, 59, 63, 68, 75) analyzed data at the local level: two (31, 37) in the midwestern United States and one each in California (38), Florida (58), Illinois (63), Louisiana (59), Maryland (33), New Jersey (68), New York (28), and Texas (75). Ten studies (31, 34, 35, 38, 42, 45, 47, 58, 59, 78) did not report the hotline under review: seven (31, 34, 35, 38, 47, 58, 59) evaluated one unspecified hotline, and three (42, 45, 78) analyzed multiple unspecified hotlines, two of which also evaluated the Lifeline (45, 78). Six studies (30, 40, 48, 62, 65, 79) evaluated the use or perceptions of hotlines more generally and as such had no applicable hotline. Various subpopulations of hotline callers were examined across these studies. Nine studies (33–35, 38, 47, 50, 63, 71, 80) focused on youths, of which two (35, 47) focused on LGBTQ+ youth callers to an LGBTQ+-specific hotline. An additional 14 studies (39, 43, 44, 46, 51–53, 57, 60, 62, 65–67, 76) examined veteran or active military personnel callers. Twenty-four studies (29, 33–36, 45, 47, 49, 53–59, 61, 64, 70, 72–74, 76, 77, 79) assessed callers at risk for suicide, four of which (33–35, 47) focused on youths at risk for suicide and three (53, 57, 76) on veterans at risk for suicide.

Descriptive Studies

Descriptive studies characterized hotline callers and responders according to a wide variety of characteristics, ranging from demographic to clinical. These studies also provided information about the hotline itself: for example, utilization patterns, services provided, and communication modalities. We identified 25 descriptive studies (28–52), most of which used quantitative methods (N=22). Two (43, 44) used qualitative methods, and one (52) used a mixed-methods approach. Regarding communication modalities, most of these studies (N=14) focused on telephone-based hotlines (28, 29, 32, 34, 36, 37, 39–42, 44, 46, 51, 52); eight (30, 31, 33, 35, 38, 43, 45, 47) reported on a combination of telephone, text, or chat exchanges; and two (49, 50) focused solely on text messaging exchanges. Ten different hotlines were assessed. Six studies (32, 33, 36, 37, 41, 45) assessed the Lifeline, five (39, 43, 44, 46, 51) focused on the VCL, and two (49, 50) focused on the CTL.

The study population for six (33–35, 38, 47, 50) of the 25 studies focused on youths, and five (39, 43, 44, 46, 51) concentrated on veterans and one (52) on active military personnel. Eight studies (29, 33–36, 45, 47, 49) evaluated callers at risk for suicide, with four (33–35, 47) focused on youths at suicide risk. Twenty-three studies (28–40, 43–52) included outcomes on caller characteristics, and five (28, 36, 38, 42, 45) included responder characteristics.

Caller characteristics.

Of the 25 descriptive studies, 18 reported the demographic, clinical, or both characteristics of callers. Two studies (40, 48) estimated that approximately 2.5% of individuals in the United States will use a hotline in their lifetime. Similarly, lifetime use of the Lifeline among youths was estimated to be 3% (33). Among studies using descriptive methods that explored differences in utilization rates according to gender, race, or ethnicity, a preponderance found greater utilization of hotlines among callers who are female (28, 29, 34, 36, 38, 40, 45, 48) and White (29, 34, 35, 47, 48, 52). Six studies (28, 38, 40, 45, 48, 50) reported increased use by those with anxiety, major depressive, or substance use disorders. Experiencing suicidality was correlated with greater hotline use (29, 48, 50). Beyond clinical characteristics, articles noted general medical health, interpersonal, and financial concerns of hotline callers (31, 44, 45, 49), as well as the use of hotlines by repeat callers (28, 36, 45, 46, 49) and third-party callers (e.g., family members or friends). In studies that reported repeat callers (28, 36, 45, 46, 49), between 14% and 35% of callers had previously contacted the hotline, and in the study reporting third-party callers (45), 13% of calls were placed by such callers.

Eight descriptive articles (29, 33–36, 45, 47, 49) focused on callers at risk for suicide. One study reported that on average, 21% of callers were at risk for suicide (45). Of callers who expressed suicidality, between 9% and 11% were identified as highest risk on the basis of risk prediction models and expressed intent of self-harm (36, 49). Two studies evaluated specific risk profiles: Gould and colleagues (36) found that Lifeline callers at imminent suicide risk most commonly showed concretized suicidal ideation (89%), hopelessness (83%), and psychological pain (82%). Szlyk and colleagues (49) reported that texters to the CTL at risk for suicide most commonly reported depression (42%) and family issues (25%).

Of six descriptive studies discussing youths (33–35, 38, 47, 50), five assessed caller demographic and clinical characteristics (34, 35, 38, 47, 50). One study (38) determined that females ages 15–16 years were the most frequent callers and that calls from youths ages ≤13 years increased significantly between 2010 and 2016. Another study, by Thompson and colleagues (50), reported that youths in rural areas had significantly lower rates of hotline use. Kerner and colleagues (38) reported that anxiety (20%), depression (17%), and suicidal ideation (14%) were the most common clinical characteristics of youths who contacted the hotlines.

Two studies (35, 47) focused on LGBTQ+ youth callers. Rhoades et al. (47) assessed homelessness and suicidality, finding that 32% of such callers had experienced homelessness. Those who had experienced homelessness reported significantly increased rates of psychiatric conditions and suicidality. Goldbach and colleagues (35) assessed youths contacting an LGBTQ+ hotline and found that the most common reason for callers’ contacting the hotline (42%) was that it was LGBTQ+ affirming.

Six descriptive studies (39, 43, 44, 46, 51, 52) examined veteran and active military callers. Three studies on the VCL reported variations in concerns by age or gender (39, 44, 46). Veteran callers ages ≥60 were found to be more likely to report loneliness and were less likely to express behavioral health concerns (39, 46), whereas female veterans were more likely to express concerns about sexual violence, report depressed mood, and mention drug overdose (44).

Because hotlines are increasingly offered through a variety of modalities, four studies explored caller communication preferences (30, 33, 38, 43). The findings of these studies suggested that the telephone is most widely preferred and that an increasing percentage of adults, including veterans, use text and chat. For example, a survey of California adults (30) found that they preferred telephone-based services and were willing to contact Web-based or text-based hotlines. Specifically, 62% of survey respondents reported that they would be “likely” or “very likely” to use a telephone hotline if they were to experience a suicidal thought; however, a sizable percentage of respondents (>40%) indicated that they would also use a Web- or text-based hotline. Although youths tended to prefer text, chat, and social networking (59% combined), they still expressed interest in telephone hotlines (41%) (33). Another study reported that 30% of youths reached a hotline via text (38). Veterans often used chat and text lines to ensure confidentiality and to make a first connection with mental health services (43).

Responder characteristics.

Five of the 25 descriptive studies (28, 36, 38, 42, 45) examined responder behavior and use of response procedures, including active engagement, collaborative problem-solving, suicide risk assessments, referrals, and in-person emergency services. Gould and colleagues (36) found that responders used active engagement, a means of collaboratively ensuring caller safety, on more than three-quarters of calls (76%). However, another study (45) identified weaknesses in collaborative problem-solving methods on about half (52%) of calls.

Besides providing services over the telephone, responders often offer referrals to outside resources and sometimes dispatch emergency services. Four studies (28, 31, 38, 45) reported rates of referral to an outside mental health resource, ranging from an average of 15% to 86% of calls. Gould and colleagues (36) found that when Lifeline callers were at imminent suicide risk, responders dispatched emergency services on almost one-fifth of calls (19%) and sent active rescues (i.e., emergency services without caller cooperation) on more than a quarter (28%) of calls.

In predicting responder behavior, three studies evaluated occupational status (36, 42, 45). Mishara et al. (42) found that call centers with only volunteers conducted suicide risk assessments more often than centers with all professional staff and that volunteer centers had significantly better ratings on the Crisis Call Outcome Rating Scale, used to evaluate call effectiveness. However, Gould and colleagues (36) reported that when callers had imminent suicide risk, volunteers were less likely than professional staff to actively engage callers over the telephone and more likely to send an active rescue.

Call and caller outcomes were associated with several response-related factors. Gould and colleagues (36) found several outcomes related to responders’ average hours per week spent answering calls: each additional hour was associated with a greater likelihood of actively engaging callers and lowering suicide risk and a reduced likelihood of sending an active rescue. Longer calls were associated with higher caller satisfaction and higher likelihood of reduced distress (45). Ramchand et al. (45) also observed that call centers in the Lifeline network, compared with centers not in the network, had a greater likelihood of reducing caller distress.

Intervention Studies

Hotline as the intervention.

We identified six studies (53–58) examining the effects of an intervention for which the mental health hotline was itself the intervention. Four of the studies (53, 56–58) used quantitative methods, and two (54, 55) used mixed-methods approaches. None of the studies used a controlled design. Almost all of these studies (53–57) were focused on national telephone-based hotlines, two (54, 55) of which evaluated several call centers across the United States at unspecified locations. One study (56) examined chat-based exchanges, three (54–56) evaluated the Lifeline, and two (53, 57) examined the VCL. All six studies focused on individuals at risk for suicide, with two (53, 57) focusing exclusively on military veterans.

Most of the studies (53–57) focused on self-reported caller outcomes, specifically related to referrals (53, 54) and reductions in suicidality (55–57). Three evaluated caller behavioral outcomes (55–57). Gould and colleagues (54) reported characteristics of Lifeline callers who were referred to a mental health provider, finding that women were more likely than men to receive a referral. Approximately 50% of suicidal callers self-reported following through with the referral. A second study by Gould and colleagues (55) quantified whether the Lifeline helped callers, according to callers’ self-reports. Most callers reported that being linked to the Lifeline prevented them from killing themselves (80%) and kept them safe (91%). A similar study by Gould et al. (56) found that those who engaged in chat-based communication with the Lifeline were less upset after their chat. Specifically, 67% of chatters found the chat to be helpful, 27% reported feeling less depressed, and 45% reported feeling less suicidal.

Studies evaluating the VCL also found positive effects. Johnson et al. (57) examined similar self-reported metrics for the VCL, including measures of caller satisfaction and perceived helpfulness of the call. The authors documented high levels of caller satisfaction (87%) and perceived helpfulness (82%). Nearly three-quarters of callers (73%) reported that the call kept them safe. Similarly, Britton and colleagues (53) observed that a large portion of VCL calls (84%) ended with a positive outcome (i.e., with resolution or referral). One-quarter (25%) of calls were resolved by the end of the call, and 59% ended with a referral. Referrals were more common when the call came from high-risk individuals, took place on a weekday, and was made between 6 a.m. and 6 p.m.

Other interventions.

We identified 14 studies (59–72) that evaluated the effects of an intervention other than the hotline itself, such as the effect of responder training on responder behavior and caller outcomes and the influence of public health campaigns and media events on hotline use or engagement. These studies primarily used quantitative methods (59–67, 69–72), including quasi-experimental (67, 69–72) and randomized controlled trial (64) designs. Eleven of the studies (59, 60, 62–67, 69–71) evaluated caller outcomes, with 10 (59, 60, 62, 63, 65–67, 69–71) focused on hotline use and one (64) on behavioral outcomes. Two studies (61, 68) included responder outcomes.

Two studies (61, 64) evaluated the effects of responder training on behavioral outcomes. Cross and colleagues (61) examined the association between trainer and responder behavior, finding that responder use of recommended response procedures (e.g., safety planning and risk assessments) was primarily related to trainer competence rather than adherence to training program content. Using a randomized controlled trial design, Gould and colleagues (64) examined the association between responder training and caller behavior, finding that callers were significantly less likely to feel depressed and suicidal by the end of calls handled by trained responders. Improvements in caller outcomes were linked to training-related responder procedures, including exploring social networks and reasons for living (64).

Five studies (60, 62, 65–67) analyzed the impact of a public health campaign on veteran hotline use or intended use, three of which (60, 66, 67) evaluated a U.S. Department of Veterans Affairs campaign. Such campaigns were associated with increases in calls to the VCL and the Lifeline (60, 66). Elder et al. (62) discovered that exposure to a variety of public health campaigns was associated with veterans’ increased intention to use a hotline among veteran households.

Three studies examined the effects of media on hotline use or engagement, specifically, the release of the television series 13 Reasons Why (13RW) and hip-hop artist Logic’s song “1-800-273-8255,” referencing the original Lifeline number (69, 71, 72). Thompson and colleagues (71) noted a brief but significant rise in CTL contacts immediately after the release of 13RW, followed by a more substantial and significant drop in contacts. Niederkrotenthaler et al. (69) found that after the release of Logic’s song, the Lifeline had a 6.9% increase in caller volume, and Torgerson and colleagues (72) found that Google searches for “suicide hotline” increased by 50% above expected volume over the subsequent month and that Twitter engagement increased by roughly 1,500% within 1 week.

Implementation Studies

We identified six studies (73–78) that examined and discussed implementation of a hotline, pilot program, or new policy or intervention. These studies used quantitative (73, 76–78) or qualitative (74, 75) methods. Two studies (74, 77) evaluated new Lifeline policies. The remaining studies (73, 75, 76, 78) each analyzed a distinct hotline. Four studies (73, 74, 76, 77) focused on those at risk for suicide, and one (75) concentrated on health care workers.

Two of the six studies quantified caller characteristics (73, 76). Each of these studies tracked the number of contacts and described characteristics of callers directly after hotline or program launch. Four implementation studies included responder-specific outcomes, three of which evaluated responder uptake of the new program or policy. These studies ranged from describing specific response procedures (73, 74) or training (77) to a description of the types of hotline workers (78). Labouliere and colleagues (77) particularly focused on the implementation of the safety planning intervention (SPI), a brief procedure designed to help manage suicidal crisis, and found that responders reported high levels of self-efficacy when using SPI. Self-efficacy predicted greater use and perceived effectiveness of SPI. Five of the studies (73–75, 77, 78) reported implementation challenges or lessons learned. Implementation barriers ranged from time constraints and privacy concerns to regulatory and financial sustainability.

Other Studies

Our literature search identified a perspective piece (79) and a systematic review (80). Hunt and colleagues (79) explored hotline use by men at suicide risk and provided directions for future research. The authors also provided perspectives on potential reasons why men might use hotlines, including perceptions of greater confidentiality and accessibility, a lack of screening assessments, and a focus on problem-solving.

Mathieu and colleagues (80) undertook a systematic review of the literature on youth hotlines. The authors identified 52 articles on youth hotlines published between 1973 and 2020. The most common concerns expressed by youths were psychosocial difficulties and suicidality. Primary service-related factors that influenced engagement and outcomes were hotline modality and responder-caller interactions.

Discussion

Despite the broad reach of mental health hotlines in the United States and the rollout of the 988 Suicide & Crisis Lifeline (21), we found that few of the reviewed studies analyzed hotlines’ impact on certain high-risk populations, assessed capacity of responders, or evaluated the effects of local chat- and text-based hotlines. Although we identified 53 studies that met our inclusion criteria, most of these studies were descriptive; that is, they focused primarily on callers’ clinical and demographic characteristics. A small pool of studies (N=6) evaluated hotlines as interventions. We also found that study location was most frequently at a national level (N=37), although only three of the studies included nationally representative samples. Of those studies that were locally focused and specified a location, five were conducted in the West, four in the South, three in the Midwest, and two in the Northeast. Although the Lifeline network includes smaller local call centers, Lifeline studies commonly examined it at a national level. Studies that examined the Lifeline at a local level often did not specify the locations. This lack of geographic information raises questions about the comparative effectiveness of local hotlines that not only may be more attuned to local resources and emergency response systems (19) but also may face distinct challenges with response capacity and variability in quality (78).

We found that callers in the descriptive studies tended to be female, younger, and White, a population that generally uses mental health care at higher rates (81, 82). Although females have higher rates of mood and anxiety disorders (83), the use of hotlines among males was much smaller than expected. Although suicide rates are elevated among older adults (84), this demographic group appears less engaged in hotline use. We found that veteran hotlines, however, do reach older men.

We also found that hotline users frequently have suicidal ideation. However, we note some evidence for challenges in effectively engaging specific high-risk populations in hotline use. The National Suicide Hotline Designation Act of 2020 emphasizes the importance of reaching LGBTQ+ youths and Indigenous and rural populations, along with the need for hotlines to be “equipped to provide specialized resources to these and other high-risk populations” (85). Although two studies focused on LGBTQ+ youths, these studies primarily evaluated caller characteristics (35, 47). No studies evaluated the effects of hotline use on this high-risk population. Two descriptive studies (34, 52) reported rates of use by Native Americans, ranging from 1% to 2% of callers. No studies focused on Indigenous populations. Rural communities had significantly lower use of hotlines (50). Given that rural and Indigenous communities experience increased rates of suicide, further research should explore how to reach these populations.

Although descriptive articles with responder outcomes focused on response procedures and occupational status (the latter of which found mixed results on benefits), the studies gave little attention to responder capacity. However, internal Lifeline data indicate that current response capacity is at only 85% of telephone calls, 56% of texts, and 30% of chats (21). Given the expected increase in hotline contacts with the 988 rollout, evaluations of responder capacity could be useful (86).

We found a growing use of text- and chat-based hotlines not only among youths but also among adults, including veterans. It was unclear whether callers, texters, and chatters expressed different concerns, although there was evidence that chatters more frequently exhibited suicidality (56) and that callers at risk for suicide had different concerns from texters (36, 49). We found few studies of responder behavior or efficacy via different communication modalities. Further research is needed on specific response procedures via these modalities and on the impacts of these procedures on outcomes among those who use chat- or text-based hotlines.

We noted positive impacts of hotlines for people at high suicide risk. Callers self-reported high satisfaction and helpfulness of hotlines. They also reported experiencing reduced distress by the end of a call, and calls often ended in referrals to outside services. Because intervention studies focused on individuals with suicidality, it is unclear from this literature what the effects of hotlines are on individuals with other mental health conditions. Moreover, the intervention studies predominantly focused on telephone-based exchanges. We found limited examination of the effects of text- and chat-based exchanges. One recent hotline intervention study (56) assessed chat interactions, finding that those using chats were less upset after the chat and that 45% reported feeling less suicidal. No studies examined the effects of text-based exchanges. Finally, we found positive relationships between public health campaigns and hotline use or intended hotline use, although these studies primarily assessed veterans’ engagement. Further research might examine the effect of hotlines and campaigns on other populations and through different modalities.

We identified several important lessons or barriers to implementation of hotlines, namely, those pertaining to data collection, privacy concerns, communication trends, and financial sustainability. For example, one study (74) reported that privacy and confidentiality concerns were significant barriers to coordination between crisis hotlines and emergency services. As jurisdictions look to increase coordination between call centers, mobile crisis response teams, and emergency services, these confidentiality concerns are likely to be a recurrent theme that could be addressed through legislative efforts and public information campaigns.

Importantly, these confidentiality concerns and the sensitive nature of call content often limit the availability of demographic, clinical, and other outcome data. Although some studies reported these data, others did not have access to this information, often making it difficult to evaluate outreach to and outcomes among individuals in high-risk populations. This lack of data has important implications for understanding who uses and benefits from 988. Investment in rigorous data collection is needed to enable evaluations after the implementation of 988.

We note two limitations of our review. First, our data abstraction effort was constrained by the availability of study information. For example, very few studies specified the type of hotline intervention (e.g., active listening line vs. line using a solution-focused approach). Likewise, standardized effect sizes such as Cohen’s d were seldom reported, and we therefore focused on more common measures such as ORs.

Second, as with any literature review, it is possible that our search parameters may have resulted in the omission of relevant studies. For example, it is possible that a study focusing on hotline callers with a specific mental health condition, such as depression or anxiety, was not captured because the term “mental health,” “behavioral health,” or “suicide” did not appear in the title or abstract of the study.

Conclusions

With the launch of 988 in July 2022, many cities, counties, and states are considering implementation or expansion of mental health emergency hotlines that are situated within a broader continuum of care. President Biden’s fiscal year 2023 budget includes an additional $700 million to strengthen local call centers (87), creating an even stronger impetus for administrators to familiarize themselves with the scope, reach, and impacts of these centers. This study should serve as a primer for those considering these issues, as well as those seeking to anticipate potential implementation challenges. We also found that a substantial need exists for evaluating interventions as they are scaled. Forthcoming funding and resourcing present a unique opportunity for this research to be conducted in parallel with expansion of hotlines.