Evaluating sustained reach and effectiveness of collaborative care models: A Cross-sectional study of the New York State Collaborative Care Medicaid Program

Evaluating sustained reach and effectiveness of collaborative care models: A Cross-sectional study of the New York State Collaborative Care Medicaid Program | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Evaluating sustained reach and effectiveness of collaborative care models: A Cross-sectional study of the New York State Collaborative Care Medicaid Program Kain Kim , Baoyi Feng , Mengxiao Luan , Jungang Zou , Amy Jones , Danielle Gadbois , Joseph E. Schwartz , Qixuan Chen , Nathalie Moise doi: https://doi.org/10.1101/2025.01.11.25320387 Kain Kim 1 Department of Medicine, Emory University School of Medicine , Atlanta, GA 101 Woodruff Cir Atlanta, 30322 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Baoyi Feng 2 Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengxiao Luan 2 Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jungang Zou 2 Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Jones 3 Primary Care Behavioral Health Integration, NYS Office of Mental Health 330 5th Ave A-B, New York, NY 10018 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Danielle Gadbois 4 Behavioral Health Integration Program Coordinator, NYS Office of Mental Health 330 5th Ave A-B, New York, NY 10018 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joseph E. Schwartz 5 Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 6 Department of Psychiatry and Behavioral Health, Renaissance School of Medicine, Stony Brook University , Stony Brok, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qixuan Chen 2 Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 7 Center for Research on Cultural & Structural Equity in Behavioral Health, Nathan Kline Institute for Psychiatric Research , Orangeburg, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nathalie Moise 5 Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Irving Medical Center , New York, NY 622 W 168th St, New York, NY 10032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: nm2562{at}cumc.columbia.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Little data exists on collaborative care (CC) sustainability. Objective Describe and determine predictors of long-term CC reach and effectiveness Design: Cross-sectional observational study of the NY State CC Medicaid Program (CCMP), involving technical assistance (TA), quality monitoring, and fee-for-quality Medicaid reimbursement codes for implementing CC. We included clinics participating in CCMP from 2012-2019 with ≥0.5 full time equivalent (FTE) care manager and available 2021 and/or 2021 data. Main Measures Clinic (size, type, region, enrollment year); and CC program (care manager FTE, caseload/care manager FTE [target 100-150], screening [proportion of clinic screened for depression], engagement [proportion of CC patients contacted/engaged monthly], and psychiatrist consultations in unremitted patients) characteristics. Outcomes were reach (proportion of screen-detected depressed patients enrolled in CC) and effectiveness (proportion of CC enrolled patients achieving remission or 50% reduction in depressive symptoms [Target 50-60%]). We used multilevel negative binomial regression models, adjusting for clustering by healthcare system and county. Results Of eligible 160 clinics, 71.2% were Federally Qualified Health Centers (FQHCs); the median caseload/care manager FTE was 55.1, reach 13.0% and effectiveness 42.0%. In multivariable analyses, key CC factors associated with reach included engagement (adjusted Rate Ratio [aRR]=3.99 [1.82, 8.76]), care manager FTE (aRR=1.06 [1.02, 1.10]), and caseload/care manager FTE (aRR=1.23 [1.17, 1.29]); smaller clinic size (aRR=0.60 [0.53, 0.69]), earlier adoption (aRR=0.40 [0.23,0.69] in 2017-2019 vs. 2012-2014), and academic/private clinics (vs. FHQC) (aRR=0.66 [0.45, 0.96]) were also predictive. Caseload/care manager FTE (aRR=1.04 [1.01, 1.07]), psychiatry consultations (aRR=1.55 [1.19, 2.00]), and FQHCs (aRR=1.19 [1.02, 1.40]) were associated with greater CC effectiveness. Conclusion Despite ongoing fiscal and TA, CC clinics particularly struggle to achieve long-term reach. While majority FQHCs limit generalizability, we provide several targets for selecting ideal settings for CC, optimizing the pace of sustainability and considering de-implementation efforts when futile. Primary Funding Source Agency for Healthcare Research Background The collaborative care model for depression (CC) is an integrated behavioral health model shown to improve access to mental health resources, depression symptoms, and quality of life compared to usual primary care, and is one of the few evidence-based interventions in general shown to reduce health disparities. 1 – 6 Over the last decade, experts have sought to target the wide range of implementation barriers, 1 , 7 – 10 with fiscal, training, and implementation support strategies showing wide ranging results. 11 – 13 CC is now widely reimbursed and it is now clear that that achieving real-world effectiveness similar to clinical trials is contingent on the level of external implementation support. 1 Despite remarkable progress in CC implementation, there has been little focus on sustainability, 14 – 16 defined as “the continued use of program components and activities for the continued achievement of desirable program and population outcomes. ” 17 The key determinants of sustained reach (i.e., percentage of eligible patients enrolled in CC) and effectiveness (i.e., percentage of enrolled patients with remission and/or >=50% reduction in depressive symptoms) remain unknown. In 2015, the New York Office of Mental Health (OMH) launched the Collaborative Care Medicaid Program (CCMP) (implementation strategy), which supported CC (the evidence-based intervention) implementation in primary care clinics across the state with training, centralized technical assistance (TA), ongoing quality monitoring and Medicaid reimbursement codes. The program resulted in broad CC adoption and fidelity, reaching the majority of Federally Qualified Health Centers (FQHCs) in NYS. 18 To optimize the pace of national CC sustainability efforts and advance the field of implementation science, we aimed to describe and investigate key predictors of sustainability indices in clinics participating in the NYS CCMP. Methods Setting and Participants During 2012-2014, the NYS OMH implemented the NYS CC Initiative, a demonstration program funded by the NYS Department of Health. 19 Having demonstrated improved outcomes in 32 early adopter clinics, OMH launched CCMP in 2015, which became the first Medicaid program in the US to reimburse CC services. 19 Over 300 clinics currently participate in CCMP, which provides clinics a monthly case rate payment for each Medicaid patient enrolled in CC. A unique feature of CCMP is its adoption of the Quality Supplemental Payment model, which incentivizes clinics to receive additional funding if benchmarks in pre-specified quality outcomes are met. To participate, primary care clinics were required to submit a billing application to OMH confirming the necessary infrastructure for CC implementation, such as a state-approved registry. Once approved, clinics were required to submit quarterly process and outcomes data used to further improve training and TA. Participating clinics received free training, TA, registry upkeep, and quality improvement activities. 19 In our study, we included all primary care sites that (1) reported ≥1 quarter’s worth of data in 2021 and/or 2022, (2) participated in NYS CCMP in 2012-2019 to mitigate unique COVID19 effects and isolate sustained programmatic effects, and (3) had ≥0.5 FTE of a care manager. Measures and Outcomes To frame presentation of our data, we employed the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) model. 20 Our primary outcomes corresponded with the long-term (“maintenance”) constructs of “reach” and “effectiveness” within RE-AIM. Our data source consisted of performance metrics reported on a monthly basis and collected from clinics by OMH quarterly. Our Institutional Review Board at Columbia University Irving Medical Center approved analyses of this quality improvement data. We calculated reach as the total sum of patients newly enrolled in CC in a given year divided by the number of unique patients who screened positive for depression (defined as a PHQ-9 score of 10 or higher) in that year; only the months with complete data were used. There were rare cases in which the enrollment number exceeded the screen positive number (e.g., the care manager may have enrolled a patient screened in a prior time period or not screened at all), in which case we modified the denominator to equal the numerator, thereby capping reach at 100%. Clinical effectiveness was reported by clinics and defined by OMH (with an optimal goal of 50-60%) as the proportion of patients enrolled in CC for 70 days or greater who demonstrated clinically significant improvement defined as either (1) a PHQ-9 ≤10 or (2) a 50% reduction from their screening/baseline PHQ-9. We calculated effectiveness similar to reach for a given year. Candidate predictors included various OMH defined metrics related to clinic and CC program characteristics: start year (the year OMH received a complete CCMP application from a clinic, including 2012-2014 (early adopters in the CC Initiative) 2015-2016 and 2017-2019, geographical region (a binary variable of NYC vs. elsewhere in NYS), FQHC status, clinic size (the average monthly number of unique screen eligible patients seen in a clinic), the average monthly care manager FTE, and the average caseload per care manager FTE. 21 Candidate predictors operationalized similar to reach above (summed numerator over summed denominator in given year) included depression screening (the proportion of all unique adult patients seen during the reporting period who received their annual PHQ-2 or PHQ-9 screening; this did not include patients being monitored for depression as part of CC), engagement (the proportion of CC enrolled patients who had at least one clinical contact with a care manager in a given month), and psychiatry consultation (defined in 2021 as the proportion of those patients enrolled for ≥70 days without improvement who either [1] had their case reviewed by the consulting psychiatrist, who then made treatment recommendations to the PCP or depression care manager, or [2] had a documented change in their treatment plan for that month; in 2022, the second criterion was removed by the OMH due to difficulty capturing data in registries). OMH definitions and targets are included in the Supplementary eText 1. Data Analysis The distributions of outcomes and predictors were examined separately for 2021 and 2022. We present medians and interquartile ranges (IQR, first and third quartiles) for numeric variables and counts with frequencies for categorical variables. We present boxplots to visually compare the 2021 and 2022 depression screening, CC engagement, effectiveness, psychiatrist consultation, and reach. To investigate the associations between reach/effectiveness and clinic-level characteristics, we fit Bayesian multilevel negative binomial regression models. The models used random intercepts to account for correlations among clinics within the same county, correlations among clinics from the same health systems, and correlations of multiple measures from the same clinics. We started with univariable models examining each predictor separately, followed by multivariable models that included all predictors. Experts posit that reach and effectiveness may be negatively associated. 22 As such, in the models predicting reach, effectiveness was included as a covariate, and in the models predicting effectiveness, reach was included as a covariate. To reduce the skewness of the covariates, clinic size was log-transformation (natural log) and caseload per care manager FTE was square root transformed. We implemented the Bayesian multilevel negative binomial models using the “ stan_glmer.nb ” function in the “ rstanarm ” package (version 2.32.1) in R. We used the default prior settings in the function and specified 2 chains, each with 4,000 iterations for the burn-in phase and 4,000 for the posterior sampling phase. We monitored the convergence of MCMC chains to obtain a R-hat statistic close to 1. The regression coefficients were obtained using posterior medians and 95% credible intervals (CIs), defined using the 2.5 th and 97.5 th percentile of the posterior draws. The regression coefficient estimates were then exponentiated to obtain rate ratios (RRs). The 95% CI that does not include 1 was considered statistically significant. Role of the Funding Source CCMP data collection is supported by OMH. The design, conduct, and analyses for this study were funded by an R01 grant from the Agency for Healthcare Research. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Results In total, 160 CCMP clinics met eligibility criteria (109 reported data in both years; 42 in 2021 only, 9 in 2022 only). Table 1 shows similar descriptive statistics in years 2021 and 2022. In 2022, CCMP clinics served a median of 474 [IQR: 159 - 938] patients per month; 56.8% were in NYC; 71.2% FQHCs. Median reach (i.e., the % of patients screened positive who enrolled in CC) was 13% (IQR 5% - 22%), clinical effectiveness 42% (IQR: 26% - 53%), caseload/FTE 55 (30 - 89), depression screening 81% (67% - 94%), engagement (i.e., proportion of enrolled patients with clinical encounters) 58% (42% - 70%) and psychiatry consultation 38% (11% - 71%). Figure 1 displays the distributions of depression screening, reach, engagement, effectiveness, and psychiatric consultation in 2021 and 2022 using boxplots. Figures 2 and 3 show the average reach and effectiveness for each county. Download figure Open in new tab Figure 1. Comparing fidelity measures between 2021 and 2022 using boxplots: + sign representing sample mean View this table: View inline View popup Download powerpoint Table 1. Descriptive statistics of outcomes and covariates for the clinics with data in 2021 or 2022: count (percentage) for categorical variables and median (Q1, Q3) for numerical variables. Table 2 shows the results of the multivariable models using Bayesian multilevel negative binomial regression for reach and effectiveness. After adjusting for other covariates, there was no difference in reach between 2021 and 2022 or between clinics in NYC versus other regions in NYS. Reach was associated with earlier adoption (2017-2019: aRR=0.40 [95% CI: 0.23, 0.69], 2012-2014: reference), smaller clinic size (aRR=0.60 [95% CI: 0.53, 0.69]), larger caseload per care manager FTE (aRR=1.23 [95% CI: 1.17, 1.29]), higher care manager FTE (aRR=1.06 [95% CI: 1.02, 1.10]), better engagement (i.e., proportion of enrolled patients with care manager clinical contact) efforts (aRR=3.99 [95% CI: 1.82, 8.76]), academic/private clinic status (aRR=0.66 [95% CI: 0.45,0.96]) and lower effectiveness (aRR=0.47 [95% CI: 0.25, 0.91]), but not psychiatrist consultation or depression screening metrics. Effectiveness was associated with higher caseload per care manager FTE (aRR=1.04 [95% CI: 1.01, 1.07]), greater psychiatrist consultations for unremitting patients (aRR=1.55 [95% CI: 1.19, 2.00]), FQHC clinic status (aRR=1.19 [95% CI: 1.02,1.40]), and lower reach (aRR: 0.56 [0.41, 0.77]). The results of the univariable models are presented in the supplementary eTable 1. View this table: View inline View popup Download powerpoint Table 2. Bayesian multivariable multilevel negative binomial regression models examining factors associated with reach and effectiveness among CCMP clinics starting before 2020. The rate ratio (RR) and 95% credible interval (CI) are presented. Discussion The purpose of this study was to describe and understand determinants of long-term effectiveness and reach of the NYS CCMP, one of the largest statewide programs of its kind. Informed by the RE-AIM framework, our study demonstrated that despite ongoing fiscal and technical assistance strategies, clinics struggled with sustained reach, and to a lesser extent, effectiveness. Our findings also suggest a possible tradeoff between reach and clinical effectiveness, both of which were associated with caseload/care manager FTE, a potential target for policymakers seeking to sustain CC programs. To date, researchers have largely focused on effectiveness and less so reach of CC programs. Experts posit that achieving population wide impact may require maximizing reach even while marginally sacrificing effectiveness. 22 Here, we found that significant predictors of long-term reach included better patient engagement efforts and care manager FTE (unadjusted for caseload) both of which should be targeted to optimize the pace of sustainability efforts. A multiple case study of the collaborative chronic care model in outpatient mental health clinics highlighted the need for increased prioritization of fundamental operational needs (e.g. mental health staffing or clinical space). 23 These resources remain integral in the sustainability phase when psychosocially complex patients and their providers become increasingly difficult to reach, particularly in FQHCs. Clinics seeking to sustain CC may address resource barriers by task-shifting duties to lower-cost administrative roles to expand care manager capacities. 14 Centralized triage and referral services may also support CC implementation efforts and maximize reach while also meeting the needs of the diverse array of patients, many of whom may require more intensive mental health services. 24 Our study results also provide valuable insights for policymakers, highlighting the need to target efforts toward specific settings, such as larger clinics, later adopters, and Federally Qualified Health Centers (FQHCs) that may have fewer resources per capita and which face greater challenges with achieving reach. Additionally, our study adds to our understanding of the drivers of sustained clinical effectiveness, which has been shown to be particularly difficult to maintain after removing initial implementation support. 17 , 25 – 28 Unutzer found that CC clinics receiving enhanced (vs. basic) implementation support are more likely to achieve the effects of clinical trials (i.e., optimally ≥50-60%). 1 We advance this literature by demonstrating that despite sustained implementation support , clinics see long-term effectiveness rates of 42% which aligns with real world effectiveness rates in CC settings with varying degrees of support. 1 Here, we found that a higher percentage of psychiatric consultations for unremitted patients (done in just 30-40% of patients) was associated with higher effectiveness, supporting the integral role of psychiatrists, even with case-based consultation, in achieving optimal outcomes. 29 Psychiatry involvement, which may see a voltage drop in the sustainability phase, should be further incentivized and revived in addition to promoting engagement with external implementation support. Our results also support recent research suggesting a reciprocal relationship between clinical effectiveness and reach. 22 This was perhaps due to a decrease in quality that may occur when too many patients are reached and, vice versa, the high degree of effectiveness that can be achieved when care managers focus on only a small group of patients. Our study further highlights the need to explicitly distinguish reach and effectiveness as unique targets in achieving sustainability. For example, capacity building, care manager training/accountability and primary care referral support may improve reach, while maximizing use of e-consults may be an avenue for increasing psychiatrist involvement. Interestingly, greater caseload/care manager FTE, which may serve as a proxy for high-quality care managers, was positively associated with both reach and effectiveness. Notably, median caseload per FTE in our study was approximately half the recommended acuity-adjusted panel size of 100-150 patients/FTE for moderately complex patients. 30 Targeting caseloads, perhaps through fee-for-quality reimbursement codes that have thus far been focused on incentivizing effectiveness, may be one approach. Finally, it has now been 10 years since the 2012-2014 OMH/DOH demonstration project found an average effectiveness of 45% and reach (defined as proportion of clinically diagnosed patients, which made up the majority of those screened, enrolled in CC) of 43%. 19 , 31 Our study suggests that reach may wane over time, perhaps due to the myriad of unique barriers to CC sustainability. 15 , 32 53,54 14 , 33 Future research is warranted to examine how best to promote reach and effectiveness long-term. It may be that CC is not be worth the effort in every setting, and identifying settings best suited for CC, perhaps via predictive modeling, while de-implementing ineffective models in others is an area of future research. There were several limitations to this study’s design that restricted our ability to generalize our findings to other states and clinics. First, the majority of our clinics were FQHCs that served Medicaid populations, potentially limiting generalizability. As the available data were collected primarily for administrative purposes and reviewed retrospectively, several variables (e.g., reach) were based on clinic self-report. However, we did mostly draw from OMH’s prespecified definitions of key metrics. Not every clinic participating in CCMP reported data to OMH, and among those reporting data, there was considerable missing data (e.g., in a given month no patients were enrolled or eligible for clinical improvement assessment or EMR/registry issues emerged). As we calculated reach based on the number of enrolled patients divided by the number who screened positive, our data may have been skewed when patients were screened one month and enrolled in another and does not account for screen positive patients who were not eligible for CC (e.g., severe psychiatric comorbidities though care managers often assist in care coordination for such patients). Finally, we could not examine patient-level characteristics such as gender, age and symptom severity due to the lack of patient-level data, 1 though this Medicaid program has a large proportion of historically marginalized patients. Despite limitations, the outcomes-driven approach of the NYS CCMP provides an opportunity to address the challenges of implementing and sustaining CC in diverse patient population. Conclusion Overall, our study adds to our understanding of the drivers of sustained reach and effectiveness in the context of ongoing implementation support and provides several targets for future interventions. Our study is also a call to action for increased attention to reach, particularly given its importance in achieving equitable access to CC. Large state-sponsored initiatives like NYS’ CCMP will continue to be integral to tackling the challenging work involved in implementing and sustaining CC in diverse patient populations and settings while also advancing our understanding of what it takes to sustain evidence-based programs, which is particularly salient to the emerging field of implementation science. Data Availability The senior author (NM) had full access to the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. The datasets used and/or analyzed in this study are available in Open Science, https://www.cos.io upon request. https://www.cos.io Authors’ contributions KK wrote the first draft of the manuscript. BF, ML, JZ, QC, and JES managed, analyzed and interpreted the data. JZ and QC drafted the data analysis and results sections. NM conceptualized the project, obtained funding and was a key contributor in writing the manuscript. AJ and DG collected and cleaned the data. All authors contributed to critical revision of the manuscript. All authors read and approved the final manuscript. Funding The design and conduct of this study were funded by an R01 grant from the Agency for Healthcare Research. Prior presentations This manuscript has not been previously presented or published elsewhere. Competing interest declaration All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare that all authors have support from the Office of Mental Health for the submitted work, no relationships with 3 rd party companies that might have an interest in the submitted work, and no financial or non-financial relationships that may be relevant to the submitted work. Ethics approval and consent to participate This study was approved by the Columbia University Institutional Review Board (study ID: AAAQ1705). Consent for publication Not applicable. Only aggregate clinic-level data (no patient level data) were used in this study. Availability of data and materials The senior author (NM) had full access to the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. The datasets used and/or analyzed in this study are available in Open Science, https://www.cos.io upon request. Acknowledgments The authors would like to thank Drs. Ian Kronish and Jennifer M. Barbecho for their critical revisions to this work. References 1. ↵ Unutzer J , Carlo AC , Arao R , et al. Variation In The Effectiveness Of Collaborative Care For Depression: Does It Matter Where You Get Your Care? Health affairs (Project Hope) . 2020 ; 39 ( 11 ): 1943 – 1950 . OpenUrl PubMed 2. Thota AB , Sipe TA , Byard GJ , et al. Collaborative Care to Improve the Management of Depressive Disorders: A Community Guide Systematic Review and Meta-Analysis . American Journal of Preventive Medicine . 2012 ; 42 ( 5 ): 525 – 538 . OpenUrl CrossRef PubMed 3. Hu J , Wu T , Damodaran S , Tabb KM , Bauer A , Huang H . The Effectiveness of Collaborative Care on Depression Outcomes for Racial/Ethnic Minority Populations in Primary Care: A Systematic Review . Psychosomatics . 2020 ; 61 ( 6 ): 632 – 644 . OpenUrl PubMed 4. Faisal MR , Salam FT , Vidyasagaran AL , et al. Collaborative care for common mental disorders in low- and middle-income countries: A systematic review and meta-analysis . J Affect Disord . 2024 ; 363 : 595 – 608 . OpenUrl PubMed 5. Wang Y , Hu M , Zhu D , Ding R , He P . Effectiveness of Collaborative Care for Depression and HbA1c in Patients with Depression and Diabetes: A Systematic Review and Meta-Analysis . Int J Integr Care . 2022 ; 22 ( 3 ): 12 . OpenUrl 6. ↵ Archer J , Bower P , Gilbody S , et al. Collaborative care for depression and anxiety problems . Cochrane Database of Systematic Reviews . 2012 ( 10 ). 7. ↵ Whitebird RR , Solberg LI , Jaeckels NA , et al. Effective Implementation of collaborative care for depression: what is needed? The American journal of managed care . 2014 ; 20 ( 9 ): 699 – 707 . OpenUrl PubMed 8. Solberg LI , Crain AL , Jaeckels N , et al. The DIAMOND initiative: implementing collaborative care for depression in 75 primary care clinics . Implementation Science . 2013 ; 8 ( 1 ): 135 . OpenUrl PubMed 9. Bauer AM , Azzone V , Goldman HH , et al. Implementation of collaborative depression management at community-based primary care clinics: an evaluation . Psychiatr Serv . 2011 ; 62 ( 9 ): 1047 – 1053 . OpenUrl CrossRef PubMed Web of Science 10. ↵ Rossom RC , Solberg LI , Magnan S , et al. Impact of a national collaborative care initiative for patients with depression and diabetes or cardiovascular disease . Gen Hosp Psychiatry . 2017 ; 44 : 77 – 85 . OpenUrl PubMed 11. ↵ Chang ET MJ , Chaney E , Lanto A , Simon B , Yano EM , Rubenstein LV . Predictors of primary care management of depression in the Veterans Affairs healthcare system . Journal of General Internal Medicine 2014 ; 29 ( 7 ): 1017 – 1025 . OpenUrl PubMed 12. Solberg LI , Crain AL , Maciosek MV , et al. A stepped-wedge evaluation of an initiative to spread the collaborative care model for depression in primary care . Annals of Family Medicine . 2015 ; 13 ( 5 ): 412 – 420 . OpenUrl Abstract / FREE Full Text 13. ↵ Bauer MS , Miller CJ , Kim B , et al. Effectiveness of Implementing a Collaborative Chronic Care Model for Clinician Teams on Patient Outcomes and Health Status in Mental Health: A Randomized Clinical Trial . JAMA Network Open . 2019 ; 2 ( 3 ): e190230 – e190230 . OpenUrl 14. ↵ Moise N , Shah RN , Essock S , et al. Sustainability of collaborative care management for depression in primary care settings with academic affiliations across New York State . Implement Sci . 2018 ; 13 ( 1 ): 128 . OpenUrl PubMed 15. ↵ Palinkas LA , Ell K , Hansen M , Cabassa L , Wells A . Sustainability of collaborative care interventions in primary care settings . J Soc Work . 2011 ; 11 . 16. ↵ Saldana L , Chamberlain P , Bradford WD , Campbell M , Landsverk J . The Cost of Implementing New Strategies (COINS): A Method for Mapping Implementation Resources Using the Stages of Implementation Completion . Child Youth Serv Rev . 2014 ; 39 : 177 – 182 . OpenUrl PubMed 17. ↵ Scheirer MA , Dearing JW . An Agenda for Research on the Sustainability of Public Health Programs . American journal of public health . 2011 ; 101 ( 11 ): 2059 – 2067 . OpenUrl CrossRef PubMed Web of Science 18. ↵ Bowen DJ , Heald A , LePoire E , et al. Population-based implementation of behavioral health detection and treatment into primary care: early data from New York state . BMC Health Services Research . 2021 ; 21 ( 1 ): 922 . OpenUrl PubMed 19. ↵ Sederer LI , Derman M , Carruthers J , Wall M . The New York State Collaborative Care Initiative: 2012-2014 . Psychiatr Q . 2016 ; 87 ( 1 ): 1 – 23 . OpenUrl PubMed 20. ↵ Holtrop JS , Estabrooks PA , Gaglio B , et al. Understanding and applying the RE-AIM framework: Clarifications and resources . J Clin Transl Sci . 2021 ; 5 ( 1 ): e126 . OpenUrl 21. ↵ Health NOoM . NYS CCMP Final Metrics . https://aims.uw.edu/nyscc/sites/default/files/NYS%20CCMP%20Final%20Metrics%2020pdf . Published 2022. Accessed 2024 . 22. ↵ Zatzick DF , Koepsell T , Rivara FP . Using target population specification, effect size, and reach to estimate and compare the population impact of two PTSD preventive interventions . Psychiatry . 2009 ; 72 ( 4 ): 346 – 359 . OpenUrl CrossRef PubMed Web of Science 23. ↵ Kim B , Sullivan JL , Brown ME , et al. Sustaining the collaborative chronic care model in outpatient mental health: a matrixed multiple case study . Implementation Science . 2024 ; 19 ( 1 ): 16 . OpenUrl PubMed 24. ↵ Wolk CB , Last BS , Livesey C , et al. Addressing Common Challenges in the Implementation of Collaborative Care for Mental Health: The Penn Integrated Care Program . Ann Fam Med . 2021 ; 19 ( 2 ): 148 – 156 . OpenUrl Abstract / FREE Full Text 25. ↵ Bauer MS , Stolzmann K , Miller CJ , Kim B , Connolly SL , Lew R . Implementing the Collaborative Chronic Care Model in Mental Health Clinics: Achieving and Sustaining Clinical Effects . Psychiatr Serv . 2021 ; 72 ( 5 ): 586 – 589 . OpenUrl PubMed 26. Chinman M , Goldberg R , Daniels K , et al. Implementation of peer specialist services in VA primary care: a cluster randomized trial on the impact of external facilitation . Implementation Science . 2021 ; 16 ( 1 ): 60 . OpenUrl PubMed 27. Jackson CB , Herschell AD , Scudder AT , et al. Making Implementation Last: The Impact of Training Design on the Sustainability of an Evidence-Based Treatment in a Randomized Controlled Trial . Administration and Policy in Mental Health and Mental Health Services Research . 2021 ; 48 ( 5 ): 757 – 767 . OpenUrl 28. ↵ Shelton RC , Cooper BR , Stirman SW . The Sustainability of Evidence-Based Interventions and Practices in Public Health and Health Care . Annual Review of Public Health . 2018 ; 39 ( Volume 39, 2018 ): 55 – 76 . OpenUrl CrossRef PubMed 29. ↵ Whitebird RR , Solberg LI , Jaeckels NA , Pietruszewski PB , Hadzic S , Unutzer J . Effective implementation of collaborative care for depression: what is needed? Am J Manag Care . 2014 ; 20 . 30. ↵ University of Washington . AIMS Center (Advancing Integrated Mental Health Solutions . AIMS Center (Advancing Integrated Mental Health Solutions . Published 2016 . Accessed December 11, 2016 31. ↵ Bowen DJ , Heald A , LePoire E , et al. Population-based implementation of behavioral health detection and treatment into primary care: early data from New York state . BMC Health Serv Res . 2021 ; 21 ( 1 ): 922 . OpenUrl PubMed 32. ↵ Blasinsky M , Goldman HH , Unutzer J . Project IMPACT: a report on barriers and facilitators to sustainability . Admin Pol Ment Health . 2006 ; 33 . 33. ↵ Dong M , Salamanca LF , Medina V , et al. Patient-level barriers and facilitators to sustaining collaborative care programs for underserved minorities: A qualitative study . General hospital psychiatry . 2020 ; 67 : 169 – 170 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted January 12, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Evaluating sustained reach and effectiveness of collaborative care models: A Cross-sectional study of the New York State Collaborative Care Medicaid Program Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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