Prevention of 90-day Detox Readmission for Opioid Use Disorder by a Community-based Life-changing Individualized Medically Assisted Evidence-based Treatment (C.L.I.M.B.) Program: a Quasi-experimental Design

doi:10.21203/rs.3.rs-962685/v1

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Prevention of 90-day Detox Readmission for Opioid Use Disorder by a Community-based Life-changing Individualized Medically Assisted Evidence-based Treatment (C.L.I.M.B.) Program: a Quasi-experimental Design

https://doi.org/10.21203/rs.3.rs-962685/v1

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Background: Evidence for community-based detox readmission prevention strategies for opioid use disorder (OUD) is scant. We evaluated a pilot program designed to provide individualized and structured treatment plan, including addressing prolonged withdrawal symptoms, family/systems assessment, contingency management, and medically assisted treatment.

Methods: A non-randomized quasi-experimental design was used to compare the pilot sites (treatment) and comparison sites before and after the program started, i.e., a simple difference-in-differences (DID) strategy. Adults 18 years and older who meet the Diagnostic and Statistical Manual of Mental Disorders version 5 criteria for OUD and had a detox admission at any facility in two study periods between 5/2018 and 12/2019 are included. Readmission in 90-days after an inpatient detox was the primary outcome. Multiple statistical estimation methods were used to adjust for potential confounding factors between treatment and control groups.

Results: A total of 2,320 unique patients with 2,443 index detox admission in the pre- and post-periods were compared. The C.L.I.M.B. patients had higher readmission compared with the controls in the pre-period (unadjusted readmission rates16.4% vs. 10.0%) and similar rates in the post-period (11.8% vs. 10.1%). All DID estimates were not statistically significant (adjusted estimates ranged from 6.0 to 9.2 percentage points difference favoring the C.L.I.M.B. program).

Conclusions: While not statistically significant, wrap-around services reduced detox readmission. It may be that the added services helped to inform people that inpatient detox is not necessary for OUD and withdrawal can be managed with a structured treatment plan.

Other Public Policy

Health Economics & Outcomes Research

Opioids and substance-related and additive disorders

community integration and recovery-oriented care

mental health care/service delivery systems

rehabilitation

Almost 500,000 Americans died from an opioid-related overdose between 1999 and 2019 and the age-adjusted synthetic opioid-involved death rate increased 15% from 9.9 to 11.4 per 100,000 population.¹ Evidence-based strategies – including reducing opioid prescribing and increasing access to treatment of opioid use disorder (OUD) – must be adapted and deployed together to address the crisis and save lives.² In the past few decades, OUD has been treated on an episodic basis with poor outcomes and high relapse rates. Many patients and providers lament the mismanagement of OUD, especially the lack of “ownership” in handling the tapering process.^3,4 Methadone, buprenorphine and naltrexone have been shown to prevent relapse in clinical trials,^5,6 but they may not be tolerated or beneficial universally.⁷ Although multiple programs exist for the treatment of OUD for specific populations (e.g., cancer or surgical patients),^8–10 many patients have histories of unemployment, homelessness, and psychiatric comorbidities and require social service interventions that are not usually found in the medical system.^11,12 The American Society of Addiction Medicine (ASAM) recommends the OUD treatment choice be based on a shared decision making process.^13,14 Strategies specifically designed for relapse prevention and withdrawal management are equally important because OUD is a chronic relapsing disease with increased fatal overdose risks in the first year of a nonfatal overdose event.¹⁵

Grounded in the community-based chronic care model, Blue Care Network (BCN) and Blue Cross Blue Shield (BCBS) of Michigan implemented a pilot program (Community-based Life-changing Individualized Medically assisted evidence-Based treatment, [C.L.I.M.B.]) on May 1 and Dec 1, 2018, respectively, for individuals 18 years and older who met the Diagnostic and Statistical Manual of Mental Disorders version 5 criteria for OUD and were admitted to inpatient detox at two sites. This paper describes and evaluates the impact of the program on detox admission prevention in 90 days after discharge from inpatient facilities. The question is: If the C.L.I.M.B. program is rolled out to sites like the pilot sites will it reduce 90-day readmission among OUD patients?

Design

Consistent with the quasi-experimental two-group pre-post design, we used the difference-in-differences (DID) method to ameliorate potential confounding bias.^16,17 The underlying assumption of the DID method is that the change in readmission rates from pre- to post-period in the comparison group is a good proxy of the counterfactual change in the pilot group had there been no pilot program (Figure S1 in supplemental materials). The target estimand is the average treatment effect on the treated which answers the question: for patients treated in the pilot sites, was the program a cause for the change in readmission rate? On the probability scale, a DID method estimates the difference of risk differences (DRD); and on the odds ratio (OR) scale, a DID method estimates the ratio of ORs (ROR). The pre-and post-periods for the BCN (a health maintenance organization [HMO]) patients were 7/1/2016 to 4/30/2018 and 5/1/2018 to 8/31/2019; the corresponding periods for the BCBSM (a preferred provider organization [PPO]) patients were 2/1/2017 to 11/30/2018 and 12/1/2018 to 3/30/2020. In the post periods, another site implemented a program like C.L.I.M.B.; thus, to avoid contamination patients whose initial detox occurred at that site were excluded. The pilot program was approved by the BCN and BCSM medical directors, and the current evaluation was approved by the Institutional Review Board of Michigan State University as non-human subject research (STUDY00000846).

Patients

Patients 18 years or older, who had detox inpatient stay for a diagnosis of OUD in any of the two pre-periods were included in the study. To ensure data completeness, a patient had to be enrolled in the health plan for 6 or more months before the initial detox in pre-period to capture baseline comorbidity; and the initial detox did not occur within 90-days of each period’s end date.

Interventions

Following guidelines set forth by the ASAM,^13,14 the C.L.I.M.B. program included services for the continuum of OUD cycle, including detoxification, residential care, intensive outpatient program, outpatient with medically assisted treatment (MAT), and smart phone support application (app) originated in 2011 at University of Wisconsin Center for Health Enablement Support System (CHESS).called Addiction-CHESS (A-CHESS), which is a comprehensive relapse prevention tool for patients with substance use disorders (SUDs). The critical tasks in each level of care (LOC) of the pilot program are described below.

a) Detoxification: A patient will stay 1-3 days in LOC (ASAM LOC 3.3 or higher) appropriate for acute withdrawal management to stabilize the patient’s physical symptoms of withdrawal and “jump start” the MAT process. The sub-acute detoxification phase can be shorter when the continuing withdrawal can be addressed in the residential LOC.

b) Residential services: A patient can stay 7-14 days in supervised living (ASAM LOC 3.0-3.1) where there is a safe, structured recovery environment with lesser temptation to relapse and have access to medical attention if required to address stabilizing withdrawal symptoms. Critical items that will be addressed beyond biomedical conditions are education about the disease, motivation to change, tools to reduce relapse potential, e.g., the A-CHESS app, comprehensive treatment plan, family/systems assessment to identify additional members who may have SUDs, and continuation of MAT where appropriate.

c) Partial hospitalization/intensive outpatient services: This LOC (ASAM LOC 2.0-2.5) would continue the work started in the residential LOC. The patient would live at home or in supportive recovery housing, ideally be back to work or school and simultaneously attending 12 step programming. They would use the A-CHESS app to identify sober supports (sponsor, sober coach) and have routine drug testing with weekly reporting to the therapist. This LOC would continue for 12-24 sessions in 2 months on the average.

d) Outpatient services: In this LOC (ASAM LOC 1.0), which could last 12 months or more, therapists and treatment team will need to monitor the patient through A-CHESS, have routine contact with the peer support/coach, use motivational interviewing and behavioral activation skills to support continued therapy, develop benchmarks and celebrate achievement.

e) A-CHESS: The smartphone app applies the science of self-determination theory¹⁸ to help patients succeed in addiction recovery. Key A-CHESS services¹⁹ include 1) a “Help” button linked to the patient’s preapproved supporters, positive and potentially distracting games, and audio-video relaxation recording; 2) cognitive behavioral therapy boosters; 3) functionality monitoring with self-assessment tools; 4) a global positioning system location tracker that will initiate a patient-defined action (e.g., contacting sober coach) when s/he approaches a high-risk location, and 5) just-in-time feedback via a counselor dashboard.

Main Measures

Primary outcome: 90-day detox readmission after an initial detox inpatient stay at any facility. Readmission was identified by the same method as the initial detox: any inpatient stay with a diagnosis of F11.x or F11.xx using the International Classification of Diseases, 10th version, Clinical Modification (ICD-10-CM) codes, and revenue codes 01x6 (x=1, 2,3,4, or 5).

Treatment groups: the National Provider Identifier codes for the two pilot facilities were used to identify patients in the pilot group (two sites).

Level of care: MAT was identified using National Drug Codes in pharmacy claims and the Current Procedural Terminology codes; and revenue codes and/or procedure codes were used to find LOC 1.0-2.5 (partial hospitalization/intensive outpatient services/outpatient) services (list of these codes available upon request).

Comorbidity: the Agency for Healthcare Research and Quality Clinical Classification Software Refined version (v2021.2)²⁰ based on the ICD-10-CM codes was used to find in medical claims the comorbid conditions 6 months prior to the initial detox in each period, including mood disorders; anxiety-, fear-, trauma- or stressor-related disorders; alcohol-, cannabis-, sedative-, stimulant- hallucinogen- or inhalant- related disorders, neoplasms; suicidal ideation/attempt or intentional self-harm; endocrine, nutritional and metabolic diseases; diseases of the nervous system, circulatory system, respiratory system, digestive system, musculoskeletal system, or genitourinary system. Emergency room visits in past 6 months were identified using revenue codes 045x (x=0-9).

Covariates: Patient’s age, sex, HMO or PPO plan types, and residential zip codes were extracted from health plan enrollment files. The 5-digit zip codes were linked to the census tracts using the U.S. Department of Housing and Urban Development zip code crosswalk files²¹ where a census tract with the highest residential ratio was chosen when multiple tracts were within the same zip code.²² Past research linked living in a disadvantaged neighborhood to worse health conditions and increased utilizations. We used the 2018 Area Deprivation Index (ADI),^23,24 2015 Childhood Opportunity Index (COI),²⁵ and 2018 Social Vulnerability Index (SVI)²⁶ to approximate the neighborhood characteristics and as proxies to patient socioeconomic status. Higher ADI rankings represent more disadvantaged neighborhoods; higher COI scores measure more opportunities; and higher SVI scores indicate more vulnerable neighborhoods. All indices were transformed to range from 0 to 100.

Analytic Approach

We compared the differences in covariates and comorbidities between the C.L.I.M.B. and control group in the pre- and post-periods using chi-square tests for categorical variables and t-tests for continuous variables. As in the tradition for propensity score analysis, we also presented the standardized differences (difference divided by the pooled standard deviation) between the two group where absolute value greater than 0.1 were indicative of clinically or practically important differences,²⁷ i.e., they are 0.1 standard deviations away from each other. We estimated the DID effects using six methods to triangulate evidence: 1) multivariable logistic regression adjustment (RA) for comorbidities and covariates; 2) augmented inverse probability weighted (IPW) marginal effects²⁸ where covariates for the outcome model and the propensity scores (PS) model were selected using logistic lasso;²⁹ 3) IPW estimation where the PS were estimated using logistic regressions; 4) IPW-RA double robust method;³⁰ 5) bias-corrected single nearest neighbor matching method³¹ based on Mahalanobis distance matrix of the covariates; and 6) PS matching with caliper 0.2. The 95% confidence intervals (CI) were estimated using the percentile-based bootstrap CI using 1,000 bootstrapped samples. All analyses were performed in Stata version 17.³²

Sensitivity Analyses

We performed two sets of sensitivity analysis. First, we excluded 123 patients (236 events) who were in both the pre- and post-periods, because the analyses may be contaminated by the correlations between observations for the same patients, especially when the patient was in different treatment groups across periods. Secondly, many randomized controlled trials (RCTs) include stringent inclusion/exclusion criteria. We applied the criteria of the MAT + A-CHESS trial¹⁹ to compare the results of C.L.I.M.B. in the community-based population versus in a trial-selected population. The trial required the patients to have no acute medical problem with immediate inpatient treatment needs, no history of psychotic disorders, willing to participate, able to read and write in English, not pregnant, willing to share health-related data with primary care physicians, and at the trial intake, abstinent from opioids for at least one week and no longer than two months, except for medications used to treat OUD. We applied as many of these criteria as possible using claims data (underlined above).

A total of 2,320 unique patients with 2,443 index detox admissions in the pre- and post-periods were included in the main analyses (Figure 1). Table 1 shows that in the pre-pilot period C.L.I.M.B. patients were more likely to be in the HMO plans, had more mood disorders, and diseases of the musculoskeletal system than controls; however, in the post-pilot period C.L.I.M.B. patients had fewer other substance-related disorders, or diseases of the nervous or digestive systems. The largest and most significant differences (Tables 1 and 2) between the groups were at the neighborhood level. C.L.I.M.B. patients were more likely to live in one the 100 largest metropolitan areas, had lower ADI, higher COI, and lower SVI scores, i.e., they were from relatively more well-to-do neighborhoods.

Table 1

Demographic characteristics, medical claims six months prior to detox, and standardized difference
	Pre-period			Post-period
	Control	CLIMB	p-value ‖	Control	CLIMB	p-value ‖
	N=1,385	N=318		N=545	N=195
Age category	N (%)	N (%)		N (%)	N (%)
18–<25	407 (29.4)	88 (27.7)	0.636	154 (28.3)	53 (27.2)	0.47
25–<35	364 (26.3)	90 (28.3)		147 (27.0)	45 (23.1)
35–<45	212 (15.3)	42 (13.2)		95 (17.4)	43 (22.1)
45+	402 (29.0)	98 (30.8)		149 (27.3)	54 (27.7)
Female	467 (33.7)	96 (30.2)	0.228	176 (32.3)	69 (35.4)	0.43
HMO	245 (17.7)	154 (48.4)	<0.001	57 (10.5)	87 (44.6)	<0.001
Comorbidity 6 months prior to index detox
Had no claims	156 (11.3)	34 (10.7)	0.770	51 (9.4)	17 (8.7)	0.79
had detox	78 (5.6)	18 (5.7)	0.984	48 (8.8)	15 (7.7)	0.63
Had emergency room visits	708 (51.1)	160 (50.3)	0.796	270 (49.5)	89 (45.6)	0.35
Had opioid use disorder diagnosis	677 (48.9)	160 (50.3)	0.645	295 (54.1)	102 (52.3)	0.66
Substance–related disorders *	331 (23.9)	72 (22.6)	0.634	174 (31.9)	44 (22.6)	0.01
Mood disorders †	550 (39.7)	148 (46.5)	0.026	236 (43.3)	84 (43.1)	0.96
Alcohol–related disorders	260 (18.8)	68 (21.4)	0.287	135 (24.8)	36 (18.5)	0.07
Anxiety/fear/trauma/stressor–related disorders	585 (42.2)	145 (45.6)	0.275	257 (47.2)	84 (43.1)	0.33
Suicidal ideation/attempt/intentional self–harm	130 (9.4)	31 (9.7)	0.842	45 (8.3)	12 (6.2)	0.35
Neoplasm	83 (6.0)	18 (5.7)	0.821	32 (5.9)	11 (5.6)	0.91
Endocrine, nutritional, and metabolic diseases	418 (30.2)	100 (31.4)	0.658	182 (33.4)	62 (31.8)	0.68
Diseases of the nervous system	569 (41.1)	120 (37.7)	0.273	229 (42.0)	66 (33.8)	0.05
Diseases of the circulatory system	448 (32.3)	92 (28.9)	0.238	185 (33.9)	57 (29.2)	0.23
Diseases of the respiratory system	381 (27.5)	83 (26.1)	0.611	155 (28.4)	58 (29.7)	0.73
Diseases of the digestive system	350 (25.3)	75 (23.6)	0.531	152 (27.9)	38 (19.5)	0.02
Diseases of the musculoskeletal system and connective tissue	653 (47.1)	130 (40.9)	0.043	238 (43.7)	78 (40.0)	0.37
Diseases of the genitourinary system	280 (20.2)	62 (19.5)	0.773	118 (21.7)	36 (18.5)	0.35
Injury, poisoning and certain other consequences of external causes	455 (32.9)	119 (37.4)	0.120	177 (32.5)	58 (29.7)	0.48
Live in one of the 100 largest metro areas	965 (69.7)	270 (84.9)	<0.001	377 (69.2)	169 (86.7)	<0.001
Neighborhood characteristics	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)
Mean ADI state rank ‡	48.4 (21.6)	44.8 (23.9)	0.009	51.3 (22.3)	44.0 (24.4)	<0.001
Mean ADI national rank	59.8 (19.4)	57.1 (22.1)	0.033	61.1 (20.3)	55.6 (23.4)	<0.01
Mean childhood opportunity index	54.1 (20.8)	56.0 (23.4)	0.149	51.7 (21.6)	57.8 (24.2)	<0.01
Mean SVI socioeconomic score §	44.9 (20.2)	42.0 (22.4)	0.022	47.0 (20.5)	39.5 (22.9)	<0.001
Mean SVI household/disability score	52.8 (18.2)	48.0 (18.7)	<0.001	54.1 (17.7)	46.0 (19.5)	<0.001
Mean SVI minority/language score	31.8 (17.7)	35.3 (16.8)	0.001	35.0 (19.0)	35.6 (17.3)	0.66
Mean SVI housing/transportation score	39.9 (14.6)	36.0 (14.7)	<0.001	42.3 (15.9)	34.1 (14.0)	<0.001
*. Including cannabis–, sedative–, stimulant– hallucinogen– or inhalant–related substances
†. Including depressive disorders, bipolar disorders, and other specified mood disorders
‡. ADI: area deprivation index
§. SVI: social vulnerability index
‖. p–values are based on chi–square tests for categorical variables and t–tests for continuous variables

Table 2

Standardized difference between C.L.I.M.B. and comparison in the pre- and post-period respectively
	Pre-period		Post-period
	Raw SD	Weighted SD	Raw SD	Weighted SD
Age category
18–<25	0.038	–0.078	0.024	0.019
25–<35	–0.046	0.025	0.089	–0.198
35–<45	0.059	–0.047	–0.119	0.105
45+	–0.039	0.090	–0.008	0.086
Female	0.075	–0.099	–0.066	0.168
HMO	–0.757	–0.040	–0.933	–0.060
Comorbidity 6 months prior to index detox
Had no claims	–0.018	0.021	–0.022	–0.083
had detox	–0.001	–0.030	0.040	–0.046
Had emergency room visits	0.016	–0.038	0.078	–0.070
Had opioid use disorder diagnosis	–0.029	0.025	0.037	–0.120
Substance–related disorders *	0.030	–0.051	0.206	–0.161
Mood disorders †	–0.139	0.143	0.005	–0.017
Alcohol–related disorders	–0.066	0.028	0.150	–0.129
Anxiety/fear/trauma/stressor–related disorders	–0.068	0.032	0.082	–0.078
Suicidal ideation/attempt/intentional self–harm	–0.012	–0.018	0.079	–0.187
Neoplasm	0.014	0.002	0.010	0.005
Endocrine, nutritional, and metabolic diseases	–0.028	0.062	0.034	–0.055
Diseases of the nervous system	0.068	–0.041	0.167	–0.223
Diseases of the circulatory system	0.073	–0.039	0.101	–0.135
Diseases of the respiratory system	0.032	–0.061	–0.029	–0.007
Diseases of the digestive system	0.039	–0.037	0.193	–0.192
Diseases of the musculoskeletal system and connective tissue	0.126	–0.103	0.074	–0.094
Diseases of the genitourinary system	0.018	–0.015	0.079	–0.026
Injury, poisoning and certain other consequences of external causes	–0.097	0.081	0.059	–0.063
Live in one of the 100 largest metro areas	–0.344	0.302	–0.404	0.274
Neighborhood characteristics
Mean ADI state rank ‡	0.163	0.022	0.317	–0.130
Mean ADI national rank	0.132	0.029	0.261	–0.138
Mean childhood opportunity index	–0.090	–0.078	–0.274	0.093
Mean SVI socioeconomic score §	0.142	0.058	0.355	–0.143
Mean SVI household/disability score	0.265	0.019	0.443	–0.258
Mean SVI minority/language score	–0.202	0.168	–0.037	0.123
Mean SVI housing/transportation score	0.267	–0.068	0.529	–0.136
*. Including cannabis–, sedative–, stimulant– hallucinogen– or inhalant–related substances
†. Including depressive disorders, bipolar disorders, and other specified mood disorders
‡. ADI: area deprivation index
§. SVI: social vulnerability index

Before using the PS for adjustments, the raw standardized differences (Table 2) showed consistent patterns as in Table 1. After weighting, the standardized differences were reduced to less than 0.1 for all except for 4 variables in the pre-period; however, weighting did not improve balance in the post-period. The residual imbalance was adjusted using these 4 variables in the nearest neighbor matching method. The few variables selected by the logistic lasso generated bi-modal PS (Figure 2) but there were good overlaps between C.L.I.M.B. and control group.

All six methods (Table 3) gave similar estimated DRDs and RORs between groups over time. Before the pilot implementation, the C.L.I.M.B. sites had significantly higher readmission rates than controls (16.4% vs. 9.3 to 11.6% estimated by various adjustment methods); however, after the pilot implementation, readmission rates decreased significantly in the C.L.I.M.B. sites to 11.8% whereas the adjusted rates in control sites did not change significantly (varying from 10.1–12.8%). The decline in readmission in the C.L.I.M.B. sites was not due to changes in patient profiles before and after the pilot because there were no statistically significant differences in all covariates in Tables 1 and 2 over time for C.L.I.M.B. patients. The first sensitivity analysis (N=2,197) led to similar results qualitatively (Table S1 in supplemental materials). Applying the exclusion criteria of an RCT in the second sensitivity analysis with N=2,121, we found two out of six methods produced statistically significant DRD and ROR (Table S2). The C.L.I.M.B. group had lower proportions of patients using intensive outpatient services in the post-period (Table S3).

Table 3

Readmission rate in pre- and post-period and C.L.I.M.B. and comparison groups
	Pre-period				Post-period				Treatment Effect
	C.L.I.M.B.	Control	RD	OR	C.L.I.M.B.	Control	RD	OR	DID	95% CI #	ROR	95% CI #
ATET * Unadjusted	16.4	10.0	6.3	1.75	11.8	10.1	1.7	1.19	–4.6	[–11.2, 2.0]	0.68	[0.37, 1.27]
ATET Adjusted	16.4	9.3	7.0	1.95	11.8	11.1	0.7	1.08	–6.4	[–14.2, 1.1]	0.72	[0.38, 1.37]
ATET AIPW † Lasso	16.4	10.3	6.0	1.70	11.8	11.8	0.04	1.00	–6.0	[–13.8, 1.6]	0.59	[0.28, 1.22]
ATET IPW ‡	16.4	10.0	6.4	1.76	11.8	12.8	–1.0	0.91	–7.4	[–15.6, 0.3]	0.51	[0.24, 1.03]
ATET IPWRA §	16.4	10.0	6.4	1.76	11.8	12.6	–0.8	0.93	–7.2	[–15.0, 0.4]	0.53	[0.26, 1.06]
ATET NNMATCH ‖	16.4	11.4	5.0	1.52	11.8	14.8	–3.0	0.77	–8.0	[–16.7, 0.3]	0.51	[0.21, 1.55]
ATET PSMATCH ¶	16.4	10.7	5.7	1.64	11.8	15.3	–3.5	0.74	–9.2	[–18.8, 0.4]	0.45	[0.21, 1.56]
* ATET = average treatment effect on the treated
† AIPW = augmented inverse–probability weighting
‡ IPW = inverse probability weighting
§ IPWRA = inverse probability weighted regression adjustment
‖ NNMATCH = nearest–neighbor matching
¶ PSMATCH = propensity–score matching
# 95% CI percentile–based bootstrap 1,000 times

The ASAM criteria advocate for individualized, assessment-driven treatment and the flexible use of services across a broad range of care, which can be offered by single or multiple providers with “(1) seamless transfer between levels of care, (2) philosophical congruence among the various providers of care; and (3) timely arrival of the patient’s clinical record at the next provider.”³³ The C.L.I.M.B. program was designed using these principles. Our evaluation found significant decrease in 90-day readmission rates in the C.L.I.M.B. group, however, the DID effects were not statistically significant.

Several reasons may explain the findings. First, many patients came to the pilot program sites from afar and after leaving the facility it was difficult to follow up with them. As OUD is a chronic disease, patient’s willingness to be treated and to follow the treatment plans is critical for recovery. However, Lee et al.⁶ found a substantial induction hurdle in initiating extended-release naltrexone as compared with the sublingual buprenorphine-naloxone combination (72% vs. 94%) in an open-label randomized controlled trial in eight U.S. community-based inpatient settings. Another small RCT³⁴ found that patients receiving long-acting injection naltrexone had twice the rate of treatment retention at 6 months compared with those taking oral naltrexone (57% vs. 28%). Simply increasing knowledge about a community-based treatment does not increase buprenorphine initiation.³⁵ Finding factors that improve treatment adherence and retention is an important next step in the design for effective intervention in the future.

Secondly, when excluding patients with acute medical problems with immediate medical inpatient treatment needs, history of psychosis, or pregnant, we found statistically significant DID using the AIPW and the IPW-RA double robust methods. However, the large effects were partly due to the increase in readmission rates in the selected control group in the post-period. Although RCTs are deemed the gold standard to establish evidence of efficacy of a treatment, practitioners tend to find many trial-tested treatments less effective in the real world. Community-based programs do not have strict inclusion/exclusion criteria as RCTs and it could be one reason that trials are not replicable in more realistic settings.

Finally, one of the key components of the pilot program was the integration of the smartphone app A-CHESS to the clinical practices. We received data from the A-CHESS developer who tracked the daily use of patients who signed up through the two sites’ free licenses in March 2019. Unfortunately, the software did not track the patient’s IDs so the results could not be linked to the claims data. Few patients used A-CHESS after they signed up on the phone at discharge (confirmed by the A-CHESS data). Other free apps existed with features that patients preferred and were easier to use. In December 2018, the Food and Drug Administration cleared the first smartphone app, reSET-O, designed by Pear Therapeutics to be used alongside the MAT to help improve retention in an outpatient setting. With a new push to combat the opioid use epidemic the Substance Abuse and Mental Health Services Administration (SAMHSA) launched a new smartphone app for doctors interested in using MAT to treat patients with OUD in March 2019. The mobile app is free and provides access to SAMHSA’s locator tool for helping doctors and patients find the nearest qualified OUD treatment center. A group of Yale experts also developed a free app, BUP Home Induction, as a companion to the medication. Compared with these tools, the A-CHESS suffers from a few shortcomings. It is not free, and it does not provide the feedbacks that are salient to the patients, e.g., the money saved for abstinence.

Our study has several limitations. Foremost, an ideal design to evaluate the effect of the C.L.I.M.B. intervention would have been a cluster randomized trial. Although we used several statistical methods to adjust for comorbidities and demographic covariates, other unmeasured confounders could bias the results. Second, the DID design relies on the assumption that the change in readmission rates from pre- to post-period in the comparison group is a good proxy of the counterfactual change in the pilot group had there been no pilot program, i.e., the parallel trends assumption. Like all assumptions, this is untestable. Despite changes in the demographics of the control groups between the two periods, the readmission rate in this group remained almost the same (10%). Third, our outcome and covariate measures were all based on administrative healthcare data. Although prevention of readmission was an important goal for the insurers, treatments for OUD are not limited to the inpatient setting. Compared with measures of relapse in RCTs (e.g., 4 consecutive weeks of opioid use by urine toxicology or self-report, or 7 consecutive days of self-reported use in Lee et al.⁶), inpatient claims do not capture all relapses. However, in the real world, health plans rely on these data to identify target population for quality improvement.

Despite the limitations, our study used a carefully quasi-experimental design to evaluate a community-based naturalist treatment of OUD. The lessons learned include that treating OUD as a chronic condition instead of an acute episodic condition for patients in the community was a potentially good option to reduce readmission. As telehealth becomes more amenable to patients, providers and insurers, there is a good opportunity to increase access to treatment.³⁶ In the year of the coronavirus disease pandemic, telehealth has risen dramatically, and it will be important to evaluate the effectiveness of telehealth in managing OUD withdrawal and preventing readmission in the future.

C.L.I.M.B.: community-based life-changing individualized medically assisted evidence-based treatment

OUD: opioid use disorder

DID: difference-in-differences

ASAM: American Society of Addiction Medicine

BCN: Blue Care Network

BCBS: Blue Cross Blue Shield

DRD: difference of risk difference

OR: odds ratio

ROR: ratio of odds ratio

PPO: preferred provider organization

HMO: health maintenance organization

ADI: area deprivation index

COI: childhood opportunity index

SVI: social vulnerability index

RA: regression adjustment

IPW: inverse probability weighted

PS: propensity scores

CI: confidence interval

RCT: randomized controlled trial

MAT: medically assisted treatment

A-CHESS: Addiction-Center for Health Enablement Support System

LOC: level of care

SUD: substance use disorder

ICD-10-CM: International Classification of Diseases, 10^th version, Clinical Modification

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of Michigan State University as non-human subject research (STUDY00000846) and no content was required.

Consent for publication

All authors approved the final version of the manuscript for submission and publication.

Availability of data and materials

All data are proprietary and not publicly available.

Competing interests

None.

Funding

Dr. Luo received an Investigator Initiated Research Program grant (#002648.MG) from the Blue Cross and Blue Shield of Michigan Foundation.

Authors' Contributions

ZL: conceptualization, methodology, writing-- original draft preparation, reviewing and editing.

CR: data curation, writing--original draft preparation and editing.

WSP: program implementation, writing--reviewing and editing.

JD: data preparation, writing--reviewing and editing.

CMR: data curation, writing--reviewing and editing.

PHG: data curation, writing--reviewing and editing.

RR: literature summary, writing--reviewing.

WB: program design and implementation, conceptualization, writing--reviewing and editing.

Acknowledgements

Michael Hoover, Informatics Delivery Lead (Contractor at BCBSM) assisted with data procurement and secure transfer.

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Prevention of 90-day Detox Readmission for Opioid Use Disorder by a Community-based Life-changing Individualized Medically Assisted Evidence-based Treatment (C.L.I.M.B.) Program: a Quasi-experimental Design

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