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Impact of COVID-19 on Substance Use Disorder Treatment: Examining the Influence of in-Person and Telehealth Intervention on Outcomes Using Real-World Data

A peer-reviewed version of this preprint was published in:
Healthcare 2025, 13(1), 84. https://doi.org/10.3390/healthcare13010084

Submitted:

29 November 2024

Posted:

02 December 2024

You are already at the latest version

Preprints on COVID-19 and SARS-CoV-2
Abstract
Background: Telehealth services have experienced a rapid implementation in addiction centres in the aftermath of the COVID pandemic. Several studies highlight the usefulness of telehealth activities. However, there are relatively fewer studies using real-world data, evaluating its effectiveness across different moments in time. The present study analyse the evolution, patient profile and effectiveness of an hybrid care modality (in-person and telehealth) versus an only in-person patient care modality. Methods: Retrospective observational study with data collected between 03/14/2019 and 06/21/2023. The electronic health records of 44930 patients were analysed according to different moments, selected based on the different health measures imposed by the COVID pandemic. Patients were classified according to whether they received an in-person or hybrid intervention. Bivariate statistics and logistic regression analysis were applied. Results: The trend over time shows an increase in the number of patients seen in addiction centres. However, there are no notable changes within the in-person care modality, and a modest increase in telehealth services is observed. Telehealth is primarily used among patients with opiate addiction, as well as with those with comorbid mental disorders. Logistic regression analysis shows that patients in a hybrid modality are more likely to remain in treatment. Conclusions: The study shows that hybrid care is associated with higher patient’s retention rates. Despite this, different profiles are mostly treated with in-person interventions rather than on hybrid modalities. Future studies should look further into how to personalize hybrid care so that it becomes more widespread among SUD patients.
Keywords: 
Substance Use
;  
Hybrid treatments
;  
Outcomes
;  
Retention
;  
COVID-19
Subject: 
Social Sciences  -   Psychology

1. Introduction

The COVID-19 pandemic has led governments in different countries to adopt measures that impact the lives of citizens. In terms of health, the pandemic represented a trigger for promoting telehealth [1]. This can be understood as the incorporation of technological means in the provision of clinical services, such as remote patient monitoring, videoconferencing, and mobile applications [2].
In the field of the treatment of patients diagnosed with substance use disorder (SUD), although telehealth activities were already available before the pandemic [3,4], after its onset, the number of protocols for incorporating telehealth into clinical practice increased, which has probably influenced the exponential expansion of these practices in addiction centres [5]. After several years of telehealth expansion, several authors have highlighted the benefits of this care modality, such as the possibility of having a more flexible and agile care resource without the need to commute to care centres [6]. However, it has also been noted that this modality makes patient-therapist interaction difficult, which can be a handicap for treatment [7].
Reviews such as that of Mark et al. [8] indicate that the results of in-person and via telehealth treatments are similar. Subsequent studies have shown that the application of telehealth techniques can be beneficial in the treatment of patients with SUD. For example, Sistad et al. [9] reported that telehealth resources can have a positive effect on the earlier stage of treatment in patients with SUD. Similarly, Gainer et al. [10] reported that the application of telehealth techniques at the beginning of treatment decreases the likelihood of dropout compared with those who only receive in-person treatment. In the case of opioid patients, different studies have suggested that telehealth treatments match and even increase retention rates of patients receiving medication for opioid use disorders [11,12,13,14]. Further evidence from other studies suggests that the quality of life of patients who receive both telehealth and in-person treatments is equivalent to that of patients who complete an exclusive telehealth intervention [15,16]. Gliske et al. [17] conducted a retrospective study of 3642 patients, classifying them according to whether they received an in-person, hybrid or telehealth treatment. Three months after the start of treatment, these authors found no notable differences among the three groups, highlighting that those patients following a hybrid program reported better levels of general health than patients receiving telehealth assistance did. After one year of follow-up, the results indicated that those following the hybrid intervention had greater retention and a lower proportion dropped out of treatment than those who participated in traditional (in-person) or online-only programmes (telehealth) [18].
Overall, the above studies suggest that, compared with in-person treatment, the use of telehealth techniques lead to results similar to those of in-person treatment, and that the adoption of hybrid treatments may be even more positive. Nevertheless, most of these studies have been conducted over specific time periods, the results of which may be influenced by the temporal proximity to the general health measures associated with the COVID-19 pandemic.
Considering the above, the present study aims to analyse different time periods to determine the impact of telehealth activities on patients' retention in treatment. In this regard, several specific aims are considered: i) to analyse the temporal evolution of in-person and hybrid treatment activity and the profile of patients adhering to each modality; and ii) to analyse whether the hybrid modality improves retention in treatment compared with the in-person modality in different time periods.

2. Materials and Methods

2.1. Desing

Retrospective observational study

2.2. Participants

The study was conducted using the information available in the electronic health records (EHR) of 44930 outpatients. These patients received treatment for addiction between 03/14/2019 and 06/21/2022 in one of the 110 centres of the Public and Subsidised Network of Addiction Centres of Andalusia (Region of Spain with more than 8,000,000 inhabitants). Patients were included if they a) had been diagnosed with opiate, stimulant (excluding patients with nicotine dependence only), hypnosedatives, hallucinogens, cannabis or alcohol dependence; b) had attended at least one treatment information session and at least one therapy session. Patients with only behavioural addictions (i.e. pathological gambling) were excluded, as well as patients who only attended the first information session of treatment.
The period was divided into four phases according to the general legal measures determined for health facilities: 1) pre-pandemic (14 March 2019 to 13 March 2020; no restrictions on patient care); 2) state of alarm (14 March to 21 June 2020; home confinement of patients. The possibility of going to addiction centres with strong restrictions of capacity in the centres, spatial limitations and use of personal protective measures); 3) transition to normality (22 June 2020 to 21 June 2021; the capacity of addiction centres is increased for patient care. The use of personal protective measures is maintained); 4) ‘New normality’ period (22 June 2021 to 21 June 2022; sanitary restrictions are eliminated).
Among the 44930 outpatients, 80.1% were male. The mean age of the patients was 40.36 years (SD = 12.83). A total of 31.6% of the patients had alcohol dependence, 28.2% had cocaine dependence, 17.1% had cannabis dependence, 19.4% of the patients had opioid dependence, and 1.9% had hypnosedative dependence.

2.3. Procedure

Patient EHRs from all care centres are centralised in a single database, allowing no duplication of data for each patient. It also includes automatic checks to detect inconsistencies in the data entered. The information on the EHR is based on the Treatment Demand Indicator (TDI) Standard Protocol 3.0 of the European Monitoring Centre for Drugs and Drug Abuse [19]. Each patient’s data included sociodemographic information, patterns of consumption, previous treatments and illnesses. It also contains information about their treatment and therapeutic process, clinical observations and outcomes.
The registration of the data and storage of the information complied with the General Health Law, and Ethical approval was granted by the Research Ethics Committee of the Andalusian Ministry of Health, which has certified compliance with the requirements for the ethical handling of the information.
The study received ethical approval from the Research Ethics Committee of the Ministry of Health in Andalusia (Cod. 0661-N-22), ensuring the ethical standards for data management.

2.4. Measures

Treatment modality. Each patient was categorised according to whether they received in-person or hybrid (in-person and telehealth) treatment in each period analysed. That is, patients with in-person treatment were those who attended the treatment centres to receive treatment. Patients categorised as hybrid had in-person treatment and at least one telehealth clinical activity.
Retention and dropout. A patient was considered to have dropped out of treatment when a period of at least six months without receiving treatment, against medical and psychological prescriptions, was observed. The time of treatment drop-out was coded as the month following the last appointment attended by the patient.

2.5. Data analysis

For the analysis of the first specific aim, uni- and bivariate statistics were applied. The chi-square test was applied to determine the associations between groups. Given the large sample sizes, to prevent type I errors, the Phi effect size was calculated, considering the presence of at least a weak effect size when phi > .20 [20].
For the second specific aim, hierarchical logistic regression analyses were applied. The overall fit of the model was tested via the omnibus test, which revealed that the model is significant and has a good fit. The R2 of each block was also calculated to observe the increases in variance explained with the introduction of the variables.
All analyses were performed with SPSS 29.0.

3. Results

3.1. Trends in the Modality of Care and Patient Profile

During the study period, a total of 33397 (74.3%) outpatients received in-person care. There were 11533 (25.7%) outpatients who received hybrid treatment. However, as shown in Table 1, the two care modalities differ in each trimester.
Patients who receive in-person treatment attend most of the scheduled appointments with the therapists at the centre. The analysis of the changes over time revealed that the number of patients remained stable and even increased after the lockdown, when there was a significant decrease. On the other hand, the average number of appointments per patient remained similar throughout the trimesters, except during the lockdown. Among patients who have received hybrid care, several aspects can be observed. The last months of the study revealed that the number of patients per trimester has increased compared with that in the pre-pandemic quarters, with the highest value occurring during the lockdown.
The clinical activity of these patients shows that they had fewer in-person appointments and, in addition, missed more appointments with professionals in the therapy centres. In contrast, these patients attended almost all the telehealth activities. During the lockdown and the quarter after, the number of telehealth activities exceeded the number of in-person appointments.
Table 2 shows the percentage of patients in each care modality during the different phases of the study. Although the above table shows that the total number of patients within the hybrid care modality increased in the last trimester, in percentual terms these patients represented a lower proportion than did those in the pre-pandemic stage.
The profile of the patients revealed that, except for those in the lockdown period, those who received in-person care were mainly women, patients with alcohol dependence, cocaine dependence and cannabis dependence. In contrast, patients with opiate dependence are more likely to use hybrid care, regardless of the phase analysed. The treatment of patients with mental disorders varies across the phases studied, and according to mental disorders. On the other hand, it is noteworthy that, during the lockdown only a greater percentage of patients with cannabis dependence received in-person treatment. The rest of the groups followed a hybrid modality.

3.2. Relation Between Treatment Modality And Treatment Retention

Table 3 shows the associations between the different study variables and treatment retention for each study period. The socio-demographic variables have scarce predictive capacity for treatment maintenance or dropout. When variables associated with dependence are introduced, the R2 values clearly increase. The highest odds ratios are observed in the group of patients with opiate dependence, reflecting the fact that these patients are the ones most likely to remain in treatment. The analysis of comorbid disorders shows that the presence of comorbid disorders is related to a greater probability of remaining in treatment. However, the increase in the variance explained with respect to the block represents approximately 2% in the different stages of the study. Finally, when the type of therapeutic modality is introduced, patients following a hybrid modality have a greater probability of being retained in treatment. Particularly noteworthy, in the last period analysed, the increase in R2 was close to 7%.

4. Discussion

The present study aimed to analyse the profiles of patients receiving either in-person or hybrid care and to determine its impact on treatment retention. Since the COVID-19 pandemic, various health measures have been implemented, progressively influencing the adoption and integration of telehealth. Therefore, unlike existing evidence, the present study included a 3-year period analysis (and one more follow-up), during which various health measures were introduced in addiction treatment centres.
With respect to the first aim, the results show that the number of patients treated increased following the pandemic restrictions, compared with the pre-pandemic period. This increase was observed among patients receiving either in-person or hybrid care. Additionally, the proportion of in-persons appointments remained stable throughout the study period, whereas the share of telehealth-related clinical activity increased. Consistent with the literature, in-person care decreased during the lockdown and was largely replaced by telehealth services for most patients.
With respect to the profile of the patients throughout the different periods, except for the lockdown period, the profile of patients receiving in-person care or telehealth services remained relatively stable over time. However, the results revealed an increase in the percentage of women, patients with alcohol use disorders, and patients with cannabis use disorders, who received in-person care without any telehealth activity. The increase in women could be unexpected, although other authors also find similar evidence [21], as the literature usually shows that women have more difficulties attending to treatment [22,23]. In that sense, it could be expected that women would utilise telehealth support more frequently than addiction treatment centres in person.
With respect to cannabis use disorder patients, previous studies have shown that interventions based on telehealth have positive outcomes in terms of satisfaction with and motivation for treatment [24,25]. Therefore, these patients are expected to be more heavily represented in the hybrid treatment group.
On the other hand, patients with opioid use disorders are clearly the most involved in telehealth activities. This could be because telehealth activity is related to follow-up on medication for opioid use disorder [12].
For patients with dual pathology, some studies have shown that telehealth services for those with mental health disorders can be comparable to in-person care [26] and may even improve their retention in treatment [27]. The present study shows that, on average, these patients receive more hybrid care across all periods. Additionally, nearly 40% of these patients receive coordinated care within mental health services. Therefore, these patients were managed by two care networks (addiction centres and mental health services). Previous studies have shown that patients with dual pathology may experience care overload and subsequently drop out of one of the care networks [28]. As a result, the adoption of telehealth measures for these patients could be an effective strategy.
Concerning the second aim, it was observed that different variables were associated with an increased probability of retention in treatment versus dropout. However, the use of a hybrid model and the diagnosis of opioid use disorder were among the variables most strongly associated with treatment retention.
This finding is even more relevant since patients in hybrid care have a lower attendance rate in drug treatment centres. Therefore, this result shows that telehealth care activity in combination with in-person care could improve patient retention. Moreover, the probability of patients receiving hybrid care was observed during all phases of the study. In that sense, this result extends the evidence for the benefits of hybrid care beyond the periods directly affected by the COVID-19 pandemic.
Despite the favourable evidence supporting hybrid care, it is important to consider some limitations to accurately interpret the results. On the one hand, the study covers a period of over three years. During this time, patients were classified as receiving in-person or hybrid care in each phase of the study. Therefore, a patient whose treatment spans different phases of the study may have received in-person care in one phase and hybrid care in another phase. While this may be considered a limitation, we believe that this analytical approach is realistic in terms of the treatment patients receive, as the analyses for each phase are such that the impact is limited. On the other hand, it is necessary to consider that the use of telehealth is not standardised among patients. As a study conducted with real data, the application of telehealth activities depends on the resources of each patient. This makes it difficult to determine the usefulness of telehealth activities and the timing of their application to improve retention.
In any case, we consider that the evidence obtained is highly valuable, as it shows that these activities improve retention in the treatment of patients with SUD, which is one of the main problems associated with the treatment of these patients.

Author Contributions

1. Conceptualization and Study Design: Narváez-Camargo, M., Mancheño-Velasco, C., Lozano-Rojas, O.: Conceived and designed the study, outlined objectives and hypotheses. Mancheño-Velasco, C., Lozano-Rojas, O., Dacosta-Sánchez, D.: Contributed to the refinement of the study design and helped define key variables. 2. Data Collection and Acquisition: - Narváez-Camargo, M., Mancheño-Velasco, C., De la Rosa-Cáceres, A., Lozano-Rojas, O.: Organized and verified dataset quality and integrity. 3. Data Analysis and Interpretation: - Dacosta-Sánchez, D., De la Rosa-Cáceres, A. Lozano-Rojas, O.: Conducted the data analysis, including statistical modelling and interpretation of findings. 4. Manuscript Preparation: Narváez-Camargo, M., Mancheño-Velasco, C., Lozano-Rojas, O.: Drafted the initial manuscript and prepared the figures/tables.- All authors: Reviewed and edited the manuscript, added critical content, ensured clarity and accuracy, provided comments on the draft, revised specific sections, approved the final version for submission and agreed to be accountable for all aspects of the work.

Funding

This work was supported by the grant “Evolución de la actividad asistencial y los resultados del tratamiento de pacientes con trastorno por consume de sustancias en Andalucía durante las diferentes fases de la pandemia Covid-19”, project EXP 2022/08882 by Delegación para el Gobierno del Plan Nacional sobre Drogas (Spain) from the European Union’s Recovery, Transformation and Resilicience Mechanism.

Acknowledgments

This study has been developed thanks to the transfer of data by the Department of Equality, Social Policies and Conciliation of the Junta de Andalucía.
Competing interests: Authors declare that they have no conflicts of interest.

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Table 1. Quarterly evolution of attendance activity.
Table 1. Quarterly evolution of attendance activity.
In-person Hybrid
Patients (n) In-person visits (n) Avg. of appointments (sd) Attendance (mean of % (sd)) Hybrid visits (n) Average of in-person visits (sd) Attendance in-person (mean of % (sd)) Average of phone calls (SD) Phone attendance (mean of % (sd))

Pre-lockdown		 Second quarter 2019 13583 12144 3.56 (2.75) 0.98 (.07) 1439 3.35 (3.24) 0.64 (0.47) 1.36 (0.86) 0.99 (0.05)
Third quarter 2019 12668 11400 3.16 (2.42) 0.99 (0.06) 1268 2.77 (2.99) 0.56 (0.49) 1.35 (1.08) 0.99 (0.03)
Fourth quarter 2019 13325 12064 3.47 (2.62) 0.99 (0.07) 1261 3.22 (3.18) 0.62 (0.48) 1.37 (0.86) 0.99 (0.06)
First quarter 2020 13879 11252 3.21 (2.34) 0.98 (0.08) 1802 2.95 (2.79) 0.68 (0.46) 1.47 (1.08) 0.99 (0.04)
Lockdown period Second quarter 2020 12081 6121 2.49 (2.02) 0.98 (0.11) 5960 1.64 (1.58) 0.38 (0.48) 2.77 (2.68) 0.99 (0.05)

Transition to normality 		Third quarter 2020 12700 9648 2.87 (2.18) 0.98 (0.07) 3052 2.42 (2.35) 0.59 (0.49) 2.87 (1.66) 0.99 (0.06)
Fourth quarter 2020 13907 11303 3.27 (2.55) 0.99 (0.07) 2604 2.80 (2.74) 0.62 (0.48) 1.66 (1.27) 0.99 (0.06)
First quarter 2021 15027 12505 3.24 (2.53) 0.99 (0.06) 2522 3.00 (3.04) 0.64 (0.48) 1.74 (1.45) 0.99 (0.06)
Second quarter 2021 15709 13437 3.49 (2.66) 0.98 (0.06) 2272 3.35 (3.30) 0.66 (0.47) 1.71 (1.44) 0.99 (0.04)
New normality Third quarter 2021 13849 12019 3.03 (2.31) 0.99 (0.05) 1830 2.89 (2.77) 0.62 (0.48) 1.61 (1.31) 0.99 (0.03)
Fourth quarter 2021 14440 12643 3.22 (2.41) 0.99 (0.06) 1797 3.21 (3.06) 0.64 (0.47) 1.60 (1.21) 0.99 (0.05)
First quarter 2022 14880 12989 3.38 (2.50) 0.99 (0.06) 1891 3.25 (3.11) 0.65 (0.47) 1.61 (1.31) 0.99 (0.06)
Second quarter 2022 14802 13081 3.32 (2.50) 0.99 (0.06) 1721 3.23 (3.03) 0.65 (0.47) 1.60 (1.19) 0.99 (0.05)
Table 2. Comparison between in-person and hybrid modalities of patient characteristics.
Table 2. Comparison between in-person and hybrid modalities of patient characteristics.
Pre-lockdown Lockdown Transition to normality New normality
In-person (62.1%) Hybrid (37.9%) Chi-Square/ Student t In-person (39.8%) Hybrid (60.2%) Chi-Square/ Student t In-person (62.9%) Hybrid (37.1) Chi-Square/ Student t In-person (68.9%) Hybrid (31.1%) Chi-Square/ Student t
Years old (M(sd)) 39.86 (13.37) 43.97 (10.63) 19.554** 40.63 (12.96) 43.35 (10.50) 12.680** 39.46 (13.11) 43.33 (10.34) 24.320** 39.74 (13.14) 43.55 (10.13) 23.183**
Male 61.2 38.8 38.505** 38.5 61.5 40.195** 61.5 38.5 88.200** 67.7 32.3 76.320**
Female 66.1 33.9 45.8 54.2 68.8 31.2 74.0 26.0
Alcohol 66.2 33.8 77.535** 43.8 56.3 32.035** 66.3 33.7 50.162** 74.0 26.0 135.953**
Cocaine 64.5 35.5 20.992** 42.7 57.3 14.604** 66.8 33.2 60.352** 72.9 27.1 73.600**
Opiates 36.9 63.1 2237.643**+ 23.1 76.9 712.475**+ 36.9 63.1 2528**+ 41.9 58.1 3013.009**+
Cannabis 78.1 21.9 469.492** 56.9 43.1 181.470** 79.8 20.2 532.343** 84.7 15.3 544.566**
Psychotic 47.7 52.3 47.552** 26.5 73.5 23.384** 46.9 53.1 61.905** 51.0 49.0 80.493**
Mood disorders 49.7 50.3 66.653** 32.6 67.4 12.997** 49.5 50.5 76.017** 56.8 43.2 71.335**
Anxiety disorders 51.5 48.5 87.315** 33.3 66.7 19.216** 52.5 47.5 86.321** 59.9 40.1 74.838**
Personality disorders 41.4 58.6 282.567** 25.3 74.7 87.424** 43.2 56.8 261.443** 49.9 50.1 267.977**
Patients in Mental Health Services 46.5 53.5 218.915** 27.8 72.7 78.532** 47.0 53.0 231.567** 53.2 46.8 243.520**
* P < .05; ** P < .01; +Phi > .20.
Table 3. Regressions analysis between patients in treatment (value 1) and patients dropping out (value 0).
Table 3. Regressions analysis between patients in treatment (value 1) and patients dropping out (value 0).
Pre-lockdown Lockdown Transition to normality New normality
Years old 1.014 (1.01-1.02)** 1.012 (1.007 – 1.016)** 1.015 (1.011 – 1.018)** 1.011 (1.008 – 1.014)**
Female 0.980 (0.887 – 1.081) 1.027 (0.916 – 1.152) 0.985 (0.904 – 1.073) 0.973 (0.892 – 1.061)
Block 1 Cox & Snell’s R2= .023 Nagelkerke R2 = .032 Cox & Snell’s R2 = .016 Nagelkerke R2 = .022 Cox & Snell’s R2 = .024 Nagelkerke R2 =.034 Cox & Snell’s R2 = .020
Nagelkerke R2 = .029
Alcohol 1.097 (0.958 – 1.258) 1.196 (1.022 – 1.399)* 1.179 (1.050 – 1.325)** 1.068 (0.937 – 1.206)
Cocaine 1.211 (1.060 – 1.385)** 1.281 (1.097 – 1.496)** 1.260 (1.123 – 1.413)** 1.150 (1.021 – 1.295)*
Opiates 3.844 (3.365 – 4.393)** 3.916 (3.355 – 4.570)** 3.995 (3.560 – 4.482)** 3.338 (2.961 – 3.764)**
Cannabis 0.993 (0.837 – 1.177) 1.149 (0.935 – 1.388) 1.039 (0.899 – 1.201) 0.933 (0.803 – 1.083)
Block 2 Cox & Snell’s R2 = 0.112 Nagelkerke R2 = .157 Cox & Snell’s R2 = .097 Nagelkerke R2 =.130 Cox & Snell’s R2 = .111 Nagelkerke R2 = .158 Cox & Snell’s R2 =.107
Nagelkerke R2 = .154
Psychotic 1.459 (1.135 – 1.875)** 1.340 (1.000 – 1.797)* 1.285 (1.024 – 1.614)* 1.354 (1.073 – 1.709)*
Mood disorders 1.256 (1.055 – 1.494)* 1.345 (1.097 – 1.648)** 1.426 (1.213 – 1.676)** 1.380 (1.172 – 1.625)**
Anxiety disorders 1.665 (1.448 – 1.915)** 1.793 (1.523 – 2.110)** 1.775 (1.565 – 2.013)** 1.694 (1.493 – 1.923)**
Personality disorders 1.365 (1.184 – 1.573)** 1.325 (1.124 – 1.562)** 1.506 (1.319 – 1.720)** 1.531 (1.336 – 1.754)**
Patients in Mental Health Services 1.309 (1.130 – 1.517)** 1.409 (1.188 – 1.673)** 1.345 (1.176 – 1.540)** 1.413 (1.231 – 1.621)
Block 3 Cox & Snell’s R2 = .128 Nagelkerke R2 = .179 Cox & Snell’s R2 = .116 Nagelkerke R2 = .156 Cox & Snell’s R2 = .130 Nagelkerke R2 = .183 Cox & Snell’s R2 = .127
Nagelkerke R2 = .183
Hybrid modality 3.134 (2.906 – 3.381)** 2.152 (1.188 – 1.673)** 2.890 (2.702 – 3.092)** 4.371 (4.081 – 4.682)**
Block 4 Cox & Snell’s R2 = .175 Nagelkerke R2 = .244 Cox & Snell’s R2 = .140 Nagelkerke R2 = .187 Cox & Snell’s R2 = .168 Nagelkerke R2 = .238 Cox & Snell’s R2 = .195
Nagelkerke R2 = .280
* P < .05; ** P < .01.
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