Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose
Abstract
:1. Introduction
1.1. Use of Patterns of Service Utilization in Planning and Providing Care to Complex Patients
1.2. Evidence-Based Care: Machine Learning and Statistical Analysis
1.3. Objectives
- Using PSUs, to what extent can we determine whether the opioid overdose cohort is homogeneous or not with respect to determinants of risk?
- How many communities constitute the opioid overdose cohort, based on how patients within this cohort interact with the host organization’s cross-continuum service system?
- To what extent can we determine the risk of a subsequent opioid overdose based on the community an opioid overdose patient belongs to and quantify it using survival analysis?
2. Methods
2.1. Addressing Data Granularity Issues
2.2. Community Detection
2.3. Survival Models
2.4. Combining Community Detection and Survival Analysis
- The encounter data were engineered as a bipartite graph consisting of nodes with edges connecting patients to Service Classes. A patient (node) is connected to a Service Class (node) when they use a service represented by the Service Class. Recall that roughly two hundred Service Classes employed for modeling in this paper consist of equivalence classes formed by the application of six code sets to the host organization Service Units to reduce granularity.
- A bipartite projection onto patients was applied (Figure 1) to the bipartite graph to create a weighted graph, where the number of services that were used by two connected patients became the weight of the edge.
- The Louvain community detection algorithm was applied to the weighted graph to uncover the communities of patients that reflect high-prevalent PSUs by Service Classes.
- For each of the generated communities, both the service engagement profile and the diagnosis profile were appended.
- Collaborating with team members with clinical backgrounds, each community of patients was labeled based on their prevalent service engagement and diagnosis profile.
- Using community belonging as a characteristic of a patient, survival analysis was used to quantify the risk of a second overdose.
- Using other patient-related characteristics, such as age, gender, and homelessness status, survival analysis was used to further quantify the risk of a second overdose.
3. Analysis
3.1. Patient Characteristics
3.2. Community Characteristics
- Community one, termed the “reciprocal group”, exhibited a proactive approach to accessing health services, for example, self-referred ambulatory addiction services. They demonstrated higher utilization rates within the service system overall, including Mental Health, and Substance Use (MHSU) and Medical/Surgical (Med/Surg) services. Notably, 80% of patients in this community utilized MHSU clinical intake and addiction clinical intake services. Predominant diagnoses within this group centered around severe addiction issues, with minimal occurrences of schizophrenia-related diagnoses. The average age of patients in this community was years.
- Community two, characterized as the “service-disengaged group”, displayed lower engagement with the service system compared to other communities. They accessed overdose-related and addiction outreach services prior to the overdose events. Diagnostic profiles within this group were not pronounced, with only 8% reporting homelessness and an average age of years.
- Community three, labeled as the “group with complex/serious health problems”, exhibited a higher frequency of encounters with Med/Surg services, particularly laboratory and medical imaging procedures. Engagement with MHSU services was comparatively lower, indicating that their engagement with the service system focused on addressing complex medical conditions rather than substance use. Diagnostic data suggested a variety of medical issues, including high rates of palliative care and alterations of awareness. The average age within this community was 46 years, with a considerable number of patients being 60 years or older.
- Community four, characterized as the “group with severe psychiatric issues”, demonstrated a high engagement with psychiatric services but low involvement with addiction services. This group exhibited a younger average age of 35 years and a notably high prevalence of schizophrenia diagnoses. Engagement with MHSU services was more prominent than with Med/Surg services.
3.3. Statistical Analyses
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Groups | Total OD % | No Second OD | Second OD | Hazard Ratio |
---|---|---|---|---|
%(n) (N = 5380) | %(n) (N = 3798) | %(n) (N = 1582) | (95% CI) | |
Age | ||||
0−20 | 05.84 (314) | 05.58 (212) | 06.45 (102) | 1.00 |
20–29 | 23.35 (1256) | 21.70 (823) | 27.37 (433) | 0.77 (0.61, 0.97) |
30–39 | 27.06 (1456) | 25.70 (977) | 30.28 (479) | 0.77 (0.61, 0.97) |
40–49 | 19.33 (1040) | 19.50 (742) | 18.84 (298) | 0.67 (0.52, 0.85) |
50–59 | 14.28 (768) | 14.90 (565) | 12.84 (203) | 0.64 (0.50, 0.83) |
60–100 | 08.75 (471) | 10.60 (404) | 04.24 (67) | 0.45 (0.32, 0.62) |
Gender | ||||
Male | 69.65 (3747) | 68.14 (2588) | 73.30 (1159) | 1.34 (1.18, 1.51) |
Female | 30.29 (1630) | 31.78 (1207) | 26.70 (423) | 1.00 |
Unknown | 00.06 (3) | 00.08 (3) | − | − |
Community ID | ||||
Community ID 1 | 20.00 (1076) | 15.70 (0595) | 30.40 (481) | 1.00 |
Community ID 2 | 30.72 (1653) | 35.00 (1331) | 20.40 (322) | 0.49 (0.42, 0.58) |
Community ID 3 | ||||
Community ID 4 | ||||
Ever Homeless | ||||
No (0) | ||||
Yes (1) |
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Bambi, J.; Olobatuyi, K.; Santoso, Y.; Sadri, H.; Moselle, K.; Rudnick, A.; Dong, G.Y.; Chang, E.; Kuo, A. Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose. Knowledge 2024, 4, 444-461. https://doi.org/10.3390/knowledge4030024
Bambi J, Olobatuyi K, Santoso Y, Sadri H, Moselle K, Rudnick A, Dong GY, Chang E, Kuo A. Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose. Knowledge. 2024; 4(3):444-461. https://doi.org/10.3390/knowledge4030024
Chicago/Turabian StyleBambi, Jonas, Kehinde Olobatuyi, Yudi Santoso, Hanieh Sadri, Ken Moselle, Abraham Rudnick, Gracia Yunruo Dong, Ernie Chang, and Alex Kuo. 2024. "Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose" Knowledge 4, no. 3: 444-461. https://doi.org/10.3390/knowledge4030024