Abstract
Background
Alcohol use disorder (AUD) is common and may complicate type 2 diabetes (T2DM) management. Little research has examined diabetes outcomes for people with T2DM and AUD, including during the window when patients start specialty addiction treatment.
Objective
To examine diabetes-related health monitoring, clinical outcomes, and acute health care use among patients with T2DM and AUD newly accessing specialty addiction treatment.
Design
This retrospective cohort study included electronic health record data from a large, integrated health care delivery system.
Patients
Adults with T2DM and an index outpatient health care visit during 2016–2021 were included. Patients whose index visit was an initial AUD-related visit in specialty addiction treatment were in the AUD group. The comparison group had no AUD or addiction medicine visits.
Main Measures
Outcomes were diabetes-related health monitoring, achievement of treatment targets, complications, and acute health care use during the 12 months post–index visit.
Key Results
The study included 222,334 adults with T2DM, 1,998 with AUD. Relative to the comparison group, participants with AUD had elevated risk for hypoglycemia (adjusted risk ratio [aRR] = 2.14; 95% confidence interval [CI] = 1.49, 3.08), cardiovascular complications (aRR = 1.43; 95% CI = 1.34, 1.53), and neuropathy (aRR = 1.26; 95% CI = 1.14, 1.41), and were less likely to be non-smokers (aRR = 0.88; 95% CI = 0.86, 0.90), after adjusting for confounding factors. In adjusted models, the AUD versus comparison group had similar or higher rates of diabetes monitoring (e.g., any glycemic test, aRR = 1.19; 95% CI = 1.17, 1.22) and metabolic control (e.g., hemoglobin A1c < 8.0%, aRR = 1.14; 95% CI = 1.11, 1.18).
Conclusions
Patients with co-occurring T2DM and AUD in an integrated health care delivery system are vulnerable to diabetes complications that could be addressed during the early phase of specialty addiction treatment.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-024-09209-4.
KEY WORDS: type 2 diabetes, alcohol use disorder, diabetes complications, diabetes monitoring
INTRODUCTION
An understudied risk factor for poor type 2 diabetes (T2DM) outcomes is alcohol use disorder (AUD), a condition diagnosed when drinking interferes with health, functioning, or social roles.1–3 AUD is common among individuals with diabetes: past-year AUD prevalence is 11% for U.S. adults age 26 or older,4 with research suggesting similar or higher rates for individuals with versus without T2DM.5–7 Research on diabetes complications among people with AUD is sparse. Studies of people with unhealthy alcohol use, a more broadly defined condition,8 suggest elevated diabetes complication rates compared to individuals with lower drinking levels,9 including for cardiovascular and cerebrovascular disease, neuropathy, nephropathy, lower-limb amputations, severe hypoglycemia, and hyperglycemia.9–14 Diabetes-related hospitalizations are higher among patients with versus without substance use disorder including AUD.15,16 Heavy alcohol use (versus moderate or no drinking) is associated with lower T2DM regimen adherence.17,18 As AUD may impede individuals’ self-management behaviors and stigmatize patients receiving primary care,19,20 having this disorder may increase diabetes complication risk by interfering with receipt of guideline-concordant health screening21,22 or with individuals’ ability to meet metabolic treatment targets. AUD often co-occurs with psychiatric disorders, other substance use disorders, and smoking, which are additional risk factors for poor diabetes outcomes.23–25
Evidence-based specialty addiction treatment helps individuals moderate or stop alcohol use26–28 and may help patients improve management of chronic illnesses such as T2DM. To guide care strategies for patients with T2DM who are newly accessing AUD treatment, it is important to characterize diabetes care processes and health outcomes within this understudied population. We used electronic health record (EHR) data including diagnostic codes to examine diabetes-related health monitoring, achievement of treatment targets, complication rates, and acute health care use among patients with T2DM and AUD newly accessing specialty addiction treatment in an integrated health care system, comparing them to individuals with neither diagnosed AUD nor specialty addiction treatment. We hypothesized that relative to the comparison group, those with AUD newly accessing treatment would have worse diabetes-related care process and health outcomes.
METHODS
Study Design, Setting and Population
This retrospective cohort study used EHR data of Kaiser Permanente Northern California (KPNC) adult members with T2DM. KPNC is an integrated health care system providing comprehensive medical care to more than 4.5 million people (> 30% of the regional population). KPNC members have a range of insurance types, including commercial insurance, Medicare, and Medicaid, and reflect the broad sociodemographic diversity of the region.29 Study reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement.30 The KPNC Institutional Review Board approved study procedures.
Eligible patients had T2DM for ≥ 12 months (since management may fluctuate in early diabetes treatment), had an index outpatient health care visit during 2016 to 2021, and were not pregnant. We used both EHR data and a linked diabetes research registry to select eligible patients. The KPNC Diabetes Registry identifies patients with diabetes based on outpatient diagnoses and information from hospitalization, pharmacy, and laboratory records and is highly accurate when compared to chart review.31,32 Of a starting population of 271,983 KPNC Diabetes Registry patients, we excluded: 13,624 (5.0%) with type 1 diabetes (because more than 50% of diabetes codes were for type 133); 20,830 (7.7%) with recent diabetes diagnosis; and, 14,836 (5.5%) with gaps in KPNC membership during the 12 months before and after the index visit, including 311 due to death.
AUD and Comparison Groups
Participants with an addiction medicine index visit and a linked AUD diagnosis were in the AUD group. To identify patients newly engaged with addiction medicine services, we additionally required the AUD group to have no addiction medicine visits in the prior 12 months. Patients without an addiction medicine visit in 2016–2021 and no AUD or other substance use disorder diagnosis in the prior 36 months (not counting tobacco-related) were in the non-AUD comparison group and their index visit was their first outpatient health care visit. Substance use disorder was an exclusion criterion for the comparison group due to common co-occurrence with AUD.23,24,34
Measures
We assessed clinical and health care use variables from the EHR during a 12-month baseline period and a 12-month follow-up period before and after the index health care visit. Patient sociodemographics included sex, age, race and ethnicity, insurance type, medical service area, and neighborhood deprivation index (NDI)35 based on census tract as a proxy for socioeconomic status during the baseline period. Visit-based diagnosis codes were used to measure substance use and psychiatric conditions (see Supplemental Table 1 for ICD diagnosis codes). We used the Charlson comorbidity index to provide a count of physical health comorbidities.36 Diabetes medications were determined from pharmacy dispensing data (capturing patients’ receipt of the medication) and included insulin, sulfonylureas, or metformin (any versus none). Body mass index (kg/m2) was categorized as underweight (< 18.5), normal (18.5–24.9), overweight (25.0–29.9), obese (30.0 +), or missing. Patient-reported smoking status was current, former, never, or missing. Primary care use was categorized by count of days with visits (0, 1, 2–3, or ≥ 4 visits).
Outcomes
Diabetes monitoring outcomes included glycemic laboratory test result (hemoglobin A1c or fasting glucose), blood pressure test results, lipid laboratory test result (total cholesterol, triglycerides, high-density lipoprotein, or low-density lipoprotein), and retinopathy screening from procedure codes (ICD-9: 95.11; ICD-10: 08J0XZZ, 08J1XZZ; CPT-4: 92227, 92228, 92250).
Achievement of treatment targets included hemoglobin A1c < 8.0%, blood pressure < 140/90 mm Hg, low-density lipoprotein cholesterol < 100 mg/dL, and non-smoking status (never or former smoking).
Diabetes complications included documented diagnoses of acute (severe hyperglycemia, hypoglycemia) and microvascular and macrovascular (cardiovascular, cerebrovascular, lower limb, nephropathy, neuropathy, peripheral vascular, retinopathy) conditions (Supplemental Table 1).
Diabetes-related acute health care use included any emergency department visit with a primary diagnosis and any hospitalization with a principal or primary diagnosis of diabetes (ICD-9: 250*, 362*, 357.2*; ICD-10: E11*, E13*), reflecting immediate treatment of severe or unexpected diabetes-related conditions that could not be managed through routine outpatient care.
Analytic Approach
We contrasted the AUD and comparison groups using unadjusted analyses (e.g., Chi square tests) and multivariable modified Poisson regression adjusting for potential baseline confounders. We adjusted for age, sex, race/ethnicity, NDI quartile, Medicaid, medical service area, index year, baseline primary care use, insulin medication, psychiatric disorder (any versus none), smoking status (not included for smoking outcome), and Charlson comorbidity index. We used mean imputation to impute missing Charlson data (n = 4,582, 2.1%) and NDI (n = 1,470, 0.8%) in analyses. We removed the Charlson comorbidity index from the analyses for diabetes complications as this could partial out variance related to the association of interest. We limited analyses for the achievement of treatment targets to participants who had the relevant screening measure (i.e., analyses for hemoglobin A1c < 8.0% were limited to those with a hemoglobin A1c test).
To examine the role of co-occurring drug use disorder (DUD), we repeated the main analyses specifically for the AUD group, comparing outcomes between those with and without a co-occurring DUD. Due to sample size limitations, these models did not adjust for NDI, medical service area, or the Charlson comorbidity index and included a simplified primary care use variable (0–1, 2–3, ≥ 4 visit days).
Given the large main sample size and multiple comparisons, two-sided p values < 0.001 were considered statistically significant. We conducted all analyses using SAS 9.4.
RESULTS
Descriptive Statistics
The study included 222,334 adults with T2DM, 1,998 with co-occurring AUD who initiated specialty addiction treatment (AUD group) and 220,336 with no AUD or specialty addiction treatment (comparison group; Table 1). The cohort was 52.2% male, 47.8% female, 21.3% Hispanic, 24.7% non-Hispanic Asian/Pacific Islander, 9.7% non-Hispanic Black, 40.7% non-Hispanic White, and 3.6% from other racial or ethnic categories. The AUD group was more likely to be male and less likely to have Asian/Pacific Islander ethnicity, relative to the comparison group. In the AUD group, 30.2% had a co-occurring DUD, most commonly cannabis (13.5%) or opioid use disorders (7.9%), and the median number of specialty addiction visits during the follow-up period was 7 (interquartile range = 26; 30.0% of participants had only 1 visit [not shown]).
Table 1.
Characteristics of 222,334 Participants with Type 2 Diabetes
| Baseline variable, no. (%) | Alcohol Use Disorder Groupa (N = 1998) |
Comparison Groupb (N = 220,336) |
P value | ||
|---|---|---|---|---|---|
| Sex | |||||
| Male | 1521 | (76.1) | 114,457 | (51.9) | < 0.001 |
| Female | 477 | (23.9) | 105,876 | (48.1) | |
| Unknown/other | 0 | (0.0) | 3 | (0.0) | |
| Age, years, mean (SD) | 57.0 | (11.3) | 64.3 | (12.7) | < 0.001 |
| Age, years | < 0.001 | ||||
| 18–29 | 12 | (0.6) | 1109 | (0.5) | |
| 30–49 | 495 | (24.8) | 26,639 | (12.1) | |
| 50–64 | 952 | (47.6) | 80,200 | (36.4) | |
| 65–105 | 539 | (27.0) | 112,388 | (51.0) | |
| Race/ethnicity | < 0.001 | ||||
| Hispanic | 528 | (26.4) | 46,925 | (21.3) | |
| Non-Hispanic Asian/Pacific Islander | 183 | (9.2) | 54,718 | (24.8) | |
| Non-Hispanic Black | 199 | (10.0) | 21,355 | (9.7) | |
| Non-Hispanic White | 1026 | (51.4) | 89,356 | (40.6) | |
| Multiracial/Native American/Unknown | 62 | (3.1) | 7982 | (3.6) | |
| Insurance type | < 0.001 | ||||
| Medicaid | 112 | (5.6) | 8341 | (3.8) | |
| Medicare | 676 | (33.8) | 117,762 | (53.4) | |
| Commercial/other | 1208 | (60.5) | 94,028 | (42.7) | |
| Missing | 2 | (0.1) | 205 | (0.1) | |
| Neighborhood Deprivation Index, quartiles | < 0.01 | ||||
| Quartile 1, lowest | 562 | (28.1) | 54,670 | (24.8) | |
| Quartile 2 | 452 | (22.6) | 54,545 | (24.8) | |
| Quartile 3 | 503 | (25.2) | 54,687 | (24.8) | |
| Quartile 4, highest | 472 | (23.6) | 54,703 | (24.8) | |
| Missing | 9 | (0.5) | 1731 | (0.8) | |
| Diabetes medications | |||||
| Insulin | 457 | (22.9) | 53,348 | (24.2) | 0.16 |
| Sulfonylurea | 688 | (34.4) | 88,502 | (40.2) | < 0.001 |
| Metformin | 1151 | (57.6) | 136,161 | (61.8) | < 0.001 |
| None of the above medications | 577 | (28.9) | 50,462 | (22.9) | < 0.001 |
| Body mass index, kg/m2 | |||||
| Median (interquartile range) | 30.6 | (26.9–34.8) | 30.3 | (26.5–35.3) | 0.73 |
| Underweight (< 18.5) | 6 | (0.3) | 637 | (0.3) | < 0.001 |
| Normal (18.5–24.9) | 216 | (10.8) | 29,809 | (13.5) | |
| Overweight (25.0–29.9) | 549 | (27.5) | 61,226 | (27.8) | |
| Obese (30.0 +) | 932 | (46.6) | 100,898 | (45.8) | |
| Missing | 295 | (14.8) | 27,766 | (12.6) | |
| Smoking status | < 0.001 | ||||
| Never | 704 | (35.2) | 122,588 | (55.6) | |
| Current | 382 | (19.1) | 12,749 | (5.8) | |
| Former | 800 | (40.0) | 68,369 | (31.0) | |
| Missing | 112 | (5.6) | 16,630 | (7.5) | |
| Substance use disorder type, any in study period | N/A | ||||
| Alcohol | 1998 | (100.0) | — | — | |
| Any drug use disorder | 604 | (30.2) | — | — | |
| Cannabis | 270 | (13.5) | — | — | |
| Opioid use | 158 | (7.9) | — | — | |
| Cocaine | 119 | (6.0) | — | — | |
| Other stimulant | 173 | (8.7) | — | — | |
| Other drug use disorder | 217 | (10.9) | — | — | |
| Charlson comorbidity index, | < 0.001 | ||||
| median (interquartile range) | 2.0 | (1.0–5.0) | 2.0 | (1.0–4.0) | |
| Missing | 9 | (0.5) | 4573 | (2.1) | |
| Psychiatric disorder, any in study period | 1259 | (63.0) | 45,472 | (20.6) | < 0.001 |
| Major depressive disorder | 917 | (45.9) | 28,480 | (12.9) | < 0.001 |
| Anxiety disorder | 898 | (44.9) | 25,369 | (11.5) | < 0.001 |
| Schizophrenia spectrum or bipolar disorder | 204 | (10.2) | 3955 | (1.8) | < 0.001 |
aThe alcohol use disorder (AUD) group included patients with an AUD diagnosis linked to a first visit to a specialty addiction medicine clinic
bThe comparison group included patients with an outpatient health care visit, no visit to a specialty addiction medicine clinic, and no AUD or drug use disorder diagnoses
Diabetes Care Processes and Health Outcomes
We evaluated differences on all outcomes between the AUD and comparison groups during the 12-month follow-up period after the index visit (Table 2). Relative to the comparison group, the AUD group received similar rates of universal and targeted diabetes monitoring (e.g., 91.1% with a glycemic test and 29.1% with retinopathy screening in the AUD group). After adjusting for baseline sociodemographic and clinical variables (Fig. 1), the AUD group had significantly higher rates of universal monitoring (e.g., glycemic test, adjusted risk ratio [aRR] = 1.19; 95% confidence interval [CI] = 1.17, 1.22). Among those with diabetes monitoring, the AUD group met metabolic treatment targets at similar rates as the comparison group (e.g., 81.3% of those with AUD had HbA1c < 8.0%). After adjusting for baseline variables, the AUD group had similar achievement of cholesterol and blood pressure targets and higher achievement of HbA1c < 8.0% (aRR = 1.14; 95% CI = 1.11, 1.18), relative to the comparison group. However, the AUD group was less likely than the comparison group to report never or former smoking status, including after adjusting for baseline variables (aRR = 0.88; 95% CI = 0.86, 0.90).
Table 2.
Diabetes Care Processes and Health Outcomes by Group
| Outcome, no. (%) | Unadjusted | Alcohol Use Disorder Group Versus Comparison Group | ||||||
|---|---|---|---|---|---|---|---|---|
| Alcohol Use Disorder Group | Comparison Group | P value | Adjusted risk ratio | (95% confidence interval) | P value | |||
| Diabetes monitoringa | ||||||||
| Universal screening | ||||||||
| Glycemic laboratory test | 1820 | (91.1) | 195,594 | (88.8) | 0.001 | 1.19 | (1.17, 1.22) | < 0.001 |
| Blood pressure check | 1816 | (90.9) | 200,021 | (90.8) | 0.87 | 1.15 | (1.13, 1.18) | < 0.001 |
| Targeted screening | ||||||||
| Lipid laboratory test | 990 | (49.5) | 127,449 | (57.8) | < 0.001 | 1.00 | (0.95, 1.06) | 0.94 |
| Retinopathy screening | 582 | (29.1) | 58,757 | (26.7) | 0.01 | 1.01 | (0.93, 1.10) | 0.83 |
| Achievement of treatment targetsb | ||||||||
| Metabolic controla | ||||||||
| Hemoglobin A1c < 8.0% | 1478 | (81.3) | 154,416 | (79.2) | 0.03 | 1.14 | (1.11, 1.18) | < 0.001 |
| Blood pressure < 140/90 mm Hg | 1721 | (94.8) | 192,229 | (96.1) | 0.004 | 1.02 | (1.00, 1.03) | 0.03 |
| Low-density lipoprotein cholesterol < 100 mg/dL | 680 | (68.7) | 94,590 | (74.2) | < 0.001 | 1.05 | (1.00, 1.11) | 0.05 |
| Other | ||||||||
| Non-smokerc | 1496 | (80.6) | 183,392 | (94.2) | < 0.001 | 0.88 | (0.86, 0.90) | < 0.001 |
| Diabetes complicationsd | ||||||||
| Acute | ||||||||
| Severe hyperglycemia | 251 | (12.6) | 11,519 | (5.2) | < 0.001 | 1.08 | (0.92, 1.28) | 0.34 |
| Hypoglycemia | 82 | (4.1) | 4,621 | (2.1) | < 0.001 | 2.14 | (1.49, 3.08) | < 0.001 |
| Macrovascular and microvascular | ||||||||
| Cardiovascular | 926 | (46.3) | 87,504 | (39.7) | < 0.001 | 1.43 | (1.34, 1.53) | < 0.001 |
| Cerebrovascular | 243 | (12.2) | 21,968 | (10.0) | 0.001 | 1.13 | (0.94, 1.35) | 0.18 |
| Lower limb | 174 | (8.7) | 10,004 | (4.5) | < 0.001 | 1.03 | (0.81, 1.31) | 0.78 |
| Nephropathy | 353 | (17.7) | 63,610 | (28.9) | < 0.001 | 1.03 | (0.92, 1.16) | 0.60 |
| Neuropathy | 489 | (24.5) | 45,514 | (20.7) | < 0.001 | 1.26 | (1.14, 1.41) | < 0.001 |
| Peripheral vascular | 111 | (5.6) | 12,264 | (5.6) | 0.98 | 1.09 | (0.83, 1.42) | 0.53 |
| Retinopathy | 363 | (18.2) | 25,508 | (11.6) | < 0.001 | 0.96 | (0.85, 1.08) | 0.50 |
| Diabetes-related acute health care usea | ||||||||
| Emergency department, any visit | 58 | (2.9) | 2,102 | (1.0) | < 0.001 | 1.52 | (0.99, 2.35) | 0.06 |
| Hospitalization, any | 35 | (1.8) | 1,518 | (0.7) | < 0.001 | 0.75 | (0.42, 1.34) | 0.33 |
Measures were ascertained during the 12 months after an index health care visit, either an alcohol use disorder (AUD)-related visit in specialty addiction medicine (AUD group; N = 1,998) or a non-addiction medicine outpatient health care visit (comparison group; N = 220,336)
aModels adjusted for age, sex, race/ethnicity, neighborhood deprivation index (NDI) quartile, Medicaid, medical service area, index year, baseline primary care use, insulin, psychiatric disorder, smoking status, and Charlson comorbidity index
bPercentages for achievement of treatment targets were derived among those with relevant screening. AUD group: hemoglobin A1c (N = 1,817), low-density lipoprotein cholesterol (N = 990), blood pressure (N = 1,816), smoking status (N = 1,857). Comparison group: hemoglobin A1c (N = 194,926), low-density lipoprotein cholesterol (N = 127,449), blood pressure (N = 200,021), smoking status (N = 194,749)
cModel did not adjust for baseline smoking status
dModels adjusted for age, sex, race/ethnicity, NDI quartile, Medicaid, medical service area, index year, baseline primary care use, insulin, psychiatric disorder, and smoking status
Figure 1.
Adjusted Risk for Diabetes Outcomes for the Alcohol Use Disorder Group Versus Comparison Group with 95% Confidence Intervals
The AUD versus comparison group had significantly higher rates of acute, microvascular, and macrovascular complications during the follow-up period. After adjusting for baseline variables, several complication types remained elevated for the AUD group, specifically hypoglycemia (aRR = 2.14; 95% CI = 1.49, 3.08), cardiovascular complications (aRR = 1.43; 95% CI = 1.34, 1.53), and neuropathy (aRR = 1.26; 95% CI = 1.14, 1.41).
Prior to statistical adjustment, the AUD group had more than twice the rates of diabetes-related ED and hospital use (2.9% and 1.8%) than the comparison group (1.0% and 0.7%). After adjusting for baseline variables, neither type of acute health care use remained significantly higher in the AUD group.
Diabetes Outcomes for Co-occurring AUD and Drug Use Disorder
For the AUD group, we examined whether outcomes differed during the follow-up period for those with a co-occurring DUD versus those with AUD but no DUD (Table 3). Across these 2 subgroups, rates of diabetes monitoring and achievement of treatment targets were similar. Individuals with co-occurring DUD were less likely to be non-smokers (73.2% versus 83.8%) and had higher rates of severe hyperglycemia (18.5% versus 10.0%) compared to those with AUD and no DUD. These differences remained significant after adjusting for sociodemographic and clinical characteristics such as baseline primary care use and psychiatric co-morbidity (e.g., severe hyperglycemia aRR = 1.56 for co-occurring DUD versus AUD but no DUD; 95% CI = 1.22, 1.98). In adjusted analyses, DUD was significantly associated with higher risk for cardiovascular complications (aRR = 1.18; 95% CI = 1.07, 1.29) compared to AUD with no DUD. Rates of other types of diabetes complications were comparable between subgroups. Relative to those without co-occurring DUD, participants with AUD and DUD were more than twice as likely to visit the ED (4.5% versus 2.2%) and hospital (3.0% versus 1.2%) for diabetes-related causes, but as with the main analyses these differences were not significant after statistical adjustment.
Table 3.
Diabetes Outcomes by Drug use Disorder Among Those with Alcohol Use Disorder
| Outcome, no. (%) | Unadjusted | Association with Drug Use Disorder | ||||||
|---|---|---|---|---|---|---|---|---|
| AUD, No Drug Use Disorder | AUD and Drug Use Disorder | P value | Adjusted risk ratioc | (95% confidence interval) | P value | |||
| Diabetes monitoringa | ||||||||
| Universal screening | ||||||||
| Glycemic laboratory test | 1272 | (91.2) | 548 | (90.7) | 0.71 | 0.99 | (0.96, 1.02) | 0.40 |
| Blood pressure check | 1263 | (90.6) | 553 | (91.6) | 0.50 | 1.00 | (0.97, 1.03) | 0.96 |
| Targeted screening | ||||||||
| Lipid laboratory test | 689 | (49.4) | 301 | (49.8) | 0.87 | 0.99 | (0.89, 1.09) | 0.81 |
| Retinopathy screening | 410 | (29.4) | 172 | (28.5) | 0.67 | 1.06 | (0.90, 1.24) | 0.48 |
| Achievement of treatment targetsb | ||||||||
| Metabolic control | ||||||||
| Hemoglobin A1c < 8.0% | 1046 | (82.3) | 432 | (79.1) | 0.11 | 1.00 | (0.96, 1.05) | 0.87 |
| Blood pressure < 140/90 mm Hg | 1195 | (94.6) | 526 | (95.1) | 0.66 | 1.00 | (0.98, 1.03) | 0.88 |
| Low-density lipoprotein cholesterol < 100 mg/dL | 471 | (68.4) | 209 | (69.4) | 0.74 | 1.08 | (0.98, 1.19) | 0.11 |
| Other | ||||||||
| Non-smoker | 1084 | (83.8) | 412 | (73.2) | < 0.001 | 0.90 | (0.85, 0.95) | < 0.001 |
| Diabetes complicationsa | ||||||||
| Acute | ||||||||
| Severe hyperglycemia | 139 | (10.0) | 112 | (18.5) | < 0.001 | 1.56 | (1.22, 1.98) | < 0.001 |
| Hypoglycemia | 53 | (3.8) | 29 | (4.8) | 0.30 | 0.97 | (0.61, 1.54) | 0.89 |
| Macrovascular and microvascular | ||||||||
| Cardiovascular | 631 | (45.3) | 295 | (48.8) | 0.14 | 1.18 | (1.07, 1.29) | < 0.001 |
| Cerebrovascular | 163 | (11.7) | 80 | (13.2) | 0.33 | 1.18 | (0.92, 1.52) | 0.19 |
| Lower limb | 107 | (7.7) | 67 | (11.1) | 0.01 | 1.29 | (0.96, 1.75) | 0.09 |
| Nephropathy | 250 | (17.9) | 103 | (17.1) | 0.64 | 1.00 | (0.81, 1.23) | > 0.99 |
| Neuropathy | 318 | (22.8) | 171 | (28.3) | 0.01 | 1.11 | (0.94, 1.32) | 0.20 |
| Peripheral vascular | 76 | (5.5) | 35 | (5.8) | 0.76 | 1.20 | (0.80, 1.81) | 0.39 |
| Retinopathy | 243 | (17.4) | 120 | (19.9) | 0.19 | 1.19 | (0.97, 1.45) | 0.09 |
| Diabetes-related acute health care usea | ||||||||
| Emergency department, any visit | 31 | (2.2) | 27 | (4.5) | 0.01 | 1.62 | (0.99, 2.65) | 0.05 |
| Hospitalization, any | 17 | (1.2) | 18 | (3.0) | 0.01 | 1.90 | (0.99, 3.66) | 0.05 |
Measures were ascertained during the 12 months after the index alcohol use disorder (AUD)-related visit in specialty addiction medicine
aN = 1,394 with AUD and no drug use disorder and N = 604 with co-occurring AUD and drug use disorder
bNo drug use disorder: hemoglobin A1c (N = 1,271), low-density lipoprotein cholesterol (N = 689), blood pressure (N = 1,263), smoking status (N = 1,294). Co-occurring drug use disorder: hemoglobin A1c (N = 546), low-density lipoprotein cholesterol (N = 301), blood pressure (N = 553), smoking status (N = 563)
cModels adjusted for age, sex, race/ethnicity, Medicaid, index year, baseline primary care use (0–1, 2–3, ≥ 4 visit days), insulin, psychiatric disorder, and smoking status (except for the outcome of non-smoker)
DISCUSSION
This study addressed a need to better understand T2DM care processes and health outcomes among individuals with co-occurring AUD starting specialty addiction treatment. Unhealthy alcohol use has been correlated with problems in diabetes management, including lower adherence to self-care tasks and less receipt of recommended preventive care,11,17,18 although little research has examined associations between AUD and glycemic control.37,38 Our analyses expanded upon current knowledge by including comprehensive measurement of diabetes management processes and outcomes, including diabetes monitoring, HbA1c status, complications, and health care use, and we adjusted for many baseline variables that could explain differences between individuals with and without AUD and addiction medicine treatment.
Contrary to our expectations, we found that participants with AUD newly accessing specialty addiction treatment did not differ from their peers without AUD in many areas of T2DM management: they had similar or higher rates of diabetes monitoring and achievement of metabolic treatment targets. Yet, participants with AUD had higher rates of most diabetes complication types and greater diabetes-related acute health care use. By focusing on the period after an initial specialty addiction treatment visit, study findings highlight comorbidities that could be addressed during the critical period when individuals access specialty treatment for AUD.
After accounting for confounding variables, participants with AUD initiating specialty addiction treatment had specific vulnerabilities. Risk for hypoglycemia was doubled for participants in the AUD versus comparison group, and severe hyperglycemia was further elevated among participants with both AUD and DUD compared to those with AUD alone. Alcohol is known to increase hypoglycemia risks, although past research has focused on risk in type 1 diabetes.12,39 Heavy alcohol use impairs hepatic glucose production, and chronic use depletes liver glycogen stores, a problem exacerbated by poor nutrition secondary to AUD.40,41 Acute and chronic cognitive dysfunction resulting from heavy alcohol use and co-occurring drug use can contribute to medication or carbohydrate intake errors leading to hypo or hyperglycemia.42,43 Altered cognition can also mask hypoglycemia symptoms such as confusion, reducing individuals’ and others’ risk awareness, delaying treatment that would prevent serious complications.42 Repeated episodes of these acute complications can further contribute to long-term cognitive dysfunction, impairing decision-making capabilities.43
Participants with AUD, and especially those with co-occurring drug use disorder, were more likely to smoke and to have cardiovascular complications than those without AUD. Tobacco use is an independent risk factor for T2DM and for cardiovascular diabetes complications; theorized mechanisms include increased central obesity, inflammation, oxidative stress, and impairment of beta cell function.44,45 Heavy alcohol use further impacts the cardiovascular system through vascular oxidative stress, increased vascular reactivity, endothelial dysfunction, dyslipidemia, disruption to the coagulation fibrinolysis balance, and increased sympathetic nervous system activation.46 Resulting atherosclerosis along with decreased arterial function and elasticity contribute to increased blood pressure and downstream hypertension, stroke, and myocardial infarction.47 Alcohol-related electrophysiological changes can lead to arrhythmias including atrial fibrillation.46–48
An elevated risk for neuropathy within the AUD group relative to the comparison group is consistent with the documented effects of heavy alcohol use on peripheral vasculature.42 Alcohol and its metabolites have neurotoxic effects on the small C fibers of the extremities, which are also damaged by hyperglycemia and insulin resistance in T2DM.49 Conversely, patients experiencing pain from diabetic neuropathy may turn to unhealthy alcohol use as a maladaptive coping behavior, creating a vicious cycle that interferes with recovery from AUD.50,51
We found comparable rates of diabetes monitoring and achievement of metabolic treatment targets between the AUD and comparison groups, which diverges from other research,21,22 yet may reflect characteristics of the study population or care delivery context. Specialty addiction treatment can involve a combination of medical, case management, and individual or group psychotherapy visits. Patients who access specialty addiction treatment may have characteristics or resources (e.g., motivation, social support)52 that increase their ability to engage with diabetes monitoring and meet metabolic goals. Patients engaged in substance use disorder treatment are more likely to meet hemoglobin A1c targets.38 The universal deployment of population-based diabetes management services in the study health care system likely helped patients with AUD manage metabolic risk factors.53 This is consistent with past research in the same health care system, which found that behavioral health comorbidity (in that study, severe mental illness) was not associated with lower lab-based monitoring or worse metabolic control among individuals with T2DM.54
Screening and intervention approaches can help mitigate the acute and long-term health risks associated with co-occurring T2DM and AUD. Among patients with diabetes, evidence supports screening and brief intervention for unhealthy alcohol use,55 medications and counseling for smoking cessation,44,56 treatments for AUD,57 and improved care coordination and integration between diabetes and behavioral health care providers.58,59 Patients in addiction treatment can benefit from enhanced linkages to primary care and encouragement to engage with preventive care and chronic condition treatment,60,61 as well as smoking cessation intervention.62 Patients and caregivers should be counseled on the symptoms and treatment of hypoglycemia. Pain, as a symptom of neuropathy that may drive alcohol use, should be treated,63 and additional attention to foot exams given neuropathy risk may be beneficial. In general, this population may require closer monitoring of a wider range of diabetes- and alcohol-related health complications beyond glycemic control and other commonly monitored metabolic indicators considering that moderate drinking may not adversely affect glycemia.64
Study limitations should be considered. The small size of the AUD group relative to the total diabetes population suggests that misclassification of AUD status occurred from using visit-based EHR diagnoses. The study health care system universally screens primary care patients for unhealthy alcohol use,65 but many patients with AUD do not continue on the path towards diagnosis and treatment.66 Thus, our non-AUD comparison group likely included patients with undiagnosed AUD, biasing results towards the null. This study focused on patients who may be amenable to intervention while seeking specialty addiction treatment, but the findings may underestimate risk for non-treated patients who may differ in AUD severity or face greater AUD-related stigma or other barriers to seeking care or attending appointments. We were not able to measure various dimensions of AUD (duration, severity, treatment outside the health system). Achievement of treatment targets depended on receipt of diabetes monitoring; there may be unique barriers to such monitoring among patients with AUD that warrant further study. We excluded patients with undiagnosed or newly diagnosed T2DM. Future research may examine the role of AUD in the underdiagnosis of T2DM and the achievement of early T2DM treatment targets, as both are associated with later management.67,68 Finally, the study setting was a large integrated health care delivery system and findings may not generalize to other care delivery contexts or to uninsured populations.
CONCLUSIONS
In this study of patients with T2DM, participants with AUD newly accessing specialty addiction treatment were at higher risk for hypoglycemia, smoking, cardiovascular disease, and neuropathy relative to patients without co-occurring AUD. While these two subgroups did not meaningfully differ in their rates of diabetes monitoring or achievement of metabolic treatment targets, due to their increased risk factors, patients with co-occurring T2DM and AUD may benefit from additional, targeted clinical supports during specialty addiction treatment.
Supplementary Information
Below is the link to the electronic supplementary material.
Authors Contribution:
Not applicable.
Funding
This study was supported by a Kaiser Permanente Northern California Community Health Grant. Dr. Iturralde is supported by K23 MH126078. Dr. Satre is supported by K24 AA025703. None of the funders had a role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Declarations:
Prior Presentations:
Not applicable.
Conflicts of Interest:
The authors have no conflicts of interest to report.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
REFERENCES
- 1.Pastor A, Conn J, MacIsaac RJ, Bonomo Y. Alcohol and illicit drug use in people with diabetes. Lancet Diabetes Endocrinol. 2020;8(3):239-248. [DOI] [PubMed] [Google Scholar]
- 2.Walter KN, Wagner JA, Cengiz E, Tamborlane WV, Petry NM. Substance use disorders among patients with type 2 diabetes: a dangerous but understudied combination. Curr Diab Rep. 2017;17(1):2. [DOI] [PubMed] [Google Scholar]
- 3.American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. Arlington, VA: American Psychiatric Publishing; 2013. [Google Scholar]
- 4.Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2021 National Survey on Drug Use and Health (HHS Publication No. PEP22-07-01-005, NSDUH Series H-57). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. 2022. https://www.samhsa.gov/data/report/2021-nsduh-annual-national-report. Accessed 5 Mar 2024.
- 5.Wu L-T, Ghitza UE, Batch BC, et al. Substance use and mental diagnoses among adults with and without type 2 diabetes: results from electronic health records data. Drug Alcohol Depend. 2015;156:162-169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lin EH, Von Korff M, consortium WWs. Mental disorders among persons with diabetes—results from the World Mental Health Surveys. J Psychosom Res. 2008;65(6):571-580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wu L-T, Ghitza UE, Zhu H, Spratt S, Swartz M, Mannelli P. Substance use disorders and medical comorbidities among high-need, high-risk patients with diabetes. Drug Alcohol Depend. 2018;186:86-93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.National Institute on Alcohol Abuse and Alcoholism. Helping patients who drink too much: a clinician’s guide, updated edition. 2005; https://my.ireta.org/sites/ireta.org/files/NIAAA_Clinicians_Guide_Helping_Patients.pdf. Accessed November 15, 2023.
- 9.Blomster JI, Zoungas S, Chalmers J, et al. The relationship between alcohol consumption and vascular complications and mortality in individuals with type 2 diabetes. Diabetes Care. 2014;37(5):1353-1359. [DOI] [PubMed] [Google Scholar]
- 10.Winhusen T, Theobald J, Kaelber DC, Lewis D. Medical complications associated with substance use disorders in patients with type 2 diabetes and hypertension: electronic health record findings. Addiction. 2019;114(8):1462-1470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Leung G, Zhang J, Lin W-C, Clark RE. Behavioral disorders and diabetes-related outcomes among Massachusetts Medicare and Medicaid beneficiaries. Psychiatr Serv. 2011;62(6):659-665. [DOI] [PubMed] [Google Scholar]
- 12.Pastor A, Conn J, Teng J, et al. Alcohol and recreational drug use in young adults with type 1 diabetes. Diabetes Res Clin Pract. 2017;130:186-195. [DOI] [PubMed] [Google Scholar]
- 13.Cooper H, Tekiteki A, Khanolkar M, Braatvedt G. Risk factors for recurrent admissions with diabetic ketoacidosis: a case–control observational study. Diabet Med. 2016;33(4):523-528. [DOI] [PubMed] [Google Scholar]
- 14.Pietraszek A, Gregersen S, Hermansen K. Alcohol and type 2 diabetes: a review. Nutr Metab Cardiovasc Dis. 2010;20(5):366-375. [DOI] [PubMed] [Google Scholar]
- 15.Leung KS, Parks J, Topolski J. Preventable hospitalizations among adult Medicaid beneficiaries with concurrent substance use disorders. Prev Med Rep. 2015;2:379-384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Stockbridge EL, Chhetri S, Polcar LE, Loethen AD, Carney CP. Behavioral health conditions and potentially preventable diabetes-related hospitalizations in the United States: findings from a national sample of commercial claims data. PloS One. 2019;14(2):e0212955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Thomas RM, Francis Gerstel PA, Williams EC, et al. Association between alcohol screening scores and diabetic self-care behaviors. Fam Med. 2012;44(8):555. [PubMed] [Google Scholar]
- 18.Ahmed A, Karter A, Liu J. Alcohol consumption is inversely associated with adherence to diabetes self‐care behaviours. Diabet Med. 2006;23(7):795-802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Matthews S, Dwyer R, Snoek A. Stigma and self-stigma in addiction. J Bioeth Inq. 2017;14(2):275-286. [DOI] [PMC free article] [PubMed]
- 20.Press KR, Zornberg GZ, Geller G, Carrese J, Fingerhood MI. What patients with addiction disorders need from their primary care physicians: a qualitative study. Subst Abuse. 2016;37(2):349-355. [DOI] [PubMed] [Google Scholar]
- 21.Leung GY, Zhang J, Lin W-C, Clark RE. Behavioral health disorders and adherence to measures of diabetes care quality. Am J Manag Care. 2011;17(2):144-150. [PubMed] [Google Scholar]
- 22.Clark RE, Weir S, Ouellette RA, Zhang J, Baxter JD. Beyond health plans: behavioral health disorders and quality of diabetes and asthma care for Medicaid beneficiaries. Med Care. 2009;47(5):545-552. [DOI] [PubMed] [Google Scholar]
- 23.John WS, Zhu H, Mannelli P, Schwartz RP, Subramaniam GA, Wu L-T. Prevalence, patterns, and correlates of multiple substance use disorders among adult primary care patients. Drug Alcohol Depend. 2018;187:79-87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Stinson FS, Grant BF, Dawson DA, Ruan WJ, Huang B, Saha T. Comorbidity between DSM-IV alcohol and specific drug use disorders in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug Alc Depend. 2005;80(1):105-116. [DOI] [PubMed] [Google Scholar]
- 25.Gonzalez JS, Hood KK, Esbitt SA, Mukherji S, Kane NS, Jacobson A.Chapter 33: Psychiatric and psychosocial issues among individuals living with diabetes. Bethesda, MD: National Institute of Diabetes and Digestive and Kidney Diseases; 2018. [PubMed]
- 26.Kranzler HR, Soyka M. Diagnosis and pharmacotherapy of alcohol use disorder: a review. JAMA. 2018;320(8):815-824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Weisner C, Ray GT, Mertens JR, Satre DD, Moore C. Short-term alcohol and drug treatment outcomes predict long-term outcome. Drug Alcohol Depend. 2003;71(3):281-294. [DOI] [PubMed] [Google Scholar]
- 28.Kline‐Simon AH, Falk DE, Litten RZ, et al. Posttreatment low‐risk drinking as a predictor of future drinking and problem outcomes among individuals with alcohol use disorders. Alcohol Clin Exp Res. 2013;37:E373-E380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gordon N.Similarity of adult Kaiser Permanente members to the adult population in Kaiser Permanente’s Northern California service area: comparisons based on the 2017/2018 cycle of the California Health Interview Survey. Oakland, CA: Kaiser Permanente Northern California Division of Research;2020. https://memberhealthsurvey.kaiser.org/Documents/compare_kp_ncal_chis2017-18.pdf Accessed August 21, 2023
- 30.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Prev Med. 2007;45(4):247-251. [DOI] [PubMed] [Google Scholar]
- 31.Karter AJ, Schillinger D, Adams AS, et al. Elevated rates of diabetes in Pacific Islanders and Asian subgroups: the Diabetes Study of Northern California (DISTANCE). Diabetes Care. 2013;36(3):574-579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV. Ethnic disparities in diabetic complications in an insured population. JAMA. 2002;287(19):2519-2527. [DOI] [PubMed] [Google Scholar]
- 33.Hampp C, Swain RS, Horgan C, et al. Use of sodium–glucose cotransporter 2 inhibitors in patients with type 1 diabetes and rates of diabetic ketoacidosis. Diabetes Care. 2020;43(1):90-97. [DOI] [PubMed] [Google Scholar]
- 34.Von Gunten CD, Wu L-T. Comorbid substance use disorder profiles and receipt of substance use disorder treatment services: a national study. J Stud Alcohol Drugs. 2021;82(2):246-256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Messer LC, Laraia BA, Kaufman JS, et al. The development of a standardized neighborhood deprivation index. J Urban Health. 2006;83:1041-1062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP. The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol. 2008;61(12):1234-1240. [DOI] [PubMed] [Google Scholar]
- 37.Ojo O, Wang X-H, Ojo OO, Ibe J. The effects of substance abuse on blood glucose parameters in patients with diabetes: a systematic review and meta-analysis. Int J Environ Res Public Health. 2018;15(12):2691. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Horigian VE, Schmidt RD, Duan R, et al. Untreated substance use disorder affects glycemic control: Results in patients with type 2 diabetes served within a network of community-based healthcare centers in Florida. Front Public Health. 2023;11:1122455. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Pedersen-Bjergaard U, Reubsaet JL, Nielsen SL, et al. Psychoactive drugs, alcohol, and severe hypoglycemia in insulin-treated diabetes: analysis of 141 cases. Am J Med. 2005;118(3):307-310. [DOI] [PubMed] [Google Scholar]
- 40.Steiner JL, Crowell KT, Lang CH. Impact of alcohol on glycemic control and insulin action. Biomolecules. 2015;5(4):2223-2246. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Jeynes KD, Gibson EL. The importance of nutrition in aiding recovery from substance use disorders: A review. Drug Alcohol Depend. 2017;179:229-239. [DOI] [PubMed] [Google Scholar]
- 42.Emanuele NV, Swade TF, Emanuele MA. Consequences of alcohol use in diabetics. Alcohol Health Res World. 1998;22(3):211. [PMC free article] [PubMed] [Google Scholar]
- 43.McCall AL. Insulin therapy and hypoglycemia. Endocrinol Metab Clin North Am. 2012;41(1):57-87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Pan A, Wang Y, Talaei M, Hu FB. Relation of smoking with total mortality and cardiovascular events among patients with diabetes mellitus: a meta-analysis and systematic review. Circulation. 2015;132(19):1795-1804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Śliwińska-Mossoń M, Milnerowicz H. The impact of smoking on the development of diabetes and its complications. Diab Vasc Dis Res. 2017;14(4):265-276. [DOI] [PubMed] [Google Scholar]
- 46.Piano MR, Mazzuco A, Kang M, Phillips SA. Cardiovascular consequences of binge drinking: an integrative review with implications for advocacy, policy, and research. Alcohol Clin Exp Res. 2017;41(3):487-496. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Wakabayashi I, Kobaba-Wakabayashi R, Masuda H. Relation of drinking alcohol to atherosclerotic risk in type 2 diabetes. Diabetes Care. 2002;25(7):1223-1228. [DOI] [PubMed] [Google Scholar]
- 48.Biddinger KJ, Emdin CA, Haas ME, et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Netw Open. 2022;5(3):e223849-e223849. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Zeng L, Alongkronrusmee D, van Rijn RM. An integrated perspective on diabetic, alcoholic, and drug-induced neuropathy, etiology, and treatment in the US. J Pain Res. 2017:219–228. [DOI] [PMC free article] [PubMed]
- 50.Maleki N, Tahaney K, Thompson BL, Oscar-Berman M. At the intersection of alcohol use disorder and chronic pain. Neuropsychology. 2019;33(6):795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Zale EL, Maisto SA, Ditre JW. Interrelations between pain and alcohol: An integrative review. Clin Psychol Rev. 2015;37:57-71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Weisner C, Mertens J, Tam T, Moore C. Factors affecting the initiation of substance abuse treatment in managed care. Addiction. 2001;96(5):705-716. [DOI] [PubMed] [Google Scholar]
- 53.Schmittdiel JA, Gopalan A, Lin MW, Banerjee S, Chau CV, Adams AS. Population health management for diabetes: health care system-level approaches for improving quality and addressing disparities. Curr Diab Rep. 2017;17(5):31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Mangurian C, Schillinger D, Newcomer JW, et al. Comorbid diabetes and severe mental illness: outcomes in an integrated health care delivery system. J Gen Intern Med. 2020;35(1):160-166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Ramsey SE, Engler PA, Harrington M, et al. Brief alcohol intervention among at-risk drinkers with diabetes. Subst Abuse. 2010;4:SART. S4071. [DOI] [PMC free article] [PubMed]
- 56.Sherman JJ. The impact of smoking and quitting smoking on patients with diabetes. Diabetes Spectr. 2005;18(4):202-208. [Google Scholar]
- 57.Walter KN, Petry NM. Patients with diabetes respond well to contingency management treatment targeting alcohol and substance use. Psychol Health Med. 2015;20(8):916-926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Chwastiak LA, Jackson SL, Russo J, et al. A collaborative care team to integrate behavioral health care and treatment of poorly-controlled type 2 diabetes in an urban safety net primary care clinic. Gen Hosp Psychiatry. 2017;44:10-15. [DOI] [PubMed] [Google Scholar]
- 59.Weisner C, Mertens J, Parthasarathy S, Moore C, Lu Y. Integrating primary medical care with addiction treatment: a randomized controlled trial. JAMA. 2001;286(14):1715-1723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Weisner CM, Chi FW, Lu Y, et al. Examination of the effects of an intervention aiming to link patients receiving addiction treatment with health care: the LINKAGE clinical trial. JAMA Psychiatry. 2016;73(8):804-814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Iturralde E, Weisner CM, Adams SR, et al. Patterns of health care use 5 years after an intervention linking patients in addiction treatment with a primary care practitioner. JAMA Netw Open. 2022;5(11):e2241338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Kalman D, Kim S, DiGirolamo G, Smelson D, Ziedonis D. Addressing tobacco use disorder in smokers in early remission from alcohol dependence: the case for integrating smoking cessation services in substance use disorder treatment programs. Clin Psychol Rev. 2010;30(1):12-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Finnerup NB, Otto M, McQuay H, Jensen TS, Sindrup SH. Algorithm for neuropathic pain treatment: an evidence based proposal. Pain. 2005;118(3):289-305. [DOI] [PubMed] [Google Scholar]
- 64.Shai I, Wainstein J, Harman-Boehm I, et al. Glycemic effects of moderate alcohol intake among patients with type 2 diabetes: a multicenter, randomized, clinical intervention trial. Diabetes Care. 2007;30(12):3011-3016. [DOI] [PubMed] [Google Scholar]
- 65.Mertens JR, Chi FW, Weisner CM, et al. Physician versus non-physician delivery of alcohol screening, brief intervention and referral to treatment in adult primary care: the ADVISe cluster randomized controlled implementation trial. Addict Sci Clin Pract. 2015;10(1):1-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Mintz CM, Hartz SM, Fisher SL, et al. A cascade of care for alcohol use disorder: Using 2015–2019 National Survey on Drug Use and Health data to identify gaps in past 12‐month care. Alcohol Clin Exp Res. 2021;45(6):1276-1286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Gopalan A, Mishra P, Alexeeff S, et al. Prevalence and predictors of delayed clinical diagnosis of Type 2 diabetes: a longitudinal cohort study. Diabet Med. 2018;35(12):1655-1662. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Laiteerapong N, Ham SA, Gao Y, et al. The legacy effect in type 2 diabetes: impact of early glycemic control on future complications (the Diabetes & Aging Study). Diabetes Care. 2019;42(3):416-426. [DOI] [PMC free article] [PubMed] [Google Scholar]
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