1. HSR Core Co-Chairs: Emily Wang, MD Kathleen McGinnis, DrPH
Members: K Akgun, K Bensley, C Brandt, L Brinkley-Rubensteine, H Chang, S Crystal, M Dugal, J Edelman, D Fiellin, J Gaither, T Ghose, A Gordon, K Gordon, R Greyson, S Haplin, D Harsono, N Kim, T Korthuis, K Kraemer, D Leaf, J Long, K Mattocks, K McInnes, D Rimland, V Marconi, M Ohl, M Perkins, C Rentsch, J Shidel, M Skanderson, H Swan, K Wang, E Williams, J Womack
First I will tell you about the HSR Core. Emily Wang and I co-chair the group and the members have diverse backgrounds and interests.

2. Recent VACS HSR Core Highlights
Peer Reviewed Publications
Gordon KS, Edelman EJ, Justice AC, Fiellin DA, Akgun K, Crystal S, Duggal M, Goulet JL, Rimland D, Bryant KJ. Minority men who have sex with men demonstrate increased risk for HIV transmission. AIDS Behav 2016; Oct 22 (Epub ahead of print).
Howell BA, Long JB, Edelman EJ, McGinnis KA, Rimland D, Fiellin DA, Justice AC, Wang EA. Incarceration history and uncontrolled blood pressure in a multi-site cohort. J Gen Intern Med 2016; Sep 12 (Epub ahead of print).
Korthuis PT, McGinnis KA, Kraemer KL, Gordon AJ, Skanderson M, Justice AC, Crystal S, Goetz MB, Gibert CL, Rimland D, Fiellin LE, Gaither JR, Wang K, Asch SM, McInnes DK, Ohl ME, Bryant K, Tate JP, Duggal M, Fiellin DA.  Quality of HIV Care and Mortality in HIV-Infected Patients.  Clin Infect Dis 2015; Sep 3.
Wang EA, McGinnis KA, Goulet J, Bryant K, Gibert C, Leaf DA, Mattocks K, Fiellin LE, Vogenthaler N, Justice AC, Fiellin DA   Food insecurity and health: data from the veterans aging cohort study.  Public Health Rep 2015 May; 130(3):
Wang E, McGinnis KA, Long J, Akgun K, Edelman J, Rimland D, Wang K, Justice AC, Fiellin DA.  Incarceration and health outcomes in HIV-infected patients: The impact of substance use, primary care engagement, and antiretroviral adherence.  Am J Addict 2015 Mar; 24(2):  
Presentations
Bensley K, et al., The association between AUDIT-C and mortality among Black, Hispanic, and White Male Patients Living with HIV. June RSA 2016.
Kraemer K, et al. Substance Use Disorder Health Services Use and Pharmacotherapy in HIV Infected and Uninfected Patients, AHSR, Sept 2015.
Robbins J, et al. Predictors of initiating opioid agonist therapy in a large U.S. cohort of HIV-infected and uninfected patients. SGIM, April 2016.
Wilson N, et al. A retrospective analysis of HIV-associated gastrointestinal Symptom Burden and Inflammation in VACS. 2nd International Workshop on Microbiome in HIV Pathogenesis, Prevention, and Treatment, Nov 2016.
Funding
R01 AA “Comparative Effectiveness of Alcohol and Drug Treatment in HIV-Infected Veterans” (PI – Kraemer)
In recent years the HSR core has published work on food insecurity, HIV transmission risk among MSM, quality of care’s association with outcomes, and incarceration. Emily Wang has spearheaded incarceration work - VACS has multiple sources of incarceration data including survey data, primary care stop codes and we’ve also explored using state level dept of justice data. Current ongoing incarceration work includes examining the association of incarceration with HIV risk factors and mortality. We have several projects related to Kevin Kraemers RO1 to compare effectiveness of alcohol and drug trt in HIV+ veterans. One project is examining pharmacotherapy use and health services use among with those substance use disorders; and another project is identifying predictors of initiating opiod agonist therapy
(using metrics such as # clinic visits, vaccine rates, ARV reciept, and monitoring of CD4/VL).
ood insecurity
Incarceration
multiple sources of data identified:
Survey data, PA DOJ data, ICD-9 codes
Multiple papers published
Microbial translocation, GI SX, and HIV
MSM and Risk for HIV transmission

3. Kathleen McGinnis VACS Scientific Meeting December 12, 2016
Validating Harmful Alcohol Use as a Phenotype for Genetic Discovery Using Phosphatidylethanol (PEth) and a Polymorphism in ADH1B
Kathleen McGinnis
VACS Scientific Meeting
December 12, 2016
Amy Justice, Jan Tate, Ke Xu, William Becker, Hongyu Zhao, Joel Gelernter, Henry Kranzler for the VACS Study Team
The paper I’m presenting is called…
Funded by U24-AA and U01-AA020790

4. Background
Longitudinal electronic health record (EHR) data offer a source of phenotype information for genetic studies
Phenotypes for unhealthy alcohol use have yet to be validated
We use EHR AUDIT-C to develop a phenotype for unhealthy alcohol use

5. Data VACS Biomarker Cohort 2005 to 2007
PEth - direct quantitative biomarker which detects alcohol use up to 21 days
ADH1B genetic marker
encodes for protein that metabolizes alcohol
variant* associated w/ reduced alcoholism
variant highly prevalent among African-Americans
We used data from multiple sources.
VACS Biomarker Cohort collected blood samples from 2005 to 2007.
Peth is a direct quantitative biomarker which detects alcohol use for up to 21 days.
ADH1B is a gene that encodes for protein that metabolizes alcohol. We examined an ADH1B genetic variant that is associated with reduced alcoholism. This genetic variant is highly prevalent among African-Americans, but is rare among other racial/ethnic groups
*Polymorphism in ADH1B (Arg369Cys)

6. Data
AUDIT-C – asked routinely at clinic visits and entered into EHR (2008+)
Closest AUDIT-C uses 1 value
Highest AUDIT-C uses 1 value but based on all data
Trajectory AUDIT-C incorporates all values
uses a modelling procedure to develop distinct trajectories
For AUDIT-C, we used data from the electronic health record. AUDIT-C has been asked routinely at clinic visits and entered into the electronic health record from 2008 and on. We wanted to examin the AUDIT-C 3 different ways:
Closest AUDIT-C is the simplest and is the closest value to the date of the PEth measurement
Highest AUDIT-C uses all data from 2008 to 2016 and the highest value is used.
Trajectory AUDIT-C is the most complicated. It uses all data from 2008 to 2016; a modelling procedure sorts all values into clusters and estimates distinct trajectories. Individuals probability of belonging to each trajectory is calculated and the person is assinged to the trajectory with the highest probability of membership.
longitudinal AUDIT-C trajectories characterized using joint trajectory modeling (AUDIT-C trajectories). In brief, the modeling procedure sorts all values of each participant’s set of AUDIT-C values into “clusters” and estimates distinct trajectories. The procedure calculates each individual’s probability of belonging to each trajectory and assigns the individual to the one with the highest probability of membership. We used a zero-inflated Poisson model for this estimation procedure (Jones et al., 2001). In developing the trajectories we evaluated 3, 4 and 5-group models.  Model fit as measured by the Bayesian Information Criterion (BIC) improved substantially increasing from 3 groups to 4, but only slightly between 4 groups and 5.  Further, the lower level group membership was quite stable with differences in assignment occurring only at the higher levels.    Because the highest level of the 4-group model only had 98 people we felt that unstable estimates would result from using a 5-group model.

7. Analysis AUDIT-C association with PEth
AUDIT-C (Closest, Highest, Trajectory)
PEth (median, 8+, 20+, 100+)
Chi-square tests for a trend
AUDIT-C association with ADH1B variant
Among African-Americans only
Chi-square tests for trend
To examine the association between AUDIT-C and Peth, We use the 3 AUDIT-C metrics I just mentioned. For Peth we chose 3 cutoffs that have been standardly used in prior research and that are recommended the lab: 8+, 20+ and We used chi squre test of trent to determine if associations were stat sign.
To examine the association between AUDIT-C and ADH1B genetic variant we again used chi squre test of trend to determine if associations were stat sign. Only af am are included in this portion of the analysis as the prevalence of the genetic variant is very low in other racial/ethnic groups

8. Characteristics N 1851 1503 HIV+ 64% 65% Male 95% 94% Age, mean (SD)
Analysis:
All Race/Ethnicity
PEth and AUDIT-C
African-American
ADH1B and AUDIT-C N
1851
1503
HIV+
64%
65%
Male
95%
94%
Age, mean (SD)
52 (9)
52 (8)
Race/Ethnicity
White
Hispanic/other
72%
21% 7%
n/a
Median Years from PEth to AUDIT-C (IQR)
2.3 ( )
Percent with ADH1B Genetic Variant For Each Race/Ethnicity
36.2%
0.3%
6.4%
This table shows characteristics for both samples included in this study. To compare Peth and AUDIT-C data, we have 1851 participants. To compare ADH1B variant and AUDIt-C among African-Americans, we have 1503 participants. There is a lot of overlap between these groups and as you can see the characteristics are similar between the groups: Of the 1851, 64% are HIV+,95% male, 72% African American, 21% white, 7 % hisp/other. Median years from peth to AUDIT-C 2.3.
At the bottom we show the percent with ADH1B genetic variant by race/ethnicity. 36% of AA have the variant compared to .3% of white and 6% of the hisp/other group.

9. Highest AUDIT-C AUDIT-C Trajectory AUDIT-C Trajectory
 Table 1
Highest AUDIT-C
Closest AUDIT-C 1
2-3 4+
AUDIT-C = 0
495
181
158
118
AUDIT-C = 1
149
142 67
AUDIT-C = 2 or 3
182
127
AUDIT-C = 4+
232
Closest and Highest
Agreement: 57%
Highest higher: 43%
Highest lower: N/A
Correlation coefficient: 0.51
 Table 2
AUDIT-C Trajectory
Closest AUDIT-C 1 2 3 4
AUDIT-C = 0
660
216 70 6
AUDIT-C = 1 43
248 60 7
AUDIT-C = 2 or 3
133
158 12
AUDIT-C = 4+ 31
128 73
Closest and Trajectory
Agreement: 62%
Trajectory higher: 20%
Trajectory lower: 18%
Correlation coefficient: 0.52
These tables show how the three AUDIT-C metrics compare to each other. the first Table compares the closest AUDIT-C to the Highest AUDIT-C. Overall, there is 57% agreement between all cells and the cells that agree are in black font on the diagnal. The Highest audit c was higher than the closest AUDIT-C 42% of the time (cells shown in red). Because we used the Highest AUDIT-C, there were no cells that were lower (Green cells are all 0).
The corr coef between closest and highest AUDIt-C is .51
The 2nd table compares the Closest AUDIT-C to the Trajectory AUDIT-C. Overall, there is 61.5% agreement between all cells The Trajectory audit c was higher than the closest AUDIT-C 20% of the time (cells shown in red). The Trajectory audit c was lower than the closest AUDIT-C 18.4% of the time (cells shown in green)
The corr coef between closest and traj audit-ci s .52
The 3rd Table compares the Highest AUDIT-C to the Trajectory AUDIT-C. Overall, there is 46.5% agreement between all cells. The Trajectory audit c was higher than the Highest AUDIT-C ~0% of the time (cells shown in red). The Trajectory audit c was lower than the Highest AUDIT-C 53.5% of the time (cells shown in green)
The Correlation coefficient between highest and trajectory audit C : 0.79
 Table 3
AUDIT-C Trajectory
Highest AUDIT-C 1 2 3 4
AUDIT-C = 0
495
AUDIT-C = 1
156
173
AUDIT-C = 2 or 3 51
337 94
AUDIT-C = 4+ 7
118
321 98
Highest and Trajectory
Agreement: 47%
Trajectory higher: N/A
Trajectory lower: 54%
Correlation coefficient: 0.79

10. PEth vs AUDIT-C Metrics
These figures show PEth versus 3 AUDIT-C metrics. The top left figure shows median PEth for each of the AUDIT-C metric categories. The blue bar shows Median PEth for the closest AUDIT-C groups, the red bar shows median peth for the highest AUDIT-C groups, the green bar shows median peth for the trajectory group. For all 3 AUDIT-C metric , Median peth increases as audit-C increases, but the gradient is steeper for trajectory audit-c. The top right figure shows the percent with peth 8+ for each audit-C group. Again, as audit-c increases, percent with peth 8+ increases. Again, The gradient is slightly higher for the trajectory (green line) . We see a similar pattern for the bottom two graphs. All associates are statistically significant.
The first figure (top left, navy bars) shows that as Closest AUDIT-C group increase, the percent with Peth 8+ also increases.
Moving to the right, we see that as the highest AUDIT-C group increases, the percent with Peth 8+ also increases. Similarly, to the right, we see that as the Trajectory AUDIT-C increases, the percent with Peth 8+ also increases though for this Trajectory figure, we see a steeper gradient (better thresholds).
The figures with green bars show the same thing except using Peth 20+ as the cutoffs. The patterns are similar- clearly all figures show a strong assoiation between increasing AUDIT-C and increased percent with PEth 20+; however for trajectory AUDIT-C, we see a steeper gradient. We see a similar pattern for the figure with red bars which uses the PEth 100+ cutoff. Tests for trend were statistically significant for all figures.
In addition, we ran models and compared r-squared and AIC. Based on the logistic regression models and Akaike Information Criterion, we found that the Highest and Trajectory AUDIT-C performed better than the closest AUDIT-C.
All chi-square tests for trend p<.05; n=1851

11. Genetic Variant by AUDIT-C Among African-Americans (n=1503)
p=.11
p=.01
p=.003
This figure compares the percent with ADH1B variant by all 3 AUDIT-C groups. To orient you to this figure, we show all 3 AUDIT-C metrics. For each metric, the lightest bar is for the lowest AUDIT-C group; the darkest bar is for the highest AUDIT-C group. Starting with the figure on the left, which uses closest AUDIT-C, we see that as AUDIT-C increases, % with ADH1B variant decreases, with the exception for the last AUDIT-C group. And the test for trend is NS. for Highest and Trajectory AUDIT-C, as AUDIT-C increases, percent with ADH1B variant decreases. For both, the p-value is stat sign. However, for the trajectory AUDIT-C, the gradient is steepest for and the p-value is also the lowest
FYI corresponding chi sq tets were .31, .08, and .01
Based on logistic regression models, closest AUDIT-C was not stat associated with ADH1B, highest AUDIT-C wasn’t stat associated but p=.08, and trajectory AUDIT-C was stat sign associated with ADH1B.
Add CI
p-values from test for trend

12. Summary Of 3 AUDIT-C metrics AUDIT-C and PEth
Highest and Trajectory were highly correlated
AUDIT-C and PEth
All 3 AUDIT-C associated with PEth
Steeper gradient for AUDIT-C Trajectory
AUDIT-C and ADH1B variant
Highest and Trajectory highly associated w/ genetic variant
Of the 3 AUDIT-C metrics, highest and trajetory were highly correlated
All 3 AUDIt-C’s were stat sign associated with PEth. There appears to be a steeper gradient for trajectory AUDIT-C
Highest and trajecory audit-c were stat sign assoc with ADH1B variant; assoc appears to be is stronger for trajecotry AUDIT-C ; We did not find a stat sign assoc between adh1b variant and closest audit-c.

13. Limitations Genetic analysis limited to African-Americans
Majority of participants male
EHR AUDIT-C
administered inconsistently and subject to under-reporting
from later timeframe than PEth
There were some limitations to this analysis.
ADH1B analysis limited to AA bec/ the ADH1B gene variant is not sufficiently present in the other racial/ethnic groups (consistent with other studies of ADH1B). So our results are not generalizable to other racial/ethnic groups. Our participants are mostly men so we cannot generalize to women.
EHR AUDIT-C has been found to be administered inconsistently and to be subject to under-reporting. Despite this, we see that it is strongly associated with less subjective alcohol measures.
The timeframe between Peth and AUDIT-C differs by a median of 2.3 years. However, for Middle age adults we believe that patterns of alcohol use remain consistent for most people. The potential effect of the time difference would be to bias our results to the null.

14. Conclusion
Despite limitations, AUDIT-C is strongly associated with PEth and ADH1B variant
AUDIT-C can be used to identify complex phenotypes such as unhealthy alcohol use
The validity of the phenotype may be enhanced through the use of longitudinal trajectories

15. Co-Authors Amy C. Justice, MD, PhD Kathleen A. McGinnis, DrPH
Jan Tate, ScD
Ke Xu, MD
William C. Becker, MD
Hongyu Zhao, PhD
Joel Gelernter, MD
Henry R. Kranzler, MD

16. Acknowledgements Consortium PI : AC Justice*
Scientific Collaborator (NIAAA): K Bryant
Affiliated PIs: S Braithwaite, K Crothers*, R Dubrow *, DA Fiellin*, M Freiberg*, V LoRe*
Participating VA Medical Centers: Atlanta (D. Rimland*, V Marconi), Baltimore (M Sajadi, R Titanji), Bronx (S Brown, Y Ponomarenko), Dallas (R Bedimo), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, H Leaf, J Leung), Pittsburgh (A Butt, K Kraemer, M Freiberg, E Hoffman), and Washington DC (C Gibert, R Peck)
Core and Workgroup Chairs: C Brandt, J Edelman, N Gandhi, J Lim, K McGinnis, KA Oursler, C Parikh, J Tate, E Wang, J Womack
Staff: H Bathulapalli, T Bohan, J Ciarleglio, A Consorte, P Cunningham, L Erickson, C Frank, K Gordon, J Huston, F Kidwai-Khan, G Koerbel, F Levin, L Piscitelli, C Rogina, S Shahrir, M Skanderson
Major Collaborators: VA Public Health Strategic Healthcare Group, VA Pharmacy Benefits Management, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Yale Center for Interdisciplinary Research on AIDS (CIRA), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD, HIV-Causal
Cross Cohort Collaborators: Richard Moore (NA-ACCORD), Jonathan Sterne (ART-CC), Brian Agan (DoD)
Major Funding by: National Institutes of Health: AHRQ (R01-HS018372), NIAAA (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799, U24-AA022001, U24 AA022007), NHLBI (R01-HL095136; R01-HL090342) , NIAID (U01-A ), NIMH (P30-MH062294), NIDA (R01DA035616), NCI (R01 CA173754) and the Veterans Health Administration Office of Research and Development (VA REA , VA IRR Merit Award) and Office of Academic Affiliations (Medical Informatics Fellowship)
*Indicates individual is also the Chair of a Core or Workgroup

17. Acknowledgements Continued
COMpAAAS/Veterans Aging Cohort Study, a CHAART Cooperative Agreement, supported by the National Institutes of Health: National Institute on Alcohol Abuse and Alcoholism (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799) and in kind by the US Department of Veterans Affairs.  In addition to grant support from NIAAA, we gratefully acknowledge the scientific contributions of Dr. Kendall Bryant, our scientific collaborator.
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18. Appendix (Extra Slides)

19. Characteristics All Race/Eth PEth and AUDIT-C African-American
ADH1B and AUDIT-C
1851
1501
Closest
AUDIT-C 1
2-3 4+
51%
19%
17%
13%
52%
18%
12%
Highest
27%
26%
29%
31%
Trajectory
Infrequent
Lower Risk
Potentially Hazardous
Consistently Hazardous
38%
34%
22% 5%
39%
33%
23%
PEth 8+
20+
100+
36%
30%
This table shows frequency of AUDIT-C for all three AUDIT-C metrics. When the closest (or single value) of aUDIT-C is used, around half fall into the lowest group, followed by 19% with 1, 17% with 2-3, nd 13% with 4+. When Highest AUDIT-C is used, a lot less fall into the lowest group (27% compred to 51%) and more fall into the higher groups. When the Trajectory is used, 38% fall into the lowest group (Infrequent), 34% into lower risk, 22% as potentially hazarous, and 5% into the consistently hazardous group. When limtied to African Americans, frequencies are similar.
We also show the frequency with each of the Peth cutoffs (note these groups aren’t mutually exclusive. 36% have peth 8+, 27% have peth 20+ and 12% have Peth Percents are slightly higher for the African American only group.
Mean Peth 39 (sd=106); median peth 3 (0-26); for AA only mean peth 45 (sd = 113); median peth 4 (2-33)
*note for peth for afam, only have for n=1342

20. Association of Closest AUDIT-C to Highest AUDIT-C1
2 or 3 4+
495
181
158
118
149
142 67
182
127
232
The following 3 slides are similar and show how the three AUDIT-C metrics compare to each other. This includes all race/ethnicities in the sample. The first slide compares the closest AUDIT-C to the Highest AUDIT-C. Overall, there is 57% agreement between all cells and the cells that agree are in black font on the diagnal. The Highest audit c was higher than the closest AUDIT-C 42% of the time (cells shown in red). Because we used the Highest AUDIT-C, no cells that were lower (Green cells are all 0).
The corr coef between closest and highest AUDIt-C is .51
Agreement: 57.2%
Highest higher than Closest: 42%
Highest lower than Closest : N/A
Correlation coefficient = 0.51

21. Association of Closest AUDIT-C to Trajectory AUDIT-C1 2 3 4
660
216 70 6 43
248 60 7
2 or 3
133
158 12 4+ 31
128 73
This slide compares the Closest AUDIT-C to the Trajectory AUDIT-C. Overall, there is 61.5% agreement between all cells and the cells that agree are in black font on the diagnal. The Trajectory audit c was higher than the closest AUDIT-C 20% of the time (cells shown in red). The Trajectory audit c was lower than the closest AUDIT-C 18.4% of the time (cells shown in green)
The corr coef between closest and traj audit-ci s .52
Agreement: 61.5%
Trajectory higher than Closest : 20.0%
Trajectory lower than Closest: 18.4%
Correlation coefficient = 0.52

22. Association of Highest AUDIT-C to Trajectory AUDIT-C1 2 3 4
495
156
173
2 or 3 51
337 94 4+ 7
118
321 98
This slide compares the Highest AUDIT-C to the Trajectory AUDIT-C. Overall, there is 46.5% agreement between all cells and the cells that agree are in black font on the diagnal. The Trajectory audit c was higher than the Highest AUDIT-C ~0% of the time (cells shown in red). The Trajectory audit c was lower than the Highest AUDIT-C 53.5% of the time (cells shown in green)
The correlation coefficient between highest and trajectory audit-c is .79
Agreement: 46.5%
Trajectory higher than Highest: ~0%
Trajectory lower than Highest: 53.5%
Correlation coefficient = 0.79

23. Methods - Variables AUDIT-C comparison Closest to PEth Highest
Trajectory - using joint trajectory modeling
PEth a direct quantitative biomarker for alcohol
Among African-Americans, test the association of AUDIT-C with a polymorphism (Arg369Cys; rs ) in ADH1B (ADH1B minor allele), which encodes an alcohol dehydrogenase isozyme
*Mean of 7 measures over median of 6.1 years

24. Timeframe of Data
This slide shows the timeframe of our data bec/ its important to note that bec of data availability, we used PEth collected 2005 to 2007 and AUDIT-C 2008+
We think its valid to compare these different timeframes bec/ in general, in a population of middle aged men, we don’t expect substantial changes in alcohol use overtime
However, we would expect results to be stronger, perhaps, if the data were for the exact same timeframe.
ADH1B is also from 2005 to 2007, but timeframe does not matter as genetic data are fixed.

25. Odds of ADH1B Minor Allele as a Function of AUDIT-C Among African Americans (n=1,503)*N OR
95% CI P
Closest AUDIT-C
 Overall p = 0.31
1 (score= 0)
792 1
N/A
2 (score =1)
274
0.83
0.62, 1.11
0.22
3 (score=2-3)
255
0.78
0.58, 1.05
0.11
4 (score 4+)
182
0.85
0.61, 1.20
0.35
Highest AUDIT-C Overall p=0.08
399
254
0.56, 1.08
0.14
387
0.72
0.54, 0.97
0.029
463
0.55, 0.95
0.021
AUDIT-C Trajectories
 Overall p =
1 (little to none)
591
2 (light use)
496
0.82
0.64, 1.05
0.12
3 (moderate use)
340
0.79
0.60, 1.05
0.10
4 (heavy use) 76
0.41
0.23, 0.73
0.003
Get rid of slide

26. PEth (8+, 20+, 100+) by AUDIT-C
PEth 8+
PEth 20+
Proportion
PEth 100+
This figure shows the percent with the 3 PEth thresholds for each of the 3 AUDIT-C metrics.
The first figure (top left, navy bars) shows that as Closest AUDIT-C group increase, the percent with Peth 8+ also increases.
Moving to the right, we see that as the highest AUDIT-C group increases, the percent with Peth 8+ also increases. Similarly, to the right, we see that as the Trajectory AUDIT-C increases, the percent with Peth 8+ also increases though for this Trajectory figure, we see a steeper gradient (better thresholds).
The figures with green bars show the same thing except using Peth 20+ as the cutoffs. The patterns are similar- clearly all figures show a strong assoiation between increasing AUDIT-C and increased percent with PEth 20+; however for trajectory AUDIT-C, we see a steeper gradient. We see a similar pattern for the figure with red bars which uses the PEth 100+ cutoff. Tests for trend were statistically significant for all figures.
In addition, we ran models and compared r-squared and AIC. Based on the logistic regression models and Akaike Information Criterion, we found that the Highest and Trajectory AUDIT-C performed better than the closest AUDIT-C.
AUDIT-C AUDIT-C AUDIT-C Group
All chi-square tests for trend p<.05; n=1851

27. Example of AUDIT-C 3 Ways
Biomarker
Closest = 1
Highest = 5
Trajectory =2