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80006
Identifying Risk factors for Non-Birth Cohort Hepatitis C Testing in a Large Healthcare System
Whitney L. Nichols1, Alexander G. Geboy1, Stephen J. Fernandez1, Peter Basch1,2,3, Idene E. Perez1,Maria Hafeez1, Amanda Smart5, Ilan Fleisher1, Dawn A. Fishbein1,4
1MedStar Health Research Institute, 2MedStar Institute for Innovation, 3MedStar Quality and Safety Institute, 4MedStar Washington Hospital Center,
Georgetown School of Medicine5
80006
• The data from this small study demonstrates that the lack of testing
outside of the BC may result in the exclusion of an at-risk HCV population.
Those who tested HCV Ab+ were more likely to be younger and white,
consistent with other recent literature but a shift in the HCV epidemic.
• Of the patients without identified RFs, 11% were HCV Ab+ (33% of all Ab+
patients) and would not have been identified if the provider had only
followed CDC recommendations.
• Of African Americans without identifiable risk factors, 65% (n=9) were
tested due to patient request. This was not statistically significant as
compared with all other groups, possibly due to low numbers. This may
show that blacks have become more aware of the risks of HCV and may be
directly influenced by HCV health initiatives educating this group.
• Limitations to this project include the small sample size and the abstraction
of RFs, which were often contained as unstructured data within the EHR.
• This study highlights the limitations of an EHR in clinical decision making,
as not all RFs are recorded in structured, searchable fields. This, coupled
with the (a) non-reporting by patients of current and historical RFs and (b)
providers not asking, may make it difficult to accurately assess testing
eligibility. Further evaluation containing a larger sample size and opiate use
as an additional RF is underway.
RESULTSBACKGROUND
• Between 130-150 million people globally have chronic hepatitis C (HCV). In
the U.S., it is estimated that 3.2 million Americans are infected with HCV.
Eliminating HCV from the United States is feasible, as reported by the
National Academy of Medicine in April 2016, though multiple barriers exist.
• CDC recommends testing in people born outside of the birth cohort (non-
BC with high risk factors (RF), such as intravenous drug use (IVDU),
abnormal LFTS, tattoos, piercings, high risk sexual behaviors, blood
transfusion before 1987 & HIV (www.cdc.gov/hepatitis/hcv/guidelines).
• Unfortunately these type of risk factors are not always identified during
office visits. This may be due to a lack of patient transparency and time
restrictions on the provider to elicit this information, among other reasons.
• This study examined RFs associated with non-BC testing. A convenience
sample was selected from a large health care system.
• The objectives of this study were to:
• Explore rationale for provider testing within the non-BC
• Explore CDC risk factor recommendation practicality and adherence
METHODS
• A MedStar-wide, primary care EHR-based Birth Cohort HCV testing protocol
went live in July of 2015. Non-BC HCV test results were collected as a
possible marker for patients at high risk for HCV.
• A convenience sample of 100 charts was selected and retrospectively
reviewed at a 1:4 case-control of the total 3,275 non-BC patients tested from
July 1, 2015 though February 29, 2016.
• A total of 18 persons were HCV Ab positive (HCV Ab+) in the overall sample
and these persons were compared to 82 randomly selected Ab negative
(HCV Ab-) controls [“Convenience Sample”]. Data on RFs was manually
abstracted from the medical records, including patient history and provider
notes.
• Patients were assessed for the following RFs: elevated liver function test
(LFTs), drug usage, high risk sexual behavior, country of origin, blood
transfusion, incarceration, other STI’s, military service, spousal infidelity,
country of origin outside the US, renal disease, occupational risk and HIV.
Additional risk factors were recorded if observed for more than one person.
• Chi-square and Fisher exact models were utilized for univariate analysis. A
p<0.05 was considered statistically significant.
CONCLUSION
Whitney Nichols, MS
MHRI
100 Irving St NW, EB 4109
Washington, DC 20010
201-877-6501
Whitney.L.Nichols@medstar.net
Author Disclosures: Dawn A. Fishbein, MD has served on an Advisory Board for BMS, Gilead and
serves as a Medical Advisor for Hepatitis Foundation International; Alexander G. Geboy has served
on an Advisory Board for Gilead Sciences, LLC. Dr. Fishbein has grant funding from Gilead Sciences.
Table 1. Demographic Characteristics
Table 2. Convenience Sample Risk Factors by HCV Result
Figure 1. HCV Ab Testing Results by Age Distribution Outside of the Birth Cohort
1
8
14
16 16
5
12
3*
5*
2
1
2
3 3
5
3
1
15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 70-75 75-80
NumberofPatients
Age
Negative
Positive
Overall Comparisons:
• The only statistically significant difference between the larger sample and
the convenience sample was between those with Medicaid versus Medicare
insurance in the HCV Ab negatives.
• Of the larger sample, 45% (n=1488) of those tested were black/African
American (b/AA); 45% (n=1483) were b/AA and HCV Ab(-) as compared with
61.1% (n=11) who were white and HCV Ab(+) (p = 0.04). Within the
convenience sample, more whites tested HCV Ab+ than all other Ab+
positive race groups (p=0.01).
Convenience Sample – Patients with identifiable Risk Factors (n = 47):
• 47% of patients had an identifiable risk factor and 25.5% (n=12) of these
were found to be HCV Ab+.
• Of the 12 positive in this group, 58.3% (n=7) were between 30-50 years old.
• 40% (n=19) were white, with 47% (n=9) testing HCV Ab+ ( p < 0.01) compared
to all other race groups.
Convenience Sample – Patients with no identifiable Risk Factors (n = 53):
• 53% of patients were tested without any identifiable risk factor
and 11.3 % (n=6) of these were found to be HCV Ab+.
• Of those positive in this group, 100% (n=6) were between 30-50 years.
• 51% (n=27) were black, with 67% (n=4) testing HCV Ab+ (NS).
*p<0.05 comparing white to non-white race in both the larger and the convenience sample
*None of the patients in either group were born between 1945 - 1965
Larger Sample Convenience Sample
HCV Ab Negatives HCV Ab Negatives HCV Ab Positives
Characteristic N (%) N (%) N (%)
Total 3275 (99.6) 82 (82.0) 18 (18.0)
Mean Age + SD 37.3 + 14.0 39 + 13.5 38 + 9.7
Sex
Female 1816 (55.5) 49 (59.8) 7 (38.9)
Male 1459 (44.5) 33 (40.2) 11 (61.1)
Race
Black 1483 (45.3) 38 (46.3) 5 (27.7)
White 1097 (33.5) 25 (30.5) 11 (61.1)*
Other 695 (21.1) 19 (23.2) 1 (5.6)
Primary Insurance
Public 1057 (32.3) 23 (28.0) 10 (55.6)
Medicare 271 (25.6) 11 (13.4) 4 (22.2)
Medicaid 786 (74.4) 12 (14.6) 6 (33.3)
Private 2088 (63.8) 57 (69.5) 8 (44.4)
Self Pay 126 (3.9) 2 (2.4) -
HCV Ab Negatives (N (%)) HCV Ab Positives (N (%))
Totals 82 (100) 18 (100)
No Identifiable RFs 47 (57.3) 6 (33.3)
Patient Requested 12 (14.6) 1 (5.5)
Identifiable RFs: 35 (42.7) 12 (66.7)
Elevated LFTs 9 (11.0) 3 (16.8)
High risk sexual behavior 4 (4.9) -
Drug use - 6 (33.3)
Spousal Infidelity 3 (3.7) 1 (5.5)
Country of origin 5 (6.1) -
STI 4 (4.9) 1 (5.5)
HIV 3 (3.7) -
More than one RF 7 (8.5) 1 (5.5)

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ID Week 2016- N-BC Risk Factors Poster Final

  • 1. 80006 Identifying Risk factors for Non-Birth Cohort Hepatitis C Testing in a Large Healthcare System Whitney L. Nichols1, Alexander G. Geboy1, Stephen J. Fernandez1, Peter Basch1,2,3, Idene E. Perez1,Maria Hafeez1, Amanda Smart5, Ilan Fleisher1, Dawn A. Fishbein1,4 1MedStar Health Research Institute, 2MedStar Institute for Innovation, 3MedStar Quality and Safety Institute, 4MedStar Washington Hospital Center, Georgetown School of Medicine5 80006 • The data from this small study demonstrates that the lack of testing outside of the BC may result in the exclusion of an at-risk HCV population. Those who tested HCV Ab+ were more likely to be younger and white, consistent with other recent literature but a shift in the HCV epidemic. • Of the patients without identified RFs, 11% were HCV Ab+ (33% of all Ab+ patients) and would not have been identified if the provider had only followed CDC recommendations. • Of African Americans without identifiable risk factors, 65% (n=9) were tested due to patient request. This was not statistically significant as compared with all other groups, possibly due to low numbers. This may show that blacks have become more aware of the risks of HCV and may be directly influenced by HCV health initiatives educating this group. • Limitations to this project include the small sample size and the abstraction of RFs, which were often contained as unstructured data within the EHR. • This study highlights the limitations of an EHR in clinical decision making, as not all RFs are recorded in structured, searchable fields. This, coupled with the (a) non-reporting by patients of current and historical RFs and (b) providers not asking, may make it difficult to accurately assess testing eligibility. Further evaluation containing a larger sample size and opiate use as an additional RF is underway. RESULTSBACKGROUND • Between 130-150 million people globally have chronic hepatitis C (HCV). In the U.S., it is estimated that 3.2 million Americans are infected with HCV. Eliminating HCV from the United States is feasible, as reported by the National Academy of Medicine in April 2016, though multiple barriers exist. • CDC recommends testing in people born outside of the birth cohort (non- BC with high risk factors (RF), such as intravenous drug use (IVDU), abnormal LFTS, tattoos, piercings, high risk sexual behaviors, blood transfusion before 1987 & HIV (www.cdc.gov/hepatitis/hcv/guidelines). • Unfortunately these type of risk factors are not always identified during office visits. This may be due to a lack of patient transparency and time restrictions on the provider to elicit this information, among other reasons. • This study examined RFs associated with non-BC testing. A convenience sample was selected from a large health care system. • The objectives of this study were to: • Explore rationale for provider testing within the non-BC • Explore CDC risk factor recommendation practicality and adherence METHODS • A MedStar-wide, primary care EHR-based Birth Cohort HCV testing protocol went live in July of 2015. Non-BC HCV test results were collected as a possible marker for patients at high risk for HCV. • A convenience sample of 100 charts was selected and retrospectively reviewed at a 1:4 case-control of the total 3,275 non-BC patients tested from July 1, 2015 though February 29, 2016. • A total of 18 persons were HCV Ab positive (HCV Ab+) in the overall sample and these persons were compared to 82 randomly selected Ab negative (HCV Ab-) controls [“Convenience Sample”]. Data on RFs was manually abstracted from the medical records, including patient history and provider notes. • Patients were assessed for the following RFs: elevated liver function test (LFTs), drug usage, high risk sexual behavior, country of origin, blood transfusion, incarceration, other STI’s, military service, spousal infidelity, country of origin outside the US, renal disease, occupational risk and HIV. Additional risk factors were recorded if observed for more than one person. • Chi-square and Fisher exact models were utilized for univariate analysis. A p<0.05 was considered statistically significant. CONCLUSION Whitney Nichols, MS MHRI 100 Irving St NW, EB 4109 Washington, DC 20010 201-877-6501 Whitney.L.Nichols@medstar.net Author Disclosures: Dawn A. Fishbein, MD has served on an Advisory Board for BMS, Gilead and serves as a Medical Advisor for Hepatitis Foundation International; Alexander G. Geboy has served on an Advisory Board for Gilead Sciences, LLC. Dr. Fishbein has grant funding from Gilead Sciences. Table 1. Demographic Characteristics Table 2. Convenience Sample Risk Factors by HCV Result Figure 1. HCV Ab Testing Results by Age Distribution Outside of the Birth Cohort 1 8 14 16 16 5 12 3* 5* 2 1 2 3 3 5 3 1 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 70-75 75-80 NumberofPatients Age Negative Positive Overall Comparisons: • The only statistically significant difference between the larger sample and the convenience sample was between those with Medicaid versus Medicare insurance in the HCV Ab negatives. • Of the larger sample, 45% (n=1488) of those tested were black/African American (b/AA); 45% (n=1483) were b/AA and HCV Ab(-) as compared with 61.1% (n=11) who were white and HCV Ab(+) (p = 0.04). Within the convenience sample, more whites tested HCV Ab+ than all other Ab+ positive race groups (p=0.01). Convenience Sample – Patients with identifiable Risk Factors (n = 47): • 47% of patients had an identifiable risk factor and 25.5% (n=12) of these were found to be HCV Ab+. • Of the 12 positive in this group, 58.3% (n=7) were between 30-50 years old. • 40% (n=19) were white, with 47% (n=9) testing HCV Ab+ ( p < 0.01) compared to all other race groups. Convenience Sample – Patients with no identifiable Risk Factors (n = 53): • 53% of patients were tested without any identifiable risk factor and 11.3 % (n=6) of these were found to be HCV Ab+. • Of those positive in this group, 100% (n=6) were between 30-50 years. • 51% (n=27) were black, with 67% (n=4) testing HCV Ab+ (NS). *p<0.05 comparing white to non-white race in both the larger and the convenience sample *None of the patients in either group were born between 1945 - 1965 Larger Sample Convenience Sample HCV Ab Negatives HCV Ab Negatives HCV Ab Positives Characteristic N (%) N (%) N (%) Total 3275 (99.6) 82 (82.0) 18 (18.0) Mean Age + SD 37.3 + 14.0 39 + 13.5 38 + 9.7 Sex Female 1816 (55.5) 49 (59.8) 7 (38.9) Male 1459 (44.5) 33 (40.2) 11 (61.1) Race Black 1483 (45.3) 38 (46.3) 5 (27.7) White 1097 (33.5) 25 (30.5) 11 (61.1)* Other 695 (21.1) 19 (23.2) 1 (5.6) Primary Insurance Public 1057 (32.3) 23 (28.0) 10 (55.6) Medicare 271 (25.6) 11 (13.4) 4 (22.2) Medicaid 786 (74.4) 12 (14.6) 6 (33.3) Private 2088 (63.8) 57 (69.5) 8 (44.4) Self Pay 126 (3.9) 2 (2.4) - HCV Ab Negatives (N (%)) HCV Ab Positives (N (%)) Totals 82 (100) 18 (100) No Identifiable RFs 47 (57.3) 6 (33.3) Patient Requested 12 (14.6) 1 (5.5) Identifiable RFs: 35 (42.7) 12 (66.7) Elevated LFTs 9 (11.0) 3 (16.8) High risk sexual behavior 4 (4.9) - Drug use - 6 (33.3) Spousal Infidelity 3 (3.7) 1 (5.5) Country of origin 5 (6.1) - STI 4 (4.9) 1 (5.5) HIV 3 (3.7) - More than one RF 7 (8.5) 1 (5.5)