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Rebecca Bridge, Division of Epidemiology and Biostatistics
University of Illinois at Chicago, Chicago, IL
Identifying Pa@erns of HIV Testing in a Kenyan District Hospital
• In Kenya, HIV is still the leading cause of morbidity and mortality.
• One national strategy is to identify new cases of HIV through universal testing in healthcare facilities.
• The aim of this study is to identify pa@erns of HIV testing in the county district hospital in Kisumu, Kenya where the HIV prevalence is ~19% (Kenya Ministry of Health, 2014) and incidence is 2nd highest in the country.
• We hypothesized that patients with the greatest risk of infection would be more likely to be tested despite recommended testing regardless of risk. Highest risk groups include women 18-‐‑25, who have the highest incidence rates, and patients with known co-‐‑infections.
• The hypothesized highest risk groups were not significantly more likely to be tested for HIV than others.
• The utility of expanded testing and strategic testing should be evaluated in the casualty department in order to identify newly infected people to link to care and adhere to the test and treat model.
• A limitation to this study is that it is not generalizable as we only have information on patients who are admi@ed.
• Another limitation is documentation in patient charts. It is not recorded if someone was offered an HIV test and whether or not they accepted, and healthcare workers may not consistently document when an HIV test is given or patient is known HIV positive.
Conclusion Results & Project Impact
• We conducted a retrospective chart review of patient records who a@ended the casualty department between 01/2014-‐‑01/2015 at Jaramogi Oginga Odinga Teaching and Referral Hospital.
• We systematically sampled and abstracted information from 5% of admi@ed patients 18+. Wri@en charts are kept only for patients who are admi@ed. After excluding those who had documentation of previous HIV testing and those known to have HIV our final sample size was 365.
• We coded casualty diagnoses using ICD-‐‑9 codes and used hierarchy coding when there was more than one diagnosis. We also recorded disposition, date of admi@ance, home county, age, and sex.
• Using chi-‐‑square analysis we characterized patients and used Poisson regression modeling to produce the relative risk of being tested based on casualty diagnosis and covariates.
Materials & Methods
Introduction
Kenya Ministry of Health. (2014). Kenya HIV County Profiles. National AIDS and STI Control Programme. Retrieved from: h@p://www.nacc.or.ke/images/documents/KenyaCountyProfiles.pdf
Literature Cited
Janet Lin, MD, MPH, Supriya Mehta, MHS, PhD, Katherine Reifler, Frank Ebai, Maseno University, and Jaramogi Oginga Odinga Teaching and Referral Hospital
Acknowledgements
In 2014, 9,071 patients 18+ years were admi@ed from the casualty department. In the sample, 26% of patients were tested for HIV. There was no significant difference in testing by gender (p-‐‑value=0.91) and no significant difference between age groups (p-‐‑value=0.50). The RR of being tested for those diagnosed with an infectious disease diagnosis was 1.34 (.84, 2.14).
Variable HIV Tested,
N= 96 n (%)
HIV Not Tested, N= 269
n (%)
Chi-‐‑square p-‐‑
value
Diagnosis, N=365 Other Infectious Genitourinary Injury Pregnancy
41 (27.2) 16 (36.4) 15 (40.5) 18 (20.7) 6 (13.0)
110 (72.8) 28 (63.6) 22 (59.5) 69 (79.3) 40 (87.0)
0.02
Sex, N=365 Female Male
53 (26.37) 43 (26.22)
148 (73.6) 121 (73.8)
0.97
Age Categories, N=365 18-‐‑25 26-‐‑39 40-‐‑64 65+
21 (23.3) 24 (22.9) 26 (31.3) 25 (28.7)
69 (76.7) 81 (77.1) 57 (68.7) 62 (71.3)
0.50
County, N=360 Other Homa Bay Siaya Kisumu**
18 (31.0) 10 (30.3) 34 (32.1) 34 (20.9)
40 (69.0) 23 (69.7) 72 (67.9) 129 (79.1)
0.16
Time Period, N=365 Jan, Feb, Mar Apr, May, Jun Jul, Aug, Sep Oct, Nov, Dec
18 (22.0) 19 (22.9) 21 (23.6) 38 (34.2)
64 (78.0) 64 (77.1) 68 (76.4) 73 (65.8)
0.16
Table 1: Distribution of HIV Testing by Variables
*Other diagnosis includes: circulatory, neurological, respiratory, digestive, blood diseases, musculoskeletal, sense organs, and endocrine diagnoses **Other county includes any county that wasn’t Homa Bay, Kisumu, or Siaya
Table 2: Gender Stratified Models-‐‑ Relative Risk of being tested for HIV by covariates and controlling for covariates
Multivariable model includes casualty diagnosis, age category, county, and time period *=Referent category
Male Female Variable Crude
RR (95% CI) N=164
Adjusted RR (95% CI)
N=164
Crude RR (95% CI)
N=201
Adjusted RR (95% CI)
N=201 Diagnosis, N=365 Other* Infectious Genitourinary Injury Pregnancy
Ref
1.61 (0.83, 3.11) 1.12 (0.39, 3.21) 0.96 (0.52, 1.78)
-‐‑
Ref
1.71 (0.91, 3.22) 1.19 (0.42, 3.41) 0.95 (0.51, 1.77)
-‐‑
Ref
1.13 (0.56, 2.25) 1.56 (0.91, 2.66) 0.50 (0.19, 1.31) 0.44 (0.19, 1.00)
Ref
1.06 (0.54, 2.07) 1.87 (1.08, 3.26) 0.53 (0.20, 1.39) 0.57 (0.23, 1.40)
Age Categories, N=365 18-‐‑25 26-‐‑39 40-‐‑64 65+*
1.77 (0.75, 4.21) 1.05 (0.43, 2.57) 2.24 (1.04, 4.80)
Ref
1.59 (0.69, 3.65) 1.07 (0.45, 2.50) 2.31 (1.12, 4.82)
Ref
0.51 (0.28, 0.96) 0.70 (0.40, 1.23) 0.56 (0.28, 1.15)
Ref
0.50 (0.26, 0.95) 0.68 (0.38, 1.21) 0.57 (0.28, 1.15)
Ref
County, N=360 Other Homa Bay Siaya Kisumu*
0.40 (0.09, 1.61) 1.47 (0.63, 3.41) 1.57 (0.89, 2.75)
Ref
0.42 (0.10, 1.65) 1.54 (0.65, 3.68) 1.57 (0.92, 2.70)
Ref
2.27 (1.32, 3.91) 1.42 (0.53, 3.80) 1.41 (0.76, 2.61)
Ref
2.30 (1.33, 3.95) 1.36 (0.57, 3.25) 1.39 (0.75, 2.54)
Ref
Time Period, N=365 Jan, Feb, Mar* Apr, May, Jun Jul, Aug, Sep Oct, Nov, Dec
Ref
1.61 (0.60, 4.34) 1.74 (0.67, 4.53) 2.50 (1.03, 6.08)
Ref
1.37 (0.53, 3.57) 1.61 (0.64, 4.07) 2.34 (0.99, 5.49)
Ref
0.82 (0.40, 1.68) 0.80 (0.39, 1.65) 1.19 (0.66, 2.14)
Ref
0.84 (0.42, 1.69) 0.79 (0.38, 1.61) 1.21 (0.68, 2.18)
