Characteristics of users of HIV self-testing in Kenya, outcomes, and factors associated with use: results from a population-based HIV impact assessment, 2018
verfasst von:
Jonathan Mwangi, Fredrick Miruka, Mary Mugambi, Ahmed Fidhow, Betty Chepkwony, Frankline Kitheka, Evelyn Ngugi, Appolonia Aoko, Catherine Ngugi, Anthony Waruru
About 20% of persons living with HIV aged 15–64 years did not know their HIV status in Kenya, by 2018. Kenya adopted HIV self-testing (HIVST) to help close this gap. We examined the sociodemographic characteristics and outcomes of self-reported users of HIVST as our primary outcome.
Methods
We used data from a 2018 population-based cross-sectional household survey in which we included self-reported sociodemographic and behavioral characteristics and HIV test results. To compare weighted proportions, we used the Rao-Scott χ-square test and Jackknife variance estimation. In addition, we used logistic regression to identify associations of sociodemographic, behavioral, and HIVST utilization.
Results
Of the 23,673 adults who reported having ever tested for HIV, 937 (4.1%) had ever self-tested for HIV. There were regional differences in HIVST, with Nyanza region having the highest prevalence (6.4%), p < 0.001. Factors independently associated with having ever self-tested for HIV were secondary education (adjusted odds ratio [aOR], 3.5 [95% (CI): 2.1–5.9]) compared to no primary education, being in the third (aOR, 1.7 [95% CI: 1.2–2.3]), fourth (aOR, 1.6 [95% CI: 1.1–2.2]), or fifth (aOR, 1.8 [95% CI: 1.2–2.7]) wealth quintiles compared to the poorest quintile and having one lifetime sexual partner (aOR, 1.8 [95% CI: 1.0–3.2]) or having ≥ 2 partners (aOR, 2.1 [95% CI: 1.2–3.7]) compared to none. Participants aged ≥ 50 years had lower odds of self-testing (aOR, 0.6 [95% CI: 0.4–1.0]) than those aged 15–19 years.
Conclusion
Kenya has made progress in rolling out HIVST. However, geographic differences and social demographic factors could influence HIVST use. Therefore, more still needs to be done to scale up the use of HIVST among various subpopulations. Using multiple access models could help ensure equity in access to HIVST. In addition, there is need to determine how HIVST use may influence behavior change towardsaccess to prevention and HIV treatment services.
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Abkürzungen
AIDS
Acquired Immunodeficiency syndrome
aOR
Adjusted odds ratio
ART
Antiretroviral therapy
CI
Confidence internal
HIV
HIV: Human immunodeficiency virus
HIVST
HIV self-testing
HTS
HIV Testing services
KENPHIA
Kenya Population-based HIV Impact Assessment
NASSEP-V
National Sample Survey and Evaluation Program version 5
PHIA
Population-based HIV Impact Assessment
PLHIV
People Living with HIV
TB
Tuberculosis
UNAIDS
The Joint United Nations Programme on HIV/AIDS
US
United States of America
WHO
World health organization
Introduction
HIV diagnosis through testing is the doorway to HIV prevention and antiretroviral therapy (ART) services [1], whose benefits are well documented [2, 3] and are critical for reducing transmissions and achieving epidemic control [4]. To attain HIV epidemic control, the Joint United Nations Programme on HIV/AIDS (UNAIDS) set 90–90-90 targets: 90% of all people living with HIV (PLHIV) knowing their HIV status; of these, 90% receiving sustained ART; and of these, 90% having viral suppression by 2020 [5]. In 2015, UNAIDS revised these targets to 95–95-95 by 2030 [6]. In addition, the UNAIDS recommended broadening testing options to attain the first target, including community-based testing, home-based self-testing, events, location-based testing, community mobilization for testing, public–private partnerships, and voluntary and provider-initiated counseling. The Kenya Ministry of Health adopted these targets in the 2014/2015–2018/2019 Kenya AIDS Strategic Framework [7].
Even with comprehensive HIV testing strategies and a global increase in the percentage of people living with HIV (PLHIV) who know their HIV-positive status (from 71% in 2015 to 84% in 2020), testing gaps still exist, especially among men and young people. About 16% of PLHIV globally and 10% of adults aged 15 years and older in eastern and southern Africa were unaware of their HIV status in 2020 [8] and about 20% of PLHIV aged 15–64 years were unaware of their HIV status in Kenya in 2018 [9]. Several studies have demonstrated high acceptability and effectiveness of HIVST as a strategy for reaching men and young people [10‐13]. In its 2015 guidelines, the World Health Organization (WHO) recommended HIV self-testing as an effective strategy to narrow the gap and increase HIV status knowledge among PLHIV [1]. In 2016, WHO’s HIVST and assisted partner notification services guidelines emphasized HIVST as a strategy to help identify PLHIV [14]. Kenya adopted these WHO guidelines and rolled out HIVST guidelines that included both oral and blood-based HIVST [15]. In Kenya, studies continue to show feasibility and acceptability of HIVST among diverse users in the population [16‐19].
Anzeige
Despite studies showing high acceptability for HIVST, few studies have looked at prevalence of HIVST use at the population level [20]. In Zimbabwe and Malawi a population based survey found 1.2% prevalence of use of HIVST [21]. In Kenya, after rolling out HIVST guidelines [15], information on the prevalence of HIVST use and the characteristics of HIVST users is limited. To address this, we used data from a population-based HIV impact assessment survey to characterize HIVST users in Kenya, HIV status outcomes, and factors associated with HIVST use.
Methods
Study design and population
The methods used in the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA) 2018 have been previously described . Briefly, KENPHIA (October 2018–February 2019) was a cross-sectional household survey targeting adults aged 15–64 years and children ≤ 14 years old. The survey was a two-stage, stratified cluster sample design with the sampling frame that comprised of all households in the country, based upon the National Sample Survey and Evaluation Program version 5, (NASSEP-V) sampling frame. In the first stage, 800 clusters within the 47 counties of Kenya were selected using a probability proportional to size method. During the second stage, a sample of households was randomly selected within each cluster, using an equal probability method. We restricted our analysis to respondents aged 15–64 years who had ever been tested for HIV.
Data collection methods
Respondents were interviewed using a standardized PHIA questionnaire regarding household and demographic characteristics, bio-behavioral factors, and use of HIV-related services such as HIV testing services (HTS) and having ever used an HIVST kit. These data were collected on tablet computers and transmitted electronically to a central database. Since receipt of test results was a requirement for participation in the biomarker component, if an individual did not want to receive his or her HIV test result, this was considered a refusal, and the survey was concluded. For respondents consenting to receive test results, HIV home-based counseling and testing were conducted in each household per national guidelines via a sequential rapid-testing algorithm. The first screening test was with Determine HIV 1/2 RT; individuals with a non-reactive test result were reported as HIV negative. No further HIV testing was performed at home. Persons with a reactive result underwent confirmatory testing at home using a second rapid test (First Response HIV 1–2.0 Card Test [Premier Medical Corporation, Mumbai, India]). Those with a reactive result on both screening and confirmatory tests were classified as HIV positive. For quality assurance, whole-blood specimens collected in the household were transported to satellite laboratories. The first 50 tests from each tester and a fraction of negative specimens were tested using the national HIV rapid testing algorithm and confirmatory testing to determine field results’ accuracy. In addition, all HIV-positive specimens were confirmed with the Geenius HIV-1/2 supplemental assay (Bio-Rad Laboratories, Redmond, WA United States).
Measures
We included the following sociodemographic characteristics for this secondary analysis: sex, residence (urban/rural), age, education, marital status, and wealth quintile. We also included sexual behavioral factors such as sexual encounters in the last 12 months, lifetime sexual partners, and age at sexual debut. We selected the variables due to their relevance in HIVST uptake. Some variables, such as residence and geographic locations, were predetermined from the sampling frame at the survey cluster level. Wealth quintiles were calculated using an established process considering household possessions and income. We categorized the age in years into age bands. Our primary outcome was the prevalence of HIVST use and characteristics associated with HIVST users. The respondents reported their sex, age, education, marital status, and household possessions, and HIVST use during face-to-face interviews. We included the HIV test results by merging the laboratory results with the individual questionnaire response datasets for respondents who consented to a blood draw and testing.
Anzeige
Analysis
We used PROC SURVEYFREQ in SAS to compare the independence of weighted proportions using the Rao-Scott chi-square statistical test, accounting for the sample design. We used jackknife weights for variance estimation. We tested for associations of sociodemographic, behavioral, and HIV testing services utilization with HIVST and presented both unadjusted and adjusted odds ratios. For the unadjusted logistic regression model, the factors were selected a priori for comparability because they were relevant for the HIV program. In the bivariate analyses, significant covariates at p < 0.05 level were then fitted into a multivariable logistic regression model. We additionally assessed for collinearity of factors in the multivariate model and determined that they were not collinear. In all analyses, p-values < 0.05 were considered statistically significant.
Results
Of the 30,384 2018 KENPHIA participants aged 15–64 years, 23,673 (77.9%) had ever tested for HIV; of these, 23,581 (99.6%) responded to the HIVST question (Fig. 1).
Fig. 1
Adolescents and adults reporting to have ever tested for HIV and self-testing, Kenya Population-Based HIV Impact Assessment (KENPHIA 2018). The figure shows how the data were subset for analysis. The percentages are not weighted. * Self-reported testing; † includes unknown; ‡ HIVST – HIV self-testing
×
Those who reported to have ever self-tested were 937, 4.0% (95% confidence interval (CI): 3.7–4.6). Most of the respondents who never had self-tested came from urban areas 50.8%, and residents of rural areas had the highest proportion of non-self-testers, 60%, (p < 0.001). The older respondents aged ≥ 50 years and younger respondents, 15–19 years had the lowest percentage of self-testers, 7.0%, and 7.3%, respectively, (p < 0.001). The highest proportion of self-testers was persons who had secondary education or higher 38.9% (95% CI: 34.3—43.5), p < 0.001, or had never been married 50.0% (95% CI: 44.9—55.1), p = 0.033, or were wealthiest 31.5% (95% CI: 25.2—37.9), p < 0.001, or had sex within the past 12 months 78.9% (95% CI: 74.9–82.4), p < 0.001, or respondents who had ≥ two lifetime sexual partners 66.5% (95% CI: 62.2—70.8), p < 0.001, and respondents who had their sexual debut at the age 15–19 years 55.4% (95% CI: 50.8—60.0), p = 0.022, (Table 1).
Table 1
Sociodemographic and behavioral characteristics and self-reported HIV self-testing status among adolescents and adults aged 15–64 years (N = 30,384) – who participated in the 2018 Kenya Population-Based HIV Impact Assessment (KENPHIA)
Total
Ever self-tested
Never self-tested
P-value
Characteristic
n
%
95% CI
n
%
95% CI
n
%
95% CI
Total
23,581
937
4.0
(3.7–4.6)a
22,644
Sex
0.082
Male
8945
44.9
(44.5—45.3)
407
48.7
(44.1—53.3)
8538
44.8
(44.3—45.2)
Female
14,636
55.1
(54.7—55.5)
530
51.3
(46.7—55.9)
14,106
55.2
(54.8—55.7)
Residence
< 0.001
Urban
9322
40.4
(38.4—42.4)
480
50.8
(45.3—56.2)
8842
40.0
(37.9—42.0)
Rural
14,259
59.6
(57.6—61.6)
457
49.2
(43.8—54.7)
13,802
60.0
(58.0—62.1)
Age, years
< 0.001
15–19
2638
12.3
(11.9—12.7)
68
7.3
(5.2—9.4)
2570
12.5
(12.1—12.9)
20–24
3493
17.3
(17.0—17.5)
198
25.7
(22.3—29.0)
3295
16.9
(16.6—17.2)
25–29
3628
17.1
(16.8—17.3)
209
24.6
(21.2—28.0)
3419
16.7
(16.4—17.0)
30–34
3675
15.0
(14.8—15.2)
153
14.0
(10.9—17.1)
3522
15.0
(14.8—15.3)
35–39
2749
12.0
(11.8—12.2)
97
9.9
(7.7—12.1)
2652
12.1
(11.8—12.3)
40–49
4099
15.5
(15.3—15.8)
135
11.4
(9.1—13.8)
3964
15.7
(15.4—16.0)
50 +
3299
10.9
(10.7—11.1)
77
7.0
(5.1—8.9)
3222
11.1
(10.9—11.3)
Education
< 0.001
No primary
1859
5.4
(4.8—6.0)
41
3.2
(2.1—4.3)
1818
5.5
(4.8—6.1)
Incomplete Primary
11,147
43.8
(42.6—45.1)
297
27.7
(23.9—31.6)
10,850
44.5
(43.3—45.8)
Complete Primary
7283
34.5
(33.3—35.6)
286
30.1
(26.6—33.7)
6997
34.6
(33.5—35.8)
Secondary
3274
16.3
(15.1—17.5)
313
38.9
(34.3—43.5)
2961
15.4
(14.2—16.5)
Marital status
0.033
Never married
5820
43.7
(42.6—44.7)
277
50.0
(44.9—55.1)
5543
43.4
(42.3—44.4)
Monogamous
5017
37.2
(36.2—38.2)
226
32.7
(27.9—37.5)
4791
37.4
(36.4—38.4)
Polygamous
340
2.0
(1.7—2.4)
17
2.4
(0.8—3.9)
323
2.0
(1.7—2.4)
Divorced / separated
1869
11.6
(10.9—12.3)
86
11.3
(8.1—14.5)
1783
11.6
(10.9—12.3)
Widowed
1122
5.5
(5.1—5.9)
30
3.6
(1.9—5.3)
1092
5.6
(5.2—6.0)
Wealth
< 0.001
Lowest
5348
17.7
(16.2—19.1)
117
9.4
(7.2—11.5)
5231
18.0
(16.6—19.5)
Second
5130
20.7
(19.5—21.9)
150
15.1
(12.1—18.2)
4980
21.0
(19.8—22.2)
Middle
5122
21.1
(19.9—22.2)
200
20.9
(17.0—24.7)
4922
21.1
(19.9—22.2)
Fourth
4684
20.7
(19.0—22.3)
238
23.1
(19.0—27.2)
4446
20.6
(18.9—22.2)
Highest
3294
19.8
(17.9—21.7)
232
31.5
(25.2—37.9)
3062
19.3
(17.4—21.2)
Sex ≤ 12 months
< 0.001
Yes
16,985
72.8
(71.8—73.7)
734
78.6
(74.9—82.4)
16,251
72.5
(71.6—73.4)
No
6596
27.2
(26.3—28.2)
203
21.4
(17.6—25.1)
6393
27.5
(26.6—28.4)
Lifetime sexual partners
< 0.001
0 partners
1513
7.8
(7.3—8.3)
31
4.0
(2.2—5.8)
1482
8.0
(7.4—8.5)
1 partner
8002
32.8
(31.6—33.9)
276
29.5
(25.4—33.7)
7726
32.9
(31.7—34.1)
2 or more
12,505
59.4
(58.2—60.7)
558
66.5
(62.2—70.8)
11,947
59.1
(57.9—60.4)
Age at the first sexual encounterb
0.022
< 15
2737
13.6
(12.9—14.3)
112
13.3
(10.5—16.1)
2625
13.6
(12.9—14.3)
15–19
12,337
58.3
(57.3—59.3)
500
55.4
(50.8—60.0)
11,837
58.4
(57.4—59.5)
20–24
4700
22.6
(21.7—23.6)
212
27.4
(22.8—31.9)
4488
22.4
(21.4—23.4)
25 +
1215
5.5
(5.0—6.0)
44
3.9
(2.6—5.3)
1171
5.6
(5.0—6.1)
AbbreviationsCI Confidence Intervals
arow percentage
bage in years
Prevalence of HIVST use varied by region, with Nyanza region having the highest prevalence, 6.4%, p = < 0.001 compared to other regions (Fig. 2).
Fig. 2
Prevalence of HIV testing and HIV testing across regions, Kenya Population-Based HIV Impact Assessment (KENPHIA 2018). The figure shows regional variation in reported HIV testing and HIV self testing. The percentages are not weighted. * Self-reported testing; † includes unknown; ‡ HIVST – HIV self-testing
×
Factors individually associated (unadjusted) with having ever self-tested for HIV were: living in an urban compared to rural setting; being 20–34 years compared to 15–19 years old; completion of primary or secondary education compared to no primary education; having never married compared to being widowed; wealth status in the second to the fifth quintile compared to the lowest quintile; having had sex in the past 12 months compared to none and having one or more partners compared to none. Factors independently (adjusted) associated with having ever self-tested for HIV were secondary education adjusted odds ratio (aOR), 3.5 [95% CI: 2.1–5.9]) compared to no primary education, being in the third (aOR, 1.7 [95% CI: 1.2–2.3]), fourth (aOR, 1.6 [95% CI: 1.1–2.2]), or fifth wealth quintiles (aOR, 1.8 [95% CI: 1.2–2.7]) compared to the first wealth quintile and one-lifetime sexual partner (aOR, 1.8 [95% CI: 1.0–3.2]) or ≥ 2 sexual partners (aOR, 2.1 [95% CI: 1.2–3.7]) compared to those with none (Table 2).
Table 2
Factors associated with HIV self-testing among adolescents and adults aged 15–64 years who participated in the 2018 Kenya Population-Based HIV Impact Assessment –(KENPHIA)
Characteristic
Number and percentages
Unadjusted
odds ratios (OR)
Adjusted
odds ratios (aOR)
Number ever tested for HIV
Number and percentage self-tested
OR (95% CI)
P-value
aOR (95% CI)
P-value
Sex
Female
14,636
530 (3.8)
refa
Male
8945
407 (4.5)
1.2 (1.0–1.4)
0.08
Residence
Urban
14,259
457 (3.4)
refa
Rural
9322
480 (5.2)
1.6 (1.2–1.9)
< .001
1.0 (0.8–1.3)
0.75
Age, years
15–19
2638
68 (2.5)
refa
20–24
3493
198 (6.2)
2.6 (1.9–3.6)
< .001
1.3 (0.9–1.9)
0.18
25–29
3628
209 (6.0)
2.5 (1.7–3.6)
< .001
1.2 (0.8–1.9)
0.38
30–34
3675
153 (3.9)
1.6 (1.1–2.3)
0.01
0.9 (0.6–1.4)
0.58
35–39
2749
97 (3.4)
1.4 (0.9–2.1)
0.10
0.7 (0.4–1.2)
0.16
40–49
4099
135 (3.0)
1.2 (0.9–1.8)
0.23
0.8 (0.5–1.2)
0.21
≥ 50
3299
77 (2.7)
1.1 (0.7–1.6)
0.67
0.6 (0.4–1.0)
0.03
Education
No primary
1859
41 (2.5)
refa
Incomplete Primary
11,147
297 (2.6)
1.5 (1.0–2.2)
0.78
1.1 (0.7–1.9)
0.63
Complete Primary
7283
286 (3.6)
1.5 (1.0–2.2)
0.04
1.4 (0.9–2.4)
0.16
Secondary
3274
313 (9.9)
4.3 (2.9–6.3)
< .001
3.5 (2.1–5.9)
< .001
Marital status
Never married
1122
30 (3.0)
refa
Monogamous
1869
86 (4.5)
1.5 (0.9–2.6)
0.09
Polygamous
5017
226 (4.1)
1.4 (0.8–2.3)
0.20
Divorced/separated
340
17 (5.3)
1.8 (0.8–4.2)
0.14
Widowed
5820
277 (5.3)
1.8 (1.1–3.0)
0.02
Wealth quintiles
First (lowest)
5348
117 (2.2)
refa
Second
5130
150 (3.0)
1.4 (1.1–1.8)
0.02
1.3 (0.9–1.7)
0.1
Third
5122
200 (4.1)
1.9 (1.4–2.6)
< .001
1.7 (1.2–2.3)
< .001
Fourth
4684
238 (4.6)
2.2 (1.6–2.9)
< .001
1.6 (1.1–2.2)
< .001
Fifth (highest)
3294
232 (6.6)
3.1 (2.2–4.5)
< .001
1.8 (1.2–2.7)
< .001
Sex in the past 12 months
No
6596
203 (3.2)
refa
Yes
16,985
734 (4.5)
1.4 (1.1–1.8)
< 0.001
1.1 (0.8–1.4)
0.54
Lifetime sexual partners
0
1513
31 (2.1)
refa
1
8002
276 (3.7)
1.8 (1.1–3.0)
0.02
1.8 (1.0–3.2)
0.04
≥ 2
12,505
558 (4.6)
2.2 (1.4–3.6)
< .001
2.1 (1.2–3.7)
0.01
AbbreviationsCI Confidence Intervals
areferent category
HIV prevalence rates were 4.9% (95% CI: 3.1%–6.7%) among respondents who had ever self-tested for HIV and 6.0% (95% CI: 5.5%–6.4%) among those who never had self-tested for HIV. HIV prevalence varied significantly comparing those who had ever self-tested vs. those who had never self-tested among; persons with incomplete primary education 12.9% vs 8.0% (p = 0.015), with secondary education 0.5% vs 2.5% (p < 0.001), were never married 0.9% vs 2.6% (p = 0.016), were in the lowest wealth quintile 13.4% vs 6.6% (p = 0.012), or who had ≥ 2 sexual partners 4% vs 7.7% (p = 0.030) (Table 3).
Table 3
HIV prevalence by reported HIV self-testing and socio-demographic and behavioral characteristics among adolescents and adults aged 15–64 years (N = 21,470) who participated in the 2018 Kenya Population-Based HIV Impact Assessment (KENPHIA)
Characteristic
HIV prevalence
Ever Self-tested
Never Self-tested
P-value*
HIV-infected/n
%
95% CI
HIV-infected/n
%
95% CI
Total
50/807
4.9
(3.1–6.7)
1394/20663
6.0
(5.5–6.4)
0.265
Sex
Male
18/352
4.4
(1.8–7.1)
383/7719
4.2
(3.6–4.7)
0.821
Female
32/455
5.3
(3.1–7.6)
1011/12944
7.4
(6.8–8.0)
0.100
Residence
Urban
17/401
3.0
(0.9–5.1)
564/7915
5.5
(4.8–6.3)
0.070
Rural
33/406
6.8
(3.9–9.7)
830/12748
6.2
(5.6–6.9)
0.676
Age, years
15–19
0/60
-
-
40/2366
1.5
(0.9–2.1)
†
20–24
7/169
2.0
(0.0–4.1)
80/2980
2.3
(1.7–3.0)
0.780
25–29
10/181
4.0
(1.2–6.8)
163/3081
4.6
(3.7–5.5)
0.672
30–34
12/134
7.2
(2.6–11.8)
252/3201
6.8
(5.8–7.9)
0.866
35–39
2/83
2.8
(0.0–6.8)
192/2416
7.0
(5.7–8.3)
0.169
40–49
9/114
8.2
(2.5–14.0)
378/3628
10.5
(9.2–11.9)
0.473
≥ 50
10/66
17.1
(3.3–30.8)
289/2991
9.6
(8.1–11.1)
0.177
Education
No primary
3/37
8.0
(0.0–17.4)
107/1652
8.8
(6.6–11.1)
0.866
Incomplete Primary
32/260
12.9
(7.9–17.9)
893/10111
8.0
(7.3–8.6)
0.015
Complete Primary
11/252
2.9
(0.9–4.9)
310/6325
4.3
(3.7–4.9)
0.259
Secondary
4/258
0.5
(0.0–0.9)
83/2560
2.5
(1.8–3.3)
< 0.001
Marital Status
Never married
5/231
0.9
(0.0–1.8)
178/4987
2.6
(2.1–3.2)
0.016
Monogamous
14/192
6.1
(2.5– 9.6)
246/4331
5.0
(4.2–5.8)
0.502
Polygamous
2/17
10.7
(0.0–26.0)
28/294
9.3
(5.7–12.9)
0.850
Divorced/separated
11/78
14.0
(2.4–25.7)
174/1643
10.9
(9.1–12.7)
0.557
Widowed
3/23
14.4
(0.0–31.6)
267/1022
28.0
(24.6–31.5)
0.198
Household Wealth
First (lowest)
12/108
13.4
(5.7–21.1)
322/4846
6.6
(5.5–7.6)
0.012
Second
10/128
5.4
(1.7–9.0)
348/4654
6.8
(5.8–7.7)
0.480
Third
14/177
7.1
(2.9–11.2)
336/4543
6.8
(5.7–8.0)
0.910
Fourth
11/202
3.1
(0.7–5.4)
262/3996
5.4
(4.5–6.3)
0.123
Fifth (highest)
3/192
2.0
(0.0–4.9)
125/2622
4.0
(3.0–5.0)
0.323
Lifetime sexual partners
0
0/22
-
-
24/1314
1.9
(0.9–2.9)
†
1
10/238
3.2
(0.5–5.9)
266/6956
3.6
(2.9–4.2)
0.779
≥ 2
33/485
5.0
(2.9–7.1)
1015/11069
7.7
(7.0–8.3)
0.030
Age at first sex, years
< 15
12/99
8.6
(2.6–14.7)
228/2454
7.6
(6.3–8.8)
0.706
15–19
29/446
5.9
(3.3–8.5)
812/10896
6.5
(5.9–7.2)
0.626
20–24
6/179
2.0
(0.0–4.1)
220/4037
4.9
(4.1–5.8)
0.062
≥ 25
1/35
1.7
(0.0–4.2)
47/1045
4.7
(2.9–6.5)
0.147
Abbreviations: CI Confidence Interval
*Rao-Scott χ-square statistical test p-values are computed for each of the categories as two-by-two tables of ever having self-tested, and the outcome is HIV prevalence
†p-value not calculated due to missing values
Discussion
Among the survey respondents who reported having had an HIV test, we found that 4.0% reported having ever taken an HIV self-test. Comparatively, among those who had had an HIV test in Malawi and Zimbabwe, 1.0% and 1.2%, respectively, reported having ever taken an HIV self-test in a population based survey [21]. The results also showed geographic variation in the prevalence of HIVST use. This geographic variation largely mirrors HIV prevalence in the country and the corresponding efforts to increase access to HIV prevention and treatment services in Kenya. The relatively low prevalence of HIVST provides an opportunity to scale up the use of HIVST kits to meet the demand for HIVST among various populations, as has been demonstrated in previous studies. For example, in a prior survey in Kenya, 70% of the respondents reported willingness to use HIVST privately or at home (men, 74%; women, 67%) [22]. Similarly, other studies have reported high acceptability rates of HIVST among the general population [23, 24] and key populations [25]. To increase access to HIVST, the Ministry of health in Kenya developed the HIVST guidelines [15], informed by multiple studies on HIVST acceptability and impact to reach populations [26, 27].
Among those reporting to have ever used an HIV self-test, we found that participants aged 20–29 years were more likely to use HIVST kits, and those older than 50 years were less likely to self-test. A study in Malawi found a similar pattern of decreasing the use of HIVST across older age groups. This was attributed to possibly frequent access to health facilities by the younger population, where HIVST are distributed [28]. These findings could help inform Kenya’s HIV testing program strategies, whose current HIVST objective is to target partners of pregnant and breastfeeding women, men and young persons to close the gaps in the knowledge of HIV status among these groups [22]. However, even though these target populations have a relatively higher prevalence of HIVST use, further scale-up is still needed to expand the prevalence of HIVST use across all age groups. A large-scale rollout of HIVST with different approaches has been practiced in Malawi, Zambia, and Zimbabwe [12]. Similarly, Kenya’s HIVST guidelines provide multiple distribution channels that include facility-based, community-based, and private-sector channels that utilize pharmacies where individuals can buy self-testing kits [15] at approximately five US Dollars [29]. At health facilities, and private pharmacies, there is an option of utilizing the HIVST under the guidance of a healthcare worker (assisted HIVST).
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Higher wealth quintiles were associated with higher HIVST prevalence, possibly because of the higher purchasing power among those respondents [30]. This finding suggests possible inequity in access to HIVST. Furthermore, in this survey, those in the lowest quintile reported a higher prevalence of HIV but reported the most insufficient use of HIVST. This finding underlines the need to ensure all populations are reached, irrespective of socioeconomic status. Demand for HIVST is price-sensitive [31, 32], and price may create inequalities to access where the pricing is considered out of reach to segments of the population. A mix of methods [33, 34], including free distribution of HIV self-tests [31], secondary distribution [26], use of vouchers [35], text message reminders [36], and internet-based approaches [37], may help promote access and use in targeted populations.
We also found higher use of HIVST by those with two or more lifetime sexual partners. This could be associated with participants’ perception of their susceptibility to infection [38]. Individuals with multiple sexual partners are at higher risk of HIV infection [39, 40] and perceived susceptibility has been described as a predictor of HIVST use [41]. Moreover, in this survey, among individuals with ≥ two lifetime sexual partners, those who reported having self-tested for HIV had a lower prevalence of HIV compared to those who had never been tested. This finding warrants further investigation to determine how use of HIVST may influence behavior change towards access of HIV prevention and treatment services.
Although HIVST offers a convenient approach to knowing one’s HIV status, linkage to treatment and other prevention services remains a challenge to be addressed [42], considering privacy and confidentiality is a key advantage of HIVST. Financial incentives [43] and interactive voice response systems [44] have demonstrated potential in increasing the linkage to HIV treatment services. Monitoring ART enrollment and population-based surveys have been proposed for programs to monitor linkage to treatment from HIVST [45]. More research is warranted to explore ways of increasing access to HIVST and linkage to prevention and treatment services among all populations.
Study strengths and limitations
The study had a large sample size from a survey distributed across the country, thus providing a nationally representative sample.
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Our findings are subject to several limitations. First, the HIVST question posed during the survey may have been subject to social-desirability bias in responses like all questions asked in face-to-face interviews. However, the HIVST prevalence is comparable to others reported elsewhere in similar PHIA surveys. Second, the KENPHIA survey was not powered to characterize HIVST use in smaller geographical regions but provided national estimates.
Conclusions
From the survey, among those who reported having ever tested for HIV, 4.0% reported having ever self-tested for HIV. Those living in urban areas had a higher prevalence of HIVST use compared to those living in rural areas. Younger age, higher education levels, being of higher wealth quintile, and having multiple lifetime sexual partners were associated with the use of HIVST. While progress has been made by the program in Kenya to roll out HIVST, more may still need to be done to scale up the use of HIVST among various subpopulations and these results could serve as a baseline. The Kenya program could explore using multiple access models to help ensure equity in access to HIVST. In addition, there is a need to determine the impact of HIVST on behavior change towards access to prevention and HIV treatment services.
Acknowledgements
We acknowledge the Kenya Ministry of Health, NASCOP, the Ministry of Planning and Devolution, and all partners for the scientific, strategic, and technical leadership through KENPHIA Principal Investigators: Dr. Peter Cherutich, Dr. Kigen Bartilol, Dr. Kevin M. De Cock, and Dr. Jessica Justman. We also thank the various planning organs of the KENPHIA through the National Executive Steering Committee, Council of Governors, and the KENPHIA Technical Working Group and Technical Sub-Committees from relevant survey partner institutions.
We thank the 14 community mobilization coordination officers, 1,600 community mobilizers, five regional coordination supervisory teams, 50 field data collection teams, six roving satellite laboratory teams, and the central laboratory team who worked tirelessly to collect high-quality data. Kenyans from all walks of life across the country participated in this survey. We also wish to acknowledge ICAP at Columbia University, who made this survey possible through financial support from PEPFAR and the Global Fund, and the CDC’s technical support.
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Informed consent
The household head consented to the household questionnaire on behalf of the household; heads of households were adults aged 18–64 years or emancipated individuals with no parent or guardian or not living with their parent/guardian. Informed consent was documented electronically on the tablet with a signature or other mark by the interviewer on the consent form’s designated field. To participate in the study, adolescents aged 15–17 years provided written assent, and their parents or guardians provided written informed consent.
Declarations
Ethics approval and consent to participate
This study was approved by the Kenya Medical Research Institute’s Ethical Review Committee, the US Centers for Disease Control and Prevention’s Institutional Review Board, and the Committee on Human Research of the University of California, San Francisco. In addition, the Kenya Ministry of Health, the National AIDS and STI Control Program, and the President’s Emergency Plan for AIDS Relief funding agencies provided concurrence. All the PHIA protocols were carried out in accordance with relevant guidelines and regulations. The Protocols were also reviewed per the CDC IRB human research protection procedures. Columbia University IRB additionally reviewed and approved the 2018 PHIA protocol.
Consent for publication
Individual data not presented. Not applicable.
Competing interests
The authors declare that they have no competing interests. The findings and conclusions of this article are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention, Ministry of Health Kenya, National AIDS & STI Control Program, National HIV Reference Laboratory, Nairobi, Kenya.
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Characteristics of users of HIV self-testing in Kenya, outcomes, and factors associated with use: results from a population-based HIV impact assessment, 2018
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Jonathan Mwangi Fredrick Miruka Mary Mugambi Ahmed Fidhow Betty Chepkwony Frankline Kitheka Evelyn Ngugi Appolonia Aoko Catherine Ngugi Anthony Waruru
