Skip to main content
PMC home page
Morbidity and Mortality Weekly Report logoLink to Morbidity and Mortality Weekly Report
. 2020 Dec 4;69(48):1801–1806. doi: 10.15585/mmwr.mm6948a1

Sex Differences in HIV Testing — 20 PEPFAR-Supported Sub-Saharan African Countries, 2019

Bakary Drammeh 1,✉, Amy Medley 1, Helen Dale 1, Anindya K De 1, Shane Diekman 1, Randy Yee 1, Tiffiany Aholou 1, Arielle Lasry 1, Andrew Auld 2, Brittney Baack 2, Wayne Duffus 1, Ebrahim Shahul 1, Vincent Wong 3, Michael Grillo 4, Teeb Al-Samarrai 5, Shabeen Ally 5, Mtemwa Nyangulu 1; MCHD2, Rose Nyirenda 6, Jacobus Olivier 7, Thato Chidarikire 8, Nompumelelo Khanyile 9, Alick A Kayange 10, Oscar Ernest Rwabiyago 11, Upendo Kategile 12, Jema Bisimba 12, Rachel A Weber 13, Gertrude Ncube 14, Onesimo Maguwu 15, Ismelda Pietersen 16, Denis Mali 17, Edington Dzinotyiweyi 18, Lisa Nelson 19, Matovu John Bosco 20, Kwarisiima Dalsone 21, Madina Apolot 19, Samson Anangwe 22, Leonard K Soo 23, Mary Mugambi 24, Andre Mbayiha 25, Placidie Mugwaneza 26, Samuel S Malamba 27, Arlene Phiri 1; MCD28, Tina Chisenga 29, Mary Boyd 30, Chanie Temesgan 31, Mesfin Shimelis 31, Teklu Weldegebreal 32, Mirtie Getachew 33, Shirish Balachandra 34, Ehui Eboi 35, Willibord Shasha 36, Nicole Doumatey 34, Didier Adjoua 37, Chidozie Meribe 38, Jerry Gwamna 38, Pamela Gado 39, Ima John-Dada 40, Elie Mukinda 41, Luc F Kamanga Lukusa 42, Lucien Kalenga 43, Sudhir Bunga 44, Victoria Achyut 45, John Mondi 44, Peter Loeto 46, Goabaone Mogomotsi 47, Jenny Ledikwe 48, Puleng Ramphalla 49, Mphotleng Tlhomola 50, Justine K Mirembe 51, Tse Nkwoh 52, Laura Eno 52, Leonard Bonono 53, Nely Honwana 54, Noela Chicuecue 55, Ana Simbine 56, Inacio Malimane 54, Lenhle Dube 57, Munamato Mirira 58, Phumzile Mndzebele 59, Alean Frawley 60, Yolanda Marina Rebello Cardo 60, Stephanie Behel 1
PMCID: PMC7714024  PMID: 33270608

Despite progress toward controlling the human immunodeficiency virus (HIV) epidemic, testing gaps remain, particularly among men and young persons in sub-Saharan Africa (1). This observational study used routinely collected programmatic data from 20 African countries reported to the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) from October 2018 to September 2019 to assess HIV testing coverage and case finding among adults (defined as persons aged ≥15 years). Indicators included number of HIV tests conducted, number of HIV-positive test results, and percentage positivity rate. Overall, the majority of countries reported higher HIV case finding among women than among men. However, a slightly higher percentage positivity was recorded among men (4.7%) than among women (4.1%). Provider-initiated counseling and testing (PITC) in health facilities identified approximately two thirds of all new cases, but index testing had the highest percentage positivity in all countries among both sexes. Yields from voluntary counseling and testing (VCT) and mobile testing varied by sex and by country. These findings highlight the need to identify and implement the most efficient strategies for HIV case finding in these countries to close coverage gaps. Strategies might need to be tailored for men who remain underrepresented in the majority of HIV testing programs.

In 2014, the Joint United Nations Programme on AIDS (UNAIDS) launched its 90–90–90 strategy for ending the global HIV pandemic: 90% of all persons living with HIV/AIDS (PLHIV) know their status; of these, 90% are receiving antiretroviral treatment (ART); and of these, 90% are virally suppressed (2). PEPFAR provides guidance on reaching these targets to all its supported countries (3). PEPFAR also collects data on standardized indicators as part of its Monitoring, Evaluation, and Reporting system (4). These data are collected by facility and community sites and are reported quarterly by each country program.

Routine program data reported to PEPFAR from October 2018 to September 2019 from 20 sub-Saharan African countries and modeling data from UNAIDS were used to identify progress toward achieving the first of the three 90–90–90 goals. These countries were selected because they collectively represent the highest HIV prevalence among PEPFAR-supported countries. Indicators used included the number of HIV tests conducted among adults, the number of HIV-positive test results, and yield (or percentage positivity) defined as the number of positive test results divided by the total number of tests reported. Results for each country are presented overall and disaggregated by sex and testing strategy. Testing strategies include index testing (offering an HIV test to the partners and biologic children of PLHIV), PITC (providers recommending an HIV test as part of routine care), VCT (HIV testing at a clinic dedicated to this purpose), and mobile testing (HIV testing offered at an ad hoc location in the community [e.g., van, workplace, or school]).The standard test statistic for the difference in proportions between men and women was computed on the basis of pooled variance formulation. P-values <0.05 were considered statistically significant for each pairwise difference and were computed under the asymptomatic normality assumption (5). PITC was used as the reference strategy for comparisons among strategies because the highest number of new HIV cases are identified by PITC in sub-Saharan Africa (1). The percentage positivity for the three other strategies (index testing, VCT, and mobile testing) was compared with PITC for each country and by sex.

From October 2018 to September 2019, PEPFAR supported 60,945,355 tests that identified 2,603,560 adults with positive HIV test results (5.0% yield; Table 1). Approximately one fifth (19.9%) of all testing occurred in South Africa. More women received tests than men (women, 40,263,510; men, 20,681,845). However, yield was slightly higher among men (970,100; 4.7% yield) than among women (1,633,460; 4.1% yield). Over one half (51.6%) of all HIV-positive results among men were reported by South Africa, Tanzania, and Zambia, and approximately one third (29.2%) were reported by South Africa alone.

TABLE 1. Adult human immunodeficiency virus (HIV) prevalence, by sex — 20 PEPFAR-supported African countries, October 2018–September 2019*.

Country All HIV tests conducted
 All HIV tests positive
No.
 No. (%)
Men Women Men Women
Rwanda
 888,336
 371,405
 516,931
 7,343 (0.8)
 2,929 (0.8)
 4,414 (0.9)
Eswatini
 305,714
 106,448
 199,266
 21,341 (7.0)
 8,697 (8.2)
 12,644 (6.3)
Botswana
 278,908
 119,530
 159,378
 14,407 (5.2)
 6,105 (5.1)
 8,302 (5.2)
Namibia
 398,722
 130,566
 268,156
 14,078 (3.5)
 5,385 (4.1)
 8,693 (3.2)
Malawi
 3,741,494
 1,362,235
 2,379,259
 122,509 (3.3)
 52,870 (3.9)
 69,639 (2.9)
South Africa
 12,131,042
 3,996,848
 8,134,194
 759,465 (6.3)
 267,255 (6.7)
 492,210 (6.1)
Zimbabwe
 2,059,970
 709,379
 1,350,591
 112,605 (5.5)
 43,340 (6.1)
 69,265 (5.1)
Kenya
 9,325,119
 3,248,359
 6,076,760
 168,809 (1.8)
 60,515 (1.9)
 108,294 (1.8)
Zambia
 4,666,548
 1,843,640
 2,822,908
 275,966 (5.9)
 111,599 (6.1)
 164,367 (5.8)
Lesotho
 739,505
 247,653
 491,852
 28,899 (3.9)
 11,453 (4.6)
 17,446 (3.5)
Uganda
 4,872,644
 1,858,346
 3,014,298
 161,742 (3.3)
 60,855 (3.3)
 100,887 (3.3)
Ethiopia
 401,572
 153,500
 248,072
 8,729 (2.2)
 3,543 (2.3)
 5,186 (2.1)
Tanzania
 6,930,758
 2,415,017
 4,515,741
 314,364 (4.5)
 121,603 (5.0)
 192,761 (4.3)
Cameroon
 839,762
 317,881
 521,881
 32,435 (3.9)
 11,648 (3.7)
 20,787 (4.0)
Mozambique
 5,651,254
 1,519,954
 4,131,300
 281,022 (5.0)
 103,433 (6.8)
 177,589 (4.3)
Nigeria
 4,309,213
 1,348,056
 2,961,157
 158,351(3.7)
 55,834 (4.1)
 102,517 (3.5)
Côte d’Ivoire
 2,200,382
 564,945
 1,635,437
 60,058 (2.7)
 19,713 (3.5)
 40,345 (2.5)
DRC
 811,233
 235,984
 575,249
 41,898 (5.2)
 16,062 (6.8)
 25,836 (4.5)
Angola
 141,292
 49,215
 92,077
 9,208 (6.5)
 3,253 (6.6)
 5,955 (6.5)
South Sudan
 251,887
 82,884
 169,003
 10,331 (4.1)
 4,008 (4.8)
 6,323 (3.7)
Total 60,945,355 20,681,845 40,263,510 2,603,560 (5.0) 970,100 (4.7) 1,633,460 (4.1)
Open in a new tab

Source: PEPFAR Monitoring, Evaluation, and Reporting data for Accountability, Transparency, and Impact Monitoring database, October 2018–September 2019.

Abbreviations: DRC = Democratic Republic of Congo; PEPFAR = U.S. President’s Emergency Plan for AIDS Relief.

* Nine of the 20 countries account for 90% of HIV prevalence: Kenya, Malawi, Mozambique, Nigeria, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe. Six countries have achieved the first 90 target (Botswana, Eswatini, Kenya, Lesotho, Namibia, and South Africa), and knowledge of HIV status is <70% in Angola (69%), DRC (49%), and South Sudan (24%).

Across the 19 countries (excluding Malawi because of limited data), PITC identified the most PLHIV (63.2%) compared with index testing (17.4%), VCT (11.0%), and mobile testing (8.4%). HIV case finding among men followed a similar pattern: PITC identified 57.7% of HIV-positive men, followed by index testing (21.4%), VCT (11.9%), and mobile testing (9.0%). Six countries accounted for 80.2% of HIV-positive men identified through PITC: South Africa (220,940), Mozambique (56,960), Tanzania (52,574), Zambia (38,991), Kenya, (28,814), and Uganda (26,421). Additional data for new HIV cases identified across the four HIV testing strategies both overall and by sex are provided (Table 2).

TABLE 2. Number and percentage of human immunodeficiency virus (HIV)–positive adults Identified, by four HIV-testing strategies, overall and by sex — 20 PEPFAR-supported countries, October 2018–September 2019*.

Country Index testing
 Provider-initiated testing
 Voluntary counseling and testing
 Mobile testing
All
 Men
 Women
 All
 Men
 Women
 All
 Men
 Women
 All
 Men
 Women
No./Total no.† (%)§ % Positive % Positive No./Total no. (%) % Positive % Positive No./Total no. (%) % Positive % Positive No./total no. (%) % Positive % Positive
Rwanda
 1.4/26.6 (5.1)
 4.6
 5.8
 2.4/545.9 (0.5)
0.5
 0.4
 2.6/259.9 (1.0)
0.8
 1.2
 0.9/55.9 (1.7)
1.0
 2.8
Eswatini
 3.5/13.9 (25.5)
 25.6
 25.4
 11.7/214.9 (5.5)
 6.6
 5.0
 4.0/46.6 (8.5)
 7.9
 9.1
 2.1/30.3 (6.9)
 6.5
 7.2
Botswana
 2.3/16.9 (13.8)
 14.6
 12.9
 9.8/187.5 (5.3)
 5.2
 5.3
 0.5/17.3 (3.0)
 2.6
 3.3
 1.7/57.1 (3.0)
 2.6
 3.4
Namibia**
 2.6/16.8 (15.6)
 15.7
 15.5
 10.1/347.8 (2.9)
 3.3
 2.7
 1.3/32.6 (3.9)
 3.8
 4.0
 0.07/1.5 (4.5)
 4.4
 4.6
Malawi††
 8.9/29.8 (30.1)
 30.5
 29.6
 29.7/1,025.5 (2.9)
 3.4
 2.7
 17.5/317.5 (5.5)
 4.9
 6.1
 5.6/63.5 (8.9)
 7.5
 11.1
South Africa
 28.5/92.4 (30.9)
 30
 31.7
 647.8/10.2 (6.3)
 6.9
 6.0
 2.2/26.3 (8.3)
 7.3
 11.2
 80.9/1,747.6 (4.6)
 4.2
 4.9
Zimbabwe
 23.8/73.8 (32.2)
 34.5
 30.2
 79.6/1,904.1 (4.2)
 4.4
 4.1
 5.0/45.5 (11.1)
 9.7
 11.9
 4.2/36.6 (11.5)
 9.4
 13.5
Kenya
 51.7/294.4 (17.6)
 14.5
 21.0
 91.7/7,379.6 (1.2)
 1.2
 1.2
 23.7/1,361.9 (1.7)
 1.4
 2.0
 1.8/289.1 (0.6)
 0.4
 0.7
Zambia
 63.6/222.2 (28.6)
 26.4
 30.7
 105.1/2,884.3 (3.6)
 3.8
 3.6
 87.3/1,455.2 (6.0)
 5.6
 6.3
 19.9/104.8 (19.0)
 15.7
 22.0
Lesotho
 4.2/17.3 (24.3)
 25.4
 23.3
 20.2/597.0 (3.4)
 4.3
 3.0
 0.4/7.8 (4.8)
 3.3
 5.7
 4.1/117.4 (3.5)
 2.7
 4.5
Uganda
 34.6/189.8 (18.2)
 16.1
 20.4
 80.3/3,231.7 (2.5)
 2.4
 2.5
 33.9/1,070.8 (3.2)
 2.9
 3.4
 12.9/380.4 (3.4)
 3.1
 3.7
Ethiopia
 2.8/26.1 (10.8)
 10.5
 11.1
 4.3/313.6 (1.4)
 1.5
 1.3
 0.9/40.8 (2.3)
 1.8
 2.4
 0.7/21.0 (3.2)
 2.5
 3.9
Tanzania
 116.5/544.0 (21.4)
 19.9
 22.9
 153.9/5,192.5 (3.0)
 3.2
 2.9
 5.0/103.5 (4.9)
 3.8
 5.8
 38.9/1,090.6 (3.6)
 3.4
 3.7
Cameroon
 5.3/46.3 (11.5)
 9.5
 14.3
 21.4/708.1 (3.0)
 2.8
 3.1
 4.5/66.2 (6.9)
 5.1
 8.3
 1.1/19.1 (5.8)
 4.5
 7
Mozambique
 40.7/151.3 (26.9)
 24.9
 28.9
 177.4/4,540.9 (3.9)
 5.7
 3.4
 54.3/863.6 (6.3)
6.1
 6.4
 8.6/95.5 (9.0)
 7.2
 11.6
Nigeria
 25.1/103.5 (24.3)
 19.5
 30.6
 79.4/3,007.8 (2.6)
 3.2
 2.4
 38.1/947.0 (4.0)
 3.3
 4.6
 15.8/250.9 (6.3)
 6.1
 6.4
Côte d’Ivoire
 14.1/88.2 (16.0)
 12.4
 20.7
 39.5/1,983.5 (2.0)
 2.4
 1.9
 0.3/5.2 (6.6)
 5.9
 7.4
 6.1/123.4 (4.9)
 4.2
 5.5
DRC
 9.3/29.7 (31.4)
 30.4
 32.6
 23.8/656.3 (3.6)
 5.1
 3.2
 1.7/37.1 (4.6)
 3.1
 7.1
 7.0/88.1 (8.0)
 6.3
 9.4
Angola
 0.7/2.0 (37.3)
 29.8
 44.3
 3.7/83.3 (4.5)
 5
 4.2
 3.5/32.7 (10.8)
 8.5
 13.2
 1.2/23.2 (5.2)
 4.5
 5.4
South Sudan
 0.5/4.9 (11.0)
 11.4
 10.7
 6.1/186.4 (3.3)
 4.5
 2.8
 2.9/48.9 (6.0)
 5.1
 6.9
 0.7/11.7 (6.4)
 4.1
 8.7
Total 440.5/1,990.0 (22.1) 20.1 24.3 1,598.2/45,255.4 (3.5) 3.9 2.7 289.7/6,786.8 (4.3) 3.8 4.6 214.4/4,607.9 (4.7) 4.2 5.0
Open in a new tab

Source: PEPFAR Monitoring, Evaluation, and Reporting data for Accountability, Transparency, and Impact Monitoring database, October 2018–September 2019.

Abbreviations: DRC = Democratic Republic of Congo; PEPFAR = U.S. President’s Emergency Plan for AIDS Relief.

* Breakdown by strategy for 19 countries (excluding Malawi because of limited data).

† Number of HIV tests positive divided by the number of HIV tests conducted. Numbers are reported in thousands with one decimal place.

§ % indicates percent yield.

Statistically significant difference between men and women at p-value <0.05. The p-values were calculated based on z-tests for differences in binomial proportions with pooled variance.

** Community index testing program yield of 22.6% overall across adults.

†† Data are from 5.5 districts (N = 28) in Malawi.

Although PITC identified more PLHIV than any other testing strategy, the percentage positivity of index testing was higher than PITC in all countries and for both sexes (Table 3).The number of PLHIV identified through index testing ranged from 500 in South Sudan (11.0% yield) to 116,500 in Tanzania (21.4% yield) (Table 2). The five countries that identified the most PLHIV through index testing were Uganda (34,585), Mozambique (40,681), Kenya (51,717), Zambia (63,587), and Tanzania (116,546), and yields varied from 17.6% (Kenya) to 28.6% (Zambia). These countries also accounted for two thirds (69.7%) of all HIV-positive men identified through index testing: Uganda (15,313), Mozambique (19,064), Kenya (22,259), Zambia (28,383), and Tanzania (52,040). The contribution of index testing to the total number of HIV-positive men identified in these five countries ranged from 18.1% (Mozambique) to 42.8% (Tanzania).

TABLE 3. Percentage positivity of four human immunodeficiency virus (HIV) testing strategies in identifying new HIV-positive adults, stratified by sex — 20 PEPFAR-supported countries, October 2018–September 2019*.

Country % Yield
Men
 Women
PITC as reference* Index testing† VCT§ Mobile testing PITC as reference Index testing VCT Mobile testing
Rwanda
 0.5
 4.6**
 0.8
 1
 0.4
 5.8**
 1.2
 2.8**
Eswatini
 6.6
 25.6**
 7.9
 6.5
 5
 25.4**
 9.1**
 7.2**
Botswana
 5.2
 14.6**
 2.6
 2.6**
 5.3
 12.9**
 3.3
 3.4
Namibia
 3.3
 15.7**
 3.8
 4.4
 2.7
 15.5**
 4.0
 4.6
Malawi
 3.4
 30.5**
 4.9**
 7.6**
 2.7
 29.6**
 6.1**
 11.1**
South Africa
 6.9
 30.0**
 7.3
 4.2**
 6
 31.7**
 11.2**
 4.9**
Zimbabwe
 4.4
 34.5**
 9.7**
 9.4**
 4.1
 30.2**
 11.9**
 13.5**
Kenya
 1.2
 14.5**
 1.4
 0.4
 1.2
 21.0**
 2.0**
 0.7
Zambia
 3.8
 26.4**
 5.6**
 15.7**
 3.6
 30.7**
 6.3**
 22.0**
Lesotho
 4.3
 25.4**
 3.3
 2.7**
 3
 23.3**
 5.7
 4.5**
Uganda
 2.4
 16.1**
 2.9**
 3.1**
 2.5
 20.4**
 3.4**
 3.7**
Ethiopia
 1.5
 10.5**
 1.8
 2.5
 1.3
 11.1**
 2.4
 3.9**
Tanzania
 3.2
 19.9**
 3.8
 3.4
 2.9
 22.9**
 5.8**
 3.7**
Cameroon
 2.8
 9.5**
 5.1**
 4.5
 3.1
 14.3**
 8.3**
 7.0**
Mozambique
 5.7
 24.9**
 6.1
 7.2**
 3.4
 28.9**
 6.4**
 11.6**
Nigeria
 3.2
 19.5**
 3.3
 6.1**
 2.4
 30.6**
 4.6**
 6.4**
Côte d’Ivoire
 2.4
 12.4**
 5.9**
 4.2**
 1.9
 20.7**
 7.4**
 5.5**
DRC
 5.1
 30.4**
 3.1
 6.3
 3.2
 32.6**
 7.1**
 9.4**
Angola
 5
 29.8**
 8.5**
 4.5
 4.2
 44.3**
 13.2**
 5.4
South Sudan 4.5 11.4** 5.1 4.1 2.8 10.7** 6.9** 8.7**
Open in a new tab

Source: PEPFAR Monitoring, Evaluation, and Reporting data for Accountability, Transparency, and Impact Monitoring database, October 2018–September 2019.

Abbreviations: DRC = Democratic Republic of Congo; PEPFAR = U.S. President’s Emergency Plan for AIDS Relief; PITC = provider-initiated testing and counseling; VCT = voluntary counseling and testing.

* PITC for HIV testing of persons at health facilities.

† Index testing of persons with new HIV diagnoses.

§ VCT to determine HIV status.

Mobile testing outside health facilities.

** Statistically significant differences between PITC as reference compared with other strategies at p-values <0.05. The p-values were calculated on the basis of z-tests for differences in binomial proportions with pooled variance.

The yield for VCT among men was significantly higher than for PITC in eight countries (Angola, Cameroon, Côte d’Ivoire, Malawi, Mozambique, Uganda, Zambia, and Zimbabwe) but was lower in the Democratic Republic of Congo (DRC) (Table 3). Similarly, mobile testing had a significantly higher yield than PITC among men in Cameroon, Côte d’Ivoire, DRC, Malawi, Mozambique, Nigeria, Uganda, and Zimbabwe but a lower yield in Botswana, Lesotho, and South Africa. Among women, VCT had a significantly higher yield than PITC in all countries except Botswana, whereas mobile testing had a significantly higher yield than PITC in all but three countries (Botswana, South Africa, and Kenya).

These findings will help guide the Ministries of Health in selecting specific testing strategies to increase HIV-testing coverage. This will help identify persons living with HIV infection.

Discussion

PITC identified the largest number of PLHIV, but index testing was more efficient at finding PLHIV as evidenced by a higher positivity rate. Several factors, including the absolute number of PLHIV identified, testing yield, cost per diagnosis, and the ability to reach persons with undiagnosed HIV infection, can help identify the best HIV-testing approach (1).

Although an efficient testing strategy in terms of identifying PLHIV, index testing is resource-intensive (1). For large yields of persons with a new HIV diagnosis to be identified, index testing requires a massive, potentially expensive, scale-up (6). However, Kenya, Tanzania, Mozambique, and Uganda have achieved substantial scale-up, demonstrating the feasibility of index testing in sub-Saharan Africa. Although the majority of PLHIV in sub-Saharan Africa receive their diagnosis through PITC, findings from this analysis show that index testing is identifying new PLHIV in many countries.

Screening tools that use a combination of clinical and behavioral questions to identify persons most likely to be HIV-positive can increase the percentage positivity of PITC. However, these screening tools must be validated to ensure high sensitivity and good specificity to avoid missed opportunities for diagnosis (3).

These findings show that men are less likely to be tested for HIV than women and represented 37% of HIV-positive results. Although women were twice as likely to be tested for HIV, approximately one third of new cases identified occurred among men. In all countries, women are routinely tested for HIV as part of antenatal testing, regardless of their clinical or behavioral risk factors. This might help explain why the number of tests conducted was higher among women compared with men, but the percent positivity rate was lower. PITC and index testing identified the most HIV-positive men, but mobile testing remains an important strategy to reach men who are unable or unwilling to attend health care facilities. Interventions that might improve coverage among men include flexible hours, male counselors, and integration of HIV testing into screening for other chronic conditions (7). Although not included in this analysis, HIV self-testing, directly offered within health facilities (8) or distributed by sex or needle-sharing partners as part of index testing (9), might also be an efficient method for reaching men.

The findings in this report are subject to at least three limitations. First, cost data are lacking. Second, data quality varies across countries despite PEPFAR’s monitoring and reporting guidance. Finally, PITC data include antenatal testing, which results in more testing among women. Testing antenatal women at lower risk also explains why the yield for PITC is lower for women than men, even though the yield is higher for women with the other three testing strategies. Strengths include the number of countries involved in the analysis and the volume of testing data collected by implementing partners.

As more PLHIV are identified, finding the remaining PLHIV will become more difficult and expensive because new cases will be harder to find. Multiple testing strategies, tailored to the epidemiologic context and targeted to populations with low access to HIV testing, can help reach these persons. Recency testing now allows programs to identify persons infected with HIV in the past year as well as clusters of incident cases. Immediate and intensified index testing efforts targeted to these clusters might reduce transmission and help countries achieve epidemic control (10).

Summary.

What is already known on this topic?

Identifying persons with HIV infection and initiating treatment are critical to ending the HIV epidemic by 2030. Despite global progress, testing gaps remain, particularly for men.

What is added by this report?

From October 2018 to September 2019, men in 20 sub-Saharan African countries were half as likely as women to receive an HIV test, and 37% of the HIV-positive results were among men. Similar sex differences were observed across HIV testing strategies based upon percent positivity rates.

What are the implications for public health practice?

These results highlight provider-initiated testing and index testing as strategies that might improve HIV testing coverage and maximize the number of persons newly identified as HIV-positive in 20 African countries.

References

  • 1.De Cock KM, Barker JL, Baggaley R, El Sadr WM. Where are the positives? HIV testing in sub-Saharan Africa in the era of test and treat. AIDS 2019;33:349–52. . 10.1097/QAD.0000000000002096 [DOI] [PubMed] [Google Scholar]
  • 2.Joint United Nations Programme on HIV/AIDS (UNAIDS). 90–90–90: an ambitious treatment target to help end the AIDS Epidemic. Geneva, Switzerland: UNAIDS; 2014. https://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf
  • 3.US President’s Emergency Plan for AIDS Relief. PEPFAR 2020 country operational plan guidance for all PEPFAR countries. Washington, DC: US President’s Emergency Plan for AIDS Relief; 2020. https://www.state.gov/wp-content/uploads/2020/01/COP20-Guidance_Final-1-15-2020.pdf
  • 4.US President’s Emergency Plan for AIDS Relief. Monitoring, evaluation, and reporting indicator reference guide, version 2.4. Washington, DC: US President’s Emergency Plan for AIDS Relief; 2019. https://www.state.gov/wp-content/uploads/2019/10/PEPFAR-MER-Indicator-Reference-Guide-Version-2.4-FY20.pdf
  • 5.Siegel and Castellan. Nonparametric statistics for the behavioral sciences. 2nd ed. New York, NY: McGraw-Hill; 1988. [Google Scholar]
  • 6.Sharma M, Smith JA, Farquhar C, et al. Assisted partner notification services are cost-effective for decreasing HIV burden in western Kenya. AIDS 2018;32:233–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Sharma M, Barnabas RV, Celum C. Community-based strategies to strengthen men’s engagement in the HIV care cascade in sub-Saharan Africa. PLoS Med 2017;14:e1002262.. 10.1371/journal.pmed.1002262 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Dovel K, Shaba F, Offorjebe OA, et al. Effect of facility-based HIV self-testing on uptake of testing among outpatients in Malawi: a cluster-randomised trial. Lancet Glob Health 2020;8:e276–87 . 10.1016/S2214-109X(19)30534-0 [DOI] [PubMed] [Google Scholar]
  • 9.Choko AT, Corbett EL, Stallard N, et al. HIV self-testing alone or with additional interventions, including financial incentives, and linkage to care or prevention among male partners of antenatal care clinic attendees in Malawi: an adaptive multi-arm, multi-stage cluster randomised trial. PLoS Med 2019;16:e1002719.. 10.1371/journal.pmed.1002719 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kim AA, Behel S, Northbrook S, Parekh BS. Tracking with recency assays to control the epidemic: real-time HIV surveillance and public health response. AIDS 2019;33:1527–9 . 10.1097/QAD.0000000000002239 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Morbidity and Mortality Weekly Report are provided here courtesy of Centers for Disease Control and Prevention

RESOURCES