| Literature DB >> 32087152 |
Catherine A Koss1, Edwin D Charlebois2, James Ayieko3, Dalsone Kwarisiima4, Jane Kabami4, Laura B Balzer5, Mucunguzi Atukunda4, Florence Mwangwa4, James Peng6, Yusuf Mwinike4, Asiphas Owaraganise4, Gabriel Chamie6, Vivek Jain6, Norton Sang3, Winter Olilo3, Lillian B Brown6, Carina Marquez6, Kevin Zhang6, Theodore D Ruel7, Carol S Camlin8, James F Rooney9, Douglas Black6, Tamara D Clark6, Monica Gandhi6, Craig R Cohen8, Elizabeth A Bukusi10, Maya L Petersen11, Moses R Kamya12, Diane V Havlir6.
Abstract
BACKGROUND: Optimal strategies for pre-exposure prophylaxis (PrEP) engagement in generalised HIV epidemics are unknown. We aimed to assess PrEP uptake and engagement after population-level HIV testing and universal PrEP access to characterise gaps in the PrEP cascade in rural Kenya and Uganda.Entities:
Mesh:
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Year: 2020 PMID: 32087152 PMCID: PMC7208546 DOI: 10.1016/S2352-3018(19)30433-3
Source DB: PubMed Journal: Lancet HIV ISSN: 2352-3018 Impact factor: 12.767
Figure 1PrEP uptake after population-level HIV testing in 16 communities in rural Kenya and Uganda
PrEP=pre-exposure prophylaxis. SEARCH=Sustainable East Africa Research in Community Health. *Empirical risk score developed on the basis of applying ensemble supervised machine learning methods to HIV seroconversion data from the first 2 years of the SEARCH test-and-treat trial, with a threshold selected to correctly classify 50% of seroconversions as at elevated risk across the three regions and minimise the number of individuals classified as at risk. Variables: age, sex, marital status, polygamy, education, occupation, alcohol, and circumcision. †Individuals neither in serodifferent partnerships nor identified by the risk score could self-identify as at risk of HIV acquisition. ‡82% initiated PrEP on the same day as seen during population-level HIV testing.
Characteristics of adult residents who tested negative for HIV infection, individuals assessed to be at elevated HIV risk, and individuals who initiated PrEP within 90 days of HIV testing in 16 communities in rural Kenya and Uganda
| Male | 31 276 (45%) | 6476 (50%) | 1733 (50%) |
| Female | 37 845 (55%) | 6459 (50%) | 1756 (50%) |
| 15–24 | 25 562 (37%) | 4800 (37%) | 978 (28%) |
| 25–34 | 14 739 (21%) | 4712 (36%) | 1197 (34%) |
| 35–44 | 10 236 (15%) | 1927 (15%) | 752 (22%) |
| 45–54 | 7177 (10%) | 991 (8%) | 393 (11%) |
| ≥55 | 11 407 (17%) | 505 (4%) | 169 (5%) |
| Less than primary level | 8640 (13%) | 716 (6%) | 224 (6%) |
| Primary school level | 42 154 (61%) | 8559 (66%) | 2371 (68%) |
| Any secondary school level and higher | 18 255 (26%) | 3637 (28%) | 890 (26%) |
| Farmer | 35 634 (52%) | 5578 (43%) | 1795 (51%) |
| Student | 13 932 (20%) | 853 (7%) | 170 (5%) |
| Fishing, bar, or transportation | 3421 (5%) | 2349 (18%) | 548 (16%) |
| Other informal sector | 8721 (13%) | 2567 (20%) | 643 (18%) |
| Other formal sector | 2972 (4%) | 713 (6%) | 149 (4%) |
| Unemployed or disabled | 4081 (6%) | 777 (6%) | 164 (5%) |
| Other or unknown | 321 (0·5%) | 87 (1%) | 16 (0·5%) |
| Not married | 20 624 (30%) | 3669 (28%) | 721 (21%) |
| Married (monogamous) | 33 595 (49%) | 6428 (50%) | 1788 (51%) |
| Married (polygamous) | 7396 (11%) | 1993 (15%) | 692 (20%) |
| Divorced, separated, or widowed | 7466 (11%) | 833 (6%) | 285 (8%) |
| By a health-care provider | 7061 (23%) | 1809 (28%) | 513 (30%) |
| By a traditional practitioner | 5748 (18%) | 1121 (17%) | 317 (18%) |
| Uncircumcised | 18 405 (59%) | 3489 (54%) | 898 (52%) |
| None | 59 979 (87%) | 10 600 (82%) | 2823 (81%) |
| 1–7 days per month | 3743 (5%) | 1079 (8%) | 257 (7%) |
| >7 days per month | 5366 (8%) | 1241 (10%) | 408 (12%) |
| Yes | 4464 (6%) | 1131 (9%) | 192 (6%) |
| Community health fair | 57 107 (83%) | 10 680 (83%) | 3395 (97%) |
| Home-based testing | 12 014 (17%) | 2255 (17%) | 94 (3%) |
| Western Kenya | 19 321 (28%) | 6418 (50%) | 1542 (44%) |
| Eastern Uganda | 25 159 (36%) | 2871 (22%) | 930 (27%) |
| Western Uganda | 24 641 (36%) | 3646 (28%) | 1017 (29%) |
Data are number (%). PrEP=pre-exposure prophylaxis.
Missing data for 72 residents (0·10%).
Missing data for 39 residents (0·06%); other formal sector occupations are teaching, government, military, health care, and factory; other informal sector occupations are shopkeeper, market vendor, hotel worker, homemaker, household worker, miner, and construction.
Missing data for 40 residents (0·06%).
Assessed among 31 276 men; missing data for 62 residents (0·20%).
Missing data for 33 residents (0·05%).
Missing data for 547 residents (0·8%); mobility is migration out of the community for at least 1 month or moved residence within the past 12 months; among individuals at elevated risk, mobility was more prevalent among participants aged 15–24 years (13%) versus participants aged 35–44 years (5%).
Factors associated with PrEP uptake among adult residents assessed to be at elevated HIV risk in 16 communities in rural Kenya and Uganda
| Male | 1 | .. | 1 | .. |
| Female | 1·04 (0·86–1·24) | 0·70 | 0·88 (0·75–1·05) | 0·16 |
| 15–24 | 0·41 (0·34–0·49) | <0·0001 | 0·55 (0·45–0·68) | <0·0001 |
| 25–34 | 0·53 (0·45–0·63) | <0·0001 | 0·61 (0·52–0·72) | <0·0001 |
| 35–44 | 1 | .. | 1 | .. |
| ≥45 | 0·94 (0·80–1·09) | 0·40 | 0·86 (0·73–1·00) | 0·052 |
| Not married | 1 | .. | 1 | .. |
| Married (monogamous) | 1·65 (1·36–1·99) | <0·0001 | 1·19 (0·96–1·48) | 0·11 |
| Married (polygamous) | 2·31 (2·02–2·63) | <0·0001 | 1·54 (1·29–1·84) | <0·0001 |
| Divorced, separated, or widowed | 2·05 (1·68–2·50) | <0·0001 | 1·59 (1·30–1·95) | <0·0001 |
| No or unknown | 1 | .. | 1 | .. |
| Yes | 2·57 (1·90–3·47) | <0·0001 | 2·02 (1·44–2·84) | 0·0005 |
| Student or other formal sector occupation | 1 | .. | 1 | .. |
| Fishing, bar, or transport | 1·25 (0·94–1·66) | 0·12 | 0·85 (0·60–1·20) | 0·34 |
| Farming or other informal sector occupation | 1·66 (1·37–2·00) | <0·0001 | 1·16 (0·91–1·48) | 0·23 |
| Unemployed, disabled, or other | 1·13 (0·91–1·41) | 0·27 | 0·98 (0·77–1·26) | 0·89 |
| Less than primary level | 1 | .. | 1 | .. |
| Primary school level | 0·87 (0·69–1·11) | 0·27 | 1·07 (0·87–1·31) | 0·54 |
| Any secondary school level or higher | 0·68 (0·52–0·90) | 0·0078 | 1·04 (0·80–1·35) | 0·78 |
| None | 1 | .. | 1 | .. |
| At least 1 day per month | 1·04 (0·89–1·23) | 0·62 | 0·96 (0·81–1·13) | 0·59 |
| No | 1 | .. | 1 | .. |
| Yes | 0·54 (0·37–0·79) | 0·0015 | 0·61 (0·41–0·91) | 0·016 |
Mixed effects logistic regression with community as random effect and variances adjusted for clustering at community level. Of 12 935 residents at elevated HIV risk, 12 850 were included in the analysis; 85 individuals (0·7%) were excluded because of missing data on covariates. PrEP=pre-exposure prophylaxis.
Other formal sector occupations are teaching, government, military, health care, and factory; other informal sector occupations are shopkeeper, market vendor, hotel worker, homemaker, household worker, miner, and construction.
Mobility is migration out of the community for at least 1 month or moved residence within the past 12 months.
Factors associated with PrEP uptake stratified by sex among men and women assessed to be at elevated HIV risk in 16 communities in rural Kenya and Uganda
| Adjusted odds ratio (95% CI) | p value | Adjusted odds ratio (95% CI) | p value | |
|---|---|---|---|---|
| 15–24 | 0·74 (0·56–0·97) | 0·027 | 0·43 (0·33–0·56) | <0·0001 |
| 25–34 | 0·72 (0·60–0·86) | 0·0003 | 0·52 (0·43–0·63) | <0·0001 |
| 35–44 | 1 | .. | 1 | .. |
| ≥45 | 1·03 (0·84–1·27) | 0·75 | 0·79 (0·61–1·01) | 0·065 |
| Not married | 1 | .. | 1 | .. |
| Married (monogamous) | 1·13 (0·87–1·47) | 0·35 | 1·60 (1·19–2·13) | 0·0016 |
| Married (polygamous) | 1·51 (1·15–1·99) | 0·0030 | 1·86 (1·45–2·40) | <0·0001 |
| Divorced, separated, or widowed | 2·15 (1·56–2·96) | <0·0001 | 1·70 (1·30–2·22) | 0·0001 |
| No or unknown | 1 | .. | 1 | .. |
| Yes | 1·54 (0·96–2·45) | 0·072 | 2·41 (1·80–3·22) | <0·0001 |
| Student or other formal sector occupation | 1 | .. | 1 | .. |
| Fishing, bar, or transport | 0·85 (0·56–1·28) | 0·44 | 0·68 (0·52–0·89) | 0·0048 |
| Farming or other informal sector occupation | 1·17 (0·89–1·55) | 0·27 | 1·02 (0·82–1·29) | 0·82 |
| Unemployed, disabled, or other | 0·86 (0·60–1·24) | 0·42 | 1·02 (0·78–1·32) | 0·90 |
| Less than primary level | 1 | .. | 1 | .. |
| Primary school level | 0·99 (0·78–1·27) | 0·95 | 1·21 (0·94–1·55) | 0·14 |
| Any secondary school level or higher | 1·11 (0·81–1·50) | 0·53 | 0·97 (0·72–1·29) | 0·81 |
| None | 1 | .. | 1 | .. |
| At least 1 day per month | 1·09 (0·94–1·25) | 0·24 | 0·74 (0·55–0·99) | 0·040 |
| No | 1 | .. | 1 | .. |
| Yes | 0·67 (0·45–1·01) | 0·059 | 0·54 (0·35–0·83) | 0·0049 |
Mixed effects logistic regression with community as random effect and variances adjusted for clustering at community level. Of 12 935 residents (6476 men and 6459 women) at elevated HIV risk, 12 850 (6426 men and 6424 women) were included in the analysis; 85 (0·7%) residents were excluded because of missing data on covariates. PrEP=pre-exposure prophylaxis.
Other formal sector occupations are teaching, government, military, health care, and factory; other informal sector occupations are shopkeeper, market vendor, hotel worker, homemaker, household worker, miner, and construction.
Mobility is migration out of the community for at least 1 month or moved residence within the past 12 months.
Figure 2PrEP programme engagement, refill, and adherence overall and by self-assessed current HIV risk up to week 72
(A) PrEP programme engagement, refill, and self-reported adherence among PrEP initiators. (B) Refill and self-reported adherence among PrEP participants reporting self-assessed current HIV risk. PrEP=pre-exposure prophylaxis. *Programme engagement is defined as attendence at a PrEP follow-up visit during scheduled visit weeks; eligibility for visit excludes participants who were withdrawn or died before the visit. †Self assessed current HIV risk was evaluated at each follow-up visit among individuals engaged in the PrEP programme.
Factors associated with self-reported adherence to PrEP among individuals who initiated PrEP and were seen at a week 24 follow-up visit in 16 communities in rural Kenya and Uganda
| Male | 1 | .. | 1 | .. |
| Female | 1·07 (0·88–1·30) | 0·51 | 0·83 (0·63–1·09) | 0·17 |
| 15–24 | 0·37 (0·28–0·50) | <0·0001 | 0·59 (0·40–0·86) | 0·0067 |
| 25–34 | 0·67 (0·52–0·87) | 0·0030 | 0·86 (0·63–1·17) | 0·33 |
| 35–44 | 1 | .. | 1 | .. |
| ≥45 | 0·87 (0·64–1·18) | 0·37 | 0·98 (0·68-1·41) | 0·90 |
| Not married | 1 | .. | 1 | .. |
| Married (monogamous) | 1·69 (1·26–2·27) | 0·0005 | 1·23 (0·81–1·88) | 0·33 |
| Married (polygamous) | 2·46 (1·76–3·45) | <0·0001 | 1·41 (0·87–2·28) | 0·17 |
| Divorced, separated, or widowed | 2·91 (1·80–4·72) | <0·0001 | 2·10 (1·12–3·95) | 0·021 |
| No or unknown | 1 | .. | 1 | .. |
| Yes | 2·76 (2·14–3·55) | <0·0001 | 1·64 (1·22–2·19) | 0·0009 |
| Student or other formal sector occupation | 1 | .. | 1 | .. |
| Fishing, bar, or transport | 1·69 (1·10–2·57) | 0·015 | 0·83 (0·47–1·47) | 0·52 |
| Farming or other informal sector occupation | 1·57 (1·09–2·28) | 0·016 | 0·88 (0·53–1·49) | 0·64 |
| Unemployed, disabled, or other | 2·01 (1·21–3·34) | 0·0071 | 1·52 (0·79–2·93) | 0·21 |
| Less than primary level | 1 | .. | 1 | .. |
| Primary school level | 0·81 (0·52–1·27) | 0·36 | 0·87 (0·51–1·45) | 0·59 |
| Any secondary school level or higher | 0·60 (0·37–0·97) | 0·037 | 0·65 (0·36–1·16) | 0·14 |
| None | 1 | .. | 1 | .. |
| At least 1 day per month | 1·05 (0·77–1·43) | 0·77 | 0·81 (0·55–1·18) | 0·27 |
| No | 1 | .. | 1 | .. |
| Yes | 1·12 (0·61–2·04) | 0·71 | 1·12 (0·56–2·26) | 0·74 |
| No | 1 | .. | 1 | .. |
| Yes | 13·46 (10·30–17·59) | <0·0001 | 12·36 (9·39–16·28) | <0·0001 |
Mixed effects logistic regression with community as random effect and variances adjusted for clustering at community level. Of 3421 individuals who initiated PrEP who were alive and not withdrawn at week 24, 1912 attended a week 24 visit, of whom 1863 are included in this analysis. 49 participants (2·6%) had missing data on covariates and are excluded from this analysis. PrEP=pre-exposure prophylaxis.
Other formal sector occupations are teaching, government, military, health care, and factory; other informal sector occupations are shopkeeper, market vendor, hotel worker, homemaker, household worker, miner, and construction.
Mobility is migration out of the community for at least 1 month or moved residence within the past 12 months.
Figure 3PrEP programme engagement, refill, and adherence at week 24 among demographic subgroups of participants who initiated PrEP
(A) PrEP programme engagement, refill, and self-reported adherence among risk groups of participants who initiated PrEP. (B) Refill and self-reported adherence among PrEP participants reporting self-assessed current HIV risk. PrEP=pre-exposure prophylaxis. *Programme engagement is defined as attendence at a PrEP follow-up visit during scheduled visit weeks; eligibility for visit excludes participants who were withdrawn or died before the visit. †Self-assessed current HIV risk was evaluated at each follow-up visit among individuals engaged in the PrEP programme. ‡Mobile individuals could be in any age group.
Figure 4Adherence to PrEP estimated from the concentration of tenofovir in hair samples
Adherence at (A) week 4 (n=166) and (B) week 24 (n=152) among subgroups of sampled participants reporting self-assessed current HIV risk and any PrEP adherence (at least one dose taken of the past three). BLQ=below the limit of quantification.
Adverse events among 3489 individuals who initiated pre-exposure prophylaxis
| Any grade 3 or 4 adverse event | 28 (0·8%) |
| Grade 3 creatinine elevation | 1 (0·03%) |
| Grade 4 creatinine elevation | 0 |
| Grade 3 adverse event possibly related to the study drug | 5 (0·1%) |
| Grade 4 adverse event possibly related to the study drug | 0 |
| Any serious adverse event | 29 (0·8%) |
| Death | 7 (0·2%) |
Grade 3 or 4 adverse events, serious adverse events, and causes of death are listed in the appendix (p 7). Adverse events possibly related to the study drug were creatinine elevation (n=1), dizziness (n=2), fatigue (n=1), and headache (n=1). All other grade 3 or 4 adverse events were judged to be unlikely (n=2) or not related to the study drug. One serious adverse event was judged to be possibly related to the study drug: a 71-year-old man was treated in hospital for urinary retention and found to have bilateral hydronephrosis and grade 3 creatinine elevation. Creatinine returned to baseline following relief of urinary obstruction and cessation of the study drug.