Introduction
Pharmacy PrEP Models in Use
Model | Description | Examples |
---|---|---|
Co-located model (i.e., no task shifting) | A cadre of healthcare worker legally authorized to initiate and manage clients on PrEP independently or under remote physician supervision (e.g., a nurse/nurse practitioner) is based in a private sector pharmacy and provides PrEP services. If involved, pharmacy providers dispense against PrEP prescriptions issued | USA: CVS Minute Clinics [18]; Walgreens HIV-specialized Pharmacies [19]; Walmart Specialty Pharmacies of the Community [20] Kenya: AGYW Nurse-Navigator Model [21••] South Africa: Pharmacies with physicians and/or NIMART-trained nurses on staff |
Partially task-shifted model | Pharmacy providers are upskilled to deliver select components of PrEP services to eligible clients (e.g., HIV risk screening, HIV testing), with components that are beyond their scope of practice (e.g., PrEP prescribing) provided remotely via telehealth consultation by a higher-cadre healthcare worker | Kenya: Pharm PrEP Refill Model [22••] South Africa: EPIC Model [23•] prior to August 2023 |
Fully task-shifted model (i.e., standalone model) | Pharmacy providers are upskilled to independently initiate (i.e., screen, prescribe) and manage eligible clients on PrEP, with access to an HIV specialist, as needed, for consultations and referrals | USA: Kelley-Ross Pharmacy One-Step PrEP program [24] Kenya: Pharm PrEP Initiation Model [25••] South Africa: EPIC Model post-August 2023 |
USA | South Africa | Kenya | |
---|---|---|---|
Population [26] | 333.2 million | 59.9 million | 54.0 million |
1.2 million | 7.5 million | 1.4 million | |
36,000 | 210,000 | 35,000 | |
0.13 | 4.2 | 0.73 | |
Total oral PrEP initiationsa [17] | 381,784 | 888,217 | 321,662 |
227,047 | 346,667 | 117,174 | |
Total number of private pharmacies | 60,000 | 4700 | 6500 |
Case Study 1: The United States
Co-located Model
Fully Task-Shifted Model
Case Study 2: South Africa
Co-located model
Partially Task-Shifted Model
Fully Task-Shifted Model
Case Study 3: Kenya
Co-located Model
Partially and Fully Task-Shifted Models
Key Findings to Date
Population | Reporting period | PrEP uptakea | PrEP continuationb | Implementation outcomesc | Willingness to pay | ||
---|---|---|---|---|---|---|---|
Co-located model (i.e., no task shifting) | |||||||
Kenya | Pintye et al. (2023)21 | Confirmed HIV-negative AGYW (≥ 15–24 years) | Oct. 2020–Mar. 2021 (5 months) | 85% (200/235) | Not assessed | Acceptability – Clients: High (assessed via interviews) [79•] | After 1 month: 69% (107/155) Median (IQR) amount: 150 (100–200) KES (~ $1 [$0.70–$1.40] USD) per follow-up visit |
Partially task-shifted modeld | |||||||
South Africa | Shipp et al. (2023) [23•] | General population age ≥ 18 | Aug. 2019–Dec. 2020 (15 months) | Not reported | Not yet published | Acceptability – Clients: High (assessed via interviews) | Not yet publishede |
Kenya | Mogere et al. (2023) [22••] | Adults (≥ 18) initiated on PrEP at a clinic | Nov. 2020–Oct. 2021 (11 months) | Not applicable | At 1 month: 39% (41/106), with only 1% (3/106) opting to refill at a pharmacy | Acceptability – Clients: Low (assessed via interviews) [79•] | Not yet publishede |
Fully task-shifted model (i.e., standalone model) | |||||||
USA | Tung et al. (2018) [68••] | General population age ≥ 18 | Mar. 2015–Feb. 2018 (35 months) | 97% (695/714) | Still “active in the service” at end of study period:f 54% (372/695) | None assessed | Not assessed |
Kenya | Ortblad et al. (2023) [25••] | General population age ≥ 18 | Nov. 2020–Oct. 2021 (11 months) | 60% (287/476) | At 1 month: 53% (153/287); At 4 months: 36% (103/287); At 7 months: 21% (51/242) | Acceptability – Clients & providers: High (assessed via survey) [80] Appropriateness – Clients: High (assessed via survey)h Cost: Financial cost per initiation visit: $1.52 USD; per continuation visit: $1.38 USD [77] Fidelity: High (assessed via standardized client actors) [78••] Feasibility: Not yet publishede | After 1 month: 98% (150/153) Median (IQR) amount: 300 (200–375) KES (~ $2.71 [$1.81-$3.39] USD) per follow-up visit |
Roche et al. (2023) [62••] | General population age ≥ 18 | Jan.–Jul. 2022 (6 months) | 97% (684/704) | At 1 month: 71% (484/684) | At enrollment: 83% (575/684) Median (IQR) amount: $3.30 ($1.60–4.10 USD) per follow-up visit [77] |
Pharmacy-delivered PrEP services | Clinic-delivered PrEP services | |||||
---|---|---|---|---|---|---|
Fully task-shifted model (N = 287) | Co-located model (N = 235) | Partners Scale-Up; at HIV clinics (N = 4898) | PrIYA Program; at MCH clinics (N = 2030) | PrIYA Program; at FP clinics (N = 278) | ||
Study description and demographics | Study duration | 11 months (Nov. 2020–Oct. 2021) | 5 months (Oct. 2020–Mar.2021) | 30 months (Jan. 2017–Jun. 2019) | 7 months (Nov. 2017–Jun. 2018) | 7 months (Nov. 2017–Jun. 2018) |
Population of interest | Anyone at risk of HIV infection (≥ 18 years) | AGYW seeking contraception services at private pharmacies (≥ 15 to 24 years) | Anyone at risk of HIV infection (≥ 18 years) | Women attending antenatal or postnatal care (≥ 15 years) | Women of reproductive age (15 to 45 years) | |
PrEP delivery location | Private community pharmacies (n = 5) | Private community pharmacies (n = 3) | Public HIV care clinics (n = 25) | Public, faith-based, & private maternal & child health clinics (n = 16) | Public family planning clinics (n = 13) | |
Implementation strategy | Pharmacy provider-led delivery | PrEP-dedicated nurse-led delivery | Healthcare provider-led delivery1 | PrEP-dedicated nurse-led delivery | PrEP-dedicated nurse-led delivery | |
Men | 163/287 (57%) | - | 2257/4898 (46%) | - | - | |
< 25 years | 127/287 (44%) | 235/235 (100%) | 969/4898 (20%) | 999/2030 (49%) | 87/278 (31%) | |
Unmarried | 178/287 (62%) | 159/200 (80%) | 432/4898 (9%) | 399/2030 (19%) | 213/278 (77%) | |
Behaviors associated with HIV risk, past 6 months | Men initiating PrEP services | |||||
Partner(s) HIV status unknown | 136/163 (83%) | - | 236/2257 (11%) | - | - | |
Partner(s) living with HIV | 4/163 (2%) | - | 1993/2257 (88%) | - | - | |
Inconsistent or no condom use | 111/163 (68%) | - | 921/2257 (41%) | - | - | |
Multiple sexual partners | 118/163 (72%) | - | 260/2257 (12%) | - | - | |
Recurrent sex with alcohol | 63/163 (39%) | - | 67/2257 (3%) | - | - | |
Transactional sex2 | 21/163 (13%) | - | 23/2257 (1%) | - | - | |
Women initiating PrEP services | ||||||
Partner(s) HIV status unknown | 96/124 (77%) | - | 553/2640 (21%) | 1178/2030 (58%) | 151/278 (54%) | |
Partner(s) living with HIV | 7/124 (6%) | - | 2098/2640 (80%) | 153/2030 (8%) | 62/278 (22%) | |
Inconsistent or no condom use | 92/124 (74%) | - | 1150/2640 (44%) | 1946/2030 (96%) | 31/278 (11%) | |
Multiple sexual partners | 46/124 (37%) | - | 305/2640 (12%) | - | 18/278 (7%) | |
Recurrent sex with alcohol | 27/124 (22%) | - | 44/2640 (2%) | - | 4/278 (1%) | |
Transactional sex2 | 9/124 (7%) | - | 44/2640 (2%) | 17/2030 (1%) | 8/278 (3%) | |
Adolescent girls and young women (15–24 years) initiating PrEP services | ||||||
Partner(s) HIV status unknown | 51/64 (80%) | 116/193 (60%) | 178/673 (26%) | 648/1129 (57%) | 64/106 (60%) | |
Partner(s) living with HIV | 2/64 (3%) | 1/200 (< 1%) | 478/673 (71%) | 48/1129 (4%) | 0/106 (0%) | |
Inconsistent or no condom use | 48/64 (75%) | 199/200 (99.5%) | 341/673 (51%) | 1081/1129 (96%) | 11/106 (10%) | |
Multiple sexual partners | 16/64 (25%) | 72/200 (36%) | 113/673 (17%) | - | 6/106 (6%) | |
Recurrent sex with alcohol | 8/64 (13%) | 30/200 (15%) | 7/673 (1%) | - | 1/106 (< 1%) | |
Transactional sex2 | 7/64 (11%) | 53/196 (27%) | 21/673 (3%) | 8/1129 (< 1%) | 2/106 (2%) | |
Client outcomes | PrEP initiation and continuation at different service locations in Kenya3 | |||||
PrEP initiation (/eligible participants) | 287/5754 (60%) | 200/235 (85%) | 4898/NR (N/A) | 2030/9376 (22%) | 278/1271 (22%) | |
PrEP continuation (1 month) | 153/287 (53%) | 105/2005 (53%) | 2806/4898 (57%) | 786/2030 (39%) | 114/2786 (41%) | |
PrEP continuation (3 months) | 106/287 (36%) | - | 2135/4898 (44%) | 441/2030 (22%) | 68/278 (25%) | |
PrEP continuation (6 months) | 51/287 (21%) | - | 1661/4898 (34%) | 189/2030 (12%) | 30/278 (11%) |
Potential Pitfalls and Future Directions
Topic | Subtopic | Possible research directions |
---|---|---|
Governance | Professional associations | Assessing support and identifying potential implementation barriers and solutions to delivering PrEP via pharmacies |
Delivery model(s) | Developing and testing models/model modifications and generating data on the reach, utilization, acceptability, feasibility, and cost of different delivery models | |
Financing | Public financing | Conducting costing studies to understand the economic viability of different types of government support for private pharmacy-based PrEP delivery (e.g., government-supplied commodities and personnel) |
Developing and testing ways for private pharmacy providers to be incorporated into national health insurance schemes as PrEP providers | ||
Private financing | Conducting a landscape assessment of current or potential private insurer coverage of PrEP services delivered via private pharmacies (e.g., which PrEP modalities are/would be covered, for whom; client out-of-pocket expenses) | |
Donor financing | Assessing donor willingness to allow donor-supplied commodities to be delivered in private pharmacies, including if clients charged a fee for provider’s time; exploring other ways donors might be willing to subsidize PrEP services delivered via private pharmacies (e.g., vouchers for key populations) | |
Billing | Designing and evaluating mechanisms for private pharmacies to request, track, and report on PrEP services rendered and/or commodities used | |
Developing and/or testing billing mechanisms for private pharmacies to receive payment for PrEP services rendered (from governments, insurers, and/or donors) | ||
Other | Assessing willingness to pay among key populations and PrEP-eligible members of the general population and willingness to provide PrEP services among different types of private pharmacies (e.g., independent pharmacies, retail chains) | |
Testing strategies to address cost barriers for priority PrEP populations (e.g., sliding scale payment systems) | ||
Conducting time-and-motion studies and unit-cost analyses to understand workflow burden and costs to pharmacies to deliver PrEP; conducting budget impact analyses to help inform governments about potential benefits of investing in pharmacy-delivered PrEP services | ||
Regulation | Regulations | Working with regulatory bodies to think through regulations needed to ensure PrEP is delivered with high fidelity at pharmacies |
Monitoring | Defining and validating a set of measures for routine monitoring of pharmacy-delivered PrEP services | |
Developing and testing strategies for providing feedback to private pharmacies on their adherence to regulations | ||
Designing and testing the effect of learning networks on pharmacy providers’ PrEP delivery skills and ability to meet regulations |