Tracking HIV Outcomes Among Key Populations in the Routine Health Information Management System: A Systematic Review

Abstract

Despite having the world’s largest HIV burden, South Africa has yet to attain the 95-95-95 targets. Accurate, complete, and timely data are critical for monitoring a country’s HIV progress. The integration of unique identifier codes (UICs) for key populations (KPs) into routine health information management systems (RHIMS) strengthens data accuracy and completeness, facilitating more targeted HIV interventions and greater accountability. This systematic review assessed how Sub-Saharan African (SSA) countries have integrated KPs’ UICs into RHIMS, highlighting key enablers, challenges, and opportunities. A comprehensive search was conducted across PubMed, Scopus, Google Scholar, MEDLINE, PLOS ONE, and various government and non-government websites to identify the published and grey literature relevant to the study objective from June 2013 to December 2024. References were managed using Zotero version 6.0.36. Two authors independently screened studies using Covidence software. The review was done in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines and was registered with the “International Prospective Register of PROSPERO) Systematic Reviews” with the registration number CRD42023440656. Out of 1735 studies screened, 361 duplicates were removed. The review found that only nine of the fifty-three SSA countries have incorporated UICs for KPs into their RHIMS through alphanumeric codes. They include Burundi, Burkina Faso, Ghana, Mali, Kenya, Uganda, Togo, Malawi, and Liberia. Facilitators for KPs’ UIC adoption included strong data security and political will, whereas barriers encompassed compromised privacy, stigma and discrimination. In South Africa, the UIC for KPs can be integrated into the new electronic medical record (EMR) system.

1. Introduction

Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) continues to pose a significant global public health challenge. In 2022, an estimated 39 million people of all ages were living with HIV globally, according to the Joint United Nations Programme on HIV/AIDS (UNAIDS) [1]. While Sub-Saharan Africa (SSA) has approximately 15% of the world’s population, it accounts for two-thirds of the 39 million people living with HIV (PLHIV) globally. The HIV prevalence among the South African general population is estimated to be around 12.7% [2]. South Africa is striving to achieve the UNAIDS 95-95-95 targets, with the broader goal of “ending AIDS as a public health threat by 2030”. To achieve this goal, a robust state “routine health information management system” (RHIMS), which disaggregates HIV data for key populations (KPs), is urgently needed to support targeted resource allocation and comprehensive progress reporting [3]. Addressing HIV among KPs in South Africa demands a strong national commitment to KP-focused programming and prevention efforts [4]. Baral and Greenall have referred to the challenges faced by KPs as “the data paradox,” in which decision-makers often ignore the existence of the highly impacted populations and neglect to develop data systems, using the resulting information gaps to justify ongoing denial [5].

The World Health Organization (WHO) defines KPs as “groups at higher risk of HIV infection, despite the epidemic type or setting, and who experience social and legal challenges that increase their exposure. These groups comprise sex workers (SWs), gay men and other men who have sex with men (MSM), transgender (TG), people who inject drugs (PWID), and people in prisons or other closed settings” [6]. In 2021, KPs and their sexual partners were disproportionately affected by HIV, accounting for an estimated 70% of new infections globally and 51% in SSA [7], underscoring the critical need for targeted public health interventions [1]. In South Africa, while the estimated HIV prevalence in the general population is 12.7%, it is significantly higher among KPs, with 55% among PWID, 40% among TG women, 27% among MSM, and 53.6% among SWs [8,9,10]. Despite this, public health data collection and reporting systems in South Africa do not disaggregate KPs and instead report them as part of the general population. This lack of disaggregation undermines effective planning, targeted allocation of resources, and effective surveillance of KP health indicators [11].

The inclusion of KPs’ unique identifier code (UIC) into RHIMS can enable countries to assess their current performance more accurately and allocate resources more effectively toward KP programmes, accelerating progress to eradicate HIV/AIDS as a key public health challenge by the year 2030 without leaving anyone behind. However, KPs remain affected by persistent social and legal obstacles, marked by stigma and discrimination that act as barriers to healthcare utilisation. Consequently, service coverage among KPs remains inadequate, perpetuating elevated HIV incidence [12]. These conditions present significant risks to effective public health programming, as KPs may understandably hesitate to disclose stigmatized or criminalised practices.

Robust and nationally representative HIV surveillance data, grounded in national ownership and sustained political will, are essential for informing effective and targeted HIV responses, particularly among KPs who contribute disproportionately to the transmission dynamics of the virus [13]. In this context, the generation of KP-specific data has become increasingly imperative to guide strategic efforts aimed at attaining the 95-95-95 UNAIDS targets. However, access to and effective use of such data continue to be constrained, primarily due to the absence of UICs, which enable consistent tracking of individuals across healthcare facilities. To address this critical gap, the WHO issued consolidated guidelines in 2017, advocating for the adoption and implementation of UICs as a means to enhance person-centred HIV patient monitoring and strengthen case-based surveillance systems [13].

The adoption of KPs’ UIC for KPs represents a critical advancement in addressing systemic challenges within HIV service delivery, particularly in the context of achieving and sustaining the 95-95-95 targets. The KPs’ UICs play a pivotal role in eliminating duplicate records, thereby enhancing the accuracy of data collection and enabling more efficient allocation of resources. Moreover, they facilitate the longitudinal tracking of individuals across various points of care, allowing for comprehensive analysis of behavioural changes and health outcomes over time. Importantly, the implementation of UICs strengthens service integration and improves the capacity to assess programme effectiveness and continuity of care. These identifiers are crucial for moving beyond traditional data collection methods to electronic patient information systems, facilitating the delivery of contemporary healthcare services while upholding strict confidentiality and data protection standards.

In many countries, non-profit organizations funded by international donors to deliver KP programs often use varying formats of UICs [14]. These organizations also operate on diverse health information systems developed by different vendors, each with distinct system architectures, which hinders effective data exchange with government systems [15]. In South Africa, government platforms like the Health Patient Registration System (HPRS) generate UICs; however, these identifiers do not specifically designate KPs or other marginalized groups [4]. The operationalization of KPs’ UICs within South Africa’s RHIMS is primarily hindered by insufficient political will to align and adequately fund the national HIV response through KP priorities within government policies and programmes.

South Africa has not yet achieved the UNAIDS 95-95-95 targets. One contributing barrier is the absence of UICs for KPs, which restricts the ability to generate accurate, disaggregated data essential for tracking progress and implementing targeted HIV interventions. This systematic review was primarily conducted to explore how SSA countries have integrated UICs for KPs into their routine health information systems. The secondary objective was to identify the facilitators, barriers, and resources influencing this process, as well as to document successes and opportunities in order to inform efforts to strengthen RHIMS and improve HIV response strategies in South Africa.

2. Materials and Methods

2.1. Systematic Review Registration and Methodological Approach

“This systematic review followed the PRISMA guidelines to maintain methodological rigor and credibility [16], and it was officially registered with PROSPERO under the registration number CRD42023440656. The review forms part of a doctoral study by the first author, which obtained the University of Johannesburg (UJ) Research Ethics Committee (REC) approval (REC-2518-2023). The doctoral study is connected to two bigger studies in the South African Medical Research Council (SAMRC)/UJ Pan African Centre for Epidemic Research (PACER) Extramural Unit, namely harnessing big heterogeneous data to evaluate the potential impact of HIV responses among KPs in generalized epidemic settings in SSA (REC-1504-2022) and assessing COVID-19 impacts on HIV prevention and treatment continuum (REC-1781-2022), which also received ethics approval from the UJ REC”.

2.2. Inclusion and Exclusion Criteria of the Studies

This systematic review included studies specifically focused on MSM, SW, PWID/UD, and TG of all races who were 18 years or older. The inclusion criteria were restricted to articles and reports published in English. Studies that solely focused on the general population, adolescent girls/boys, young women/men, and incarcerated individuals were not included in this review.

2.3. Data Sources and Search Strategies

An extensive search approach was utilised to locate the relevant literature across multiple electronic databases and the grey literature sources, ensuring broad coverage of studies aligned with the review objectives from January 2013 to December 2024, focusing on the integration of KPs’ UIC in SSA. The search utilised several databases, including PubMed, PLOS ONE, Scopus, Google Scholar, and MEDLINE. The grey literature sources covered official reports, presentations, dissertations, conference abstracts, and posters. Furthermore, manual searches were performed across relevant sources and the websites of major multilateral organizations, including WHO, UNAIDS, PEPFAR, the Global Fund, and the CDC. Studies which might have been missed were also manually hand-searched. Additional reports were identified by reviewing the reference lists of the studies included in the review. The search strategy terms included “key population” OR “hidden population” OR “vulnerable people” OR “Priority population” AND “unique” OR “personal” OR “identifier” OR “code” OR “biometric” OR “UIC” OR “UPI” OR “Unique identifier code” OR “Unique personal identifier” OR “Finger print” OR “health information management system” OR “health management information system” OR “HMIS” OR “routine health information management system” OR “RHIMS” OR “health information system” OR “HIS” OR “District Health Information System” OR “DHIS” OR “Electronic medical register” OR “Electronic information system” AND “Sub-Saharan Africa”. During this search strategy development stage, parentheses were not used; instead, the advanced search functions of each database were employed. These functions allowed each group of keywords to be entered into separate fields, with the appropriate Boolean operators (AND, OR) applied between them. Consequently, the databases automatically structured the search logic correctly, eliminating the need for manual use of parentheses. This approach ensured both the accuracy and consistency of the search across all databases consulted. The selected time frame, 1 January 2013 to 31 December 2024, was chosen due to the scarcity of the literature on this topic. Only articles written in English were evaluated. The study selection process is presented in the PRISMA diagram in Figure 1 below. It was developed with guidance from a UJ librarian, who also assisted in retrieving articles that were initially inaccessible.

2.4. Study Selection and Data Extraction

Following the database searches, the results were imported into Zotero (version 6.0.36) for efficient citation management and organization. Title, abstract, and full-text screening were conducted using Covidence software for data management (version 2.0). Two authors (MR and EP) independently assessed all retrieved titles and abstracts to assess their eligibility according to the predefined inclusion and exclusion criteria, and also removed duplicate records. This was followed by a thorough full-text review of all eligible articles. Data were extracted by two authors (MR and EP), with verification conducted by a third author (RPM) to maintain precision and uniformity. One author (MR) independently carried out the full-text review and data extraction, and another author (EP) subsequently cross-verified the results for accuracy. Differences were subsequently resolved through consensus with the input of a third author (RPM). The following details were extracted from eligible studies: the author and year, country name, study title, KP group, type of UIC, and the level where KPs’ UIC was included in RHIMS. A narrative and descriptive analysis was done on the studies that met the inclusion criteria for this review.

2.5. Quality Appraisal

The quality of the included studies was assessed using the Critical Appraisal Skills Programme Systematic Review (CASPSR) checklist (Table S1), which evaluates aspects such as validity, accuracy, and applicability of the findings. Two reviewers independently assessed each study, and final inclusion decisions were made based on their evaluations. Additionally, the PRISMA checklist (Table S2) was used to ensure that the systematic review process was transparent, complete, and well-documented.

2.6. Summary of the Systematic Review Selection Process

A total of nine countries served as the settings for the studies included in the review, including Malawi, Burundi, Uganda, Ghana, Liberia, Mali, Burkina Faso, Togo, and Kenya. A total of 1735 studies were found through a grey and published literature search. A total of 361 duplicates were removed, leaving 1374 reports eligible for screening, which resulted in the exclusion of 1199 irrelevant studies. Of the 175 records sought for retrieval, none were inaccessible, thanks to the support of the librarian. A full-text review was conducted on 175 studies, and 102 were further excluded due to reasons such as an intervention not related to UIC (82) and studies conducted outside SSA (20). Of the 73 studies that reported on UIC use, a further 65 were excluded as they were either conducted outside SSA or focused on non-KP groups, resulting in only eight studies meeting the final inclusion criteria with KP UIC implementation in SSA, as shown in

Table 1. Overview of the characteristics of included studies focusing on the implementation of unique identifier codes among key populations in sub-Saharan Africa.

	Authors, Year	Country	Title	Key Populations	UIC Type	Level Where UIC Was Incorporated on RHIMS
1.	National AIDS and STI Commission Programme (NASCOP), 2015 [17]	Kenya	“Unique identifier code for Key population programmes in Kenya”	FSW/MSM/TG/PWUD/PWID	Alphanumeric code	National
2.	Ghana Aids Commission (GAC), 2020 [18]	Ghana	“National HIV & AIDS strategic plan 2021–2025”	FSW and MSM	Alphanumeric code	National
3.	Rucinski et al., 2022 [19]	Malawi	“HIV testing approaches to optimize prevention and treatment for Key and priority populations in Malawi”	FSW, MSM, TGW, and MSW	Alphanumeric code	Sub-national
4.	West African Health Organization (WAHO), 2020 [20]	Togo	“ECOWAS regional strategy for HIV, tuberculosis, hepatitis B & C and sexual and reproductive health and rights among key populations”.	FSW, MSM, TGW, and MSW	Alphanumeric code	National
5.	Family Health International (FHI) 360, 2019 [21]	Liberia	“LINKAGES Liberia quarterly progress report 1 July–30 September 2019”	MSM, FSW and TG	Alphanumeric code	National
6.	Chapman et al., 2020 [22]	Uganda and Burundi	“Changing the landscape of data and digital health solutions”	FSW, TG, PWID, MSM, and people in prisons and closed settings	Alphanumeric code and Biometric fingerprint	Sub-national
7.	Bore et al., 2017 [23]	Mali	“Improving key population tracking and links to HIV Services using unique identifier codes in Mali”	FWS, partners of FSW, and MSM	Alphanumeric code	Sub-national
8.	Zan et al., 2016 [24]	Burkina Faso and Togo	“Strategies and resources for implementing HIV prevention, care, and treatment programming with key populations in West Africa”	FSW and MSM	Alphanumeric code	Sub-national

Notes: “NASCOP, National AIDS and STIs Control Programme; GAC, Ghana AIDS Commission; WAHO, West Africa Health Organization; FSW, Female Sex Workers; TG, Transgender People; PWID, People Who Inject Drugs; PWUD, People Who Use Drugs; and MSM, Men Who have Sex with Men”.

. Among the 65 excluded studies and the eight included studies, a total of 21 provided rich information on the resources required for KP UIC adoption. These findings are summarised in Table 3. One study was conducted in both Uganda and Burundi, resulting in a total of eight studies and nine countries, highlighting the dearth of published literature on KPs’ UIC in SSA. Only two reports (from Mali and Malawi) were published, while the remaining six were the grey literature.

3. Results

The results section outlines how the selected studies incorporated KPs’ UIC into the RHIMS in SSA to achieve the primary objective, as illustrated in Figure 1 and Table 1. All nine countries that have incorporated the KPs’ UIC into their national RMHIS have used an alphanumeric code-based unique patient identification system. Countries have used various algorithms to generate UICs for KPs, as shown in

Table 2. Methods used to create alphanumeric codes by country.

Country Name	UIC Creation Method
Kenya	County code (2-digit) + Sub-County code (3-digit) + Ward code (3-digit) + Implementing partner code (3-digit) + Hotspot code (3-digit) + KP type (e.g., 01) + first 2 letters of first name + first 2 letters of middle name + first 2 letters of last name + month of birth (2-digit) + 4-digit serial number
Ghana	KP type + first 2 letters of first name + first 2 letters of middle name + first 2 letters of surname + month of birth (2-digit)
Malawi	Country code + Initials of health facility name + last 2 digits of year of enrolment + population type code (e.g., 00, 01, 02, 03)
Liberia	County code (2-digit) + Health facility name initials + last 2 digits of year of enrolment + 4-digit serial number + population type code (e.g., 00, 01, 02, 03)
Mali	First letter of KP type + last 2 digits of birth year + first 2 letters of client’s last name + first 2 letters of mother’s last name + first 3 letters of country of origin + first 2 letters of birth town
Burkina Faso	Gender + last 2 digits of birth year + first letter of last name + first letter of first name + first 2 letters of mother’s first name
Togo	Not fully specified; national alphanumeric UIC used across stakeholders at national level
Uganda	Not fully specified; alphanumeric code + biometric fingerprint
Burundi	Not fully specified; alphanumeric code + biometric fingerprint

. In addition to the key findings, we have summarised resources in Table 3, barriers and facilitators in Table 4, and factors related to feasibility and acceptability in Table 5. Together, these tables provide a comprehensive overview of the challenges, opportunities, and practical considerations, supporting informed decision-making for the effective integration of UICs into RHIMS. Furthermore, additional studies were included based on their reporting on unique health patient identifiers, regardless of the population group or location, although this was not the primary focus of the analysis.

3.1. Studies That Included KPs’ UIC on the Government RHIMS

Kenya’s “National AIDS and STI Control Program” (NASCOP) has adopted a nationwide strategy to address HIV/AIDS among KPs by implementing a system that generates alphanumeric UICs for tracking and managing KP data [17]. In 2019, the “Ghana AIDS Commission” (GAC), with support from the “Centers for Disease Control and Prevention” (CDC), launched the “Ghana Key Population Unique Identifier System” (GKPUIS) [18]. The Ghanaian “National HIV & AIDS Strategic Plan 2021–2025” places a strong emphasis on addressing the needs of KPs, including FSW and MSM. Their national-level KPs’ UIC strategy was designed to provide tailored interventions and support for KP communities as part of the broader effort to combat HIV/AIDS. In Malawi, a simple alphanumeric algorithm has been adopted to generate KPs’ UIC, with implementation confined to sub-national regions. The KPs covered included FSW, MSM, and male sex workers. The LINKAGES programme in Malawi, implemented by FHI 360 in collaboration with the “National AIDS and STI Control Program” (NACP) and the County Health Team (CHT), implemented a national UIC for PLHIV [19].

Togo is the only country in the “Economic Community of West African States” (ECOWAS) that has a national UIC for KPs that is implemented across the whole HIV prevention system by stakeholders. The inclusion of alphanumeric KP UIC operates at the national level on their health information system [20]. In Liberia, the successful implementation of KPs’ UIC involved collaboration among the “Linkages across the Continuum of HIV Services for KPs Affected by HIV (LINKAGES) Project”, KP stakeholders, the NACP, and the County Health Team (CHT) [21]. Uganda and Burundi are among SSA countries that took innovative steps to include KPs’ UIC on their RHIMS, focusing on FSW, MSM, TG, PWID, and individuals in incarceration and other closed settings [22]. The two countries employed both alphanumeric codes and biometric fingerprints at the sub-national level.

Data for Implementation (Data.FI), which is a global project that supports countries in improving RHIMS, used a “privacy by design” KPs’ UIC. The alphanumeric KPs’ UIC allows health facilities to securely access clients’ health records without requiring the client to disclose their identity. In Mali, they have taken the initiative to improve KPs tracking and linkage to HIV services using KPs’ UIC for MSM, FWS, and their partners at the sub-national level. The government of Mali has adopted the KPs’ UIC system for the delivery of services to KPs [23]. In Burkina Faso and Togo, the USAID/West Africa Regional HIV/AIDS Prevention and Care Project (PACTE-VIH) introduced a UIC for every KP member who underwent HIV testing. Their KPs’ UIC strategy focused on FSW and MSM at the sub-national level. The governments of Burkina Faso and Togo have also adopted the UIC system and plan to implement it in all interventions targeting KPs [24]. The summary of the characteristics of the selected studies is reflected in Table 1 above.

3.2. Methods Used to Create KPs’ UIC by Country

Countries have used various methods to generate UICs for KPs, as shown in Table 2. Most commonly, alphanumeric codes are constructed using a combination of personal and programmatic details such as gender, NGO name, date of birth, national ID number, and parental names. However, this method can lead to duplicate records when individuals change or forget their information, or when different people share similar details, resulting in information mix-ups. To address these challenges, some countries have introduced biometric systems, although such systems also have limitations, including privacy concerns and reliance on individuals’ willingness to provide data.

3.3. Resources Required for the Implementation of KPs’ UICs

A total of 21 studies were evaluated as they listed resources utilised for UIC implementation in SSA countries. Most studies mentioned finance, biometric software, fingerprint scanning devices, barcoded stickers, electricity, power backups, computers, laptops, tablets, Wi-Fi routers, 3G cards, and printers as the most crucial resources for implementing UIC [11,25,26,27,28,29,30,31]. Studies reported that the availability of human resources, such as IT technicians and field support staff, was essential for installing and maintaining biometric software and hardware [11,17,23,27,30,32,33,34]. Studies have also mentioned financial resources needed for capacity building to provide persons with the necessary expertise and understanding to operate and manage these systems effectively [17,23,33,35]. Most reports indicated that funding and technical support to establish the KPs’ UIC systems came from international donors like PEPFAR and the Global Fund [17,23,36,37,38]. The results showed that government officials coordinated the integration of KPs’ UIC systems into their national health information systems at different levels and took ownership thereof [17,23,33,39]. An alphanumeric code-based unique patient identification system relies on resources such as operating manuals and SOPs for UIC creation [17,21,22,23,40]. Table 3 summarises the resources required for the adoption of KPs’ UIC mentioned by the studies.

Table 3. Resources required for the implementation of KPs’ UIC.

	Author and Year	Country	UIC Type	Target Population	Title	Resources Mentioned
1.	National AIDS and STIs Control Programme (NASCOP), 2015 [17]	Kenya	Alphanumeric code	FSW/MSM/TG/PWUD/PWID	“Unique Identifier Code for Key Population Programmes in Kenya”	Technical support
2.	Family Health International (FHI) 360, 2019 [21]	Liberia	Alphanumeric code	MSM, FSW and TG	“Linkages across the Continuum of HIV Services for Key Populations Affected by HIV (LINKAGES) Project”	SOP for UIC creation
						Technical support
						Human resources
						Capacity building
						Finance
3.	Chapman et al., 2020 [22]	Uganda and Burundi	Alphanumeric code and Biometric fingerprint	FSW, TG, PWID, MSM, and people in prisons and closed settings	“Changing the landscape of data and digital health solutions”	Mobile and web based UIC App
						Human resources
						Finance
						Capacity building
4.	Bore et al., 2017 [23]	Mali	Alphanumeric code	FWS, partners of FSW and MSM	“Improving Key Population Tracking and Links to HIV Services Using Unique Identifier Codes in Mali”	Cellular reception
						Electricity
5.	Zan et al., 2016 [24]	Burkina Faso and Togo	Alphanumeric code	FSW and MSM	“Strategies and Resources for Implementing HIV Prevention, Care, and Treatment Programming with Key Populations in West Africa”	UIC system
						Technical support
6.	Odei-Lartey et al., 2016 [25]	Ghana	Biometric fingerprint	General population	“The application of a biometric identification technique for linking community and hospital data in rural Ghana”	UIC system
						UIC card
						Field notebooks
						Desktop computers
						Laptops
						Laptop spare batteries
						Fingerprint scanning device
						License key for fingerprint scanning devices
						Server space to store backups
7.	Wall et al., 2015 [27]	Zambia	Biometric fingerprint	FSW	“Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers”	Fingerprint scanners
						Tablets
						Portable single-finger multi-spectral imaging sensor
						Technical support
						Finance
						Capacity building
						Central server
8.	You et al., 2020 [28]	South Africa	Biometric fingerprint	FSW	“Facilitators and barriers to incorporating digital technologies into HIV care among cisgender female sex workers living with HIV in South Africa”	Biometric devices
9.	Harichund et al., 2013 [29]	South Africa	Biometric fingerprint	HIV negative adult Females	“Participant verification: Prevention of co-enrolment in clinical trials in South Africa”	Computers
						Laptops
						Biometric software
						Internet connection fees
						IT technicians to install Biometric software.
						3G card for back-up
10.	Snidal et al., 2015 [30]	Uganda	Biometric fingerprint	TB patients	“Use of eCompliance, an Innovative Biometric System for Monitoring of Tuberculosis Treatment in Rural Uganda”	Notebook computer
						Biometric software
						Fingerprint scanner
						Training
						Implementation cost
						Hardware maintenance
						Software support
11.	Jaafa et al., 2021 [31]	Kenya	Biometric fingerprint	Clients on HIV care and treatment	“Implementation of Fingerprint Technology for Unique Patient Matching and Identification at an HIV Care and Treatment Facility, in Western Kenya: Cross-sectional Study”	Capacity building
						Java Web Start (Oracle Corporation)
12.	Sharkey et al., 2021 [32]	Zambia	Biometric fingerprint and manual fingerprint	HIV-Negative couples	“A cluster randomized trial to reduce HIV risk from outside partnerships in Zambian HIV-Negative couples using a novel behavioural intervention, “Strengthening Our Vows”: Study protocol and baseline data”	Tablet-based biometric software
						Fingerprint ink
						Papers
						Wi-Fi router
13.	Dalhatu et al., 2023 [34]	Nigeria	Biometric fingerprint	General ART patients	“From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria’s Journey to a National Data Repository for Decision-Making and Patient Care”	Internet services
						Computer hardware
						Human capacity
						Skillsets
						Personnel
14.	Radmanovic, 2021 [36]	Uganda	Biometric fingerprint	Fisherfolks	“Biometric fingerprint technology for estimating frequent HIV testing and HIV incidence among mobile men women in fishing communities along Lake Victoria, Uganda”	Biometric identification system
15.	Family Health International (FHI) 360, 2019 [40]	Mali	Alphanumeric code	FSW and MSM	“Unique identifier code create continuity and improve client tracking. A success story”	Capacity building
						UIC system
						Finance
						Human resources
16.	Harkaway N. 2019 [41]	Burkina Faso and Togo	Alphanumeric code	FSW and MSM	“The Unique Identifier Code (UIC) as a System to track Key Populations—The Experience of PACTE-VIH Project in Burkina Faso and Togo”	Capacity building
						User manuals
						Technical support
17.	Bengtson et al., 2021 [42]	Malawi	Biometric fingerprint	Pregnant women with HIV and HCWs	“Improving monitoring of engagement in HIV care for women in Option B+: a pilot test of biometric fingerprint scanning in Lilongwe, Malawi”	Wireless internet
						Battery-powered, handheld biometric fingerprint scanner
						Web-based application to register fingerprints.
						Bluetooth technology
						Tablets
						Fingerprint scanners
						Centralized cloud-based location
18.	Mazanderani et al., 2018 [43]	South Africa	Alphanumeric code	Babies done PCR testing after birth	“Leveraging the road to health booklet as a unique patient identifier to monitor the prevention of mother-to-child transmission programme”	Barcoded stickers
						Barcoded sticker printer
						Laminated barcoded peal-out card
						Adhesive strips
19.	Nyamhuno, 2019 [44]	South Africa	Biometric fingerprint	FSW	“Assessing the acceptability of biometrics in HIV prevention programme by Hillbrow sex workers”	Biometric devices
						Computers
20.	Global Fund, 2022 [45]	South Africa	Biometric fingerprint	AGYW	“Global Fund Grants in South Africa: Audit report”	Finance
						Biometric devices
						Capacity building
						Human resources
21.	White et al., 2018 [46]	Uganda	Biometric fingerprint	TB patient contacts	“Feasibility, acceptability, and adoption of fingerprint scanning during contact investigation for tuberculosis in Kampala, Uganda: A parallel-convergent, mixed-methods analysis”	Digital scanner
						Training on fingerprints and biometric scanners

Notes:NASCOP, National AIDS and STIs Control Programme; FHI, Family Health International, FSW, Female Sex Workers; TG, Transgender People; PWID, People Who Inject Drugs; PWUD, People Who Use Drugs; AYGW, Adolescent Girls and Young Women, MSM, Men Who have Sex with Men; SOP, Standard Operating Procedure; HCW, Health Care Worker; PCR, Polimerase Chain Reaction; TB, Tuberculosis.

Table 3. Resources required for the implementation of KPs’ UIC.

	Author and Year	Country	UIC Type	Target Population	Title	Resources Mentioned
1.	National AIDS and STIs Control Programme (NASCOP), 2015 [17]	Kenya	Alphanumeric code	FSW/MSM/TG/PWUD/PWID	“Unique Identifier Code for Key Population Programmes in Kenya”	Technical support
2.	Family Health International (FHI) 360, 2019 [21]	Liberia	Alphanumeric code	MSM, FSW and TG	“Linkages across the Continuum of HIV Services for Key Populations Affected by HIV (LINKAGES) Project”	SOP for UIC creation
						Technical support
						Human resources
						Capacity building
						Finance
3.	Chapman et al., 2020 [22]	Uganda and Burundi	Alphanumeric code and Biometric fingerprint	FSW, TG, PWID, MSM, and people in prisons and closed settings	“Changing the landscape of data and digital health solutions”	Mobile and web based UIC App
						Human resources
						Finance
						Capacity building
4.	Bore et al., 2017 [23]	Mali	Alphanumeric code	FWS, partners of FSW and MSM	“Improving Key Population Tracking and Links to HIV Services Using Unique Identifier Codes in Mali”	Cellular reception
						Electricity
5.	Zan et al., 2016 [24]	Burkina Faso and Togo	Alphanumeric code	FSW and MSM	“Strategies and Resources for Implementing HIV Prevention, Care, and Treatment Programming with Key Populations in West Africa”	UIC system
						Technical support
6.	Odei-Lartey et al., 2016 [25]	Ghana	Biometric fingerprint	General population	“The application of a biometric identification technique for linking community and hospital data in rural Ghana”	UIC system
						UIC card
						Field notebooks
						Desktop computers
						Laptops
						Laptop spare batteries
						Fingerprint scanning device
						License key for fingerprint scanning devices
						Server space to store backups
7.	Wall et al., 2015 [27]	Zambia	Biometric fingerprint	FSW	“Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers”	Fingerprint scanners
						Tablets
						Portable single-finger multi-spectral imaging sensor
						Technical support
						Finance
						Capacity building
						Central server
8.	You et al., 2020 [28]	South Africa	Biometric fingerprint	FSW	“Facilitators and barriers to incorporating digital technologies into HIV care among cisgender female sex workers living with HIV in South Africa”	Biometric devices
9.	Harichund et al., 2013 [29]	South Africa	Biometric fingerprint	HIV negative adult Females	“Participant verification: Prevention of co-enrolment in clinical trials in South Africa”	Computers
						Laptops
						Biometric software
						Internet connection fees
						IT technicians to install Biometric software.
						3G card for back-up
10.	Snidal et al., 2015 [30]	Uganda	Biometric fingerprint	TB patients	“Use of eCompliance, an Innovative Biometric System for Monitoring of Tuberculosis Treatment in Rural Uganda”	Notebook computer
						Biometric software
						Fingerprint scanner
						Training
						Implementation cost
						Hardware maintenance
						Software support
11.	Jaafa et al., 2021 [31]	Kenya	Biometric fingerprint	Clients on HIV care and treatment	“Implementation of Fingerprint Technology for Unique Patient Matching and Identification at an HIV Care and Treatment Facility, in Western Kenya: Cross-sectional Study”	Capacity building
						Java Web Start (Oracle Corporation)
12.	Sharkey et al., 2021 [32]	Zambia	Biometric fingerprint and manual fingerprint	HIV-Negative couples	“A cluster randomized trial to reduce HIV risk from outside partnerships in Zambian HIV-Negative couples using a novel behavioural intervention, “Strengthening Our Vows”: Study protocol and baseline data”	Tablet-based biometric software
						Fingerprint ink
						Papers
						Wi-Fi router
13.	Dalhatu et al., 2023 [34]	Nigeria	Biometric fingerprint	General ART patients	“From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria’s Journey to a National Data Repository for Decision-Making and Patient Care”	Internet services
						Computer hardware
						Human capacity
						Skillsets
						Personnel
14.	Radmanovic, 2021 [36]	Uganda	Biometric fingerprint	Fisherfolks	“Biometric fingerprint technology for estimating frequent HIV testing and HIV incidence among mobile men women in fishing communities along Lake Victoria, Uganda”	Biometric identification system
15.	Family Health International (FHI) 360, 2019 [40]	Mali	Alphanumeric code	FSW and MSM	“Unique identifier code create continuity and improve client tracking. A success story”	Capacity building
						UIC system
						Finance
						Human resources
16.	Harkaway N. 2019 [41]	Burkina Faso and Togo	Alphanumeric code	FSW and MSM	“The Unique Identifier Code (UIC) as a System to track Key Populations—The Experience of PACTE-VIH Project in Burkina Faso and Togo”	Capacity building
						User manuals
						Technical support
17.	Bengtson et al., 2021 [42]	Malawi	Biometric fingerprint	Pregnant women with HIV and HCWs	“Improving monitoring of engagement in HIV care for women in Option B+: a pilot test of biometric fingerprint scanning in Lilongwe, Malawi”	Wireless internet
						Battery-powered, handheld biometric fingerprint scanner
						Web-based application to register fingerprints.
						Bluetooth technology
						Tablets
						Fingerprint scanners
						Centralized cloud-based location
18.	Mazanderani et al., 2018 [43]	South Africa	Alphanumeric code	Babies done PCR testing after birth	“Leveraging the road to health booklet as a unique patient identifier to monitor the prevention of mother-to-child transmission programme”	Barcoded stickers
						Barcoded sticker printer
						Laminated barcoded peal-out card
						Adhesive strips
19.	Nyamhuno, 2019 [44]	South Africa	Biometric fingerprint	FSW	“Assessing the acceptability of biometrics in HIV prevention programme by Hillbrow sex workers”	Biometric devices
						Computers
20.	Global Fund, 2022 [45]	South Africa	Biometric fingerprint	AGYW	“Global Fund Grants in South Africa: Audit report”	Finance
						Biometric devices
						Capacity building
						Human resources
21.	White et al., 2018 [46]	Uganda	Biometric fingerprint	TB patient contacts	“Feasibility, acceptability, and adoption of fingerprint scanning during contact investigation for tuberculosis in Kampala, Uganda: A parallel-convergent, mixed-methods analysis”	Digital scanner
						Training on fingerprints and biometric scanners

Notes:NASCOP, National AIDS and STIs Control Programme; FHI, Family Health International, FSW, Female Sex Workers; TG, Transgender People; PWID, People Who Inject Drugs; PWUD, People Who Use Drugs; AYGW, Adolescent Girls and Young Women, MSM, Men Who have Sex with Men; SOP, Standard Operating Procedure; HCW, Health Care Worker; PCR, Polimerase Chain Reaction; TB, Tuberculosis.

3.4. Facilitators and Barriers for the Implementation of KPs’ UIC

In this study, barriers refer to obstacles or challenges that may hinder the successful implementation of KPs’ UIC, while facilitators are factors that may support or promote its adoption [15]. Barriers can include resistance from KPs, inadequate healthcare professionals, a lack of resources, technological challenges, and organizational constraints. Conversely, facilitators include strong leadership, clear communication, positive attitudes among KPs and healthcare providers, adequate training, and alignment with existing systems. Seven studies included in this review listed barriers and facilitators that play roles in adopting KPs’ UIC.

3.4.1. Facilitators

Studies indicated that the ability of UIC systems to maintain privacy and confidentiality is a critical facilitator for their uptake [27,28,35,41,47,48]. The presence of peers and the identification of officials collecting personal details or fingerprints for creating the UIC were listed as facilitators for KPs’ UIC in the studies [27,44]. A study conducted in Burkina Faso and Togo reported that UICs provide for client movement between service providers and ensure that services are accessed only by the intended beneficiaries, which ensures continuity of follow-up visits, thereby enhancing the quality of care and data simultaneously [41]. A study done among SWs reported that they preferred to use the biometric fingerprint systems at their place of work, particularly during off-peak hours and in the presence of other sex workers [27,28,44].

3.4.2. Barriers

The studies retrieved reported that the most common barriers are possible data leakage, fear of the police, criminals accessing fingerprints, fear of the unknown, misconception about technology, privacy disclosure, unknown officials taking fingerprints, low literacy levels, poor internet connection, system maintenance, and the absence of peers [27,28,41,44,47,49]. Technical challenges mentioned in those studies included interruptions caused by intermittent electricity and internet, system failures, camera malfunctions, computer/software errors, poor maintenance of biometric systems, low skill levels of operators, and poor understanding of technology. High turnover rates among trained healthcare providers who operated the KPs’ UIC system were seen as a barrier in the PACTE-VIH project’s study [33]. Cultural and religious concerns were mentioned as barriers in the Kenya study, which assessed the practicality and user acceptance of a biometric system for UIC [26]. Table 4 summarises the facilitators and barriers for the inclusion of KPs’ UIC in the RHIMS.

Table 4. Facilitators and barriers for the implementation of KPs’ UIC.

	Authors (Year)	Country Name	Study Title	KP	UIC Type	Facilitators	Barriers
1.	Wall et al., 2015 [27]	Zambia	“Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers”	FSW	Biometric fingerprint	Knowledge about technology	Misconception about technology
						Location	Fear of the unknown
						Time	Absence of peers
						Confidentiality	Identity of officials taking fingerprints
						Presence of peers
						Trust
						Identity of officials taking fingerprints
						Incentives
2.	You et al., 2020 [28]	South Africa	“Facilitators and barriers to incorporating digital technologies into HIV care among cisgender female sex workers living with HIV in South Africa”	FSW	Biometric fingerprint	Convenience	Long nails
					Iris scan	Security	Fear of police
					Voice recognition	Reliability	Fear that the scanner may cause damage to the eyes
						Anonymity	System failure
						Confidentiality
						Precision
						Accuracy
3.	Family Health International (FHI) 360, 2019 [40]	Mali	“Unique identifier code creates continuity and improves client tracking	FSW and MSM	Alphanumeric code	Precision
						Accuracy
						Trust
						Confidentiality
4.	Harkaway, 2019 [41]	Burkina Faso and Togo	“The Unique Identifier Code (UIC) as a System to track Key Populations The Experience of PACTE-VIH Project in Burkina Faso and Togo”	FSW and MSM	Alphanumeric code	Anonymity	Low literacy
						Confidentiality	System maintenance
						Security and protection	Turnover of healthcare providers
						Precision
						Allows movement between services
						Accuracy
						Easy to generate
5.	Nyamhuno, 2019 [44]	South Africa	“Assessing the acceptability of biometrics in HIV prevention programme by Hillbrow sex workers”	FSW	Biometric fingerprint	Knowledge	Fear of the unknown
						Faith or trust	Poor relationship with officials collecting fingerprints
						Presence of peers	Privacy concerns
							Fear of deportation (foreigners)
							Fear that biometrics may inhibit them from getting new jobs
							Connection between clinic and police
							Criminals accessing fingerprints
6.	Prata et al., 2021 [47]	Togo	“A mixed-methods study to explore opportunities and challenges with using a mHealth approach to engage men who have sex with men in HIV prevention, treatment and care in Lomé, Togo”	MSM	Biometric fingerprint	Confidentiality
7.	Njoroge, 2019 [49]	Kenya	“The Last Mile: Use of Innovative Technologies to Attain the UNAIDS 90-90-90 Target”		Biometric iris scan	Not used in any government identification processes, thus eliminating the fear of being arrested	Data leakage
							Time
							Religious/cultural concerns
							Lack of understanding
							Fear that biometric cameras will exacerbate eye problems.
							Poor internet connection
							System failure
							Computer/software error
							ID Number generation failure
							Poor image quality
							Eye deformity

Notes: FSW, Female Sex Workers; FHI, Family Health International; MSM, Men Who have Sex with Men; UIC, Unique Identifier Code; UNAIDS, the Joint United Nations Programme on HIV/AIDS, and ID; Identity.

Table 4. Facilitators and barriers for the implementation of KPs’ UIC.

	Authors (Year)	Country Name	Study Title	KP	UIC Type	Facilitators	Barriers
1.	Wall et al., 2015 [27]	Zambia	“Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers”	FSW	Biometric fingerprint	Knowledge about technology	Misconception about technology
						Location	Fear of the unknown
						Time	Absence of peers
						Confidentiality	Identity of officials taking fingerprints
						Presence of peers
						Trust
						Identity of officials taking fingerprints
						Incentives
2.	You et al., 2020 [28]	South Africa	“Facilitators and barriers to incorporating digital technologies into HIV care among cisgender female sex workers living with HIV in South Africa”	FSW	Biometric fingerprint	Convenience	Long nails
					Iris scan	Security	Fear of police
					Voice recognition	Reliability	Fear that the scanner may cause damage to the eyes
						Anonymity	System failure
						Confidentiality
						Precision
						Accuracy
3.	Family Health International (FHI) 360, 2019 [40]	Mali	“Unique identifier code creates continuity and improves client tracking	FSW and MSM	Alphanumeric code	Precision
						Accuracy
						Trust
						Confidentiality
4.	Harkaway, 2019 [41]	Burkina Faso and Togo	“The Unique Identifier Code (UIC) as a System to track Key Populations The Experience of PACTE-VIH Project in Burkina Faso and Togo”	FSW and MSM	Alphanumeric code	Anonymity	Low literacy
						Confidentiality	System maintenance
						Security and protection	Turnover of healthcare providers
						Precision
						Allows movement between services
						Accuracy
						Easy to generate
5.	Nyamhuno, 2019 [44]	South Africa	“Assessing the acceptability of biometrics in HIV prevention programme by Hillbrow sex workers”	FSW	Biometric fingerprint	Knowledge	Fear of the unknown
						Faith or trust	Poor relationship with officials collecting fingerprints
						Presence of peers	Privacy concerns
							Fear of deportation (foreigners)
							Fear that biometrics may inhibit them from getting new jobs
							Connection between clinic and police
							Criminals accessing fingerprints
6.	Prata et al., 2021 [47]	Togo	“A mixed-methods study to explore opportunities and challenges with using a mHealth approach to engage men who have sex with men in HIV prevention, treatment and care in Lomé, Togo”	MSM	Biometric fingerprint	Confidentiality
7.	Njoroge, 2019 [49]	Kenya	“The Last Mile: Use of Innovative Technologies to Attain the UNAIDS 90-90-90 Target”		Biometric iris scan	Not used in any government identification processes, thus eliminating the fear of being arrested	Data leakage
							Time
							Religious/cultural concerns
							Lack of understanding
							Fear that biometric cameras will exacerbate eye problems.
							Poor internet connection
							System failure
							Computer/software error
							ID Number generation failure
							Poor image quality
							Eye deformity

Notes: FSW, Female Sex Workers; FHI, Family Health International; MSM, Men Who have Sex with Men; UIC, Unique Identifier Code; UNAIDS, the Joint United Nations Programme on HIV/AIDS, and ID; Identity.

3.5. Feasibility and Acceptability of Implementing KPs’ UIC

Feasibility in healthcare intervention examines its practicality by assessing whether it can be successfully implemented, considering logistical factors. Acceptability gauges the suitability of the intervention based on the cognitive and emotional responses of individuals involved, including service providers, clients, and stakeholders [50]. In this systematic review, the selected feasibility studies evaluated the practicality of the inclusion of KPs’ UIC by considering effectiveness, cost, regulatory compliance, and integration into existing systems to guide informed decision-making for adoption in the South African context [27,29,31]. Acceptability studies evaluated how KPs perceived and responded to the use of UIC. The effectiveness of KPs’ UIC is influenced by its acceptance and satisfaction by those directly involved [36,44,46]. In this review, nine reports were assessed for the details on the feasibility and acceptability [25,27,29,31,32,36,42,44,46].

3.5.1. Feasibility

The implementation of KPs’ UIC in SSA health settings has been shown to be feasible, as mentioned in several studies listed in Table 5. Studies conducted in rural areas of low-income countries in SSA, such as Kenya, Ghana, Malawi, and Uganda, reported the practicality of implementing KPs’ UIC in relatively resource-limited settings [25,27,31,42,46]. Although these studies indicated that implementation of KPs’ UIC is feasible, they highlighted areas that need attention, including the training of service providers, investment in technology and regular maintenance, network, and electricity availability. Results from this review indicated a few instances of technology failure, low biometric device sensitivity, and specificity, which led to patient misidentification. The studies recommended combining biometric fingerprint identification using multiple fingers with alphanumeric codes to improve feasibility, especially in rural settings, where some clients have diminished fingerprint sensitivity resulting from manual labour [31,36,42].

3.5.2. Acceptability

Studies have indicated that KPs’ UICs are acceptable in SSA, as they allow patient data to be linked across different facilities through individual longitudinal service records, and their careful handling may result in increased enrolment and retention in HIV care for KPs [27,31,42,44,46,51]. The KPs’ UIC facilitates data disaggregation, thereby uncovering hidden inequalities, informing targeted interventions, and guiding resource allocation [52]. The reports from nine countries that have incorporated the KPs’ UIC at government RHIMS also reaffirm that SSA countries have accepted the utilisation of KPs’ UIC [17,18,19,20,22,23,38,48]. Studies have reported that UICs help prevent patient co-enrolment in multiple healthcare facilities, double-counting, and patient-misidentification, thereby improving data accuracy [26,27,44,46]. Key recommendations from these studies include strong frameworks, guidelines, and standard operating procedures for protecting unauthorized access to personal information.

Table 5. Feasibility and acceptability of implementing KPs’ UIC.

	Author and Year	Country	Study Objective	Target Population	UIC Type
1	Odei-Lartey et al., 2016 [25]	Ghana	“To assess the feasibility of using fingerprint identification to link community data and hospital data in a rural African setting”	General population	Biometric fingerprint
2	Wall et al., 2015 [27]	Zambia	“To detail the feasibility, including technical challenges, of implementing an electronic fingerprint linked data capture system in clinics in Zambia, and the acceptability and barriers to uptake of this system among FSWs”	FSW	Biometric fingerprint
3.	Harichund et al., 2013 [29]	South Africa	“To report the development and feasibility of a digital fingerprint-based participant identification method to prevent co-enrolment at multiple clinical trial sites”	HIV-negative adult females	Biometric fingerprint
4.	Jaafa et al., 2021 [31]	Kenya	“To evaluate the performance and acceptability of fingerprint technology for unique patient matching and identification in the LMIC setting of Kenya”	Clients on HIV care and treatment	Biometric fingerprint
5.	Sharkey et al., 2021 [32]	Zambia	“Assess the ability of an e-fingerprinting system to enhance follow-up and detection of study outcomes, multiple enrolments, and potential spillover effect”	HIV-negative couples	Biometric fingerprint and manual fingerprint
6.	Radmanovic, 2021 [36]	Uganda	“To assess the plausibility of applying novel technologies (i.e., fingerprint technology) to assess HIV healthcare (i.e., HIV testing) services among mobile fisherfolks in Uganda”	Fisherfolks	Biometric fingerprint
7.	Nyamhuno, 2019 [44]	South Africa	“To investigate the acceptability of biometrics by sex workers in the Hillbrow Health Precinct programme”	FSW	Biometric fingerprint
8.	White et al., 2018 [46]	Uganda	“To understand the feasibility, acceptability, and adoption of digital fingerprinting for patient identification in a household tuberculosis contact investigation study in Kampala, Uganda”	TB patient contacts	Biometric fingerprint
9.	Bengtson et al., 2021 [42]	Malawi	“To evaluate the feasibility and acceptability of using biometric fingerprint scanning to accurately identify women and register HIV visits at two large urban antenatal clinics in Lilongwe, Malawi”	Pregnant women with HIV and HCWs	Biometric fingerprint

Table 5. Feasibility and acceptability of implementing KPs’ UIC.

	Author and Year	Country	Study Objective	Target Population	UIC Type
1	Odei-Lartey et al., 2016 [25]	Ghana	“To assess the feasibility of using fingerprint identification to link community data and hospital data in a rural African setting”	General population	Biometric fingerprint
2	Wall et al., 2015 [27]	Zambia	“To detail the feasibility, including technical challenges, of implementing an electronic fingerprint linked data capture system in clinics in Zambia, and the acceptability and barriers to uptake of this system among FSWs”	FSW	Biometric fingerprint
3.	Harichund et al., 2013 [29]	South Africa	“To report the development and feasibility of a digital fingerprint-based participant identification method to prevent co-enrolment at multiple clinical trial sites”	HIV-negative adult females	Biometric fingerprint
4.	Jaafa et al., 2021 [31]	Kenya	“To evaluate the performance and acceptability of fingerprint technology for unique patient matching and identification in the LMIC setting of Kenya”	Clients on HIV care and treatment	Biometric fingerprint
5.	Sharkey et al., 2021 [32]	Zambia	“Assess the ability of an e-fingerprinting system to enhance follow-up and detection of study outcomes, multiple enrolments, and potential spillover effect”	HIV-negative couples	Biometric fingerprint and manual fingerprint
6.	Radmanovic, 2021 [36]	Uganda	“To assess the plausibility of applying novel technologies (i.e., fingerprint technology) to assess HIV healthcare (i.e., HIV testing) services among mobile fisherfolks in Uganda”	Fisherfolks	Biometric fingerprint
7.	Nyamhuno, 2019 [44]	South Africa	“To investigate the acceptability of biometrics by sex workers in the Hillbrow Health Precinct programme”	FSW	Biometric fingerprint
8.	White et al., 2018 [46]	Uganda	“To understand the feasibility, acceptability, and adoption of digital fingerprinting for patient identification in a household tuberculosis contact investigation study in Kampala, Uganda”	TB patient contacts	Biometric fingerprint
9.	Bengtson et al., 2021 [42]	Malawi	“To evaluate the feasibility and acceptability of using biometric fingerprint scanning to accurately identify women and register HIV visits at two large urban antenatal clinics in Lilongwe, Malawi”	Pregnant women with HIV and HCWs	Biometric fingerprint

4. Discussion

4.1. Main Findings

The inclusion of KP UICs in RHIMS could speed up advancement toward reaching the 95-95-95 goals while ensuring no one is left behind. A recent UNAIDS report indicated that the goal is within reach, although certain countries, including SA, face challenges in providing comprehensive HIV-related data for KPs [1]. This systematic review described the existing literature, both published and grey, on how SSA countries have incorporated the KPs’ UIC into their national RHIMS.

The findings revealed that only nine out of more than 50 SSA countries, accounting for less than one-third, have incorporated KP UICs into their state RHIMS [17,18,19,20,23,48]. Among the countries incorporating KPs’ UIC in SSA, only four (Ghana, Togo, Liberia, and Kenya) have done so at the national level using alphanumeric codes. This study supports evidence from a report by FHI 360 which cited few countries that have incorporated KPs’ UIC on national RHIMS using alphanumeric codes in Central American countries (Panama, Nicaragua, Guatemala, El Salvador, Costa Rica, and Belize), Denmark, Papua New Guinea, Afghanistan, Tajikistan, and Uzbekistan [47].

The results obtained are consistent with those reported by the AIDS Projects Management Group Health report, which assessed 65 countries across six regions and found only 11 countries using KPs’ UIC nationally [53]. In this regard, there are several reasons why SSA countries are not adopting UICs for KPs. One significant factor is the widespread criminalisation of KP groups. A study conducted across five SSA countries found that homosexual practices among MSM and TG individuals are criminalised in 36 countries within the region, with penalties including fines, imprisonment, and the death penalty [54].

The “Global State of Harm Reduction 2020” report highlighted that people who inject drugs are criminalised in the majority of SSA countries, where individuals are frequently targeted by law enforcement operations [55]. Criminalising sex work likewise grants police and other law enforcement officials the authority to harass sex workers [56]. Therefore, SSA countries that criminalise some KPs’ activities are less likely to prioritize collecting KP data on government health information systems, due to political sensitivities, fear of legitimizing criminalised behaviours, and concerns over public backlash. As a result, KP data are frequently excluded from national data collection tools, leading to significant gaps in health planning and service delivery.

Balancing the healthcare needs of KPs with the requirements of the law involves ensuring access to essential HIV services while advocating for legal reforms that protect public health and human rights, without further criminalising vulnerable groups. The Department of Health should ensure that KPs, regardless of legal status or behaviour, have safe, stigma-free access to long-term HIV prevention, care, and treatment services, and their data are captured on government tools in a way that represents them. KPs themselves accept the collection of UICs in HIV programs, while concerns regarding privacy and data security highlight the need to build trust and uphold strict confidentiality standards.

The government of South Africa should consider the adoption of KPs’ UIC in TIER.Net or a new EMR to enhance accurate data collection and reporting. This integration is essential for strengthening national monitoring, such as the National Strategic Plan (NSP), and international reporting through the Global AIDS Monitoring (GAM) report. It will also support tracking progress toward ending HIV/AIDS by 2030 and support the attainment of Sustainable Development Goal (SDG) 3, aimed at ensuring good health and promoting well-being for everyone. Despite the criminalisation of certain behaviours associated with KPs, governments can still implement mechanisms for health data collection that help track HIV progress while seeking to protect individuals from legal risks. By using anonymous identifiers like UICs, countries work towards balancing public health goals with legal frameworks.

4.2. Required Resources for Implementing KPs’ UIC

Importantly, the operationalization of KPs’ UIC systems necessitates a robust IT infrastructure. This includes secure data processing and storage through robust computer hardware, reliable internet services for real-time communication, a stable electricity supply to prevent disruptions and data loss, power backups for continuous operation during outages, Wi-Fi connections for wireless communication in mobile or remote areas, and dedicated IT support for timely issue resolution and system maintenance [17,25,27,28,30,44]. Robust offline capabilities to ensure seamless workflow during periods of limited access to electricity and the internet are important.

In SSA, the implementation of UIC faces multiple challenges, including the assignment of multiple UICs to one person, the loss or forgetting of system-generated identifiers often associated with a smart card, and inappropriate and varied recording of the UIC by health personnel. Additional challenges include the high costs of procuring and maintaining RHIMS, weak system integration, lack of finance, limited computer literacy, and inconsistent access to electricity and internet connectivity [57].

Crucially, multilateral stakeholders played a pivotal role in providing financial support, human resources, and technical assistance. Active government involvement in coordinating and integrating KPs’ UIC with national health information systems, ensuring maintenance and sustainability, has been key to the success observed in the reported countries [17,20,22,23,27,28,30,35,36,43,48]. Government officials across various countries played a pivotal role in integrating KPs’ UIC with health information systems, coordinating stakeholders, and supporting the implementation of KPs’ UIC.

In Liberia, successful implementation involved collaboration among KP stakeholders, the “National AIDS and STI Control Program” (NACP), and the County Health Team (CHT). Ghana demonstrated commitment to sustainability as the Ghana AIDS Commission partnered with developmental organizations to establish the “Ghana KP Unique Identifier System” (GKPUIS). Ghana and Kenya’s governments took ownership of the UIC systems [38,40]. The “South African National AIDS Council” (SANAC) unites government, civil society organizations, and the private sector to address HIV, TB, and STIs collectively. The SANAC and NDoH are responsible for playing a leading role in advocating for and coordinating the integration of KPs’ UIC within the government’s public RHIMS.

4.3. Facilitators and Barriers for Implementing KPs’ UIC

This study highlighted some key individual-level facilitators for the inclusion of KPs’ UIC on RHIMS, including a clear understanding of technology, trust, and positive relationships with healthcare providers, privacy, security, and data precision [27,28,41]. Countries such as Australia, Canada, China, India, Denmark, Switzerland, Sweden, Norway, and New Zealand have acknowledged the facilitators of UIC, which encompass enhanced confidentiality, patient safety, improved care quality, informed policy formulation, better care coordination, data quality, cost-effectiveness, efficient program implementation monitoring, and streamlined sharing of patient records [58].

South Africa is currently advancing the implementation of the National Health Insurance (NHI), which seeks to improve access to quality healthcare services for all citizens. To achieve this goal, it is essential to establish an EMR system that not only registers and monitors patients across various healthcare providers but also enables the identification of KPs to ensure inclusive, targeted, and equitable healthcare delivery [59]. The adoption of KPs’ UIC can also be incorporated into the new EMR.

Apart from the facilitators reported in this review, the barriers noted encompassed general fear of the unknown, unclear intentions for data collection through fingerprint scanners, concerns about data ending up with the police, and lack of political will [44,47,49]. Our results are aligned with a United States study conducted by Matthew and colleagues, who cautioned that implementing HIV data collection through biometric scanning for ID verification may deter participation [60].

In 2022, the annual data breach report revealed an increase in data compromises in the USA, with specific incidents highlighting the vulnerability of health data [61]. Examples of countries that leaked HIV related personal health data include the Scotland HIV Organization, which leaked 65 records, the London HIV Clinic, which leaked 780 records, and the Singapore Ministry of Health, which experienced a leakage of 14,200 records [62,63,64]. In Kenya, a KP organization stopped the implementation of a biometric system for KPs and released a report titled “Everyone said no,” expressing concerns about the potential misuse of biometric data, particularly the risk of “function creep” where the police could use health data for arrests [65].

The implementation of KPs’ UIC in South Africa is largely hindered by insufficient political commitment to align national HIV response with KP priorities in government plans and programs. South Africa’s approach to tackling HIV is guided by the “National Strategic Plan for HIV, TB, and STIs (2023–2028)”, which highlights the importance of a comprehensive response that meets both the prevention and treatment needs of key and priority populations [66]. However, KP programmes in South Africa are mainly implemented by organizations financed through international donors such as PEPFAR (USAID and CDC), Global Fund, and the NDoH high transmission area (HTA) program, and they utilise different UIC formats due to the lack of harmonization at the national level.

4.4. Factors Influencing the Feasibility and Acceptability of Implementing KPs’ UIC

The review underscores the feasibility and acceptability of collecting data for KPs using a UIC, either through biometric fingerprint or alphanumeric codes at the individual level [21,25,29,31,34,36,42,44,46,64]. Biometric fingerprint technology emerges as a tool that not only enhances data quality through better-quality patient identification but also ensures accurate person-centred data management and improved data security for KPs [31,36,42,60]. Our study findings support the reported feasibility of implementing the KPs’ UIC, as documented by PASMO in Central America, particularly in Belize and Guatemala, for FSW, MSM, and TG individuals [67]. Similarly, Mongolia implemented KPs’ UIC for MSM and FWS in East and Central Asia, whereas nations in the Latin America and Caribbean region, such as Paraguay, Bolivia, and Haiti, successfully implemented KPs’ UIC [3,68].

While this review has confirmed the feasibility and acceptability of including KPs’ UIC on RHIMS in some countries, it is noteworthy that, despite this, many SSA countries still criminalise some KP activities due to a weak criminal justice system. Meanwhile, some SSA countries that have adopted KP UICs have demonstrated notable progress toward meeting the 95-95-95 targets. These include Burundi (89-98-90) [69], Kenya (96-98-97) [70], Malawi (88-97-97) [71], and Liberia (80-98-95) [72], which have all reported encouraging outcomes. More attention should be directed toward legal reform as a key intervention. A few years ago, some countries made a positive move toward public health. For example, Angola repealed laws banning same-sex sexual relationships, the U.S. state of Oregon decriminalised the possession of personal-use drugs, and the Northern Territory in Australia legalised sex [11]. Thus, the inclusion of KPs’ UIC on the government RHIMS in SSA will depend on political will.

4.5. Strengths and Limitations of the Study

This review employed a rigorous and systematic approach, exploring the status quo of RHIMS that has integrated KP UICs across SSA. To promote transparency and avoid duplication, the review was registered with PROSPERO, and the PRISMA guidelines were followed to ensure consistency and enhance the study’s credibility. A comprehensive literature search was carried out across multiple academic databases and repositories of governmental and non-governmental organizations in SSA, with the assistance of a qualified librarian. Zotero software was used to manage references, while Covidence facilitated the screening process in accordance with the PRISMA flow diagram. Two independent reviewers conducted the screening, and discrepancies during the full-text review were resolved by a third reviewer.

Multilateral stakeholders play a critical role in providing financial resources, technical assistance, and expertise to address the specific needs of KPs. However, the need for sustained financial support may contribute to selective reporting, where only successful projects are published to maintain donor confidence, potentially overshadowing challenges or failures. Some lessons may have been missed, particularly those contained in unpublished work, results published in non-English languages, or research hosted on platforms with restricted access, potentially giving us only a partial view of the evidence. Additionally, by confining our review to SSA, we may have introduced a regional bias, as relevant findings from other parts of the world were excluded.

5. Conclusions

Due to the criminalisation of KP activities in SSA, very few countries have incorporated KP UICs into their RHIMS, with only a limited number successfully implementing UICs at the national level. Despite concerns about privacy and data security, KPs are generally accepting these systems when proper safeguards are in place. The operationalization of UIC systems requires robust IT infrastructure, including secure data processing, reliable internet, and dedicated support, while addressing barriers such as high costs, inconsistent data recording, and a lack of political will. Facilitators include enhanced confidentiality, improved care coordination, and better policy formulation. For South Africa, integrating KPs’ UICs into tools like TIER.Net or new EMRs would improve data accuracy, strengthen national and international reporting, and support the goal of ending HIV/AIDS by 2030, in line with SDG 3.

Recommendations

The KPs’ UIC in South Africa may be incorporated into the new EMR. Further studies may engage multilateral stakeholders and health managers to gather their perspectives, as their support is crucial for ensuring technical assistance and data management policy change. Piloting of revised paper-based clinical stationery and electronic data collection tools may assist with understanding the feasibility issues.