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Try out PMC Labs and tell us what you think. Learn More. Robust population size estimates of female sex workers and other key populations in South Africa face multiple methodological limitations, including inconsistencies in surveillance and programmatic indicators. This has, consequently, challenged the appropriate allocation of resources and benchmark-setting necessary to an effective HIV response.

The survey also included several multiplier-based population size estimation methods. The objective of our study was to present the selected population size estimation methods used in an IBBS survey and the subsequent participatory process used to estimate the of female sex workers in three South African cities. Inwe used respondent-driven sampling to recruit independent samples of female sex workers for IBBS surveys in Johannesburg, Cape Town, and eThekwini. Following data analysis, investigators consulted civil society stakeholders to present survey and size estimates and facilitated stakeholder vetting of individual estimates to arrive at consensus point estimates with upper and lower plausibility bounds.

In total, and female sex workers participated in the survey in Johannesburg, Cape Town, and eThekwini, respectively. For size estimation, investigators calculated preliminary point estimates as the median of the multiple estimation methods embedded in the IBBS survey and presented these to a civil society-convened stakeholder group. Stakeholders vetted all estimates in light of other data points, including programmatic experience, ensuring inclusion only of plausible point estimates in median calculation. After vetting, stakeholders adopted three consensus point estimates with plausible ranges: Johannesburg; Cape Town ; eThekwiniUsing several population size estimates methods embedded in an IBBS survey and a participatory stakeholder consensus process, the South Africa Health Monitoring Survey produced female sex worker size estimates representing approximately 0.

In data-sparse environments, stakeholder engagement and consensus is critical to vetting of multiple empirically based size estimates procedures to ensure adoption and utilization of data-informed size estimates for coordinated national and subnational benchmarking. It also has the potential to increase coherence in national and key population-specific HIV responses and to decrease the likelihood of duplicative and wasteful resource allocation.

We recommend building cooperative and productive academic-civil society partnerships around estimates and other strategic information dissemination and sharing to facilitate the incorporation of additional data as it becomes available, as these additional data points may minimize the impact of the known and unknown biases inherent in any single, investigator-calculated method.

FSWs work in diverse settings, including along major transport routes, at public venues such as urban street corners, parks, bars, and taverns, as well as in more closed spaces such as private homes, where they mainly interact with clients using social media platforms [ 4 ].

HIV prevalence and behavioral have been reported elsewhere [ 2 ]. Briefly, we estimated that In this paper, we have described these survey methods, PSE methods andand the consensus process through which FSW stakeholders adopted PSEs and plausible ranges PRs for purposes of strategic planning, policy making, advocacy, and programming.

We used respondent-driven sampling RDS methods [ 9 - 12 ] that have been subsequently adapted for key populations HIV surveillance and population size estimation purposes [ 13 - 19 ]. Recruitment of each sample began with seeds identified by stakeholders and study staff during pre-IBBS formative assessment; each seed recruited up to 3 additional FSWs from their social and professional networks, who recruited up to 3 additional FSWs, and so on in Markov chains, as shown in Table 1.

Respondent-driven sampling sample size and recruitment statistics for three samples of female sex workers in South Africa. The study procedures consisted of a behavioral survey and biological testing for HIV. Eligible candidates were those who were born biologically female; aged 16 years or older; had exchanged sex for money with someone other than a primary partner in the 30 days; and had lived, worked, or socialized in the urban area where they were recruited for the 6 months.

Participants provided written informed consent for study procedures and separate written informed consent for rapid HTS per South African guidelines. Survey data collection commenced in July and concluded in February Laboratory and statistical analyses of biological and behavioral survey data followed the Strengthening the Reporting of Observational Studies in Epidemiology RDS guidelines [ 20 ], and the full description of laboratory methods has been provided in the SAHMS final report [ 2 ]. In the next sections, we have described the background and methodological approach to each population size estimation method.

The final estimate was reached by taking the average of the two median estimates and ranges. The unique object multiplier is a 2-step method commonly used in conducting population size estimation of key populations. The first step involves distributing unique, memorable objects in advance of the survey throughout the study area to the members of the population of interest. The objects were determined through stakeholder consultation in each city.

In eThekwini, lavender-colored bracelets were distributed, while compact make-up kits were used in Johannesburg and Cape Town. In each city, study staff and stakeholder volunteers distributed objects to FSWs throughout the study area a few weeks prior to survey launch, varying days and times in order to achieve the largest distribution. To avoid distribution biases and errors in the first step of this process, we relied on the advice of individual volunteers and staff who were familiar with the local FSWs, or who were themselves local FSWs, to minimize the possibility that individuals would receive multiple objects or that objects would be distributed to nonpopulation members.

The s of objects distributed at a particular time and geographic area eg, street intersection, brothel were recorded and varied to ensure that different individuals and subpopulations would be encountered in each object distribution event. Finally, with each brief interaction, staff screened women to verify their FSW status and whether they had ly received the object.

The 2-step principles and calculation for the unique event multiplier are similar to the unique object. In the first step, in advance of the survey launch in each city, staff and stakeholders sponsored a memorable launch event, with the theme and name of the event determined through stakeholder input in each city and the event publicized through FSW stakeholders and social networks. Staff and stakeholders counted each woman who entered the event and screened all women to confirm FSW status.

Each count was recorded; discrepancies between counters were resolved through discussion until a count deemed to be reasonable was arrived at by all counters. In the second step, survey participants were asked if they attended the event, with the event identified by its name and date.

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In this 2-step process, staff first obtained de-duplicated counts of FSWs who utilized any clinical HIV or community-based service eg, HIV testing, attendance at an advocacy workshop from partnering stakeholder organizations between January 1 and June 16, Study investigators calculated a point estimate for the FSW population in each city that was the median of a plausible range of individual point estimates derived from the sources described above.

Investigators excluded point estimates as implausible in calculating the median if they were outside of an obvious range of reasonableness—for example, a preliminary point estimate could not be less than the survey sample size in each city, or it would suggest that more than half the adult female population were engaged in sex work. The investigators adopted the median of the plausible estimates as the preliminary PSE, with the largest reasonable point estimate as an upper plausibility bound and the lowest reasonable point estimate as the lower plausibility bound.

Using this range of estimates, investigators then invited input on the preliminary PSEs, including their a priori exclusion of implausiblefrom a stakeholder committee following a consensus process described by colleagues in the San Francisco Department of Public Health [ 22 ] and ly implemented in Tanzania [ 23 ] and Ghana [ 24 ].

The study investigators convened a meeting with stakeholders who were familiar with the three FSW populations to present the preliminary PSEs and associated upper and lower plausible bounds. The stakeholder group included representatives of NDOH, civil society human rights advocacy and health services organizations represented on the SANAC, and other academic experts. The PSE and crude data were distributed to stakeholders in advance of an in-person stakeholder meeting.

At this meeting, investigators reviewed all the individual PSE methods outlined above, discussed the variation between and limitations of each method, and identified their a priori implausible estimates. Upon achieving consensus on the plausible range of PSEs, the investigators calculated preliminary median PSEs and upper and lower plausible bounds. Preliminary PSEs were also compared with census data from to back-calculate the proportion of the adult female population engaging in sex work in each city to demonstrate where the estimate lay within a range of reasonableness, including comparison to other PSE studies and assumptions from other contexts.

In this case, the group considered PSEs derived from a national rapid assessment of the sex worker population commissioned by SANAC and presented by Konstant et al [ 7 ] to assess whether the preliminary median PSEs and PRs were sensitive to the. were reported as counts and proportions of the adult female population aged above 15 years.

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This process provided the opportunity to reconsider any point estimates that investigators had excluded a priori. At the conclusion of the meeting, the group was invited to reject, amend and recalculate, or adopt the preliminary PSEs as consensus PSEs. We recruited FSWs across the three sites. In Johannesburg, recruitment began in August and continued for 25 weeks, recruiting a total of women through 5 seeds.

The Cape Town site launched in July and was open for 28 weeks, with a final sample of through 6 seeds. The eThekwini study site began recruiting participants in September and was operational for 22 weeks, with women included in the final sample recruited through 3 seeds. PSEs for each city and the survey counts on which they are based, for example, the count of participants in the survey who recalled receiving the unique object, have been listed by estimation method in Table 2.

In Johannesburg, the WOTC produced the lowest estimate at FSWs range and was ultimately deemed implausibly low by consensus and excluded from calculation of the median. The service multiplier result was deemed an unreasonably low estimate as it produced an estimate equal to the survey sample size.

ly published literature has estimated the Johannesburg FSW population at 10, [ 7 ]. This value was deemed outside the range of plausibility by stakeholder consensus and was excluded from calculation of the median. The unique event resulted in an estimate of FSWs. However, this estimate was judged to be highly implausible since it was well below the de-duplicated data provided by service providers and, therefore, excluded from the final analysis.

This is very likely attributable to a misunderstanding regarding the unique event attendance question among eThekwini survey participants. Prior literature has estimated the FSW population in this city at [ 7 ]. The Modified Delphi consensus process meeting with stakeholders endorsed the investigator recommendations on preliminary point estimates median of all estimatesresulting in the exclusion of unreasonable from calculating the median.

The point estimate became the median of the remaining estimates, rounded up. Stakeholders were given the option of accepting the highest and lowest plausible estimate as the PR; in Cape Town and eThekwini, they relied on expert opinion to round the upper boundary down. This study is, to our knowledge, the first published study of its kind for South Africa where the incorporation of stakeholder consensus into the analysis of IBBS data was an integral component of the population size estimation methodology.

The estimates derived from our methodology in these cities are largely consistent with estimates by Konstant et al, derived from different methodologies [ 7 ]. While stakeholders acknowledged that the PSEs appeared to be lower than they had expected a result also reported by Konstant et alstakeholders were persuaded to rely on these as they were based upon empirical methodologies that were consistently and transparently applied to the IBBS PSE data.

Thus, these consensus PSEs were acknowledged by stakeholders to be data informed and usable for their purposes of programmatic planning and benchmarking. In fact, we substantially agree and would contend that while greater accuracy is of course a goal, it is unlikely to be achieved through a single method with enough rigor to achieve scientific consensus on bias and accuracy anytime soon. The virtue of the individual PSE methods and the consensus process described in this paper lies in their utility to public health planning and action.

Individually, the multiplier methods that we selected for inclusion in the SAHMS are available, easy to implement, rigorous enough to be reproducible, and—critically—transparent in their limitations and are generally easily understood by stakeholders. Moreover, s that do not align with stakeholder opinion or experience are not likely to be adopted or utilized, which essentially throws good money after bad.

None of this should be interpreted as our endorsement of methodological sloppiness or indiscriminate guessing; it is simply a recognition that lives are at stake and avoidable infection, illness, and death should be prioritized over methodological debates in the meantime. As discussed ly, reasonable people may disagree on whether the are accurate or precise enoughand we acknowledge that there is no empirical way to validate consensus point PSEs.

Nearly every step in the process is vulnerable to biases introduced through both random and human error; as facilitators of the consensus process, investigators have a duty to be ruthlessly and transparently skeptical of all in light of other available evidence and stakeholder experience so that reversion to the mean of empirically collected and analyzed data is privileged over indiscriminate guessing. In particular, we are aware of the emerging consensus in the scientific community that Delphi methods such as WOTC have become less necessary or desirable to be included in multimethods comparisons.

We report it here only because it was a method considered by this stakeholder group inand the purpose of this paper is to describe stakeholder consensus methodology and the generated through it, more than to validate or invalidate any individual PSE methodology. We are aware of the major empirical limitations of similar Delphi methods; they have been perhaps less robust than, for example, multiplier methods.

We substantially agree, and there may be enough, more empirical and robust, methodologies now available that a recommendation to exclude them in the future would not be unwarranted. These consensus PSEs are primarily informed by point estimates from the more empirically satisfying and theoretically reproducible multiplier methods, yet we caution that even these point estimates must be understood and qualified as being subject to several biases embedded in these methods.

For example, it is not possible to independently validate that unique object or event counts include only individuals who are true population members. Self-report bias may have been introduced in multiplier methods relying on socially desirable affirmative answers to questions about, for example, being in possession of a make-up kit object or getting HIV tested in the last 6 months service.

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For all these reasons, it is advisable to discuss proposed multiplier method procedures with the population during presurvey assessments such as phrasing of recapture survey questions to avoid misunderstandings and biased responses.

Furthermore, it is important to monitor and document the implementation of both sides of the capture-recapture methods carefully. In the absence of these recommendations, it may otherwise not be possible for investigators or stakeholders to make reasoned, qualitative judgments about the plausibility of the individual or the range of preliminary PSE. Similarly, nonservice delivery venues where FSWs are likely to be enumerated eg, brothels, the internet may be more difficult for investigators to access than for RDS recruitment to penetrate.

In this sense, failure to demonstrate substantial network transition out of service provider-related networks suggests either optimal service coverage of the population highly improbable in sex work-criminalized environments or methods-implementation limitations that must be identified and acknowledged in analysis.

Successive sampling SS -PSEs are possible to calculate from RDS data [ 28 ] and, on their face, may appear more methodologically and empirically satisfying. We did not include SS-PSEs here only because these have not been vetted by this stakeholder group, and the participatory stakeholder process is the subject of this paper as much as the estimates it produced.

For this reason, we could not recommend reliance on any single method and continue to endorse vetting and triangulation of multiple empirical methodologies by stakeholders and technical experts in a participatory process. At the end of the day, a PSE has no inherent value unless it is adopted and used consistently by all stakeholders in government, civil society, and Global Health financing partners.

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