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ISSN 2155-6113
Journal of AIDS & Clinical Research
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Modelling HIV Intervention among Most-at-Risk/Key Population: Case Study of FWSS in Nigeria

Sampson Akwafuo*, Andrew Shattock and Armin R Mikler

University of North Texas, Denton, TX, USA

Corresponding Author:
Sampson Akwafuo
University of North Texas
Denton, TX, United States
Tel: 4694516075
E-mail: [email protected]

Received date: September 10, 2017; Accepted date: September 21, 2017; Published date: September 28, 2017

Citation: Akwafuo S, Shattock A, Mikler AR (2017) Modelling HIV Intervention among Most-at-Risk/Key Population: Case Study of FWSS in Nigeria. J AIDS Clin Res 8:732. doi:10.4172/2155-6113.1000732

Copyright: © 2017 Akwafuo S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Using FWSS in Nigeria as a case study, this research develops a novel risk equation for estimating new infections among FWSS, their clients and communities. It uses a hybrid SUDT and SIT structural model. It considers number of contacts, number of protected and unprotected sexual acts, population and other existing values as base inputs. Simulation of the model was done using python programming. The model also estimates the impacts of these interventions on the clients of the sex workers, their female partners and the general population. The levels of the program implementation, needed on each scenario, to achieve the required number of averted new infections are also modelled. This model can be used to estimate the risk of a population set to a sexually transmitted disease. Public health workers can use the model to prepare a fit for- purpose intervention program for specific community members.

Keywords

FWSS; FSW; MARPs; Simulation; Mathematical modelling; Python programming

Introduction

Nigeria, with a population of 186 Million (2016 estimate), is the largest country in Africa. With over 3.5 million HIV positive persons, Nigeria has the second highest HIV burden globally. An estimated 60% of new HIV infections in Western and Central Africa in 2015 occurred in Nigeria [1]. Men who have Sex with Men (MSM), Female who Sell Sex (FWSS) and People who Injects Drugs (PWID) account for about 32% of new HIV infections in Nigeria and are the worst affected population sets by the epidemic. FWSS account for 20% of new infections in Nigeria. The country level prevalence for Brothel-Based Female who Sell Sex (BBFWSS) was 19.4% in 2014.

Using FWSS in Nigeria as a case study, this research develops a novel risk equation for estimating new infections among FWSS, their clients and communities. It uses a hybrid SUDT and SIT structural model. It considers number of contacts, number of protected and unprotected sexual acts, population and other existing values as base inputs. Simulation of the model was done using python programming. The model also estimates the impacts of these interventions on the clients of the sex workers, their female partners and the general population. The levels of the programme implementation, needed on each scenario, to achieve the required number of averted new infections are also modelled. This model can be used to estimate the risk of a population set to a sexually transmitted disease. Public health workers can use the model to prepare a fit-for-purpose intervention program for specific community members.

Objective

The objective of this research is to develop a mathematical model to estimate how many indirect HIV infections will be averted among FWSS, their clients and the general population, attributable to prevention programs targeting female sex works in Nigeria. The model also estimates the impacts of these interventions on the clients of the sex workers, their female partners and the general population. The model includes a risk ratio used to estimate the impact of the programme on each of the sub-population sets. The number of new infections averted on the sex workers and their clients, attributable to different scenarios and levels of the programme implementation is presented in this paper.

Methodology

A mathematical model was developed, using python programming language. A hybrid S-U-D-T and S-I-T structures were used in designing the model. The I group of FWSS was further divided into U and D while S-I-T was used for other population groups. A Susceptible (S) group includes all members of the population set; The Undiagnosed (U) group includes infected members of the population that are yet to be tested. The Diagnosed are the tested and confirmed members of the population set. The Infected (I) group includes both tested and yet-tobe tested infected persons (I=U+D); The Treatment group involves all persons on treatment and care [2].

The model considers various factors affecting the implementations of the program. Current values of the model variables, as shown in the Table 1 below, served as baseline inputs to the model [3]. The variables include initial prevalence of HIV among FWSS, their clients; proportion of sex acts that are protected; initial population of the target group; duration of the intervention; number of sexual contacts per FWSS, average number of sexual acts per week, etc. A specific risk equation was developed for the FSW, incorporating the current value of each variable [4]. Three Scenarios of the model was estimated over a period of five years. The first scenario entails putting all infected FWSS on treatment, irrespective of their CD4 or WHO staging and keeping other variables constant. The model considers the impact of this scenario on the clients of the sex workers. Putting only eligible FSWs on treatment and increasing condom distribution was the second scenario. Universal access to treatment for all FSWs and their clients was then modelled. An uncertainty analysis was also carried out as part of the model [5] (Figures 1-7).

Parameters Description Symbol Value
(FWSS) Proportion of FWSS that are infected pSW 19.5
Initial prevalence in GF Proportion of Gen Female that are infected pGF 4.1
Initial prevalence in GM Proportion of  Gen Male that are infected pGM 3.2
Efficacy of condom Efficacy of condom ɛ 0.95
Proportion of FWSS that are infected with STI Proportion of FWSS that are infected with STI psti 28.5
Maturity rate Rate of maturity into adulthood for gen population m  15
No of acts 1 Number of acts in FWSS-Clients n1 200
No of acts 2 Number of acts in Gen Population n2 80
No of contacts 1 Number of contacts of the FWSS c1 432
No of contacts 2 Number of contacts of the Clients c2 50
No of contacts 3 Number of contacts of the GF, GM c3 2
Beta Rate of transmission (general unprotected sex) ß 0.01
Initial Population of GM Initial male population size NGM 39,208,214
Initial Population of FSW Initial FSW  population size NSW 308340
Initial Population of Clients Initial Clients  population size NCL 18500400
Initial Population of GF Initial Female population size NGF 38942286
Death rate among susceptible Natural death rate among susceptible FWSS, GF, CL & GM d 0.014
Death rate among infected Death rate among infected FWSS, GF, CL & GM µHIV 0.12
Death rate among those treatment Death rate among FWSS, GF, CL & GM on treatment µT 0.03
Tethar FSW Rate of migration from Undiagnosed to Diagnosed FWSS Ѱsw 0.6
Tethar Treatment FSW Rate of migration from diagnosed to treatment FSW ФSW 0.05
Tethar Treatment Rate of migration from infected to treatment ФT 0.33
Rate of migration/upwards zero Rate of migration from SGF to SFSW; and IGF to UFSW go 1
Rate of migration/downwards zero Rate of migration from SFSW and UFSW to SGF & IGF ho 1
Rate of migration/upwards  one Rate of migration from IGF & TGF to DFSW and TFSW g1 1
Rate of migration/downwards one Rate of migration from DFSW and TFSW to IGF & TGF h1 1
Rate of migration (Gen Male) Rate of migration from GM to Clients g2 1
Rate of migration (Gen Male) Rate of migration from Clients to GM h2 1
Rate of migration/upwards  zero Rate of migration from SGF to SFSW; and IGF to UFSW go 1
Rate of migration/downwards zero Rate of migration from SFSW and UFSW to SGF & IGF ho 1
Rate of migration/upwards  one Rate of migration from IGF & TGF to DFSW and TFSW g1 1
Rate of migration/downwards one Rate of migration from DFSW and TFSW to IGF & TGF h1 1
Rate of migration (Gen Male) Rate of migration from GM to Clients g2 1
Rate of migration (Gen Male) Rate of migration from Clients to GM h2 1

Table 1: Parameters, description, symbol and values used.

Figure 1: Schematic diagram of the FWSS sexual network. The size of the circles represents the relative size of the sub-populations. The arrows represent the predominant direction of HIV infection from infected people to uninfected people.

Figure 2: Interaction and model structure of female who sell sex (formerly known as FSW) and the general female (GF).

Figure 3: Interaction and model structure of the clients (CL) and general male (GM).

Figure 4: Impacts of treatment of clients on the general population.

Figure 5: Effects of increased testing on infected clients.

Figure 6: Impacts of treatment of FSW on the general population.

Figure 7: Increase in the treatment of clients reduces infection in general female.

Assumptions

• Net rate of migration from SFWSS group to SGF is the same as SGF>>SFWSS, (go/ho), UFSW and different from IGF to DFSW and TFSW

• There are different rate of migration for each of the sub-groups

• There are different death rate for Susc, Und, Diag, Infected and Treatment group

• No significant sexual contact between FSW and GF

• Individuals in the infected population, not on ART, lives for extra 10 to 15years

• Maturity rate of 15 years

• Only GF within the age range of 15-49 are considered

Modelling Equations

Equations for FSW

equation

Risk equation for FSW

equation

Equation for general female

equation

Risk equation for general female

equation

Client equation

equation

Client risk equation

equation

General male equation

equation

General male risk equation

equation

Results and Discussion

It was observed that if the status quo (37% of eligible positive FSW on treatment) is maintained, the new infection rate will gradually increase by 3.6% in five years’ time. Putting 80% of eligible positive FSWs on treatment will avert 2789 new infections in the same duration and reduce the current rate of new infections to 0.7. A slight decrease of 0.3% would be experienced in the general female population. Putting all FSWs on treatment returns a 89.7% reduction on the number of new infections among clients of FSW.

Conclusion and Recommendations

The simulation model reveals the efficiency of treatment in reducing the rate of new infections among FSWs, their clients and general female. The models reveal the importance of the investing in the FSW intervention programs now, rather in the future. The model outputs can be used to calculate the Quality Adjusted Life Years (QALY) to be gained during the intervention. A slight contribution of the total number of condom distributed to a reduction in new infection rate was also noticed. Further modelling scenarios are required to effectively infer on the efficiency of the intervention programs.

References

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