A New Model of How Risk Factors Affects Sexually Transmitted

Disease Incidences among MSM Population in Surakarta, Indonesia

using Structural Equation Modelling

Ancella Soenardi

, Reti Anggraeni

, Ari Natalia Probandari

, Endra Yustin

, Prasetyadi Mawardi

Harijono Kariosentono

Sebelas Maret University, Faculty of Medicine, Department of Dermatovenerology, Indonesia

Dr. Moewardi General Hospital, Department of Dermatovenerology, Indonesia

Sebelas Maret University, Faculty of Medicine, Department of Public Health, Indonesia

Keywords: MSM, Risk Factor, Sexually Transmitted Diseases, Structrual Equation Modelling

Abstract: Risk factors affecting Sexually Transmitted Diseases (STD) are complex, covering from sociodemographic

factor, sexual risk behavior factor and risky health behaviour factor to name a few. Structural Equation

Modelling (SEM) with path analysis can be used to illustrate the complex and correlated roles of each factors.

This cross sectional study was done in Surakarta. Population in this study was MSM population who visited

monthly mobile clinic for Voluntary Consultation Testing (VCT) testing and MSM patients who visited VCT

clinic of Dr Moewardi General Hospital between March 2017 to August 2017. A total 190 subjects were asked

to answer questionnaires about the risk factors and to provide biological samples (blood, urethral and anal

swab) to detect STD including syphilis, urethral and rectal gonorrhea, and non specific utethral and rectal

infection. Physical examination was used to diagnose genital warts. There were 67 (35,3 %) STD cases,

comprising of syphilis 39 subjects (58.2%), genital warts 25 subjects (37.3%), non specific proctitis 14

subjects (20.9%) and gonorrhea 1 subject (1.5%). The SEM model indicated that sociodemographic factor

has a direct effect to sexual behavior strongly by 0.86 point, and sexual behavior itself has a direct effect to

STD prevalence by 0.28 point. Sociodemographic factor however has a negative direct effect to STD

prevalence by -0.54 point. The model shows that by controlling sociodemographic factors especially single

and low educated individuals may enable us to reduce STD prevalence through control of sexual behavior.

1 INTRODUCTION

Sexually transmitted Diseases (STD) includes many

diseases caused by various pathogens, transmitted

mainly through sexual intercourse. STD and its

complications are one of five main problems of health

cares in developing countries (Diez, 2011). New

cases of STD each year globally reached 340 million,

especially among men and women aged 15-49 years

old, with South East Asia in the lead (Newman,

2015).

Men who have sex with men (MSM) are high risk

population for acquiring STD. In South East Asia, it

is estimated that the number of MSM reached 4-5

million men (WHO, 2010). In Indonesia, there are

approximately 0,9-1,2 million MSM, while in Central

Java, the number of MSM population is predicted to

about 190 – 240 thousand people (Kementrian

Kesehatan Republik Indonesia, 2014). Data from

Health Department of Surakarta only stated 6 STD

cases in MSM population within April – June 2016

(Kementrian Kesehatan RI, 2016). This shows that

STD prevalence in MSM is still an iceberg

phenomenon.

Incidence, prevalence and distribution of STD is

largely determined by complex role of various risk

factors such as demographic factos, economy, social,

sexual behaviour and risky health behavior (Aral,

2008). Some factors that may be associated with

increasing STD in MSM are loss of fear of HIV

transmission and an increase in oral sex role in STD

transmission (Illa, 2008). MSM in Indonesia is a

minority group and is therefore rarely included in

surveillance program by the National Health

Department, especially for STD screening. MSM

may play a significant role in HIV transmission in the

Soenardi, A., Anggraeni, R., Probandari, A., Yustin, E., Mawardi, P. and Kariosentono, H.

A New Model of How Risk Factors Affects Sexually Transmitted Disease Incidences among MSM Population in Surakarta, Indonesia using Structural Equation Modelling.

DOI: 10.5220/0008150200350039

In Proceedings of the 23rd Regional Conference of Dermatology (RCD 2018), pages 35-39

ISBN: 978-989-758-494-7

future, hence it is important for the government

authorities to understand the problem and to take

appropriate action.

Structural Equation Modelling (SEM) is a

multivariate statistical modeling technique widely

used in behavioral sciences to describe relationships

among observed variables. Path analysis is

considered a version of SEM. SEM has a very similar

function to multiple regression, however, SEM is

considered to be a stronger analysis technique

because it involves interaction modelling,

nonlinearity, correlated independent variables and

multiple latent independents variables measured

through many indicators (Schumacker, 2010).

The purpose of this study is to describe a model of

how risk factors affect the incidence of STD among

MSM population in Surakarta, Indonesia, using SEM.

By understanding the model, it may be easier to

combat STD among MSM population and to reduce

HIV risk transmission.

2 METHODS AND RESULTS

This cross-sectional study was conducted in

Surakarta, Indonesia, starting from March 2017 until

August 2017. Target population was MSM

population in Surakarta. In co-operation with the

Health Department of Surakarta, 190 MSM who

visited the mobile clinic for HIV screening or the

Voluntary Consultation Testing (VCT) Clinic of dr

Moewardi General Hospital, were asked to provide

biological samples for the screening of syphilis,

urethral and rectal gonorrhoeae and non specific

urethral and rectal infections. A dermatologist also

examined the study subjects physically for the

presence of anogenital warts. All MSM who supplied

biological samples were asked to voluntarily

participate in the behavioral survey using a structured

questionnaire. The ethics committee of dr. Moewardi

General Hospital has approved this study.

Syphilis infection was examined using a rapid

immunochromatographic assay that detects

antibodies against T. pallidum, SD Bioline Syphilis

3.0

(SD Biostandard Diagnostics Private Limited,

Gurgaon, Haryana, India). Screening for anogenital

warts was done through physical examination only.

Urethral and rectal swabs were taken to screen for

urethral and rectal gonorrhea and non-specific

urethral and rectal infections. The survey consists of

19 questions covering the sociodemographic, sexual

risk behavior and risky health behavior factors.

The data from the questionnaire were collected

and measured statistically using structural equation

modelling (SEM) path analysis type to see which

variables predominantly affect the dependent

variable, which were STD infection Statistical

analysis were considered significant if p value < 0.05,

with a 95% confidence interval. The statistical

analysis was done using AMOS

for Windows.

From 190 subjects, there were 67 (35.3%) cases

of STD, comprising of syphilis 39 subjects (58.2%),

genital warts 25 subjects (37.3%), non specific

proctitis 14 subjects (20.9%) and gonorrhea 1 subject

(1.5%) (Table 1). Data for sociodemographic factor,

sexual risk behavior and risky health behavior factor

can be seen in Table 2.

Model specification with SEM using path analysis

consist of 19 independent variables categorized into

three risk factors, the sociodemographic, sexual

behaviour risk and risky health behaviour factor. Our

model indicated p = 0.160, p>0.05, CMIN = 1,114 <

2; GFI = 0,920 > 0,90; RMSEA = 0,025 < 0,05; RMR

= 0,043 < 0,05 which resembled that the model fits

the empirical data well. The model indicated that

sociodemographic factor has a direct effect to sexual

behaviour strongly by 0.86 point, and sexual

behaviour itself has a direct effect to STD prevalence

by 0.28 point. Sociodemographic factor however has

a negative direct effect to STD prevalence by -0.54

point. Marital status (-0,84) and education level (-

0.33) has a negative effect on sociodemographic

factor. The number of sexual partner within 1 year

also has a negative effect on sexual behaviour (-1.65).

Sexual orientation (0.56) and previous HIV

examination (0.42) had a strong positive effect on

sexual behaviour. There was correlation between the

number of sexual partner within 1 year and

sociodemographic factor (0.20), and also the

correlation between place to meet partner and STD

prevalence (0.17). In this study, no significant

correlation between risky health behaviour and STD

prevalence were found (Figure 1).

3 DISCUSSION

This study showed that the prevalence of STD in

Surakarta is comparable to a study done by Rumana

et al (2013) in cities such as Tangerang, Jogjakarta

and Makassar in 2009, which were 32% of 599

RCD 2018 - The 23rd Regional Conference of Dermatology 2018

Table 1: Subjects Description of the study.

Table 2: Risk factores for Sexuallly Transmitted Diseases.

A New Model of How Risk Factors Affects Sexually Transmitted Disease Incidences among MSM Population in Surakarta, Indonesia using

Structural Equation Modelling

Figure 1. Path analysis of risk factors for STD.

MSM subjects (Rumana, 2013). However, the

number of syphilis cases in this study is much higher

than a study done by Morineau et al (2011) with 4.3%

of 749 MSM in Bandung (Morineau, 2011).

The high number of syphilis cases in Surakarta

demonstrated that syphilis may be a hidden disease,

with a lot of MSM population unaware of having this

disease. The low number of non specific proctitis in

this study may be due to a difference in examination

technique, where this study only used simple

laboratory examination using gram examination from

penile and rectal swab, while study such as Morineau

et al (2011) indicated a high prevalence of rectal

chlamydia and rectal gonorrhea using PCR

examination which is more accurate in detecting C.

trachomatis and N. gonorrhea (Morineau, 2011).

The data from Health of Department of Surakarta

indicates only 6 cases of STD within April – June

2016 (Kementrian Kesehatan RI, 2006), while our

study shows quite a lot of STD cases. This shows the

importance of creating a program to screen STD

regularly especially in high risk population such as

MSM so that any STD detected can be intervened as

early as possible to cut the transmission of HIV.

The SEM model indicated that risk factors for

STD are dependent to each other, hence to fight STD,

one must consider these risk factors as a whole,

instead of just handling one risk factor only. For

example, path analysis indicates that the most

influential sociodemographic factors are marital

status and education, especially those who are single

and low educated. The most influential sexual

behaviour factor is number of sexual partner within 1

year, sexual orientation and previous HIV

examination. By looking at this model, we can

conclude that by controlling sociodemographic factor

such as the low educated and single MSM, may

reduce STD prevalence through control of sexual

behaviour.

Some limitation of this study includes a small

number of samples and the simple physical and

laboratory examination used to diagnose STD. There

is a also a chance of recall bias when the subjects were

asked to answer questionnaires, for example when

they were asked about the number of sex partner until

now and age at first sexual intercourse. Variables

chosen to describe the model may not cover the

complexity of factors affecting STD, for example we

have not included variables such as knowledge and

perception about STD in this study.

We hoped that this study may be used by the

Health Department of Surakarta and Indonesia in

generating a regular STD screening program in MSM

population. We recommend that non profit

organization of MSM population can improve the role

of field facilitator in educating clients about the

importance of regular STD screening to detect STD

as early as possible, so as to reduce HIV transmission.

RCD 2018 - The 23rd Regional Conference of Dermatology 2018

4 CONCLUSION

The prevalence of STD, especially syphilis is still

high in Surakarta, compared to other cities such as

Bandung in 2011. Controlling sociodemographic

factors especially single and low educated

indivuduals may enable us to reduce STD prevalence

through control of sexual behaviour.

ACKNOWLEDGEMENT

The authors are grateful for the help of Health

Department of Surakarta, VCT Clinic of dr Moewardi

General Hospital and Mitra Alam Non profit

Organization for their co-operation to complete this

study

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