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Dispatch
HIV Epidemic among Young Thai
Men, 1991–2000
Kalyanee Torugsa,* Scott Anderson,† Nucharee Thongsen,* Narongrid
Sirisopana,* Achara Jugsudee,‡ Pitak Junlananto,‡ Sorachai Nitayaphan,*
Suebpong Sangkharomya,* and Arthur E. Brown*
*Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand;
†State University of New York at Cortland, Cortland, New York, USA; and
‡Army Institute of Pathology, Bangkok, Thailand
Suggested citation for this article: Torugsa
K, Anderson S, Thongsen N, Sirisopana N, Jugsudee A, Junlananto P, et
al. HIV epidemic among young Thai man, 1991-2000. Emerg Infect Dis [serial
online] 2003 Jul [date cited]. Available from: URL: http://www.cdc.gov/ncidod/EID/vol9no7/02-0653.htm
Characterization
of the HIV epidemic in Thailand has benefited from the systematic testing
of young men upon entry into the military. These data, which have shown
that public health measures can reverse an HIV epidemic, have been reanalyzed
with current geographic information systems methods. The resulting maps,
thus far, are the best means of visualizing the geography of the dynamic
HIV epidemic in Thailand.
In few nations is the HIV epidemic characterized as well as it is in
Thailand. As elsewhere, sentinel surveillance of high-risk populations
is performed and more general populations are partially assessed by HIV
testing of blood donors and pregnant women. In addition, Thailand has
benefited from another measure to assess the HIV epidemic in the general
population. Since very early in the epidemic, young men have been tested
for HIV at the time they entered into military service. Military medicine
and public health leaders recognized that, in addition to using this information
to benefit individual men, maintaining this information in a confidential
database linked to area of residence would allow monitoring of national
demographic trends in the epidemic. Data generated in this way have provided
key evidence that public health policy has reversed the trend of increasing
HIV infections (1), preventing an estimated 200,000 HIV
infections by 2000 (2).
The HIV prevalence data on young Thai men is provided to public health
policy makers on a regular basis. It has also been published in the scientific
literature (3,4) with data presented that uses provinces
as the scale of analysis. More recently, this unique dataset has been
analyzed by using geographic information systems (GIS). GIS methods allow
enhanced visualization of the trends, both geographic and chronologic,
within this well-documented HIV epidemic. Using a GIS approach, we present
the Royal Thai Army dataset, expanded to include the decade of 1991–2000
and data precision refined to the district level.
Methods
The methods of recruiting young men into the Thai military have been
described previously (3,4). Approximately 50,000–60,000
men, mostly 21 years of age, are selected each year by lottery in their
home province (the province in which they are listed on the house registration).
The system produces a representative national sampling of Thai men. Because
of their young age, HIV prevalence in these annual recruit classes may
serve as a proxy for HIV incidence. Induction into the military occurs
in May (M) and November (N) of each year. At the time when blood is collected,
recruits provide information about the location of their main residence
(including province and district) during the previous 2 years (3).
Although the actual locations where infections occurred are unknown, residential
data enables analysis of the association between HIV prevalence and this
key geographic marker.
To refine the HIV prevalence analyses, geographic localization uses districts
as the first administrative subunit of provinces. When data were analyzed
by using annual classes grouped at the district level, calculations of
the percentage testing positive for HIV were statistically unreliable
because the number of men tested in some rural districts was so small.
Therefore, we merged data in two ways to decrease variability attributable
to the small sample size of the prevalence figures: classes were combined
across time, and districts were combined across space. Sixteen classes
of men recruited from 1991 to 2000 were combined temporally into four
larger classes, each representing discrete 2-year periods: 1) N91, M92,
N92, M93; 2) N94, M95, N95, M96; 3) N96, M97, N97, M98; and 4) N98, M99,
N99, M00. Data on classes recruited from the M91, N93, M94, and N00 lotteries
were not available for analysis (M91, before full implementation; N93
and M94, protocol under revision; N00, completed after dataset closed).
However, even after combining classes into these 2-year periods, a number
of districts still had numbers too low for statistical reliability.
We also merged some districts with neighboring districts so that each
had a minimum denominator of 20 in the HIV prevalence calculation. Numbers
of >20 persons provide minimal, but acceptable, reliability
in the percentage-positive calculations. Districts with <20 were combined
with other districts according to the following protocol, following a
sequence of priorities: we combined districts if they were in the same
province, had historic connections (formerly part of single larger district),
had similarly small numbers tested, had similar demographics, and had
similar topography or other geographic features.
For the GIS analysis, data tables provided in Excel files (Microsoft
Corp., Redmond, WA) by the Armed Forces Research Institute of Medical
Sciences were joined to district-level GIS maps obtained from the Thai
Environmental Institute and National Statistical Office. We used Arcview
3.2a software (ESRI, Redlands, CA) to create dot density and choropleth
maps. (A choropleth map uses shades or colors to demonstrate the geographic
distribution of a range of values.)
Results
and Discussion
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Figure
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Click to view
enlarged image
Figure 1. Dot density map of young men who
tested positive for HIV at time of entry into the Royal Thai Army,
Thailand, November 1991–May 2000...
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Figure
2
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Click
to view enlarged image
Figure 2. Choropleth maps of HIV prevalence
in four classes of young men at time of entry into the Royal Thai
Army, Thailand, 1991–2000... |
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Figure 1 provides visual evidence of the geographic
distribution of the epidemic. Each of the 10,043 dots represents a recruit
who tested positive for HIV infection (positive for HIV-1 by enzyme immunoassay
and Western blot) among the 442,923 recruits tested. Arcview software
uses an algorithm that allows the random placement of dots within a geographic
area (in this case a district). By creating these dots at the district
level but presenting them at the province level, we show the vivid geographic
pattern of infection while illustrating the human impact of the epidemic.
The dot distribution does not distinguish between population density and
HIV prevalence. Nevertheless, these absolute numbers provide important
information regarding the potential HIV-related impact on the healthcare
system and various other social structures.
The evolution of the HIV epidemic over the course of the decade is seen
in the four maps shown in Figure 2. These maps
use gradients of color shading to distinguish the percentage of men who
tested positive for HIV during each 2-year period. The initial high prevalence
of HIV in men from the upper north region is illustrated, as is the decline
in prevalence over time (which likely occurred as a result of public health
interventions). The relative persistence of HIV prevalence in some districts
in the far south of the country is also shown, suggesting that locale-specific
features of the epidemic need to be well understood in that area, potentially
leading to changes in public health interventions.
Previous analyses conducted at the province level identified regional
variation quite clearly, but at a less detailed level (3,4).
The maps from our study indicate considerable variation even to the district
level of analysis, which suggests important local factors may be at work
in determining rates of transmission. The HIV epidemic in Thailand seems
to have largely missed some districts and devastated others, even within
the same province. Chiang Mai and Chiang Rai Provinces are examples of
this phenomenon. These maps also show that, although the growth of the
epidemic has slowed in most parts of the country, the epidemic has not
decreased in some districts in south Thailand. Choropleth maps best demonstrate
the changing prevalence of HIV among recruits from each district. The
dot map best demonstrates the intensity of the epidemic within and across
provinces. Both types of imagery are required to describe and understand
the epidemic and to suggest locales in which public health initiatives
are needed. In areas where the intensity of the epidemic is highest, health
and social services may require additional resources to serve persons
with the disease. In areas where the prevalence rates are highest or are
increasing, socially and culturally appropriate interventions may be needed
to strengthen and focus HIV programs.
These GIS maps are the most accurate maps available to date and are built
upon the unique HIV prevalence datasets collected over a decade by the
Royal Thai Army. GIS mapping at this descriptive level enhances visualization
of these data (5,6). More sophisticated methods of multivariate
GIS visualization could enhance this analysis even more but would require
the Royal Thai Army to assess recruits at induction on other variables,
especially risk factors, or to use recruits’ home addresses rather than
districts or provinces of residence. Nonetheless, changes in time and
place of chronic viral infection can be added to the recognized areas
of GIS application in epidemiology, transmission of vector-borne and water-borne
diseases, and environmental health (7). The maps also
illustrate the point that political borders may have no relationship to
epidemiologic boundaries. Differences in HIV prevalence often occur, not
only between regions and provinces but also within provinces and across
provincial borders. When an epidemic can be visualized at this level of
detail and accuracy, researchers can formulate and address questions regarding
the bases of these differences. At the same time, policy makers can better
direct intervention strategies and more finely assess the outcomes of
these interventions. Coordination of data collection and joint GIS analysis
by neighboring countries would enhance regional understanding of the emergence
and spread of HIV epidemics and monitoring of control programs.
Dr. Torugsa is a
research scientist at the Armed Forces Research Institute of Medical
Sciences (AFRIMS) in Bangkok, Thailand. Her research interests are in
infectious disease epidemiology, and her current focus is the establishment
of a disease surveillance and outbreak response system in the Royal
Thai Army Medical Department.
References
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- Sirisopana N, Torugsa K, Mason CJ, Markowitz LE, Jugsudee A, Supapongse
T, et al. Correlates
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