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R E S E A R C H A R T I C L E Open Access
Analyzing spatial clustering and the spatiotemporal
nature and trends of HIV/AIDS prevalence using
GIS: the case of Malawi, 1994-2010
Leo C Zulu
1*
, Ezekiel Kalipeni
2
and Eliza Johannes
3
Abstract
Background: Although local spatiotemporal analysis can improve understanding of geographic variation of the
HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent
declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on
pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for
2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level.
Methods: Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to
generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and
2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district
level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were
also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV
prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering.
Results: Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district
levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’while declining significantly
after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999)
localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots”clustered among eleven
southern districts/cities while a “coldspot”captured configurations of six central region districts. Preliminary multiple
regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R
2
= 0.688):
mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever,
and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of
high HIV-prevalence districts.
Conclusions: Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible
underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that
intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis
management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.
Keywords: HIV/AIDS, GIS, Spatiotemporal analysis, Clustering, Hotspots, Coldspots, Drivers, Malawi, Africa
* Correspondence: zulu@msu.edu
1
Department of Geography, Michigan State University, Geography Building,
Auditorium Road, East Lansing, MI 48824, USA
Full list of author information is available at the end of the article
© 2014 Zulu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited.
Zulu et al. BMC Infectious Diseases 2014, 14:285
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Background
Introduction
Understanding the nature and causes of spatial variation
in HIV prevalence is essential to understanding and ad-
dressing the epidemic. Geospatial analytical methods, in-
cluding geographic information systems (GIS), are an
essential tool for achieving this. Yet even with the sig-
nificant increase in the use of such geospatial tools in
understanding public health problems in planning and
implementing interventions and assessing their out-
comes, geographically explicit studies of HIV/AIDS in
sub-Saharan Africa are still very limited [1-3]. Existing
studies are limited predominantly to coarse continental
or cross-country analyses (e.g., [2,4-6]). Reasons for lim-
ited GIS use include scarcity of reliable spatially coded
data. While a detailed review of the GIS/public health
literature is beyond the scope of this article (but see
[7-10]), Nykiforuk and Flaman [11] identify four categor-
ies of GIS use from a review of 621cases published be-
tween 1990 and 2007, namely: disease surveillance, risk
analysis, access to health services and planning, and pro-
filing community-health service utilization. Our study
fits the surveillance and, partly, risk-analysis categories.
Malawi provides a fitting setting because it is one of
the six low-income countries with the highest HIV-
prevalence rates globally [12], and requires more effective
interventions even with recent declines in prevalence.
Understanding spatio-temporal patterns of the HIV
epidemic(s) in Malawi is limited to broad regional charac-
terizations or urban/rural differences (e.g., [13-15]), and
spatially differentiated knowledge of the underlying drivers
is limited [3,8].
This study uses primarily HIV prevalence data for
pregnant women attending antenatal clinics (ANCs) in
Malawi and spatial statistical tools to: 1) examine spatio-
temporal trends and clustering of HIV prevalence in
Malawi from 1994 to 2010, and 2) identify for the year
2010 variables associated with HIV prevalence and map
their spatial clustering and variation relative to HIV
“hotspots”and “coldspots”. We use women’s HIV data
from 19 ANCs to address objective 1 for the selected
years 1994, 1996, 1999, 2001, 2003, 2005, 2007, and
2010; and data from 54 ANCs to address objective 2 for
2010 (See Figure 1). Most notably, this study maps the
spatial distribution and clustering of the identified fac-
tors in order to begin matching configurations of ex-
planatory variables with particular clusters of HIV
hotspots at district level for potential spatial targeting of
HIV interventions in Malawi.
The 2012 global AIDS report placed Malawi among 25
countries with declines of 50% or more in new cases of
adult (age 15-49) HIV infection globally. Malawi is also
among countries reporting positive behavioral change or
covering 60-79% of eligible people in antiretroviral
therapy (2001- 2011), and is among 32 countries with a
25-49% decline in HIV deaths (2005-2011) [12]. Yet at
10.0% in 2011 [12,16], HIV prevalence remains a formid-
able challenge 26 years after the first diagnosis in1985.
Among sex workers, prevalence was up to 70%, while
923,058 of Malawi’s 14 million people were living with
HIV/AIDS in 2010 [13]. Nearly 600,000 children were
orphaned due to HIV/AIDS [17], and there are glaring
gender and urban/rural disparities. HIV prevalence was
2.2 times higher among female than male youth aged
15-24, and 2.8 times higher among urban women than
their rural counterparts (22.7% versus 10.5%) in 2010
[14]. Further, decline in HIV prevalence in the general
population has been modest –from 11.8% in 2004 to
10.6% in 2010. As with other epidemics, understanding
such spatial variation in HIV/AIDS prevalence and its
drivers within a social, spatial and temporal context is
crucial for spatial targeting of interventions and re-
sources [1,7,18].
This article is divided into five parts. First, we briefly
review the literature on the use of GIS and spatial ana-
lysis in analyzing HIV prevalence, and on commonly
found drivers of HIV risk and prevalence. A presentation
of broader spatiotemporal patterns of HIV prevalence at
national and regional scale, including the significant
urban/rural divide, follows. Third, the overall spatial
structure of the epidemic is examined based on spatial
statistical analysis of the presence, nature, temporal
trends and implications of spatial dependency in preva-
lence rates. This knowledge is used to decompose spatial
patterns of HIV prevalence first into continuous surfaces
using spatial interpolation techniques in order to show
continuous spatiotemporal variation nationally, and sec-
ond at district level after spatial aggregation (averages).
The fourth section presents analysis of local clustering
patterns, or “hotspots”and “coldspots,”of HIV preva-
lence at district level.
a
The fifth part presents models of
indicative drivers of HIV prevalence identified from mul-
tiple regression analysis and mapping of the spatial vari-
ation and clustering of the drivers. Study findings and
implications are then discussed, and limitations pre-
sented before offering a concluding section. The meth-
odology used is presented at the end.
Use of GIS and geospatial analytical methods in
understanding HIV prevalence
Among the limited but growing uses of GIS in HIV ana-
lyses in Africa, very few studies have analyzed spatiotem-
poral variation in HIV prevalence, including clustering
patterns, at meso (e.g., district) or lower scales, where
better understanding can enhance the effectiveness of
interventions [19].
b
Occasional studies during early parts
of the epidemic demonstrated the value of geographic
analysis in understanding HIV prevalence, risk, and
Zulu et al. BMC Infectious Diseases 2014, 14:285 Page 2 of 21
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spread; including in mapping the distribution of at-risk
populations of commercial sex workers and uncircum-
cised males [20,21]. However, most of the few recent
GIS-based analyses of HIV/AIDS in Africa have been
mainly at coarse continental scale with limited national
policy relevance (e.g., [5,6]), or at fine local scales requiring
heavy data collection/use but with a limited spatial scope,
e.g., assessment of local HIV variability/clustering and
risk in a rural sub-district in northern KwaZulu-Natal
Province, South Africa [1,22].
c
Very few exceptions have
analyzed country-wide spatial variation and clustering of
HIV prevalence, and their drivers. Messina et al. [3] con-
ducted such a study involving sex differentiated spatial
variation in HIV prevalence using 2007 demographic
and health survey (DHS) data, GIS, and regression analysis
of HIV drivers at “community”(neighborhood or village-
clusters) scale in the Democratic Republic of the Congo
(DRC). Moise and Kalipeni [23] recently used GIS and
HIV sentinel data for pregnant women attending ANCs
in Zambia to analyze spatiotemporal patterns of HIV
Figure 1 Malawi’s administrative boundaries and location of sentinel antenatal clinic. The figure shows the original 19, expanded 54
(in 2007), and overlapping ANC network for HIV surveillance. It also shows Malawi’s three regions, 28 districts, and four major cities, but Likoma
(Island) was left out of the district regression analysis.
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prevalence from 1994 to 2004. They also used spatial stat-
istical regression modeling to identify possible drivers
of HIV prevalence for 2004 at the district scale, where
health-related data are reported and planning and decision-
making done. Despite limitations involving sample
representativeness and paucity of data compared to
population-based (e.g. DHS) HIV data, ANC data are
more readily available, allow longitudinal trend analysis,
and can be re-scaled to district or other scales using
spatial interpolation techniques [2,6]. Our study uniquely
goes beyond HIV “hotspot”analysis to map the spatial
variation of likely drivers allowing identification of config-
urations of explanatory variables for particular clusters
of HIV hotspot districts for more effective intervention
targeting.
Common drivers of HIV/AIDS prevalence
HIV epidemics are diverse and complex to deal with be-
cause their drivers are numerous, diverse and vary over
space and time. Given that most HIV transmission in
sub-Saharan Africa is through heterosexual intercourse,
the major proximate driver is known −having unpro-
tected sex with an infected person, and the higher the
number of cumulative and concurrent sexual partners,
the higher the risk of transmission [12,14,24]. Yet recent
research shows that there are underlying HIV drivers that
go deeper than biomedical or narrow epidemiological sus-
ceptibility factors. These include diverse socio-economic,
demographic, cultural, historical, and geographic factors
and their configurations that affect the vulnerability of
particular groups of men and women who engage in such
risky sexual behavior to HIV infection [25-29].
For Malawi, reported underlying HIV drivers have in-
cluded high levels of poverty (and wealth), low literacy,
high rates of unprotected casual and transactional sex,
low male and female condom use, cultural (e.g., widow
cleansing and/or ‘hyena customs’) and religious factors,
gender inequity and low social and economic status of
women, high-risk livelihoods, high migration/mobility
levels, high incidences of sexually transmitted diseases
and tuberculosis, and geographic factors mainly involv-
ing poor access to health services or increased exposure
to risks. For instance, several curable sexually transmit-
ted diseases can increase the risk of HIV transmission
2-20 times per sexual contact, and prevalence is higher
among those with poor access to prompt treatment [15].
Poverty has forced some women into commercial sex
or other risky sexual behavior in order to survive, in-
creasing their risk of contracting/spreading HIV/AIDS
[30,31]. Therefore, conditions of high unemployment,
and low and insecure wages that lead to such behavior
may help explain high HIV rates among the urban
poor. Tough economic conditions have also driven
international male labor migration, including into
mining hubs in South Africa where men are separated
from their spouses for extended time periods and vul-
nerable to casual sex and HIV infection, and spreading
HIV in their areas on their return [32,33]. Geographic-
ally, high HIV infection has been associated with close
proximity to major transportation networks which have
provided transmission arteries for HIV within and across
countries, and to urban and trading centers and trans-
port networks linking them [3,6,34].
Methods
HIV prevalence data and temporal trends at national,
regional and urban/rural scales
HIV/AIDS data came from mean HIV prevalence rates
among pregnant women attending a longitudinally rich
network of 19 ante-natal clinics (ANCs) where HIV sur-
veillance has been conducted from 1994 to 2010. In
2007, the network was nearly tripled to 54 ANCs while
maintaining the original 19, allowing continuity and spa-
tiotemporal analysis (Figure 1). Data included the loca-
tion (latitude and longitude coordinates) of the ANCs,
allowing their mapping. HIV data collection frequency
was annual from 1994-1999 and subsequently largely
biennial. Although limited demographic information is
also collected, only HIV prevalence was available to the
authors for all the years, sourced from U.S. based Centra
Technology Inc. (1994-2003) and HIV/syphilis surveil-
lance reports produced by Malawi’s National AIDS
Commission, NAC [13,35,36]. Malawi uses standard
sampling, HIV testing, and data analysis methods and
models recommended by the Joint United Nations Pro-
gram on HIV/AIDS (UNAIDS) and the World Health
Organization (WHO) (e.g., [37,38]).
d
Despite known lim-
itations of such sentinel based HIV prevalence estimates
[18,39], they remain the major source of HIV data in
Malawi and other African countries, and the only longi-
tudinal record for analyzing the spatiotemporal trends
targeted in this study without projecting to the general
population or generating predictions.
e
Spatial dependence in HIV prevalence, spatial
interpolation, and spatiotemporal trends
First, we plotted HIV prevalence rates for pregnant
women 15 to 49 years old [13,17] from 1995 to 2010) to
provide a broad, multi-scalar, spatiotemporal perspective
of the HIV epidemics at national, regional, urban and
rural scales. Then GIS tools were used to 1) empirically
test for spatial dependency in HIV prevalence nationally,
2) produce a continuous surface of HIV prevalence at
1 × 1 km spatial resolution for visualization and gener-
ation of prevalence estimates at district level for cluster/
hotspot and regression analysis. GIS analysis was con-
ducted with AcrGIS desktop 10.0 (Redlands, CA: Environ-
mental Systems Research Institute, Inc., 1999).
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The presence and nature of HIV spatial autocorrel-
ation (or dependency) was assessed empirically for each
of the available 17 data years spanning 1994 to 2010 for
the original 19 ANCs, using the global Moran’s I statistic
[40]. The presence of spatial autocorrelation can suggest
HIV clustering, sometimes indicative of hierarchical ex-
pansionary spread in urban areas and across districts
[41]. Moran’s I is based on Waldo Tobler’s first law of
geography: “everything is related to everything else, but
close things are more related than distant things”[42]. If
this law applies, HIV prevalence rates should be similar
among neighboring districts than among non-neighbors.
Moran’s I tests the null hypothesis that measured values
at one location are independent of values at other loca-
tions (i.e., HIV prevalence is randomly dispersed). Its
value varies from -1 to 1. Positive values indicate pres-
ence of spatial autocorrelation, zero means total spatial
randomness, and negative values indicate dissimilar values
clustered next to one another. A statistically significant
Moran’s I (p < 0.05) leads to rejection of the null hypoth-
esis and indicates the presence of spatial autocorrelation.
Global Moran’s I is computed as follow:
I¼
NXiXjwij Xi‐
XðÞXj‐
X
XiXjwij
XiXi‐
XðÞ
2
where N is the number of spatial units (sentinel ANCs),
X
i
is the measured value for feature I (up to N), X
j
is the
measured value for a neighboring point j (up to N-1),
and W
ij
represents a weight measure of the influence of
neighboring feature j on measured value at I derived
from the row-standardized spatial weight matrix.
In order to produce smooth surfaces of HIV preva-
lence for visualization and data generation at district
level, the Inverse Distance Weighted (IDW) spatial
interpolation method was used for the selected years
(1994, 1996, 1999, 2001, 2003, 2005, 2007 and 2010).
These years were chosen for trend continuity while
including years of positively autocorrelated HIV preva-
lence (partly justifying use of IDW) and early years
(1996, 1999, 2001) of non-significant and/or negative
autocorrelation which nevertheless illuminate spatiotem-
poral patterns. Spatial interpolation methods apply
mathematical models to measured point values of a
continuous variable at known locations to predict values
at locations that do not have values, thereby creating a
continuous surface [43,44]. In predicting values, interpolation
methods generally use distance-based weights that assign
more influence to measured values nearest an unmeasured
location than to measured values located farther away.
Deterministic interpolators, including IDW, use weights
based only on distance between measured and unmeas-
ured points while geostatistical (or stochastic, e.g., kriging)
use sophisticated weights combining distance with prob-
abilistic statistical models of the spatial variation among
measured points. IDW produced stable and reasonably reli-
able predictions for cross-year comparisons with the small
sample size (from 19 ANCs). It has been used reliably with
small-medium samples in HIV studies [2,23], at times pre-
ferred over (potentially superior) kriging whose performance
often suffers more with small samples because of probability
distribution requirements [44,45]. With IDW, we used a
variable setting of 6-10 points to predict values at each un-
known location based on iterative testing to minimize mean
error and root mean square error (RMSE). We then used
GIS tools to extract HIV estimates for the 31 ‘districts’
(27 of Malawi’s 28 districts and four main cities of Blantyre,
Lilongwe, Zomba and Mzuzu, Figure 1) by averaging preva-
lencevaluesinconstituent1×1kmspatialcells.
Local spatiotemporal variation in HIV prevalence and
cluster/‘hotspot’analysis
Two local measures of spatial association were used within
ArcGIS 10.0 to indicate “where the clusters or outliers are
located”and “what type of spatial correlation is most im-
portant”[46]. Anselin Local Moran’sI[46]allowedustode-
tect core clusters/outliers of districts with extreme HIV
prevalence values unexplained by random variation, and to
classify them into hotspots (high values next to high, HH),
“coldspots”(low values next to low, LL) and spatial outliers
(high amongst low, HL or vice versa, LH). Local Moran’sI
tests the same null hypothesis of absence of spatial depend-
ence (for polygon features) when its expected value is -1/
(N - 1). It has been used in studies to identify HIV hotspots
[15,18,23]. Further, the local Getis-Ord statistic, Gi*was
used to provide additional information indicating the
intensity and stability of core hotspot/coldspot clusters
[47,48]. The statistical significance of a Z-score assigned
to each district identified the presence and intensity of
local clusters of hotspots and coldspots of HIV preva-
lence within a radius of 80 km, relative to the hypothesis
of spatial randomness. This fixed distance, identified it-
eratively as maximizing autocorrelation (global Moran’s
I) and maintaining stability across years [44], defined
the neighborhood search for a particular district, in-
cluding for analysis with Anselin’sLocalMoran’sI.The
Getis-OrdGi* index is calculated as:
G
i¼X
n
j¼1
wi;jxj‐
XX
n
j¼1
wi;j
S
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
nX
n
j¼1
w2
i;j
‐X
n
j¼1
wi;j
!
2
n‐1
v
u
u
u
t
ð1Þ
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Where x
j
is HIV prevalence for district j,w
i,j
is the
spatial weight between districts iand j,nis the total
number of districts (31), and
X¼X
n
j¼1
xj
nð2Þ
S¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
n
j¼1
x2
j
n
‐
XðÞ
2
v
u
u
u
u
tð3Þ
Regression analysis and indicative drivers of HIV
prevalence for 2010
In order to identify and illustrate potential linking of in-
dicative drivers of observed spatial variation of HIV
prevalence to particular hotspot/coldspot clusters of dis-
tricts, we conducted multiple regression analysis of HIV
prevalence for 2010 only, and then mapped the spatial
distribution and clustering of selected factors. HIV
prevalence per district among pregnant women attend-
ing ANCs, the dependent variable, was estimated using
GIS as explained earlier for cluster analysis, but using
the latest sentinel HIV data (2010) from the 54 ANCs
(instead of 19). The year 2010 also matched dates of
available explanatory factors closely.
Independent variables were chosen based on the litera-
ture (background section) and availability at district
level. Main sources of independent variables were na-
tional surveys conducted by the Malawi National statis-
tical Office (NSO) and GIS-generated data. Surveys
included the 1998 census, 2011 Welfare Monitoring
Survey (WMS) and 2010/2011 Integrated Health Survey,
IHS3 [49-51]. Variables included socio-demographic
(e.g., education, poverty/wealth/consumption, population
density and mobility, employment, often by age/sex),
HIV awareness and behavior (value and use of condoms,
self-reported HIV testing in 2010 or ever, and gap be-
tween awareness and behavior on HIV testing). Syphilis
prevalence for 2010 was the only socio-biological vari-
able used that was available to us [13]. Geographic vari-
ables were also used to address underlying factors
related to access to HIV related amenities/services
(distance/time to roads, public transport, and health
facilities), mobility and exposure to higher risks (proxim-
ity to cities), and elevation, sourced from surveys or gen-
erated using GIS processing (Table 1). The starting pool
of independent variables was 37. They are not necessar-
ily the most important in explaining variation in HIV
prevalence, but the subsequent multi-stage statistical
screening process in correlation analysis and later
stepwise regression analysis narrowed them down to
some significant factors that adequately reflect observed
spatial variation among pregnant women in the 31 dis-
tricts of Malawi. All the data used in this study are pub-
licly available, aggregated secondary data (see Table 1)
which do not have any personal information or identify-
ing information that can be linked to particular indivi-
duals or communities. Consequently, there were no
significant ethical concerns, or approval (or permission)
needed to use the data. All sources, however, have been
acknowledged.
Correlation analysis against HIV prevalence was used
to screen the initial variable pool, yielding the 18 statisti-
cally significant (p ≤0.10) ones listed in Table 1 (the full
list is available on request). We used further correlation
analysis among the 18 to narrow the significant variables
to 13 (variable names marked with the superscript
a
in
Table 1) by removing highly correlated variables (gener-
ally r > 0.7, p ≤0.05). For instance, mean distance to
health facilities was dropped because it was highly corre-
lated (r = 0.784, p = 0.000) with distance to main roads but
slightly less correlated with HIV prevalence. We entered
the 13 independent variables into SPSS 20.0 (IBM SPSS
Statistics for Windows, Version 20.0. Armonk, NY: IBM
Corp.) for multiple regression using forward stepwise
entry after standardizing them to Z values to stabilize vari-
ability and curb observed remnant multi-collinearity.
Several collinearity diagnostics and partial significance sta-
tistics were used to further limitmulticollinearity problems
and to pick a “best”model among the four produced.
Variables in the “best”model had to have a Variance In-
flation Factor (VIF) below 2, Condition Index below 30,
and tolerance values above 0.5 to signify non-significant
collinearity. Additional diagnostics on the “best”model
confirmed multi-collinearity and heteroskedasticity not
to be significant problems. Cluster/hotspot analysis and
mapping were performed on variables from the “best”
model and ancillary correlation analysis used to explain
observations.
The narrow focus of the second study objective on
identifying and mapping spatial patterns of indicative
explanatory factors of HIV prevalence, rather than produ-
cing predictive models, and their basis on the literature,
should mitigate concerns over automated variable-selection
methods [52]. For the same reasons, we maintained a sim-
ple ordinary least squares (OLS) model. Additional spatial
diagnostics on the “best”OLS model using the GEODA
spatial statistical software (GeoDa Center for Geospatial
Analysis and Computation, Arizona State University) con-
firmed expected breach of the OLS independence assump-
tion given the significant spatial dependence. Although the
corrective spatial lag model [51,53] and significant of its
autocorrelative coefficients and individual variable coeffi-
cients were generally an improvement on the OLS model,
except a slight decline in the significance of one variable, it
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did not sufficiently change the essentials of the OLS model.
Thus,wereportonlyresultsoftheOLSmodelforpur-
poses of this study.
Results
Temporal and spatial trends in HIV prevalence at national
and regional scales
In Malawi, two broad geographic trends emerge of HIV/
AIDS prevalence among pregnant women attending
ANCs: 1) a significant overall decline in prevalence since
the peak of the epidemic in 1999, and 2) multiple
geographically defined HIV ‘epidemics’with diverse spa-
tiotemporal trends. National median HIV prevalence in-
creased from 16% in 1995 and peaked at 22.8% in 1999
before declining to 10.6% in 2010 –an average annual drop
of 1.1% (Figure 2A). The Southern Region consistently had
the highest HIV prevalence, of 7.0% higher than the na-
tional prevalence (1996 and 2007), but narrowing to a 4.4%
gap by 2010 (Figure 2A). The Northern Region had the
lowest before its trajectory essentially merged with the
Central Region’s from 2003. An urban/rural divide con-
trasts a severe urban epidemic with a less intense and vari-
able, and slower/lower peaking rural epidemic (Figure 2B).
However, the intensity of the two epidemics has been con-
verging from a 2.8-fold difference in HIV prevalence (28%
urban versus 10% rural) in 1995 to 1.5-fold by 2010. The
urban epidemic peaked earliest and highest (1996 at 27%),
and declined slower (average 0.73% annually) than the na-
tional epidemic to 16.1% in 2010. The semi-urban epidemic
varied considerably (1995 to 1999), and then settled below
the urban trajectory. The rural epidemic was relatively stag-
nant between 10% and 15%.
Table 1 Summary of selected variables significantly correlated with 2010 HIV prevalence
Variable name Variable type, description Mean Min. Max. SD Pearson r Variable source
Dependent variable
HIV_Y10 Estimated HIV prevalence, 2010 (%) 11.65 6.00 22.40 4.32 1.000 2010 HIV/syphilis report,
NAC 2011
Socio-demographic
POPDEN08
a
Persons per km
2
, 2008 433.94 36.00 3006.00 797.20 0.474*** 1998 census, NSO 2008
ED_S_PRIM
a
Attended senior primary sch. (%) 29.068 19.70 45.10 6.180 -0.445*** WMS 2011, NSO 2012
MIGRGROSS
a
Gross migration (in and out migration), 2008 (%) 35.839 19.00 102.00 18.817 0.376** 1998 census, NSO 2008
UNEMP_FEM
a
Unemployed female, 2011 (%) 22.494 1.30 73.50 18.197 0.415* WMS 2011, NSO 2012a
POPP25_49 Population, age 25_49, 2008 (%) 44.958 41.50 56.00 3.949 0.309** 1998 census, NSO 2008
URB_DISTR
a
District share of total urban population, 2008 (%) 3.213 0.00 33.7.00 8.160 0.382** 1998 census, NSO 2008
CONSUME
a
Per capita consumption, 2011 (MK) 54076 26645 152907 28017 0.46** IHS3, NSO 2012b
UNEMP_MALE Unemployed male, 2011 (%) 19.77 2.20 63.20 16.133 0.320 WMS 2011, NSO 2012a
HIV awareness and behavior
TEST_EVER2
a
Percentage that had taken an HIV test ever 66.542 51.50 84.50 7.833 510*** WMS 2011, NSO 2012a
HTEST_POSS2
a
Percentage who know a confidential HIV test is possible 84.439 63.50 97.10 9.684 0.323* WMS 2011, NSO 2012a
Socio-biological
SYPHILIS
a
Proportion of women positive for syphilis (%, 2010) 0.285 0.03 1.32 0.268 0.462*** 2010 HIV, syphilis report,
NAC 2011
Geographic (proximity, access, exposure)
DIST_HF Mean dist. to HF 18.915 4.00 38.60 8.790 −0.567*** GIS derived, 2001 HF
data, MoH
Dist_HF30_44
a
Mean dist. to HF, age 30_44 16.161 5.90 34.30 6.898 0.365** WMS 2011, NSO 2012a
DIST_RD
a
Mean dist. to main roads (km) 6.224 1.00 16.40 3.489 -0.605*** GIS based, roads layer from
Malawi Surveys Dept.
DISTCITY
a
Mean dist. to major city (km) 53.761 0.00 164.83 40.323 -0.540*** GIS based, cities map, NSO
T_RD30_44 Mean time to all-weather road, age 30_44 (min) 8.961 1.10 28.60 5.135 0.370** WMS 2011, NSO 2012a
T_TRANS30_44
a
Mean time to transport, age 30_44 (min) 12.329 4.40 25.20 4.882 0.396** WMS 2011, NSO 2012a
DIST_HF45_59
a
Mean dist. to HF, age 45_59 13.542 5.30 33.3 4.307 0.302* WMS 2011, NSO 2012a
Variables were screened from an initial list of 37. Superscript
a
indicates one of 13 variables entered in to the regression model. The superscripts *, **, and ***
indicate that the correlation is significant at the 0.05, 0.01 and 0.00 level (2-tailed, respectively. HF is health facility, IHS3 is the Third (2010/11) Integrated Health
Survey, MK is the Malawi Kwacha (Malawi’s currency), MoH is Ministry of Health, NAC is the Malawi National AIDS Commission, NSO is the Malawi National
Statistical Office, and WMS is the Welfare Monitoring Survey.
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Spatial autocorrelation, GIS interpolation mapping and
spatiotemporal trends
Spatial analysis showed the presence of positive spatial auto-
correlation (global Moran’s I > 0) in HIV prevalence among
pregnant women for eight of the 11 available data years, five
of them statistically significant (p < 0.01, see Figure 3) and
confirming the presence of spatial structure. However, there
was significant temporal variation in the spatial dependence
of HIV prevalence, including early years (1995, 1996, 1999)
years of negative autocorrelation followed by a general
increase in the size and significance of Moran’sI(range-1
to 1) from -0.09 in 1999 to peak at 0.474 in 2007.
Continuous surfaces of HIV prevalence (Figures 4
and 5) and district estimates extracted thereof (Figures 6
and 7) confirmed and spatially unpacked regional and
urban/rural variation in HIV prevalence among pregnant
women. In addition, Figures 4 and 5 capture general in-
tensification of the HIV epidemic in prevalence and
spatial extent from 1994 to 1999. HIV intensity attenu-
ated gradually from above 25% in 1999 to generally
below 12.5% nationally by 2010. Sub-epicenters emerged
around Nkhata Bay district and Mzuzu city in the
Northern Region and Lilongwe City and Mchinji district
in the Central Regions, but had dissipated by 2003. Dis-
trict prevalence estimates (Figures 6 and 7) confirmed the
persistently high rates in the south, general intensification
from 1994 to 1999 and subsequent decline in prevalence
and spatial extent of pockets of high/low prevalence.
Figure 2 National, regional, rural, and urban trends in HIV prevalence for pregnant women, 1995-2010. A shows the national median
prevalence rate (labels on chart, percent) relative to trends for the northern, central and southern regions. Bshows temporal trends in HIV
prevalence by residence type: urban, semi-urban and rural. Data sources: Government of Malawi 2012, NAC 2011.
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Change analysis based on continuous images (Figure 8)
and district-level analysis (Figure 9) showed spatial vari-
ation across the four time periods analyzed dominated by
the initial expansion (1994-1999) and subsequent decline
in HIV prevalence. Most of the initial prevalence increases
were in the Central Region and northern parts of the
SouthernRegion,withpocketsinthenorth.Overall,major
decreases in HIV prevalence (darker green areas in
Figures 8 and 9) had occurred mainly in the Southern Re-
gion, with some pockets in the Central and Northern
Figure 3 Global Moran’s I and Spatial Dependence in HIV Prevalence, 1994-2010. The black bars in Figure 3 show global Moran’s I values in
years for which the statistic (and spatial autocorrelation) was statistically significant at p≤0.01, the dark grey at p = 0.05 or p = 0.10), and the light grey
bars for years with no statistical significance. The positive Moran’s I values indicate positive autocorrelation, i.e., HIV prevalence values at neighboring
locations were similarly high or low, while negative values indicate negative autocorrelation with high prevalence values next to low ones.
Figure 4 Interpolated spatiotemporal trends of the HIV/AIDS Epidemic among pregnant women, 1994 –2001. Continuous images
produced by interpolating (IDW method at 1 km spatial resolution) HIV prevalence (%) among pregnant women attending the original 19 HIV
sentinel centers in 1994, 1996, 1999 and 2001.
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regions. However, by the period 2003-2010, declines in HIV
prevalence were clearly dominated by the Northern and
Southern regions. Three Central Region districts (Lilongwe
district/City, Dedza and Salima) had increased in HIV
prevalence slightly by 2010 relative to 1994 while one central
(Salima) and two northern (Chitipa and Karonga) districts
had gained after 1999, peak of the epidemic.
Local spatial variation and hotspot analyses of HIV prevalence
Unpacking observed spatial patterns further through local
spatial analysis revealed statistically significant clustering of
districts into ‘hotspots’and ‘coldspots’of HIV prevalence
and significant change over time. The Anselin Local Moran’s
I showed core clustering of high HIV-prevalence districts
next to high ones (HH) consistently located in the southern
region and variously composed mainly of 11 districts (Blan-
tyre, Blantyre City, Chikwawa, Chiradzulu, Mulanje,
Mwanza, Neno, Phalombe, Thyolo, Zomba, Zomba City)
(Figures 10 and 11). While relatively stable during the years
that had statistically significant autocorrelation (Figure 3),
the hotspot cluster, located at the southern end by 2003, had
expanded to include southeastern districts of Chiradzulu,
Mulanje and Phalombe by 2007 before shrinking to nine
districts and shifting slightly northwards by 2010. Analysis
also showed a core “coldspot”cluster of low next to low
(LL) districts variously composed of six Central Region
districts (Kasungu, Dowa, Ntchisi, Nkotakota, Salimaand
Dedza). The coldspot cluster was largest in 2003 and 2005
but had shrunk to 2-3 districts by 2007 and 2010.
Statistically significant spatial outliers (HL, LH clustering)
were evident only for 1995 and 1996, years that had negative
spatial autocorrelation (Figure 3). This illustrates empirically
that the lack of significant global (first order) patterns of
positive autocorrelation at district level for these years was
because of the dominance of more localized (second order)
spatial variability in HIV prevalence relative to primary order
spatial patterns. Thus, Lilongwe City had exceptionally high
HIV prevalence next to a low prevalence neighborhood (HL
in Figure 10) in 1995 and 1996, while Zomba City had sig-
nificantly low prevalence surrounded by high prevalence dis-
tricts (LH). Further, these two years had the least (first
order) hotspot clustering (Figures 10 and 11). These years of
localized variability in HIV prevalence represent fast HIV
spread (Figures 4-9), suggesting potential spatial expansion-
ary trend of the epidemic, e.g. outward from Lilongwe City
and inward into Zomba City from neighboring Blantyre
City, Chiradzulu and Mulanje.
Potential drivers of HIV prevalence for 2010 and their
local spatial patterns
Ordinary Least Squares (OLS) regression of estimated
district-level HIV prevalence for 2010 produced four models
Figure 5 Interpolated spatiotemporal trends of the HIV/AIDS Epidemic among pregnant women, 2003 –2010. Continuous images
produced by interpolating (IDW method at 1 km spatial resolution) HIV prevalence (%) among pregnant women attending the original 19 HIV
sentinel centers in 2003, 2005, 2007 and 2010.
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and we chose Model 4 as the “best”model based on collin-
earity diagnostics and explanatory power [see Table 2].
Model 4 had four variables, each statistically significant (p =
0.024 to p = 0.000) and no significant multi-collinearity
problems (tolerance 0.59-0.89 and VIF 1.12-1.69). The four
variables used in all four models in Table 2 were selected
through a multi-stage statistical process from an initial pool
of 37 variables drawn from the literature and based on avail-
ability at district level. The model F statistic was highly sig-
nificant (p = 0.000) and explained 68.8% of the variance (R
2
= 0.64, adjusted R
2
= 0.62).
Mean travel time to nearest public transport for ages
30-44 was positively associated with HIV prevalence and
had the highest influence (highest Beta value) in explain-
ing prevalence among the four variables. The longer it
took to travel to the nearest public transport, the higher
the HIV prevalence at district level. Spatial analysis
of this variable revealed a core HH cluster covering
Mulanje and Phalombe Districts within the southern
HIV hotspot, and a matching coldspot (Ntchisi district)
in central Malawi. Analysis with the Getis-OrdGi* statis-
tic added Thyolo and Zomba districts as secondary and
tertiary intensity clusters, respectively. However,
this variable was essentially uncorrelated (r = -0.045,
p = 0.810) to the other geographic and third most influ-
ential variable in the model, mean distance to main
roads. HIV prevalence decreased with mean distance
from main roads. Its local spatial variation revealed a
single coldspot (areas closest to main roads) capturing
Blantyre City and neighboring Blantyre Rural and Chir-
adzulu districts. However, differentiated hotspot analysis
with the Getis-OrdGi* statistic showed a secondary cold-
spot cluster which was closely matched with the core
HIV hotspot cluster for 2010 (Figures 12 and 13).
The proportion that had ever taken an HIV test was the
only behavioral, and the second most influential, explana-
tory factor for HIV prevalence, and was (counterintuitively)
positively associated with HIV prevalence (Table 2). Only
Zombacityemergedasacorehotspotforthisvariableand
Lilongwe City as a spatial outlier (Figure 12). The percent-
age of those who had taken at least an HIV test was highest
in Lilongwe city (81.9% of adults, hence an HL outlier) in
relation to the exceptionally low Lilongwe Rural (52.9%,
hence an LH outlier). However, analysis with the Getis-
Figure 6 Spatiotemporal trends in estimated average HIV prevalence among pregnant women by district, 1994-2001. District/city
estimates of HIV/AIDS prevalence were derived by averaging prevalence for all 1-km cells in the interpolated surfaces falling within each district
and major city for 1994, 1996, 1999 and 2001. These rates are indicative and only for assessing spatial patterns and temporal change, rather than
authoritative district estimates.
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OrdGi* statistic revealed a secondary intensity hotspot
cluster (GiZScore = 1.96-2.58 SDs) perfectly spatially
matching the eight core HIV hotspots for 2010.
Education, in particular the percentage of the population
that had attended senior primary school (grades 6-8), was sig-
nificantly negatively correlated with HIV prevalence, although
it had the lowest Beta value among the four “best”model vari-
ables. This suggests that the more pregnant women who have
a little education (senior primary) the less likely they are to
have HIV/AIDS. Cluster analysis for this variable shows only
three northern districts (Chitipa, Karonga and Mzimba) stand-
ing out as hotspots. Analysis with Getis-OrdGi* statistic adds
two secondary hotspot districts (Nkhata Bay and Mzuzu City)
and one coldspot district. None of the HIV hotspot districts
were in education “hotspots.”
Discussion
HIV clustering and spatiotemporal patterns of HIV
prevalence in Malawi
This study has provided a visually powerful and empirically
derived, multi-scalar analysis of spatiotemporal variation in
HIV prevalence among pregnant women attending ANCs
in Malawi from 1994 to 2010. It goes beyond current broad
characterizations at national, regional and urban/rural vari-
ation in HIV prevalence (Figure 2) to local variation at
district and lower (continuous) levels (Figures 4, 5, 6, 7, 8,
9, 10, 11, 12, and 13). GIS tools allowed the generation of
HIV prevalence estimates at scales where HIV data are not
traditionally collected (district and continuous) from point
HIV sentinel data at 19 ANCs for spatiotemporal pattern
analysis and from 54 ANCs for use in multivariate analysis
of possible drivers and their spatial variation. Observed
widespread spatial variation in HIV prevalence reveal the
HIV epidemic as an aggregation of several spatially defined
sub-epidemics −national, regional, urban, rural, and local
(clusters). Findings broadly call into question the use/ef-
fectiveness of one-size-fits-all interventions and policies
under such circumstances.
The most prominent temporal trend was the general de-
cline in HIV prevalence after rapid spread/intensification up
to 1999. For Malawi, the trend positively reflects on the con-
certed planning and considerable human and financial re-
sources (most from donor aid) applied to prevention and
treatment efforts by government and other agencies over
Figure 7 Spatiotemporal trends in estimated average HIV prevalence among pregnant women by district, 2003-2010. District/city
estimates of HIV/AIDS prevalence were derived by averaging prevalence for all 1-km cells in the interpolated surfaces falling within each district
and major city for 1994, 1996, 1999 and 200. These rates are indicative and only for assessing spatial patterns and temporal change. They are not
authoritative district estimates.
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the past decade [17]. However, the impressive rates of na-
tional decline in HIV prevalence among ANC-attending
pregnant women are 1) slowing down from 1.1% annually
betweeen1999 and 2010, to 0.91% and 0.88% annually
for the periods 2003-2010 and 2005-2010, respectively
(Figures 4-11) tempered by a modest decline in the gen-
eral population (from 12.0% in 2004 to 10.6% in 2010,
0.2% annually) based on MDHS data [14] −a cautionary
tale against complacency.
Another significant spatiotemporal trend was a slow
but emergent spatial evening in HIV prevalence as the
epidemic stabilized and then declined after an initial
period of localized spatial heterogeneity and explosive
spatial spread and intensification to a peak in 1999, as
illustrated empirically in temporal patterns of global
Moran’s I (Figure 3) and in various ways in Figures 4-11.
First, there was a general narrowing in the prevalence gap
among the regional sub-epidemics (more so for the north
and center, and between the urban/semi-urban and rural
epidemics. Second, the negative autocorrelation evident in
1995 and 1996 indicated localized heterogeneity with high
next to low prevalence pockets confirmed as spatial out-
liers in clustering patterns (Figures 10 and 11), which are
generally associated with expansionary spatial processes
and rapid spread [54]. Subsequent increases in the level
and significance of positive spatial autocorrelation
(Figure 3) further indicate relative spatial evening of the
epidemic with districts of similar HIV prevalence more
clustered together. The (low) decrease in total statistically
significant clustering (12 core hotspot and coldspot cluster
districts 2001 to 2010 relative to 14 in 1994 and 1999) also
indicates spatial evening as districts that stand out from
the rest decrease. The fact that the spatial and population
center of gravity was firmly located in rural areas where
the majority (85%) of Malawians lived [49] and the HIV
epidemic was more stable and lower in intensity (at least
among pregnant women), may have provided the inertia
that kept the national epidemic in relative check. The rural
epidemic’s declining trend may thus be a key turning point
nationally.
Identification of a hotspot cluster of 5-11 districts
making up the HIV epicenter consistently located in the
Figure 8 Temporal change in continuous HIV prevalence among pregnant women for various periods between 1994 and 2010.
Negative values (shades of green) represent a decrease (%) in prevalence and positive values an increase, for the continuous surfaces for the
time periods 1994-1999, 1999-2010, 2003-2010 and 1994-2010.
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south was the most significant outcome of cluster ana-
lysis. Potential explanations include the long history of
urbanization in the south forming a hub for HIV trans-
mission. Cluster analysis revealed a hierarchy among the
four biggest cities in the country in HIV prevalence rela-
tive to surrounding districts. Blantyre, the first city in
Malawi and the commercial capital, anchored the HIV
hotspot/epicenter every year of analysis, followed by
Zomba (twice in a hotspot cluster), then Lilongwe (twice
an HL outlier but part of a primary or secondary cold-
spot in most of the study years), and then Mzuzu city
[Figures 10 and 11]. The Southern Region was also the
most densely populated [49], and had the highest levels
of rural poverty [50] and prevalence of syphilis [17]. The
2010 MDHS also showed that women and men in the
Southern Region had the highest percentage of those
who had two or more sexual partners in the previous
12 months and mean number of sexual partners per life-
time, while men had the lowest percentage reporting
using a condom during last sexual intercourse (22.9%)
[14]. Elevated labor migrancy, particularly returning
Malawian mine workers from South Africa late1980s/
early 1990s, may partly explain emergence of an HIV
sub-epicenter from 1994 to 1999 (Figures 6-9) among the
northern districts of Nkhata Bay and Rumphi, and Mzuzu
city [32,55].
Potential drivers of HIV prevalence for 2010 and their
local spatial patterns
Geographic variables, particularly mean travel time to
nearest road, had the most explanatory influence on
HIV prevalence among the variables in the “best”regres-
sion model (Table 2), but the two geographic variables
in the model were virtually uncorrelated and they ap-
peared to capture subtly different aspects of “access”.
The spatial variation of these two variables in relation
to HIV prevalence (and hotspots) also differed. Main
roads generally link places to main towns and cities and
this variable mainly captured the urban/rural divide. In-
creasing distance from main roads reflected increasing
distance from urban areas and the factors that elevated
HIV prevalence there, while increasingly capturing more
Figure 9 Temporal change in district-level estimates of HIV prevalence among pregnant women for various periods between 1994 and
2010. Negative values (shades of green) represent a decrease (%) in prevalence and positive values an increase, for the continuous surfaces for
the time periods 1994-1999, 1999-2010, 2003-2010 and 1994-2010.
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isolated and sparsely populated areas with limited mobil-
ity and sexual mixing and access to health services and
conditions that are generally associated with lower risks
of HIV infection. This explains the negative relationship
with HIV prevalence. Cluster analysis of distance from
main roads revealed only one statistically significant core
cluster, a coldspot (districts exceptionally close to main
roads and urban areas, hence high HIV risk areas) clus-
ter of Blantyre city and neighboring Blantyre Rural and
Chiradzulu districts. This suggests that distance to cities
(or urban influences) is a major explaining factor for high
HIV rates in this subset of the core HIV hotspot districts,
although hotspot analysis with the Getis-OrdGi* statistic
expanded the primary coldspot cluster to 8 districts which
virtually matched the larger core HIV hotspot.
In contrast, core and secondary hotspots for mean
travel time to nearest transport (30-44 age group), which
varied positively with HIV prevalence, matched three
rural HIV hotspot districts of Mulanje, Phalombe and
Thyolo spatially. These districts (including Zomba Rural
as a tertiary HH cluster) have hilly terrain, which is a
physical obstacle to travel and access to health services.
Further, high concentration of commercial farms (mainly
tea) forced many people into smaller remaining areas,
making for some of the highest population densities in
relation to available arable land. The dense populations,
including the predominantly male commercial farm workers
(a high HIV risk group in Malawi [56]), facilitated increased
sexual mixing and risky sexual encounters, and HIV spread.
These areas need increased access to HIV information and
other services, including through mobile services.
Our findings are generally consistent with recent stud-
ies elsewhere in Africa. Tanser et al. [1] also found an in-
verse significant relationship between HIV prevalence
and distance to the main road in Kwazulu-Natal, South
Africa. Travel time to nearest public transport captures
a similar relationship in the inverse direction (longer/far-
ther from transport, lower HIV rates). Studies reveal a
mixed role of education, suggesting that it is mediated
by complex factors. The 2004 and 2010 MDHS studies
Figure 10 Spatiotemporal patterns of HIV hotspots and outliers by district, 1994-2001. Estimates of district HIV prevalence were based on
the original 19 ANCs to allow longitudinal continuity from 1994 to 2010. Figure 10 shows the years 1994, 1995, 1996, 1999 and 2001. The year
1995 is included along with 1996 to illustrate the presence of outliers during periods of significant negative autocorrelation (Figure 3).
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Figure 11 Spatiotemporal patterns of HIV hotspots and outliers by district, 2003-2010. Estimates of district HIV prevalence were based on
the original 19 ANCs for longitudinal depth. Figure 11 shows the years 2003, 2005, 2007 and 2010.
Table 2 Summary of regression coefficients for possible drivers of HIV prevalence
Model 1 Model 2 Model 3 Model 4
Variable description Beta Tolerance VIF Beta Tolerance VIF Beta Tolerance VIF Beta Tolerance VIF
Attended senior primary sch. (%) −0.287* 0.836 1.196
Percentage that had taken an HIV test
ever
0.435** 0.621 1.611 0.360** 0.591 1.691
Mean time to transport, age
30_44 (min)
0.369** 0.998 1.002 0.483*** 0.899 1.113 0.507** 0.892 1.121
Mean dist. to main roads (km) 0.605*** 1.000 1.000 0.589*** 0.998 1.002 −0.341* 0.656 1.523 −0.291* 0.641 1.560
R
2
Model 1 0.366 Model 2 0.5 Model 3 0.62 Model 4 0.688
R
2
adjusted 0.344 0.467 0.467 0.64
F-ratio 16.757 14.662 14.354 14.130
F-ratio significance .000 0.000 0.000 0.000
N = 31 districts. Significance of the coefficients for individual variables was * for p = 0.05, ** for p = 0.001, and *** for p = 0.001 or lower. Tolerance is proportion of
the variance explained by the variable alone and VIF is the Variance Inflation Factor, both collinearity diagnostic statistics. Model 4 was chosen as the
“best”model.
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show the level of education generally positively associ-
ated with HIV prevalence [14]. However, Moise and
Kalipeni found a negative relationship for literacy rates
above 20% in Zambia based on a spatial lag model.
Given our more specific definition of education, and
controlling for HIV testing, distance to main roads and
time to main transport, the negative relation with the
percentage of those who had attained this modest
(senior primary education) suggests some level of educa-
tion is good for AIDS prevention.
Further, we found that the percentage of people in the
larger population (men and women) who had taken at
least one HIV test ever was positively associated with
HIV prevalence. The secondary hotspot clusters for HIV
testing revealed by the Getis-OrdGi* statistic captured
seven districts that closely matched the core HIV preva-
lence hotspot for the women’s ANC sample for 2010
(Figure 13, B4). This finding that levels of HIV testing
were highest in districts that already had high HIV
prevalence is plausible because the sense of risk and
value of HIV testing would likely be heightened in such
areas. Indeed, the 2011 Malawi Welfare Monitoring
Survey found that the main reason given by 44% of
non-tested respondents for not testing was that they
did not feel at risk or in need of an HIV test [51]. During
Malawi’s 2010/11 fiscal year, 1.773 million people
(28% of the sexually active population) were tested for
HIV in Malawi [12]. However, HIV testing was higher
among urban residents who are generally wealthier and
more educated, have better access to testing facilities,
but also a higher risk of being infected than rural resi-
dents [3,6,14]. This relationship explains the emergence
of Lilongwe City as a core HL outlier and Zomba City as
a core hotspot (Figure 12, A4). However, Blantyre City
does not appear as an outlier probably because the core
HIV cluster in the south is more extensive and includes
Figure 12 Distribution of core spatial clusters and outliers of HIV prevalence relative to main explanatory variables. The core clusters
and outliers of HIV prevalence and of identified main explanatory variables are based on the Anselin Local Mora’s I. The variables displayed are:
HIV, A1) 2010 HIV prevalence, A2) mean distance to main roads (km), A3) mean travel time to main public transport for the 30-44 age group,
A4) percentage reporting having ever had an HIV test, A5) percentage who had attained senior primary education, and A6) 2010 syphilis
prevalence (%).
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Blantyre and several (rural) districts (Figure 12, HIV,
A1). Further nuanced analysis may be needed on HIV
testing behavior and its impact on HIV prevention.
Although the four variables included in the “best”model
explained a relatively high proportion of the variance in
HIV prevalence (68.8%, see Table 2), closer examination
suggests that these variables were essentially proxies for
underlying factors related to geographic/physical and
socio-economic access or exposure to health services and
other amenities, HIV prevention knowledge, personal re-
sources, sexual networks and risky sex and other factors
that influence HIV-related behavior and risk, mainly medi-
ated by urban/rural residence or proximity and poverty/
wealth and education. Further, some variables not in-
cluded in the final model were interesting. For instance,
the core and secondary clustering patterns (hotspots) for
2010 syphilis prevalence, highly significant in binary corre-
lations (Table 1), were a near-perfect match with HIV
hotspot districts (Figures 12 and 13), suggesting that syph-
ilis treatment/management also needs special attention.
Therefore, more detailed analysis needs to be done,
focusing on local HIV variation at finer spatial scales
and drivers of HIV incidence to better inform policy, ra-
ther than on prevalence as was done in this study. Never-
theless, HIV incidence is often closely associated with
prevalence, and as in Tanser et al. [1], we also assumed
that time lags between cause and effect would not signifi-
cantly model outcomes given our emphasis on preliminary
associations.
Limitations of the study
While this study contributes significantly to advancing
spatiotemporal analysis of HIV/AIDS in Malawi, Africa,
and the developing world generally, it has limitations.
The longitudinal depth of HIV sentinel data and caution
with the interpolation process including consistency
Figure 13 Spatial patterns and intensity of HIV prevalence hotspots relative to patterns of main explanatory variables. The intensity of
hotspots/coldspots of HIV prevalence and of identified main explanatory variables are based on the Getis-OrdGi* ZScore measured in standard
deviations. The variables displayed are: HIV, B1) 2010 HIV prevalence, B2) mean distance to main roads (km), B3) mean travel time to main public
transport for the 30-44 age group, B4) percentage reporting having ever had an HIV test, B5) percentage who had attained senior primary
education, and B6) 2010 syphilis prevalence (%).
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across years allowed adequate assessment of spatiotem-
poral patterns/trends among pregnant women, but the
small number (19) of sentinel ANCs, used in the spatio-
temporal analysis means that the interpolated products
and especially the derived district estimates are and
should be treated as indicative, although the HIV data
for the regression analysis were from a larger sample
(54 points). Local spatial-statistical pattern (hotspot)
analysis also came with uncertainties, including the
choice of method or search distance to define a neigh-
borhood. In this study, we empirically determined it as
the distance at which global Moran’s I was highest for
most years, 80 km. Further research is needed to deter-
mine the spatial limit of the spatial dependence of HIV
on neighboring values at different spatial scales to better
guide interventions. Finally, although our choice of an
OLS regression model in the presence of spatial depend-
ence breached the assumption of independence of mea-
surements, OLS was sufficient for the study purpose
of identifying indicative explanatory factors for observed
spatial variation (as opposed to producing predictive
models). Moreover, a corrective spatial lag model of HIV
prevalence using standardized variables in the “best”OLS
model based on spatial diagnostics was performed within
the GEODA spatial statistical software [53,57]. Despite im-
provements in the explanatory power of the model and
significance of coefficients for all but one of the four inde-
pendent variables (the p-value for distance to roads fell
slightly from 0.043 to 0.06), the spatial lag model did not
change the directions of the variable relationships nor
their relative importance.
f
In the interest of space, we left
out the spatial lag model findings.
Conclusion
This study has 1) shown spatiotemporal trends in HIV
prevalence at multiple scales (national, regional, district
and sub-district/continuous) in Malawi from 1994 to 2010
using spatial analysis of data from pregnant women attend-
ing HIV sentinel surveillance centers and GIS tools and 2)
identified five socio-demographic, behavioral, socio-
biological and geographic variables found to be signifi-
cantly associated with HIV prevalence in multiple ordinary
least squares (OLS) regression analysis and mapped their
spatial variation at district level in relation to the spatial
distribution of HIV hotspots, coldspots and spatial outliers.
A varying core hotspot of 6-11 districts was found in the
south and a coldspot of 1-6 districts in the center, but the
epidemic was slowly leveling out spatially in terms of
prevalence. Findings illustrated the importance of spatially
explicit geographic analysis to enhance understanding of
the spatial and temporal variation and nature of the HIV
pandemic along with configurations of factors that shape it
in particular locations, with potential to enhance spatial
targeting of HIV interventions and policies.
The results indicate that for Malawi there were several
geographically differentiated HIV/AIDS epidemics rather
than a single one. Further, the results of our analysis
offer the most spatially explicit longitudinal analysis of
HIV prevalence in the country that we are aware of. The
study joins a small but growing number of studies with
similar spatial specificity including mapping the spatial
variation of proximate and underlying factors that
influence HIV prevalence. Despite acknowledged short-
comings associated with limited sample size, this study
demonstrates the broader importance of using explicit
spatial analysis to understand the geographic nature of
the epidemic, examine spatiotemporal trends, and use
this knowledge for more effective spatial targeting to
combat the HIV/AIDS pandemic.
Endnotes
a
The presence of spatial dependence implies that
HIV prevalence values in one district are similar to and
dependent on values in neighboring districts. It is also
called spatial autocorrelation.
b
However, analysis of spatial disease clustering is more
common with other diseases in developed regions of the
world [1], with some on HIV/AIDS (e.g., [19]).
c
Very localized analysis has included issues of access
to HIV anti-retroviral treatment in part of Karonga dis-
trict, northern Malawi [22].
d
Target sample sizes are 300, 500, and 800 women at
rural, semi-urban, and urban ANCs, respectively.
e
Concerns involve sample representativeness –use of
purposive sampling favoring urban/high risk areas over
rural ones, paucity of ANCs, and narrow population of
pregnant women attending ANCs relative to the larger
population. Limited access and historical depth reduce
utility of emerging population-based HIV survey data,
e.g. the MDHS.
f
The R square increased from 0.688 to (pseudo) 0. 837,
the Log likelihood increased from -70.757 to -61.932,
while the Akaike info criterion and Schwartz criterion
decreased from 151.515 to 135.863 and 158.685 to
144.467, respectively. These changes are indicative that
the spatial lag model is better than the OLS model, and
confirm that space matters as a factor in HIV prevalence
among pregnant women attending antenatal clinics in
Malawi [57].
Abbreviations
ANC: Antenatal clinic; CI: Confidence interval; DHS: Demographic and health
survey; DRC: The Democratic Republic of the Congo; GIS: Geographic
information systems; GoM: Government of Malawi; HH: High-high hotspot
(clustering of districts with outstandingly high HIV or other values); HF: Health
facility; HL: Hi-Low hotspot (an outlier high-value district surrounded by
low value ones); HTC: HIV testing and counseling; IDW: Inverse distance
weighted; IHS: Integrated health survey; LISA: Local measures of spatial
autocorrelation; LH: Low-High outlier (a coldspot district surrounded by high
value ones); LL: Low-Low coldspot (clustering of districts with very low values);
MDHS: Malawi demographic and health survey; MK: Malawi Kwacha (Malawi’s
Zulu et al. BMC Infectious Diseases 2014, 14:285 Page 19 of 21
http://www.biomedcentral.com/1471-2334/14/285
currency); MoH: Ministry of health; NAC: National AIDS Commission;
NSO: Malawi National Statistical Office; OLS: Ordinary least squares; RMSE:
Root mean square error; SD: Standard deviations; STI: Sexually transmitted
infection; UNAIDS: The Joint United Nations Program on HIV/AIDS; WHO:
The World Health Organization; WMS: Welfare monitoring survey.
Competing interests
Authors have no competing interests to report in relation to this article.
Authors’contributions
LCZ conceived the study, conducted the GIS and statistical analysis and
wrote the first draft. EK participated in the conceptualization of the study,
contributed to the literature review and revisions of the draft manuscript.
EMJ participated in the design of the study and helped to draft and review
the manuscript. All authors approved the final manuscript.
Acknowledgements
Authors are grateful in acknowledging the help of Imelda Moise and Sarah
Hession for commenting on earlier drafts of the manuscript.
Author details
1
Department of Geography, Michigan State University, Geography Building,
Auditorium Road, East Lansing, MI 48824, USA.
2
Department of Geography,
216 Ho Science Center, Colgate University, 13 Oak Drive, Hamilton, NY 13346,
USA.
3
Institute for Defense Analyses, 4850 Mark Center Drive, Alexandria, VA
22311-1882, USA.
Received: 4 July 2013 Accepted: 2 May 2014
Published: 23 May 2014
References
1. Tanser F, Bärnighausen T, Cooke GS, Newell ML: Localized spatial
clustering of HIV infections in a widely disseminated rural South African
epidemic. Int J Epidemiol 2009, 38:1008–1016.
2. Kalipeni E, Zulu L: Using GIS to model and forecast HIV/AIDS rates in
Africa, 1986–2010. Prof Geogr 2008, 60:33–53.
3. Messina JP, Emch M, Muwonga J, Mwandagalirwa K, Edidi SB, Mama N,
Okenge A, Meshnick SR: Spatial and socio-behavioral patterns of HIV
prevalence in the Democratic Republic of Congo. Soc Sci Med 2010,
71:1428–1435.
4. Mayer JD: The geographical understanding of HIV/AIDS in sub-Saharan
Africa. NorskGeografiskTidsskrift - Norwegian J Geogr 2005, 59:6–13.
5. Uthman O, Yahaya I, Ashfaq K, Uthman M: A trend analysis and
sub-regional distribution in number of people living with HIV and
dying with TB in Africa, 1991 to 2006. I J Health Geogr 2009, 8:65.
6. Kalipeni E, Zulu L: HIV and AIDS in Africa: a geographic analysis at
multiple spatial scales. GeoJournal 2012, 77:505–523.
7. Cromley EK, McLafferty S: GIS and Public Health. 6td edth edition. New York:
Guilford Press; 2002.
8. Tanser F, le Sueur D: The application of geographical information systems
to important public health problems in Africa. Int J Health Geogr 2002, 1:4.
9. Foody GM: GIS: Health applications. ProgPhysGeogr 2006, 30:691–695.
10. Higgs G: The role of GIS for health utilization studies: literature review.
Health Serv Outcomes Res Method 2009, 9:84–99.
11. Nykiforuk CIJ, Flaman LM: Geographic information systems (GIS) for
health promotion and public health: a review. Health Promot Pract 2011,
12:63–73.
12. UNAIDS Global Report: UNAIDS Report on the Global Aids Epidemic; 2012.
Geneva: Joint United Nations Program on HIV/AIDS (UNAIDS); 2012.
13. NAC: HIV and Syphilis Sero–Survey and National HIV Prevalence and AIDS
Estimates Report for 2010. Lilongwe, Malawi: National AIDS Commission
(NAC); 2011.
14. NSO, ICF Macro: Malawi Demographic and Health Survey 2010. Zomba,
Malawi; Calverton, Maryland, USA: National Statistical Office (NSO) and ICF
Macro; 2011.
15. Geubbels E, Bowie C: Epidemiology of HIV/AIDS in adults in Malawi.
Malawi Med J 2006, 18:99–121.
16. Malawi: HIV and AIDS estimates 2011. Geneva: UNAIDS; 2012.
17. GoM: 2012: Global AIDS Response Progress Report: Malawi Country Report for
2010 and 2011. Lilongwe, Malawi: Government of Malawi (GoM); 2012.
18. Peng ZH, Yue-Jia C, Reilly KH, Lu W, Qian-Qia Q, Zheng-Wei D, Guo-Wei D,
Ding KQ, Yu RB, Chen F, Wang N: Spatial distribution of HIV/AIDS in
Yunnan province, People’s Republic of China. Geospatial Health 2011,
5:177–182.
19. Getis A, Ord JK: The analysis of spatial association by use of distance
statistics. Geogr Anal 1992, 24:189–206.
20. Obbo C: HIV transmission through social and geographical networks in
Uganda. Soc Sci Med 1993, 36:949–955.
21. Webb D: Mapping the AIDS pandemic: geographical progression of HIV
in South Africa 1990-93. Nurs RSA 1994, 9:20–21.
22. Houben R, van Boeckel T, Mwinuka V, Mzumara P, Branson K, Linard C,
Chimbwandira F, French N, Glynn J, Crampin A: Monitoring the impact of
decentralized chronic care services on patient travel time in rural Africa -
methods and results in northern Malawi. Int J Health Geogr 2012, 11:49.
23. Moise I, Kalipeni E: Applications of geospatial analysis to surveillance
data: a spatial examination of HIV/AIDS prevalence in Zambia.
Geo J 2012, 77:525–540.
24. Epstein H, Morris M: Concurrent partnerships and HIV: an inconvenient
truth. J Int AIDS Soc 2011, 14:13.
25. Kalipeni E, Oppong J, Zerai A: HIV/AIDS, gender, agency and
empowerment issues in Africa. Soc Sci Med 2007, 64:1015–1018.
26. Nyindo M: Complementary factors contributing to the rapid spread of
HIV-1 in sub-Saharan Africa: a review. East Afr Med J 2005, 82, 1:40–46.
27. Kalipeni E, Ghosh J, Mkandawire-Valhmu L: The multiple dimensions of
vulnerability to HIV/AIDS in Africa: A social science perspective. In
Women, AIDS and Access to Health Care in Sub-Saharan Africa: Approaches
from the Social Sciences. Edited by Mundi M. Barcelona, Spain: Medicus
Mundi Catalunya; 2007:45–60.
28. Yeboah IEA: HIV/AIDS and the construction of sub-Saharan Africa:
heuristic lessons from the social sciences for policy. Soc Sci Med 2007,
64:1128–1150.
29. Oppong JR, Harold A: Disease, ecology and environment. In A Companion
of Medical Geography. Edited by Brown T, McLafferty S, Graham M.
Chichester, West Sussex, U.K: Wiley-Blackwell; 2009.
30. Kalipeni E, Ghosh J: Concern and practice among men about HIV/AIDS in
low socioeconomic income areas of Lilongwe, Malawi. Soc Sci Med 2007,
64:1116–1127.
31. Dodoo FNA, Zulu EM, Ezeh AC: Urban–rural differences in the
socioeconomic deprivation–sexual behavior link in Kenya. Soc Sci Med
2007, 64:1019–1031.
32. Chirwa WC: Migrant labor, sexual networking and multi-partnered sex in
Malawi. Health Transit Rev 1997, 7(Supplement 3):5–15.
33. Coffee M, Lurie MN, Garnett GP: Modelling the impact of migration on the
HIV epidemic in South Africa. AIDS 2007, 21:343–350. 310.1097/
QAD.1090b1013e328011dac328019.
34. Kumwenda NI, Taha TE, Hoover DR, Markakis D, Liomba GN, Chiphangwi JD,
Celentano DD: HIV-1 incidence among male workers at a sugar estate in
rural Malawi. JAIDS J Acquir Immune Defic Syndr 2001, 27:202–208.
35. NAC: Hiv and Syphilis Sero–Survey and National Hiv Prevalence and Aids
Estimates Report for 2005. Lilongwe, Malawi: National AIDS Commission,
NAC; 2005.
36. NAC: Hiv and Syphilis Sero–Survey and National Hiv Prevalence and Aids
Estimates Report for 2007. Lilongwe, Malawi: National AIDS Commission,
NAC; 2008.
37. UNAIDS WHO: Guidelines for Effective Use of Data from Hiv Surveillance
Systems. Geneva: WHO; 2000.
38. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance: The
Pre-Surveillance Assessment: Guidelines for Planning Serosurveillance of HIV,
Prevalence of Sexually Transmitted Infections and the Behavioral Components
of Second Generation Surveillance of HIV. Geneva, Switzerland: UNAIDS and
WHO; 2005.
39. UNAIDS, WHO: Guidelines on Estimating the Size of Populations Most At Risk
to HIV. Geneva, Switzerland: UNAIDS/WHO Working Group on Global HIV/
AIDS and STI Surveillance; 2010.
40. Moran PAP: Notes on continuous stochastic phenomena. Biometrika 1950,
37:17–33.
41. Golub A, Gorr WL, Gould PR: Spatial diffusion of the HIV/AIDS epidemic:
modeling implications and case study of aids incidence in Ohio.
Geogr Anal 1993, 25:85–100.
42. Tobler WR: A computer movie simulating urban growth in the Detroit
region. Econ Geogr 1970, 46:234–240.
Zulu et al. BMC Infectious Diseases 2014, 14:285 Page 20 of 21
http://www.biomedcentral.com/1471-2334/14/285
43. Mitasova H, Mitas L, Brown WM, Gerdes DP, Kosinovsky I, Baker T: Modeling
spatially and temporally distributed phenomena: new methods and
tools for GRASS GIS. Int J GeogrInfSyst 1995, 9:433–446.
44. Krivoruchko K: Spatial Statistical Data Analysis for GIS Users. Redlands, CA:
ESRI Press; 2011.
45. EPA: Developing Spatially Interpolated Surfaces and Estimating Uncertainty.
Triangle Park, NC, USA: United States Environmental Protection Agency; 2004.
46. Anselin L, Sridharan S, Gholston S: Using exploratory spatial data analysis
to leverage social indicator databases: the discovery of interesting
patterns. Soc Indic Res 2007, 82:287–309.
47. Anselin L: Local indicators of spatial association—LISA. Geogr Anal 1995,
27:93–115.
48. Anselin L, Getis O: Spatial statistical analysis and geographic information
systems. Ann Reg Sci 1992, 26:19–33.
49. NSO: The 2008 Population and Housing Census: Main Report. Zomba, Malawi:
National Statistical Office (NSO); 2009.
50. NSO: Integrated Household Survey 2010-2011: Household Socio-Economic
Characteristics Report. Zomba, Malawi: National Statistical Office (NSO); 2012.
51. NSO: Welfare Monitoring Survey 2011. Zomba, Malawi: National Statistical
Office (NSO); 2012.
52. Altman DG, Andersen PK: Bootstrap investigation of the stability of a Cox
regression model. Stat Med 1989, 8:771–783.
53. Anselin L, Syabri I, Kho Y: Geoda: an introduction to spatial data analysis.
Geogr Anal 2006, 38:5–22.
54. Williams BG, Gouws E: The epidemiology of human immunodeficiency virus
in South Africa. Philos Trans R Soc Lond B Biol Sci 2001, 356:1077–1086.
55. Chirwa WC: Aliens and AIDS in southern Africa: the Malawi-South Africa
debate. Afr Aff 1998, 97:53–79.
56. NSO: Malawi Biological and Behavioral Surveillance Survey 2006 and
Comparative Analysis of 2004 BSS and 2006 BBSS. Lilongwe, Malawi:
National AIDS Commission, NAC; 2007.
57. Anselin L: Exploring Spatial Data with GeoDa
TM
: A Workbook. Urbana-Champaigl,
IL: Spatial Analysis Laboratory, Department of Geography, University of Illinois,
Urbana-Champaign; 2005.
doi:10.1186/1471-2334-14-285
Cite this article as: Zulu et al.:Analyzing spatial clustering and the
spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the
case of Malawi, 1994-2010. BMC Infectious Diseases 2014 14:285.
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