Dery et al. Malaria Journal  (2015) 14:142 
DOI 10.1186/s12936-015-0667-6RESEARCH Open AccessBaseline malaria vector transmission dynamics in
communities in Ahafo mining area in Ghana
Dominic B Dery1*, Kwaku P Asante1, Charles Zandoh1, Lawrence G Febir1, Charles Brown2, George Adjei1,
Yaw Antwi-Dadzie3, Emmanuel Mahama1, Kofi Tchum1, David Dosoo1, Seeba Amenga-Etego1, Robert Adda1,
Christine Mensah4, Kwabena B Owusu-Sekyere3, Chris Anderson3, Gary Krieger5 and Seth Owusu-Agyei1Abstract
Background: Malaria vector dynamics are relevant prior to commencement of mining activities. A baseline entomology
survey was conducted in Asutifi and Tano (referred to as Ahafo) in the Brong-Ahafo geo-political region of Ghana during
preparatory stages for mining by Newmont Ghana Gold Limited.
Methods: Between November 2006 and August 2007, eight Centre for Disease Control light traps were set daily
(Monday-Friday) to collect mosquitoes. Traps were hanged in rooms that were selected from a pool of 1,100
randomly selected houses. Types of materials used in construction of houses were recorded and mosquito
prevention measures were assessed from occupants.
Results: A total of 5,393 mosquitoes were caught that comprised Anopheles gambiae (64.8%), Anopheles funestus
(4.2%), as well as Culicines, comprising of Culex (30.4%) and Aedes species (0.6%). The entomological inoculation
rate in Asutifi (279 infective bites/person/month) and Tano (487 infective bites/person/month) demonstrate
relatively high malaria transmission in Ahafo. The presence or absence of Anopheles vectors in rooms was
influenced by the type of roofing material (OR 2.33, 95%CI: 1.29-4.22, p = 0.01) as well as the presence of eaves
gaps (OR 1.80, 95%CI: 1.37-2.37, p < 0.01). It was also associated with bed net availability in the room (OR 1.39,
95%CI: 1.08-1.80, p = 0.01). Over 80% of the houses were roofed with corrugated zinc sheets. Over 60% of the
houses in Ahafo had no eaves gaps to give access to mosquito entry and exit into rooms and mosquito bed net
coverage was over 50%. Other measures used in preventing mosquito bites included; coil (22.1%), insecticide
spray (9.4%), repellent cream (4.0%) and smoky fires (1.1%), contributed minimally to individual mosquito
preventive measures in impact areas. Similarly, levels of protection; coil (16.9%), insecticide spray (2.8%) and
repellent cream (0.3%) for the non-impact areas, depict low individual prevention measures.
Conclusions: The survey identified areas where intensified vector control activities would be beneficial. It also
demonstrates that transmission in Asutifi and Tano is high even before the commencement of mining
operations. This study serves as baseline information to assess impact of mining activities in relation to future
vector control interventions.
Keywords: Malaria transmission, Ahafo, Anopheles gambiae, Anopheles funestus* Correspondence: bonereme@gmail.com
1Kintampo Health Research Centre, Ghana Health Service, Ministry of Health,
P.O. Box 200, Kintampo, Ghana
Full list of author information is available at the end of the article
© 2015 Dery et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Dery et al. Malaria Journal  (2015) 14:142 Page 2 of 8Background
Knowledge of vector bionomics is essential in understand-
ing the epidemiology and implementing control of malaria
[1]. Today, malaria can be prevented, diagnosed and
treated with a combination of well-established strategies.
Vector control is one of the key control tools and involves
the use of insecticide-treated mosquito nets (ITN), indoor
residual spraying and larval control activities [2]. Malaria
vector control is an important strategy recognized in the
global policy frameworks [3,4] of which Ghana is no
exception.
Ghana is endowed with significant mineral deposits
[5]. These deposits have attracted both large inter-
national mining companies and small-scale artisanal
miners. Mining activities can lead to land degradation,
changes in landscape, deforestation and changes in hu-
man behaviour [6]. More importantly, mining activities
may lead to a change in the abundance and distribution
of malaria vectors [7,8]. Mining activities may also lead
to enhanced household income and improved housing
construction, which can impact on malaria transmission.
The attraction of mosquitoes into a house or room is
dependent on various factors including construction mate-
rials and room characteristics [9,10]. Studies in Sri Lanka
and Kenya [11,12] found that the risk of getting malaria
was greater for inhabitants occupying the poorest type of
houses which was characterized by incomplete construc-
tion with thatched roofs and walls made of mud or cadjan
(woven coconut palm leaves). This was compared with
better-constructed houses made of complete brick and
plastered walls and tiled roofs. The Kenyan study specific-
ally found 84% (OR 0.16, 95% CI 0.07-0.39, P < 0.0001)
and 87% (OR 0.13, 95% CI 0.03-0.5, P = 0.0004) reduction
in the odds of finding Anopheles gambiae s.l. and Anoph-
eles funestus in better constructed houses compared with
poorly constructed houses [13]. Thus, malaria transmis-
sion may be affected by improvement in housing con-
struction as a result of enhanced income generation in
mining areas.
Malaria assessment, including vectorial capacity deter-
mination, are important prior to introduction of mining ac-
tivities as this will inform monitoring of vector dynamics
over time so as to plan effective control interventions. Prior
to the start of active mining in the Ahafo area of Ghana,
Newmont Gold Ghana Ltd (NGGL) commissioned a var-
iety of environmental, social and health baseline studies.
The present study was commissioned by NGGL in order
to determine basic malaria entomological indices including
transmission dynamics in the mining area.
Methods
Study areas and communities
The study was conducted in two areas in the Brong Ahafo
Region of Ghana; Asutifi and Tano (Figure 1). Asutifi liesbetween latitudes 6°40′ and 7°15′ North and Longitudes
2°15′ and 2°45′ West. Tano lies between latitudes 7°00′N
and 7°25′ N and between longitudes 1°45 W and 2°30 W.
Asutifi had a population of approximately 97,977 and land
surface area of 1,500 square kilometres. Tano had a total
population of approximately 155,100 at the time of survey.
The study area falls within the wet semi-equatorial forest
zone where mean annual rainfall is about 1200 mm per
annum. The quarterly rainfall pattern in the area (Figure 2)
depicts rainfall throughout the year and reflects two rain-
ing seasons in the area. The major economic activities in
the study areas besides gold mining are farming of cash
crops, such as cocoa, palm oil, yam and plantain.
Studied communities were grouped into “impact” and
“non-impact” communities based on the assumption that
mining operations could have an impact on the health and
socio-economic status of the population. “Impact” area
means communities either in the Asutifi or Tano area
whose health and/or socio-economic indices are likely to
be directly affected by mining activities being undertaken
and located within 25 Km radius from the mine sites. The
“non-impact” area represents communities in either the
Asutifi or Tano areas that are not likely to be affected
directly by the mining activities and located outside the
25 kilometres radius from the mines site.
The area has a health system conforming to the basic
primary care model established by the Ghana Health Ser-
vice. Communities are served by a local health clinic that
manages basic health problems. All health facilities in the
study area render both clinical and public health services.
Malaria is the leading reported cause of outpatient attend-
ance in all health facilities in the study area.
Study design and methods
Houses were randomly selected from among those used
in a district-wide household morbidity survey [14] and
mosquito traps were set using CDC light traps. All the
houses in the morbidity survey that had children less
than five years of age formed the sampling frame from
which rooms were selected for the mosquito light
trappings.
A total of 1,100 houses were randomly selected from
enumerated houses in the Asutifi and Tano areas and used
for the weekly trappings. Eight CDC light traps were set
daily; four were set simultaneously in Asutifi and Tano re-
spectively, starting from November 2006 and ending in
August 2007. Traps were set at the foot-end of the person
(s) sleeping in the selected room and hanged approxi-
mately 1.6 metres above the floor. Untreated mosquito nets
were provided to household members whose rooms were
used on the night of trap setting as an ethical requirement.
The characteristics were recorded for each house where
a trap was set; these included the types of materials used
for roof construction, walls and floor; presence of eaves
Dery et al. Malaria Journal  (2015) 14:142 Page 3 of 8
Figure 1 Map of Asutifi and Tano area in Ghana (Nov 2006-Aug 2007).
Ave rainfall (mm)
35.00
2006
30.00
2007
25.00
20.00
15.00
10.00
5.00
0.00
January-March April-June July-September October-December
Figure 2 Bi-monthly rainfall pattern in Asutifi and Tano area in Ghana (2006–2007).
Dery et al. Malaria Journal  (2015) 14:142 Page 4 of 8gaps, number of windows and doors. Malaria vector pre-
ventive measures were assessed one week prior to the date
of survey on occupants in each room that received a light
trap.
Mosquitoes caught were chloroformed and morpho-
logically identified using Anopheline morphological
identification keys [15]. They were then stored in 1.5 ml
eppendorf tubes and transported on weekly basis to the
Kintampo Health Research Centre laboratories for
enzyme-linked immunosorbent assay (ELISA) [16]. An
average cut-off point of 0.2 nm absorbance wavelength
was considered positive. Positive samples were re-tested
for confirmation.Data management
All data collected in the field or the laboratory were
logged for traceability, and then batched for double data
entry and processing using Microsoft® Access.
Statistical analysis
The entomological inoculation rate (EIR) was calculated
as the product of the proportion of Anopheles positive
by ELISA also termed as sporozoite rate (SR) and the
man biting rate, which was estimated as the geometric
mean of Anopheles caught by CDC light traps per night.
The monthly EIR (EIRm) was calculated as the product
of EIR for a night (EIRn) and estimated number of days
in a month (30). The presence or absence of Anopheline
species in houses in relation to housing construction
and location was modeled using logistic regression con-
trolling for the main confounding variables.Table 1 Mosquito abundance and EIRs in Asutifi area in Ghan
Location VILLAGE An. gambiae An. funestus Culex
n (%) n (%) n (%)
IMPACT ATRONIE 41 (4.7) 1 (0.5) 47 (4.6
GYEDU 48 (5.5) 13 (6.8) 81 (7.9
KENYASI NO.2 44 (5.1) 2 (1.1) 108 (1
NKASEIM 67 (7.7) 42 (22.1) 95 (9.3
NTOTROSO 309 (35.7) 14 (7.4) 175 (1
WAMAHINSO 38 (4.4) 1 (0.5) 48 (4.7
KENYASI NO.1 201 (23.2) 16 (8.4) 125 (1
NON-IMPACT ASUKESE 34 (3.9) 9 (4.7) 37 (3.6
DADIESOABA 50 (5.8) 25 (13.2) 102 (1
KENSERE 11 (1.3) 34 (17.9) 48 (4.7
MEHAME 22 (2.5) 33 (17.4) 153 (1
Total 865 190 1,019
TOTALa 2,088a
SR-Ag; Sporozoite Rate for An. gambiae, MBR; Man Biting Rate.
SR-Af; Sporozoite Rate for An. funestus, EIRn; Entomological Inoculation Rate/night.
EIRm; Entomological Inoculation Rate/month, SR; Sporozoite Rate (Ag + Af).
Superscript (a) - Total mosquitoes caught in Asutifi n; number caught.Ethics
Approval for the study was obtained from the Kintampo
Institutional Ethics Committee (Federal Wide Assurance
number: 00011103). Participants were consented the
night before setting a light trap. Untreated mosquito
nets were provided to occupants of rooms on the night
of setting trap to ensure they were protected while en-
suring that mosquitoes were not repelled. The Kintampo
Health Research Centre ethics committee approved this
method.
Results
Mosquito vector abundance and EIRs in Asutifi and Tano
A total of 942 CDC light trap-nights were set within the
period (November 2006 – August 2007). A total of 5,393
mosquitoes were collected which comprised An. gam-
biae (64.8%), An. funestus (4.2%), Culex species (30.4%)
and Aedes species (0.6%). Mosquito abundance was gen-
erally higher in the impact areas of Asutifi compared
with the non-impact areas (Table 1). Ntotroso recorded
the highest number of mosquitoes caught in the impact
area and Mehame recorded the highest mosquitoes
caught in non-impact area. An. gambiae and An. funes-
tus vector abundance in the two areas were significantly
different. Anopheles gambiae was much higher (ten-fold)
in impact areas compared to An. funestus. However, in
non-impact areas abundance of both species were nearly
equal.
Relatively higher numbers of mosquitoes were caught
in non-impact areas than impact areas in Tano (Table 2).
Afisipakrom recorded the highest number of mosquitoes
caught in impact area and Bredi recorded the highesta (Nov 2006 – Aug 2007)
sp. Aedes sp. SR-Ag SR-Af SR MBR EIRn EIRm
n (%)
) 7 (50.0) 0.1 0.0 0.1 6.0 0.6 18.0
) 0 (0.0) 0.3 0.0 0.3 17.0 4.3 127.5
0.6) 0 (0.0) 0.3 0.0 0.3 2.0 0.5 15.0
) 1 (7.1) 0.1 0.1 0.2 7.0 1.2 35.7
7.2) 6 (42.9) 0.1 0.0 0.1 8.0 0.3 9.6
) 0 (0.0) 0.1 0.0 0.1 6.0 0.3 9.0
2.3) 0 (0.0) 0.1 0.1 0.2 8.0 1.0 31.2
) 0 (0.0) 0.2 0.0 0.2 5.0 0.8 24.0
0.0) 0 (0.0) 0.1 0.0 0.1 17.0 1.4 40.8
) 0 (0.0) 0.2 0.0 0.0 5.0 0.2 6.0
5.0) 0 (0.0) 0.1 0.0 0.1 7.0 0.6 16.8
14 1.4 0.2 1.4 6.9 9.3 278.6
Dery et al. Malaria Journal  (2015) 14:142 Page 5 of 8
Table 2 Mosquito abundance and EIRs in Tano area in Ghana (Nov 2006 – Aug 2007)
Location VILLAGE An. gambiae An. funestus Culex sp. Aedes sp. SR-Ag SR-Af SR MBR EIRn EIRm
n (%) n (%) n (%) n (%)
IMPACT ADROBAA 94 (3.6) 1 (2.7) 20 (3.2) 0 (0.0) 0.1 0.0 0.1 11.0 0.6 16.5
AFISIPAKROM 448 (17.0) 2 (5.4) 23 (3.7) 0 (0.0) 0.1 0.0 0.1 36.0 1.8 54.0
BECHEM 93 (3.5) 3 (8.1) 182 (29.4) 0 (0.0) 0.1 0.0 0.1 6.0 0.8 23.4
SUSUANSO 17 (0.6) 1 (2.7) 24 (3.9) 0 (0.0) 0.1 0.0 0.1 4.0 0.2 7.2
TEEKYEERE 61 (2.3) 1 (2.7) 10 (1.6) 0 (0.0) 0.2 0.0 0.2 8.0 1.2 36.0
TANOSO 27 (1.0) 1 (2.7) 31 (5.0) 1 (50.9) 0.2 0.0 0.2 4.0 0.7 21.6
YAMFO 18 (0.7) 0 (0.0) 45 (7.3) 1 (50.9) 0.3 0.0 0.3 3.0 0.8 25.2
NON-IMPACT BOMAA 69 (2.6) 5 (13.5) 43 (6.9) 8 (47.1) 0.3 0.0 0.3 5.0 1.5 45.0
BREDI 1321 (50.2) 0 (0.0) 16 (2.6) 0 (0.0) 0.0 0.0 0.0 274.0 5.5 164.4
DERMA 147 (5.6) 1 (2.7) 104 (16.8) 1 (5.9) 0.1 0.0 0.1 11.0 1.1 33.0
DWOMO 217 (8.2) 13 (35.1) 39 (6.3) 1 (5.9) 0.0 0.0 0.0 31.0 0.9 27.9
MANKRAHO 64 (2.4) 8 (21.6) 64 (10.3) 4 (23.5) 0.2 0.0 0.2 6.0 1.0 28.8
TECHIMANTIA 56 (2.1) 1 (2.7) 18 (2.9) 1 (5.9) 0.1 0.0 0.1 4.0 0.4 13.2
Total 2,632 37 619 17 1.7 0 1.6 486.8
TOTALb 3,305b
3,497 (64.8) 227 (4.2) 1,638 (30.4) 31 (0.6)
TOTAL (Tano + Asutifi) 5,393
SR-Ag; Sporozoite Rate for An. gambiae, MBR; Man Biting Rate.
SR-Af; Sporozoite Rate for An. funestus, EIRn; Entomological Inoculation Rate/night.
EIRm; Entomological Inoculation Rate/month, SR; Sporozoite Rate (Ag + Af).
Superscripts (b) - Total mosquitoes caught in Tano n; number caught.number of mosquitoes in non-impact area. Most Anoph-
eles vectors caught were An. gambiae with nearly negligible
numbers of An. funestus vectors.
EIRs were higher for An. gambiae than An. funestus in
both impact and non-impact areas in Asutifi (Table 1). A
similar trend is observed in Tano (Table 2). EIR per month
in Asutifi ranged between nine infective bites/person/
month and 128 ib/p/m in impact area. In the non-impact
area it ranged between 6–41 ib/p/m (Table 1). EIRs in
Tano ranged between 7–54 ib/p/m in impact area and
13–164 ib/p/m in non-impact area (Table 2).
Housing characteristics and mosquito preventive
measures
Between 80% - 90% of houses in the study area were
roofed with corrugated zinc sheets; with 94.7% and
82.6% in impact and non-impact areas, respectively. A
few houses were roofed with logs (1.8% in impact, 7.7%
in non-impact) and thatch (3.5% in impact; 9.7% in non-
impact) (Table 3). Over sixty percent (67.5%) of houses
in impact area had no eaves gaps compared with 62.9%
of houses in non-impact area. Nearly half (48%) of the
houses surveyed had a bed net. In preventing mosquito
bites, 22.1% of respondents used mosquito coils in im-
pact areas as against 16.9% in non-impact areas. The use
of insecticide sprays was 9.4% in impact areas and 2.8%
in non-impact places. Four percent of respondents’applied repellent creams in impact areas but a negligible
0.3% applied creams in non-impact areas. A negligible
number had used strongly scented leaves (0.5%) or
smoky fire (1.1%) to prevent mosquito bites in impact
areas but none was recorded in non-impact areas.
The types of construction materials correlated posi-
tively with high abundance of Anopheles in rooms, i.e.
the presence of grass or thatch roof was associated with
a significant increase in calculated odds ratio (Table 4).
There was significant association in abundance of An.
gambiae and An. funestus at three levels; roof type, pres-
ence of eaves gaps and bed nets in rooms of individuals.
The latter association (presence of bed nets) is further
explained by the fact that majority of the nets were
untreated.
Discussion
The abundance of mosquitoes in the two areas (Tano
and Asutifi) differed slightly. Unlike Asutifi where mos-
quito abundance was highest in impact area of commu-
nities, Tano experienced the reverse within the surveyed
period. This observation could be attributed to micro-
ecological differences and rainfall in the two areas. The
total number of mosquitoes caught in this survey is high
against the background of a relatively short period of
collection. Similar surveys [9,10,17] in which the period
of collection was longer (one or two years) using the
Dery et al. Malaria Journal  (2015) 14:142 Page 6 of 8
Table 3 Characteristics of houses and mosquito Table 4 Factors that influence abundance of Anopheles
preventive measures by individuals in Ahafo area of gambiae and Anopheles funestus in rooms in Ahafo area
Ghana (Nov 2006-Aug 2007) of Ghana (Nov 2006-Aug 2007)
Variables (N = 942) Impact (N = 551) Non-impact (N = 391) Variables Number Odds 95% p
n (%) n (%) p-valuea (%) ratio Confidence
interval
Presence of eaves gaps
Presence of eaves
Yes 179 (32.5) 145 (37.1) 0.14 gaps
No 372 (67.5) 246 (62.9) No 302 (49.0) 1
Type of roof Yes 206 (63.4) 1.80 (1.37, 2.37) <0.01
Corrugated zinc roof 522 (94.7) 323 (82.6) <0.01 Type of roof
Mud concrete/logs roof 10 (1.8) 30 (7.7) Corrugated zinc roof 442 (52.4) 1
Thatch roof 19 (3.5) 38 (9.7) Mud concrete/logs roof 25 (62.5) 1.52 (0.79, 2.92) 0.01
Bed net use Thatch roof 41 (71.9) 2.33 (1.29, 4.22)
Yes 249 (45.3) 201 (51.3) 0.07 Bed net use
No 301 (54.7) 191 (48.7) No 246 (50.0) 1
Insecticide spray Yes 262 (58.2) 1.39 (1.08, 1.80) 0.01
Yes 52 (9.4) 11 (2.8) <0.01 Insecticide spray
No 499 (90.6) 380 (97.2) No 468 (53.3) 1
Mosquito coil Yes 40 (63.5) 1.52 (0.90, 2.59) 0.12
Yes 122 (22.1) 66 (16.9) 0.05 Mosquito coil
No 429 (77.9) 325 (83.1) No 390 (52.1) 1
Smoky fire Yes 118 (61.1) 1.44 (1.05, 1.99) 0.03
Yes 6 (1.1) 0 (0.0) 0.04* Smoky fire
No 545 (98.9) 391 (100.0) No 502 (53.8) 1
Scented leaves Yes 6 (85.7) 5.16 (0.62, 11.05) 0.13
Yes 3 (0.5) 0 (0.0) 0.27* Scented leaves
No 548 (99.5) 391 (100.0) No 505 (53.8) 1
Repellent creams Yes 3 (0.59) 0.44 (0.04, 4.92) 0.51
Yes 22 (4.0) 1 (0.3) <0.01* Repellent creams
No 529 (96.0) 390 (99.7) No 493 (53.7) 1
*: Fisher’s Exact test. Yes 15 (65.2) 1.62 (0.68, 3.85) 0.28
a: Pearson Chi-squared test.
Area
Impact 290 (52.7) 1
Non-Impact 218 (55.6) 1.12 (0.87, 1.46) 0.38
same collection method (CDC light traps) produced
lower mosquito collections.
There were more mosquitoes caught in mud/log and
grass/thatched roofed houses compared to zinc corru-
gated roofed houses. This could be attributed to the
conducive nature thatched roofed and mud houses
present for mosquito entry. This observation could also
be linked to the fact that these types of houses present
conducive environment for mosquitoes to rest indoors.
Improvement in socio-economic standards may result in
improved housing architecture such as the use of window
screens that can reduce malaria transmission by reducing
man-vector contact. Eventually this could lead to a de-
creased mosquito burden inside rooms and subsequent
decrease in malaria transmission levels.
EIRs in the surveyed communities demonstrate very
high malaria transmission levels by Anopheles vectors(7–128 infective bites per person per month). This is
comparable with EIRs documented in the Kintampo area
where the same investigators documented 269 infective
bites per person per year [18]. EIRs varied significantly
between communities; therefore, control strategies need
to be planned in concert with detailed communities vector
indices; abundance, speciation and EIRs. In Tano, EIRs in
the impact communities were significantly higher in three
of seven communities while in the non-impact communi-
ties two of six communities had higher EIRs.
Since infective An. gambiae vectors were detected by
ELISA in each month and for the entire period of the
survey, it may be concluded that An. gambiae vectors in
Asutifi and Tano are capable of sustaining an infective
Dery et al. Malaria Journal  (2015) 14:142 Page 7 of 8biting pattern throughout the year. Some communities
experienced higher malaria transmission than others,
which is a clear indication of how local vector populations
influence malaria intensity even when the communities
are not very far apart, in most cases less than 5 km apart.
Anopheles funestus vectors has not be incriminated as a
major vector in the studied area as the numbers caught in
communities were low with negligible sporozoites rates.
Mining activities is reported to have an impact on mal-
aria vector dynamics [19]. Environmental manipulation
to accommodate project-related infrastructure develop-
ments can create favourable habitats for malaria vectors,
most importantly An. gambiae [20]. The Ahafo area has
experienced changes since mining activities commenced
and therefore environmental changes might impact on
vector densities. Also, mining activities attract several
categories of workers and camp followers who might
create settlements with poorly constructed houses and
poor drainage creating conducive grounds for malaria
transmission. The changes in behaviour of reservoir
hosts and the ability of pathogens to adapt to new reser-
voir hosts in newly-created habitats also influence the
patterns of disease [21]. Integrated vector control mea-
sures [22] are appropriate even more in the context of
mining areas since the environment is greatly changed
by mining and human activities. Continued malaria vector
monitoring is, therefore, recommended in the ongoing
mining areas in Tano and Asutifi.
Conclusions
The survey documented important malaria vector indices
associated with a large mining development in a rural area.
At the community level, malaria vector transmission is
primarily determined by prevailing environment, housing
characteristics and vector composition. Preventive mea-
sures, such as the use of treated bed nets or indoor re-
sidual spraying, need to be considered within the local
context in order to maximize cost-effectiveness. For ex-
ample high kdr + levels in key mosquito vectors will im-
pact the design and implementation of IRS and possibly
even ITN distribution. Developing a pre-project baseline
is critical and helps establish the underlying community
situation and facilitates a scientifically rational control
programme that can be sequentially monitored. It is in
both the host communities and the project’s best interest
to have an objective foundation before active construction
begins, during peak activity and for long-term continuous
operations.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
All authors contributed equally to the planning and implementation of the
study. The manuscript was drafted by DBD and KPA. All authors read and
approved the final version for publication.Acknowledgements
Our sincere gratitude goes to the individuals who agreed for traps to be set
in their rooms. Same also goes to the chiefs and elders in communities for
granting approval to enter their communities and Newmont Gold Ghana
and the Ghana Health Service for support in carrying out this research.
Author details
1Kintampo Health Research Centre, Ghana Health Service, Ministry of Health,
P.O. Box 200, Kintampo, Ghana. 2College of Health Sciences, University of
Ghana, Legon, Ghana. 3Newmont Ghana Gold Limited, C825/26 Lagos
Avenue, East Legon, Accra, Ghana. 4HealthLink Consulting, P.O. Box AN 6811,
Accra-North, Ghana. 5Newfields, 730 17th Street, Suite 925, Denver, CO
80202, USA.
Received: 22 September 2014 Accepted: 25 March 2015
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