RESEARCH Open Access Malaria epidemiology in the Ahafo area of Ghana Kwaku P Asante1*, Charles Zandoh1, Dominic B Dery1, Charles Brown2, George Adjei1, Yaw Antwi-Dadzie3, Martin Adjuik4, Kofi Tchum1, David Dosoo1, Seeba Amenga-Etego1, Christine Mensah5, Kwabena B Owusu-Sekyere3, Chris Anderson3, Gary Krieger6 and Seth Owusu-Agyei1 Abstract Background: Plasmodium falciparum malaria remains endemic in sub-Saharan Africa including Ghana. The epidemiology of malaria in special areas, such as mining areas needs to be monitored and controlled. Newmont Ghana Gold Limited is conducting mining activities in the Brong Ahafo Region of Ghana that may have an impact on the diseases such as malaria in the mining area. Methods: Prior to the start of mining activities, a cross-sectional survey was conducted in 2006/2007 to determine malaria epidemiology, including malaria parasitaemia and anaemia among children < 5 years and monthly malaria transmission in a mining area of Ghana. Results: A total of 1,671 households with a child less than five years were selected. About 50% of the household heads were males. The prevalence of any malaria parasitaemia was 22.8% (95% CI 20.8 - 24.9). Plasmodium falciparum represented 98.1% (95% CI 96.2 - 99.2) of parasitaemia. The geometric mean P. falciparum asexual parasite count was 1,602 (95% CI 1,140 - 2,252) and 1,195 (95% CI 985 - 1,449) among children < 24 months and ≥ 24 months respectively. Health insurance membership (OR 0.60, 95% CI 0.45 - 0.80, p = 0.001) and the least poor (OR 0.57, 95% CI 0.37 - 0.90, p = 0.001) were protected against malaria parasitaemia. The prevalence of anaemia was high among children < 24 months compared to children ≥ 24 months (44.1% (95% CI 40.0 - 48.3) and 23.8% (95% CI 21.2 - 26.5) respectively. About 69% (95% CI 66.3 - 70.9) of households own at least one ITN. The highest EIRs were record in May 2007 (669 ib/p/m) and June 2007 (826 ib/p/m). The EIR of Anopheles gambiae were generally higher than Anopheles funestus. Conclusion: The baseline malaria epidemiology suggests a high malaria transmission in the mining area prior to the start of mining activities. Efforts at controlling malaria in this mining area have been intensified but could be enhanced with increased resources and partnerships between the government and the private sector. Background Plasmodium falciparum malaria remains endemic in sub-Saharan Africa [1]. In Ghana, the burden of malaria remains high with about 323 per 1,000 cases reported among children < 5 years in 2008; and there is limited evidence of a decrease in recent years (2002 - 2008) [2]. Malaria studies carried out in the middle belt of Ghana report of high transmission of approximately 269 infec- tive bites per person per year and a parasite prevalence of not less than 50% at all times in the year among chil- dren < 5 years [3]. The burden of malaria in Ghana and other endemic countries will continue to result in anaemia, cerebral malaria, and severe malaria, if the use of currently available tools for malaria interventions such as rapid diagnosis and appropriate treatment, use of insecticide-treated nets (ITNs) and indoor residual spraying are not intensified as part of the strategies to control malaria. A complex matrix of factors could miti- gate the successful use of the available tools; of special note are access to health care, household socioeconomic status, parental formal education, health education about malaria control and nutritional status of indivi- duals. However, there have been reports of encouraging malaria control efforts in some countries in sub-Saharan Africa, including Zanzibar [4], Kenya [5,6] and Tanzania [7], which can be replicated in other African countries given the resources from public and private partnerships and political commitment available recently. * Correspondence: kwakupoku.asante@kintampo-hrc.org 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 Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 © 2011 Asante 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 cited. Newmont Ghana Gold Limited, a mining company in Ghana[8] recognizes that its mining activities may have an impact on malaria and other public health diseases in the mining area. For example, mining activities could alter the breeding potential of mosquitoes as well as increase migration into the mining area and this could precipitate poor environmental factors that may lead to diseases including malaria. Alternatively, environmental alterations as a result of urbanization and the mining activities may decrease mosquito breeding sites and, therefore, the burden of malaria. With this realization, the Ghana Health Service in partnership with Newmont Ghana Gold Limited con- ducted a household and malaria epidemiology study as a baseline prior to the start of mining activities of New- mont Ghana Gold Limited in 2006/2007 to plan and evaluate malaria control programme within the area. This effort to control malaria in a mining area builds on a similar partnership in another mining area in Obuasi, Ghana that has led to a successful malaria control pro- gramme in the area [9]. This baseline malaria epidemio- logical data will be used for monitoring and evaluating malaria interventions specifically targeted at the mining area. The Ahafo baseline data are being reported in further attempts to contribute to the body of data required for monitoring global malaria burden. Methods Study area The study was carried out in four districts in the Brong Ahafo Region of Ghana; the Asutifi District (between latitudes 6°40’ and 7°15’ North and Longitudes 2°15’ and 2°45’ West), the Tano North and South (Tano N/S) Dis- tricts (between latitudes 7°00’N and 7°25’ N and between longitudes 1°45 W and 2°30 W), and the Techi- man municipality (between latitudes 7°31’N and 8°0’ N and between longitudes 1°60 W and 2°00 W). The study areas are in the wet semi-equatorial forest zone where the mean annual rainfall is about 1,200 mm per annum. Farming of cash crops, such as Cocoa, Oil Palm, Coffee are the major economic activity in the study commu- nities. All health facilities in the study area implement both clinical and public health services under the super- vision of the Brong Ahafo Regional Health Directorate. Malaria is the leading cause of morbidity and mortality in the region. About eighty percent of residents in the study area belong to the Akan ethnicity. Study design and methods A household cross-sectional study was carried out between July 2006 and March 2007 to determine base- line household morbidity and malaria transmission prior to the start of mining activities in the area. The districts in the mining area were categorized into two; the Impact area involving communities within the Asutifi, and Tano N/S districts whose health and/or socio-eco- nomic indices are likely to be affected by mining activ- ities being undertaken and located within approximately 25 Km radius from the mine sites. The Non-impact area representing communities within the Asutifi, Tano North or Tano South districts that are not likely to be affected by the mining activities and located more than 25 Km from the mines site (Figure 1). The Techiman municipality was purposively selected as a control area without mining activities but under the same local poli- tical and health administration as the other districts. The control area was selected for future comparisons after the beginning of mining activities. Within each area, communities were purposively selected based on geographical location to ensure representation of all locations within each area. Eleven (11) out of twenty (20); thirteen (13) out of nineteen (19) and eight (8) out of eighteen (18) communities were selected to represent the study areas in the Asutifi, Tano N/S and Techiman areas respectively. Household selection All households in the selected communities were enum- erated and encoded into a database. Each household member in the community including children less than five (5) years was given a unique identification number as part of the household morbidity survey. All house- holds who were respondents for the household morbid- ity survey and with children less than five (5) years in the database formed the sampling frame. One child less than five (5) years was randomly selected per household using a computer software for anthropometric assess- ment, hematological and malaria parasitaemia. Household ownership of ITNs and other household characteristics were assessed using a pre-tested struc- tured questionnaire which were administered by trained fieldworkers. The quality of the ITNs was assessed by direct observation for holes. Malaria parasitaemia was determined by collecting fin- ger prick blood samples from participants to prepare thick and thin blood smears. The smears were stained with 10% Giemsa and air-dried. They were examined under the microscope using ×10 eyepieces and ×100 objective. Malaria parasites were counted against 200 white blood slides on the thick smear. A slide was declared negative only after 200 oil immersion fields of the thick smear had been examined. Parasites were expressed per microlitre, assuming a total white cell count of 8,000 per microlitre. Species identification was done using the thin smear. A sub-sample (10%) of the blood slides were randomly selected and sent to an independent expert microscopists for examination and the results compared with > 90% concordance. Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 2 of 14 Part of the blood from the finger prick was collected into Hemocue microcuvettes and haemoglobin levels were determined using a calibrated hemocue (HemoCue GmbH, Germany). All children found to be unwell, parasitaemic or anaemia during the survey were referred to a study clinician and treated. Entomological surveys were carried out in the Asutifi and the Tano N/S districts from November 2006 to Figure 1 Study area map. Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 3 of 14 August 2007. The dry season period (Nov. 2006 - Feb. 2007) and wet season period (May 2007 - Aug. 2007) were purposefully planned to compare transmission with or without the influence of rainfall in a mining area. No entomology work was carried out in the Techi- man district as similar studies were carried out in the Kintampo district, an adjacent district with similar rain- fall pattern in 2004 [3]. Centre for Disease Control (CDC) light trap catches were performed concurrently on each night of mosquito collection in both impact and non-impact communities. Index persons were ran- domly selected from the Asutifi/Tano household enu- meration database. A total of about 1,100 possible compounds were randomly selected from enumerated households in the Asutifi and Tano N/S districts to receive mosquito CDC light traps. The house of the “index” person was the house of choice to set traps. Traps were hung at the foot end adjacent to the “index” net of the “index” person. Untreated mosquito nets were provided to household members whose rooms were used on the night of trap setting to ensure they were protected while ensuring that mosquitos were not repelled. This method was approved by ethics commit- tees. Four traps were set weekly and mosquitoes col- lected were chloroformed and morphologically identified using Anopheline morphological identification keys [10]. They were stored in 1.5 ml eppendorf tubes and trans- ported on weekly basis to the Kintampo Health Research Centre laboratories for ELISA as per the pro- cedures of Wirtz et al [11]. An average cut-off point of 0.2 nm absorbance wavelength was considered positive. Positive samples were re-tested and PCR analysis per- formed as described [11-13]. The entomologic inocula- tion rate (EIR) was calculated as the product of the proportion of Anopheles positive by ELISA also termed as sporozoite rate (SR) and the Human Biting Rate which is estimated as the geometric mean of Anopheles caught per CDC light traps set. Identification of Anopheles gambiae s.l A total of 200 morphologically identified Anopheles gambiae s.l. specimens were further analysed by poly- merase chain reaction (PCR) in order to establish mem- bers of the species complex and molecular forms composition [14]. Data management and statistical analysis All data collected in the field or the laboratory were logged for traceability, and then batched for double data entry and processing using Microsoft® Visual FoxPro 6.0. All data management processes were done at the computer laboratory of the Kintampo Health Research Centre. Data analysis was done using StataCorp Stata 10, TX USA. Averages and proportions were used to provide descriptions of age- and parasite prevalence, density, infection rates and the proportions of parasite clones that persisted during the survey. Socio-economic status was calculated for each household using World Bank asset scores for Ghana, based on ownership of a television, radio, refrigerator, car, motorcycle, bicycle, farm, electricity in house, ceiling, floor cover, and num- ber of individuals/sleeping room. The anthropometric indices height-for-age (HA), weight-for-age (WA), weight-for-height (WH), were expressed as Z-scores using the WHO Anthro for personal computers, Version 3.1, 2010: Software for assessing growth and develop- ment of the world’s children. A child was identified as being malnourished if s/he scored < -2 in one of the HA, WA, or WH indices. Malaria parasitaemia, anaemia (haemoglobin < 10 g/ dL) and ITN usage were the main outcome variables. Odds ratios were calculated with their 95% confidence interval to assess the relationships between exposure and outcome variables using univariate and multivariate logistic regression models. The Wald’s test and likeli- hood ratio test were used to estimate P values for expo- sure variables with two (2) levels and more than two (2) levels respectively in both the univariate and multivari- ate logistic regression models. P values less than 0.05 were considered statistically significant. Missing values were not included in the logistic regression models. Ethical approval Ethical approval to conduct the study was granted by the Kintampo Health Research Centre Institutional Ethics Committee. Community entry involved explaining the study to key community opinion leaders followed by community durbars/meetings. At these meetings, the study’s aims, objectives, risk and benefits were explained to all participants. A signed/thumb printed written con- sent was obtained from each respondent. All data col- lected are kept confidential. Results Household characteristics A total of 1,671 households with a child less than five years were selected. Response rate was approximately 100%. About 50% of the household heads were males (Table 1). Majority of household heads were between ages 30-39 years (27.8%) and 40-49 years (27.3). Majority of chil- dren (63.6%; 95% CI 61.2 - 65.9) in the households were ≥ 24 months. Health insurance coverage was 42.3%, (95% CI 40.5 - 45.3). Malaria parasitaemia A total of 1,671 children less than five years were sur- veyed for malaria parasitaemia and anaemia. The Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 4 of 14 prevalence of any malaria parasitaemia was 22.8% (95% CI 20.8 - 24.9). Plasmodium falciparum constituted 98.1% (95% CI 96.2 - 99.2) of parasitaemia. Plasmodium malariae was uncommon -1.9% (95% CI 0.81 - 3.8); no Plasmodium ovale or Plasmodium vivax was identified; there were no mixed infections. The geometric mean P. falciparum asexual parasite count was 1602 (95% CI 1,140 - 2,252) and 1,195 (95% CI 985 - 1,449) among children < 24 months and ≥ 24 months respectively. The proportion of children with parasite counts at var- ious cut-off points was similar among the two age groups (Figure 2). Majority of the children (< 24 months: 74%, 95% CI 64.4 - 82.9; ≥ 24 months: 82.9%, 95% CI 76.7 - 86.1) had low parasite counts < 5,000/μL. A univariate logistic regression model analysis was fit prior to multivariate regression models. Malaria parasi- taemia was significantly associated with the socioeco- nomic quintile (p < 0.001) with the strength of the association decreasing along the better socioeconomic quintiles. The least poor were significantly less likely to have malaria parasitaemia compared to the poor, OR 0.40 (95% CI 0.27 - 0.60) (Table 2). Health insurance membership protected against malaria parasitaemia (OR 0.51, 95% CI 0.40 - 0.66, p < 0.001). Household ITN ownership was weakly associated with malaria parasitae- mia (OR 0.80, 95% CI 0.62 - 1.03, p = 0.08). Children ≥ 24 months were significantly more likely to have parasi- taemia compared to children < 24 months (OR 1.95, 95% CI 1.50 - 2.53, p < 0.001) (Table 3). In the multi- variate logistic regression model, there were strong evi- dence of associations between malaria parasitaemia and Table 1 Household characteristics (N = 1671) n % Gender of household head Male 840 50.3 Female 818 49.0 Missing 13 0.8 Age group of household head 15 - 29 182 10.9 30-39 454 27.2 40-49 445 26.6 50-59 229 13.7 60+ 321 19.2 Missing 40 2.4 Child age < 24 months 609 36.4 ≥ 24 months 1062 63.6 Household head educational level None 603 36.1 Primary 203 12.2 Middle/JHS 645 38.6 > middle/JHS 208 12.5 Missing 12 0.7 Household socioeconomic status Poorest 357 21.4 Second 333 19.9 Third 341 20.4 Fourth 333 19.9 Least poor 300 18.0 Missing 7 0.4 Health Insurance membership No 950 56.9 Yes 714 42.7 Missing 7 0.4 Household size ≤ 6 712 42.6 > 6 952 57.0 Missing 7 0.4 Area of residence Asutifi impact 246 14.7 Asutifi non impact 150 9.0 Tano N/S impact 580 34.7 Tano N/S non impact 360 21.5 Techiman 328 19.6 Missing 7 0.4 JHS-Junior High School Figure 2 Geometric Mean Parasite Density levels in the cohort ((< 24 mo, n = 94; > 24 mo, n = 279). Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 5 of 14 household membership with health insurance (p = 0.001); child age (p < 0.001) and anaemia (p < 0.001). Anaemia The prevalence of anaemia (haemoglobin < 10 g/dL) among all children < 5 years was 30.5% (95% CI 28.2 - 32.8). The prevalence of anaemia was high among chil- dren < 24 months compared to children ≥ 24 months 44.1% (95% CI 40.0 - 48.3) and 23.8% (95% CI 21.2 - 26.5) respectively. About 32.7% (95% CI 28.9 - 36.7) of the children < 24 months and 19.0% (95% CI 16.6 - 21.5) of children > = 24 months had haemoglobin levels between 8.0 - 9.9 g/dL (Figure 3). In the univariate logistic regression model, children living in households with a male as a household head were significantly more likely to be anaemic compared to children living in households with females as house- hold heads (OR 1.30; 95% CI 1.05 - 1.60, p = 0.016); childhood anaemia was less likely among household Table 2 Analysis of household attributes associated with malaria parasitaemia Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Gender of household head Female 176/796 (22.1) 1 - 1 - Male 188/827 (22.7) 1.04 (0.82 - 1.31) 0.764 0.99 (0.77 - 1.27) 0.954 Age group of household head 15 - 29 39/180 (21.7) 1 - 1 - 30 - 39 74/447 (16.6) 0.71 (0.46 - 1.10) 0.003* 0.75 (0.47 - 1.20) 0.042* 40 - 49 107/428 (25.0) 1.21 (0.79 - 1.83) 1.26 (0.79 - 2.03) 50 - 59 65/225 (28.9) 1.47 (0.93 - 2.32) 1.38 (0.82 - 2.34) 60+ 71/317 (22.4) 1.04 (0.67 - 1.62) 1.12 (0.66 - 1.91) Household head educational level None 164/586 (28.0) 1 - 1 - Primary 42/200 (21.0) 0.68 (0.47 - 1.01) < 0.001* 0.78 (0.51 - 1.18) 0.499* Middle/JHS 125/631 (19.8) 0.64 (0.49 - 0.83) 0.80 (0.58 - 1.10) > middle/JHS 31/207 (15.0) 0.45 (0.30 - 0.69) 0.86 (0.52 - 1.41) Household socioeconomic status Poorest 103/348 (29.6) 1 - 1 - Second 81/323 (25.1) 0.80 (0.57 - 1.12) < 0.001* 0.88 (0.60 - 1.27) 0.047* Third 83/334 (24.9) 0.79 (0.56 - 1.1) 0.91 (0.61 - 1.34) Fourth 52/327 (15.9) 0.45 (0.31 - 0.65) 0.59 (0.38 - 0.92) Least poor 43/296 (14.5) 0.40 (0.27 - 0.60) 0.56 (0.35 - 0.91) Member of Health Insurance No 250/927 (27.0) 1 - 1 - Yes 112/701 (16.0) 0.51 (0.40 - 0.66) < 0.001 0. 60 (0.45 - 0.80) 0.001 Household ITN ownership No 124/498 (24.9) 1 - 1 - Yes 232/1107 (21.0) 0.80 (0.62 - 1.03) 0.079 0.81 (0.62 - 1.07) 0.147 Household size ≤ 6 130/696 (18.7) 1 - 1 - > 6 232/932 (24.9) 1.44 (1.13 - 1.84) 0.003 1.15 (0.85 - 1.54) 0.364 Area of residence Asutifi impact 50/236 (21.2) 1 - 1 - Asutifi non impact 34/148 (23.0) 1.11 (0.68 - 1.82) < 0.001* 0.85 (0.49 - 1.47) 0.065* Tano N/S impact 94/575 (16.4) 0.73 (0.50 - 1.07) 0.92 (0.60 - 1.40) Tano N/S non impact 103/358 (28.8) 1.50 (1.02 - 2.21) 1.43 (0.93 - 2.20) Techiman 81/311 (26.1) 1.31 (0.88 - 2.0) 1.27 (0.79 - 2.02) b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval; * -Likelihood ratio p-values; JHS-Junior High School Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 6 of 14 heads with characteristics, such as educational level > middle/Junior high school (OR 0.61; 95% CI 0.43 - 0.87, p = 0.029), the least poor socioeconomic status (OR 0.57 95% CI 0.40-0.80, p = 0.001) and household ITN ownership (OR 0.75 95% CI 0.61-0.93, p = 0.010). There was however no significant association between child- hood anaemia and household health insurance member- ship or household size (Table 4). Children ≥ 24 months were significantly less likely to be anaemic compared to children < 24 months (OR 0.41 95% CI 0.33-0.51, p < 0.001) (Table 5). Attributes such as sex and age of household head, area of residence and child age remain significant in their relationship with anaemia in the mul- tivariate logistic regression model. ITN ownership About 68.7% (95% CI 66.3 - 70.9) of households own at least one ITN. The median ITN per household was 2 (range 1 - 7). About 18.1% (95% CI 15.9 - 20.5) of ITNs observed for quality had holes in them. About 86.4% (95% CI 84.3 - 88.4) of household that owned ITNs had at least one person sleeping under an ITN the night before the interview. Majority of ITNs (64.9%, 95% CI 62.1 - 67.7) were obtained free of charge as part of a national campaign while 26.4% (95% CI 23.8 - 29.0) bought their ITNs on the open market at commercial prices. There was no significant association between household ITN ownership and household head charac- teristics such as sex and educational level in the univari- ate logistic regression model (Table 6). There was however a significant association between ITN owner- ship and attributes such as health insurance member- ship and area of residence that remained significant in the multivariate logistic regression models. Households with children ≥ 24 months were significantly less likely to own an ITN (Table 7) Mosquito abundance and entomological inoculation rates (EIRs) A total of 5,257 mosquitoes were caught from 818 light traps set; Anopheles gambiae comprised of 64.1% (n = 3,371) of the mosquitoes caught, Anopheles funestus 4.3% (n = 227) and non-Anopheles species 31.6% (n = 1,659) which were predominantly Culex and Aedes spe- cies of mosquitoes. A few Mansonia species were recorded in Asutifi (4) and Tano N/S districts (2). Table 3 Analysis of child attributes associated with malaria parasitaemia Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Child age < 24 months 93/600 (15.5) 1 - 1 - ≥ 24 months 273/1035 (26.4) 1.95 (1.50 - 2.53) < 0.001 2.44 (1.79, 3.34) < 0.001 Height for age z-score Normal 302/1403 (21.5) 1 - 1 - Abnormal 59/209 (28.2) 1.43 (1.03 - 1.99) 0.031 1.17 (0.79 - 1.73) 0.442 Weight for age z-score Normal 333/1519 (21.9) 1 - 1 - Abnormal 30/98 (30.6) 1.57 (1.01 - 2.46) 0.047 1.54 (0.84 - 2.85) 0.166 Weight for height z-score Normal 340/1500 (22.7) 1 - 1 - Abnormal 18/104 (17.3) 0.71 (0.42 - 1.20) 0.206 0.56 (0.30 - 1.05) 0.071 Anaemia Non-anaemic 216/1130 (19.1) 1 - 1 - Anaemic 150/505 (29.7) 1.79 (1.40 - 2.28) < 0.001 1.70 (1.30 - 2.22) < 0.001 b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval Figure 3 Haemoglobin levels in the cohort of children (< 24 mo, n = 585; > 24 mo, n = 1035). Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 7 of 14 EIRs for both Anopheles gambiae and Anopheles funestus were generally high. In the impact and non- impact areas of the Asutifi district, the median EIR was 46 ib/p/m (range 0-826 ib/p/m) and 51 ib/p/m (0 - 216) in the impact and non impact areas respectively. The highest was recorded in June 2007 at both impact and non impact areas respectively (Figure 4). EIRs in Tano N/S districts were also high, median 65 ib/p/m (range 48 - 219 ib/p/m) and 102 (range 14 - 669 ib/p/m) in the impact and non-impact area respectively with the high- est in May 2007 (Figure 5). The EIR of Anopheles gambiae were generally higher than Anopheles funestus. In Tano N/S alone, the median EIR for Anopheles gambiae alone was 73 ib/p/m (range: 39 - 438 ib/p/m) compared to Anopheles funestus med- ian 0 ib/p/m (range 0 - 5 ib/p/m) alone (Figure 6). Table 4 Analysis of household attributes associated with anaemia Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Gender of household head Female 226/818 (27.6) 1 - 1 - Male 278/840 (33.1) 1.30 (1.05 - 1.60) 0.016 1.28 (1.01 - 1. 61) 0.038 Age group of household head 15 - 29 78/182 (42.9) 1 - 1 - 30 - 39 122/454 (26.9) 0.49 (0.34 - 0.70) 0.001* 0.55 (0.37 - 0.82) 0.024* 40 - 49 120/445 (27.0) 0.49 (0.34 - 0.71) 0.56 (0.37 - 0.86) 50 - 59 78/229 (34.1) 0.69 (0.46 - 1.03) 0.64 (0.40 - 1.03) 60+ 97/321 (30.2) 0.58 (0.40 - 0.84) 0.49 (0.31 - 0.80) Household head educational level None 206/603 (34.2) 1 - 1 - Primary 63/203 (31.0) 0.87 (0.62 - 1.22) 0.029* 0.72 (0.49 - 1.06) 0.080* Middle/JHS 185/645 (28.7) 0.78 (0.61 - 0.98) 0.70 (0.52 - 0.95) > middle/JHS 50/208 (24.0) 0.61 (0.43 - 0.87) 0.84 (0.40 - 0.99) Household socioeconomic status Poorest 123/357 (34.5) 1 - 1 - Second 120/333 (36.0) 1.07 (0.78 - 1.47) 0.001* 1.17 (0.82 - 1.68) 0.191* Third 92/341 (27.0) 0.70 (0.51 - 0.97) 0.83 (0.57 - 1.22) Fourth 102/333 (30.6) 0.84 (0.61 - 1.16) 0.99 (0.67 - 1.46) Least poor 69/300 (23.0) 0.57 (0.40 - 0.80) 0.74 (0.48 - 1.14) Member of Health Insurance No 250/927 (27.0) 1 - 1 - Yes 112/701 (16.0) 0.51 (0.40 - 0.66) 0.010 1.00 (0.77 - 1.30) 0.971 Household ITN ownership No 313/950 (33.0) 1 - 1 - Yes 193/714 (27.0) 0.75 (0.61 - 0.93) 0.010 0.91 (0.69 - 1.20) 0.514 Household size ≤ 6 201/712 (28.2) 1 - 1 - > 6 305/952 (32.0) 1.20 (0.97 - 1.48) 0.095 1.28 (0.97 - 1.68) 0.080 Area of residence Asutifi impact 84/246 (34.2) 1 - 1 - Asutifi non impact 86/150 (57.3) 2.59 (1.71 - 3.93) < 0.001* 2.22 (1.38 - 3.55) < 0.001* Tano N/S impact 115/580 (19.8) 0.48 (0.34 - 0.67) 0.47 (0.32 - 0.68) Tano N/S non impact 139/360 (38.6) 1.21 (0.86 - 1.70) 0.97 (0.66 - 1.43) Techiman 82/328 (25.0) 0.64 (0.45 - 0.92) 0.49 (0.32 - 0.75) b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval; * -Likelihood ratio p-values; JHS-Junior High School Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 8 of 14 Polymerase chain reaction analysis Mosquitoes randomly selected and amplified by PCR were all An. gambiae sensu-stricto as confirmed by the size of the amplified DNA fragment of 390 base pairs. One hundred out of the 200 mosquitoes identified as An. gambiae s.s. were randomly selected for the identifi- cation to the molecular form by enzyme digestion with Hha1. All 100 mosquitoes were S-forms shown by restriction analysis. Two hundred mosquitoes were randomly selected to determine knockdown resistance (kdr) mutations. One hundred and ninety three out of the 200 selected mos- quitoes (97%) possessed the kdr mutation (kdr+), whilst seven (3%) did not (kdr-). Discussion The study assessed the epidemiology of malaria in a rural forest area of the Brong Ahafo region in Ghana. The prevalence of malaria parasitaemia and anaemia among children less than five years and malaria ento- mology were determined. Though renewed efforts in controlling malaria in sub-Saharan Africa has led to reports of decline in the burden of malaria in some parts of Africa [4-7,9], there is little evidence in Ghana to suggest a decline in out-patient attendance due to malaria and malaria mortality amidst the intensified delivery of malaria control interventions [2]. In this sur- vey, a malaria epidemiology survey in a forest area of Ghana including a mining area was conducted to plan malaria control programmes in these areas. The prevalence of P. falciparum among children gives an estimation of the burden of malaria in a particular area. In the study area, the prevalence of malaria parasi- taemia was relatively low 22.8% (95% CI 20.8 - 24.9) compared to reports from other rural parts of Ghana; 58% in a forest savanna transitional area of Kintampo in 2004 [3]; between 55.5 - 69.3% in savanna area in north- ern Ghana between 2000 and 2002 [15,16]. The preva- lence of malaria parasitaemia in the area was however comparable to reports in urban semi-humid areas - 12.8% - 37.8% in 2005 [17]. The relationship between malnutrition and malaria are inconsistent in the literature. Studies conducted in Ethiopia [18] and southern Ghana[19] showed no evi- dence of relation between malnutrition and anaemia contrary to studies conducted in urban Equatorial Gui- nea [20], in western Kenya [21] and reviews by Caulfield et al [22] that suggest a strong relationship between malnutrition and malaria. In this study, there was no association of malaria parasitaemia with malnutrition. In 2003, the government of Ghana introduced the National Health Insurance Scheme with the aim of reducing financial constraints as a gap in accessing health. Household membership of the insurance scheme provides children to seek health in health facilities clo- sest to them. Common illnesses, such as malaria, are treated without direct cash payment at the point of ser- vice. In this study, health insurance membership was protective against malaria parasitaemia but had no sig- nificant relationship with anaemia. Participants who sub- scribe to the health insurance may regularly seek health care and may have been treated for malaria thus, the observed protection of malaria parasitaemia by health insurance membership. ITNs are known to prevent malaria infection by redu- cing human contact with the malaria vector and thus able to reduce all-cause mortality in children less than five years old by 17% in northern Ghana [23] and about 44% in Kenya [24]. ITN ownership has increased in recent times due to the support of local government Table 5 Analysis of child attributes associated with anaemia Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Child age < 24 months 259/609 (42.5) 1 - 1 - ≥ 24 months 247/1062 (23.3) 0.41 (0.33 - 0.51) < 0.001 0.32 (0.25, 0.42) < 0.001 Height for age z-score Normal 424/1429 (29.7) 1 - 1 - Abnormal 76/211 (26.0) 1.33 (0.99 - 1.81) 0.062 1.62 (1.11 - 2.37) 0.012 Weight for age z-score Normal 457/1550 (29.5) 1 - 1 - Abnormal 43/99 (43.4) 1.84 (1.22 - 2.77) 0.004 1.52 (0.86 - 2.68) 0.147 Weight for height z-score Normal 456/1523 (29.9) 1 - 1 - Abnormal 41/105 (39.1) 1.50 (0.99 - 2.25) 0.051 1.17 (0.70 - 1.98) 0.546 b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 9 of 14 and international donors such as USAID and their pri- vate partners [25]. In 2003, ITN ownership in Ghana was less than 10% but had increased to about 33% in 2008 [26]. In this study ITN ownership (at least one) was lowest in the Asutifi area (range 43% - 53.3%) com- pared to 76.8% in the Techiman area. This difference in household ITN ownership was due to mass distribution of ITNs to all households with children less than two years as part of Child Survival strategies throughout Ghana. The relatively low coverage in Asutifi is because this survey was carried out prior to the free distribution of treated bed-nets in the Child Survival Campaign. The coverage determined in the study area are similar to the coverage of 45.6% assessed in 2008 for the same area during the Ghana Health and Demographic Survey [26]. The coverage of ITNs could further be increased and maintained at the World Health Organization target of ≥80% if private companies such as Newmont Ghana Gold Limited and other partners could support the Ghana Health Service and the communities with addi- tional ITNs. Despite the benefits of ITNs in controlling malaria, its patronage has not been quite encouraging and its usage is for some reasons other than for malaria control. For Table 6 Analysis of household attributes associated with household ITN ownership Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Gender of household head Female 561/804 (69.8) 1 - 1 - Male 555/823 (67.4) 0.90 (0.73 - 1.11) 0.309 0.88 (0.70 - 1.10) 0.271 Age group of household head 15 - 29 127/180 (70. 6) 1 - 1 - 30 - 39 327/446 (73.3) 1.15 (0.78 - 1.68) 0.018* 1.06 (0.70 - 1.60) < 0.001* 40 - 49 303/439 (69.0) 0.93 (0.64 - 1.36) 0. 69 (0.45 - 1.06) 50 - 59 140/227 (61.7) 0.67 (0.44 - 1.02) 0.43 (0.27 - 0.70) 60+ 205/316 (64.9) 0.77 (0.52 - 1.14) 0.47 (0.29 - 0.77) Household head educational level None 406/593 (68.5) 1 - 1 - Primary 132/203 (65.0) 0.86 (0.61 - 1.20) 0.131* 0.91 (0.62 - 1.32) 0.402* Middle/JHS 433/637 (68.0) 0.98 (0.77 - 1.24) 0.98 (0.73 - 1.32) > middle/JHS 152/202 (75.3) 1.40 (0.97 - 2.01) 1.35 (0.86 - 2.10) Household socioeconomic status Poorest 237/348 (68.1) 1 - 1 - Second 214/330 (64.9) 0.86 (0.63 - 1.19) 0.013* 1.0 (0.70 - 1.43) 0.148* Third 226/338 (66.9) 0.95 (0.69 - 1.30) 0.97 (0.67 - 1.41) Fourth 223/330 (67.6) 0.98 (0.71 - 1.35) 1.05 (0.72 - 1.55) Least poor 226/294 (76.9) 1.56 (1.09 - 2.21) 1.55 (1.01 - 2.38) Member of Health Insurance No 608/937 (64.9) 1 - 1 - Yes 518/703 (73.7) 1.51 (1.22 - 1.88) < 0.001 1.35 (1.04 - 1.74) 0.022 Household size ≤ 6 462/697 (66.3) 1 - 1 - > 6 664/943 (70.4) 1.21 (0.98 - 1.49) 0.075 1.37 (1.05 - 1.79) 0.020 Area of residence Asutifi impact 107/246 (43.5) 1 - 1 - Asutifi non impact 80/150 (53.3) 1.48 (0.99 - 2.23) < 0.001* 1.44 (0.91 - 2.27) < 0.001* Tano N/S impact 423/562 (75.3) 3.95 (2.88 - 5.43) 3.55 (2.53 - 4.99) Tano N/S non impact 268/359 (74.7) 3.83 (2.27 - 5.41) 3.90 (2.68 - 5.67) Techiman 248/323 (76.8) 4.30 (2.99 - 6.16) 4.84 (3.22 - 7.26) b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval; * -Likelihood ratio p-values; JHS-Junior High School Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 10 of 14 instance, in Savalou, Benin mosquito nets were often seen as a means of protection against mosquitoes and other biting insects in order to sleep better rather than as a means of preventing malaria [27]. Several reasons including: lack of money or expensive ITN, unavailabil- ity of ITN, no provision for nets to fit sleeping space account for the low mosquito net usage. In this study, it was encouraging to note that ITN use was high; about 86.4% (95% CI 84.3 - 88.4) of household that owned ITNs had at least one person sleeping under an ITN the night before the interview. This is consistent with the high coverage of ITN use among children in the same area determined in 2008 [26]. Though there was a high ITN ownership and use, there was no evidence of pro- tection against malaria parasitaemia after adjusting for age, anaemia, malnutrition and other household. This may be due to poor quality of ITNs (about 18% of household nets had holes) or improper use of ITNs. One of the commonest causes of anaemia in sub- Saharan Africa is malaria. In this study, the prevalence of anaemia among all children < 5 years was moderately high 30.5% (95% CI 28.2 - 32.8) with children less than 24 months having a higher risk of anaemia. Although high, it is still lower than reported in other parts of Ghana and follows the same trend as malaria parasitae- mia [3,15,16,28]. There are other causes of anaemia in children such as malnutrition, hookworm infection, and sickle cell disease but their contribution to anaemia in malaria endemic regions has been found to be minimal compared malaria [29,30]. In this study, there was a Table 7 Analysis of child attributes associated with household ITN ownership Attribute n/N (%) ORb, (95% CI) p ORg, (95% CI) p Unadjusted Adjusted Child age < 24 months 491/596 (82.3) 1 - 1 - ≥ 24 months 635/1044 (60.8) 0.33 (0.26 - 0.42) < 0.001 0.26 (0.20 - 0.35) < 0.001 Height for age z-score Normal 960/1399 (68. 6) 1 - 1 - Abnormal 147/210 (70.0) 1.07 (0.78 - 1.46) 0.687 0.98 (0.68 - 1.42) 0.935 Weight for age z-score Normal 1042/1519 (68. 6) 1 - 1 - Abnormal 71/99 (71.7) 1.16 (0.74 - 1.82) 0.517 0.95 (0.53 - 1.72) 0.877 Weight for height z-score Normal 1028/1494 (68.8) 1 - 1 - Abnormal 73/103 (70.9) 1.10 (0.71 - 1.71) 0.661 0.95 (0.54 - 1.57) 0.767 Anaemia Non-anaemic 782/1138 (68.7) 1 - 1 - Anaemic 344/502 (68.5) 0.99 (0.79 - 1.24) 0.939 1.24 (0.96 - 1.60) 0.104 b-Univariate logistic regression odds ratio (OR); g-multivariate logistic regression odds ratio (OR); CI-confidence interval Figure 4 Monthly EIRs in Asutifi district (November 2006- August 2007). Figure 5 Monthly EIRs in Tano N/S district (November 2006- August 2007). Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 11 of 14 weak-association between abnormal height for age scores and anaemia; and a stronger association between malaria parasitaemia and anaemia after adjusting for age, ITN use and other household characteristics. Other common causes of anaemia such as hookworm infesta- tion and sickle cell disease were not determined in this study. There are few longitudinal malaria entomology studies carried out in mining communities in Ghana and other parts of the world. The abundance of mosquitoes in the Asutifi and Tano N/S districts differed slightly due to several contributing factors. Unlike Asutifi where the mosquito abundance was highest in the impact area, Tano experienced the reverse within the six month sur- veyed period. Though reasons for these differences in vector densities were not investigated, they may be attributed to environmental factors, such as changes in climate or vegetation, rainfall pattern, temperature; human factors such as human created breeding sites from urbanization; and intrinsic vector characteristics as previously reported [31,32]. The EIRs calculated in this survey were as high as the EIRs found in the Kintampo area (269 ib/p/y) two years prior to this survey and used as an estimate for the con- trol. The high EIRs recorded occurred in months that experienced moderate rainfall; June-July in Asutifi and February-May in Tano N/S, which indicates the impact of rainfall in vector populations and their breeding potential. Moderate rainfall creates pockets of water which favours breeding of larvae unlike torrential rains which washes away larval breeding sites of the major malaria vectors in Africa [32]. As such, changes in vege- tation or environment that promotes moderate rainfall could possibly begin to experience high malaria trans- mission if appropriate vector control activities are not planned. EIRs varied significantly between areas or com- munities and, therefore, control strategies need to be planned in concert with detail area vector indices such as abundance, speciation and EIRs. The low infectivity rate of An. funestus in this survey may probably be because a large proportion of young vectors with a low potential to transmit malaria may have been caught in the light traps; however this could not be confirmed since samples were not subjected to physiological age grading technique (parity detection) in this study. All An. gambiae s. l. analysed by PCR were of the S-mole- cular forms which is widespread in West Africa and other parts of Africa including the Kintampo area esti- mated for the control area [31-35]. The high percentage of kdr+ mutation reported in this study should prompt the malaria control programme to mount a surveillance on insecticide resistance existing especially since there could be an exacerbation due to the potential of cross-resistance between the insecticides used for farming and ITNs in this predominantly farm- ing area. At the time of the survey in 2006, the study area in the Asutifi and Tano N/S districts were being prepared for mining activities by Newmont Ghana Gold Limited. Part of the mining preparatory activities may include clearing away some forest vegetation that could poten- tially lead to clean water pools and subsequently change the breeding characteristics for mosquitoes that could transmit malaria and other vector borne diseases such as Yellow fever and Dengue if left unmanaged. This phenomenon of mosquito scourge associated with industrial activities was reported many years ago in the Panama Canal [36] and recently in the copper mines of DRC and Zambia [37,38]. In both cases, the industrial activity was significantly affected until an effective malaria control programme including environmental vector control was implemented. In the Zambian case, copper production increased significantly with declining burden of malaria, thus paying off the investment made in the malaria control. Additionally, a mining area has the potential of attracting migrant workers which increases the population with poor sanitation if uncontrolled. This malaria survey was conducted as part of pre- paratory activities to control malaria and other poten- tial vector borne diseases in the area. It demonstrates the mining company ’s commitment in controlling malaria in their area of operations while protecting its workforce to minimize cost due to healthcare and maximize productivity. Newmont Ghana Gold Limited and the Ghana Health Service have intensified malaria control activities in the area since the completion of the survey. These activities include in-door residual spraying, larviciding, free ITN distribution to employ- ees (three ITNs per employee) and community mem- bers (two ITNs per household); refurbishment of a community health centre and training of health staff Figure 6 Monthly EIRs by Anopheles gambiae and Anopheles funestus in Asutifi and Tano N/S districts (November 2006- August 2007). Ag = Anopheles gambiae. Af = Anopheles funestus (November 2006-August 2007) Asante et al. Malaria Journal 2011, 10:211 http://www.malariajournal.com/content/10/1/211 Page 12 of 14 for accurate malaria diagnosis [39]. The impact of the interventions in the community is yet to be evaluated; however, a marked decrease (8% in 2006 to 1.8% in 2009) in malaria incidence among the mining work force has been noted [40]. Newmont Ghana Gold Lim- ited’s support to control malaria and other public health diseases, such as tuberculosis and HIV/AIDS, has been recognized by the Global Business Coalition on HIV/AIDS, Tuberculosis and Malaria in 2010 [40]. Similar private cooperate involvement in malaria con- trol has successfully been supported by companies, such as Anglogold Ashanti in Obuasi area of Ghana [9], Konkola Copper Mine in Zambia [41] and Exxon Mobil in Angola, Cameroon, Chad, Equatorial Guinea and Nigeria [42]. Conclusion The malaria epidemiology suggests a high malaria trans- mission in the mining area prior to the start of mining activities. Efforts at controlling malaria in this mining area have been intensified but could be enhanced with increased resources and partnerships between govern- ment and the private sector. Periodic evaluations will be key to ascertain the impact of the malaria control activ- ities currently ongoing in the mining area. Acknowledgements We are extremely grateful to the participants, parents of the young children involved as well as the community opinion leaders for their interest and support. We are thankful to the Ghana Health Service, especially Prof Agyeman Badu Akosa, former Director-General of Ghana Health Service; Alhaj (Dr) Bin Ibrahim, former Brong Ahafo Regional Director of Health Services, Dr. George Bonsu, former Brong Ahafo Regional Deputy-Director in charge of Public Health; and The District/Municipal Directors for Asutifi, Tano North, Tano South and Techiman study areas. We are appreciative of the support provided by the management of Newmont Ghana Gold Limited for the conduct of the study. We are grateful to the management and staff of Kintampo Health Research Centre and scientist who contributed to the conduct of the study, analysis and report writing. Kintampo Health Research Centre is a member of the INDEPTH Network 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. 4INDEPTH Network Secretariat, 11 Mensah Wood Street, East Legon P. O. Box KD 213, Kanda, Accra, Ghana. 5HealthLink Consulting P.O. Box AN 6811, Accra-North Ghana. 6Newfields 730 17th Street Suite 925 Denver, CO 80202, USA. Authors’ contributions KPA led in the proposal writing with contribution from CM, CZ, GK, KT, DD and SOA. KPA, CZ, DD, and KT coordinated the data collection during the study with support from YAD and CA on behalf of Newmont Ghana Gold Limited. Laboratory analysis was performed by DBD, CB, KT and DD. Data management and analysis was done by KPA, GD, MA, SAE and SOA. KPA wrote the manuscript with contribution from all authors. 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