Serologic Markers for Detecting Malaria in Areas of Low Endemicity, Somalia, 2008

EID Journal Home > Volume 16, Number 3–March 2010

Volume 16, Number 3–March 2010
Research
Serologic Markers for Detecting Malaria in Areas of Low Endemicity, Somalia, 2008
Teun Bousema, Randa M. Youssef, Jackie Cook, Jonathan Cox, Victor A. Alegana, Jamal Amran, Abdisalan M. Noor, Robert W. Snow, and Chris Drakeley
Author affiliations: London School of Hygiene and Tropical Medicine, London, UK (T. Bousema, J. Cook, J. Cox, C. Drakeley); University of Alexandria, Alexandria, Egypt (R.M. Youssef); Kenya Medical Research Institute–Wellcome Trust Research Programme, Nairobi, Kenya (R.M. Youssef, V.A. Alegana, J. Amran, A.M. Noor, R.W. Snow); Roll Back Malaria–World Health Organization, Hargeisa, Somalia (J. Amran); and University of Oxford, Oxford, UK (A.M. Noor, R.W. Snow)

Suggested citation for this article

Abstract
Areas in which malaria is not highly endemic are suitable for malaria elimination, but assessing transmission is difficult because of lack of sensitivity of commonly used methods. We evaluated serologic markers for detecting variation in malaria exposure in Somalia. Plasmodium falciparum or P. vivax was not detected by microscopy in cross-sectional surveys of samples from persons during the dry (0/1,178) and wet (0/1,128) seasons. Antibody responses against P. falciparum or P. vivax were detected in 17.9% (179/1,001) and 19.3% (202/1,044) of persons tested. Reactivity against P. falciparum was significantly different between 3 villages (p<0.001); clusters of seroreactivity were present. Distance to the nearest seasonal river was negatively associated with P. falciparum (p = 0.028) and P. vivax seroreactivity (p = 0.016). Serologic markers are a promising tool for detecting spatial variation in malaria exposure and evaluating malaria control efforts in areas where transmission has decreased to levels below the detection limit of microscopy.

Sub-Saharan Africa has the highest incidence of malaria caused by Plasmodium falciparum. Almost all areas where P. falciparum parasite prevalence is >50% in the general population are located in Africa (1). However, malaria is not uniformly distributed (1,2) and many parts of Africa are characterized by low transmission intensity of malaria (1). These areas are considered suitable for intensive malaria control and disease elimination (3,4).

Assessing malaria transmission intensity and evaluating interventions are complicated at low levels of malaria transmission. Assessing transmission intensity directly by determining the exposure to malaria-infected mosquitoes (entomologic inoculation rate [EIR]) is difficult when mosquito numbers are low, sometimes below the detection limits of commonly used trapping methods (5,6), and spatial and temporal variations in mosquito densities necessitate long-term intensive sampling (5,7,8). Determination of malaria parasite prevalence in the human population is a commonly used alternative (9), but it also becomes less reliable as an indicator of transmission intensity when endemicity is low (3,9,10). Therefore, an alternative method is needed to assess transmission intensity, evaluate interventions, and obtain information for control programs in areas of low endemicity.

Prevalence of antibodies against malaria parasites has been explored as a means of assessing malaria transmission intensity (11–13). Antibody seroconversion rates are less susceptible to seasonal fluctuations in malaria exposure (11,12), show a tight correlation with EIR (12,13), and show potential to detect recent changes in malaria transmission intensity (14). Serologic markers could be particularly useful in areas of low endemicity, where it may be easier to detect relatively long-lasting antibody responses than a low prevalence of malaria infections in the human population or infected mosquitoes. We used serologic markers of exposure to determine spatial variation in malaria transmission intensity in an area of low endemicity in Somalia (15).

Methods
Study Area
This study was conducted in the Gebiley District in Somaliland in northwestern Somalia. The district has a predominantly arid landscape with a few seasonal rivers and patches of irrigated farmlands. It is an area of intense seasonal rainfall with an average annual precipitation of 59.9 mm (2004–2007) and 2 peaks in rainfall in April and August. Three moderately sized communities were randomly selected from census maps by using spatial random sampling techniques in Arcview version 3.2 (Environmental Systems Research Institute, Redlands, CA, USA) (16). These communities were the villages of Xuunshaley (9.72140°N, 43.42416°E), Badahabo (9.68497°N, 43.65616°E), and Ceel-Bardaale (9.81777°N, 43.47455°E). The research protocol was reviewed and approved by the Research Ethics Review Committee of the World Health Organization (RPC246-EMRO) and the Ethical Committee of the Ministry of Health and Labor, Republic of Somaliland.

Data Collection
Two cross-sectional surveys were conducted. The first survey was conducted in March 2008 to determine parasite carriage at the end of the dry season (16). The purpose of the survey and the procedures were first discussed with the clan elders; thereafter, each household was visited, and informed consent was sought from each head of household. Households that agreed to participate were geolocated by using a global positioning system (Garmin eTrex; Garmin International, Inc., Olathe, KS, USA), and information was collected on demographic characteristics, bed net use, and travel history of the participants. Distance to seasonal rivers or other water bodies and distance to the nearest livestock enclosure was determined by using the global positioning system.

Individual written consent was obtained from all literate adults; illiterate adults provided consent by a thumbprint in the presence of an independent literate adult witness. For children <18 years of age, consent was obtained from parents or guardians, and children 12–18 years of age who could not write also provided consent by a thumb print.

One fingerprick blood sample was obtained from each respondent for the preparation of a P. falciparum antigen–specific rapid diagnostic test (RDT) (Paracheck-Pf; Orchid Biomedical Systems, Goa, India) sample and thick and thin blood smears. One-hundred high-power microscopic fields were examined and an additional 100 fields were examined if the first 100 fields were negative. RDT results were used for treatment with sulfadoxine-pyrimethamine and 3 doses of artesunate according to national guidelines. A second cross-sectional survey was conducted at the end of the wet season (August–September 2008) by using procedures identical to those described above, except that part of the fingerprick blood sample was placed on filter paper (3 MM; Whatman, Maidstone, UK) as described by Corran et al. (17).

Entomologic Surveys
Presence of Anopheles spp. mosquitoes in the area was determined by larvae collections in all permanent water bodies (artificial rain water reservoirs, wells, boreholes, stagnant storage pits, and riverbeds) in the 3 villages at the end of the wet season. Locally produced 250-mL dippers with a white surface were used. Five to 10 dips were made in the large water bodies and the presence of Anopheles spp. larvae was visually assessed and recorded.

Elution of Serum
Filter paper samples were stored at 4°C with desiccant until processed. A 3.5-mm blood spot, equivalent to ≈3 mL of blood (17), was punched from the filter paper and placed in a labeled well of a low-binding 96-well titer plate. A total of 300 mL of reconstitution buffer (phosphate-buffered saline [PBS], 0.05%Tween, and 0.1% [wt/vol] sodium azide) was added, and plates were sealed and rocked gently at room temperature overnight and subsequently stored at 4°C. The reconstituted blood spot solution was equivalent to a 1:100 dilution of whole blood or a 1:200 dilution of serum.

ELISAs
All reconstituted filter paper spots were tested at a final serum dilution equivalent of 1:1,000 for human immunoglobulin G antibodies against P. falciparum merozoite surface protein 119 (MSP-119) and 1:2,000 for antibodies against apical membrane antigen 1 (AMA-1) by using described ELISA methods (12,17). Briefly, recombinant MSP-119 (Wellcome genotype) and AMA-1 (3D7) were coated overnight at 4°C at a concentration of 0.5 mg/mL. Plates were washed by using PBS, 0.05% Tween 20 (PBS/T) and blocked for 3 h with 1% (wt/vol) skim milk powder in PBS/T. Positive controls (a pool of hyperimmune serum) and negative controls (European malaria-negative volunteers) were added in duplicate to each plate. The plates were washed and horseradish peroxidase–conjugated rabbit anti-human immunoglobulin G (Dako, Roskilde, Denmark) (1:5,000 dilution in PBS/T) was added to all wells. Plates were developed for 20 min by using an o-phenylenediamine dihydrochloride substrate solution. Reactions were stopped with 2 mol/L H2SO4. Plates were read immediately at 492 nm and optical density (OD) values recorded. For P. vivax, an identical protocol was used with MSP-119 (0.5 mg/mL) (18) and AMA-1 (0. 5 mg/mL). Serum in this protocol was used at 1:1,000 dilutions for both antigens.

Data Management and Statistical Analyses
Data were double-entered and imported into STATA version 10 (StataCorp LP, College Station, TX, USA). Duplicate OD results were averaged and normalized against the positive control sample on each plate. A cutoff value above which samples were considered antibody positive was defined by using a mixture model as described (17). Distribution of normalized OD values was fitted as the sum of 2 Gaussian distributions by using maximum-likelihood methods. The mean OD of the Gaussian distribution corresponding to the seronegative population plus 3 SD values was used as the cutoff value for seropositivity (J. Cook et al., unpub. data). A separate cutoff value was generated for each antigen (MSP-119 and AMA-1) for each species (P. vivax and P. falciparum). The seroconversion rate was estimated by fitting a simple reversible catalytic model to the measured seroprevalence by age in years by using maximum-likelihood methods. The serologic-derived annual EIR was then estimated by using the MSP-119 seroconversion rate and a calibration curve derived from determined values (11).The titer of antibody responses was estimated by using the formula dilution/[maximum OD/(OD test serum – minimum OD) – 1]; the median titer and interquartile range (IQR) are given. Because of low overall antibody prevalence, antibody responses were combined by species to determine the presence of any reactivity against P. falciparum or P. vivax. As a quantitative measure of reactivity to either malaria species, the highest titer in the MSP-119 and AMA-1 ELISAs was used.

Factors associated with P. falciparum or P. vivax seroreactivity were determined for each village separately by using generalized estimating equations adjusting for correlation between observations from the same household. The following factors were tested in the models: age in years, distance to the nearest seasonal river (in 100 m), distance to the nearest enclosure of livestock (in 100 m), number of household members, number of houses in a 100-m radius, roofing material, wall material, floor material, travel history, recent or regular bed net use, and an indicator of household wealth. The household wealth index was calculated on the basis of principal component analysis on characteristics such as ownership of a television, radio, telephone, bicycle, motorbike, cattle, and access to electricity (19). Variables that were significant at p = 0.10 in univariate analyses were added to the multivariate model and retained in the final multivariate model if their association with immune responses was statistically significant at p<0.05.

For detection of spatial clusters in immune responses, age-adjusted log10-transformed ODs were calculated as described by Wilson et al (20). First, Loess lines were fitted to scatter plots of age against log-transformed ODs for each antigen separately. For P. falciparum MSP-119 and P. vivax AMA-1, the linear regression was split at 49 and 46 years of age. Log-transformed ODs were adjusted for age by linear regression. SaTScan software (21) was used for detection of spatial clustering in log-transformed age-adjusted OD values by using the normal probability model. A circular-shaped window was used to systematically scan the area of each village separately; statistical significance of the clusters was explored by using 999 Monte Carlo replications to ensure adequate power for defining clusters. The upper limit was specified as 50% of the village population. Significant increases in ODs were detected by calculation of the likelihood ratio for each window. Only clusters were reported that appeared for MSP-119, AMA-1, and their combined age-adjusted ODs. Maps were made by using ArcGIS version 9.1 (Environmental Systems Research Institute).

Results
The 2 cross-sectional surveys were completed in March (dry season, n = 1,178) and August–September (wet season, n = 1,128) 2008. These surveys were characterized by a clear seasonality with no rainfall detected during November 2007–March 2008 and a median monthly rainfall of 114.5 mm in April–August 2008. None of the survey participants were positive by rapid diagnostic test, and P. falciparum or P. vivax parasites were not detected on any of the examined blood slides (Table 1). Available hospital records indicated 2/283 slide-confirmed, RDT-confirmed malaria cases in the study area in July and August 2008 (T. Bousema, unpub. data). Travel history was not available for these persons. During August–September 2008, a total of 464 potential breeding sites were examined in Xuunshaley (n = 40), Badahabo (n = 42), and Ceel-Bardaale (n = 382). In Ceel-Bardaale, 158 Anopheles mosquito larvae were found at 81 of 382 examined sites. In the 2 other villages, no Anopheles larvae were observed.

Suggested Citation for this Article
Bousema T, Youssef RM, Cook J, Cox J, Alegana VA, Amran J, et al. Serologic markers for detecting malaria in areas of low endemicity, Somalia, 2008. Emerg Infect Dis [serial on the Internet]. 2010 Mar [date cited].
http://www.cdc.gov/EID/content/16/3/392.htm

DOI: 10.3201/eid1603.090732

please, open here to see the full-text:
http://www.cdc.gov/eid/content/16/3/392.htm