• 1.

    King CH, Dickman K, Tisch DJ, 2005. Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis. Lancet 365: 15611569.

    • Search Google Scholar
    • Export Citation
  • 2.

    Adegnika AA, Kremsner PG, 2012. Epidemiology of malaria and helminth interaction: a review from 2001 to 2011. Curr Opin HIV AIDS 7: 221224.

    • Search Google Scholar
    • Export Citation
  • 3.

    Degarege A, Degarege D, Veledar E, Erko B, Nacher M, Beck-Sague CM, Madhivanan P, 2016. Plasmodium falciparum infection status among children with Schistosoma in sub-Saharan Africa: a systematic review and meta-analysis. PLoS Negl Trop Dis 10: e0005193.

    • Search Google Scholar
    • Export Citation
  • 4.

    Degarege A, Erko B, 2016. Epidemiology of Plasmodium and helminth coinfection and possible reasons for heterogeneity. BioMed Res Int 2016: 3083568.

    • Search Google Scholar
    • Export Citation
  • 5.

    Ezeamama AE, McGarvey ST, Acosta LP, Zierler S, Manalo DL, Wu HW, Kurtis JD, Mor V, Olveda RM, Friedman JF, 2008. The synergistic effect of concomitant schistosomiasis, hookworm, and Trichuris infections on children’s anemia burden. PLoS Negl Trop Dis 2: e245.

    • Search Google Scholar
    • Export Citation
  • 6.

    Melo GC, Reyes-Lecca RC, Vitor-Silva S, Monteiro WM, Martins M, Benzecry SG, Alecrim M, Lacerda MV, 2010. Concurrent helminthic infection protects schoolchildren with Plasmodium vivax from anemia. PLoS One 5: e11206.

    • Search Google Scholar
    • Export Citation
  • 7.

    Butler SE, Muok EM, Montgomery SP, Odhiambo K, Mwinzi PM, Secor WE, Karanja DM, 2012. Mechanism of anemia in Schistosoma mansoni-infected school children in western Kenya. Am J Trop Med Hyg 87: 862867.

    • Search Google Scholar
    • Export Citation
  • 8.

    Verani JR, Abudho B, Montgomery SP, Mwinzi PNM, Shane HL, Butler SE, Karanja DMS, Secor WE, 2011. Schistosomiasis among young children in Usoma, Kenya. Am J Trop Med Hyg 84: 787791.

    • Search Google Scholar
    • Export Citation
  • 9.

    Samuels AM, Matey E, Mwinzi PN, Wiegand RE, Muchiri G, Ireri E, Hyde M, Montgomery SP, Karanja DM, Secor WE, 2012. Schistosoma mansoni morbidity among school-aged children: a SCORE project in Kenya. Am J Trop Med Hyg 87: 874882.

    • Search Google Scholar
    • Export Citation
  • 10.

    Foo KT, Blackstock AJ, Ochola EA, Matete DO, Mwinzi PN, Montgomery SP, Karanja DM, Secor WE, 2015. Evaluation of point-of-contact circulating cathodic antigen assays for the detection of Schistosoma mansoni infection in low-, moderate-, and high-prevalence schools in western Kenya. Am J Trop Med Hyg 92: 12271232.

    • Search Google Scholar
    • Export Citation
  • 11.

    Kimathi N, Micheni J, Muriithi A, 2002. Clinical Guidelines for Diagnosis and treatment of Common conditions in Kenya. Nairobi, Kenya: Ministry of Health, GOK.

  • 12.

    World Health Organization, 2013. Schistosomiasis Progress Report 2001–2011 and Strategic Plan 2012–2020. Geneva, Switzerland: WHO.

  • 13.

    Skov T, Deddens J, Petersen MR, Endahl L, 1998. Prevalence proportion ratios: estimation and hypothesis testing. Int J Epidemiol 27: 9195.

  • 14.

    Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C, 1985. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol 122: 904914.

    • Search Google Scholar
    • Export Citation
  • 15.

    Liang K-Y, Zeger SL, 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 1322.

  • 16.

    Akinosoglou KS, Solomou EE, Gogos CA, 2012. Malaria: a haematological disease. Hematology 17: 106114.

  • 17.

    Foote EM, Sullivan KM, Ruth LJ, Oremo J, Sadumah I, Williams TN, Suchdev PS, 2013. Determinants of anemia among preschool children in rural, western Kenya. Am J Trop Med Hyg 88: 757764.

    • Search Google Scholar
    • Export Citation
  • 18.

    Suchdev PS, Ruth LJ, Earley M, Macharia A, Williams TN, 2014. The burden and consequences of inherited blood disorders among young children in western Kenya. Matern Child Nutr 10: 135144.

    • Search Google Scholar
    • Export Citation
 
 
 

 

 

 

 

 

 

Relative Contribution of Schistosomiasis and Malaria to Anemia in Western Kenya

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  • 1 Rollins School of Public Health, Emory University, Atlanta, Georgia;
  • | 2 Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia;
  • | 3 Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya

Because anemia is one of the markers of morbidity associated with schistosomiasis, it has been proposed as a potential measure to evaluate the impact of control programs. However, anemia is also a common consequence of malaria, and schistosomiasis and malaria are often co-endemic. To estimate the attributable fraction of anemia due to Schistosoma mansoni and Plasmodium falciparum infections, we applied a log-binomial model to four studies measuring these parameters of a combined 5,849 children in western Kenya. In our studies, malaria contributed 23.3%, schistosomiasis contributed 6.6%, and co-infection contributed 27.6% of the anemia. We conclude that in areas where S. mansoni and P. falciparum are co-endemic, the contribution of schistosomiasis to anemia is masked by anemia resulting from malaria, thus limiting anemia as a useful measure for schistosomiasis control programs in these settings.

Schistosomiasis mansoni has been associated with subtle morbidities such as anemia, fatigue, malnutrition, and impaired cognitive development as well as more obvious morbidities, such as hepatosplenic disease. Anemia has been proposed as a marker for morbidity reduction in schistosomiasis control programs because it is one of the most easily quantified of the subtle morbidities and is affected by presence and intensity of schistosome infection.1 However, in many areas where individuals are at risk for schistosomiasis, they are additionally at risk for malaria, which also causes anemia. Furthermore, in Africa, these diseases often overlap geographically. Thus, the effect of co-infection and the potential impact of interaction of schistosomiasis and malaria on anemia may affect the usefulness of anemia as a marker of schistosomiasis morbidity, though this phenomenon is not well understood.26

We wished to evaluate the use of anemia prevalence as a morbidity outcome measure in schistosomiasis control programs in an area where malaria is co-endemic with schistosomiasis. To do so, we determined the fraction of anemia attributable to each infection and assessed whether having both schistosomiasis and malaria has a synergistic effect on anemia. Our results may provide a better evidence base for schistosomiasis, malaria, and anemia public health program planning and execution.

We evaluated the attributable fraction of anemia due to schistosomiasis in children in western Kenya, an area where malaria is endemic. For this analysis, four cross-sectional datasets containing measures of anemia, malaria, and schistosomiasis in children in western Kenya were combined.710 Each of the datasets was generated from studies that were reviewed and approved by the Scientific and Ethical Review Committees of the Kenya Medical Research Institute and at the U.S. Centers for Disease Control and Prevention (CDC) based either on formal reliance on KEMRI human subjects protections, or review by the CDC Institutional Review Board. Data were collected from 2006 to 2011 in children between the ages of 1 and 15. No systematic treatment of schistosomiasis had been implemented in this area before 2012, and a single mass drug administration of mebendazole for soil-transmitted helminth control was implemented in 2009. Access to datasets was granted by co-investigators, and all datasets were already de-identified before incorporation into the analysis. The datasets are listed in Table 1.

Table 1

Datasets combined for the analysis of attributable fraction of anemia from schistosomiasis and malaria

Date of studyNAge range (years)Study typeReference
1June 2006, May 20072,7459–12School based7
2September–December 20074841–15Community8
3February–April 20118227–8School based9
4October 2010–April 20111,7988–12School based10

We defined anemia as < 12 g/dL hemoglobin according to Kenyan national guidelines,11 schistosomiasis as ≥ 1 egg seen in any stool sample, and malaria as ≥ 1 parasite seen in any blood smear. We used the World Health Organization guidelines for schistosome infection intensity: 1–99 eggs per gram (epg) is low, 100–400 epg is moderate, and > 400 epg is high.12

A log-binomial model was used to estimate the attributable fraction of anemia due to schistosomiasis.13 The outcome of anemia was modeled with the presence of schistosomiasis, presence of malaria, and potential confounders (gender, age, season, and body mass index). We also added a schistosomiasis by malaria interaction term to assess how the attributable fraction of anemia due to schistosomiasis varied by malaria status. The calculation of attributable fraction was conducted using Bruzzi et al.’s approach, which consists of 1) calculating an odds ratio (OR) point estimate from a logistic model for the exposure, schistosomiasis, and 2) using that value in the following equation:
1([1N]×[NexpOR+{NNexp}])
where N is the total number of people with the disease of interest (anemia), Nexp is the number of people with the disease who have the exposure of interest (schistosomiasis), and OR is the previously calculated adjusted odds ratio point estimate from the logistic regression model for the exposure.14 Whether the attributable fraction of anemia by schistosomiasis varied with malaria status was determined by testing the statistical significance of the interaction variable.14 We controlled for study by including a categorical study variable in the regression model. Generalized estimating equations were used to control for clustering at the school level.15

There were 2,461 (42.1%) children with anemia in our combined dataset. The log-binomial model indicated that although schistosomiasis and malaria both contribute to anemia separately, they also contribute to anemia through an interaction effect. In children without schistosomiasis, the prevalence of anemia was 1.72 (95% confidence interval [CI]: 1.47–2.00; P ≤ 0.001) times higher in those with malaria than in those without malaria. In children without malaria, the prevalence of anemia was 1.43 (95% CI: 1.16–1.78; P ≤ 0.001) times higher in those with schistosomiasis than in those without schistosomiasis. In addition, we see that there is an interactive effect of these two infections on anemia. In children with S. mansoni infection, the prevalence of anemia in children with malaria was 1.98 (95% CI: 1.57–2.05) times higher than in those without malaria (P < 0.0001). In contrast, there was no effect of schistosomiasis on the anemia of children with malaria. In addition, the attributable fraction of anemia from each of these diseases differs considerably. Using Bruzzi et al.’s equations, we found that the attributable fraction of anemia due to malaria is 23.3% (95% CI: 17.8–28.0%), due to schistosomiasis is 6.6% (95% CI: 3.0–9.6%), and due to the interaction of the two diseases is 27.6% (95% CI: 20.3–28.6%).14

These results suggest that the effect of malaria on anemia may limit the utility of anemia as a measure of schistosomiasis program impact in populations where co-infection is common. The interaction effect found here exemplifies the importance of taking a broad view when assessing public health problems, as infection with more than one pathogen may lead to worse outcomes than might be expected with single infections. However, there are some limitations in the methods used here. Although research is beginning to suggest that anemia due to S. mansoni infection results from inflammation,7 the biological mechanism that causes the massive cyclical intravascular hemolysis of malaria-associated anemia is not fully understood.16 This lack of understanding in biological mechanisms prohibits a more granular, directed study of the interaction seen here. In addition, information about other known causes of anemia that may influence these results, such as inadequate nutrition, bacterial or hookworm infection, and presence of sickle cell disease, was not available for inclusion in the analysis.17,18 Lastly, these studies were all conducted in western Kenya, near Lake Victoria, so these results may not be generalizable to different locations where co-infection is possible and other species of schistosome are prevalent. Further investigation with other co-morbidities and in other geographic settings is thus required before adopting anemia as a universal marker for progress in programs relating to schistosomiasis.

Acknowledgments:

This work was supported in part by the University of Georgia Research Foundation, Inc., which was funded by the Bill & Melinda Gates Foundation for the SCORE project. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is published with permission of the Director, Kenya Medical Research Institute. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC.

REFERENCES

  • 1.

    King CH, Dickman K, Tisch DJ, 2005. Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis. Lancet 365: 15611569.

    • Search Google Scholar
    • Export Citation
  • 2.

    Adegnika AA, Kremsner PG, 2012. Epidemiology of malaria and helminth interaction: a review from 2001 to 2011. Curr Opin HIV AIDS 7: 221224.

    • Search Google Scholar
    • Export Citation
  • 3.

    Degarege A, Degarege D, Veledar E, Erko B, Nacher M, Beck-Sague CM, Madhivanan P, 2016. Plasmodium falciparum infection status among children with Schistosoma in sub-Saharan Africa: a systematic review and meta-analysis. PLoS Negl Trop Dis 10: e0005193.

    • Search Google Scholar
    • Export Citation
  • 4.

    Degarege A, Erko B, 2016. Epidemiology of Plasmodium and helminth coinfection and possible reasons for heterogeneity. BioMed Res Int 2016: 3083568.

    • Search Google Scholar
    • Export Citation
  • 5.

    Ezeamama AE, McGarvey ST, Acosta LP, Zierler S, Manalo DL, Wu HW, Kurtis JD, Mor V, Olveda RM, Friedman JF, 2008. The synergistic effect of concomitant schistosomiasis, hookworm, and Trichuris infections on children’s anemia burden. PLoS Negl Trop Dis 2: e245.

    • Search Google Scholar
    • Export Citation
  • 6.

    Melo GC, Reyes-Lecca RC, Vitor-Silva S, Monteiro WM, Martins M, Benzecry SG, Alecrim M, Lacerda MV, 2010. Concurrent helminthic infection protects schoolchildren with Plasmodium vivax from anemia. PLoS One 5: e11206.

    • Search Google Scholar
    • Export Citation
  • 7.

    Butler SE, Muok EM, Montgomery SP, Odhiambo K, Mwinzi PM, Secor WE, Karanja DM, 2012. Mechanism of anemia in Schistosoma mansoni-infected school children in western Kenya. Am J Trop Med Hyg 87: 862867.

    • Search Google Scholar
    • Export Citation
  • 8.

    Verani JR, Abudho B, Montgomery SP, Mwinzi PNM, Shane HL, Butler SE, Karanja DMS, Secor WE, 2011. Schistosomiasis among young children in Usoma, Kenya. Am J Trop Med Hyg 84: 787791.

    • Search Google Scholar
    • Export Citation
  • 9.

    Samuels AM, Matey E, Mwinzi PN, Wiegand RE, Muchiri G, Ireri E, Hyde M, Montgomery SP, Karanja DM, Secor WE, 2012. Schistosoma mansoni morbidity among school-aged children: a SCORE project in Kenya. Am J Trop Med Hyg 87: 874882.

    • Search Google Scholar
    • Export Citation
  • 10.

    Foo KT, Blackstock AJ, Ochola EA, Matete DO, Mwinzi PN, Montgomery SP, Karanja DM, Secor WE, 2015. Evaluation of point-of-contact circulating cathodic antigen assays for the detection of Schistosoma mansoni infection in low-, moderate-, and high-prevalence schools in western Kenya. Am J Trop Med Hyg 92: 12271232.

    • Search Google Scholar
    • Export Citation
  • 11.

    Kimathi N, Micheni J, Muriithi A, 2002. Clinical Guidelines for Diagnosis and treatment of Common conditions in Kenya. Nairobi, Kenya: Ministry of Health, GOK.

  • 12.

    World Health Organization, 2013. Schistosomiasis Progress Report 2001–2011 and Strategic Plan 2012–2020. Geneva, Switzerland: WHO.

  • 13.

    Skov T, Deddens J, Petersen MR, Endahl L, 1998. Prevalence proportion ratios: estimation and hypothesis testing. Int J Epidemiol 27: 9195.

  • 14.

    Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C, 1985. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol 122: 904914.

    • Search Google Scholar
    • Export Citation
  • 15.

    Liang K-Y, Zeger SL, 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 1322.

  • 16.

    Akinosoglou KS, Solomou EE, Gogos CA, 2012. Malaria: a haematological disease. Hematology 17: 106114.

  • 17.

    Foote EM, Sullivan KM, Ruth LJ, Oremo J, Sadumah I, Williams TN, Suchdev PS, 2013. Determinants of anemia among preschool children in rural, western Kenya. Am J Trop Med Hyg 88: 757764.

    • Search Google Scholar
    • Export Citation
  • 18.

    Suchdev PS, Ruth LJ, Earley M, Macharia A, Williams TN, 2014. The burden and consequences of inherited blood disorders among young children in western Kenya. Matern Child Nutr 10: 135144.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Susan P. Montgomery, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA. E-mail: smontgomery@cdc.gov

Authors’ addresses: Emily M. Valice and Juan S. Leon, Rollins School of Public Health, Emory University, Atlanta, GA, E-mails: evalice@umich.edu and juan.leon@emory.edu. Ryan E. Wiegand, John M. Williamson, Aaron Samuels, Jennifer R. Verani, W. Evan Secor, and Susan P. Montgomery, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, E-mails: fwk2@cdc.gov, jow5@cdc.gov, iyp2@cdc.gov, qzr7@cdc.gov, was4@cdc.gov, and smontgomery@cdc.gov. Pauline N. M. Mwinzi, Diana M. S. Karanja, and Elizabeth Ochola, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya, E-mails: pmwinzi65@gmail.com, diana@cohesu.com, and eochola@kemri.org.

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