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Presenter Disclosures

Presenter Disclosures. Polycarp O. Kogembo. No relationships to disclose. (1) The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months:.

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  1. Presenter Disclosures Polycarp O. Kogembo No relationships to disclose. (1) The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months:

  2. The Association Between Malaria Prevalence and Housing Structures in the Bondo District of Kenya Presented by Polycarp O. Kogembo 141st Meeting of the American Public Health Association Boston, MA November 2013

  3. Introduction to the Study • Introduction • Literature Review • Research Question • Hypotheses • Theoretical Framework

  4. Introduction • 3.3 billion people live in regions with a high risk of malaria (World Health Organization [WHO], 2010). • The highest malaria mortality is found in 35 countries worldwide; 30 of those countries are in Sub-Saharan Africa, and the other five are in Asia. Approximately 98% of the world’s malaria transmission occurs within those countries (WHO, 2010). • 89% of global malaria transmission occurs in Africa (WHO, 2010). Because most of the African population lives in malaria-endemic regions without protection from mosquito bites, malaria infection continues in Africa today at an alarming rate (WHO, 2010).

  5. Literature Review • Children with less immunity are at a higher risk of missing school because of malaria infection and may become critically ill (Clarke et al., 2004). • Between 40% to 59% of school-aged children suffer from malaria (Clarke et al., 2004). • A study in Kisumu, Kenya, revealed that housing structures influence mosquito density (Atieli, Menya, Githeko, & Scott, 2009). • There is a gap in the literature that focuses on these factors, which need to be addressed by research.

  6. Research Question In the Bondo District in Kenya, do people who live in houses with structural deficiencies (i.e., abutments) report higher prevalence of malaria than do those who live in structurally sound houses?

  7. Hypotheses • Null hypothesis (H0): • In the Bondo District in Kenya, there is no association between housing structure and malaria prevalence. In other words, people who live in houses with structural deficiencies (i.e., abutments) do not report higher prevalence of malaria than do those who live in structurally sound houses. • Alternative hypothesis (H1): • In the Bondo District in Kenya, there is an association between housing structure and malaria prevalence. In other words, people who live in houses with structural deficiencies (i.e., abutments) tend to report higher prevalence of malaria than do those who live in structurally sound houses.

  8. Theoretical Framework • The theory on which this research was based is called ecologic theory(Bronfenbrenner, 1979). • Living systems interact with their environments and with other living systems. • Environments in which humans live directly impact human health and ultimately influence whether people live with or without diseases. • In environments in which disease is prevalent (e.g., in the Bondo District where malaria is rampant), children become anemic before they are born because their parents are anemic, particularly because their mothers are infected with malaria.

  9. Methodology • Research Design • Assumptions • Ethical Prodcedures • Malaria Survey Questionnaire • Description of Variables

  10. Research Design • The 155 homeowners who participated in this study • Were at least 18 years of age; • Had lived in the Bondo District for a minimum of 1 year; • Were living in either poorly structured houses with abutments or mosquito-screened houses without abutments. • This study was based on a cross-sectional quantitative design using a survey with probability sampling. • Participants were randomly recruited from heavily populated areas near schools. • The researcher recruited participants by approaching them and talking to them in their private homes.

  11. Assumptions • The data collected from homeowners and predictors associated with malaria exposure were accurately obtained from participants. • The instrument of the study accommodated the ethnic culture in the Luo community and was written in both Luo and English. • The researcher had proper training to limit interview biases. • The study population was defined as a general population of people who live in poorly structured houses in the Bondo District of the Nyanza Province in Kenya.

  12. Ethical Procedures • IRB approval from Walden University and from the Office of Health in the Bondo District of Kenya • Training and certification in research ethics through the National Institutes of Health • Informed consent in both English and Luo • Explained voluntary participation, risks and benefits, and confidentiality • Compensated with US $1 and an informative malaria flyer from Bondo District Department of Public Health • Participants’ confidentiality maintained at all times

  13. Figure 1. Sample technique based on arrangement of homesteads and households.

  14. Malaria Survey Questionnaire • Modified from another already-established questionnaire to fit current research conditions. • Created two versions (i.e., English and Luo). • Tested for reliability and validity through pilot study. • Assessed the following: • Inter-class reliability • Construct validity (i.e., content validity) • Internal reliability • Test-Retest • Inter-Rater

  15. Results & Discussions • Pilot Study • Descriptive Analyses • Results of Preliminary Analyses • Primary Analyses & Interpretations • Limitations • Recommendations • Summary

  16. Pilot Study • N =15 participants • Cronbach’s α on each set of survey items to determine inter-item reliability (.69 < α < .99) • Intraclass correlations among responses to determine similarities in response patterns • Ranged from .13 (for how mosquitoes enter houses) to .87 (for family member diagnosed with malaria) • No changes made to survey based on results from pilot study

  17. Descriptive Analyses: Demographics • Each region in the Bondo was equally represented. • Percentages • Female: 77.6% • Primary Education: 64.1% • More Than 10 Years in the Bondo District: 80.6% • Luo as Primary Language: 77.0% • Luo as Ethnicity: 98.7% • Means and Standard Deviations • Number of Children in Home: M = 3.43 and SD = 2.15 • Age (in Years): M = 37.46 and SD = 13.60 • Number of Adults in Home: M = 2.05 and SD = .88

  18. Descriptive Analyses: IVs • Indicators of stronger housing structure included • Housing materials of wood, concrete, stone, and brick • Concrete floors • Screened windows and doors • Indicators of weaker housing structure included • Grass and mud as housing materials for the roof, walls, and/or floors • Open eaves, abutments, and/or windows • Ability for mosquitoes to enter through abutments, walls, and/or roofs • Housing Structure Score: M = 6.54 and SD = 2.94

  19. Descriptive Analyses: DVs • Percentages • Participant Diagnosed With Malaria: 95.5% • Spouse Diagnosed With Malaria: 39.4% • Child Diagnosed With Malaria: 89.0% • Means and Standard Deviations • Number of Times Diagnosed With Malaria in the Past 12 Months: M = 4.92 and SD = 3.53 • Number of Malaria Symptoms: M = 3.14 and SD = 1.11)

  20. Results of Preliminary Analyses • Use mosquito nets • Thick bush • Participant bitten while sleeping • Family member bitten while sleeping • Level of education • Average number of mosquito bites per night • Level of education • Time in Bondo District • Total number of children in house • Total number of adults in house

  21. Primary Analyses: Participant • Logistic regression to predict odds of participant diagnosed with malaria from housing structure score • Housing structure did not significantly affect the odds of participants’ being diagnosed with malaria (OR = 1.18). • Using mosquito nets INCREASEDthe odds of participants’ being diagnosed with malaria (OR = 12.56). • Logistic regression to predict odds of participant diagnosed with malaria from presence of abutment • Having an open abutment did not significantly affect the odds of participants’ being diagnosed with malaria (OR = .66).

  22. Interpretations: Participant • People may not understand whether their sicknesses were the result of malaria or of other diseases that present like malaria (e.g., influenza). • Despite results to the contrary in this study, results from other studies have revealed that structurally deficient houses increase the risk of mosquito bites and malaria infections (Ayele, Zewotir, & Mwambi, 2012). • Those who use mosquito nets are more likely to report malaria diagnoses than are those who do not use mosquito nets, perhaps because those who use mosquito nets are doing so after being diagnosed with malaria (Zhou et al., 2011).

  23. Primary Analyses: Child • Logistic regression to predict odds of child diagnosed with malaria from housing structure score • Housing structure did not significantly affect the odds of having a child diagnosed with malaria (OR = .94). • Having more children in the house INCREASEDthe odds of having a child diagnosed with malaria (OR= 2.24). • Logistic regression to predict odds of child diagnosed with malaria from presence of abutment • Housing structure did not significantly affect the odds of having a child diagnosed with malaria (OR = 1.18). • Being female REDUCEDthe odds of having a child diagnosed with malaria (OR= .26).

  24. Primary Analyses: Spouse • Logistic regression to predict odds of spouse diagnosed with malaria from housing structure score • Housing structure did not significantly affect the odds of having a spouse diagnosed with malaria (OR = 1.03). • Being female DECREASEDthe odds (OR = .27) and being bitten while sleeping INCREASEDthe odds of having a spouse diagnosed with malaria (OR= 5.83). • Logistic regression to predict odds of spouse diagnosed with malaria from presence of abutment • Having an open abutment INCREASEDthe odds of having a spouse diagnosed with malaria (OR = 5.46). • Having more children in a house (OR = 2.28) and using nets (OR = 7.24) INCREASEDthe odds of having a spouse diagnosed with malaria.

  25. Interpretations: Child & Spouse • More people living in a household increases the chance that a child in the household has been diagnosed with malaria because people who live in the same household are exposed to one another and are more likely to be bitten by the same mosquitoes in a given period. • In the Bondo District, most of the houses are structurally poor, and mosquitoes can easily access sleeping humans. The same mosquitoes may bite the same people many times, especially when they are sleeping (Iwashita et al., 2010).

  26. Primary Analyses: Times Infected • Linear regression to predict number of times infected from housing structure score • Housing structure did not significantly affect the number of times infected with malaria (Beta = .07). • Having more bites per night INCREASEDthe odds of being infected with malariaa greater number of times (Beta = .23). • Linear regression to predict number of times infected from presence of abutment • Having an abutment did not significantly affect the number of times infected with malaria (Beta = -.01). • Having more bites per night INCREASEDthe odds of being infected with malaria a greater number of times(Beta = .23)

  27. Primary Analyses: Malaria Symptoms • Linear regression to predict number of malaria symptoms from housing structure score • Housing structure did not significantly affect the number of malaria symptoms (Beta = -.02) • Living longer in the Bondo District INCREASEDthe odds of having more malaria symptoms (Beta = .19) • Linear regression to predict number of malaria symptoms from presence of abutment • Housing structure did not significantly affect the number of malaria symptoms (Beta = .03) • Living longer in the Bondo District (Beta = .19) and using mosquito nets (Beta = .16) INCREASEDthe odds of having more malaria symptoms.

  28. Interpretations: Times Infected & Malaria Symptoms • The structural soundness of housing was not associated with the number of times infected with malaria or with the number of malaria symptoms experienced by participants. • More mosquito bites per night was associated with more times diagnosed with malaria because more exposure to mosquito bites increases the odds that some of those mosquitoes have malaria parasites (Okiro et al., 2009). • Most participants (80.6%) have lived in the Bondo District for more than 10 years and have been diagnosed with malaria several times.

  29. Limitations • Cross-sectional survey was used to measure participants only once, so cause and effect could not be confirmed. • Self-reported data may not be accurate. • Sample size is somewhat limited by the fact that there was not much variability in participants’ exposure to malaria (i.e., nearly all were exposed), which makes it more difficult to find a statistical relationship between this variable and others.

  30. Recommendations • Future researchers investigating the relationship between malaria prevalence and housing structure should build experimental modern houses to some standard that will not allow mosquitoes and other insects in the houses. • Future researchers should use other types of research methods (e.g., longitudinal studies) to investigate the relationship between malaria prevalence and housing structure (Castro, Tsuruta, Kanamori, Kannady, & Mkude, 2009).

  31. Summary • The purpose of this quantitative study was to investigate the association between malaria prevalence and poor housing structures in the Bondo District in the Nyanza Province of Kenya (Atieli et al., 2009). • The results of this research may lead to • New ways to reduce malaria exposure(e.g., education about how to use mosquito nets) • Improved housing in malaria-affected communities • Design and funding for effective, sustainable, and meaningful intervention techniques that can significantly reduce the burdens of malaria in affected populations (e.g., equitable and affordable distribution of mosquito nets in malaria-affected communities)

  32. References • Atieli, H., Menya, D., Githeko, A., & Scott, T. (2009, May). House design modifications reduce indoor resting malaria vector densities in rice irrigation scheme areas in western Kenya. Malaria Journal, 8(1), 108. doi:10.1186/1475-2875-8-108 • Ayele, D. G., Zewotir, T. T., & Mwambi, H. G. (2012, June). Prevalence and risk factors of malaria in Ethiopia. Malaria Journal, 11(1), 195. doi:10.1186/1475-2875-11-195 • Bronfenbrenner‎, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press. • Castro, M. C., Tsuruta, A., Kanamori, S., Kannady, K., & Mkude, S. (2009, April). Community-based environmental management for malaria control: Evidence from a small-scale interventions in Dar es Salaam, Tanzania. Malaria Journal, 8(1), 57. doi:10.1186/1475-2875-8-57 • Clarke, S. E., Brooker, S., Njagi, J. K., Njau, E., Estambale, B., Muchiri, E., & Magnussen, P. (2004). Malaria morbidity among school children living in two areas of contrasting transmission in western Kenya. American Journal of Tropical Health and Hygiene, 71(6), 732–738. Retrieved from http://www.ajtmh.org/content/71/6/732.full • Iwashita, H., Dida, G., Futami, K., Sonye, G., Kaneko, S., Horio, M., . . . Minakawa, N. (2010). Sleeping arrangement and house structure affect bed net use in villages along Lake Victoria. Malaria Journal, 9(1),176. doi:10.1186/1475-2875-9-176 • Lindsay, S. W., Jawara, M., Pain, K., Pinder, M., Walraven, G. E. L., & Emerson, P. M. (2010). Changes in house design reduce exposure to malaria mosquitoes. Malaria Journal, 7(1), 2.

  33. References, cont. • Okiro, E. A., Alegana, V. A., Noor, A. M., Mutheu, J. J., Juma, E., & Snow, R. W. (2009). Malaria pediatric hospitalization between 1999 and 2008 across Kenya. BMC Medicine, 7, 75. doi:10.1186/1741-7015-7-75 • Shanks, G. D., Hay, S. I., Omumbo, J. A., & Snow, R. W. (2005). Malaria in Kenya’s western highlands. Emerging Infectious Diseases, 11, 1425–1432. doi:10.3201/eid1109.041131 • Shikwati, J. S. (2003, May). Kenya environmental ethics: The DDT story. Fraser Forum, 24–25. Retrieved from http://www.heartland.org/custom/semod_policybot/pdf/12353.pdf • Spalding, M. D., Eyase, F. L., Akala, H. M., Bedno, S. A., Prigge, S. T., Coldren, R. L., . . . Waters, N. C. (2010). Increased prevalence of the pfdhfr/phdhpsquintuple mutant and rapid emergence of pfdhpsresistance mutations at codons 581 and 613 in Kisumu, Kenya. Malaria Journal, 9(1), 338. doi:10.1186/1475-2875-9-338 • World Health Organization. (2010). World malaria report 2010. Retrieved from http://www.who.int/malaria/world_malaria_report_2010/worldmalariareport2010.pdf • Zhou, G., Afrane, Y. A., Vardo-Zalik, A. M., Atielie, H, Zhong, D., Wamae, P., . . . Yan, G. (2011, May). Changing patterns of malaria epidemiology between 2002 and 2010 in western Kenya: The fall and rise of malaria. PLoS ONE, 6(5), e20318. doi:10.1371/journal.pone.0020318

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