Nigeria Malaria Indicator Survey [MIS8]
![Nigeria Malaria Indicator Survey [MIS8]](/img2/70/mis8.webp)
NIGERIAMalaria IndicatorSurvey (MIS)2010
NigeriaMalaria Indicator Survey2010Final ReportNational Population CommissionFederal Republic of NigeriaAbuja, NigeriaNational Malaria Control ProgrammeFederal Republic of NigeriaAbuja, NigeriaMEASURE DHSICF InternationalCalverton, MarylandUnited StatesJanuary 2012Government of NigeriaInvesting in our futureTheGlobal FundTo Fight AIDS, Tuberculosis and MalariaThe World Bank
The 2010 Nigeria Malaria Indicator Survey (2010 NMIS) was implemented by the National Population Commission(NPC) and the National Malaria Control Programme (NMCP) from October 2010 through December 2010. ICFInternational provided technical assistance through the MEASURE DHS programme, a project funded by the UnitedStates Agency for International Development (USAID), which provides support and technical assistance in theimplementation of population and health surveys in countries worldwide.Funding for the 2010 NMIS was provided by the NMCP, Global Fund [through the Society for Family Health (SFH)and Yakubu Gowon Centre (YGC)], World Bank, United Kingdom Department for International Development(DFID) [through the Support to Nigeria Malaria Programme (SuNMaP)], and USAID [through the MEASURE DHSprogramme at ICF International].Additional information about the 2010 NMIS may be obtained from the headquarters of the National PopulationCommission, Plot 2031, Olusegun Obasanjo Way, Zone 7 Wuse, PMB 0281, Abuja, Nigeria; Telephone: (234) 09523-9173; Fax: (234) 09 523-1024.Information about the DHS programme may be obtained from the MEASURE DHS Project, ICF International,11785 Beltsville Drive, Suite 300, Calverton, MD 20705, United States; Telephone: 301-572-0200; Fax:301-572-0999; E-mail: info@measuredhs.com; Internet: http://www.measuredhs.com.Cover photos (left to right): Dr. Abimbola G. Olayemi, National Malaria Control Programme (NMCP); 2006 Alfredo L. Fort, Courtesy of Photoshare; 2008 Margaret F. McCann, Courtesy of Photoshare; 2008 Devon Golaszewski, Courtesy of PhotoshareRecommended citation:National Population Commission (NPC) [Nigeria], National Malaria Control Programme (NMCP) [Nigeria], andICF International. 2012. Nigeria Malaria Indicator Survey 2010. Abuja, Nigeria: NPC, NMCP, and ICFInternational.
CONTENTSPageTABLES AND FIGURES .vFOREWORD . viiPREFACE .ixACRONYMS .xiMAP OF NIGERIA . xiiCHAPTER 1INTRODUCTION1.1History, Geography, and Economy . 11.1.1 History . 11.1.2 Geography. 11.1.3 Economy . 21.2Background on Malaria in Nigeria . 21.2.1 Malaria Transmission. 31.2.2 Strategic Direction for Malaria Control . 41.2.3 Long-Lasting Insecticidal Net Campaigns . 51.2.4 Sources of Malaria Data in Nigeria . 81.3Objectives of the 2010 Nigeria Malaria Indicator Survey . 81.4Methodology of the Nigeria Malaria Indicator Survey . 91.4.1 Survey Organisation . 91.4.2 Sample Design . 101.4.3 Questionnaires . 111.4.4 Anaemia and Malarial Testing . 111.4.5 Pretest Activities . 131.4.6 Training of Field Staff . 131.4.7 Data Collection . 141.4.8 Data Processing . 141.5Response Rates . 15CHAPTER 2CHARACTERISTICS OF HOUSEHOLDS2.1Population by Age and Sex . 172.2Household Composition . 182.32.4Household Environment . 192.3.1 Drinking Water . 192.3.2 Household Sanitation Facilities . 202.3.3 Housing Characteristics . 21Household Possessions. 232.5Wealth Index . 24Contents iii
CHAPTER 3CHARACTERISTICS OF RESPONDENTS3.1General Characteristics of Women . 273.2Educational Attainment of Women. 283.3Literacy of Women . 29CHAPTER 4KNOWLEDGE OF MALARIA AND FEVER MANAGEMENT4.1Women’s Knowledge of Malaria. 314.1.1 Knowledge of Malaria Symptoms . 314.1.2 Knowledge of Causes of Malaria and Age Groups Most Likely to be Affected by Malaria . 324.1.3 Knowledge of Ways to Avoid Malaria . 334.1.4 Knowledge of Malaria Treatment . 364.2Exposure to Malaria Prevention Messages . 384.3Management of Fever among Children . 394.4Treatment of Fever among Household Members . 41CHAPTER 5MALARIA PREVENTION5.1Mosquito Nets . 435.1.1 Background . 435.1.2 Ownership of Mosquito Nets . 435.1.3 Indoor Residual Spraying . 475.1.4 Use of Mosquito Nets by Persons in the Household . 485.1.5 Use of Mosquito Nets by Children under Age 5 . 505.1.6 Use of Mosquito Nets by Women . 525.1.7 Reasons for Not Using a Mosquito Net . 545.2Intermittent Preventive Treatment of Malaria in Pregnancy . 56CHAPTER 66.1ANAEMIA AND MALARIA IN CHILDRENAnaemia and Malaria among Children . 596.1.1 Anaemia Prevalence among Children . 606.1.2 Malaria Prevalence among Children . 626.1.3 Malaria Prevalence and Body Temperature among Children . 656.1.4 Malaria Species Identification . 66REFERENCES . 67APPENDIX ASAMPLE IMPLEMENTATION . 69APPENDIX BESTIMATES OF SAMPLING ERRORS . 73APPENDIX C DATA QUALITY TABLES. 79APPENDIX D PERSONS INVOLVED WITH THE 2010 NIGERIA MALARIA INDICATORSURVEY (NMIS) . 81APPENDIX Eiv ContentsQUESTIONNAIRES . 89
TABLES AND FIGURESPageCHAPTER 1INTRODUCTIONTable 1.1Table 1.2Table 1.3States covered by universal mass LLIN campaigns and the lead partners involved. 7Treatment for children with positive malaria test results on RDTs . 13Results of the household and individual interviews . 15Figure 1.1Figure 1.2Figure 1.3Duration of Malaria Transmission Season . 3Long-Lasting Insecticidal Net Campaign Scale Up . 6Map of LLIN Distribution Coverage . 7CHAPTER 2CHARACTERISTICS OF HOUSEHOLDSTable 2.1Table 2.2Table 2.3Table 2.4Table 2.5Table 2.6Table 2.7Household population by age, sex, and residence . 17Household composition . 18Household drinking water . 19Household sanitation facilities . 20Household characteristics . 22Household durable goods . 24Wealth quintiles. 25Figure 2.1Population Pyramid . 18CHAPTER 3CHARACTERISTICS OF RESPONDENTSTable 3.1Table 3.2Table 3.3Background characteristics of respondents. 27Educational attainment: Women . 29Literacy: Women . 30CHAPTER 4KNOWLEDGE OF MALARIA AND FEVER MANAGEMENTTable 4.1Table 4.2Table 4.3Table 4.4Table 4.5Table 4.6Table 4.7Table 4.8Table 4.9Table 4.10Knowledge of malaria symptoms . 31Knowledge of causes of malaria and people most likely to be seriously affected by malaria . 33Knowledge of ways to avoid malaria. 34Knowledge of ways pregnant women can prevent getting malaria . 35Knowledge of malaria treatment in adults and children . 37Exposure to malaria prevention messages . 38Source of exposure to malaria prevention messages. 39Prevalence, diagnosis, and prompt treatment of children with fever . 40Type and timing of antimalarial drugs taken by children with fever . 41Fever and treatment of fever among household members . 42Tables and Figures v
CHAPTER 5MALARIA PREVENTIONTable 5.1Table 5.2Table 5.3Table 5.4Table 5.5Table 5.6Table 5.7Table 5.8Table 5.9Table 5.10Table 5.11Household possession of mosquito nets . 44Source and cost of mosquito nets . 48Indoor residual spraying against mosquitoes . 47Source of indoor residual spraying by organization . 48Use of mosquito nets by persons in the household . 49Use of mosquito nets by children . 50Use of mosquito nets by all women . 53Use of mosquito nets by pregnant women . 54Reason for not using the net the night preceding the interview . 55Antenatal care . 56Prophylactic use of antimalarial drugs and use of intermittent preventive treatment (IPTp)by women during pregnancy . 58Figure 5.1Figure 5.2Figure 5.3Figure 5.4Trends in Ownership of ITNs: Percent of Households with at Least One ITN . 44Differentials in the Household Ownership of ITNs . 45Differentials in ITN Usage among Children Under Age Five . 51Trends in Net Use among Children Under Age Five . 52CHAPTER 6ANAEMIA AND MALARIA IN CHILDRENTable 6.1Table 6.2Table 6.3Table 6.4Table 6.5Coverage of testing for anaemia and malaria in children . 60Prevalence of anaemia in children. 61Malaria prevalence in children . 63Fever prevalence among children with and without malaria body temperature (axillary). 65Malaria species . 66Figure 6.1Malaria Prevalence among Children 6-59 Months by Residence and Zone (according toMicroscopy) . 64Malaria Prevalence among Children 6-59 Months by Mother’s Education and WealthQuintile (according to Microscopy) . 64Figure 6.2APPENDIX ASAMPLE IMPLEMENTATIONTable A.1Table A.2Sample allocation of clusters and households . 70Sample implementation . 71APPENDIX BESTIMATES OF SAMPLING ERRORSTable B.1Table B.2Table B.3Table B.4Table B.5Table B.6Table B.7Table B.8Table B.9Table B.10List of selected variables for sampling errors, Nigeria MIS 2010 . 75Sampling errors for National sample, Nigeria MIS 2010 . 75Sampling errors for Urban sample, Nigeria MIS 2010 . 75Sampling errors for Rural sample, Nigeria MIS 2010 . 76Sampling errors for North Central sample, Nigeria MIS 2010 . 76Sampling errors for North East sample, Nigeria MIS 2010 . 76Sampling errors for North West sample, Nigeria MIS 2010 . 77Sampling errors for South East sample, Nigeria MIS 2010 . 77Sampling errors for South South sample, Nigeria MIS 2010. 77Sampling errors for South West sample, Nigeria MIS 2010. 78APPENDIX C DATA QUALITY TABLESTable C.1Table C.2vi Tables and FiguresHousehold age distribution . 79Age distribution of eligible and interviewed women. 80
FOREWORDRecent malaria control efforts have received a huge boost of support with an influx of resourcesin the drive
The 2010 Nigeria Malaria Indicator Survey (2010 NMIS) was implemented by the National Population Comm
WORLD MALARIA REPORT 2016 iii Contents Foreword iv Acknowledgements vii Abbreviations xi Key points xii 1. Global targets, milestones and indicators 2 2. Investments in malaria programmes and research 7 2.1 Total expenditure for malaria control and elimination 8 2.2 Funding for malaria-related research 11 2.3 Malaria expenditure per capita for malaria control and elimination 12
6 Malaria Each year, 300-500 million people become ill with malaria One to Two million die (most 5 y.o.) 2,000 – 8,000 children die every day World’s leading killer of children ( 5 y.o.) 200-300 children die from malaria each hr Malaria in pregnancy: 10-20% of LBW Families and communities suffer worldwide The Intolerable Burden of Malaria
Apr 02, 2021 · Attributable fraction (l) 43.7%Model PPV for density cut-off (lc) 85.9%Model Source data and model results Fever classification according cut-off True Malaria True No Malaria Total Malaria Fever (density cut off) 281 (Nc x lc) 46 327 (Nc) Non Malaria fever 5 324 329 Total fevers 286 (Nx l) 370 656 (N) Synthetic 2x2 table created from data .
Malaria might affect educational attainment through several mechanisms. First, malaria in pregnant women can result in anemia and interruption of in utero nutri- . Hoyt Bleakley (2007b) used malaria eradication campaigns in the United States, Brazil, Colombia, and Mexico to estimate the effect of childhood exposure to malaria in pre .
ENSURING APPROPRIATE TREATMENT OF MALARIA FACILITATOR'S MANUAL 3 . TRAINING PROGRAME ON MALARIA DIAGNOSIS AND TREATMENT Overview of the Training Programme Introduction to the course . Malaria is a major public health problem in Cameroon. You will already know something on how to diagnose and treat malaria.
The 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2015-16 TDHS-MIS) was implemented by the National Bureau of Statistics (NBS) and Office of the Chief Government Statistician (OCGS), Zanzibar, in collaboration with the Ministry of Health, Community Development, Gender, Elderly
In 2017, Nigeria introduced its first Building Energy Efficiency Code, which sets minimum standards for energy efficiency for new buildings in Nigeria. 2030 NDC TARGET The country's 2030 NDC target is 442.5 MtCO 2 e by 2030. There is no Climate Action Tracker analysis for Nigeria. All figures exclude land use emissions. NIGERIA Nigeria's GHG
Classical approach to management is a set of homogeneous ideas on the management of organizations that evolved in the late 19 th century and early 20 century. This perspective emerges from the industrial revolution and centers on theories of efficiency. As at the end of the 19th century, when factory production became pervasive and large scale organizations raised, people have been looking for .
