Role of improved housing and high altitude significantly reduce malaria prevalence among school-age children in mainland Tanzania.

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Mbaraka John Remiji, Felista Mwingira, Gerald Kiwelu, Tajiri Laizer, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7744849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background : Malaria remains a major public health concern in Tanzania, with school-aged children carrying a significant burden. This study assessed the impact of housing structure on malaria prevalence among school-aged children Methods : A cross-sectional survey was conducted among 6,554 school-age children from 184 districts across all 26 regions, covering 650 public primary schools in mainland Tanzania in 2021. A multi-stage cluster sampling methodology was used to ensure both geographical and demographic representation. Results : Residing in improved houses significantly reduced malaria infection prevalence among SAC (aOR: 0.48, CI: 0.33–0.70, p < 0.001). Male experienced higher malaria infection than female (aOR: 1.40, CI: 1.14–1.72, p = 0.001). Compared to school-aged children living below 750 meters above sea level, those residing at elevations of 1,250–1,750 meters had 53% lower odds of malaria infection (aOR: 0.47; CI: 0.29–0.78; p = 0.003), with an even more noticeable 93% reduction observed among those living above 1,750 meters (aOR: 0.07; CI: 0.01–0.36; p = 0.002). Sleeping under an ITN was associated with a 51% lower malaria infection (aOR: 0.49, CI: 0.34–0.71, p < 0.001). SAC in urban areas were 63% less likely to have malaria compared to those in rural settings (aOR: 0.37, CI: 0.21–0.68, p = 0.001). Conclusion. Housing improvements should be prioritized to ensure access to malaria interventions, especially in marginalized rural communities. Male children and those with severe anemia were most at risk, while higher-altitude residence, urban living, and consistent use of insecticide-treated nets were protective. Integrated, context-specific strategies combining housing, nutrition, and behavior change interventions are essential. Multi-sectoral programs linking health, housing, and social services can sustainably reduce malaria transmission and protect vulnerable children. Housing structure Malaria transmission Anemia Altitude Mosquito vectors Insecticide-treated nets Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Malaria remains a major global public health concern, with an estimated 263 million cases and nearly 600,000 deaths reported in 2023, the majority of which occurred in sub-Saharan Africa ( 1 ). While interventions such as insecticide-treated nets (ITNs), indoor residual spraying (IRS), and antimalarial treatments have significantly reduced the malaria burden across endemic countries (Bhatt et al., 2015), transmission continues in many regions despite the sustained use of these frontline interventions ( 2 , 3 ). This persistence underscores the gaps with existing intervention and opportunities with complementary strategies the need for complementary strategies to consolidate and expand malaria control gains. There are several promising prototypes of interventions that have emerged as complementary strategies to address this gap. Among these, housing improvement has gained increasing recognition as an effective approach for reducing human-vector contact and, consequently, human exposure to malaria transmission and infection ( 4 – 7 ). While ITNs and IRS remain central to malaria reduction in sub-Saharan Africa ( 8 – 11 ). Alternative strategies like Larval source management (LSM) is most effective in environments with few, fixed, and easily identifiable breeding sites, such as urban areas, arid regions, or places with permanent water sources, and requires sustained technical and community support. ( 12 , 12 – 17 ). Emerging tools such as spatial repellents, mosquito coils, and attractive targeted sugar baits are under evaluation for residual and outdoor transmission, but their large-scale impact is uncertain ( 18 – 20 ), and mosquito coils pose potential respiratory health risks from prolonged smoke exposure ( 21 , 22 ). Housing improvements like window and door screening, closed eaves, ceilings, and solid construction provide passive, chemical-free, long-term protection independent of individual behavior. Improved housing has consistently demonstrated equal or greater effectiveness than insecticide-treated nets (ITNs) in reducing malaria risk, as studies across specific African countries including Tanzania, Kenya, Uganda, Equatorial Guinea, Cameroon, Ethiopia, Gambia, and Nigeria show that better housing design lowers malaria transmission by limiting human vector contact ( 7 , 23 – 39 ). Systematic reviews and meta-analyses, including Cochrane and multi-country studies, confirm the significant impact of improved housing on malaria reduction across sub-Saharan Africa ( 40 – 46 ) Despite promising results, robust evidence on housing improvement’s impact on malaria reduction across diverse epidemiological and ecological settings in Tanzania remains limited. Understanding this relationship is critical, as malaria transmission varies widely with climatic, ecological, and socio-economic factors, causing significant heterogeneity in risk and intervention effectiveness. This study assesses the role of housing in reducing malaria transmission across Tanzania. Using nationally representative data on malaria infection, household characteristics, and spatial context from the school malaria and nutrition survey, we generate evidence on housing improvement as a scalable, context-specific control strategy. METHODOLOGY Study Area Tanzania covers approximately 945,500 km 2 , of which 883,749 km 2 is land area and 61,500 are inland water bodies with several lakes and rivers and coast along the Indian Ocean. Tanzania lies 1–12 degrees south of the equator and 29–41 degrees east. The country shares borders with eight countries: Kenya and Uganda to the north; Rwanda, Burundi, the Democratic Republic of Congo, and Zambia to the west; and Malawi and Mozambique to the South. The Indian Ocean borders the country to the east. The 2022 projected population according to the 2022 national census was 61,741,120 whereby at least 17,833,205 (30.89%) are children aged between 5–16 years. About 93% of the Tanzanian population is at risk of malaria; however, the transmission intensity is heterogeneous across the different geographic zones ( 47 ). Study Design We utilized data from the School Malaria and Nutrition Survey, a cross-sectional study of public primary school-aged children (SAC) and their household heads. The methodological details of the survey have been published previously ( 47 ). A total of 650 primary public schools were involved in the study. Study Population and Eligibility The study was conducted in public primary schools and households across mainland Tanzania, covering all 26 regions and 184 councils. Participants were school-aged children (5–16 years) enrolled in public primary schools and their household heads or representatives. Eligible children were those aged 5–16 years whose parents or guardians provided consent, while eligible households were those that agreed to participate. Children absent from school or unwell on the survey day were excluded. Sampling Technique and data collection A multi-stage cluster sampling design was employed to achieve representative coverage across all 26 regions and councils of mainland Tanzania. Stratification accounted for geographic features, malaria prevalence, demographic characteristics, proximity, and population density. Within selected strata, wards and schools were randomly chosen, and systematic sampling of school-aged children (SAC) from standards 1 to 7 was conducted, ensuring an equal sex ratio. All selected children underwent malaria testing and demographic data collection, while subsets were additionally assessed through interviews and hemoglobin measurement. Systematic sampling procedures were also used to link selected SAC to their households. The questionnaire used in this study was developed specifically for this survey. Data was collected from the participants using a pre-tested and validated semi-structured questionnaire. The questionnaire was loaded into the Open Data Kit (ODK) using the KoboCollect® platform. The study enumerators were thoroughly trained prior to the survey. The questionnaire collected reported information on socio-demographic characteristics, household characteristics, household size, history of fever, use of mosquito bed nets, and treatment-seeking behaviour of the participants. Study Period. The 2021 School Malaria and Nutrition Survey included 650 schools, with data collection conducted from 2 October to 24 November 2021. Laboratory work Finger-prick blood samples were collected from each SAC to detect malaria parasites using the malaria Rapid Diagnostic Test (mRDT). SACs who tested positive for malaria were administered antimalarial drugs based on national guidelines. Hemoglobin (Hb) concentration was measured using a portable HemoCue® analyzer, and SACs with Hb concentration below 8.0 g/dl were referred to nearby health facilities for further examination and care. Principal component analysis Housing status was assessed by systematically recording multiple housing-related variables to reflect improved versus unimproved structures and materials. Each original variable was transformed into a binary indicator, with durable or protective features coded as “improved” and less durable or temporary features coded as “unimproved.” Floor materials such as cement, ceramic tiles, and hardwood were considered improved, while mud, sand, or animal dung were classified as unimproved. Similarly, ceilings made of board wood, gypsum board, wood, cement, or traditional sealing were considered improved, whereas unsealed or unspecified materials were categorized as unimproved. Wall materials were classified based on durability and protective capacity, with concrete, stone-cement, wood, concrete blocks, iron sheets, and burnt mud bricks coded as improved, and weaker or temporary materials such as stone-mud, bamboo-mud, unburnt mud bricks, grass, or poles classified as unimproved. Roofing was considered improved if constructed from iron sheets, concrete/cement, or asbestos, and unimproved if made of grass, mud, bamboo, plastics, or other temporary coverings. Eave design was coded as improved when closed, and unimproved when open or partially closed. Window quality was assessed based on material and screening; glass or screened windows were considered improved, whereas wood, iron sheets, plastics, or other types were classified as unimproved. Protective window features, including wire screens, bed nets, or plastic coverings, were similarly coded as improved. Following recording, all new binary variables were subjected to one-hot encoding, allowing each housing component to be analyzed as a distinct indicator. This approach provided a comprehensive and systematic measure of housing quality, capturing structural and protective features relevant to malaria exposure and other health outcomes. Variables in the study The outcome variable, malaria status, was defined based on the results of the malaria rapid diagnostic test (mRDT), with individuals testing positive coded as 1 and those testing negative coded as 0. Independent variables and their categories in the study included age groups ( 5 – 8 , 9 – 12 , 13 – 16 ), sex of pupil (female, male), level of education of household head (No formal, primary, secondary, college/university), marital status (married, single), family size (≤ 4, > 4), number of under 15 children (1–2,≥3 ), elevation (below 750, 750 to 1250, 1250 to 1750, above 1750) meters above sea level, SAC receive LLIN at school (not received, yes-received), anemia status (normal, mild, moderate, severe), LLIN use last night (did not sleep, slept), number of individuals who slept under one LLIN ( 1 – 2 , 3 – 4 ), residence status (rural, urban), medium malaria transmission (mosquito, others), knowledge of malaria signs and symptoms (none, 1 to 3, 4 to 11), and occupation of household head (agriculture, employed, unemployed). Anemia classification Hemoglobin levels were classified by age and sex following the WHO 2024 guidelines, allowing standardized identification of anemia severity in children and adolescents. This categorization provides a framework to assess nutritional and health status across different age and sex groups and to examine associations with malaria risk and other health outcomes (Table 1 ). Table 1 Classification and cut-offs of hemoglobin concentration levels by age and sex (According to the WHO guideline for anemia (hemoglobin cutoffs), 2024) Age and sex* No anemia (g/dl) Mild anemia (g/dl) Moderate anemia (g/dl) Severe anemia (g/dl) 5–11 years (male and female) ≥ 11.5 11.0–11.4 8.0–10.9 < 8.0 12–14 years, female ≥ 12.0 11.0–11.9 8.0–10.9 < 8.0 12–14 years, male ≥ 12.0 11.0–11.9 8.0–10.9 < 8.0 15–16 years, female ≥ 12.0 11.0–11.9 8.0–10.9 < 8.0 15–16, years male ≥ 13.0 11.0–12.9 8.0–10.9 < 8.0 Data Analysis Data was cleaned and analyzed in R software (v4.2.4). Descriptive statistics are presented as means (± standard deviation) for continuous variables and frequencies (%) for categorical variables. To assess factors associated with malaria infection, we employed a generalized linear mixed-effects model (GLMM) with a binomial distribution and logit link. The model included random intercepts for school and ward to account for the clustered design and potential spatial autocorrelation. Fixed effects were specified a priori based on known biological and social risk factors, including housing quality, sex, age, parental occupation and education, household size, altitude, anemia status, insecticide-treated net (ITN) use, and urbanicity. Prior to model fitting, observations with missing outcome data (malaria RDT result) were excluded. We assessed multicollinearity among all explanatory variables using variance inflation factors (VIF); all retained variables had a VIF < 5, indicating no substantial collinearity. Model convergence was verified, and fit was assessed via residual diagnostics and examination of variance components for the random effects. Results are reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). All analytical procedures adhered to best practices to ensure robustness and reproducibility RESULTS This study included 6,554 school-aged children from 184 councils across all 26 regions of mainland Tanzania. Malaria prevalence was highest among children aged 13–16 years (16.3%), especially those living in unimproved housing (22.6%). Males had higher prevalence (13.1%) than females (10.3%), with greater differences in unimproved housing (20.3% vs. 17.3%). The Lake Zone showed the highest prevalence (22.8%), rising to 30.1% in unimproved housing, while Central and Southern Highlands had the lowest rates. Agricultural households experienced higher prevalence (14.8%) compared to employed heads of households (4.3%). Lower education levels correlated with increased prevalence, notably 18.6% among households without formal education. Larger households (≥ 5 members). Areas below 750 meters asl elevation showed 11.4% prevalence, increasing to 22.0% in unimproved housing. Those SAC who didn’t sleep under ITNs raised prevalence from 17.5% overall to 25.5% in unimproved housing. Severe anemia was strongly associated with malaria, reaching 41.7% overall and 55.6% in unimproved housing. Rural households had higher prevalence (13.2%) than urban areas (5.3%), with rural unimproved housing at 18.7% (Table 2 ). Table 2 Malaria Prevalence by Demographic, Socio-Economic, Geographic, and Housing Characteristics Among School Aged Children. Variable Name Overall Unimproved Semi improved Improved Overall Total, N = 6,559 Prevalence n = 769(11.7%) Total, N = 1,642 Prev, N = 309(18.8%) Total, N = 3,245 Prev, N = 370(11.4%) Total, N = 1,642 Prev, N = 83(5.1%) N n (%) n (%) n (%) n (%) n (%) n (%) n (%) Age Group (Years) 5 to 8 1621 155 (9.6) 346 60 (17.3) * 825 77 (9.3) 445 17 (3.8) 9 to 12 2463 270 (11.0) 607 105 (17.3) 1199 128 (10.7) 646 34 (5.3) 13 to 16 915 149 (16.3) 283 64 (22.6) 476 72 (15.1) 148 10 (6.8) Pupil Sex Female 3215 331 (10.3) 811 140 (17.3) * 1601 153 (9.6) 789 36 (4.6) Male 3339 438 (13.1) 831 169 (20.3) 1640 217 (13.2) 852 47 (5.5) Occupation household head Agriculture 4661 689 (14.8) 1518 295(19.4) 2417 323(13.4) 702 66 (9.4) Employed 1782 76 (4.3) 110 11 (10.0) 776 46 (5.9) 890 17 (1.9) Unemployed 111 4 (3.6) 13 3 (23.1) 50 1 (2.0) 48 0 (0.0) Education level household head Non-Formal 505 94 (18.6) 249 48 (19.3) 204 41 (20.1) 46 3 (6.5) Primary 4744 610 (12.9) 1314 249 (18.9) 2520 297 (11.8) 889 60 (6.7) Secondary 1046 53 (5.1) 73 12 (16.4) 445 27 (6.1) 525 13 (2.5) College and above 264 12 (4.5) 6 0 (0.0) 76 5 (6.6) 182 7 (3.8) Marital Status Married 5567 646 (11.6) 1375 249 (18.1) * 2769 317 (11.4) 1399 74 (5.3) Single 992 123 (12.4) 267 60 (22.5) 476 53 (11.1) 243 9 (3.7) Family Size ≤ 4 2552 245 (9.6) 582 85 (14.6) 1256 123 (9.8) 701 34 (4.9) 5 and above 4007 524 (13.1) 1060 224 (21.1) 1989 247 (12.4) 941 49 (5.2) Elevation (m asl) Below 750 2121 242 (11.4) 514 113 (22.0) 923 100 (10.8) 678 29 (4.3) 750 to 1250 2425 408 (16.8) 619 150 (24.2) 1242 208 (16.7) 549 48 (8.7) 1250 to 1750 1680 117 (7.0) 431 46 (10.7) 869 60 (6.9) 371 6 (1.6) Above 1750 333 2 (0.6) 78 0 (0.0) 211 2 (0.9) 44 0 (0.0) ITN use last night Didn’t Sleep 546 84 (15.4) 152 33 (21.7) 289 45 (15.6) 104 5 (4.8) Slept 3258 352 (10.8) 704 126 (17.9) 1664 190 (11.4) 875 32 (3.7) Residence Status Rural 5357 705(13.2) 1449 271 (18.7) 2677 347 (13.0) 1202 80 (6.7) Urban 1202 64 (5.3) 193 38 (19.7) 568 23 (4.0) 440 3 (0.7) The study demonstrated (Fig. 2 ) a strong inverse relationship between housing quality and malaria prevalence. The risk of malaria was nearly four times higher in unimproved housing compared to improved housing, confirming that housing quality is a major modifiable risk factor for the disease. Malaria Prevalence by Housing Type and Anemia Status Malaria prevalence increased sharply with anemia severity across all housing types, with the highest rates consistently observed in unimproved housing (13.4% in normal, 24.7% in mild, 27.4% in moderate, and 55.6% in severe anemia), followed by semi-improved housing (6.7%, 13.2%, 18.7%, and 50.0%, respectively) and the lowest in improved housing (2.3%, 2.8%, 7.0%, and 14.3%). This clear gradient demonstrates that both poor housing quality and worsening anemia status independently and synergistically heighten malaria risk, highlighting the dual need for structural housing interventions and improved anemia control to reduce malaria burden ( Fig. 3 ). Spatial distribution of malaria prevalence by housing type In several regions, malaria prevalence consistently increases with decreasing housing quality. In Geita, prevalence rises from 17.1% in improved housing to 39.4% in semi-improved and 37.8% in unimproved housing. Simiyu shows a similar trend, with 23.1% in improved, 22.6% in semi-improved, and 36.5% in unimproved housing. Shinyanga follows, reporting 11.3%, 25.8%, and 37.8% prevalence for improved, semi-improved, and unimproved housing, respectively. In Mtwara, prevalence is 17.0% in improved, 16.3% in semi-improved, and 35.8% in unimproved housing. Rukwa records 0.0% in improved housing, rising to 13.6% and 37.0% in semi-improved and unimproved categories. Pwani shows 4.4%, 15.2%, and 31.9% across the same housing categories, while Katavi reports 12.5%, 20.3%, and 33.3%. These data demonstrate a clear pattern: unimproved housing is consistently associated with substantially higher malaria prevalence compared to improved housing ( Fig. 4 ). Generalized Linear Mixed Model analysis In both bivariate and multivariate analyses, housing improvement was strongly associated with reduced malaria risk. Compared to unimproved housing, improved housing significantly lowered the odds of malaria infection (adjusted OR 0.48; 95% CI 0.33–0.70; p < 0.001), while semi-improved housing showed a non-significant protective trend (adjusted OR 0.82; 95% CI 0.64–1.05; p = 0.115). Male pupils had higher odds of infection than females (adjusted OR 1.40; 95% CI 1.14–1.72; p = 0.001). Elevation above 1250 meters was associated with significantly lower malaria risk, with the highest elevations (> 1750 m) showing the greatest protection (adjusted OR 0.07; 95% CI 0.01–0.36; p = 0.002). Anemia status showed a strong dose-response relationship with malaria risk: moderate (adjusted OR 2.61; 95% CI 1.92–3.56; p < 0.001) and severe anemia (adjusted OR 8.66; 95% CI 2.63–28.52; p < 0.001) markedly increased odds of infection. Use of insecticide-treated nets (ITNs) was protective (adjusted OR 0.49; 95% CI 0.34–0.71; p < 0.001), and urban residence was associated with lower malaria risk compared to rural areas (adjusted OR 0.37; 95% CI 0.21–0.68; p = 0.001). Other factors such as age group, household head occupation, education level, and family size showed no significant adjusted associations. (Table 3 ). Table 3 Factors Associated with Malaria Risk among School-Aged Children in Tanzania Variable Name Bivariate Multivariate Household category Crude odd ratio (95% CI) P value Adjusted Odds Ratio (95% CI) P value Unimproved 1 1 1 1 Semi improved 0.72(0.57–0.92) 0.009 0.82(0.64–1.05) 0.115 Improved 0.37(0.26–0.53) < 0.001 * 0.48(0.33–0.70) < 0.001 Age Group (years) 5 to 8 1 1 1 1 9 to 12 1.18(0.93–1.50) 0.171 1.07(0.83–1.36) 0.615 13 to 16 1.47(1.07–2.02) 0.018 1.23(0.89–1.71) 0.215 Pupil Sex Female 1 1 1 1 Male 1.38(1.13–1.68) 0.002 1.4(1.14–1.72) 0.001 Occupation household head Unemployed 1 1 1 1 Agriculture 2.29(0.73–7.17) 0.154 2.41(0.75–7.73) 0.14 Employed 0.91(0.29–2.88) 0.867 1.21(0.37–4.00) 0.756 Education level household head No Formal 1 1 1 1 Primary 0.79(0.56–1.11) 0.17 0.78(0.55–1.11) 0.172 Secondary 0.42(0.27–0.68) < 0.001 * 0.61(0.37–1.00) 0.052 College and above 0.27(0.12–0.58) 0.001 * 0.52(0.22–1.25) 0.144 Family Size ≤ 4 1 1 1 1 5 and above 0.98(0.78–1.23) 0.861 0.94(0.74–1.19) 0.609 Elevation group (m asl) Below 750 1 1 1 1 750 to 1250 1.78(1.13–2.81) 0.014 1.42(0.93–2.15) 0.103 1250 to 1750 0.59(0.35–1.00) 0.049 0.47(0.29–0.78) 0.003 Above 1750 0.06(0.01–0.36) 0.002 0.07(0.01–0.36) 0.002 Anemia Status Normal 1 1 1 1 Mild 1.62(1.15–2.29) 0.005 1.53(1.08–2.16) 0.017 Moderate 2.53(1.87–3.43) < 0.001 * 2.61(1.92–3.56) < 0.001 Severe 8.81(2.51–30.86) 0.001 * 8.66(2.63–28.52) < 0.001 ITN use last night No 1 1 1 1 Yes 0.49(0.35–0.70) < 0.001 * 0.49(0.34–0.71) < 0.001 Residence Status Rural 1 1 1 1 Urban 0.23(0.11–0.45) < 0.001 * 0.37(0.21–0.68) 0.001 Discussion Our study demonstrates that malaria risk among children is strongly influenced by housing quality, with those living in improved houses experiencing substantially lower odds of infection, while semi-improved housing showed a non-significant protective effect. Male children were consistently at higher risk, and infection prevalence declined sharply in highland areas, highlighting the role of environmental and demographic factors. Regional differences in malaria burden were largely explained by variation in housing quality, and at the individual level, children with mild, moderate, or severe anemia were particularly vulnerable. Protective factors, including insecticide-treated net use and urban residence, were associated with significantly reduced infection risk. This study revealed that improved housing was strongly associated with lower malaria risk in Tanzania. Similar findings across sub-Saharan Africa support housing modification as an effective intervention ( 42 , 43 , 46 ). For instance, studies in Tanzania report that improved housing reduces indoor mosquito densities and human–vector contact ( 7 , 29 , 48 ). The most likely protective mechanism is the physical barrier that limits mosquito entry, thereby reducing human exposure to infectious bites. This effect is particularly relevant in African settings where the dominant malaria vectors, such as Anopheles gambiae s.s. and An. funestus s.s. , feed and rest indoors, with peak biting occurring late at night when most people are indoors and asleep( 49 – 54 ). Although An. arabiensis exhibits more opportunistic behavior, feeding outdoors and on animals, it also exploits indoor environments depending on ecological conditions and intervention coverage ( 15 , 31 , 55 ). Improved housing therefore provides a protective barrier across diverse transmission settings by limiting exposure to both highly endophagic species and more adaptable vectors. In Tanzania, the interplay between Anopheles arabiensis and An. funestus significantly influences malaria control strategies ( 56 , 57 ). While An. arabiensis is often more abundant and exhibits outdoor feeding and resting behaviors, it is less efficient in transmitting malaria, with lower sporozoite rates ( 28 , 58 ). In contrast, An. funestus is typically more efficient, feeding predominantly on humans indoors late at night and exhibiting higher sporozoite rates ( 28 , 59 , 60 ). However, the widespread use of insecticides has driven behavioral resistance in An. arabiensis , increasing its tendency to feed outdoors ( 56 , 60 ). This shift in feeding behavior reduces the effectiveness of indoor-based interventions, highlighting the need for integrated control strategies that address both vector species and their evolving behaviors ( 61 ). Housing improvements exploit these differences by creating durable physical barriers that substantially reduce the risk of infectious bites. This is critical because even a few infectious mosquitoes can sustain transmission. Beyond entomological effects, housing improvements represent a durable, user-independent intervention that complements insecticide-based tools. Unlike ITN or IRS, housing modifications are not dependent on nightly adherence or repeated program delivery. They can therefore strengthen resilience against insecticide resistance and behavioral adaptations in mosquito populations. Moreover, investment in housing contributes to broader development goals by improving health, well-being, and quality of life, thereby providing long-term benefits beyond malaria. These findings highlight the need for integrated malaria control. Housing improvements should be recognized as a core, sustainable strategy that complement insecticide-based tools and supports elimination goals. This study observed significant regional spatial heterogeneity in malaria risk across Tanzania, consistent with national malaria stratification and recent local studies ( 62 , 63 , 63 – 66 ). The observed regional variability in malaria risk is likely driven by differences in housing quality and mirrors broader socio-economic and structural determinants that influence vector exposure and transmission dynamics. Households with lower socio-economic status are more likely to live in unimproved housing that allows mosquito entry and often face limited access to healthcare services, thereby perpetuating a cycle of malaria transmission and poverty ( 29 , 46 , 67 ). Addressing these disparities requires a holistic approach, integrating economic empowerment strategies such as microfinance through Village Community Bank (VICOBA), conditional cash transfers under programs like Tanzania Social Action Fund (TASAF), and market-based housing finance ( 68 , 69 ). Moreover, macroeconomic evidence shows that investments in malaria control, including housing upgrades, not only reduce transmission but also enhance labor productivity, decrease healthcare costs, and contribute to GDP growth ( 70 – 73 ); World Bank, 2020; Barrett et al., 2023). Taken together, these findings highlight the need for a multi-sectoral approach that integrates public health strategies with socio-economic development to achieve equitable and sustainable malaria elimination. We observed significantly higher malaria risk among male than female among school-aged children, consistent with recent findings across sub-Saharan Africa ( 47 , 74 – 76 ). Large community-based surveys across five regions in mainland Tanzania reported higher infection odds among men, particularly where housing quality was poor and net use was low ( 77 ). In Zanzibar, case–control study further demonstrates that occupations dominated by men, such as farming, fishing, and night watch, significantly increase exposure during peak mosquito-biting hours ( 75 ). Taken together, these findings highlight the limits of conventional indoor interventions in addressing male-specific and outdoor exposures. To close this gap, malaria control must integrate WHO-recommended spatial repellents with targeted social and behaviour change campaigns tailored to male-dominated occupations and social routines ( 78 ). Such complementary strategies are essential to ensure that malaria control programmes equitably protect all demographic groups The protective effect of increasing elevation against malaria observed in this study aligns with established findings that altitude influences transmission intensity through cooler temperatures, reduced mosquito survival, and slower parasite development ( 47 ). Recent studies in sub-Saharan Africa have reinforced the inverse relationship between altitude and malaria risk, showing that higher elevations are associated with lower malaria prevalence in the western Kenyan highlands ( 79 ), that malaria incidence in Rwanda is lowest around 1600 meters above sea level ( 80 ), and that malaria hotspots in Mount Elgon, Uganda, vary across altitudinal zones in association with environmental factors such as rainfall and vegetation ( 81 ). While this relationship is well-established, it remains critical to consider in malaria control planning, as climate change could alter local temperature and precipitation patterns, potentially expanding transmission into higher-elevation areas previously considered low-risk. Consequently, surveillance and intervention strategies must remain adaptive to anticipate and respond to shifting malaria risk profiles driven by environmental changes. This study found a strong statistical association between malaria infection and anemia in SAC, with higher malaria exposure associated with lower hemoglobin levels. These results align with recent evidence from sub-Saharan Africa demonstrating that malaria significantly increases the risk of anemia ( 82 ). For example, a study in Ethiopia reported that malaria-infected school-aged children were over 13 times more likely to be anemic than uninfected peers ( 83 ). The mechanistic basis includes malaria-induced hemolysis and bone marrow suppression; while pre-existing anemia may further compromise host immunity, increasing susceptibility to infection. Together, these findings reinforce the need for integrated interventions that combine malaria prevention, nutritional programs, iron supplementation, and enhanced case management, particularly in high-risk pediatric populations. This study has several notable limitations. Its cross-sectional design precludes causal inference, limiting the ability to determine the directionality of associations between risk factors and malaria infection. Key socio-economic and behavioral variables were not fully captured, and seasonal transmission dynamics and finer-scale environmental factors were not incorporated, which may affect the precision of risk estimates. Despite these limitations, the study provides valuable insights through its large, nationally representative dataset, offering practical evidence to guide targeted resource allocation and inform malaria control and elimination strategies across Tanzania. In conclusion, housing improvements should be prioritized to ensure equitable access to malaria interventions, particularly among marginalized rural communities. Male children and those with severe anemia remain most vulnerable, while residence at higher altitudes, urban settings, and consistent use of insecticide-treated nets provide protection. Sustainable malaria control requires integrated, context-specific strategies that combine housing improvements with nutritional support and behavior change interventions. Future research should focus on strengthening multi-sectoral collaboration across health, housing, and social services to achieve lasting reductions in malaria transmission and safeguard vulnerable children in endemic regions. Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. SMPS surveys were approved by the National Health Research Ethics Committee, which is a sub-committee of the Medical Research Coordinating Committee of the National Institute of Medical Research with reference number NIMR/HQ/R.8c/Vol.I/1857 (2021). The Principal Investigator and some Co-Investigators obtained certificates for the protection of human subjects and research information prior to the surveys. Other Co-Investigators, national supervisors, and field staff were trained using standard operating procedures on how to ensure the protection of human subjects. The survey applied a passive, opt-out approach for parental permission approval whereby once children were selected for the survey during the preparatory days; a memo was written by respective schoolteachers to parents/guardians. It was assumed that parents/guardians approved their children’s participation if they did not express their disapproval. In addition, approval was sought from each level before and during fieldwork exercise including permission letter from President’s Office, Regional Administrative and Local Government (PO-RALG); written school committees’ approval, and verbal assent from all surveyed children aged above 8 years old. Before participation, the purpose of the study, procedures involved, confidentiality measures, and the voluntary nature of participation were clearly explained to each selected household. Written informed consent was then obtained from the head of household or an authorized representative to confirm their agreement for the household’s participation in the interview. Only households that provided written informed consent were included in the survey. Participants were informed that they could withdraw at any time without any consequences. A child with a positive test result was treated with artemether–lumefantrine (ALu), as recommended in the National Malaria Diagnosis and Treatment Guidelines and when deemed necessary was referred to the nearest health facility. Data availability statement. Given the current situation and limitation to sharing government data, any requests for additional data supporting the findings for this study can be made to the Tanzania Ministry of Health at https://www.moh.go.tz/contact or send email directly to the Permanent Secretary at [email protected] Competing interests The authors declare that they have no competing interests. Funding This study is a secondary analysis and did not receive any funding. Author contributions. Mbaraka John Remiji : Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing. Felista Mwingira: Review & Editing. Gerald Kiwelu: Software, Validation . Tajiri Laizer: Software, Validation . Nicodem Govella: Conceptualization, Methodology, Writing - Review & Editing and Supervision Samson S. Kiware: Conceptualization, Review & Editing, Supervision. Frank Chacky : Resources, Project Administration, Funding Acquisition. All authors have read and agreed to the published version of the manuscript. Acknowledgements The SMPS study was conducted by the Tanzania Ministry of Health through the National Malaria Control Program, in collaboration with research Institutions and academia namely, National Institute for Medical Research, Ifakara Health Institute, Muhimbili University of Health and Allied Sciences, University of Dar es Salaam, Sokoine University of Agriculture, Tanzania Food and Nutrition Center. The authors are grateful to all Ministry of Health, President’s Office Regional Administration and Local Government, Ministry of Education, Science and Technology, institutions, partners, field team, data entry clerks, and investigation team and individuals who contributed to this school survey including surveying school children for their voluntary participation in the survey. Authors would like to extend their appreciation to Julieth Silao, David Dadi, Victor Alegana, Benjamin Kamala, Brigita Msofe, Witness Saitot, Abdallah Lusasi, Anna David, Pendael Machafuko, Bwire Wilson, Charles Dismas Mwalimu, Agnes Mpinga, Fidelis Mgohamwende, Humphrey Mkali and Wiggins Aaron, Janice Maige, Praise Michael, for their valuable inputs and advice. References WHO. World malaria report 2024: addressing inequity in the global malaria response. 2024. Ashton RA, Chanda B, Chishya C, Muyabe R, Kaniki T, Mambo P, et al. Why does malaria transmission continue at high levels despite universal vector control? Quantifying persistent malaria transmission by Anopheles funestus in Western Province, Zambia. Parasit Vectors. 2024;17(1):429. WHO. World Health Organization. World malaria report 2023 - Google Scholar [Internet]. 2023 [cited 2025 Feb 12]. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7744849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533006805,"identity":"17409669-4231-42eb-87a6-08e805f4d7ad","order_by":0,"name":"Mbaraka John Remiji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYLCCBB4JOTYJxgbStBjDtEgQrSuxAaqWsBbd2YePbnggY5HeJ93cwPBxD0OdPCH3mZ1LS7sBdFhum8zBBsYZzxgIe8nsDI8ZRItEYgMzzwEGCWZCDjM7w/8NpCWdDaaFjbAWHjaQlgS4Fh7CWtjADjMEOezgjAMSkjMIa2F+dvNnT528/Iz0hw8+HLDhJxhiYMDYA6EPkBCTP4hVOApGwSgYBSMSAABN+Dg80tihEwAAAABJRU5ErkJggg==","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":true,"prefix":"","firstName":"Mbaraka","middleName":"John","lastName":"Remiji","suffix":""},{"id":533006806,"identity":"8ed33d7f-7163-45f0-941c-423c39653914","order_by":1,"name":"Felista Mwingira","email":"","orcid":"","institution":"University of Dar Es Salaam","correspondingAuthor":false,"prefix":"","firstName":"Felista","middleName":"","lastName":"Mwingira","suffix":""},{"id":533006807,"identity":"04df0591-9237-4129-b681-5dd9f12e2f9d","order_by":2,"name":"Gerald Kiwelu","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Gerald","middleName":"","lastName":"Kiwelu","suffix":""},{"id":533006808,"identity":"64362020-bf75-423d-b9a3-3ececd917743","order_by":3,"name":"Tajiri Laizer","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Tajiri","middleName":"","lastName":"Laizer","suffix":""},{"id":533006809,"identity":"5d475234-9949-4317-8122-7dd29d51be6b","order_by":4,"name":"Nicodemus Govella","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Nicodemus","middleName":"","lastName":"Govella","suffix":""},{"id":533006811,"identity":"2678d6dd-5b9e-472c-9ec6-cfb084fa5331","order_by":5,"name":"Samson S. 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13:56:49","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":211950,"visible":true,"origin":"","legend":"","description":"","filename":"03ccbe5e191d40668725a9e18f8741711enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/feda345c8797271194fc421e.xml"},{"id":94399179,"identity":"b2f09de2-35aa-424c-955f-566dbd208034","added_by":"auto","created_at":"2025-10-27 13:57:23","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123236,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/2c8480045caabf1ae9b0568d.jpeg"},{"id":94396970,"identity":"ace9f51f-5e04-4c75-ac6c-9e0e48f77280","added_by":"auto","created_at":"2025-10-27 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13:57:11","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108633,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/3f4de8dc7917499a244cdba6.png"},{"id":94396654,"identity":"46885364-ae24-4e6d-a460-7056653cce25","added_by":"auto","created_at":"2025-10-27 13:56:09","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":207402,"visible":true,"origin":"","legend":"","description":"","filename":"03ccbe5e191d40668725a9e18f8741711structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/2b1ac47981df97484c5f4e99.xml"},{"id":94397789,"identity":"f268cdbf-c712-48ae-90c5-8053054b0cf4","added_by":"auto","created_at":"2025-10-27 13:56:48","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":222572,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/070ccd61a04de6d3214bca03.html"},{"id":94396580,"identity":"98f1871d-4244-4b0c-8871-8714dfc1fe00","added_by":"auto","created_at":"2025-10-27 13:56:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13790,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Housing Status Categories from Principal Component Analysis (PCA)\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/a210e905b5c7abf00b66ca2e.png"},{"id":94397135,"identity":"e24ca6f0-4e2b-4072-9006-d9ec25ccf652","added_by":"auto","created_at":"2025-10-27 13:56:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMalaria Prevalence by Housing Quality, Comparative Analysis and Percentage Changes\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/6e196994c197e244ffd5d253.png"},{"id":94397609,"identity":"259cc834-a207-4a7c-88de-eff4abe4c8b0","added_by":"auto","created_at":"2025-10-27 13:56:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21939,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMalaria Prevalence by Anemia Severity and Housing Quality\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/052bfe4a6a9bb7de11672997.png"},{"id":94399032,"identity":"81a26cc6-302a-4d31-8266-758d6fe787e6","added_by":"auto","created_at":"2025-10-27 13:57:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40077,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of Malaria Prevalence Among School-Aged Children in 2021, by regions and Housing Status: (A) unimproved housing (B) semi-improved housing, (C) improved housing.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/54040ea9e602af8b1c6f58e5.png"},{"id":94459253,"identity":"19a817e7-556d-4578-80dd-3a4bca07a564","added_by":"auto","created_at":"2025-10-27 14:52:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1583492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7744849/v1/439ba9ee-8ad0-4295-ab3a-b8acc9023b61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRole of improved housing and high altitude significantly reduce malaria prevalence among school-age children in mainland Tanzania.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains a major global public health concern, with an estimated 263\u0026nbsp;million cases and nearly 600,000 deaths reported in 2023, the majority of which occurred in sub-Saharan Africa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While interventions such as insecticide-treated nets (ITNs), indoor residual spraying (IRS), and antimalarial treatments have significantly reduced the malaria burden across endemic countries (Bhatt et al., 2015), transmission continues in many regions despite the sustained use of these frontline interventions (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This persistence underscores the gaps with existing intervention and opportunities with complementary strategies the need for complementary strategies to consolidate and expand malaria control gains.\u003c/p\u003e\u003cp\u003eThere are several promising prototypes of interventions that have emerged as complementary strategies to address this gap. Among these, housing improvement has gained increasing recognition as an effective approach for reducing human-vector contact and, consequently, human exposure to malaria transmission and infection (\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While ITNs and IRS remain central to malaria reduction in sub-Saharan Africa (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Alternative strategies like Larval source management (LSM) is most effective in environments with few, fixed, and easily identifiable breeding sites, such as urban areas, arid regions, or places with permanent water sources, and requires sustained technical and community support. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Emerging tools such as spatial repellents, mosquito coils, and attractive targeted sugar baits are under evaluation for residual and outdoor transmission, but their large-scale impact is uncertain (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and mosquito coils pose potential respiratory health risks from prolonged smoke exposure (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHousing improvements like window and door screening, closed eaves, ceilings, and solid construction provide passive, chemical-free, long-term protection independent of individual behavior. Improved housing has consistently demonstrated equal or greater effectiveness than insecticide-treated nets (ITNs) in reducing malaria risk, as studies across specific African countries including Tanzania, Kenya, Uganda, Equatorial Guinea, Cameroon, Ethiopia, Gambia, and Nigeria show that better housing design lowers malaria transmission by limiting human vector contact (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Systematic reviews and meta-analyses, including Cochrane and multi-country studies, confirm the significant impact of improved housing on malaria reduction across sub-Saharan Africa (\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDespite promising results, robust evidence on housing improvement\u0026rsquo;s impact on malaria reduction across diverse epidemiological and ecological settings in Tanzania remains limited. Understanding this relationship is critical, as malaria transmission varies widely with climatic, ecological, and socio-economic factors, causing significant heterogeneity in risk and intervention effectiveness.\u003c/p\u003e\u003cp\u003eThis study assesses the role of housing in reducing malaria transmission across Tanzania. Using nationally representative data on malaria infection, household characteristics, and spatial context from the school malaria and nutrition survey, we generate evidence on housing improvement as a scalable, context-specific control strategy.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eTanzania covers approximately 945,500 km\u003csup\u003e2\u003c/sup\u003e, of which 883,749 km\u003csup\u003e2\u003c/sup\u003e is land area and 61,500 are inland water bodies with several lakes and rivers and coast along the Indian Ocean. Tanzania lies 1\u0026ndash;12 degrees south of the equator and 29\u0026ndash;41 degrees east. The country shares borders with eight countries: Kenya and Uganda to the north; Rwanda, Burundi, the Democratic Republic of Congo, and Zambia to the west; and Malawi and Mozambique to the South. The Indian Ocean borders the country to the east. The 2022 projected population according to the 2022 national census was 61,741,120 whereby at least 17,833,205 (30.89%) are children aged between 5\u0026ndash;16 years. About 93% of the Tanzanian population is at risk of malaria; however, the transmission intensity is heterogeneous across the different geographic zones (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eWe utilized data from the School Malaria and Nutrition Survey, a cross-sectional study of public primary school-aged children (SAC) and their household heads. The methodological details of the survey have been published previously (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). A total of 650 primary public schools were involved in the study.\u003c/p\u003e\n\u003ch3\u003eStudy Population and Eligibility\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in public primary schools and households across mainland Tanzania, covering all 26 regions and 184 councils. Participants were school-aged children (5\u0026ndash;16 years) enrolled in public primary schools and their household heads or representatives. Eligible children were those aged 5\u0026ndash;16 years whose parents or guardians provided consent, while eligible households were those that agreed to participate. Children absent from school or unwell on the survey day were excluded.\u003c/p\u003e\n\u003ch3\u003eSampling Technique and data collection\u003c/h3\u003e\n\u003cp\u003eA multi-stage cluster sampling design was employed to achieve representative coverage across all 26 regions and councils of mainland Tanzania. Stratification accounted for geographic features, malaria prevalence, demographic characteristics, proximity, and population density. Within selected strata, wards and schools were randomly chosen, and systematic sampling of school-aged children (SAC) from standards 1 to 7 was conducted, ensuring an equal sex ratio. All selected children underwent malaria testing and demographic data collection, while subsets were additionally assessed through interviews and hemoglobin measurement. Systematic sampling procedures were also used to link selected SAC to their households.\u003c/p\u003e\u003cp\u003eThe questionnaire used in this study was developed specifically for this survey. Data was collected from the participants using a pre-tested and validated semi-structured questionnaire. The questionnaire was loaded into the Open Data Kit (ODK) using the KoboCollect\u0026reg; platform. The study enumerators were thoroughly trained prior to the survey. The questionnaire collected reported information on socio-demographic characteristics, household characteristics, household size, history of fever, use of mosquito bed nets, and treatment-seeking behaviour of the participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Period.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 2021 School Malaria and Nutrition Survey included 650 schools, with data collection conducted from 2 October to 24 November 2021.\u003c/p\u003e\n\u003ch3\u003eLaboratory work\u003c/h3\u003e\n\u003cp\u003eFinger-prick blood samples were collected from each SAC to detect malaria parasites using the malaria Rapid Diagnostic Test (mRDT). SACs who tested positive for malaria were administered antimalarial drugs based on national guidelines. Hemoglobin (Hb) concentration was measured using a portable HemoCue\u0026reg; analyzer, and SACs with Hb concentration below 8.0 g/dl were referred to nearby health facilities for further examination and care.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal component analysis\u003c/h2\u003e\u003cp\u003eHousing status was assessed by systematically recording multiple housing-related variables to reflect improved versus unimproved structures and materials. Each original variable was transformed into a binary indicator, with durable or protective features coded as \u0026ldquo;improved\u0026rdquo; and less durable or temporary features coded as \u0026ldquo;unimproved.\u0026rdquo; Floor materials such as cement, ceramic tiles, and hardwood were considered improved, while mud, sand, or animal dung were classified as unimproved. Similarly, ceilings made of board wood, gypsum board, wood, cement, or traditional sealing were considered improved, whereas unsealed or unspecified materials were categorized as unimproved.\u003c/p\u003e\u003cp\u003eWall materials were classified based on durability and protective capacity, with concrete, stone-cement, wood, concrete blocks, iron sheets, and burnt mud bricks coded as improved, and weaker or temporary materials such as stone-mud, bamboo-mud, unburnt mud bricks, grass, or poles classified as unimproved. Roofing was considered improved if constructed from iron sheets, concrete/cement, or asbestos, and unimproved if made of grass, mud, bamboo, plastics, or other temporary coverings. Eave design was coded as improved when closed, and unimproved when open or partially closed. Window quality was assessed based on material and screening; glass or screened windows were considered improved, whereas wood, iron sheets, plastics, or other types were classified as unimproved. Protective window features, including wire screens, bed nets, or plastic coverings, were similarly coded as improved.\u003c/p\u003e\u003cp\u003eFollowing recording, all new binary variables were subjected to one-hot encoding, allowing each housing component to be analyzed as a distinct indicator. This approach provided a comprehensive and systematic measure of housing quality, capturing structural and protective features relevant to malaria exposure and other health outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariables in the study\u003c/h3\u003e\n\u003cp\u003eThe outcome variable, malaria status, was defined based on the results of the malaria rapid diagnostic test (mRDT), with individuals testing positive coded as 1 and those testing negative coded as 0.\u003c/p\u003e\u003cp\u003eIndependent variables and their categories in the study included age groups (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), sex of pupil (female, male), level of education of household head (No formal, primary, secondary, college/university), marital status (married, single), family size (\u0026le;\u0026thinsp;4, \u0026gt;\u0026thinsp;4), number of under 15 children (1\u0026ndash;2,\u0026ge;3 ), elevation (below 750, 750 to 1250, 1250 to 1750, above 1750) meters above sea level, SAC receive LLIN at school (not received, yes-received), anemia status (normal, mild, moderate, severe), LLIN use last night (did not sleep, slept), number of individuals who slept under one LLIN (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), residence status (rural, urban), medium malaria transmission (mosquito, others), knowledge of malaria signs and symptoms (none, 1 to 3, 4 to 11), and occupation of household head (agriculture, employed, unemployed).\u003c/p\u003e\n\u003ch3\u003eAnemia classification\u003c/h3\u003e\n\u003cp\u003e Hemoglobin levels were classified by age and sex following the WHO 2024 guidelines, allowing standardized identification of anemia severity in children and adolescents. This categorization provides a framework to assess nutritional and health status across different age and sex groups and to examine associations with malaria risk and other health outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eClassification and cut-offs of hemoglobin concentration levels by age and sex\u003c/b\u003e (According to the WHO guideline for anemia (hemoglobin cutoffs), 2024)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge and sex*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo anemia (g/dl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMild anemia (g/dl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate anemia (g/dl)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSevere anemia (g/dl)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;11 years (male and female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u0026ndash;11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;14 years, female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u0026ndash;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;14 years, male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u0026ndash;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;16 years, female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u0026ndash;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;16, years male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u0026ndash;12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.0\u0026ndash;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData was cleaned and analyzed in R software (v4.2.4). Descriptive statistics are presented as means (\u0026plusmn;\u0026thinsp;standard deviation) for continuous variables and frequencies (%) for categorical variables.\u003c/p\u003e\u003cp\u003eTo assess factors associated with malaria infection, we employed a generalized linear mixed-effects model (GLMM) with a binomial distribution and logit link. The model included random intercepts for school and ward to account for the clustered design and potential spatial autocorrelation. Fixed effects were specified \u003cem\u003ea priori\u003c/em\u003e based on known biological and social risk factors, including housing quality, sex, age, parental occupation and education, household size, altitude, anemia status, insecticide-treated net (ITN) use, and urbanicity.\u003c/p\u003e\u003cp\u003ePrior to model fitting, observations with missing outcome data (malaria RDT result) were excluded. We assessed multicollinearity among all explanatory variables using variance inflation factors (VIF); all retained variables had a VIF\u0026thinsp;\u0026lt;\u0026thinsp;5, indicating no substantial collinearity. Model convergence was verified, and fit was assessed via residual diagnostics and examination of variance components for the random effects. Results are reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). All analytical procedures adhered to best practices to ensure robustness and reproducibility\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis study included 6,554 school-aged children from 184 councils across all 26 regions of mainland Tanzania. Malaria prevalence was highest among children aged 13\u0026ndash;16 years (16.3%), especially those living in unimproved housing (22.6%). Males had higher prevalence (13.1%) than females (10.3%), with greater differences in unimproved housing (20.3% vs. 17.3%). The Lake Zone showed the highest prevalence (22.8%), rising to 30.1% in unimproved housing, while Central and Southern Highlands had the lowest rates. Agricultural households experienced higher prevalence (14.8%) compared to employed heads of households (4.3%). Lower education levels correlated with increased prevalence, notably 18.6% among households without formal education. Larger households (\u0026ge;\u0026thinsp;5 members). Areas below 750 meters asl elevation showed 11.4% prevalence, increasing to 22.0% in unimproved housing. Those SAC who didn\u0026rsquo;t sleep under ITNs raised prevalence from 17.5% overall to 25.5% in unimproved housing. Severe anemia was strongly associated with malaria, reaching 41.7% overall and 55.6% in unimproved housing. Rural households had higher prevalence (13.2%) than urban areas (5.3%), with rural unimproved housing at 18.7% (Table\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMalaria Prevalence by Demographic, Socio-Economic, Geographic, and Housing Characteristics Among School Aged Children.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eUnimproved\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSemi improved\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eImproved\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;6,559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;769(11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal, N\u0026thinsp;=\u0026thinsp;1,642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrev, N\u0026thinsp;=\u0026thinsp;309(18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal, N\u0026thinsp;=\u0026thinsp;3,245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePrev, N\u0026thinsp;=\u0026thinsp;370(11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal, N\u0026thinsp;=\u0026thinsp;1,642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePrev, N\u0026thinsp;=\u0026thinsp;83(5.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Group (Years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 to 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (17.3) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17 (3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9 to 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e128 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34 (5.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13 to 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10 (6.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePupil Sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e331 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e140 (17.3) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e153 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36 (4.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e438 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e169 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e217 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47 (5.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation household head\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e689 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e295(19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e323(13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e66 (9.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17 (1.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level household head\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Formal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e610 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e249 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e297 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13 (2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e646 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e249 (18.1) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e317 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e74 (5.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9 (3.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e123 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34 (4.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e524 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e224 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e247 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElevation (m asl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29 (4.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e750 to 1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e208 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48 (8.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1250 to 1750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6 (1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 1750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eITN use last night\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDidn\u0026rsquo;t Sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5 (4.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e126 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e190 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32 (3.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e705(13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e347 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e80 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe study demonstrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) a strong inverse relationship between housing quality and malaria prevalence. The risk of malaria was nearly four times higher in unimproved housing compared to improved housing, confirming that housing quality is a major modifiable risk factor for the disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMalaria Prevalence by Housing Type and Anemia Status\u003c/h2\u003e\u003cp\u003eMalaria prevalence increased sharply with anemia severity across all housing types, with the highest rates consistently observed in unimproved housing (13.4% in normal, 24.7% in mild, 27.4% in moderate, and 55.6% in severe anemia), followed by semi-improved housing (6.7%, 13.2%, 18.7%, and 50.0%, respectively) and the lowest in improved housing (2.3%, 2.8%, 7.0%, and 14.3%). This clear gradient demonstrates that both poor housing quality and worsening anemia status independently and synergistically heighten malaria risk, highlighting the dual need for structural housing interventions and improved anemia control to reduce malaria burden \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSpatial distribution of malaria prevalence by housing type\u003c/h2\u003e\u003cp\u003eIn several regions, malaria prevalence consistently increases with decreasing housing quality. In Geita, prevalence rises from 17.1% in improved housing to 39.4% in semi-improved and 37.8% in unimproved housing. Simiyu shows a similar trend, with 23.1% in improved, 22.6% in semi-improved, and 36.5% in unimproved housing. Shinyanga follows, reporting 11.3%, 25.8%, and 37.8% prevalence for improved, semi-improved, and unimproved housing, respectively. In Mtwara, prevalence is 17.0% in improved, 16.3% in semi-improved, and 35.8% in unimproved housing. Rukwa records 0.0% in improved housing, rising to 13.6% and 37.0% in semi-improved and unimproved categories. Pwani shows 4.4%, 15.2%, and 31.9% across the same housing categories, while Katavi reports 12.5%, 20.3%, and 33.3%. These data demonstrate a clear pattern: unimproved housing is consistently associated with substantially higher malaria prevalence compared to improved housing \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGeneralized Linear Mixed Model analysis\u003c/h2\u003e\u003cp\u003eIn both bivariate and multivariate analyses, housing improvement was strongly associated with reduced malaria risk. Compared to unimproved housing, improved housing significantly lowered the odds of malaria infection (adjusted OR 0.48; 95% CI 0.33\u0026ndash;0.70; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while semi-improved housing showed a non-significant protective trend (adjusted OR 0.82; 95% CI 0.64\u0026ndash;1.05; p\u0026thinsp;=\u0026thinsp;0.115). Male pupils had higher odds of infection than females (adjusted OR 1.40; 95% CI 1.14\u0026ndash;1.72; p\u0026thinsp;=\u0026thinsp;0.001). Elevation above 1250 meters was associated with significantly lower malaria risk, with the highest elevations (\u0026gt;\u0026thinsp;1750 m) showing the greatest protection (adjusted OR 0.07; 95% CI 0.01\u0026ndash;0.36; p\u0026thinsp;=\u0026thinsp;0.002). Anemia status showed a strong dose-response relationship with malaria risk: moderate (adjusted OR 2.61; 95% CI 1.92\u0026ndash;3.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and severe anemia (adjusted OR 8.66; 95% CI 2.63\u0026ndash;28.52; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) markedly increased odds of infection. Use of insecticide-treated nets (ITNs) was protective (adjusted OR 0.49; 95% CI 0.34\u0026ndash;0.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and urban residence was associated with lower malaria risk compared to rural areas (adjusted OR 0.37; 95% CI 0.21\u0026ndash;0.68; p\u0026thinsp;=\u0026thinsp;0.001). Other factors such as age group, household head occupation, education level, and family size showed no significant adjusted associations. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors Associated with Malaria Risk among School-Aged Children in Tanzania\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude odd ratio (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAdjusted Odds Ratio (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnimproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemi improved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72(0.57\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.82(0.64\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImproved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37(0.26\u0026ndash;0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.48(0.33\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Group (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 to 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9 to 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18(0.93\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.07(0.83\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13 to 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47(1.07\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.23(0.89\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePupil Sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38(1.13\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.4(1.14\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation household head\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.29(0.73\u0026ndash;7.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2.41(0.75\u0026ndash;7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91(0.29\u0026ndash;2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.21(0.37\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level household head\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Formal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79(0.56\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.78(0.55\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42(0.27\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.61(0.37\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27(0.12\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.52(0.22\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98(0.78\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.94(0.74\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElevation group (m asl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow 750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e750 to 1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78(1.13\u0026ndash;2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.42(0.93\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1250 to 1750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59(0.35\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.47(0.29\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove 1750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06(0.01\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.07(0.01\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnemia Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62(1.15\u0026ndash;2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.53(1.08\u0026ndash;2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.53(1.87\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2.61(1.92\u0026ndash;3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.81(2.51\u0026ndash;30.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.66(2.63\u0026ndash;28.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eITN use last night\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49(0.35\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.49(0.34\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23(0.11\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.37(0.21\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study demonstrates that malaria risk among children is strongly influenced by housing quality, with those living in improved houses experiencing substantially lower odds of infection, while semi-improved housing showed a non-significant protective effect. Male children were consistently at higher risk, and infection prevalence declined sharply in highland areas, highlighting the role of environmental and demographic factors. Regional differences in malaria burden were largely explained by variation in housing quality, and at the individual level, children with mild, moderate, or severe anemia were particularly vulnerable. Protective factors, including insecticide-treated net use and urban residence, were associated with significantly reduced infection risk.\u003c/p\u003e\u003cp\u003eThis study revealed that improved housing was strongly associated with lower malaria risk in Tanzania. Similar findings across sub-Saharan Africa support housing modification as an effective intervention (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). For instance, studies in Tanzania report that improved housing reduces indoor mosquito densities and human\u0026ndash;vector contact (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The most likely protective mechanism is the physical barrier that limits mosquito entry, thereby reducing human exposure to infectious bites. This effect is particularly relevant in African settings where the dominant malaria vectors, such as \u003cem\u003eAnopheles gambiae s.s.\u003c/em\u003e and \u003cem\u003eAn. funestus s.s.\u003c/em\u003e, feed and rest indoors, with peak biting occurring late at night when most people are indoors and asleep(\u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Although \u003cem\u003eAn. arabiensis\u003c/em\u003e exhibits more opportunistic behavior, feeding outdoors and on animals, it also exploits indoor environments depending on ecological conditions and intervention coverage (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Improved housing therefore provides a protective barrier across diverse transmission settings by limiting exposure to both highly endophagic species and more adaptable vectors.\u003c/p\u003e\u003cp\u003eIn Tanzania, the interplay between \u003cem\u003eAnopheles arabiensis\u003c/em\u003e and \u003cem\u003eAn. funestus\u003c/em\u003e significantly influences malaria control strategies (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). While \u003cem\u003eAn. arabiensis\u003c/em\u003e is often more abundant and exhibits outdoor feeding and resting behaviors, it is less efficient in transmitting malaria, with lower sporozoite rates (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In contrast, \u003cem\u003eAn. funestus\u003c/em\u003e is typically more efficient, feeding predominantly on humans indoors late at night and exhibiting higher sporozoite rates (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). However, the widespread use of insecticides has driven behavioral resistance in \u003cem\u003eAn. arabiensis\u003c/em\u003e, increasing its tendency to feed outdoors (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). This shift in feeding behavior reduces the effectiveness of indoor-based interventions, highlighting the need for integrated control strategies that address both vector species and their evolving behaviors (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Housing improvements exploit these differences by creating durable physical barriers that substantially reduce the risk of infectious bites. This is critical because even a few infectious mosquitoes can sustain transmission. Beyond entomological effects, housing improvements represent a durable, user-independent intervention that complements insecticide-based tools. Unlike ITN or IRS, housing modifications are not dependent on nightly adherence or repeated program delivery. They can therefore strengthen resilience against insecticide resistance and behavioral adaptations in mosquito populations. Moreover, investment in housing contributes to broader development goals by improving health, well-being, and quality of life, thereby providing long-term benefits beyond malaria. These findings highlight the need for integrated malaria control. Housing improvements should be recognized as a core, sustainable strategy that complement insecticide-based tools and supports elimination goals.\u003c/p\u003e\u003cp\u003eThis study observed significant regional spatial heterogeneity in malaria risk across Tanzania, consistent with national malaria stratification and recent local studies (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan additionalcitationids=\"CR64 CR65\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). The observed regional variability in malaria risk is likely driven by differences in housing quality and mirrors broader socio-economic and structural determinants that influence vector exposure and transmission dynamics. Households with lower socio-economic status are more likely to live in unimproved housing that allows mosquito entry and often face limited access to healthcare services, thereby perpetuating a cycle of malaria transmission and poverty (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Addressing these disparities requires a holistic approach, integrating economic empowerment strategies such as microfinance through Village Community Bank (VICOBA), conditional cash transfers under programs like Tanzania Social Action Fund (TASAF), and market-based housing finance (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Moreover, macroeconomic evidence shows that investments in malaria control, including housing upgrades, not only reduce transmission but also enhance labor productivity, decrease healthcare costs, and contribute to GDP growth (\u003cspan additionalcitationids=\"CR71 CR72\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e); World Bank, 2020; Barrett et al., 2023). Taken together, these findings highlight the need for a multi-sectoral approach that integrates public health strategies with socio-economic development to achieve equitable and sustainable malaria elimination.\u003c/p\u003e\u003cp\u003eWe observed significantly higher malaria risk among male than female among school-aged children, consistent with recent findings across sub-Saharan Africa (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Large community-based surveys across five regions in mainland Tanzania reported higher infection odds among men, particularly where housing quality was poor and net use was low (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In Zanzibar, case\u0026ndash;control study further demonstrates that occupations dominated by men, such as farming, fishing, and night watch, significantly increase exposure during peak mosquito-biting hours (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Taken together, these findings highlight the limits of conventional indoor interventions in addressing male-specific and outdoor exposures. To close this gap, malaria control must integrate WHO-recommended spatial repellents with targeted social and behaviour change campaigns tailored to male-dominated occupations and social routines (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Such complementary strategies are essential to ensure that malaria control programmes equitably protect all demographic groups\u003c/p\u003e\u003cp\u003eThe protective effect of increasing elevation against malaria observed in this study aligns with established findings that altitude influences transmission intensity through cooler temperatures, reduced mosquito survival, and slower parasite development (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Recent studies in sub-Saharan Africa have reinforced the inverse relationship between altitude and malaria risk, showing that higher elevations are associated with lower malaria prevalence in the western Kenyan highlands (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), that malaria incidence in Rwanda is lowest around 1600 meters above sea level (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), and that malaria hotspots in Mount Elgon, Uganda, vary across altitudinal zones in association with environmental factors such as rainfall and vegetation (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). While this relationship is well-established, it remains critical to consider in malaria control planning, as climate change could alter local temperature and precipitation patterns, potentially expanding transmission into higher-elevation areas previously considered low-risk. Consequently, surveillance and intervention strategies must remain adaptive to anticipate and respond to shifting malaria risk profiles driven by environmental changes.\u003c/p\u003e\u003cp\u003eThis study found a strong statistical association between malaria infection and anemia in SAC, with higher malaria exposure associated with lower hemoglobin levels. These results align with recent evidence from sub-Saharan Africa demonstrating that malaria significantly increases the risk of anemia (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). For example, a study in Ethiopia reported that malaria-infected school-aged children were over 13 times more likely to be anemic than uninfected peers (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). The mechanistic basis includes malaria-induced hemolysis and bone marrow suppression; while pre-existing anemia may further compromise host immunity, increasing susceptibility to infection. Together, these findings reinforce the need for integrated interventions that combine malaria prevention, nutritional programs, iron supplementation, and enhanced case management, particularly in high-risk pediatric populations.\u003c/p\u003e\u003cp\u003eThis study has several notable limitations. Its cross-sectional design precludes causal inference, limiting the ability to determine the directionality of associations between risk factors and malaria infection. Key socio-economic and behavioral variables were not fully captured, and seasonal transmission dynamics and finer-scale environmental factors were not incorporated, which may affect the precision of risk estimates. Despite these limitations, the study provides valuable insights through its large, nationally representative dataset, offering practical evidence to guide targeted resource allocation and inform malaria control and elimination strategies across Tanzania.\u003c/p\u003e\u003cp\u003eIn conclusion, housing improvements should be prioritized to ensure equitable access to malaria interventions, particularly among marginalized rural communities. Male children and those with severe anemia remain most vulnerable, while residence at higher altitudes, urban settings, and consistent use of insecticide-treated nets provide protection. Sustainable malaria control requires integrated, context-specific strategies that combine housing improvements with nutritional support and behavior change interventions. Future research should focus on strengthening multi-sectoral collaboration across health, housing, and social services to achieve lasting reductions in malaria transmission and safeguard vulnerable children in endemic regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. SMPS surveys were approved by the National Health Research Ethics Committee, which is a sub-committee of the Medical Research Coordinating Committee of the National Institute of Medical Research with reference number NIMR/HQ/R.8c/Vol.I/1857 (2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Principal Investigator and some Co-Investigators obtained certificates for the protection of human subjects and research information prior to the surveys. Other Co-Investigators, national supervisors, and field staff were trained using standard operating procedures on how to ensure the protection of human subjects. The survey applied a passive, opt-out approach for parental permission approval whereby once children were selected for the survey during the preparatory days; a memo was written by respective schoolteachers to parents/guardians. It was assumed that parents/guardians approved their children\u0026rsquo;s participation if they did not express their disapproval. In addition, approval was sought from each level before and during fieldwork exercise including permission letter from President\u0026rsquo;s Office, Regional Administrative and Local Government (PO-RALG); written school committees\u0026rsquo; approval, and verbal assent from all surveyed children aged above 8 years old. Before participation, the purpose of the study, procedures involved, confidentiality measures, and the voluntary nature of participation were clearly explained to each selected household. Written informed consent was then obtained from the head of household or an authorized representative to confirm their agreement for the household\u0026rsquo;s participation in the interview. Only households that provided written informed consent were included in the survey. Participants were informed that they could withdraw at any time without any consequences. A child with a positive test result was treated with artemether\u0026ndash;lumefantrine (ALu), as recommended in the National Malaria Diagnosis and Treatment Guidelines and when deemed necessary was referred to the nearest health facility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the current situation and limitation to sharing government data, any requests for additional data supporting the findings for this study can be made to the Tanzania Ministry of Health at https://www.moh.go.tz/contact or send email directly to the Permanent Secretary at [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study is a secondary analysis and did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eMbaraka John Remiji\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing. \u003cstrong\u003eFelista Mwingira:\u0026nbsp;\u003c/strong\u003eReview \u0026amp; Editing.\u003cstrong\u003e\u0026nbsp;Gerald Kiwelu:\u0026nbsp;\u003c/strong\u003eSoftware, Validation\u003cstrong\u003e. Tajiri Laizer:\u0026nbsp;\u003c/strong\u003eSoftware, Validation\u003cstrong\u003e. Nicodem Govella:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing - Review \u0026amp; Editing and Supervision \u003cstrong\u003eSamson S. Kiware:\u003c/strong\u003e Conceptualization, Review \u0026amp; Editing, Supervision. \u003cstrong\u003eFrank Chacky\u003c/strong\u003e: Resources, Project Administration, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SMPS study was conducted by the Tanzania Ministry of Health through the National Malaria Control Program, in collaboration with research Institutions and academia \u0026nbsp;namely, National Institute for Medical Research, Ifakara Health Institute, Muhimbili University of Health and Allied Sciences, University of Dar es Salaam, Sokoine University of Agriculture, Tanzania Food and Nutrition Center. The authors are grateful to all Ministry of Health, President\u0026rsquo;s Office Regional Administration and Local Government, Ministry of Education, Science and Technology, institutions, partners, field team, data entry clerks, and investigation team and individuals who contributed to this school survey including surveying school children for their voluntary participation in the survey. Authors would like to extend their appreciation to Julieth Silao, David Dadi, Victor Alegana, Benjamin Kamala, Brigita Msofe, Witness Saitot, Abdallah Lusasi, Anna David, Pendael Machafuko, Bwire Wilson, Charles Dismas Mwalimu, Agnes Mpinga, Fidelis Mgohamwende, Humphrey Mkali and Wiggins Aaron, Janice Maige, Praise Michael, for their valuable inputs and advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. 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PLoS ONE. 2025 July;17(7):e0327378.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Housing structure, Malaria transmission, Anemia, Altitude, Mosquito vectors, Insecticide-treated nets","lastPublishedDoi":"10.21203/rs.3.rs-7744849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7744849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Malaria remains a major public health concern in Tanzania, with school-aged children carrying a significant burden. This study assessed the impact of housing structure on malaria prevalence among school-aged children\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A cross-sectional survey was conducted among 6,554 school-age children from 184 districts across all 26 regions, covering 650 public primary schools in mainland Tanzania in 2021. A multi-stage cluster sampling methodology was used to ensure both geographical and demographic representation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Residing in improved houses significantly reduced malaria infection prevalence among SAC (aOR: 0.48, CI: 0.33–0.70, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001). Male experienced higher malaria infection than female (aOR: 1.40, CI: 1.14–1.72, p = 0.001). Compared to school-aged children living below 750 meters above sea level, those residing at elevations of 1,250–1,750 meters had 53% lower odds of malaria infection (aOR: 0.47; CI: 0.29–0.78; \u003cem\u003ep\u003c/em\u003e = 0.003), with an even more noticeable 93% reduction observed among those living above 1,750 meters (aOR: 0.07; CI: 0.01–0.36; \u003cem\u003ep\u003c/em\u003e = 0.002). Sleeping under an ITN was associated with a 51% lower malaria infection (aOR: 0.49, CI: 0.34–0.71, p \u0026lt; 0.001). SAC in urban areas were 63% less likely to have malaria compared to those in rural settings (aOR: 0.37, CI: 0.21–0.68, p = 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003cbr\u003e\n \u003c/strong\u003eHousing improvements should be prioritized to ensure access to malaria interventions, especially in marginalized rural communities. Male children and those with severe anemia were most at risk, while higher-altitude residence, urban living, and consistent use of insecticide-treated nets were protective. Integrated, context-specific strategies combining housing, nutrition, and behavior change interventions are essential. Multi-sectoral programs linking health, housing, and social services can sustainably reduce malaria transmission and protect vulnerable children.\u003c/p\u003e","manuscriptTitle":"Role of improved housing and high altitude significantly reduce malaria prevalence among school-age children in mainland Tanzania.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 00:59:34","doi":"10.21203/rs.3.rs-7744849/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"123078387145829398757559379766438864141","date":"2026-03-23T14:06:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36092506660946053322869904209693383445","date":"2026-01-13T13:45:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T11:56:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229353089567806138011107856212335707556","date":"2025-10-16T08:44:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-11T18:57:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-11T18:32:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T18:23:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-09T07:34:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-10-09T07:10:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ffaa85b-6ede-4e57-bee4-3ba63061cae3","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-26T00:59:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 00:59:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7744849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7744849","identity":"rs-7744849","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}