Livestock ownership and reproductive characteristics, but not maize production, are associated with anemia in women in malaria-endemic low-income setting

Livestock ownership and reproductive characteristics, but not maize production, are associated with anemia in women in malaria-endemic low-income setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Livestock ownership and reproductive characteristics, but not maize production, are associated with anemia in women in malaria-endemic low-income setting Taddese Alemu Zerfu, Wegderes Ketema, Amare Abera, Abera Belay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4105146/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: It is unclear whether common agricultural practices, such as livestock farming and maize production, affect the burden of malaria and subsequent anemia status among reproductive-age women in a low-income setting. Methods : A community-based cross-sectional study was conducted among women of reproductive age, comparing areas with high versus low maize production intensity in rural central Ethiopia. The study areas were categorized as high (> 50%) or low (≤ 10%) maize production clusters based on the percentage of cultivated land. Data were collected from 450 randomly selected households (250 from each cluster). Descriptive and bivariate statistics were used to outline the participants' profiles and the association of variables. Multivariate linear regression was applied to identify determinants of mean hemoglobin concentration levels. Results : The mean (± SD) hemoglobin concentration remained statistically consistent across high 13.59 (± 1.39 g/dl) and low 13.39 (± 1.65 g/dl) maize-intensity clusters (P > 0.05). Women's ownership of livestock (β, 0.13; 95% CI: 0.01, 1.12), chicken production (β, 0.21; 95% CI: 0.34, 1.01), and women's parity (β, 0.67; 95% CI: 0.17, 0.61) significantly increased hemoglobin concentration levels (P < 0.05). Conversely, gravidity (β, -0.82; 95% CI: -0.65, -0.21) and the frequency of abortions (β, -0.23; 95% CI: 0.31, 1.06) significantly decreased hemoglobin concentration levels (P < 0.05). Conclusion : Regardless of maize production intensity, women's reproductive characteristics and livestock farming (especially chicken production) were identified as independent predictors of hemoglobin levels. Therefore, promoting women's reproductive health care and livestock-based agricultural practices could enhance the health status of agrarian women in low-income settings. Anemia hemoglobin maize livestock keeping women Figures Figure 1 Figure 2 Figure 3 Introduction Malaria is an acute febrile illness caused by protozoan parasites of Plasmodium species. It disproportionately affects young children and pregnant women, putting them at a higher risk of complications and death from infection 1,2 . The parasites invade red blood cells, causing them to rupture, resulting in a decrease in the red blood cell count, often resulting in anemia 3 . Malaria during pregnancy can have serious consequences, including fetal death, prematurity, low birth weight, severe maternal anemia, and other complications 2 . Malarial anemia is a significant health challenge in regions in which malaria is prevalent. However, it remains poorly understood, and its treatment is not simple 1,2 . The relationship between malaria and iron is intricate 1,2 . The mechanisms that lead to anemia during malaria are extremely diverse and involve environmental, immunological, dietary, and other factors that act differently 3 . Agriculture is an environmental factor associated with malaria. Irrigation is considered to be one of the primary links between agriculture and malaria 4 . Certain agro-ecological changes also be associated with an increased risk of malaria transmission 5,6 . A direct relationship was identified between intensified maize cultivation and malaria transmission. It was found that the scent of maize pollen attracting female mosquitoes for oviposition and provides nourishment for their larvae 7 . Maize pollen is commonly found on the surface of water near maize plantations during the wet season, which accelerates the rapid and frequent development of mosquito larvae into pupae, resulting in larger adult mosquitoes 7 . Interestingly, while mosquitoes are attracted to the scent of maize pollen, they seem to be repelled by the odor of chickens 9 . Although progress has been made in many countries towards eliminating malaria, there has been a resurgence of malaria cases and related deaths in certain countries in Sub-Saharan Africa (SSA) 10 . While there has been a significant decrease in malaria-associated morbidity and mortality over the past two decades, these issues still pose major public health problems in Ethiopia. Roughly 68% of the population is at risk of malaria, and at least one-third of women suffer from anemia 12,13 . We investigated the relationship between maize production intensity, cattle ownership, and the likelihood of anemia among rural women of reproductive age in Central Ethiopia. Methods Study area, design, and participants. We used a community-based comparative cross-sectional design, employing a cluster analysis to categorize households based on the intensity of maize production. The survey took place in twenty-four villages located in two districts in Arsi, Oromia, Ethiopia. These villages were selected because they had similar malaria burden and incidence rates, average annual temperature, and agroclimatic conditions, but differed in terms of maize production levels. The Arsi Zone is divided into four agro-climatic areas, primarily due to differences in altitude. The main agro-climatic zones are moderately cool (40%) and cool (34%), with the remainder classified as moderately warm (20%) or cold (6%). The main crops grown in this zone include cereals, pulses, and oil seeds. Maize, sorghum, and oats are grown in specific areas, albeit not on a large scale 15 . Sample size and Sampling Method The sample size was calculated using Epi-Info version 7 with the following parameters and assumptions: a 95% significance level (2-sided), 80% power, and 24% anemia prevalence among women 16 with an anticipated 10% lower prevalence among women in low-maize-intensity clusters. This calculation yielded 225 participants in each district. The sample size was increased to 450 to account for a 10% nonresponse rate. A village was designated as high maize intensity (cluster) if more than 50% of the cultivated land was maize and low intensity if less than 10% of the land was covered by maize. This classification was based on agricultural data in the area, information gathered from the district’s agricultural officers, and field survey observations conducted by the investigators. A multistage sampling design was used to select samples. Initially, two comparable districts (similar in malaria burden but differing in agro-ecology or maize production intensity) were chosen in collaboration with the zonal health and agricultural bureaus. The selected districts, each comprising 35 to 43 kebeles (the smallest administrative unit with a population of approximately 5,000 or 1,000 households), were included in the study. On average, each kebele contained 16 to 23 villages (obtained) with 40 to 50 households. Subsequently, 24 villages (12 districts) were chosen by employing a cluster sampling method. From each village, 18–20 households were selected using systematic random sampling. The study included apparently healthy women aged 15–49 years who were permanent residents of the selected villages. One woman from each household was also interviewed. Data collection and management Motivated and experienced health workers proficient in speaking the local language (Afan Oromo) collected data in the field. The lead investigator (TAZ) and other field staff provided three days of intensive training on data collection, respondent handling, and data quality assurance methods. A pre-tested interviewer administered a questionnaire, initially prepared in English, and then translated into the local language ‘Afan Oromo’, to gather socio-demographic, knowledge, and malaria-related information. Measurements Anthropometric measurements The same data collectors took anthropometric measurements twice (duplicate) following standardized procedures recommended by the World Health Organization (WHO). Women were weighed to the nearest 100 g on electronic scales with a weighing capacity of 140 kg. Height was measured to the nearest millimeter with a portable device equipped with calibrated and standardized height gauges (SECA 206 body meter). The mid-upper arm circumference (MUAC) of the left arm was measured to the nearest millimeter using a non-stretch measuring tape. Hemoglobin measurements A blood sample was collected through a finger prick, and hemoglobin concentration was measured using a portable HemoCue photometer (AB Leo Diagnostics, Helsingborg, Sweden). The hemoglobin readings were adjusted for altitude (Sullivan et al., 2008), and women with values below 12 g/dl were considered anemic. Analysis of the Data and Quality Assurance The cleaned data were input into SPSS Version 20 and later imported into STATA 14 for statistical analysis. Descriptive analysis (Fisher's exact test) and bivariate analysis (independent-sample t-test) were conducted to explore the associations between different categories. The mean hemoglobin levels between the two groups were compared using an independent sample t-test. Multivariate linear logistic regression analysis was performed to identify independent predictors of mean hemoglobin concentration. To ensure data accuracy, one supervisor (typically the head of the health center) and two senior researchers were responsible for overseeing the data collection. The investigator was present in the field throughout the data collection process to verify the completeness and quality of the collected data. The field supervisors reviewed the completed questionnaires daily to check for consistency and completeness. Additionally, approximately 10% of the households were re-interviewed by supervisors in the lead investigator's presence. Ethical considerations The study protocol was approved by the institutional review boards of the College of Natural and Computational Sciences at Addis Ababa University and the Oromia Regional Health Bureau. This study adhered to the principles of the Helsinki Declaration of 1975, as revised in 1983. Verbal informed consent was obtained from eligible participants in the presence of local administrators after detailed explanation of the study's objectives and methods. Results A total of 443 women of reproductive age participated in the study. Of these, 214 (48.3%) were from high-maize-intensity clusters, while 229 (51.7%) were from low-maize-intensity clusters. The response rate was 98.4%. Most participants (71.7%) were under 30 years old, and a significant proportion (40%) had no formal education. Most participants (76.7%) owned less than one hectare of land and had access to clean and safe water sources (87.8%), Table 1 . Among the women, 35.2% were moderately undernourished, while 13.3% were severely undernourished (MUAC <21 cm). On average, 83.3% of women reported owning livestock, with 76.5% from high maize intensity clusters and 89.5% from low maize intensity clusters. The most owned livestock were oxen (62.3%), cows (52.8%), and donkeys (46.3%). Chickens (20.3%) and cows (13.4%) were the most kept animals in the same house, Table 2 Awareness of malaria appears to be universal, as only a few (2.7%) women reported being unaware of or not having heard of it. Chills (84.4%), fever (54.5%), and headaches (46.6%) were the most common symptoms of malaria. However, headache as a symptom of malaria was reported by more women from high maize intensity clusters (56.8%) than from low maize intensity clusters (36.8%). Convulsions, as a sign of malaria, were the least known symptoms (7.4%) in both clusters, Table 3 . Mosquitoes were identified as a key mode of malaria transmission by 58.8% of the respondents, person-to-person transmission (29.9%), eating maize cane/sugarcane (9.7%), and contaminated food (5.3%). When asked about mosquito breeding sites, stagnant water (72.5%) and household utensils (19.4%) were the most frequently mentioned. Rainy seasons, mainly from September to November, were identified as the peak season for mosquito breeding. Approximately 70% of respondents identified September to November as the peak season for malaria transmission, while 17.9% reported June to August as a high season for malaria transmission (Table 3). There were no statistically significant differences in mean hemoglobin concentration (13.59 ± 1.39 g/dl versus 13.39 ± 1.65 g/dl; P = 0.19) or anemia prevalence (5.4% versus 9.4%; P = 0.56) between high and low maize intensity clusters (Table 4 & Figure 1). Women who owned livestock had a significantly higher mean hemoglobin concentration (14.03 ± 1.16 g/dl) compared to those who did not (13.37 ± 1.68 g/dl). Among individual livestock, only chicken ownership showed a statistically significant difference: women with at least one chicken had a significantly higher mean hemoglobin concentration (13.76 ± 1.21 g/dl) compared to those without (13.09 ± 2.01 g/dl). Ownership of other domestic animals did not result in significant differences in mean hemoglobin concentrations between the two groups. Likewise, multivariate logistic regression analysis (Table 5) indicated that a one-unit increase in livestock and chicken ownership raised hemoglobin concentration by 0.13 g/dl and 0.21 g/dl (β 0.13 & 0.21; 95% CI: 0.01, 1.12 & 0.34, 1.01) respectively. An increase of one unit in women's parity level also raised mean hemoglobin concentration level by 0.67 g/dl (β 0.67; 95% CI: 0.17, 0.61). Conversely, a unit increase in gravidity and the number of previous abortions reduced hemoglobin concentration levels by 0.82 g/dl (β -0.82; 95% CI: -0.65, -0.21) and 0.23 g/dl (β -0.23; 95% CI: 0.31, 1.06) respectively. The pattern of maize production intensity and malaria experience among women in the high-versus low-maize-producing clusters converged over time. Sixty-two percent of women in high maize intensity clusters had experienced malaria at least once in their lives, which was significantly higher than the prevalence (52.4%) among women in low-maize-intensity clusters. No significant difference in malaria incidence was observed between the two groups in the recent and current periods (Figure 2). The prevalence of anemia in women with lifetime, recent, and current malaria experience was 60.6%, 45.4%, and 24.2%, respectively (Figure 3). Discussion Employing a community-based comparative cross-sectional study design, we examined the associations between maize production intensity, malaria prevalence, and anemia prevalence in a rural resource-limited setting in Ethiopia. The respondents' level of awareness of malaria and its mode of transmission across the two clusters was very high. Although the lifetime malaria experience was significantly higher among women from high maize intensity clusters, the mean hemoglobin concentration and anemia prevalence did not differ significantly between the two clusters. Ownership of at least one livestock in the household in general, and chicken in particular, was significantly associated with a higher mean hemoglobin concentration. Most women in our study were aware of malaria and had at least one sign or symptom of the disease. This high level of awareness observed in the area is consistent 18 or even higher than many other study sites 19 in Ethiopia and elsewhere 20 . This could be attributed to the successful and efficient implementation of primary healthcare interventions in Ethiopia, including the rural health extension program 21 , which aims to improve the community's awareness of common communicable diseases, including malaria, through door-to-door visits. It could also be that because the study participants lived in a malaria-endemic area, they frequently experienced the symptoms, enough to recognize and associate them with malaria. Interestingly, although some participants mentioned maize as a potential mosquito breeding site and related maize cane consumption as a potential malaria transmission vehicle, the awareness of both groups regarding maize production intensity and malaria risk was consistent. This finding aligns with previous evidence suggesting that gravid Anopheles arabiensis (female mosquitoes) are attracted by sugarcane pollen volatiles and oviposits in response to maize pollen odors 22 . The recent and current prevalence of malaria experiences across the two clusters with different maize production intensities were not statistically significant, but the lifetime experience of malaria differed between the two groups. The observed higher lifetime malaria experience in the high-intensity maize production cluster is consistent with previous studies that linked maize production intensity with an elevated risk of malaria 23 . However, the lack of difference in recent and current malaria experiences between the two groups may be linked to intensified and successful national malaria prevention and control activities in recent years 24 . On the other hand, our analysis did not find any association between the prevalence of anemia and mean hemoglobin levels, as well as maize production intensity. These factors were not statistically significant across clusters of high versus low maize production intensity (villages). The prevalence of malaria and anemia remained constant during the study period, which supports the lack of association between anemia and mean hemoglobin levels. Since this study is one of the few cross-sectional studies that examine the relationship between maize production intensity, malaria, and anemia, further research using different designs such as case-control or prospective cohorts, as well as other longitudinal designs, with better control for background differences and potential confounders, is necessary to investigate the observed associations and trends in our study. The study also revealed that livestock and chicken ownership and domestication were linked to a significantly higher mean hemoglobin concentration. This could be attributed to better socioeconomic status and subsequent consumption. Various studies conducted in Ethiopia and other parts of the world have pointed out that household assets and income levels are associated with the risk of anemia 26,27 , as anemia tends to be more prevalent in low socioeconomic groups. For instance, an investigation from Afghanistan demonstrated that agricultural assets, such as ownership of sheep and chickens, reduce the risk of anemia, partly due to self-production consumption in the context of inadequate market functioning 28 . A prior study from Ethiopia indicated that mosquitoes are deterred by the scent of chickens 9 . This study has several limitations that should be considered when interpreting the findings. Although we compared two clusters in different districts based on their maize production intensity using a cross-sectional comparative analysis, it is unlikely that some of the statistically significant differences observed between the two categories are solely due to the classification factor. Additionally, data regarding malaria experiences were obtained from secondary sources (health facilities), and mothers were interviewed based on memory and presumed diagnoses; therefore, such findings may not be as accurate as direct testing and reporting. Despite these limitations, our study has several strengths. We collected and cross-checked data from both health facilities and communities in resource-limited rural settings, where evidence is scarce. We also addressed the burden of anemia from a new perspective, focusing on agriculture and malaria, which are not widely available in the literature. Furthermore, the sample size in both groups was sufficiently large and the response rate was high. Generally, we found that women from clusters with high maize intensity had a higher incidence of malaria compared to those with low intensity. However, there was no significant difference in malaria experience between the two groups. On the other hand, owning at least one livestock animal in the household, especially chickens, was associated with higher mean hemoglobin concentration and a lower prevalence of anemia. To improve the health status of women in low-income settings, it is recommended to focus on promoting women's reproductive healthcare and livestock-based agricultural practices such as livestock production. To support our findings, which contradict the previous hypothesis of no association between maize production and the spread of malaria in similar settings, large-scale studies integrating nutrition, agriculture, and health are needed. Declarations Acknowledgments This project was funded by the Innovative Methods and Metrics (IMMANA). We would like to express our gratitude to Professor Mike Faber, Professor William Masters, Dr. Kaleba Baye, and Dr. Yohanes Siyoum for their support and supervision throughout the project. We also appreciate Tufts University for their management of funds and supervision. Furthermore, special thanks go to the Global Academy of Agriculture and Food Security at the University of Edinburgh (UoE) and the International Food Policy Research Institute (IFPRI). The principal investigator (TAZ) dedicated part of his paid time to analyzing the data, as well as writing and revising the manuscript. Financial Support: The study was funded by Innovative Methods and Metrics for Agriculture and Nutrition Actions (IMMANA Fellowship) Authors’ contributions to manuscript: Conceptualized and designed research: T.A.Z, AA, AB; Conducted data collection, data analysis and interpretation and drafted the manuscript: T.A.Z., WK; Revised the manuscript and supervised all the work: AA, AB, and WK. Supported acquisition of the financial support. TAZ. All authors have read and approved the manuscript for submission. Perceived conflicts of interest: Authors declare no competing interests. Authors’ current addresses: Taddese Zerfu: International Food Policy Research Institute (IFPRI), P.O.Box. 5986, Addis Ababa, Ethiopia Amare Abera: Wollo University, Department of Biomedical Sciences, Dessie, Ethiopia Abera Belay: Addis Ababa Science and Technology University, Department of Food Science and Applied Nutrition and center of excellence for Bioprocessing and Biotechnology; Addis Ababa, Ethiopia Wegderes Ketema: Dire Dawa city administration, Dire Dawa, Ethiopia References World Health Organization. WHO guidelines for malaria. Published online 2023. Okoyo C, Githinji E, Muia RW, et al. Assessment of malaria infection among pregnant women and children below five years of age attending rural health facilities of Kenya: A cross-sectional survey in two counties of Kenya. PLoS One . 2021;16(9):e0257276. doi:10.1371/journal.pone.0257276 Mohandas N, An X. Malaria and human red blood cells. 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PLoS ONE . 2016;11(6). doi:10.1371/journal.pone.0156878 Tables Table 1: Socio-demographic and anthropometric characteristics of women in high and low/no maize producing villages in rural Arsi, Central Ethiopia. Maternal characteristic High maize density area, n (%) Low/no maize producing area, n (%) Total n (%) Total households, n (%) 214 (48.3) 229 (51.7) 443 (100) Age (years) 15 -29 30 -44 45-49 166 (72.5) 58 (25.3) 5 (2.2) 151 (70.9) 52 (24.4) 10 (4.7) 318 (71.7) 110 (24.9) 15 (3.4) Educational Status Unable to read & write. Primary education Secondary education Tertiary education 90 (42.1) 94 (43.9) 28 (13.1) 2 (0.9) 82 (35.8) 119 (52) 26 (11.4) 2 (0.9) 172 (38.8) 213 (49.1) 54 (12.2) 4 (0.9) Ethnic background Oromo Amhara Guraghe Others 167 (78.0) 31 (14.5) 9 (4.2) 7 (3.3) 215 (93.9) 5 (2.2) 2 (0.9) 7 (3.1) 382 (86.2) 36 (8.1) 11 (2.5) 14 (3.2) Height (cm) 150 6 (2.0) 18 (7.9) 202 (94.4) 6 (2.0) 18 (7.9) 205 (89.5) 8 (1.8) 28 (6.3) 407 (91.9) MUAC (cm) 23 25 (11.7) 65 (30.4) 124 (57.9) 34 (14.8) 91 (39.7) 104 (45.4) 59 (13.3) 156 (35.2) 228 (51.5) Land size (Hectares) 2 157 (75.5) 43 (20.7) 8 (3.8) 178 (77.7) 41 (17.9) 10 (4.4) 335 (76.7) 84 (19.2) 18 (4.1) Water source⃰ Safe Unsafe 157 (86.0) 30 (14.0) 205 (89.5) 24 (10.5) 389 (87.8) 54 (12.2) ⃰ Safe water sources include bottled water, pipe water and any treated (protected water); unsafe water sources include water from river, unprotected spring or any other unprotected or untreated water sources Table 2: Livestock ownership and domestication characteristics of women among high versus low/no maize producing villages in rural Arsi, Central Ethiopia Livestock ownership and domestication status High maize density area, n (%) Low/no maize producing area, n (%) Total n (%) Ownership of Domestic animals Have at least one (any) Cow Ox (Oxen) Sheep Goat Chicken Donkey Horse Dog Others 163 (76.5) 109 (51.9) 120 (56.1) 25 (11.7) 50 (23.4) 63 (29.4) 73 (34.1) 4 (1.9) 20 (9.3) 6 (2.8) 205 (89.5) 123 (53.7) 156 (68.1) 34 (14.8) 87 (38) 117 (51.1) 132 (42.6) 4 (1.7) 19 (8.3) 0 (0) 368 (83.3) 232 (52.8) 276 (62.3) 59 (13.3) 137 (13.3) 180 (40.6) 205 (46.3) 8 (1.8) 39 (8.8) 6 (1.4) Domestication of animals 1 Cow or ox Sheep (goat) Chicken Donkey (Horse) Calf 26 (12.1) 10 (4.7) 26 (12.1) 12 (5.6) 15 (7.0) 32 (14.0) 13 (5.7) 64 (27.9) 31 (13.5) 20 (8.7) 58 (13.1) 23 (5.2) 90 (20.3) 43 (9.7) 35 (7.9) 1 Domestication of animals means that the animals sleep with humans in the same house Table 3. Knowledge about malaria, malaria transmission and prevention of women among high versus low/no maize producing villages in rural Arsi, Central Ethiopia Knowledge High (> 50%) maize intensity area, n (%) Low (< 10%) maize intensity area, n (%) Total n (%) Heard about Malaria Yes 211 (98.6) 220 (96.1) 431 (97.3) Knows signs of malaria Fever Headache Chills Loss of Appetite Joint pain Convulsion 122 (57.8) 120 (56.8) 171 (81) 49 (23.2) 38 (18) 13 (6.0) 113 (51.3) 81 (36.8) 193 (84.3) 58 (26.4) 47 (21.3) 19 (8.6) 235 (54.5) 201 (46.6) 364 (84.4) 107 (24.8) 85 (19.7) 32 (7.4) Known mode of transmission Mosquito bite Person to person Eating contaminated food Eating sugar cane Eating maize cane Other causes 125 (59.2) 75 (35.5) 12 (5.7) 5 (2.4) 12 (5.6) 19 (9) 127 (57.7) 54 (24.5) 11 (5) 7 (3.2) 12 (5.5) 11 (5) 252 (58.5) 129 (29.9) 23 (5.3) 12 (2.8) 30 (6.9) 30 (6.9) Knowledge of mosquito breeding site Stagnant water Running water Maize and/or maize pollen Household utensils 162 (75.5) 4 (1.9) 10 (4.7) 43 (20.1) 159 (69.4) 4 (1.7) 5 (2.2) 43 (18.8) 321 (72.5) 8 (1.18) 15 (3.4) 86 (19.4) Knowledge on mosquito breeding season Rainy season (Jun - August) Autumn (Sept - November) Others 40 (18.7) 148 (69.2) 26 (12.1) 38 (16.6) 162 (70.7) 24 (10.5) 78 (17.9) 310 (70) 50 (11.3) Table 4: Mean hemoglobin concentration according to maize production intensity and Livestock ownership of women among in rural Arsi, Central Ethiopia Variable No (%) Hemoglobin level (Mean ± SD) P - value ⃰ Maize production intensity High Low/No 214 (49.4) 229 (50.6) 13.59 ± 1.39 13.39 ± 1.65 0.19 Owns livestock (at least one) Yes No 368 (84.9) 74 (15.1) 14.03 ± 1.16 13.37 ± 1.68 0.02 * Cow Yes No 253 (53.6) 180 (46.4) 13.62 ± 1.73 13.27 ± 1.49 0.30 Ox (Oxen) Yes No 276 (63.7) 156 (46.3) 13.47 ± 1.65 13.51 ± 1.57 0.79 Chicken Yes No 180 (41.6) 255 (58.4) 13.76 ± 1.21 13.09 ± 2.01 0.00 * Sheep/goat Yes No 59 304 13.51 ± 1.94 13.48 ± 1.56 0.96 Donkey/Horse Yes No 205 230 13.34 ± 1.80 13.61 ± 1.43 0.07 ⃰ Independent sample t-test Table 5: Linear regression analysis of the effect of maize production intensity, livestock ownership and women’s reproductive characteristics of on mean hemoglobin concentration, rural Arsi, Central Ethiopia. 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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-4105146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279880313,"identity":"af4723b4-0100-45ff-925a-2b077af800f2","order_by":0,"name":"Taddese Alemu Zerfu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACA4YHQJK9gfkAkJKQIaieB6QlAcQ4wAaiJHhI0cJjABUgAOzZD2/8kPDHJp9HIufzqxs1FjwM7IePbsBrC09asUQCT5plj0TuNuucY0CH8aSl3cDvsBwDiQSJwwb2QC3GOWxALRI8Zvi18L8x/pFg8N8A6LBnxjn/iNEikWMmkZBwAKSF+XFuGzFabjwrs0g4kGzAw/PMjDm3T4KHjZBf2PuTN9/48MfOgIc9+fHnnG91cvzsh4/h1YIM2CTAJLHKQYD5AymqR8EoGAWjYOQAAGcBQXY1X1Q4AAAAAElFTkSuQmCC","orcid":"","institution":"International Food Policy Research Institute (IFPRI)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Taddese","middleName":"Alemu","lastName":"Zerfu","suffix":""},{"id":279880314,"identity":"4a681189-a39c-48be-9b3d-add848605248","order_by":1,"name":"Wegderes Ketema","email":"","orcid":"","institution":"Project Hope - Ethiopia Office","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wegderes","middleName":"","lastName":"Ketema","suffix":""},{"id":279880315,"identity":"c46d0e26-23b8-4d36-aecf-3da87e929d09","order_by":2,"name":"Amare Abera","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amare","middleName":"","lastName":"Abera","suffix":""},{"id":279880316,"identity":"07817001-05af-4414-8169-c06a4b253093","order_by":3,"name":"Abera Belay","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abera","middleName":"","lastName":"Belay","suffix":""}],"badges":[],"createdAt":"2024-03-15 05:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4105146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4105146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53018421,"identity":"9739e256-e32a-484f-99e8-9ea71aecc9db","added_by":"auto","created_at":"2024-03-19 16:15:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34584,"visible":true,"origin":"","legend":"\u003cp\u003eMaize production intensity and anemia status among women of high versus low/no maize producing villages in rural Arsi, Central Ethiopia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4105146/v1/ce7cb53e320841c74bad3d68.png"},{"id":53018422,"identity":"11a39bb9-6e09-482d-9fcd-a6bf174b1c1f","added_by":"auto","created_at":"2024-03-19 16:15:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40423,"visible":true,"origin":"","legend":"\u003cp\u003eMaize production intensity and malaria experience among women of high versus low/no maize producing villages in rural Arsi, Central Ethiopia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4105146/v1/00b62bac5d78bbf364f90f86.png"},{"id":53018420,"identity":"b35aa5f6-05d3-44b1-afb2-efd2ded4b1fe","added_by":"auto","created_at":"2024-03-19 16:15:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14782,"visible":true,"origin":"","legend":"\u003cp\u003eAnemia status and malaria experience (lifetime, recent and current) among women of reproductive age group in rural Arsi, Central Ethiopia.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4105146/v1/7095d33f6259d0c3c0a40eda.png"},{"id":53103921,"identity":"d125ed98-715e-46b9-a8fa-bd43408293cd","added_by":"auto","created_at":"2024-03-20 15:47:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":516786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4105146/v1/f9a487c7-8d48-4946-84af-767b19d68686.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Livestock ownership and reproductive characteristics, but not maize production, are associated with anemia in women in malaria-endemic low-income setting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria is an acute febrile illness caused by protozoan parasites of Plasmodium species. It disproportionately affects young children and pregnant women, putting them at a higher risk of complications and death from infection\u003csup\u003e1,2\u003c/sup\u003e. The parasites invade red blood cells, causing them to rupture, resulting in a decrease in the red blood cell count, often resulting in anemia \u003csup\u003e3\u003c/sup\u003e. Malaria during pregnancy can have serious consequences, including fetal death, prematurity, low birth weight, severe maternal anemia, and other complications\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMalarial anemia is a significant health challenge in regions in which malaria is prevalent. However, it remains poorly understood, and its treatment is not simple\u003csup\u003e1,2\u003c/sup\u003e. The relationship between malaria and iron is intricate\u003csup\u003e1,2\u003c/sup\u003e. The mechanisms that lead to anemia during malaria are extremely diverse and involve environmental, immunological, dietary, and other factors that act differently \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAgriculture is an environmental factor associated with malaria. Irrigation is considered to be one of the primary links between agriculture and malaria\u003csup\u003e4\u003c/sup\u003e. Certain agro-ecological changes also be associated with an increased risk of malaria transmission\u003csup\u003e5,6\u003c/sup\u003e. A direct relationship was identified between intensified maize cultivation and malaria transmission. It was found that the scent of maize pollen attracting female mosquitoes for oviposition and provides nourishment for their larvae\u003csup\u003e7\u003c/sup\u003e. Maize pollen is commonly found on the surface of water near maize plantations during the wet season, which accelerates the rapid and frequent development of mosquito larvae into pupae, resulting in larger adult mosquitoes\u003csup\u003e7\u003c/sup\u003e. Interestingly, while mosquitoes are attracted to the scent of maize pollen, they seem to be repelled by the odor of chickens \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough progress has been made in many countries towards eliminating malaria, there has been a resurgence of malaria cases and related deaths in certain countries in Sub-Saharan Africa (SSA)\u003csup\u003e10\u003c/sup\u003e. While there has been a significant decrease in malaria-associated morbidity and mortality over the past two decades, these issues still pose major public health problems in Ethiopia. Roughly 68% of the population is at risk of malaria, and at least one-third of women suffer from anemia \u003csup\u003e12,13\u003c/sup\u003e. We investigated the relationship between maize production intensity, cattle ownership, and the likelihood of anemia among rural women of reproductive age in Central Ethiopia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy area, design, and participants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used a community-based comparative cross-sectional design, employing a cluster analysis to categorize households based on the intensity of maize production. The survey took place in twenty-four villages located in two districts in Arsi, Oromia, Ethiopia. These villages were selected because they had similar malaria burden and incidence rates, average annual temperature, and agroclimatic conditions, but differed in terms of maize production levels.\u003c/p\u003e \u003cp\u003eThe Arsi Zone is divided into four agro-climatic areas, primarily due to differences in altitude. The main agro-climatic zones are moderately cool (40%) and cool (34%), with the remainder classified as moderately warm (20%) or cold (6%). The main crops grown in this zone include cereals, pulses, and oil seeds. Maize, sorghum, and oats are grown in specific areas, albeit not on a large scale\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample size and Sampling Method\u003c/h2\u003e \u003cp\u003eThe sample size was calculated using Epi-Info version 7 with the following parameters and assumptions: a 95% significance level (2-sided), 80% power, and 24% anemia prevalence among women \u003csup\u003e16\u003c/sup\u003e with an anticipated 10% lower prevalence among women in low-maize-intensity clusters. This calculation yielded 225 participants in each district. The sample size was increased to 450 to account for a 10% nonresponse rate.\u003c/p\u003e \u003cp\u003eA village was designated as high maize intensity (cluster) if more than 50% of the cultivated land was maize and low intensity if less than 10% of the land was covered by maize. This classification was based on agricultural data in the area, information gathered from the district\u0026rsquo;s agricultural officers, and field survey observations conducted by the investigators.\u003c/p\u003e \u003cp\u003eA multistage sampling design was used to select samples. Initially, two comparable districts (similar in malaria burden but differing in agro-ecology or maize production intensity) were chosen in collaboration with the zonal health and agricultural bureaus. The selected districts, each comprising 35 to 43 kebeles (the smallest administrative unit with a population of approximately 5,000 or 1,000 households), were included in the study. On average, each kebele contained 16 to 23 villages (obtained) with 40 to 50 households. Subsequently, 24 villages (12 districts) were chosen by employing a cluster sampling method. From each village, 18\u0026ndash;20 households were selected using systematic random sampling. The study included apparently healthy women aged 15\u0026ndash;49 years who were permanent residents of the selected villages. One woman from each household was also interviewed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and management\u003c/h2\u003e \u003cp\u003eMotivated and experienced health workers proficient in speaking the local language (Afan Oromo) collected data in the field. The lead investigator (TAZ) and other field staff provided three days of intensive training on data collection, respondent handling, and data quality assurance methods. A pre-tested interviewer administered a questionnaire, initially prepared in English, and then translated into the local language \u0026lsquo;Afan Oromo\u0026rsquo;, to gather socio-demographic, knowledge, and malaria-related information.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric measurements\u003c/h2\u003e \u003cp\u003eThe same data collectors took anthropometric measurements twice (duplicate) following standardized procedures recommended by the World Health Organization (WHO). Women were weighed to the nearest 100 g on electronic scales with a weighing capacity of 140 kg. Height was measured to the nearest millimeter with a portable device equipped with calibrated and standardized height gauges (SECA 206 body meter). The mid-upper arm circumference (MUAC) of the left arm was measured to the nearest millimeter using a non-stretch measuring tape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHemoglobin measurements\u003c/h2\u003e \u003cp\u003eA blood sample was collected through a finger prick, and hemoglobin concentration was measured using a portable HemoCue photometer (AB Leo Diagnostics, Helsingborg, Sweden). The hemoglobin readings were adjusted for altitude (Sullivan et al., 2008), and women with values below 12 g/dl were considered anemic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the Data and Quality Assurance\u003c/h2\u003e \u003cp\u003eThe cleaned data were input into SPSS Version 20 and later imported into STATA 14 for statistical analysis. Descriptive analysis (Fisher's exact test) and bivariate analysis (independent-sample t-test) were conducted to explore the associations between different categories. The mean hemoglobin levels between the two groups were compared using an independent sample t-test. Multivariate linear logistic regression analysis was performed to identify independent predictors of mean hemoglobin concentration. To ensure data accuracy, one supervisor (typically the head of the health center) and two senior researchers were responsible for overseeing the data collection. The investigator was present in the field throughout the data collection process to verify the completeness and quality of the collected data. The field supervisors reviewed the completed questionnaires daily to check for consistency and completeness. Additionally, approximately 10% of the households were re-interviewed by supervisors in the lead investigator's presence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the institutional review boards of the College of Natural and Computational Sciences at Addis Ababa University and the Oromia Regional Health Bureau. This study adhered to the principles of the Helsinki Declaration of 1975, as revised in 1983. Verbal informed consent was obtained from eligible participants in the presence of local administrators after detailed explanation of the study's objectives and methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 443 women of reproductive age participated in the study. Of these, 214 (48.3%) were from high-maize-intensity clusters, while 229 (51.7%) were from low-maize-intensity clusters. The response rate was 98.4%. Most participants (71.7%) were under 30 years old, and a significant proportion (40%) had no formal education. Most participants (76.7%) owned less than one hectare of land and had access to clean and safe water sources (87.8%), \u003cstrong\u003eTable 1\u003c/strong\u003e. Among the women, 35.2% were moderately undernourished, while 13.3% were severely undernourished (MUAC \u0026lt;21 cm). On average, 83.3% of women reported owning livestock, with 76.5% from high maize intensity clusters and 89.5% from low maize intensity clusters. The most owned livestock were oxen (62.3%), cows (52.8%), and donkeys (46.3%). Chickens (20.3%) and cows (13.4%) were the most kept animals in the same house, \u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAwareness of malaria appears to be universal, as only a few (2.7%) women reported being unaware of or not having heard of it. Chills (84.4%), fever (54.5%), and headaches (46.6%) were the most common symptoms of malaria. However, headache as a symptom of malaria was reported by more women from high maize intensity clusters (56.8%) than from low maize intensity clusters (36.8%). Convulsions, as a sign of malaria, were the least known symptoms (7.4%) in both clusters,\u003cstrong\u003e\u0026nbsp;Table 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eMosquitoes were identified as a key mode of malaria transmission by 58.8% of the respondents, person-to-person transmission (29.9%), eating maize cane/sugarcane (9.7%), and contaminated food (5.3%). When asked about mosquito breeding sites, stagnant water (72.5%) and household utensils (19.4%) were the most frequently mentioned. Rainy seasons, mainly from September to November, were identified as the peak season for mosquito breeding. Approximately 70% of respondents identified September to November as the peak season for malaria transmission, while 17.9% reported June to August as a high season for malaria transmission \u003cstrong\u003e(Table 3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant differences in mean hemoglobin concentration (13.59 ± 1.39 g/dl versus 13.39 ± 1.65 g/dl; P = 0.19) or anemia prevalence (5.4% versus 9.4%; P = 0.56) between high and low maize intensity clusters \u003cstrong\u003e(Table 4 \u0026amp; Figure 1).\u003c/strong\u003e Women who owned livestock had a significantly higher mean hemoglobin concentration (14.03 ± 1.16 g/dl) compared to those who did not (13.37 ± 1.68 g/dl). Among individual livestock, only chicken ownership showed a statistically significant difference: women with at least one chicken had a significantly higher mean hemoglobin concentration (13.76 ± 1.21 g/dl) compared to those without (13.09 ± 2.01 g/dl). Ownership of other domestic animals did not result in significant differences in mean hemoglobin concentrations between the two groups.\u003c/p\u003e\n\u003cp\u003eLikewise, multivariate logistic regression analysis \u003cstrong\u003e(Table 5)\u003c/strong\u003e indicated that a one-unit increase in livestock and chicken ownership raised hemoglobin concentration by 0.13 g/dl and 0.21 g/dl (β 0.13 \u0026amp; 0.21; 95% CI: 0.01, 1.12 \u0026amp; 0.34, 1.01) respectively. An increase of one unit in women's parity level also raised mean hemoglobin concentration level by 0.67 g/dl (β 0.67; 95% CI: 0.17, 0.61). Conversely, a unit increase in gravidity and the number of previous abortions reduced hemoglobin concentration levels by 0.82 g/dl (β -0.82; 95% CI: -0.65, -0.21) and 0.23 g/dl (β -0.23; 95% CI: 0.31, 1.06) respectively.\u003c/p\u003e\n\u003cp\u003eThe pattern of maize production intensity and malaria experience among women in the high-versus low-maize-producing clusters converged over time. Sixty-two percent of women in high maize intensity clusters had experienced malaria at least once in their lives, which was significantly higher than the prevalence (52.4%) among women in low-maize-intensity clusters. No significant difference in malaria incidence was observed between the two groups in the recent and current periods (Figure 2). The prevalence of anemia in women with lifetime, recent, and current malaria experience was 60.6%, 45.4%, and 24.2%, respectively \u003cstrong\u003e(Figure 3).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEmploying a community-based comparative cross-sectional study design, we examined the associations between maize production intensity, malaria prevalence, and anemia prevalence in a rural resource-limited setting in Ethiopia. The respondents' level of awareness of malaria and its mode of transmission across the two clusters was very high. Although the lifetime malaria experience was significantly higher among women from high maize intensity clusters, the mean hemoglobin concentration and anemia prevalence did not differ significantly between the two clusters. Ownership of at least one livestock in the household in general, and chicken in particular, was significantly associated with a higher mean hemoglobin concentration.\u003c/p\u003e \u003cp\u003eMost women in our study were aware of malaria and had at least one sign or symptom of the disease. This high level of awareness observed in the area is consistent\u003csup\u003e18\u003c/sup\u003e or even higher than many other study sites \u003csup\u003e19\u003c/sup\u003e in Ethiopia and elsewhere\u003csup\u003e20\u003c/sup\u003e. This could be attributed to the successful and efficient implementation of primary healthcare interventions in Ethiopia, including the rural health extension program\u003csup\u003e21\u003c/sup\u003e, which aims to improve the community's awareness of common communicable diseases, including malaria, through door-to-door visits. It could also be that because the study participants lived in a malaria-endemic area, they frequently experienced the symptoms, enough to recognize and associate them with malaria.\u003c/p\u003e \u003cp\u003eInterestingly, although some participants mentioned maize as a potential mosquito breeding site and related maize cane consumption as a potential malaria transmission vehicle, the awareness of both groups regarding maize production intensity and malaria risk was consistent. This finding aligns with previous evidence suggesting that gravid Anopheles arabiensis (female mosquitoes) are attracted by sugarcane pollen volatiles and oviposits in response to maize pollen odors\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe recent and current prevalence of malaria experiences across the two clusters with different maize production intensities were not statistically significant, but the lifetime experience of malaria differed between the two groups. The observed higher lifetime malaria experience in the high-intensity maize production cluster is consistent with previous studies that linked maize production intensity with an elevated risk of malaria\u003csup\u003e23\u003c/sup\u003e. However, the lack of difference in recent and current malaria experiences between the two groups may be linked to intensified and successful national malaria prevention and control activities in recent years\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, our analysis did not find any association between the prevalence of anemia and mean hemoglobin levels, as well as maize production intensity. These factors were not statistically significant across clusters of high versus low maize production intensity (villages). The prevalence of malaria and anemia remained constant during the study period, which supports the lack of association between anemia and mean hemoglobin levels. Since this study is one of the few cross-sectional studies that examine the relationship between maize production intensity, malaria, and anemia, further research using different designs such as case-control or prospective cohorts, as well as other longitudinal designs, with better control for background differences and potential confounders, is necessary to investigate the observed associations and trends in our study.\u003c/p\u003e \u003cp\u003eThe study also revealed that livestock and chicken ownership and domestication were linked to a significantly higher mean hemoglobin concentration. This could be attributed to better socioeconomic status and subsequent consumption. Various studies conducted in Ethiopia and other parts of the world have pointed out that household assets and income levels are associated with the risk of anemia\u003csup\u003e26,27\u003c/sup\u003e, as anemia tends to be more prevalent in low socioeconomic groups. For instance, an investigation from Afghanistan demonstrated that agricultural assets, such as ownership of sheep and chickens, reduce the risk of anemia, partly due to self-production consumption in the context of inadequate market functioning\u003csup\u003e28\u003c/sup\u003e. A prior study from Ethiopia indicated that mosquitoes are deterred by the scent of chickens\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. Although we compared two clusters in different districts based on their maize production intensity using a cross-sectional comparative analysis, it is unlikely that some of the statistically significant differences observed between the two categories are solely due to the classification factor. Additionally, data regarding malaria experiences were obtained from secondary sources (health facilities), and mothers were interviewed based on memory and presumed diagnoses; therefore, such findings may not be as accurate as direct testing and reporting.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study has several strengths. We collected and cross-checked data from both health facilities and communities in resource-limited rural settings, where evidence is scarce. We also addressed the burden of anemia from a new perspective, focusing on agriculture and malaria, which are not widely available in the literature. Furthermore, the sample size in both groups was sufficiently large and the response rate was high.\u003c/p\u003e \u003cp\u003eGenerally, we found that women from clusters with high maize intensity had a higher incidence of malaria compared to those with low intensity. However, there was no significant difference in malaria experience between the two groups. On the other hand, owning at least one livestock animal in the household, especially chickens, was associated with higher mean hemoglobin concentration and a lower prevalence of anemia. To improve the health status of women in low-income settings, it is recommended to focus on promoting women's reproductive healthcare and livestock-based agricultural practices such as livestock production. To support our findings, which contradict the previous hypothesis of no association between maize production and the spread of malaria in similar settings, large-scale studies integrating nutrition, agriculture, and health are needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Innovative Methods and Metrics (IMMANA). We would like to express our gratitude to Professor Mike Faber, Professor William Masters, Dr. Kaleba Baye, and Dr. Yohanes Siyoum for their support and supervision throughout the project. We also appreciate Tufts University for their management of funds and supervision. Furthermore, special thanks go to the Global Academy of Agriculture and Food Security at the University of Edinburgh (UoE) and the International Food Policy Research Institute (IFPRI). The principal investigator (TAZ) dedicated part of his paid time to analyzing the data, as well as writing and revising the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFinancial Support:\u003c/strong\u003e The study was funded by Innovative Methods and Metrics for Agriculture and Nutrition Actions (IMMANA Fellowship)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions to manuscript:\u0026nbsp;\u003c/strong\u003eConceptualized and designed research: T.A.Z, AA, AB; Conducted data collection, data analysis and interpretation and drafted the manuscript: T.A.Z., WK; Revised the manuscript and supervised all the work: AA, AB, and WK. Supported acquisition of the financial support. TAZ. All authors have read and approved the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived conflicts of interest:\u0026nbsp;\u003c/strong\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ current addresses:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaddese Zerfu: International Food Policy Research Institute (IFPRI), P.O.Box. 5986, Addis Ababa, Ethiopia\u003c/p\u003e\n\u003cp\u003eAmare Abera: Wollo University, Department of Biomedical Sciences, Dessie, Ethiopia\u003c/p\u003e\n\u003cp\u003eAbera Belay: Addis Ababa Science and Technology University,\u0026nbsp;Department of Food Science and Applied Nutrition and center of excellence for Bioprocessing and Biotechnology; Addis Ababa, Ethiopia\u003c/p\u003e\n\u003cp\u003eWegderes Ketema: Dire Dawa city administration, Dire Dawa, Ethiopia\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization. WHO guidelines for malaria. Published online 2023.\u003c/li\u003e\n \u003cli\u003eOkoyo C, Githinji E, Muia RW, et al. Assessment of malaria infection among pregnant women and children below five years of age attending rural health facilities of Kenya: A cross-sectional survey in two counties of Kenya. \u003cem\u003ePLoS One\u003c/em\u003e. 2021;16(9):e0257276. doi:10.1371/journal.pone.0257276\u003c/li\u003e\n \u003cli\u003eMohandas N, An X. Malaria and human red blood cells. \u003cem\u003eMed Microbiol Immunol\u003c/em\u003e. 2012;201(4):593-598. doi:10.1007/s00430-012-0272-z\u003c/li\u003e\n \u003cli\u003eIjumba JN, Lindsay SW. Impact of irrigation on malaria in Africa: paddies paradox. \u003cem\u003eMedical Vet Entomology\u003c/em\u003e. 2001;15(1):1-11. doi:10.1046/j.1365-2915.2001.00279.x\u003c/li\u003e\n \u003cli\u003eJaleta KT, Hill SR, Seyoum E, et al. Agro-ecosystems impact malaria prevalence: large-scale irrigation drives vector population in western Ethiopia. \u003cem\u003eMalar J\u003c/em\u003e. 2013;12(1):350. doi:10.1186/1475-2875-12-350\u003c/li\u003e\n \u003cli\u003eStresman GH. Beyond temperature and precipitation: Ecological risk factors that modify malaria transmission. \u003cem\u003eActa Tropica\u003c/em\u003e. 2010;116(3):167-172. doi:10.1016/j.actatropica.2010.08.005\u003c/li\u003e\n \u003cli\u003eYe-ebiyo Y, Pollack RJ, Spielman A. Enhanced development in nature of larval Anopheles arabiensis mosquitoes feeding on maize pollen. \u003cem\u003eAmerican Journal of Tropical Medicine and Hygiene\u003c/em\u003e. 2000;63(1-2):90-93. doi:10.4269/AJTMH.2000.63.90\u003c/li\u003e\n \u003cli\u003eDahl A, Gal\u0026aacute;n C, Hajkova L, et al. The onset, course and intensity of the pollen season. In: \u003cem\u003eAllergenic Pollen: A Review of the Production, Release, Distribution and Health Impacts\u003c/em\u003e. Vol 9789400748. ; 2013:29-70. doi:10.1007/978-94-007-4881-1_3\u003c/li\u003e\n \u003cli\u003eJaleta KT, Hill SR, Birgersson G, Tekie H, Ignell R. Chicken volatiles repel host-seeking malaria mosquitoes. \u003cem\u003eMalaria Journal\u003c/em\u003e. 2016;15(1). doi:10.1186/s12936-016-1386-3\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. \u003cem\u003eWorld Malaria Report 2016\u003c/em\u003e.; 2016. doi:10.1071/EC12504\u003c/li\u003e\n \u003cli\u003eUN DESA. World Population Expected to Reach 9.7 Billion by 2050. United Nations Department of Economic and Social Affairs.\u003c/li\u003e\n \u003cli\u003eGebremedhin S, Enquselassie F. Correlates of anemia among women of reproductive age in Ethiopia: Evidence from Ethiopian DHS 2005. \u003cem\u003eEthiopian Journal of Health Development\u003c/em\u003e. 2011;25(1):22-30. doi:10.4314/ejhd.v25i1.69842\u003c/li\u003e\n \u003cli\u003eEDHS 2016 Team. Ethiopian Demographic and Health Survey. \u003cem\u003eReport\u003c/em\u003e. Published online 2016.\u003c/li\u003e\n \u003cli\u003eCSA. Population Projection of Ethiopia for All Regions At Wereda Level from 2014 \u0026ndash; 2017. \u003cem\u003eJournal of Ethnobiology and Ethnomedicine\u003c/em\u003e. 2013;3(1):28. doi:10.1186/1746-4269-3-28\u003c/li\u003e\n \u003cli\u003eEtefa OF, Forsido SF, Kebede MT. Postharvest Loss, Causes, and Handling Practices of Fruits and Vegetables in Ethiopia: Scoping Review. \u003cem\u003eJournal of Horticultural Research\u003c/em\u003e. 2022;30(1):1-10. doi:10.2478/johr-2022-0002\u003c/li\u003e\n \u003cli\u003eZerfu TA, Baye K, Faber M. Dietary diversity cutoff values predicting anemia varied between mid and term of pregnancy: a prospective cohort study. \u003cem\u003eJ Health Popul Nutr\u003c/em\u003e. 2019;38(1):44. doi:10.1186/s41043-019-0196-y\u003c/li\u003e\n \u003cli\u003eTang AM, Dong K, Deitchler M, Chung M, Maalouf-Manasseh Z, Tumilowicz A WC. \u003cem\u003eUse of Cutoffs for Mid-Upper Arm Circumference ( MUAC ) as an Indicator or Predictor of Nutritional and Health- Related Outcomes in Adolescents and Adults : A Systematic Review\u003c/em\u003e.; 2013.\u003c/li\u003e\n \u003cli\u003eSixpence A, Nkoka O, Chirwa GC, et al. Levels of knowledge regarding malaria causes, symptoms, and prevention measures among Malawian women of reproductive age. \u003cem\u003eMalar J\u003c/em\u003e. 2020;19(1):225. doi:10.1186/s12936-020-03294-6\u003c/li\u003e\n \u003cli\u003eBirhanu Z, Yihdego YY ebiyo, Yewhalaw D. Caretakers\u0026rsquo; understanding of malaria, use of insecticide treated net and care seeking-behavior for febrile illness of their children in Ethiopia. \u003cem\u003eBMC Infectious Diseases\u003c/em\u003e. 2017;17(1). doi:10.1186/s12879-017-2731-z\u003c/li\u003e\n \u003cli\u003eSingh R, Godson II, Singh S, Singh RB, Isyaku NT, Ebere UV. High prevalence of asymptomatic malaria in apparently healthy schoolchildren in Aliero, Kebbi state, Nigeria. \u003cem\u003eJ Vector Borne Dis\u003c/em\u003e. 2014;51(2):128-132.\u003c/li\u003e\n \u003cli\u003eKefyalew T, Kebede Z, Getachew D, et al. Health worker and policy-maker perspectives on use of intramuscular artesunate for pre-referral and definitive treatment of severe malaria at health posts in Ethiopia. \u003cem\u003eMalar J\u003c/em\u003e. 2016;15(1):507. doi:10.1186/s12936-016-1561-6\u003c/li\u003e\n \u003cli\u003eWondwosen B, Birgersson G, Tekie H, Torto B, Ignell R, Hill SR. Sweet attraction: sugarcane pollen-associated volatiles attract gravid Anopheles arabiensis. \u003cem\u003eMalar J\u003c/em\u003e. 2018;17(1):90. doi:10.1186/s12936-018-2245-1\u003c/li\u003e\n \u003cli\u003eKebede A, McCann JC, Kiszewski AE, Ye-Ebiyo Y. New evidence of the effects of agro-ecologic change on malaria transmission. \u003cem\u003eAm J Trop Med Hyg\u003c/em\u003e. 2005;73(4):676-680.\u003c/li\u003e\n \u003cli\u003eEthiopian public health institute. \u003cem\u003eEthiopian National Malaria Indicators Survey\u003c/em\u003e.; 2016.\u003c/li\u003e\n \u003cli\u003eSeleshe S, Jo C, Lee M. Meat consumption culture in Ethiopia. \u003cem\u003eKorean Journal for Food Science of Animal Resources\u003c/em\u003e. 2014;34(1):7-13. doi:10.5851/kosfa.2013.34.1.7\u003c/li\u003e\n \u003cli\u003eMbule MA, Byaruhanga YB, Kabahenda M, Lubowa A. Determinants of anaemia among pregnant women in rural Uganda. \u003cem\u003eRural and remote health\u003c/em\u003e. 2013;13(2):2259.\u003c/li\u003e\n \u003cli\u003eGari T, Loha E, Deressa W, et al. Anaemia among children in a drought affected community in south-central Ethiopia. \u003cem\u003ePLoS ONE\u003c/em\u003e. 2017;12(3). doi:10.1371/journal.pone.0170898\u003c/li\u003e\n \u003cli\u003eFlores-Martinez A, Zanello G, Shankar B, Poole N. Reducing anemia prevalence in Afghanistan: Socioeconomic correlates and the particular role of agricultural assets. \u003cem\u003ePLoS ONE\u003c/em\u003e. 2016;11(6). doi:10.1371/journal.pone.0156878\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Socio-demographic and anthropometric characteristics of women in high and low/no maize producing villages in rural Arsi, Central Ethiopia.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh maize density area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow/no maize producing area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eTotal households, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e214 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e229 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e443 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003cp\u003e15 -29\u003c/p\u003e\n \u003cp\u003e30 -44\u003c/p\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e166 (72.5)\u003c/p\u003e\n \u003cp\u003e58 (25.3)\u003c/p\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e151 (70.9)\u003c/p\u003e\n \u003cp\u003e52 (24.4)\u003c/p\u003e\n \u003cp\u003e10 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e318 (71.7)\u003c/p\u003e\n \u003cp\u003e110 (24.9)\u003c/p\u003e\n \u003cp\u003e15 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eEducational Status\u003c/p\u003e\n \u003cp\u003eUnable to read \u0026amp; write.\u003c/p\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003cp\u003eTertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (42.1)\u003c/p\u003e\n \u003cp\u003e94 (43.9)\u003c/p\u003e\n \u003cp\u003e28 (13.1)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82 (35.8)\u003c/p\u003e\n \u003cp\u003e119 (52)\u003c/p\u003e\n \u003cp\u003e26 (11.4)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e172 (38.8)\u003c/p\u003e\n \u003cp\u003e213 (49.1)\u003c/p\u003e\n \u003cp\u003e54 (12.2)\u003c/p\u003e\n \u003cp\u003e4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eEthnic background\u003c/p\u003e\n \u003cp\u003eOromo\u003c/p\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003cp\u003eGuraghe\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e167 (78.0)\u003c/p\u003e\n \u003cp\u003e31 (14.5)\u003c/p\u003e\n \u003cp\u003e9 (4.2)\u003c/p\u003e\n \u003cp\u003e7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e215 (93.9)\u003c/p\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003cp\u003e7 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e382 (86.2)\u003c/p\u003e\n \u003cp\u003e36 (8.1)\u003c/p\u003e\n \u003cp\u003e11 (2.5)\u003c/p\u003e\n \u003cp\u003e14 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003cp\u003e\u0026lt;145\u003c/p\u003e\n \u003cp\u003e145 - 150\u003c/p\u003e\n \u003cp\u003e\u0026gt;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (2.0)\u003c/p\u003e\n \u003cp\u003e18 (7.9)\u003c/p\u003e\n \u003cp\u003e202 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (2.0)\u003c/p\u003e\n \u003cp\u003e18 (7.9)\u003c/p\u003e\n \u003cp\u003e205 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (1.8)\u003c/p\u003e\n \u003cp\u003e28 (6.3)\u003c/p\u003e\n \u003cp\u003e407 (91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eMUAC (cm)\u003c/p\u003e\n \u003cp\u003e\u0026lt; 21\u003c/p\u003e\n \u003cp\u003e21 -23\u003c/p\u003e\n \u003cp\u003e\u0026gt; 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (11.7)\u003c/p\u003e\n \u003cp\u003e65 (30.4)\u003c/p\u003e\n \u003cp\u003e124 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34 (14.8)\u003c/p\u003e\n \u003cp\u003e91 (39.7)\u003c/p\u003e\n \u003cp\u003e104 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59 (13.3)\u003c/p\u003e\n \u003cp\u003e156 (35.2)\u003c/p\u003e\n \u003cp\u003e228 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eLand size (Hectares)\u003c/p\u003e\n \u003cp\u003e\u0026lt; 1\u003c/p\u003e\n \u003cp\u003e1 - 2\u003c/p\u003e\n \u003cp\u003e\u0026gt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e157 (75.5)\u003c/p\u003e\n \u003cp\u003e43 (20.7)\u003c/p\u003e\n \u003cp\u003e8 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e178 (77.7)\u003c/p\u003e\n \u003cp\u003e41 (17.9)\u003c/p\u003e\n \u003cp\u003e10 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e335 (76.7)\u003c/p\u003e\n \u003cp\u003e84 (19.2)\u003c/p\u003e\n \u003cp\u003e18 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.53153153153153%\" valign=\"top\"\u003e\n \u003cp\u003eWater source⃰\u003c/p\u003e\n \u003cp\u003eSafe\u003c/p\u003e\n \u003cp\u003eUnsafe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e157 (86.0)\u003c/p\u003e\n \u003cp\u003e30 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.027027027027028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e205 (89.5)\u003c/p\u003e\n \u003cp\u003e24 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.117117117117118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e389 (87.8)\u003c/p\u003e\n \u003cp\u003e54 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e⃰ \u003cem\u003eSafe water sources include bottled water, pipe water and any treated (protected water); unsafe water sources include water from river, unprotected spring or any other unprotected or untreated water sources\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Livestock ownership and domestication characteristics of women among high versus low/no maize producing villages in rural Arsi, Central Ethiopia\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"685\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.03649635036496%\" valign=\"top\"\u003e\n \u003cp\u003eLivestock \u003cstrong\u003eownership and domestication status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh maize density area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow/no maize producing area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16058394160584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.03649635036496%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOwnership of Domestic animals\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHave at least one (any)\u003c/p\u003e\n \u003cp\u003eCow\u003c/p\u003e\n \u003cp\u003eOx (Oxen)\u003c/p\u003e\n \u003cp\u003eSheep\u003c/p\u003e\n \u003cp\u003eGoat\u003c/p\u003e\n \u003cp\u003eChicken\u003c/p\u003e\n \u003cp\u003eDonkey\u003c/p\u003e\n \u003cp\u003eHorse\u003c/p\u003e\n \u003cp\u003eDog\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e163 (76.5)\u003c/p\u003e\n \u003cp\u003e109 (51.9)\u003c/p\u003e\n \u003cp\u003e120 (56.1)\u003c/p\u003e\n \u003cp\u003e25 (11.7)\u003c/p\u003e\n \u003cp\u003e50 (23.4)\u003c/p\u003e\n \u003cp\u003e63 (29.4)\u003c/p\u003e\n \u003cp\u003e73 (34.1)\u003c/p\u003e\n \u003cp\u003e4 (1.9)\u003c/p\u003e\n \u003cp\u003e20 (9.3)\u003c/p\u003e\n \u003cp\u003e6 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e205 (89.5)\u003c/p\u003e\n \u003cp\u003e123 (53.7)\u003c/p\u003e\n \u003cp\u003e156 (68.1)\u003c/p\u003e\n \u003cp\u003e34 (14.8)\u003c/p\u003e\n \u003cp\u003e87 (38)\u003c/p\u003e\n \u003cp\u003e117 (51.1)\u003c/p\u003e\n \u003cp\u003e132 (42.6)\u003c/p\u003e\n \u003cp\u003e4 (1.7)\u003c/p\u003e\n \u003cp\u003e19 (8.3)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16058394160584%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e368 (83.3)\u003c/p\u003e\n \u003cp\u003e232 (52.8)\u003c/p\u003e\n \u003cp\u003e276 (62.3)\u003c/p\u003e\n \u003cp\u003e59 (13.3)\u003c/p\u003e\n \u003cp\u003e137 (13.3)\u003c/p\u003e\n \u003cp\u003e180 (40.6)\u003c/p\u003e\n \u003cp\u003e205 (46.3)\u003c/p\u003e\n \u003cp\u003e8 (1.8)\u003c/p\u003e\n \u003cp\u003e39 (8.8)\u003c/p\u003e\n \u003cp\u003e6 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.03649635036496%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomestication of animals\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCow or ox\u003c/p\u003e\n \u003cp\u003eSheep (goat)\u003c/p\u003e\n \u003cp\u003eChicken\u003c/p\u003e\n \u003cp\u003eDonkey (Horse)\u003c/p\u003e\n \u003cp\u003eCalf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (12.1)\u003c/p\u003e\n \u003cp\u003e10 (4.7)\u003c/p\u003e\n \u003cp\u003e26 (12.1)\u003c/p\u003e\n \u003cp\u003e12 (5.6)\u003c/p\u003e\n \u003cp\u003e15 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.401459854014597%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32 (14.0)\u003c/p\u003e\n \u003cp\u003e13 (5.7)\u003c/p\u003e\n \u003cp\u003e64 (27.9)\u003c/p\u003e\n \u003cp\u003e31 (13.5)\u003c/p\u003e\n \u003cp\u003e20 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16058394160584%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 (13.1)\u003c/p\u003e\n \u003cp\u003e23 (5.2)\u003c/p\u003e\n \u003cp\u003e90 (20.3)\u003c/p\u003e\n \u003cp\u003e43 (9.7)\u003c/p\u003e\n \u003cp\u003e35 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDomestication of animals means that the animals sleep with humans in the same house\u003cbr\u003e\u0026nbsp;Table 3. Knowledge about malaria, malaria transmission and prevention of women among high versus low/no maize producing villages in rural Arsi, Central Ethiopia\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKnowledge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e (\u0026gt; 50%) \u003cstrong\u003emaize intensity area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e (\u0026lt; 10%) \u003cstrong\u003emaize intensity area, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003eHeard about Malaria\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e211 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e220 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e431 (97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003eKnows signs of malaria\u003c/p\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003cp\u003eLoss of Appetite\u003c/p\u003e\n \u003cp\u003eJoint pain\u003c/p\u003e\n \u003cp\u003eConvulsion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e122 (57.8)\u003c/p\u003e\n \u003cp\u003e120 (56.8)\u003c/p\u003e\n \u003cp\u003e171 (81)\u003c/p\u003e\n \u003cp\u003e49 (23.2)\u003c/p\u003e\n \u003cp\u003e38 (18)\u003c/p\u003e\n \u003cp\u003e13 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e113 (51.3)\u003c/p\u003e\n \u003cp\u003e81 (36.8)\u003c/p\u003e\n \u003cp\u003e193 (84.3)\u003c/p\u003e\n \u003cp\u003e58 (26.4)\u003c/p\u003e\n \u003cp\u003e47 (21.3)\u003c/p\u003e\n \u003cp\u003e19 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e235 (54.5)\u003c/p\u003e\n \u003cp\u003e201 (46.6)\u003c/p\u003e\n \u003cp\u003e364 (84.4)\u003c/p\u003e\n \u003cp\u003e107 (24.8)\u003c/p\u003e\n \u003cp\u003e85 (19.7)\u003c/p\u003e\n \u003cp\u003e32 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003eKnown mode of transmission\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mosquito bite\u003c/p\u003e\n \u003cp\u003ePerson to person\u003c/p\u003e\n \u003cp\u003eEating contaminated food\u003c/p\u003e\n \u003cp\u003eEating sugar cane\u003c/p\u003e\n \u003cp\u003eEating maize cane\u003c/p\u003e\n \u003cp\u003eOther causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125 (59.2)\u003c/p\u003e\n \u003cp\u003e75 (35.5)\u003c/p\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003cp\u003e5 (2.4)\u003c/p\u003e\n \u003cp\u003e12 (5.6)\u003c/p\u003e\n \u003cp\u003e19 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e127 (57.7)\u003c/p\u003e\n \u003cp\u003e54 (24.5)\u003c/p\u003e\n \u003cp\u003e11 (5)\u003c/p\u003e\n \u003cp\u003e7 (3.2)\u003c/p\u003e\n \u003cp\u003e12 (5.5)\u003c/p\u003e\n \u003cp\u003e11 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e252 (58.5)\u003c/p\u003e\n \u003cp\u003e129 (29.9)\u003c/p\u003e\n \u003cp\u003e23 (5.3)\u003c/p\u003e\n \u003cp\u003e12 (2.8)\u003c/p\u003e\n \u003cp\u003e30 (6.9)\u003c/p\u003e\n \u003cp\u003e30 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003eKnowledge of mosquito breeding site\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStagnant water\u003c/p\u003e\n \u003cp\u003eRunning water\u003c/p\u003e\n \u003cp\u003eMaize and/or maize pollen\u003c/p\u003e\n \u003cp\u003eHousehold utensils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e162 (75.5)\u003c/p\u003e\n \u003cp\u003e4 (1.9)\u003c/p\u003e\n \u003cp\u003e10 (4.7)\u003c/p\u003e\n \u003cp\u003e43 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e159 (69.4)\u003c/p\u003e\n \u003cp\u003e4 (1.7)\u003c/p\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003cp\u003e43 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e321 (72.5)\u003c/p\u003e\n \u003cp\u003e8 (1.18)\u003c/p\u003e\n \u003cp\u003e15 (3.4)\u003c/p\u003e\n \u003cp\u003e86 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.62237762237762%\" valign=\"top\"\u003e\n \u003cp\u003eKnowledge on mosquito breeding season\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRainy season (Jun - August)\u003c/p\u003e\n \u003cp\u003eAutumn (Sept - November)\u003c/p\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.174825174825173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40 (18.7)\u003c/p\u003e\n \u003cp\u003e148 (69.2)\u003c/p\u003e\n \u003cp\u003e26 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (16.6)\u003c/p\u003e\n \u003cp\u003e162 (70.7)\u003c/p\u003e\n \u003cp\u003e24 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.587412587412587%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78 (17.9)\u003c/p\u003e\n \u003cp\u003e310 (70)\u003c/p\u003e\n \u003cp\u003e50 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Mean hemoglobin concentration according to maize production intensity and Livestock ownership of women among in rural Arsi, Central Ethiopia\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eVariable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin level\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003csup\u003e⃰\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"37\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"37\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eMaize production intensity\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eLow/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e214 (49.4)\u003c/p\u003e\n \u003cp\u003e229 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.59 \u0026plusmn; 1.39\u003c/p\u003e\n \u003cp\u003e13.39 \u0026plusmn; 1.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"65\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eOwns livestock (at least one)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e368 (84.9)\u003c/p\u003e\n \u003cp\u003e74 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.03 \u0026plusmn; 1.16\u003c/p\u003e\n \u003cp\u003e13.37 \u0026plusmn; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eCow\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e253 (53.6)\u003c/p\u003e\n \u003cp\u003e180 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.62 \u0026plusmn; 1.73\u003c/p\u003e\n \u003cp\u003e13.27 \u0026plusmn; 1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eOx (Oxen)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e276 (63.7)\u003c/p\u003e\n \u003cp\u003e156 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.47 \u0026plusmn; 1.65\u003c/p\u003e\n \u003cp\u003e13.51 \u0026plusmn; 1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eChicken\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180 (41.6)\u003c/p\u003e\n \u003cp\u003e255 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.76 \u0026plusmn; 1.21\u003c/p\u003e\n \u003cp\u003e13.09 \u0026plusmn; 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eSheep/goat\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.51 \u0026plusmn; 1.94\u003c/p\u003e\n \u003cp\u003e13.48 \u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.45454545454545%\" valign=\"top\"\u003e\n \u003cp\u003eDonkey/Horse\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.575757575757576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.78787878787879%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.34 \u0026plusmn; 1.80\u003c/p\u003e\n \u003cp\u003e13.61 \u0026plusmn; 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e⃰ \u003cem\u003eIndependent sample t-test\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Linear regression analysis of the effect of maize production intensity, livestock ownership and women\u0026rsquo;s reproductive characteristics of on mean hemoglobin concentration, rural Arsi, Central Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anemia, hemoglobin, maize, livestock keeping, women","lastPublishedDoi":"10.21203/rs.3.rs-4105146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4105146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It is unclear whether common agricultural practices, such as livestock farming and maize production, affect the burden of malaria and subsequent anemia status among reproductive-age women in a low-income setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A community-based cross-sectional study was conducted among women of reproductive age, comparing areas with high versus low maize production intensity in rural central Ethiopia. The study areas were categorized as high (\u0026gt; 50%) or low (≤ 10%) maize production clusters based on the percentage of cultivated land. Data were collected from 450 randomly selected households (250 from each cluster). Descriptive and bivariate statistics were used to outline the participants' profiles and the association of variables. Multivariate linear regression was applied to identify determinants of mean hemoglobin concentration levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mean (± SD) hemoglobin concentration remained statistically consistent across high 13.59 (± 1.39 g/dl) and low 13.39 (± 1.65 g/dl) maize-intensity clusters (P \u0026gt; 0.05). Women's ownership of livestock (β, 0.13; 95% CI: 0.01, 1.12), chicken production (β, 0.21; 95% CI: 0.34, 1.01), and women's parity (β, 0.67; 95% CI: 0.17, 0.61) significantly increased hemoglobin concentration levels (P \u0026lt; 0.05). Conversely, gravidity (β, -0.82; 95% CI: -0.65, -0.21) and the frequency of abortions (β, -0.23; 95% CI: 0.31, 1.06) significantly decreased hemoglobin concentration levels (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Regardless of maize production intensity, women's reproductive characteristics and livestock farming (especially chicken production) were identified as independent predictors of hemoglobin levels. Therefore, promoting women's reproductive health care and livestock-based agricultural practices could enhance the health status of agrarian women in low-income settings.\u003c/p\u003e","manuscriptTitle":"Livestock ownership and reproductive characteristics, but not maize production, are associated with anemia in women in malaria-endemic low-income setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 16:15:34","doi":"10.21203/rs.3.rs-4105146/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"963fd96e-9282-4034-b2ee-70d0525fc4cc","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-20T15:39:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 16:15:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4105146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4105146","identity":"rs-4105146","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}