Seasonal prevalence of dietary diversity and its associated factors among households in West Gojjam Zone, Ethiopia: Longitudinal cross-sectional study

Seasonal prevalence of dietary diversity and its associated factors among households in West Gojjam Zone, Ethiopia: Longitudinal cross-sectional study | 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 Seasonal prevalence of dietary diversity and its associated factors among households in West Gojjam Zone, Ethiopia: Longitudinal cross-sectional study Melesse Belayneh Yayeh, Memme Girly Makua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6907682/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 Seasonal variations significantly influence household dietary diversity, particularly in developing countries such as Ethiopia, where evidence remains limited. Therefore, this study offers valuable information for policymakers, program directors, and stakeholders to better plan interventions on dietary diversity within the context. Objective This study aimed to assess the seasonal prevalence of dietary diversity and to identify its associated factors among households in the West Gojjam Zone, northwest Ethiopia. Methods A longitudinal study using cross-sectional surveys was conducted from December 1, 2023, to September 30, 2024. A total of 3,360 household heads were selected through a multistage stratified sampling technique. Data were collected during the seasonal transition months, December, March, June and September, using questionnaires administered by the interviewees. Data entry and cleaning were performed using EPI Data version 4.3, and SPSS version 29 statistical analyzes, including multivariate logistic regression to identify factors associated with dietary diversity. Results The seasonal prevalence of inadequate dietary diversity among households was 55.5% in December, increased to 60.5% in March, and reached 73.5% in June. September had the lowest rate of inadequate diversity at 40.5%. The key factors positively associated with inadequate dietary diversity were surveyed in June (AOR = 3.40; 95% CI 2.54–4.52) and March (AOR = 1.13; 95% CI: 1.09–3.16), female-headed households (AOR = 2.01; 95% CI: 1.38–2.35), Orthodox (AOR = 2.01; 95% CI: 1.38–2.35) and followers of Muslim religion (AOR = 1.80; 95% CI: 1.17–3.42), employed households (AOR = 0.39; 95% CI: 0.20–0.62), category of poorest wealth (AOR = 1.65; 95% CI: 1.29–2.09), not using agricultural input (AOR = 1.28; 95% CI: 1.07–1.53) and poor knowledge on dietary diversity (AOR = 1.35; 95% CI: 1.14–2.07). Conclusions This study highlights notable seasonal variations in the diversity of household foods, with the highest inadequacy in June and the lowest in September. Key risk factors included female-headed households, Orthodox and Muslim religious affiliation, low wealth status, not using agricultural input, and poor knowledge of dietary diversity. Employment was found to be protective. Developing strategies that address economic disparities, cultural influences, and seasonal food group availability are essential to improve dietary diversity between households. Dietary diversity households Seasonal prevalence Ethiopia Figures Figure 1 Figure 2 Figure 3 Introduction Dietary diversity refers to the number of diverse food groups consumed in a specific time period and is a key indicator of overall dietary adequacy [ 1 ]. It reflects nutrient intake and dietary quality, serves as a proxy indicator of socioeconomic status [ 2 ]. Dietary diversity is considered high when all food groups are consumed. However, if fewer than three food groups are consumed per day or fewer than five per week, the dietary diversity is classified as low. On the contrary, the consumption of four to five food groups per day or six to eight per week indicates medium dietary diversity [ 3 ]. Nutritionally adequate dietary diversity is defined as the consumption of six or more food groups daily or nine or more food groups weekly [ 4 ]. In low-income rural areas of sub-Saharan Africa (SSA), where most of the population still relies on small-scale agricultural production fed by rain as a principal source of livelihood [ 5 , 6 ], poor diet quality remains an intractable challenge. Households in these settings are especially vulnerable to seasonal changes in dietary diversity due to fluctuations in food availability and accessibility [ 7 ]. Among the previous studies that have examined seasonal differences in dietary diversity, some have relied on data from only two time periods (ie, post-harvest vs. lean season, or beginning of lean season vs. end of lean season [ 8 ], while others have used data from more than two time points[ 9 ]. With seasonal fluctuations in agricultural production, populations that rely heavily on agricultural productions with little access to animal-source foods are more likely to experience seasonal food shortages [ 10 ]. Household dietary diversity is a relevant problem in the pre-harvest season, where most households consume four or fewer food groups in these seasons [ 11 ]. Households in developing countries, including Ethiopia, typically face a seasonal food shortage for the period between cereal stock depletion and the next harvest, particularly in areas where people depend on the annual harvest of the staple crop following a single rainy season [ 12 , 13 ]. Studies have shown that seasonality has an effect on dietary patterns, and on both energy and nutrient intakes [ 13 , 14 ] For example, previous research in Nepal and Ethiopia demonstrated the existence of seasonal variation from 36.5% to 78.5 % in, dietary diverity, and food consumption [ 7 , 15 , 16 ] and in Ethiopia, several cross-sectional studies have reported inadequate household dietary diversity ranging from 65.7–83.3% [ 17 – 19 ]. In Ethiopia, the availability and access of household food groups is strongly affected by seasonality; many rural households are only able to produce enough food to meet their needs for six months of the year or fewer, and consequently face severe food scarcities during the lean (pre- harvest) season [ 16 , 20 , 21 ] and our previously finding indicated that child under nutrition and household food insecurity strongly influenced by seasonal variations [ 16 , 21 ]. Among the food groups in each season on average, cereals 97% followed by legumes 90%, while animal source foods such as meat, eggs, and fish were less frequently consumed [ 22 ]. Similarly, another studied found that cereals were the most dominant food group consumed by 96.4% of households followed by pulses, which was consumed by 82% of households. Nutrition-dense food commodities, such as lean meat, vegetables and fruits, were the least consumed food groups by households in Ethiopia. [ 2 , 23 ] Several factors are associated with the diversity of the diet of the household. These include sociodemographic characteristics such as age, sex [ 17 ], residence [ 24 ], marital status [ 2 ], education [ 2 , 24 ] and occupation, as well as socioeconomic status indicators like wealth index [ 2 ], savings accounts [ 23 ], land size, and food security [ 16 ]. Agricultural-related factors, including the use of agricultural inputs [ 25 ], and the number of livestock owned [ 23 ], also play a significant role. In addition, knowledge and attitudes [ 24 ] toward dietary diversity, along with seasonal variations [ 13 , 16 , 21 ], significantly influence the level of dietary diversity within households. Although the cross-sectional prevalence of household dietary diversity is well documented, [ 2 , 17 , 23 , 26 ]. There is little evidence exists regarding the seasonality of household dietary diversity and its associated factors. Therefore, the evidence remains scant and concerns about an increased incidence of seasonal prevalence of dietary diversity among households. The present study seeks to generate evidence on seasonal variations in household dietary diversity using longitudinal data collected through four survey rounds conducted at different periods within the local agricultural calendar. Specifically, its objective is to assess the seasonal prevalence of household dietary diversity and its associated factors in the West Gojjam Zone, northwest Ethiopia. The findings will be valuable for policy planners, program directors, and other stakeholders in planning, selecting, and designing context-specific strategies to reduce inadequate dietary diversity between households. Methods Study Setting and Period The study was carried out in the West Gojjam Zone, located in northwest Ethiopia, approximately 387 km from Addis Ababa and 185 km from Bahir Dar, the capital of the Amhara region. Administratively, the zone comprises 12 districts and 6 town administrations. It is served by 110 health centers, four primary hospitals, and one referral hospital. The projected total population of the zone is 2,124,626, including 472, 139 households, and 643,826 children under the age of five years old. This study specifically focused on three districts: Jabitehnane: Population of 246,376 with 57,297 households, Bure Zuria: Population of 141,248 with 32,849 households and Degadamot: Population of 192,127 with 44,681 households. Data Collection Period Data collection was carried out in four separate rounds, each timed to reflect the seasonal stages of the local agricultural cycle. Data were collected during transition periods at the end of one season and the beginning of the next. Data were collected from December 1 to 30, 2023, March 1 to 26, 2024, June 1 to 23, 2024 and September 1 to 20, 2024. Study Design A longitudinal cross-sectional study design was used to assess seasonal variations in household dietary diversity and its associated factors. Study Population The target population consisted of heads of households residing in the selected kebeles (the smallest administrative in the district of Ethiopia). These individuals were the main respondents in assessing seasonal changes in household dietary diversity and determining the underlying contributing factors. Inclusion Criteria Household heads who had been living in the selected kebeles for more than one year were included in the study. Exclusion Criteria Household heads who were critically ill or had diagnosed mental disorders were excluded from the study. Additionally, households that had celebrated a festive event within the 24 hours prior to data collection were also excluded. Sample size determination The sample size to evaluate household dietary diversity was calculated using a single population proportion formula n = \(\:\frac{{\left(\frac{Z}{2}\right)}^{2}*PQ}{{d}^{2}}\) = \(\:\frac{{\left(\frac{Z}{2}\right)}^{2}*P(1-p)}{{d}^{2}}\) , n = Sample size; P = prevalence of household food dietary diversity of 46.3% in the study area [ 27 ]; Z = standard normal distribution value for the 95% confidence interval (1.96); d = margin error, set at 5%. The sample size was calculated as 382 households and to account for potential non-response, a 10% adjustment (38 households) was added, bringing the sample size to 420. To improve representativeness and account for the multistage sampling design, a design effect of 2 was applied, yielding an adjusted sample size of 840 households per round. Since the data were collected in four separate rounds to capture seasonal variations, the total sample size in all rounds was: 840×4 = 3,360 households. Sampling Techniques A multistage sampling method was used to select study participants. In the first stage, three districts were purposively selected from different agroecosystems: Jabitehnane (including Jiga, Mankusa, and Woynima) represents the lowlands; Bure Zuria (including Wundegi, Shakwa, and TiyaTiya) represents the midlands; and Degadamot (including Shangie, Dekulkana, and Shewa) represents the hilly and mountainous highlands. In the second stage, nine kebeles were randomly selected using a simple random sampling technique. A comprehensive list of all households within each selected kebele was compiled. In the final stage, eligible households from selected kebeles were chosen through systematic random sampling. The household lists maintained by the health extension workers served as the sampling framework, ensuring that women who met the inclusion criteria were accurately identified and enrolled (Fig. 1 (a)). Data Collection Instrument Data were collected using structured and pretested questionnaires, covering information on sociodemographic, socioeconomic, agricultural, knowledge-related, and seasonal factors. The primary outcome variable, the dietary diversity of the household, was measured using a dichotomous indicator developed by the Food and Agriculture Organization [ 4 ]. Initially, 16 food groups were considered in the assessment: cereals; white tubers and roots; vitamin A rich vegetables and tubers; dark green leafy vegetables; other vegetables; vitamin A rich fruits; other fruits; organ meat; flesh meat; eggs; fish and seafood; legumes, seeds and nuts; milk and milk products; oils and fats; sweets; and spices, condiments, and beverages. For analysis, these were consolidated into 12 broader food groups: cereals; white tubers and roots; legumes, pulses, and nuts; vegetables; milk and milk products; meat products; fruits; eggs; fish and other seafood; oils and fats; sweets; and spices, condiments, and beverages [ 3 ]. Data on household dietary intake were collected asking respondents to recall all food items consumed by any household member in the previous 24 hours, including foods eaten during lunch, dinner, and any snacks or meals between. A combination of open recall and list-based methods was used. Once the foods were recalled, the data collectors underlined the relevant items within each food group on a provided list and marked “1” in the corresponding column if at least one food of the group was consumed. If no items from a food group were mentioned, a 0 was recorded. Based on the Household Dietary Diversity Score (HDDS), households consuming foods from seven or more food groups were classified as having adequate dietary diversity, while those with fewer than seven were classified as having inadequate dietary diversity.[ 23 ]. Data Collection Procedures Data were collected through face-to-face interviews using a structured questionnaire adapted from previously validated sources. The data collection period ran from December 1, 2023, to September 20, 2024, and was strategically timed to correspond to the four main agricultural seasons of the study area. Winter (December–February), autumn (March–May), summer (June–August), and spring (September–November). The interviews were conducted at the end of each season and the beginning of the next, specifically during the following intervals. December 1–25, 2023; March 1–25, 2024; June 1–22, 2024; and September 1–20, 2024. The distribution of participants by month is presented (Fig. 1 (b)). Each interview session lasted approximately 20 to 30 minutes. Study variables Household dietary diversity (HDD) served as the outcome variable in this study. Independent variables included a range of factors: sociodemographic characteristics (age, sex, marital status, place of residence, educational level, religion, occupation, and household size); socioeconomic factors (wealth index, presence of a savings account and land size); agricultural input use; food insecurity status; community participation in dietary diversity programs; knowledge of dietary diversity; and season-related factors. Operational Definitions Dietary diversity Refers to the number of distinct food groups consumed by an individual in a 24-hour period. [ 4 , 28 ] Household Dietary Diversity Definition as the number of food groups consumed by any member of a household over the previous 24 hours [ 3 ]. Adequate Dietary Diversity Also referred to as high dietary diversity, this is when a household consumes seven or more food groups in a 24-hour period. On the contrary, inadequate dietary diversity is categorized as low dietary diversity (three or fewer food groups) or medium dietary diversity (four to six food groups). [ 3 , 17 ] Household Wealth A proxy indicator of a household's living standard, measured by the total value of household assets, including productive and durable goods, domestic animals, and housing characteristics [ 19 ] Household Food Insecurity (HFI) was assessed using the Household Food Insecurity Access Scale (HFIAS). Based on total HFIAS scores, households were categorized into four levels: food secure (scores 0–1), mildly food insecure (scores 2–4), moderately food insecure (scores 11–17) and severe food insecure (scores greater than 17). For analytical purposes, households classified as food secure were coded as '0', while all levels of food insecurity (mild, moderate, and severe) were combined and coded as '1'. [ 28 ]. Data Quality Assurance The quantitative questionnaire was adapted from standardized sources, including Demographic and Health Surveys (DHS) and relevant previous studies. To ensure linguistic consistency and cultural appropriateness, the tool was translated into Amharic and subsequently translated back into English. A comprehensive protocol was developed to guide data collectors and supervisors, focusing on data quality assurance, ethical considerations, task preparation, effective communication, and a thorough understanding of key questionnaire components. A two-day training workshop was conducted for all data collectors and supervisors, which included sessions on obtaining informed consent and proper administration of the questionnaire. To assess the reliability and operational feasibility of the tool, a pilot test was carried out in the Dembecha district involving 42 household heads who were not part of the main study sites. Insights from the pre-test were used to correct typographical errors and refine question phrasing; some items were added while others were removed to enhance clarity and relevance. It is important to note that the qualitative component of the study was not subjected to pre-testing. Data Analysis Data were entered and validated using EPI Data version 3.4, then exported to SPSS version 29 for analysis. After categorizing the variables, descriptive statistics were generated, including frequencies, percentages, tables, and figures. Binary logistic regression was performed to identify predictors of household dietary diversity. Variables with a bivariate p-value ≤ 0.25 were included in the multivariable model to control for potential confounders. Both crude and adjusted odds ratios (COR and AOR) with 95% confidence intervals (CI) were reported, and statistical significance was defined as p < 0.05. Model adequacy was confirmed using the Hosmer–Lemeshow goodness-of-fit test (p = 0.987). The multicollinearity diagnostics showed variance inflation factors (VIF) ranging from 1.236 to 4.145 and tolerance values between 0.291 and 0.934, indicating that there are no collinearity problems. Ethical Considerations This study received ethical approval from the Department of Health Studies of the University of South Africa (UNISA) on 13 October 2023 (Ref. 117606980_CERC_CHS_2023). Additional permissions were granted by the Amhara Public Health Institute and the West Gojjam Zone Administration. Participants were fully informed about the voluntary nature of the study, assured that no unnecessary pressure or special incentives would be applied, and made aware of their right to withdraw at any time without consequence. Written informed consent was obtained from all participants and personal identifiers were removed to ensure anonymity. All data were securely stored in a password-protected database. Research was carried out in full compliance with ethical principles, including respect for autonomy, promotion of well-being, prevention of harm, and fairness throughout the study process. Results Sociodemographic characteristics of the participants Of the total of 3,360 participants contacted, 3,325 (99% response rate) completed all four phases of the study. The mean age of the participants was 35.8 years (SD ± 9). Most of the respondents were 25 to 34 years old, 49.2% (n = 1,634), followed by those 35 to 44 years old, 29.1% (n = 968). Regarding sex, the majority were male, 85.7% (n = 2,848). Most of the participants were married, 85.9% (n = 2,858) and 80.2% (n = 2,667) lived in rural settings. Most identified themselves as Orthodox Christians 89.8%, (n = 2,986). Additionally, 62.5% (n = 2,076) of the participants had no formal education, followed by 30.9% (n = 1,028) with primary education. Most of the participants were farmers, 83.5%, occupation (n = 2,776), and 58.3% (n = 1,937) of the households had 1–3 members (Table 1 ). Table 1 Sociodemographic characteristics of study participants in the four-phase study, West Gojjam Zone, Northwest Ethiopia (N = 3325) Variables Category Frequency Percent Age in years 18–24 623 18.7 25–34 1634 49.2 34–44 968 29.1 > 45 99 3.0 Sex Female 476 14.3 Male 2848 85.7 Marital status Single 372 11.2 Married 2858 85.9 Divorced 82 2.5 Widowed 12 0.4 Residence Urban 657 19.8 Rural 2667 80.2 Religion Orthodox 2986 89.8 Muslim 169 5.1 Protestant 169 5.1 Educational status No education 2076 62.5 Primary education 1028 30.9 Secondary education 160 4.8 Higher education 60 1.8 Occupation status Farmer 2776 83.5 Employed 181 5.4 Merchant 367 11.0 Household members 1–3 members 1937 58.3 4–5 members 1295 39.0 6 and above 92 2.8 Economic and Agricultural Characteristics Regarding the wealth index, the highest proportion of respondents was classified as the poorest 35.3% (n = 1174), followed by the poorest 20.9% (n = 696), while the richest groups made up 10.2% (n = 339) of the study participants. Most of the respondents, 63.3% (n = 2005), had savings accounts. Regarding the size of the land, just over half 54.2% (n = 1801) owned less than 1.5 hectares. 53.1% (n = 1766) of the participants reported by 53.1% (n = 1766) of participants (Table 2 ). Table 2 Economic and agricultural characteristics of the study participants in the West Gojjam Zone, Northwest Ethiopia, 2024 (N = 3325) Variables Category Frequency Percent Wealth Index Poorest 1174 35.3 Poorer 696 20.9 Medium 602 18.1 Richer 513 15.4 Richest 339 10.2 Saving accounts No 1219 36.7 Yes 2105 63.3 Land size ≥ 1.5 hectares 1523 45.8 < 1.5 hectares 1801 54.2 Agricultural input use Yes 1558 46.9 No 1766 53.1 Knowledge of dietary diversity and food insecurity characteristics Knowledge about dietary diversity was generally low, with 51.8% (n = 1722) demonstrating poor knowledge. Concern about the status of food insecurity among households varied, with more than half, 53.3% (n = 1773) experiencing mild food insecurity, 32.2% (n = 1070) classified as food secure, 11.3% (n = 375) facing moderate food insecurity, and 3.2% (n = 3.2) experiencing severe food insecurity. Regarding community participation (trainings, meetings, workshops, food cooking demonstrations) in activities related to dietary diversity, the majority of respondents, 74.8% (n = 74.8) reported no involvement (Table 3 ). Table 3 Knowledge of dietary diversity and food insecurity among households in the west Gojjam zone, northwest Ethiopia, 2024 (N = 3325) Variables Category Frequency Percent Knowledge about DDs Poor 1722 51.8 Good 1602 48.2 Food insecurity Secure 1070 32.2 Mild 1773 53.3 Moderate 375 11.3 Sever 106 3.2 Community participation in dietary diversity No 2488 74.8 Yes 836 25.2 Consumption of diuretics between households Most households consumed cereals, grains, and cereal products, with a prevalence of 98.6% (n = 3278). Legumes and pulses were also commonly consumed, reported by 73.5% (n = 2443) of households, followed closely by spices, condiments, and beverages at 75.2% (n = 2498). 69.9% (n = 2222), while 83.4% (n = 2771) of households used roots, tubers, and nuts. Sugar consumption was reported by 56.8% (n = 1889) of the households. However, the intake of vegetables and fruits rich in vitamin A was lower, at 38.8% (n = 1191). Animal source foods were consumed less frequently, with meat and other animal products at 28.5% (n = 948), milk and milk products at 27.8% (n = 925), eggs at 25.2% (n = 837), and other fruits at 25.5% (n = 848) (Fig. 2). Prevalence of seasonal dietary diversity In this study, the prevalence of inadequate dietary diversity among households was 55.5% (95% CI 50.9, 59.0) in December, in March, 60.5% (95% CI: 57.3, 64.9) of households reported inadequate dietary diversity, and in June, 73.5% (95% CI: 70.6, 76.6) of households had inadequate dietary diversity. On the contrary, September showed the lowest rate of inadequate dietary diversity at 40.5% (95% CI: 36.1, 44.9), with the highest proportion of adequate dietary diversity at 59.5% (95% CI: 55.1, 63.9) (Fig. 3). Factors associated with household dietary diversity Bivariate binary logistic regression analysis revealed that season, sex, religion, educational status, occupational status, wealth index, agriculture input use, and knowledge about dietary diversity were significantly associated with dietary diversity. However, the age of the household head, marital status, residence, savings account, land size, participation in activities of dietary diversity and food insecurity in the household were not significantly associated with dietary diversity. Multivariate logistic regression analysis identified several factors significantly associated with dietary diversity in the household, including season, sex, religion, occupation, wealth index, not using agricultural input and knowledge of dietary diversity. However, the September season surveyed, the farmer's occupation, medium, and rich wealth index were not significantly associated with dietary diversity. Consequently, households surveyed in June were significantly more likely to have inadequate dietary diversity compared to those surveyed in December (AOR = 3.403; 95% CI 2.540, 4.523) and participants in March were 1.13 times more likely to have inadequate dietary diversity in their household compared to households surveyed in December (AOR = 1.130 (1.093, 3.164). Female households had higher odds of inadequate dietary diversity than male households (AOR = 2.012; 95% CI: 1.380, 2.352). Among household head religions, Orthodox religious followers were 2 times more likely to have inadequate dietary diversity than protestant followers (AOR = 2.012; 95%CI:1.380, 2.35) and Muslim religious followers were 1.8 times higher likelihood of inadequate dietary diversity compared to Protestant religious followers (AOR = 1.799; 95% CI: 1.166, 3.420). Employed household heads were less likely to have inadequate dietary diversity compared to merchants (AOR = 0.39; 95% CI: 0.244, 0.624). Households in the poorest wealth category had 1.65 times higher odds of inadequate dietary diversity than those in the richest category (AOR = 1.645; 95% CI: 1.294, 2.092). The absence of use of agricultural inputs also increased the likelihood of inadequate dietary diversity (AOR = 1.280; 95% CI: 1.074, 1.525). Finally, household heads who had poor knowledge about dietary diversity were 1.35 times more likely to have inadequate dietary diversity (AOR = 1.352; 95% CI 1.142, 2.073) than encounters. The model showed good fit according to the Hosmer and Lemeshow test (p = 0.913) (Table 4 ). Table 4 Multivariable logistic regression analysis of factors associated with dietary diversity among households in the West Gojjam Zone, Ethiopia Character Dietary Diversity Crude OR (95%CI) Adjusted OR (95%CI) Inadequate Adequate Season December 234 606 1 1 March 227 611 0.962(0.616, 2.178) 1.130(1.093, 3.164)* June 326 486 3.260(2.489, 4.269) 3.403(2.540, 4.523)** September 221 613 0.934(0.612, 2.173) 0.89(0.589, 1.159) Household head sex Male 1471 1377 1 1 Female 309 167 1.732(1.415, 2.120) 2.012(1.380, 2.352)** Household head religion Orthodox 1624 1362 1.686(1.231, 2.309) 2.012(1.380, 2.35)* Muslim 86 83 1.465(0.954, 2.252) 1.799(1.166, 3.420)* Protestant 70 99 1 1 Household head occupation Farmer 1553 1223 1.291(1.038, 1.605) 1.137(0.862, 1.499) Employed 45 136 0.336(0.227, 0.499) 0.39(0.244, 0.624)** Merchant 182 185 1 1 Wealth index Poorest 576 598 1.669(1.304, 2.137) 1.645(1.294, 2.092)* Poorer 398 298 1.560(1.264, 1.25) 1.452(1262, 3.037)* Medium 289 313 0.959(0.757 1.167) 1.121(0.987, 1.885) Richer 308 205 1.387(0.948, 2.675) 2.139(0.969, 3.916) Richest 209 130 1 1 Agricultural input use (fertilizer, improved seed, insecticide) No 889 669 1.305(1.138 1.497) 1.280(1.074, 1.525)** Yes 891 875 1 1 Knowledge about DDS Poor 973 749 1.280(1.116, 1.467) 1.352(1.142, 2.073)* Good 807 795 1 1 Hosmer and Lemeshow test, p value = 0.913 Note: **: p value < 0.001, *: P value < 0.05 significantly associated. Discussion The purpose of this study was to determine the seasonal prevalence of dietary diversity and to identify its associated factors among households in the West Gojjam Zone, Ethiopia. The highest prevalence of inadequate dietary diversity was found in June (73.5%), followed by March (60.9%), while December (44.5%) and September (33.5%) had lower rates. Overall, dietary diversity showed significant seasonal variation, with higher inadequacy during the pre-harvest months (March and June) compared to the post-harvest months (September and December). The key factors associated with dietary diversity included season, sex, religion, occupation, wealth index, agricultural input use, and knowledge of dietary diversity. In this study, the pre-harvest seasons (March and June) exhibited the highest prevalence of inadequate dietary diversity—60.5% and 73.5%, respectively, while the post-harvest seasons (September and December) showed much lower levels of 40.5% and 55.5%. This seasonal pattern is consistent with the findings from studies in Ethiopia [ 16 , 17 ], Uganda [ 29 ], and Kenya [ 30 ]. The possible explanations for these variations could include similar in study participants, data collection instruments, and the specific months when data were collected. The seasonal prevalence of inadequate dietary diversity observed in this study (33.5–73.5%) was higher than reported in studies conducted in Ethiopia [ 20 ], and Kenya (36.5–62.4%) [ 31 ]. The possible explanations for these discrepancies include differences in study design, such as the inclusion of children and women as household respondents in this study, the timing of data collection across different seasons, and variations in the local context and agricultural practices in the study settings. In this study, most households consumed cereals, grains, and cereal products, with a prevalence of 98.6% and followed by legumes and pulses, which were also commonly consumed by 73.5% of households. However, the intake of vegetables and fruits rich in vitamin A was lower; Animal-derived foods were consumed less frequently, with meat and other animal products; milk and milk products, eggs, and other fruits. This study in agreement with other studies [ 2 ], which indicated ccereals were the most dominant food group consumed by 96.4% of households followed by pulses, which was consumed by 82% of households, while meat, vegetables, and fruits were the food groups by households in Ethiopia[ 2 ]This might be the sociocultural, norm, and socioeconomic status of the study participants. Seasonal variation significantly influences dietary diversity, particularly in agrarian communities where food availability is closely related to agricultural cycles. In this study, the pre-harvest (lean) season was found to be a significant predictor of inadequate dietary diversity between households. This is consistent with studies of 11 demographic and health survey report[ 32 ] and Ethiopia[ 16 , 33 ]. This study demonstrates lower dietary diversity during preharvest periods, when food stocks are depleted, leading to a greater vulnerability to malnutrition. This could be justified by seasonal depletion of food stocks and reduced income during preharvest periods, which limit access to diverse foods. These structural factors are widely recognized as key drivers of fluctuations in dietary diversity in agrarian settings. Reliance on subsistence agriculture makes households vulnerable to seasonal dietary diversity. In this study, the odds of having inadequate household dietary diversity were 2 times higher among women-headed households compared to men-headed households. This finding aligns with studies from Uganda [ 34 ] and Ethiopia [ 2 , 33 ]. This might be due to gender dynamics often influence food allocation and consumption within households, especially in patriarchal societies such as Ethiopia, may have less access to purchase diverse foods due to cultural norms prioritizing men’s nutrition [ 17 ] and have lower dietary diversity because of economic disadvantages [ 20 ]. However, other studies have reported better dietary diversity in female households, possibly due to women’s greater autonomy in food related decisions and have dietary diversity in the household [ 17 , 32 ]. These mixed findings highlight the importance of considering contextual gender roles when examining dietary outcomes. In the present study, religion emerged as a significant factor associated with the diversity of the family's diet. Household followers of the Orthodox Christian family members had twice the odds of being at risk of inadequate dietary diversity compared to those who followed the Protestant religion. This finding is consistent with previous studies [ 26 , 35 ]. This might be because religious affiliation can influence dietary diversity through its impact on food choices, restrictions, and cultural practices. For example, Orthodox Christian fasting periods in Ethiopia involve abstaining from animal-based foods, which can temporarily reduce dietary diversity. More broadly, religious dietary laws shape the inclusion or exclusion of specific food groups, thus influencing overall dietary patterns. In the current study, occupation was identified as an important factor influencing the diversity of the diet of the home. Employed household heads were less likely to have inadequate dietary diversity compared to merchants. Employers currently have paid less than farmers. This may be because farming households may have better access to fresh produce and diverse crops; however, they are also more vulnerable to seasonal food shortages that limit year-round dietary diversity [ 20 , 36 ]. In contrast, households engaged in non-farming occupations often benefit from more stable income, allowing them to purchase a wider variety of foods from markets. Previous Ethiopian studies support this distinction, indicating that while farmers have access to home-grown food items, their diets are more affected by seasonal fluctuations, whereas urban and wage-earner households rely heavily on market access for dietary diversity [ 2 , 4 ]. In the current study, the wealth index emerged as a strong predictor of household dietary diversity. Households in the poorest wealth category had 64.5% higher odds of experiencing inadequate dietary diversity compared to those in the richest category. This finding is consistent with studies conducted in Ethiopia [ 37 ] and Northern Uganda [ 24 ]. This may be explained by the fact that low-income households are more vulnerable to high food prices, seasonal fluctuations, and limited access to nutritious foods, often leading to reduced consumption of animal-based products and greater dependence on cereal-based diets. Furthermore, households that did not use agricultural input were 1.3 times more likely to experience inadequate dietary diversity compared to their counterparts. This finding is consistent with studies conducted in Ethiopia [ 25 ], Kenya [ 38 ] and Malawi [ 39 ] which indicate that the use of agricultural inputs, such as improved seeds, fertilizers, and irrigation, is positively associated with increased crop diversity and, consequently, better dietary diversity. Finally, knowledge of dietary diversity was identified as a significant determinant of dietary diversity among households. Households who had poor knowledge about dietary diversity were 3.9 times more likely to have inadequate dietary diversity compared to their counterparts. This finding is consistent with previous studies [ 40 , 41 ]. The justification for this association lies in the fact that households with greater awareness and understanding of the benefits of consuming a variety of food groups are more likely to make informed dietary choices, thus improving their dietary diversity. This is consistent with recent literature that emphasizes the role of nutrition education in improving dietary practices and food choices [ 42 ]. The strengths of this study include a large sample size, determining seasonal prevalence, and using standardized tools. However, its cross-sectional design limits the capture of temporal dietary variations, the reliance on self-reported data can cause recall bias, and the study conducted only in West Gojjam restricts generalizability to other Ethiopian regions with different cultural and ecological contexts. Conclusion This study found that household dietary diversity varied significantly over months, with the highest proportion of low diversity occurring in June and March, highlighting periods of increased nutritional vulnerability. Key factors influencing dietary diversity included season, sex, religion, occupation, wealth index, agricultural input use, and knowledge of dietary diversity. To address these challenges, strategies should prioritize nutrition-sensitive interventions during critical months, such as food aid, community education, and income-generating activities. Efforts should also enhance access to agricultural inputs and address socioeconomic disparities through targeted policies. Finally, seasonal monitoring of dietary diversity is crucial to enable timely, evidence-based responses. Abbreviations AOR Adjusted Odds Ratio COR Crude Odds Ratio DDS Dietary Diversity HDDS Household Dietary Diversity Declarations Acknowledgements We express our sincere gratitude to the University of South Africa, the Faculty of Human Sciences, and the Department of Health Studies for their support. We also extend our thanks to the West Gojjam Zone and district administrators. Finally, we acknowledge the data collectors, supervisors, and study participants for their invaluable contributions. Ethics Approval and Consent to Participate This study involved human participants and received ethical clearance from the Health Research Ethics Committee of the Department of Health Studies, University of South Africa (UNISA) (Reference: 11760680_CREC_CHS_2023). A permission letter was provided by the Amhara Public Health Institute (Reference: APHI/03/2053) and the West Gojjam Zone (Reference: 1443/11/2016) to conduct the study in the selected districts. All participants gave their informed written consent in accordance with the ethical principles outlined in the Declaration of Helsinki. Consent for publication All authors consented to the submission of this manuscript for publication. Availability of Data and Materials The primary data generated and analyzed in this study are included in this article. Any clarifications can be addressed by contacting the corresponding author. Competing Interests The authors declare that they have no competing interests. Funding This study was funded by Bahir Dar University (Grant ID: BDU1789/2023), which supported the data collection budget. The funding organization had no role in the design, supervision, analysis, or interpretation of the study and did not impose restrictions on publication. Authors’ Contributions MBY and MGM conceived the study, collected the data, and performed analysis and interpretation. MBY drafted the first version of the manuscript, while MGM contributed to the study design and substantially revised the final draft. All authors read and approved the final manuscript. Clinical trial number Not applicable. References Hatløy A, Torheim LE, Oshaug A. Food variety–a good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur J Clin Nutr. 1998;52(12):891–8. Jateno W, Alemu BA, Shete M. Household dietary diversity across regions in Ethiopia: Evidence from Ethiopian socio-economic survey data. PLoS ONE. 2023;18(4):e0283496. Kennedy G, DMC. Guidelines for measuring household and individual dietary diversity. Rome: Food and Agriculture Organization of the United Nations; 2011. pp. 2–35. FAO. Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations; 2010. pp. 7–34. Christiaensen LDLKJ. The role of agriculture in poverty reduction: an empirical perspective. J Dev Econ. 2011;(96):23–254. Food and Agriculture Organization (FAO). Dietary diversity: Linking food and nutrition security to sustainable food systems. Rome: FAO. 2021;25–52. Arsenault JE, Nikiema L, Allemand P, Ayassou KA, Lanou H, Moursi M, et al. Seasonal differences in food and nutrient intakes among young children and their mothers in rural Burkina Faso. J Nutr Sci. 2014;3:e55. Savy M, Martin-Prével Y, Traissac P, Eymard-Duvernay S, Delpeuch F. Dietary diversity scores and nutritional status of women change during the seasonal food shortage in rural Burkina Faso. J Nutr. 2006;136(10):2625–32. Somé JW, Jones AD. The influence of crop production and socioeconomic factors on seasonal household dietary diversity in Burkina Faso. PLoS ONE. 2018;13(5):e0195685. Adams AM. Seasonal variations in energy balance among agriculturalists in central Mali: compromise or adaptation? Eur J Clin Nutr. 1995;49(11):809–23. Guizzo Dri G, Spencer PR, da Costa R, Sanders KA, Judge DS. The seasonal relationships between household dietary diversity and child growth in a rural Timor-Leste community. Matern Child Nutr. 2022;18(3). Toorang F, HoushiarRad A, Abdollahi M, Esmaili M, Ebrahimpour Koujan S. Seasonality in Iranian Fruit and Vegetable Dietary Intake. Thrita J Med Sci. 2013;2(2):58–63. Becquey E, Delpeuch F, Konaté AM, Delsol H, Lange M, Zoungrana M, et al. Seasonality of the dietary dimension of household food security in urban Burkina Faso. Br J Nutr. 2012;107(12):1860–70. Baye K, Mekonnen D, Choufani J, Yimam S, Bryan E, Griffiths JK et al. Seasonal variation in maternal dietary diversity is reduced by small-scale irrigation practices: A longitudinal study. Matern Child Nutr. 2022;18(2). Belayneh M, Loha E, Lindtjørn B. Seasonal variation of household food insecurity and household dietary diversity on wasting and stunting among young children in a drought prone area in South Ethiopia: a cohort study. Ecol Food Nutr. 2021;60(1):44–69. Yayeh MB, Makua MG. Seasonal variation in the prevalence of household food insecurity and its associated factors in the West Gojjam Zone, Ethiopia. Discover Public Health. 2025;22(1):116. Mekuria G, Wubneh Y, Tewabe T. Household dietary diversity and associated factors among residents of finote selam town, north west Ethiopia: a cross sectional study. BMC Nutr. 2017;3(1):28. Degye GMK. Measuring diet quantity and quality dimensions of food security in rural Ethiopia. Dev Agric Econ. 2013;174–85. Central statistical Agency[Ethiopia]. Mini Ethiopian Demographic Health survey: key indicators report. The DHS Program ICF. 2019. 2019. pp. 1–34. Hirvonen K, Taffesse AS, Hassen IW. Seasonality and household diets in Ethiopia. Public Health Nutr. 2016;19(10):1723–30. Yayeh MB, Makua MG. Seasonal prevalence of child undernutrition and its associated factors in the west Gojjam zone, Ethiopia. Archives Public Health. 2025;83(1):146. Belayneh M, Loha E, Lindtjørn B. Seasonal Variation of Household Food Insecurity and Household Dietary Diversity on Wasting and Stunting among Young Children in A Drought Prone Area in South Ethiopia: A Cohort Study. Ecol Food Nutr. 2021;60(1):44–69. Aliyo A, Golicha W, Fikrie A. Household Dietary Diversity and Associated Factors among Rural Residents of Gomole District, Borena Zone, Oromia Regional State, Ethiopia. Health Serv Res Manag Epidemiol. 2022;9:23333928221108030. Kolliesuah NP, Olum S, Ongeng D. Status of household dietary diversity and associated factors among rural and urban households of Northern Uganda. BMC Nutr. 2023;9(1):83. Sibhatu KT, Krishna VV, Qaim M. Production diversity and dietary diversity in smallholder farm households. Proceedings of the National Academy of Sciences. 2015;112(34):10657–62. Workicho A, Belachew T, Feyissa GT, Wondafrash B, Lachat C, Verstraeten R, et al. Household dietary diversity and Animal Source Food consumption in Ethiopia: evidence from the 2011 Welfare Monitoring Survey. BMC Public Health. 2016;16(1):1192. Motbainor A, Worku AKA. Level and determinants of food insecurity in East and west Gojjam zones of Amhara Region, Ethiopia: A community based comparative cross-sectional study. BMC Public Health. 2016;16(503):1–13. Coates J, BP. SA, &. Household food insecurity access scale (HFIAS) for measurement of food access: Indicator guide. Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development. 2007;3–17. Jordan I, Röhlig A, Glas MG, Waswa LM, Mugisha J, Krawinkel MB, et al. Dietary Diversity of Women across Agricultural Seasons in the Kapchorwa District, Uganda: Results from a Cohort Study. Foods. 2022;11(3):344. Ngala SA. Evaluation of dietary diversity scores to assess nutrient adequacy among rural Kenyan women. Wageningen University; 2015. Waswa LM, Jordan I, Krawinkel MB, Keding GB. Seasonal Variations in Dietary Diversity and Nutrient Intakes of Women and Their Children (6–23 Months) in Western Kenya. Front Nutr. 2021;8:636872. Arimond M, Ruel MT. Dietary diversity is associated with child nutritional status: evidence from 11 demographic and health surveys. J Nutr. 2004;134(10):2579–85. Endalifer ML, Andargie G, Mohammed B, Endalifer BL. Factors associated with dietary diversity among adolescents in Woldia, Northeast Ethiopia. BMC Nutr. 2021;7(1):27. Oyet SM, Kaahwa RM, Muggaga C, Ongeng D, Okello-Uma I. Household dietary diversity and associated factors in rural and peri-urban areas of Mbale District, Eastern Uganda. BMC Public Health. 2025;25(1):303. Gulema H, Demissie M, Worku A, Yadeta TA, Tewahido D, Berhane Y. Intrahousehold food allocation social norms and food taboos in rural Ethiopia: The case of adolescent girls. Heliyon. 2024;10(11):e32295. Demilew YM, Alene GD, Belachew T. Dietary practices and associated factors among pregnant women in West Gojjam Zone, Northwest Ethiopia. BMC Pregnancy Childbirth. 2020;20(1):18. Jateno W, Alemu BA, Shete M. Household dietary diversity across regions in Ethiopia: Evidence from Ethiopian socio-economic survey data. PLoS ONE. 2023;18(4):e0283496. Olwande J, Smale M, Mathenge MK, Place F, Mithöfer D. Agricultural marketing by smallholders in Kenya: A comparison of maize, kale and dairy. Food Policy. 2015;52:22–32. Jones AD, Shrinivas A, Bezner-Kerr R. Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy. 2014;46:1–12. Girard AW, Self JL, McAuliffe C, Olude O. The Effects of Household Food Production Strategies on the Health and Nutrition Outcomes of Women and Young Children: A Systematic Review. Paediatr Perinat Epidemiol. 2012;26(s1):205–22. Fekadu Y, Mesfin A, Haile D, Stoecker BJ. Factors associated with nutritional status of infants and young children in Somali Region, Ethiopia: a cross- sectional study. BMC Public Health. 2015;15(1):846. Food and Agriculture Organization (FAO). Dietary diversity: Linking food and nutrition security to sustainable food systems. Rome: FAO. 2021;25–52. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6907682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482777573,"identity":"a60bbf95-e8db-4b2b-864c-8a0d58b9c0f7","order_by":0,"name":"Melesse Belayneh Yayeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFAC5gYog4fxAYjkI6yFEa6F2QBEspGihU0CRBHUotve2PjwR8VheYPjvccqv+bYybAxMD98dAOPFrMzB5uNec4cNtxw5lzabdltyUCHsRkb5+DTciOxTZqx7TDjhhs5ZrcltzEDtfCwSePVcv9h+8+f/w7bb7j/xqxYcls9EVpuMLYx8DYcTtxwg8eM8eO2w0RoOZPYLM1zLD155pkcY2nGbcd52JgJ+eX44YMff9RY2/YdP2P48ee2ant+9uaHj/FpgYJmBoUDwITAA2IzE1YOAnUM8g3AWP1BnOpRMApGwSgYYQAAekxOHX1s8HsAAAAASUVORK5CYII=","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Melesse","middleName":"Belayneh","lastName":"Yayeh","suffix":""},{"id":482777574,"identity":"1c1b869e-ef33-4546-8d96-f8aec681240c","order_by":1,"name":"Memme Girly Makua","email":"","orcid":"","institution":"University of South Africa (UNISA)","correspondingAuthor":false,"prefix":"","firstName":"Memme","middleName":"Girly","lastName":"Makua","suffix":""}],"badges":[],"createdAt":"2025-06-16 17:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6907682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6907682/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86526692,"identity":"298c1ce7-e857-46f3-81ac-208e51a90e6a","added_by":"auto","created_at":"2025-07-11 16:04:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182102,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram illustrating the sampling process for the study on seasonal prevalence of dietary diversity among households in West Gojjam, northwest Ethiopia, 2024[21].\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6907682/v1/b8e112124128adb02ec3c38f.png"},{"id":86526690,"identity":"968abf8f-ba93-42f8-8758-be71c99ba0fa","added_by":"auto","created_at":"2025-07-11 16:04:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFood group consumption among households in the West Gojjam zone, Ethiopia, 2024 (n=3325)\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6907682/v1/9269d0352470fafa487b271f.png"},{"id":86526691,"identity":"1e48ca99-914e-496a-849c-4c9b29db91b3","added_by":"auto","created_at":"2025-07-11 16:04:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21860,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal prevalence of dietary diversity scores among households in the West Gojjam zone, Ethiopia, 2024\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6907682/v1/ca4dbaad6a1705bbee39e2f1.png"},{"id":95193813,"identity":"bc94e4a7-7776-4052-abb0-23067f858fd5","added_by":"auto","created_at":"2025-11-05 10:54:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6907682/v1/e417dc42-da51-4479-a0ce-6fa3803b3e59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal prevalence of dietary diversity and its associated factors among households in West Gojjam Zone, Ethiopia: Longitudinal cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDietary diversity refers to the number of diverse food groups consumed in a specific time period and is a key indicator of overall dietary adequacy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It reflects nutrient intake and dietary quality, serves as a proxy indicator of socioeconomic status [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Dietary diversity is considered high when all food groups are consumed. However, if fewer than three food groups are consumed per day or fewer than five per week, the dietary diversity is classified as low. On the contrary, the consumption of four to five food groups per day or six to eight per week indicates medium dietary diversity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nutritionally adequate dietary diversity is defined as the consumption of six or more food groups daily or nine or more food groups weekly [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn low-income rural areas of sub-Saharan Africa (SSA), where most of the population still relies on small-scale agricultural production fed by rain as a principal source of livelihood [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], poor diet quality remains an intractable challenge. Households in these settings are especially vulnerable to seasonal changes in dietary diversity due to fluctuations in food availability and accessibility [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among the previous studies that have examined seasonal differences in dietary diversity, some have relied on data from only two time periods (ie, post-harvest vs. lean season, or beginning of lean season vs. end of lean season [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while others have used data from more than two time points[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. With seasonal fluctuations in agricultural production, populations that rely heavily on agricultural productions with little access to animal-source foods are more likely to experience seasonal food shortages [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHousehold dietary diversity is a relevant problem in the pre-harvest season, where most households consume four or fewer food groups in these seasons [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Households in developing countries, including Ethiopia, typically face a seasonal food shortage for the period between cereal stock depletion and the next harvest, particularly in areas where people depend on the annual harvest of the staple crop following a single rainy season [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that seasonality has an effect on dietary patterns, and on both energy and nutrient intakes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] For example, previous research in Nepal and Ethiopia demonstrated the existence of seasonal variation from 36.5% to 78.5 % in, dietary diverity, and food consumption [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and in Ethiopia, several cross-sectional studies have reported inadequate household dietary diversity ranging from 65.7\u0026ndash;83.3% [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Ethiopia, the availability and access of household food groups is strongly affected by seasonality; many rural households are only able to produce enough food to meet their needs for six months of the year or fewer, and consequently face severe food scarcities during the lean (pre- harvest) season [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and our previously finding indicated that child under nutrition and household food insecurity strongly influenced by seasonal variations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Among the food groups in each season on average, cereals 97% followed by legumes 90%, while animal source foods such as meat, eggs, and fish were less frequently consumed [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, another studied found that cereals were the most dominant food group consumed by 96.4% of households followed by pulses, which was consumed by 82% of households. Nutrition-dense food commodities, such as lean meat, vegetables and fruits, were the least consumed food groups by households in Ethiopia. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSeveral factors are associated with the diversity of the diet of the household. These include sociodemographic characteristics such as age, sex [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], residence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], marital status [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], education [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and occupation, as well as socioeconomic status indicators like wealth index [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], savings accounts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], land size, and food security [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Agricultural-related factors, including the use of agricultural inputs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the number of livestock owned [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], also play a significant role. In addition, knowledge and attitudes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] toward dietary diversity, along with seasonal variations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], significantly influence the level of dietary diversity within households.\u003c/p\u003e\u003cp\u003eAlthough the cross-sectional prevalence of household dietary diversity is well documented, [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. There is little evidence exists regarding the seasonality of household dietary diversity and its associated factors. Therefore, the evidence remains scant and concerns about an increased incidence of seasonal prevalence of dietary diversity among households. The present study seeks to generate evidence on seasonal variations in household dietary diversity using longitudinal data collected through four survey rounds conducted at different periods within the local agricultural calendar. Specifically, its objective is to assess the seasonal prevalence of household dietary diversity and its associated factors in the West Gojjam Zone, northwest Ethiopia. The findings will be valuable for policy planners, program directors, and other stakeholders in planning, selecting, and designing context-specific strategies to reduce inadequate dietary diversity between households.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Setting and Period\u003c/h3\u003e\n\u003cp\u003eThe study was carried out in the West Gojjam Zone, located in northwest Ethiopia, approximately 387 km from Addis Ababa and 185 km from Bahir Dar, the capital of the Amhara region. Administratively, the zone comprises 12 districts and 6 town administrations. It is served by 110 health centers, four primary hospitals, and one referral hospital. The projected total population of the zone is 2,124,626, including 472, 139 households, and 643,826 children under the age of five years old. This study specifically focused on three districts: Jabitehnane: Population of 246,376 with 57,297 households, Bure Zuria: Population of 141,248 with 32,849 households and Degadamot: Population of 192,127 with 44,681 households.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Period\u003c/h2\u003e\u003cp\u003eData collection was carried out in four separate rounds, each timed to reflect the seasonal stages of the local agricultural cycle. Data were collected during transition periods at the end of one season and the beginning of the next. Data were collected from December 1 to 30, 2023, March 1 to 26, 2024, June 1 to 23, 2024 and September 1 to 20, 2024.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eA longitudinal cross-sectional study design was used to assess seasonal variations in household dietary diversity and its associated factors.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe target population consisted of heads of households residing in the selected kebeles (the smallest administrative in the district of Ethiopia). These individuals were the main respondents in assessing seasonal changes in household dietary diversity and determining the underlying contributing factors.\u003c/p\u003e\n\u003ch3\u003eInclusion Criteria\u003c/h3\u003e\n\u003cp\u003eHousehold heads who had been living in the selected kebeles for more than one year were included in the study.\u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003eHousehold heads who were critically ill or had diagnosed mental disorders were excluded from the study. Additionally, households that had celebrated a festive event within the 24 hours prior to data collection were also excluded.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSample size determination\u003c/h2\u003e\u003cp\u003eThe sample size to evaluate household dietary diversity was calculated using a single population proportion formula n\u003cb\u003e=\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\left(\\frac{Z}{2}\\right)}^{2}*PQ}{{d}^{2}}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\left(\\frac{Z}{2}\\right)}^{2}*P(1-p)}{{d}^{2}}\\)\u003c/span\u003e\u003c/span\u003e, n\u0026thinsp;=\u0026thinsp;Sample size; P\u0026thinsp;=\u0026thinsp;prevalence of household food dietary diversity of 46.3% in the study area [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; Z\u0026thinsp;=\u0026thinsp;standard normal distribution value for the 95% confidence interval (1.96); d\u0026thinsp;=\u0026thinsp;margin error, set at 5%. The sample size was calculated as 382 households and to account for potential non-response, a 10% adjustment (38 households) was added, bringing the sample size to 420. To improve representativeness and account for the multistage sampling design, a design effect of 2 was applied, yielding an adjusted sample size of 840 households per round. Since the data were collected in four separate rounds to capture seasonal variations, the total sample size in all rounds was: 840\u0026times;4\u0026thinsp;=\u0026thinsp;3,360 households.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling Techniques\u003c/h3\u003e\n\u003cp\u003eA multistage sampling method was used to select study participants. In the first stage, three districts were purposively selected from different agroecosystems: Jabitehnane (including Jiga, Mankusa, and Woynima) represents the lowlands; Bure Zuria (including Wundegi, Shakwa, and TiyaTiya) represents the midlands; and Degadamot (including Shangie, Dekulkana, and Shewa) represents the hilly and mountainous highlands. In the second stage, nine kebeles were randomly selected using a simple random sampling technique. A comprehensive list of all households within each selected kebele was compiled. In the final stage, eligible households from selected kebeles were chosen through systematic random sampling. The household lists maintained by the health extension workers served as the sampling framework, ensuring that women who met the inclusion criteria were accurately identified and enrolled \u003cb\u003e(Fig.\u0026nbsp;1 (a)).\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eData Collection Instrument\u003c/h3\u003e\n\u003cp\u003eData were collected using structured and pretested questionnaires, covering information on sociodemographic, socioeconomic, agricultural, knowledge-related, and seasonal factors. The primary outcome variable, the dietary diversity of the household, was measured using a dichotomous indicator developed by the Food and Agriculture Organization [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Initially, 16 food groups were considered in the assessment: cereals; white tubers and roots; vitamin A rich vegetables and tubers; dark green leafy vegetables; other vegetables; vitamin A rich fruits; other fruits; organ meat; flesh meat; eggs; fish and seafood; legumes, seeds and nuts; milk and milk products; oils and fats; sweets; and spices, condiments, and beverages. For analysis, these were consolidated into 12 broader food groups: cereals; white tubers and roots; legumes, pulses, and nuts; vegetables; milk and milk products; meat products; fruits; eggs; fish and other seafood; oils and fats; sweets; and spices, condiments, and beverages [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData on household dietary intake were collected asking respondents to recall all food items consumed by any household member in the previous 24 hours, including foods eaten during lunch, dinner, and any snacks or meals between. A combination of open recall and list-based methods was used. Once the foods were recalled, the data collectors underlined the relevant items within each food group on a provided list and marked \u0026ldquo;1\u0026rdquo; in the corresponding column if at least one food of the group was consumed. If no items from a food group were mentioned, a 0 was recorded. Based on the Household Dietary Diversity Score (HDDS), households consuming foods from seven or more food groups were classified as having adequate dietary diversity, while those with fewer than seven were classified as having inadequate dietary diversity.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Procedures\u003c/h2\u003e\u003cp\u003eData were collected through face-to-face interviews using a structured questionnaire adapted from previously validated sources. The data collection period ran from December 1, 2023, to September 20, 2024, and was strategically timed to correspond to the four main agricultural seasons of the study area. Winter (December\u0026ndash;February), autumn (March\u0026ndash;May), summer (June\u0026ndash;August), and spring (September\u0026ndash;November). The interviews were conducted at the end of each season and the beginning of the next, specifically during the following intervals. December 1\u0026ndash;25, 2023; March 1\u0026ndash;25, 2024; June 1\u0026ndash;22, 2024; and September 1\u0026ndash;20, 2024. The distribution of participants by month is presented (Fig.\u0026nbsp;1 (b)). Each interview session lasted approximately 20 to 30 minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStudy variables\u003c/h2\u003e\u003cp\u003eHousehold dietary diversity (HDD) served as the outcome variable in this study. Independent variables included a range of factors: sociodemographic characteristics (age, sex, marital status, place of residence, educational level, religion, occupation, and household size); socioeconomic factors (wealth index, presence of a savings account and land size); agricultural input use; food insecurity status; community participation in dietary diversity programs; knowledge of dietary diversity; and season-related factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOperational Definitions\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDietary diversity\u003c/strong\u003e\u003cp\u003eRefers to the number of distinct food groups consumed by an individual in a 24-hour period. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHousehold Dietary Diversity\u003c/strong\u003e\u003cp\u003eDefinition as the number of food groups consumed by any member of a household over the previous 24 hours [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdequate Dietary Diversity\u003c/strong\u003e\u003cp\u003eAlso referred to as high dietary diversity, this is when a household consumes seven or more food groups in a 24-hour period. On the contrary, inadequate dietary diversity is categorized as low dietary diversity (three or fewer food groups) or medium dietary diversity (four to six food groups). [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHousehold Wealth\u003c/strong\u003e\u003cp\u003eA proxy indicator of a household's living standard, measured by the total value of household assets, including productive and durable goods, domestic animals, and housing characteristics [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003eHousehold Food Insecurity (HFI) was assessed using the Household Food Insecurity Access Scale (HFIAS). Based on total HFIAS scores, households were categorized into four levels: food secure (scores 0\u0026ndash;1), mildly food insecure (scores 2\u0026ndash;4), moderately food insecure (scores 11\u0026ndash;17) and severe food insecure (scores greater than 17). For analytical purposes, households classified as food secure were coded as '0', while all levels of food insecurity (mild, moderate, and severe) were combined and coded as '1'. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eData Quality Assurance\u003c/h2\u003e\u003cp\u003eThe quantitative questionnaire was adapted from standardized sources, including Demographic and Health Surveys (DHS) and relevant previous studies. To ensure linguistic consistency and cultural appropriateness, the tool was translated into Amharic and subsequently translated back into English. A comprehensive protocol was developed to guide data collectors and supervisors, focusing on data quality assurance, ethical considerations, task preparation, effective communication, and a thorough understanding of key questionnaire components.\u003c/p\u003e\u003cp\u003eA two-day training workshop was conducted for all data collectors and supervisors, which included sessions on obtaining informed consent and proper administration of the questionnaire. To assess the reliability and operational feasibility of the tool, a pilot test was carried out in the Dembecha district involving 42 household heads who were not part of the main study sites. Insights from the pre-test were used to correct typographical errors and refine question phrasing; some items were added while others were removed to enhance clarity and relevance. It is important to note that the qualitative component of the study was not subjected to pre-testing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData were entered and validated using EPI Data version 3.4, then exported to SPSS version 29 for analysis. After categorizing the variables, descriptive statistics were generated, including frequencies, percentages, tables, and figures. Binary logistic regression was performed to identify predictors of household dietary diversity. Variables with a bivariate p-value\u0026thinsp;\u0026le;\u0026thinsp;0.25 were included in the multivariable model to control for potential confounders. Both crude and adjusted odds ratios (COR and AOR) with 95% confidence intervals (CI) were reported, and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Model adequacy was confirmed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test (p\u0026thinsp;=\u0026thinsp;0.987). The multicollinearity diagnostics showed variance inflation factors (VIF) ranging from 1.236 to 4.145 and tolerance values between 0.291 and 0.934, indicating that there are no collinearity problems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e This study received ethical approval from the Department of Health Studies of the University of South Africa (UNISA) on 13 October 2023 (Ref. 117606980_CERC_CHS_2023). Additional permissions were granted by the Amhara Public Health Institute and the West Gojjam Zone Administration. Participants were fully informed about the voluntary nature of the study, assured that no unnecessary pressure or special incentives would be applied, and made aware of their right to withdraw at any time without consequence. Written informed consent was obtained from all participants and personal identifiers were removed to ensure anonymity. All data were securely stored in a password-protected database. Research was carried out in full compliance with ethical principles, including respect for autonomy, promotion of well-being, prevention of harm, and fairness throughout the study process.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic characteristics of the participants\u003c/h2\u003e\u003cp\u003eOf the total of 3,360 participants contacted, 3,325 (99% response rate) completed all four phases of the study. The mean age of the participants was 35.8 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;9). Most of the respondents were 25 to 34 years old, 49.2% (n\u0026thinsp;=\u0026thinsp;1,634), followed by those 35 to 44 years old, 29.1% (n\u0026thinsp;=\u0026thinsp;968). Regarding sex, the majority were male, 85.7% (n\u0026thinsp;=\u0026thinsp;2,848). Most of the participants were married, 85.9% (n\u0026thinsp;=\u0026thinsp;2,858) and 80.2% (n\u0026thinsp;=\u0026thinsp;2,667) lived in rural settings. Most identified themselves as Orthodox Christians 89.8%, (n\u0026thinsp;=\u0026thinsp;2,986). Additionally, 62.5% (n\u0026thinsp;=\u0026thinsp;2,076) of the participants had no formal education, followed by 30.9% (n\u0026thinsp;=\u0026thinsp;1,028) with primary education. Most of the participants were farmers, 83.5%, occupation (n\u0026thinsp;=\u0026thinsp;2,776), and 58.3% (n\u0026thinsp;=\u0026thinsp;1,937) of the households had 1\u0026ndash;3 members (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of study participants in the four-phase study, West Gojjam Zone, Northwest Ethiopia (N\u0026thinsp;=\u0026thinsp;3325)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003ein years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eReligion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrthodox\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtestant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducational\u003c/p\u003e\u003cp\u003estatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003cp\u003estatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMerchant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHousehold members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3 members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ndash;5 members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEconomic and Agricultural Characteristics\u003c/h2\u003e\u003cp\u003eRegarding the wealth index, the highest proportion of respondents was classified as the poorest 35.3% (n\u0026thinsp;=\u0026thinsp;1174), followed by the poorest 20.9% (n\u0026thinsp;=\u0026thinsp;696), while the richest groups made up 10.2% (n\u0026thinsp;=\u0026thinsp;339) of the study participants. Most of the respondents, 63.3% (n\u0026thinsp;=\u0026thinsp;2005), had savings accounts. Regarding the size of the land, just over half 54.2% (n\u0026thinsp;=\u0026thinsp;1801) owned less than 1.5 hectares. 53.1% (n\u0026thinsp;=\u0026thinsp;1766) of the participants reported by 53.1% (n\u0026thinsp;=\u0026thinsp;1766) of participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEconomic and agricultural characteristics of the study participants in the West Gojjam Zone, Northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;3325)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eWealth Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSaving accounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLand size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.5 hectares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.5 hectares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAgricultural input use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eKnowledge of dietary diversity and food insecurity characteristics\u003c/h2\u003e\u003cp\u003eKnowledge about dietary diversity was generally low, with 51.8% (n\u0026thinsp;=\u0026thinsp;1722) demonstrating poor knowledge. Concern about the status of food insecurity among households varied, with more than half, 53.3% (n\u0026thinsp;=\u0026thinsp;1773) experiencing mild food insecurity, 32.2% (n\u0026thinsp;=\u0026thinsp;1070) classified as food secure, 11.3% (n\u0026thinsp;=\u0026thinsp;375) facing moderate food insecurity, and 3.2% (n\u0026thinsp;=\u0026thinsp;3.2) experiencing severe food insecurity. Regarding community participation (trainings, meetings, workshops, food cooking demonstrations) in activities related to dietary diversity, the majority of respondents, 74.8% (n\u0026thinsp;=\u0026thinsp;74.8) reported no involvement (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKnowledge of dietary diversity and food insecurity among households in the west Gojjam zone, northwest Ethiopia, 2024 (N\u0026thinsp;=\u0026thinsp;3325)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKnowledge about DDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eFood insecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCommunity participation\u003c/p\u003e\u003cp\u003ein dietary diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eConsumption of diuretics between households\u003c/h2\u003e\u003cp\u003eMost households consumed cereals, grains, and cereal products, with a prevalence of 98.6% (n\u0026thinsp;=\u0026thinsp;3278). Legumes and pulses were also commonly consumed, reported by 73.5% (n\u0026thinsp;=\u0026thinsp;2443) of households, followed closely by spices, condiments, and beverages at 75.2% (n\u0026thinsp;=\u0026thinsp;2498). 69.9% (n\u0026thinsp;=\u0026thinsp;2222), while 83.4% (n\u0026thinsp;=\u0026thinsp;2771) of households used roots, tubers, and nuts. Sugar consumption was reported by 56.8% (n\u0026thinsp;=\u0026thinsp;1889) of the households. However, the intake of vegetables and fruits rich in vitamin A was lower, at 38.8% (n\u0026thinsp;=\u0026thinsp;1191). Animal source foods were consumed less frequently, with meat and other animal products at 28.5% (n\u0026thinsp;=\u0026thinsp;948), milk and milk products at 27.8% (n\u0026thinsp;=\u0026thinsp;925), eggs at 25.2% (n\u0026thinsp;=\u0026thinsp;837), and other fruits at 25.5% (n\u0026thinsp;=\u0026thinsp;848) (Fig.\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of seasonal dietary diversity\u003c/h2\u003e\u003cp\u003eIn this study, the prevalence of inadequate dietary diversity among households was 55.5% (95% CI 50.9, 59.0) in December, in March, 60.5% (95% CI: 57.3, 64.9) of households reported inadequate dietary diversity, and in June, 73.5% (95% CI: 70.6, 76.6) of households had inadequate dietary diversity. On the contrary, September showed the lowest rate of inadequate dietary diversity at 40.5% (95% CI: 36.1, 44.9), with the highest proportion of adequate dietary diversity at 59.5% (95% CI: 55.1, 63.9) (Fig.\u0026nbsp;3).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFactors associated with household dietary diversity\u003c/h2\u003e\u003cp\u003eBivariate binary logistic regression analysis revealed that season, sex, religion, educational status, occupational status, wealth index, agriculture input use, and knowledge about dietary diversity were significantly associated with dietary diversity. However, the age of the household head, marital status, residence, savings account, land size, participation in activities of dietary diversity and food insecurity in the household were not significantly associated with dietary diversity.\u003c/p\u003e\u003cp\u003eMultivariate logistic regression analysis identified several factors significantly associated with dietary diversity in the household, including season, sex, religion, occupation, wealth index, not using agricultural input and knowledge of dietary diversity. However, the September season surveyed, the farmer's occupation, medium, and rich wealth index were not significantly associated with dietary diversity. Consequently, households surveyed in June were significantly more likely to have inadequate dietary diversity compared to those surveyed in December (AOR\u0026thinsp;=\u0026thinsp;3.403; 95% CI 2.540, 4.523) and participants in March were 1.13 times more likely to have inadequate dietary diversity in their household compared to households surveyed in December (AOR\u0026thinsp;=\u0026thinsp;1.130 (1.093, 3.164). Female households had higher odds of inadequate dietary diversity than male households (AOR\u0026thinsp;=\u0026thinsp;2.012; 95% CI: 1.380, 2.352). Among household head religions, Orthodox religious followers were 2 times more likely to have inadequate dietary diversity than protestant followers (AOR\u0026thinsp;=\u0026thinsp;2.012; 95%CI:1.380, 2.35) and Muslim religious followers were 1.8 times higher likelihood of inadequate dietary diversity compared to Protestant religious followers (AOR\u0026thinsp;=\u0026thinsp;1.799; 95% CI: 1.166, 3.420). Employed household heads were less likely to have inadequate dietary diversity compared to merchants (AOR\u0026thinsp;=\u0026thinsp;0.39; 95% CI: 0.244, 0.624). Households in the poorest wealth category had 1.65 times higher odds of inadequate dietary diversity than those in the richest category (AOR\u0026thinsp;=\u0026thinsp;1.645; 95% CI: 1.294, 2.092). The absence of use of agricultural inputs also increased the likelihood of inadequate dietary diversity (AOR\u0026thinsp;=\u0026thinsp;1.280; 95% CI: 1.074, 1.525). Finally, household heads who had poor knowledge about dietary diversity were 1.35 times more likely to have inadequate dietary diversity (AOR\u0026thinsp;=\u0026thinsp;1.352; 95% CI 1.142, 2.073) than encounters. The model showed good fit according to the Hosmer and Lemeshow test (p\u0026thinsp;=\u0026thinsp;0.913) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression analysis of factors associated with dietary diversity among households in the West Gojjam Zone, Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDietary Diversity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCrude OR\u003c/p\u003e\u003cp\u003e(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdequate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.962(0.616, 2.178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.130(1.093, 3.164)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.260(2.489, 4.269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.403(2.540, 4.523)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.934(0.612, 2.173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89(0.589, 1.159)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eHousehold head sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.732(1.415, 2.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.012(1.380, 2.352)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHousehold head religion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrthodox\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.686(1.231, 2.309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.012(1.380, 2.35)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.465(0.954, 2.252)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.799(1.166, 3.420)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtestant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eHousehold head occupation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.291(1.038, 1.605)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.137(0.862, 1.499)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.336(0.227, 0.499)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39(0.244, 0.624)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMerchant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eWealth index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.669(1.304, 2.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.645(1.294, 2.092)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.560(1.264, 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.452(1262, 3.037)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.959(0.757 1.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.121(0.987, 1.885)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.387(0.948, 2.675)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.139(0.969, 3.916)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAgricultural input use (fertilizer, improved seed, insecticide)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.305(1.138 1.497)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.280(1.074, 1.525)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eKnowledge about DDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.280(1.116, 1.467)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.352(1.142, 2.073)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eHosmer and Lemeshow test, p value\u0026thinsp;=\u0026thinsp;0.913\u003c/p\u003e\u003cp\u003eNote: **: p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, *: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significantly associated.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this study was to determine the seasonal prevalence of dietary diversity and to identify its associated factors among households in the West Gojjam Zone, Ethiopia. The highest prevalence of inadequate dietary diversity was found in June (73.5%), followed by March (60.9%), while December (44.5%) and September (33.5%) had lower rates. Overall, dietary diversity showed significant seasonal variation, with higher inadequacy during the pre-harvest months (March and June) compared to the post-harvest months (September and December). The key factors associated with dietary diversity included season, sex, religion, occupation, wealth index, agricultural input use, and knowledge of dietary diversity.\u003c/p\u003e\u003cp\u003eIn this study, the pre-harvest seasons (March and June) exhibited the highest prevalence of inadequate dietary diversity\u0026mdash;60.5% and 73.5%, respectively, while the post-harvest seasons (September and December) showed much lower levels of 40.5% and 55.5%. This seasonal pattern is consistent with the findings from studies in Ethiopia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Uganda [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and Kenya [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The possible explanations for these variations could include similar in study participants, data collection instruments, and the specific months when data were collected. The seasonal prevalence of inadequate dietary diversity observed in this study (33.5\u0026ndash;73.5%) was higher than reported in studies conducted in Ethiopia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Kenya (36.5\u0026ndash;62.4%) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The possible explanations for these discrepancies include differences in study design, such as the inclusion of children and women as household respondents in this study, the timing of data collection across different seasons, and variations in the local context and agricultural practices in the study settings.\u003c/p\u003e\u003cp\u003eIn this study, most households consumed cereals, grains, and cereal products, with a prevalence of 98.6% and followed by legumes and pulses, which were also commonly consumed by 73.5% of households. However, the intake of vegetables and fruits rich in vitamin A was lower; Animal-derived foods were consumed less frequently, with meat and other animal products; milk and milk products, eggs, and other fruits. This study in agreement with other studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which indicated ccereals were the most dominant food group consumed by 96.4% of households followed by pulses, which was consumed by 82% of households, while meat, vegetables, and fruits were the food groups by households in Ethiopia[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]This might be the sociocultural, norm, and socioeconomic status of the study participants.\u003c/p\u003e\u003cp\u003eSeasonal variation significantly influences dietary diversity, particularly in agrarian communities where food availability is closely related to agricultural cycles. In this study, the pre-harvest (lean) season was found to be a significant predictor of inadequate dietary diversity between households. This is consistent with studies of 11 demographic and health survey report[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and Ethiopia[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study demonstrates lower dietary diversity during preharvest periods, when food stocks are depleted, leading to a greater vulnerability to malnutrition. This could be justified by seasonal depletion of food stocks and reduced income during preharvest periods, which limit access to diverse foods. These structural factors are widely recognized as key drivers of fluctuations in dietary diversity in agrarian settings. Reliance on subsistence agriculture makes households vulnerable to seasonal dietary diversity.\u003c/p\u003e\u003cp\u003eIn this study, the odds of having inadequate household dietary diversity were 2 times higher among women-headed households compared to men-headed households. This finding aligns with studies from Uganda [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Ethiopia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This might be due to gender dynamics often influence food allocation and consumption within households, especially in patriarchal societies such as Ethiopia, may have less access to purchase diverse foods due to cultural norms prioritizing men\u0026rsquo;s nutrition [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and have lower dietary diversity because of economic disadvantages [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, other studies have reported better dietary diversity in female households, possibly due to women\u0026rsquo;s greater autonomy in food related decisions and have dietary diversity in the household [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These mixed findings highlight the importance of considering contextual gender roles when examining dietary outcomes.\u003c/p\u003e\u003cp\u003eIn the present study, religion emerged as a significant factor associated with the diversity of the family's diet. Household followers of the Orthodox Christian family members had twice the odds of being at risk of inadequate dietary diversity compared to those who followed the Protestant religion. This finding is consistent with previous studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This might be because religious affiliation can influence dietary diversity through its impact on food choices, restrictions, and cultural practices. For example, Orthodox Christian fasting periods in Ethiopia involve abstaining from animal-based foods, which can temporarily reduce dietary diversity. More broadly, religious dietary laws shape the inclusion or exclusion of specific food groups, thus influencing overall dietary patterns.\u003c/p\u003e\u003cp\u003eIn the current study, occupation was identified as an important factor influencing the diversity of the diet of the home. Employed household heads were less likely to have inadequate dietary diversity compared to merchants. Employers currently have paid less than farmers. This may be because farming households may have better access to fresh produce and diverse crops; however, they are also more vulnerable to seasonal food shortages that limit year-round dietary diversity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, households engaged in non-farming occupations often benefit from more stable income, allowing them to purchase a wider variety of foods from markets. Previous Ethiopian studies support this distinction, indicating that while farmers have access to home-grown food items, their diets are more affected by seasonal fluctuations, whereas urban and wage-earner households rely heavily on market access for dietary diversity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the current study, the wealth index emerged as a strong predictor of household dietary diversity. Households in the poorest wealth category had 64.5% higher odds of experiencing inadequate dietary diversity compared to those in the richest category. This finding is consistent with studies conducted in Ethiopia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and Northern Uganda [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This may be explained by the fact that low-income households are more vulnerable to high food prices, seasonal fluctuations, and limited access to nutritious foods, often leading to reduced consumption of animal-based products and greater dependence on cereal-based diets.\u003c/p\u003e\u003cp\u003eFurthermore, households that did not use agricultural input were 1.3 times more likely to experience inadequate dietary diversity compared to their counterparts. This finding is consistent with studies conducted in Ethiopia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Kenya [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Malawi [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] which indicate that the use of agricultural inputs, such as improved seeds, fertilizers, and irrigation, is positively associated with increased crop diversity and, consequently, better dietary diversity.\u003c/p\u003e\u003cp\u003eFinally, knowledge of dietary diversity was identified as a significant determinant of dietary diversity among households. Households who had poor knowledge about dietary diversity were 3.9 times more likely to have inadequate dietary diversity compared to their counterparts. This finding is consistent with previous studies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The justification for this association lies in the fact that households with greater awareness and understanding of the benefits of consuming a variety of food groups are more likely to make informed dietary choices, thus improving their dietary diversity. This is consistent with recent literature that emphasizes the role of nutrition education in improving dietary practices and food choices [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strengths of this study include a large sample size, determining seasonal prevalence, and using standardized tools. However, its cross-sectional design limits the capture of temporal dietary variations, the reliance on self-reported data can cause recall bias, and the study conducted only in West Gojjam restricts generalizability to other Ethiopian regions with different cultural and ecological contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that household dietary diversity varied significantly over months, with the highest proportion of low diversity occurring in June and March, highlighting periods of increased nutritional vulnerability. Key factors influencing dietary diversity included season, sex, religion, occupation, wealth index, agricultural input use, and knowledge of dietary diversity. To address these challenges, strategies should prioritize nutrition-sensitive interventions during critical months, such as food aid, community education, and income-generating activities. Efforts should also enhance access to agricultural inputs and address socioeconomic disparities through targeted policies. Finally, seasonal monitoring of dietary diversity is crucial to enable timely, evidence-based responses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCrude Odds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDDS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDietary Diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDDS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHousehold Dietary Diversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the University of South Africa, the Faculty of Human Sciences, and the Department of Health Studies for their support. We also extend our thanks to the West Gojjam Zone and district administrators. Finally, we acknowledge the data collectors, supervisors, and study participants for their invaluable contributions.\u003c/p\u003e\n\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThis study involved human participants and received ethical clearance from the Health Research Ethics Committee of the Department of Health Studies, University of South Africa (UNISA) (Reference: 11760680_CREC_CHS_2023). A permission letter was provided by the Amhara Public Health Institute (Reference: APHI/03/2053) and the West Gojjam Zone (Reference: 1443/11/2016) to conduct the study in the selected districts. All participants gave their informed written consent in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors consented to the submission of this manuscript for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe primary data generated and analyzed in this study are included in this article. Any clarifications can be addressed by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by Bahir Dar University (Grant ID: BDU1789/2023), which supported the data collection budget. The funding organization had no role in the design, supervision, analysis, or interpretation of the study and did not impose restrictions on publication.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; Contributions\u003c/p\u003e\n\u003cp\u003eMBY and MGM conceived the study, collected the data, and performed analysis and interpretation. MBY drafted the first version of the manuscript, while MGM contributed to the study design and substantially revised the final draft. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHatl\u0026oslash;y A, Torheim LE, Oshaug A. Food variety\u0026ndash;a good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur J Clin Nutr. 1998;52(12):891\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJateno W, Alemu BA, Shete M. Household dietary diversity across regions in Ethiopia: Evidence from Ethiopian socio-economic survey data. PLoS ONE. 2023;18(4):e0283496.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKennedy G, DMC. Guidelines for measuring household and individual dietary diversity. Rome: Food and Agriculture Organization of the United Nations; 2011. pp. 2\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFAO. Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations; 2010. pp. 7\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristiaensen LDLKJ. The role of agriculture in poverty reduction: an empirical perspective. J Dev Econ. 2011;(96):23\u0026ndash;254.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFood and Agriculture Organization (FAO). Dietary diversity: Linking food and nutrition security to sustainable food systems. Rome: FAO. 2021;25\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArsenault JE, Nikiema L, Allemand P, Ayassou KA, Lanou H, Moursi M, et al. Seasonal differences in food and nutrient intakes among young children and their mothers in rural Burkina Faso. J Nutr Sci. 2014;3:e55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSavy M, Martin-Pr\u0026eacute;vel Y, Traissac P, Eymard-Duvernay S, Delpeuch F. Dietary diversity scores and nutritional status of women change during the seasonal food shortage in rural Burkina Faso. J Nutr. 2006;136(10):2625\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSom\u0026eacute; JW, Jones AD. The influence of crop production and socioeconomic factors on seasonal household dietary diversity in Burkina Faso. PLoS ONE. 2018;13(5):e0195685.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams AM. Seasonal variations in energy balance among agriculturalists in central Mali: compromise or adaptation? Eur J Clin Nutr. 1995;49(11):809\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuizzo Dri G, Spencer PR, da Costa R, Sanders KA, Judge DS. The seasonal relationships between household dietary diversity and child growth in a rural Timor-Leste community. Matern Child Nutr. 2022;18(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToorang F, HoushiarRad A, Abdollahi M, Esmaili M, Ebrahimpour Koujan S. Seasonality in Iranian Fruit and Vegetable Dietary Intake. Thrita J Med Sci. 2013;2(2):58\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecquey E, Delpeuch F, Konat\u0026eacute; AM, Delsol H, Lange M, Zoungrana M, et al. Seasonality of the dietary dimension of household food security in urban Burkina Faso. Br J Nutr. 2012;107(12):1860\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaye K, Mekonnen D, Choufani J, Yimam S, Bryan E, Griffiths JK et al. Seasonal variation in maternal dietary diversity is reduced by small-scale irrigation practices: A longitudinal study. Matern Child Nutr. 2022;18(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelayneh M, Loha E, Lindtj\u0026oslash;rn B. Seasonal variation of household food insecurity and household dietary diversity on wasting and stunting among young children in a drought prone area in South Ethiopia: a cohort study. Ecol Food Nutr. 2021;60(1):44\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYayeh MB, Makua MG. Seasonal variation in the prevalence of household food insecurity and its associated factors in the West Gojjam Zone, Ethiopia. Discover Public Health. 2025;22(1):116.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMekuria G, Wubneh Y, Tewabe T. Household dietary diversity and associated factors among residents of finote selam town, north west Ethiopia: a cross sectional study. BMC Nutr. 2017;3(1):28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDegye GMK. Measuring diet quantity and quality dimensions of food security in rural Ethiopia. Dev Agric Econ. 2013;174\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCentral statistical Agency[Ethiopia]. Mini Ethiopian Demographic Health survey: key indicators report. The DHS Program ICF. 2019. 2019. pp. 1\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirvonen K, Taffesse AS, Hassen IW. Seasonality and household diets in Ethiopia. Public Health Nutr. 2016;19(10):1723\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYayeh MB, Makua MG. Seasonal prevalence of child undernutrition and its associated factors in the west Gojjam zone, Ethiopia. Archives Public Health. 2025;83(1):146.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelayneh M, Loha E, Lindtj\u0026oslash;rn B. Seasonal Variation of Household Food Insecurity and Household Dietary Diversity on Wasting and Stunting among Young Children in A Drought Prone Area in South Ethiopia: A Cohort Study. Ecol Food Nutr. 2021;60(1):44\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAliyo A, Golicha W, Fikrie A. Household Dietary Diversity and Associated Factors among Rural Residents of Gomole District, Borena Zone, Oromia Regional State, Ethiopia. Health Serv Res Manag Epidemiol. 2022;9:23333928221108030.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolliesuah NP, Olum S, Ongeng D. Status of household dietary diversity and associated factors among rural and urban households of Northern Uganda. BMC Nutr. 2023;9(1):83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSibhatu KT, Krishna VV, Qaim M. Production diversity and dietary diversity in smallholder farm households. Proceedings of the National Academy of Sciences. 2015;112(34):10657\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorkicho A, Belachew T, Feyissa GT, Wondafrash B, Lachat C, Verstraeten R, et al. Household dietary diversity and Animal Source Food consumption in Ethiopia: evidence from the 2011 Welfare Monitoring Survey. BMC Public Health. 2016;16(1):1192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMotbainor A, Worku AKA. Level and determinants of food insecurity in East and west Gojjam zones of Amhara Region, Ethiopia: A community based comparative cross-sectional study. BMC Public Health. 2016;16(503):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoates J, BP. SA, \u0026amp;. Household food insecurity access scale (HFIAS) for measurement of food access: Indicator guide. Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development. 2007;3\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJordan I, R\u0026ouml;hlig A, Glas MG, Waswa LM, Mugisha J, Krawinkel MB, et al. Dietary Diversity of Women across Agricultural Seasons in the Kapchorwa District, Uganda: Results from a Cohort Study. Foods. 2022;11(3):344.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNgala SA. Evaluation of dietary diversity scores to assess nutrient adequacy among rural Kenyan women. Wageningen University; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaswa LM, Jordan I, Krawinkel MB, Keding GB. Seasonal Variations in Dietary Diversity and Nutrient Intakes of Women and Their Children (6\u0026ndash;23 Months) in Western Kenya. Front Nutr. 2021;8:636872.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArimond M, Ruel MT. Dietary diversity is associated with child nutritional status: evidence from 11 demographic and health surveys. J Nutr. 2004;134(10):2579\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEndalifer ML, Andargie G, Mohammed B, Endalifer BL. Factors associated with dietary diversity among adolescents in Woldia, Northeast Ethiopia. BMC Nutr. 2021;7(1):27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOyet SM, Kaahwa RM, Muggaga C, Ongeng D, Okello-Uma I. Household dietary diversity and associated factors in rural and peri-urban areas of Mbale District, Eastern Uganda. BMC Public Health. 2025;25(1):303.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGulema H, Demissie M, Worku A, Yadeta TA, Tewahido D, Berhane Y. Intrahousehold food allocation social norms and food taboos in rural Ethiopia: The case of adolescent girls. Heliyon. 2024;10(11):e32295.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemilew YM, Alene GD, Belachew T. Dietary practices and associated factors among pregnant women in West Gojjam Zone, Northwest Ethiopia. BMC Pregnancy Childbirth. 2020;20(1):18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJateno W, Alemu BA, Shete M. Household dietary diversity across regions in Ethiopia: Evidence from Ethiopian socio-economic survey data. PLoS ONE. 2023;18(4):e0283496.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlwande J, Smale M, Mathenge MK, Place F, Mith\u0026ouml;fer D. Agricultural marketing by smallholders in Kenya: A comparison of maize, kale and dairy. Food Policy. 2015;52:22\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones AD, Shrinivas A, Bezner-Kerr R. Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy. 2014;46:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGirard AW, Self JL, McAuliffe C, Olude O. The Effects of Household Food Production Strategies on the Health and Nutrition Outcomes of Women and Young Children: A Systematic Review. Paediatr Perinat Epidemiol. 2012;26(s1):205\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFekadu Y, Mesfin A, Haile D, Stoecker BJ. Factors associated with nutritional status of infants and young children in Somali Region, Ethiopia: a cross- sectional study. BMC Public Health. 2015;15(1):846.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFood and Agriculture Organization (FAO). Dietary diversity: Linking food and nutrition security to sustainable food systems. Rome: FAO. 2021;25\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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},"keywords":"Dietary diversity, households, Seasonal prevalence, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-6907682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6907682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSeasonal variations significantly influence household dietary diversity, particularly in developing countries such as Ethiopia, where evidence remains limited. Therefore, this study offers valuable information for policymakers, program directors, and stakeholders to better plan interventions on dietary diversity within the context.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to assess the seasonal prevalence of dietary diversity and to identify its associated factors among households in the West Gojjam Zone, northwest Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA longitudinal study using cross-sectional surveys was conducted from December 1, 2023, to September 30, 2024. A total of 3,360 household heads were selected through a multistage stratified sampling technique. Data were collected during the seasonal transition months, December, March, June and September, using questionnaires administered by the interviewees. Data entry and cleaning were performed using EPI Data version 4.3, and SPSS version 29 statistical analyzes, including multivariate logistic regression to identify factors associated with dietary diversity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe seasonal prevalence of inadequate dietary diversity among households was 55.5% in December, increased to 60.5% in March, and reached 73.5% in June. September had the lowest rate of inadequate diversity at 40.5%. The key factors positively associated with inadequate dietary diversity were surveyed in June (AOR\u0026thinsp;=\u0026thinsp;3.40; 95% CI 2.54\u0026ndash;4.52) and March (AOR\u0026thinsp;=\u0026thinsp;1.13; 95% CI: 1.09\u0026ndash;3.16), female-headed households (AOR\u0026thinsp;=\u0026thinsp;2.01; 95% CI: 1.38\u0026ndash;2.35), Orthodox (AOR\u0026thinsp;=\u0026thinsp;2.01; 95% CI: 1.38\u0026ndash;2.35) and followers of Muslim religion (AOR\u0026thinsp;=\u0026thinsp;1.80; 95% CI: 1.17\u0026ndash;3.42), employed households (AOR\u0026thinsp;=\u0026thinsp;0.39; 95% CI: 0.20\u0026ndash;0.62), category of poorest wealth (AOR\u0026thinsp;=\u0026thinsp;1.65; 95% CI: 1.29\u0026ndash;2.09), not using agricultural input (AOR\u0026thinsp;=\u0026thinsp;1.28; 95% CI: 1.07\u0026ndash;1.53) and poor knowledge on dietary diversity (AOR\u0026thinsp;=\u0026thinsp;1.35; 95% CI: 1.14\u0026ndash;2.07).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study highlights notable seasonal variations in the diversity of household foods, with the highest inadequacy in June and the lowest in September. Key risk factors included female-headed households, Orthodox and Muslim religious affiliation, low wealth status, not using agricultural input, and poor knowledge of dietary diversity. Employment was found to be protective. Developing strategies that address economic disparities, cultural influences, and seasonal food group availability are essential to improve dietary diversity between households.\u003c/p\u003e","manuscriptTitle":"Seasonal prevalence of dietary diversity and its associated factors among households in West Gojjam Zone, Ethiopia: Longitudinal cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 16:04:08","doi":"10.21203/rs.3.rs-6907682/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":"4a2c5380-455e-494a-aaa7-e1e6f1ce666c","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T10:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 16:04:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6907682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6907682","identity":"rs-6907682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}