Assessing and Enhancing Dietary Diversity among Women of Reproductive Age in Tanzania: Insights from the Tanzania National Panel Survey Wave 4 Data

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This study seeks to create a proxy for MDD-W in Tanzania, which is general missing in national-level surveys. We analyzed data from Tanzania National Panel survey wave 4 (TNPS) from which sixty food items were considered and grouped into 10 food categories as per Food and Agriculture Organization guide (2021). We considered only female household members aged 15–49 years. Data of women of reproductive age (WRA) were linked, person’s unique identifiers, with other characteristics related to food consumption and household’s data files. Grain, roots and tuber were consumed by majority (56.62%) while meat, poultry and fish by 19.3% and the rest of food groups by less than 3%. Slightly more than half (57%) of the women consumed from at least two food groups and collective MDD-W of the group was 1.83%. For that reason, none of the women attained a minimum dietary diversity for women of reproductive age. The findings suggest a potential contributor to micronutrient deficiencies in Tanzania and the insights from the study highlight the need for diversified diets to improve nutrition and health outcomes for this specific group under the study. Micronutrients Women of reproductive age minimum dietary diversity health outcomes Tanzania. Figures Figure 1 Introduction Exploring Minimum Dietary Diversity of Women of reproductive age (MDD-W) is a matter of public health importance given their vulnerability due to increased nutrition needs for physiological demands of pregnancy and lactation 1 . The food and Agriculture Organization (FAO) defines MDD-W as a measure for whether women have consumed a minimum number of diverse food groups over a specific reference period (usually 24 hours). It is defined as the proportion of women of reproductive age (WRA) who have consumed foods from at least five out of ten specified food groups. To meet the MDD-W criteria, a woman must have consumed at least one food item from each of the five different food groups out of the ten listed within the reference period. High prevalence of MDD-W among a group of WRA is therefore a proxy for better micronutrient adequacy in a given population .Wide range of potential factors for attainment of MDD-W have been reported in various settings including that of socioeconomic and food security variables having greatest effect on MDD-W and purchasing practices, like going to the market, showing a positive effect in a study where only one in three women reached the minimum threshold 2 and elsewhere where MDD-W was compromised by peri-urbanicity of dwelling place as there were more food insecurity because of high levels of poverty, unemployment, and lack of land 3 . Such other similar exploration of MDD-W has been documented reported across multiplicity of geo-political settings in Low- and Middle-Income Countries (LMICs) 4-7 and developed countries 8 similarly. Monitoring of MDD-W would, as such, inform on social economic profile of causes and effect and provide guidance on targeted interventions and policy intents towards improving nutrition and overall health outcomes 1 . In this study we used secondary data from National Panel Survey (NPS) wave four to established proxy MDD-W in Tanzania. This dataset has been missing in the final information products from NPS series and other National level surveys. Given the current status on triple burden of malnutrition and national response strategies, it is high time that measuring of MDD-W becomes a priority to inform National, region and global milestones of the nutrition agendas. Methods We analyzed data from Tanzania National Panel survey wave 4 (TNPS) collected by National Bureau of Statistics. Data was accessed via the Living Standards Measurement Study (LSMS) of the World Bank Microdata Library 9 . The TNPS is a nationally representative household survey that collects information on the living standards of the Tanzanian population, including their socio-economic characteristics, consumption, agricultural production, and non-farm income generating activities. The survey follows the same households over time to track changes in living conditions. In Module J of the TNPS, household members in particular an adult woman were asked, prompted, to recall the foods consumed by all members of the household over the past 7 days. Figure 1 map of Tanzania showing the study area (prepared by Aleswa Swai). Data Selection: Sixty food items were grouped into 10 food groups (Grain, roots, tubers; Pulses; Nuts and seeds; Milk and milk products; Meat, poultry, fish; Eggs; Dark green leafy vegetables; Vit A rich fruits and vegetables; Other vegetable; Other fruits) as per Food and Agriculture Organization guide 1 . We considered only female household members aged 15- 49 years. The TNPS (2014-2015)-wave 4 data model used unique identifiers to link data of women of reproductive age with other characteristics related to food consumption and household’s data files. Computation of MDD-W index: To measure the MDD-W , food items (at least 15g) consumed from at least five out of the ten defined food groups the previous day or night were considered. The resulting index is associated with a higher probability of nutrient adequacy for 11 micronutrients which includes vitamin A, thiamine, riboflavin, niacin, vitamin B-6, folate, vitamin B-12, vitamin C, calcium, iron and zinc. According to FAO 1 Data Analysis: STATA version 15.0 was used for the analysis for which the 60 food items were included in the model for creation of 10 food categories and generation of the MDD-W index. Food group with minimum consumption score (%) was considered the MDD-W for the sample population. From the NPS data, women age 15 to 49 years and who had a “yes” response to have consumed any of the sixty food items were selected. The sixty food items were grouped into 10 groups for alignment with guideline by FAO for measurement of MDD-W index. The MDD-W index score was investigated as an outcome variable for selected potential social demographic characteristics. Logistic regression was to ascertain association between the social demographic factors with a dependent variable. Tables were used to describe the findings, and Microsoft Word was used to write the narration. Ethical Considerations: Inherit those for primary study, informed consent was obtained and data were de-identified to protect privacy. Limitations: Bias in the original data collection, lack of control over the data collection process, and missing or incomplete information. Some of food items were lumped together hence limit choices and local vegetables and fruits were not included. Results Table 1a and 1b provides a summary of the distribution of the demographic and social-economic characteristics of the women in the study. Middle,20-35 years old, was the most prevalent (54%) band in the study of the reproductive age group from 15 – 49 years. Majority (84%) of the respondents had been to school, predominantly (72%) at primary school level and Swahili native language in Tanzania, was the most common (54%) spoken amongst them. MDD-W of the group was 1.83% and collectively grain, roots and tubers were the most (57%) common dietary intakes followed by meat, poultry and fish which scored 19% as shown on table 1.a. On table 1.b the MDD-W of 1.83% was once again and thus confirmed for pulses with age factor considered and grain, roots and tubers remained the most common dietary intakes all across age groups. There was much more proportion of women in the age group of 15-19 consuming milk and milk products than in other age categories (p value = 0.045) and no other food category had variation in consumption by age factor. Table 2 and table 3 are results of the univariate and multivariate analyses respectively for MDD-W as dependent variable against other relevant input variables of the intended model as shown in the tables. None of the potential risk factors or covariates showed an association at univariate level but yet at multivariate level there was 59% (p= 0.044) reduced odds of meeting the MDD-W with each (one) unit increase in education level. Discussion Our findings have shown reciprocal association of education level with higher score in MDD for women of reproductive age. Similar findings were reported elsewhere in which there was nearly 3-fold odds of attaining affirmative minimum dietary diversity with education of high school and above but much higher odds with education of high school completed grade 8 and attended elementary education 10 . An observed paradox, in this study, of reciprocal association of MDD-W with increasing education level has thus been reconfirmed. Further research efforts might, as such, be useful for additional light shedding to decision and policy makers. There has been reports of more than 50% MDD-W scores in several other areas of similar settings 11 , 12 . An observed 1.83% MDD-W score in our study convoyed by asymmetric consumption patterns of carbohydrates than other food categories across all the age groups might suggest for an influence of cultural issues related to staple food characteristics of the pertinent population, which might suggest for further exploration. Carbohydrates rich food groups are staple food crops grown in tropical and subtropical areas making it a potentially valuable food source for developing countries, Tanzania included. Such a low score of 1.83% in MDD-W suggests for hidden hunger in terms of micro nutrient deficiency which is supported by findings from the Tanzania Demographic and Health and Malaria Indicators Survey (TDHS-MIS) of 2022 which showed 42% of women of child bearing age and 59% of under-five are anemic 13 . Furthermore, while our findings have shown less than three food groups, at lowest, being consumed by women of reproductive age, the TDHS-MIS 2022 has shown only one quarter (25%) of women of reproductive age consumed five or more out of ten food groups 13 . Conclusion and recommendation By understanding the dietary patterns of women of reproductive age, targeted interventions and policies can be developed to effectively address nutritional deficiencies. For that matter, insights from our study highlight the need for diversified diets to improve nutrition and health outcomes for women of reproductive age. Furthermore, efforts towards promoting good eating behaviors, food fortification and bio fortification, as well as promoting not only cultivation of diversifies crops, but also promote its consumption at household level and should be emphasized for public health benefit. Declarations Ethics approval and consent to participate: Inherit those for primary survey (National Panel Survey wave 4), informed consent was obtained from participants and data were de-identified to protect privacy. Consent for publication: Ethical clearance was obtained from Tanzania National Institute for Medical Research Availability of data and materials: Data was accessed via the Living Standards Measurement Study (LSMS) of the World Bank Microdata Library ( https://microdata.worldbank.org/index.php/home ) Competing interests: No any competing interest Funding: There were no funds for this secondary data analysis. Authors' contributions AZS, ORM and JJM conceptualized the study and data collation and cleaning were performed by AZS, JK and ORM. AZS, JK and ORM analyzed the data. AZS wrote the first draft of the manuscript and all authors provided creative inputs during manuscript drafting and revisions. All authors read and approved the final manuscript. Acknowledgement: We are acknowledging world bank microdata bank and Tanzania Nation Bureau of Statistics (NBS) for providing data to facilitate this secondary analysis as well as the Tanzania Food and Nutrition center for offering an environment for the analysis, processes and writing of results. References FAO. (2021). Minimum dietary diversity for women an updated guide to measurement - from collection to action. Rome, Italy. https://doi.org/10.4060/cb3434en Custodio, E., Kaykatire, F., Fortin, S., Thomas, A.-C., Kameli, Y., Nkuzimana, C., . . . Martin-Prevel, Y. (2020). Minimum dietary diversity among women of reproductive age in urban Burkina Faso. nutrition, Medical and child. https://doi.org/10.1111/mcn.12897 Chakona, G., & Shackleton, C. (2017). Minimum Dietary Diversity Scores for Women Indicate Micronutrient Adequacy and Food Insecurity Status in South African Towns. https://doi.org/10.3390/nu9080812 Girma, T. A., & Molla, K. A. (2021). Maternal minimum dietary diversity and associated factors among pregnant women, Southwest Ethiopia. BMC Nutrition. https://doi.org/10.1186/s40795-021-00474-8 Derrick, K., Florence, N., Norah, N., N., N. R., Kenneth, K., Sheila, N., . . . Daraus, B. (2024). Prevalence and determinants of minimum dietary diversity for women of reproductive age in Uganda. BMC Nutrition. https://doi.org/10.1186/s40795-024-00858-6 Adubra, L., Savy, M., Fortin, S., Niamké, Y. K., Kodjo, E., Fainke, K., . . . Martin-Prevel, Y. (2019). The Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) Indicator Is Related to Household Food Insecurity and Farm Production Diversity: Evidence from Rural Mali. Current Developments in Nutrition. https://doi.org/10.1093/cdn/nzz002 Bellows, A., Canavan, C. R., & Fawzi, W. W. (2019). The Relationship Between Dietary Diversity Among Women of Reproductive Age and Agricultural Diversity in Rural Tanzania. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119892405 Stefanie, V., De, V. S., Inge, H., Michel, M., & Van, O. H. (2010). Overall and within-food group diversity are associated with dietary quality in Belgium. Public Health Nutrition. doi:10.1017/S1368980010001606 World bank. (2023, November). The worldbank microdata library. Retrieved from https://microdata.worldbank.org/index.php/home Kuma, M.N; Tamiru, D; & Belachew, T (2021). Level and predictors of dietary diversity among pregnant women in rural South-West Ethiopia: a community-based cross-sectional study. BMJ. doi:10.1136/ bmjopen-2021-055125 Abel, G.T & Abebaw, M.K (2021). Maternal minimum dietary diversity and associated factors among pregnant women, Southwest Ethiopia. BMC Nutrition. https://doi.org/10.1186/s40795-021-00474-8 Saaka, M; Mutaru, S; & Osman, S.M (2021). Determinants of dietary diversity and its relationship with the nutritional status of pregnant women. Journal of Nutrition Science. https://doi.org/10.1017/jns.2021.6 Ministry of Health United Republic of Tanzania & Zanzibar, National Bureau of Statistics(Dodoma) & Office of the Chief Government Statistician (Zanzibar) & The DHS Program ICF, Maryland (USA). (2023). Tanzania Demographic and Health Survey and Malaria Indicator Survey 2022 report. https://dhsprogram.com/pubs/pdf/SR282/SR282.pdf Tables Table 1a: Social-Demographic Characteristics of Respondents (N=3884) Characteristics n = 3,884 % Cum Age 15-19 813 20.93 20.93 20-35 2106 54.22 75.15 36-49 965 24.85 100 Employment Employed 2128 54.94 54.94 Not Employed 1745 45.06 100 Ever go to School Yes 3251 83.94 83.94 No 622 16.06 100 Read And Write Swahili 2108 54.43 54.43 English 48 1.24 55.67 Kiswahili And English 912 23.55 79.22 Other 1 0.03 79.24 No 804 20.76 100 Education Level Primary Level 1940 70.91 70.91 Secondary Level 764 27.92 98.83 Higher Education 32 1.17 100 Food Groups Grain,roots,tubers 1,390 56.62 56.62 Pulses 45 1.83 58.45 Nuts and seeds 128 5.21 63.66 Milk and milk products 125 5.09 68.75 Meat,Poultry,Fish 474 19.31 88.06 Eggs 54 2.20 90.26 Dark green leafy vegetables 63 2.57 92.83 Vit A rich fruits and vegetables 65 2.65 95.48 Other vegetable 52 2.12 97.60 Other Fruits 59 2.40 100 Marital Status Married 2230 57.58 57.58 Not Married 1643 42.42 100 Residence Rural 1984 59.19 59.19 Urban 1368 40.81 100 MDDW = 1.83% One food group 1648 42.43 42.43 Two food groups 2236 57.57 100 * None of the women met MDD-W Table 1b: Minimum dietary diversity of women by age categories Table 2: Univariate association MDDW Odds ratio Std. Error Z P> |z| 95% interval Age 1.0533460 0.0506395 1.08 0.280 0.9586272 - 1.157424 Employment 0.9693729 0.0633266 -0.48 0.634 0.8528725 - 1.101787 Education Level 0.8843629 0.0703713 -1.54 0.123 0.7566551 - 1.033625 Marital Status 0.9109075 0.0598740 -1.42 0.156 0.8008014 - 1.036153 Residence 0.9315492 0.1309708 -0.50 0.614 0.7071824 - 1.227100 *MDDW = Minimum Dietary Diversity of Women Table 3: Multivariate association MDDW Coef. Std. Err. t P> | t | 95% conf. Interval Age 20-35 0.0286213 0.0615611 0.46 0.642 -0.0922664 0.1495090 36-49 0.0105102 0.0686817 0.15 0.878 -0.1243602 0.1453805 Employment Not Employed 0.0062837 0.0397233 0.16 0.876 -0.071721 0.0842883 Residence Urban -0.0187071 0.039999 -0.47 0.640 -0.092848 0.0721086 Marital Status Not married -0.0103697 0.0420015 -0.25 0.805 -0.092848 0.0721086 Education Level Secondary Level 0.0558447 0.0439315 1.27 0.204 -0.0304237 0.1421131 Higher Education -0.4097104 0.2034123 -2.01 0.044 -0.8091513 -0.0102695 _Cons 0.5610483 0.0673988 8.32 0.000 0.4286971 0.6933995 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. 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The food and Agriculture Organization (FAO) defines MDD-W as a measure for whether women have consumed a minimum number of diverse food groups over a specific reference period (usually 24 hours). It is defined as the proportion of women of reproductive age (WRA) who have consumed foods from at least five out of ten specified food groups. To meet the MDD-W criteria, a woman must have consumed at least one food item from each of the five different food groups out of the ten listed within the reference period. High prevalence of MDD-W among a group of WRA is \u0026nbsp;therefore \u0026nbsp;a proxy for better micronutrient adequacy in a given population .Wide range of potential factors for attainment of MDD-W have been reported in various settings including that of socioeconomic and food security variables having \u0026nbsp;greatest effect on MDD-W and purchasing practices, like going to the market, showing a positive effect in a study where only one in three women reached the minimum threshold\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; and elsewhere where MDD-W was compromised by peri-urbanicity of dwelling place as there were more food insecurity because of high levels of poverty, unemployment, and lack of land\u003csup\u003e3\u003c/sup\u003e. Such other similar exploration of MDD-W has been documented reported across multiplicity of geo-political settings in Low- and Middle-Income Countries (LMICs)\u003csup\u003e4-7\u003c/sup\u003e and developed countries\u003csup\u003e8\u003c/sup\u003e similarly.\u003c/p\u003e\n\u003cp\u003eMonitoring of MDD-W would, as such, inform on social economic profile of causes and effect and provide guidance on targeted interventions and policy intents towards improving nutrition and overall health outcomes\u003csup\u003e1\u003c/sup\u003e. \u0026nbsp; In this study we used secondary data from National Panel Survey (NPS) wave four to established proxy MDD-W in Tanzania. This dataset has been missing in the final information products from NPS series and other National level surveys. Given the current status on triple burden of malnutrition and national response strategies, it is high time that measuring of MDD-W becomes a priority to inform National, region and global milestones of the nutrition agendas. \u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe analyzed data from Tanzania National Panel survey wave 4 (TNPS) collected by National Bureau of Statistics. Data was accessed via the Living Standards Measurement Study (LSMS) of the World Bank Microdata Library\u003csup\u003e9\u003c/sup\u003e. The TNPS is a nationally representative household survey that collects information on the living standards of the Tanzanian population, including their socio-economic characteristics, consumption, agricultural production, and non-farm income generating activities. The survey follows the same households over time to track changes in living conditions. In Module J of the TNPS, household members in particular an adult woman were asked, prompted, to recall the foods consumed by all members of the household over the past 7 days. Figure 1 map of Tanzania showing the study area (prepared by Aleswa Swai).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Selection:\u003c/strong\u003e Sixty food items were grouped into 10 food groups (Grain, roots, tubers; Pulses; Nuts and seeds; Milk and milk products; Meat, poultry, fish; Eggs; Dark green leafy vegetables; Vit A rich fruits and vegetables; Other vegetable; Other fruits) as per Food and Agriculture Organization guide\u003csup\u003e1\u003c/sup\u003e. We considered only female household members aged 15- 49 years. The TNPS (2014-2015)-wave 4 data model used unique identifiers to link data of women of reproductive age with other characteristics related to food consumption and household\u0026rsquo;s data files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComputation of MDD-W index:\u0026nbsp;\u003c/strong\u003eTo measure the MDD-W\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003efood items (at least 15g) consumed from at least five out of the ten defined food groups the previous day or night were considered. The resulting index is associated with a higher probability of nutrient adequacy for 11 micronutrients which includes vitamin A, thiamine, riboflavin, niacin, vitamin B-6, folate, vitamin B-12, vitamin C, calcium, iron and zinc. According to FAO\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis:\u0026nbsp;\u003c/strong\u003eSTATA version 15.0 was used for the analysis for which the 60 food items were included in the model for creation of 10 food categories and generation of the MDD-W index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFood group with minimum consumption score (%) was considered the MDD-W for the sample population. From the NPS data, women age 15 to 49 years and who had a \u0026ldquo;yes\u0026rdquo; response to have consumed any of the sixty food items were selected. The sixty food items were grouped into 10 groups for alignment with guideline by FAO for measurement of MDD-W index. The MDD-W index score was investigated as an outcome variable for selected potential social demographic characteristics. Logistic regression was to ascertain association between the social demographic factors with a dependent variable. Tables were used to describe the findings, and Microsoft Word was used to write the narration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e Inherit those for primary study, informed consent was obtained and data were de-identified to protect privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e Bias in the original data collection, lack of control over the data collection process, and missing or incomplete information. Some of food items were lumped together hence limit choices and local vegetables and fruits were not included.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1a and 1b provides a summary of the distribution of the demographic and social-economic characteristics of the women in the study. Middle,20-35 years old, was the most prevalent (54%) band in the study of the reproductive age group from 15 \u0026ndash; 49 years. Majority (84%) of the respondents had been to school, predominantly (72%) at primary school level and Swahili native language in Tanzania, was the most common (54%) spoken amongst them. MDD-W of the group was 1.83% and collectively grain, roots and tubers were the most (57%) common dietary intakes followed by meat, poultry and fish which scored 19% as shown on table 1.a.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn table 1.b the MDD-W of 1.83% was once again and thus confirmed for pulses with age factor considered and grain, roots and tubers remained the most common dietary intakes all across age groups. There was much more proportion of women in the age group of 15-19 consuming milk and milk products than in other age categories (p value = 0.045) and no other food category had variation in consumption by age factor.\u003c/p\u003e\n\u003cp\u003eTable 2 and table 3 are results of the univariate and multivariate analyses respectively for MDD-W as dependent variable against other relevant input variables of the intended model as shown in the tables. None of the potential risk factors or covariates showed an association at univariate level but yet at multivariate level there was 59% (p= 0.044) reduced odds of meeting the MDD-W with each (one) unit increase in education level. \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings have shown reciprocal association of education level with higher score in MDD for women of reproductive age. Similar findings were reported elsewhere in which there was nearly 3-fold odds of attaining affirmative minimum dietary diversity with education of high school and above but much higher odds with education of high school completed grade 8 and attended elementary education\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. An observed paradox, in this study, of reciprocal association of MDD-W with increasing education level has thus been reconfirmed. Further research efforts might, as such, be useful for additional light shedding to decision and policy makers.\u003c/p\u003e \u003cp\u003eThere has been reports of more than 50% MDD-W scores in several other areas of similar settings\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. An observed 1.83% MDD-W score in our study convoyed by asymmetric consumption patterns of carbohydrates than other food categories across all the age groups might suggest for an influence of cultural issues related to staple food characteristics of the pertinent population, which might suggest for further exploration. Carbohydrates rich food groups are staple food crops grown in tropical and subtropical areas making it a potentially valuable food source for developing countries, Tanzania included. Such a low score of 1.83% in MDD-W suggests for hidden hunger in terms of micro nutrient deficiency which is supported by findings from the Tanzania Demographic and Health and Malaria Indicators Survey (TDHS-MIS) of 2022 which showed 42% of women of child bearing age and 59% of under-five are anemic\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, while our findings have shown less than three food groups, at lowest, being consumed by women of reproductive age, the TDHS-MIS 2022 has shown only one quarter (25%) of women of reproductive age consumed five or more out of ten food groups\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion and recommendation","content":"\u003cp\u003eBy understanding the dietary patterns of women of reproductive age, targeted interventions and policies can be developed to effectively address nutritional deficiencies. For that matter, insights from our study highlight the need for diversified diets to improve nutrition and health outcomes for women of reproductive age. Furthermore, efforts towards promoting good eating behaviors, food fortification and bio fortification, as well as promoting not only cultivation of diversifies crops, but also promote its consumption at household level and should be emphasized for public health benefit.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eInherit those for primary survey (National Panel Survey wave 4), informed consent was obtained from participants and data were de-identified to protect privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eEthical clearance was obtained from Tanzania National Institute for Medical Research\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData was accessed via the Living Standards Measurement Study (LSMS) of the World Bank Microdata Library ( https://microdata.worldbank.org/index.php/home )\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNo any competing interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There were no funds for this secondary data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAZS, ORM and JJM conceptualized the study and data collation and cleaning were performed by AZS, JK and ORM. AZS, JK and ORM analyzed the data. AZS wrote the first draft of the manuscript and all authors provided creative inputs during manuscript drafting and revisions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are acknowledging world bank microdata bank and Tanzania Nation Bureau of Statistics (NBS) for providing data to facilitate this secondary analysis as well as the Tanzania Food and Nutrition center for offering an environment for the analysis, processes and writing of results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFAO. (2021). Minimum dietary diversity for women an updated guide to measurement - from collection to action. Rome, Italy. https://doi.org/10.4060/cb3434en\u003c/li\u003e\n\u003cli\u003eCustodio, E., Kaykatire, F., Fortin, S., Thomas, A.-C., Kameli, Y., Nkuzimana, C., . . . Martin-Prevel, Y. (2020). Minimum dietary diversity among women of reproductive age in urban Burkina Faso. nutrition, Medical and child. https://doi.org/10.1111/mcn.12897 \u003c/li\u003e\n\u003cli\u003eChakona, G., \u0026amp; Shackleton, C. (2017). Minimum Dietary Diversity Scores for Women Indicate Micronutrient Adequacy and Food Insecurity Status in South African Towns. https://doi.org/10.3390/nu9080812 \u003c/li\u003e\n\u003cli\u003eGirma, T. A., \u0026amp; Molla, K. A. (2021). Maternal minimum dietary diversity and associated factors among pregnant women, Southwest Ethiopia. BMC Nutrition. https://doi.org/10.1186/s40795-021-00474-8 \u003c/li\u003e\n\u003cli\u003eDerrick, K., Florence, N., Norah, N., N., N. R., Kenneth, K., Sheila, N., . . . Daraus, B. (2024). Prevalence and determinants of minimum dietary diversity for women of reproductive age in Uganda. BMC Nutrition. https://doi.org/10.1186/s40795-024-00858-6 \u003c/li\u003e\n\u003cli\u003eAdubra, L., Savy, M., Fortin, S., Niamk\u0026eacute;, Y. K., Kodjo, E., Fainke, K., . . . Martin-Prevel, Y. (2019). The Minimum Dietary Diversity for Women of Reproductive Age (MDD-W) Indicator Is Related to Household Food Insecurity and Farm Production Diversity: Evidence from Rural Mali. Current Developments in Nutrition. https://doi.org/10.1093/cdn/nzz002 \u003c/li\u003e\n\u003cli\u003eBellows, A., Canavan, C. R., \u0026amp; Fawzi, W. W. (2019). The Relationship Between Dietary Diversity Among Women of Reproductive Age and Agricultural Diversity in Rural Tanzania. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119892405 \u003c/li\u003e\n\u003cli\u003eStefanie, V., De, V. S., Inge, H., Michel, M., \u0026amp; Van, O. H. (2010). Overall and within-food group diversity are associated with dietary quality in Belgium. Public Health Nutrition. doi:10.1017/S1368980010001606 \u003c/li\u003e\n\u003cli\u003eWorld bank. (2023, November). The worldbank microdata library. Retrieved from https://microdata.worldbank.org/index.php/home \u003c/li\u003e\n\u003cli\u003eKuma, M.N; Tamiru, D; \u0026amp; Belachew, T (2021). Level and predictors of dietary diversity among pregnant women in rural South-West Ethiopia: a community-based cross-sectional study. BMJ. doi:10.1136/ bmjopen-2021-055125\u003c/li\u003e\n\u003cli\u003eAbel, G.T \u0026amp; Abebaw, M.K (2021). Maternal minimum dietary diversity and associated factors among pregnant women, Southwest Ethiopia. BMC Nutrition. https://doi.org/10.1186/s40795-021-00474-8\u003c/li\u003e\n\u003cli\u003eSaaka, M; Mutaru, S; \u0026amp; Osman, S.M (2021). Determinants of dietary diversity and its relationship with the nutritional status of pregnant women. Journal of Nutrition Science. https://doi.org/10.1017/jns.2021.6\u003c/li\u003e\n\u003cli\u003eMinistry of Health United Republic of Tanzania \u0026amp; Zanzibar, National Bureau of Statistics(Dodoma) \u0026amp; Office of the Chief Government Statistician (Zanzibar) \u0026amp; The DHS Program ICF, Maryland (USA). (2023). Tanzania Demographic and Health Survey and Malaria Indicator Survey 2022 report. https://dhsprogram.com/pubs/pdf/SR282/SR282.pdf\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1a: Social-Demographic Characteristics of Respondents (N=3884)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en = 3,884\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e20-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e2106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e54.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e75.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e36-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e2128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e54.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e54.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eNot Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e45.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver go to School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e3251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e83.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e83.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e16.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRead And Write\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eSwahili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e2108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e54.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e54.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e55.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eKiswahili And English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e79.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e79.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e20.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003ePrimary Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e70.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e70.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eSecondary Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e98.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eGrain,roots,tubers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1,390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e56.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e56.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003ePulses \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e58.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eNuts and seeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp; 5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e63.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eMilk and milk products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;5.09 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e68.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eMeat,Poultry,Fish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e19.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e88.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eEggs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e90.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eDark green leafy vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e92.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eVit A rich fruits and vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e95.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eOther vegetable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e97.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eOther Fruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e2230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e57.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e57.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eNot Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e42.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e59.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e59.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e40.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDDW = 1.83%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eOne food group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e1648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e42.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e42.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41%;\"\u003e\n \u003cp\u003eTwo food groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e2236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e57.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e*\u003cstrong\u003eNone of the women met MDD-W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1b: Minimum dietary diversity of women \u0026nbsp; by age categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" width=\"635\" height=\"226\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Univariate association\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026gt; |z|\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1.0533460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e0.0506395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e0.9586272 \u0026nbsp; - \u0026nbsp; \u0026nbsp; \u0026nbsp;1.157424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.9693729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e0.0633266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e0.8528725 \u0026nbsp; - \u0026nbsp; \u0026nbsp; \u0026nbsp;1.101787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.8843629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e0.0703713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e0.7566551 \u0026nbsp; - \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.033625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.9109075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e0.0598740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e0.8008014 \u0026nbsp; \u0026nbsp;- \u0026nbsp; \u0026nbsp; 1.036153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.9315492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e0.1309708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e0.7071824 \u0026nbsp; \u0026nbsp;- \u0026nbsp; \u0026nbsp; 1.227100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1923%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9359%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.77564%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e*MDDW = Minimum Dietary Diversity of Women\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Multivariate association\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Err.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026gt; | t |\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% conf. Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e20-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0286213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0615611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.0922664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.1495090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e36-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0105102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0686817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.1243602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.1453805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNot Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0062837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0397233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.071721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0842883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.0187071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.039999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.092848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0721086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.0103697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0420015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.092848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0721086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eSecondary Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0558447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0439315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.0304237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.1421131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.4097104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.2034123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.8091513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e-0.0102695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1538%;\"\u003e\n \u003cp\u003e_Cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.5610483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.0673988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.4286971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0.6933995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Micronutrients, Women of reproductive age, minimum dietary diversity, health outcomes, Tanzania.","lastPublishedDoi":"10.21203/rs.3.rs-5395681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5395681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudying the Minimum Dietary Diversity of Women of reproductive age (MDD-W) is of paramount public health importance, considering their vulnerability due to intensified nutritional necessities during pregnancy and lactation. This study seeks to create a proxy for MDD-W in Tanzania, which is general missing in national-level surveys. We analyzed data from Tanzania National Panel survey wave 4 (TNPS) from which sixty food items were considered and grouped into 10 food categories as per Food and Agriculture Organization guide (2021). We considered only female household members aged 15\u0026ndash;49 years. Data of women of reproductive age (WRA) were linked, person\u0026rsquo;s unique identifiers, with other characteristics related to food consumption and household\u0026rsquo;s data files. Grain, roots and tuber were consumed by majority (56.62%) while meat, poultry and fish by 19.3% and the rest of food groups by less than 3%. Slightly more than half (57%) of the women consumed from at least two food groups and collective MDD-W of the group was 1.83%. For that reason, none of the women attained a minimum dietary diversity for women of reproductive age. The findings suggest a potential contributor to micronutrient deficiencies in Tanzania and the insights from the study highlight the need for diversified diets to improve nutrition and health outcomes for this specific group under the study.\u003c/p\u003e","manuscriptTitle":"Assessing and Enhancing Dietary Diversity among Women of Reproductive Age in Tanzania: Insights from the Tanzania National Panel Survey Wave 4 Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:07:35","doi":"10.21203/rs.3.rs-5395681/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":"acdc5b5d-7daa-434c-b3b7-d91576756c81","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-28T20:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 15:07:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5395681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5395681","identity":"rs-5395681","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}