Food Crop Diversity, Women’s Income-Earning Activities, and Distance to Markets in Relation to Maternal Dietary Quality in Tanzania

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Study Justification:
– Women’s dietary diversity and quality are limited in low- and middle-income countries (LMICs).
– Nutrition-sensitive interventions that promote food crop diversity and women’s access to income could improve diets and address the double burden of malnutrition in LMICs.
Study Highlights:
– The study examined the associations among food crop diversity and women’s income-earning activities with women’s diet quality in rural Tanzania.
– Maternal overweight and obesity were high in the study population.
– Food crop diversity was positively associated with diet quality, and this association was strengthened by proximity to markets.
– Women’s salaried employment also improved diet quality.
Study Recommendations:
– Programs to improve women’s diet quality should consider improving market access and women’s access to income, in addition to diversifying production.
Key Role Players Needed to Address Recommendations:
– Government agencies responsible for agriculture and rural development
– Non-governmental organizations (NGOs) working on women’s empowerment and nutrition
– Local community leaders and organizations
– Agricultural extension workers
– Women’s cooperatives and self-help groups
Cost Items to Include in Planning Recommendations:
– Infrastructure development to improve market access (e.g., road construction, transportation systems)
– Training and capacity building for women in income-earning activities
– Support for women’s cooperatives and self-help groups
– Promotion and marketing campaigns to increase demand for diverse food crops
– Monitoring and evaluation of program implementation and impact

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is a cluster-randomized, prospective trial, which provides a high level of evidence. The sample size is large (880 women) and the data analysis includes controlling for socio-economic factors. The associations between food crop diversity, women’s income-earning activities, and women’s diet quality are statistically significant. However, there are a few suggestions to improve the evidence: 1) The abstract could provide more information about the methods used to assess food crop diversity, women’s income-earning activities, and women’s diet quality. 2) The abstract could also include information about potential limitations of the study, such as any biases or confounding factors that may have influenced the results. 3) Finally, the abstract could mention the implications of the findings and how they can be applied in practice, such as recommendations for interventions or policies to improve women’s diet quality in rural Tanzania.

Background: Women’s dietary diversity and quality are limited in low- and middle-income countries (LMICs). Nutrition-sensitive interventions that promote food crop diversity and women’s access to income could improve diets and address the double burden of malnutrition in LMICs. Objectives: We examined the associations among food crop diversity and women’s income-earning activities with women’s diet quality, as well as effect modification by access to markets, in the context of small-holder food production in rural Tanzania. Methods: Data from a cross-sectional study of 880 women from Rufiji, Tanzania, were analyzed. Women’s dietary intake was assessed using a food frequency questionnaire. The prime diet quality score (PDQS; 21 food groups; range, 0-42), a unique diet-quality metric for women that captures the healthy and unhealthy aspects of diet, was computed. Generalized estimating equation linear models were used to evaluate the associations of food crop diversity and women’s income-earning activities with PDQS, while controlling for socio-economic factors. Results: Maternal overweight (24.3%) and obesity (13.1%) were high. The median PDQS was 19 (IQR, 17-21). Households produced 2.0 food crops (SD ± 1.0) yearly. Food crop diversity was positively associated with PDQS (P < 0.001), but the association was strengthened by proximity to markets (P for interaction = 0.02). For women living close (<1.1 km) to markets, producing 1 additional food crop was associated with a 0.67 (95% CI, 0.22-1.12) increase in PDQS, versus a 0.40 (95% CI, 0.24-0.57) increase for women living farther away. The PDQS increased with women's salaried employment (estimate, 0.96; 95% CI, 0.26-1.67). Conclusions: Household food production may interact with access to markets for sales and purchases, while nonfarm income also improves women's diet quality in rural Tanzania. Programs to improve women's diet quality should consider improving market access and women's access to income (source of empowerment), in addition to diversifying production.

The study population participated in a cluster-randomized, prospective Homestead Agriculture and Nutrition (HANU) project set in the rural Rufiji district in Eastern Tanzania (ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT03311698","term_id":"NCT03311698"}}NCT03311698). Out of 16 villages within the Rufiji Health and Demographic Surveillance System, 10 were randomly selected, pair-matched based on location and other factors, and randomly assigned to the intervention or control arms (40). Participants were women enrolled in the study conducted to evaluate the association of integrated homestead food production with maternal and child health and nutrition. We analyzed data from 880 women participating in a midline assessment conducted from August to October 2017. The midline study included an extensive dietary intake assessment, unlike the study baseline. Baseline data collection was also before differences in dietary intake and quality would have occurred. Details of the parent study are published elsewhere (40). The eligibility criteria for the study included households with a woman of reproductive age (18–49 years) with at least 1 child aged 6–36 months and access to land for vegetable production. Study participants provided informed consent. Study research assistants used semi-structured questionnaires to collect data on household demographics and socio-economic status, maternal anthropometric data, dietary intake, hemoglobin levels, and household agriculture production for the study. The main exposure, food crop diversity, was determined by classifying all crops grown by households (self-reported) in the previous year into 7 groups based on the FAO's Minimum Dietary Diversity for Women (MDD-W) index. The 7 crop groups included in the score were 1) grains, roots and tubers, and plantains; 2) legumes (beans, peas, and lentils); 3) nuts and seeds; 4) vitamin A−rich dark green vegetables; 5) other vitamin A−rich fruits and vegetables; 6) other vegetables; and 7) other fruits. We excluded animal-source foods (dairy; meats, poultry, and fish; and eggs). A similar classification has been used elsewhere (23, 41). Food crop diversity was determined as the sum of food crop groups produced by the household in the previous year. Additionally, we considered including livestock production in the computation of production diversity in a sensitivity analysis and created an 8 food-group variable instead of the original (7 food-group) indicator that excludes animal-source foods. An alternative method of evaluating the diversity of on-farm production is to use a measure of species richness (24). Crop species richness has been used as an indicator of diversity of on-farm production in previous studies (22, 23, 42). We assessed crop species' richness as the number of food crop species produced by households in the previous year, out of 37 food crops. Cash crop diversity was evaluated as the number of cash crops grown by the household in the previous year out of 3 crops: cashew, sesame, and cotton. We determined the main cash crops based on the study data, and sesame was the most commonly sold food crop (31.5%), while cashew was sold by 9.4% of households. We also determined that cotton was a cash crop based on literature (43). Market food diversity (MFD) was determined based on the diversity of foods sold in smaller, local markets using the 10 food groups for the MDD-W (including ASF). A similar approach has been taken in previous research in Ethiopia (41). A total of 27 key informants selected by project staff in collaboration with local leaders in each village recalled foods sold in their local markets in the previous year, and all foods were classified into the 10 food groups. MFD was determined as the total number of food groups sold in the market based on each informant's recall. We computed a median score of MFD based on available informants per village. MFD was calculated at the village level and each household from the village was assigned the median MFD. We calculated distance to market for each study household to 2 larger, regional markets that had GPS coordinates using the Stata program. The 2 larger markets identified by informants in the study were Kibiti market for Kibiti A, Kibiti B, Mchukwi A, and Mchukwi B villages and Ikwiriri market for Umwe North, Umwe South, Mgomba North, Mgomba South, Ikwiriri South, and Ikwiriri Centre villages. MFD was calculated for local markets closer to the respondents’ villages, and the distance to markets was calculated for 2 larger regional markets. A similar approach of referencing 2 types of markets has been taken in another study (44). We considered women's participation in nonfarm economic activities and women's receipt of wages or salaried employment. We asked women whether they participated in nonfarm economic activities (yes/no) or wage and salary employment (yes/no). Finally, a livestock diversity score was defined as the number of livestock species owned by the household. The primary outcome of the study, women's diet quality, was assessed using the PDQS, a food group−based dietary score based on self-reported women's dietary intake. Women's dietary intake was assessed using a food frequency questionnaire (FFQ). The FFQ was composed of a list of 79 common local foods and was validated in the Tanzanian context (45). Women were asked to recall foods consumed in the previous month and the frequency of consumption of the foods, with options of frequency of consumption of 0 times in a month, 1–3 times per month, 1 time per week, 2–4 times per week, 5–6 times per week, 1 time per day, 2–3 times per day, 4–5 times per day and 6 or more times per day. Foods consumed by women in the previous month, as reported by the FFQ, were classified into 21 food groups for the PDQS. Foods were classified into 14 healthy food groups [dark green leafy vegetables, other vitamin A−rich vegetables (including carrots), cruciferous vegetables, other vegetables, whole citrus fruits, other fruits, fish, poultry, legumes, nuts, low-fat dairy, whole grains, eggs, and liquid vegetable oils] included in the PDQS (13, 46). In addition, foods were classified into 7 unhealthy food groups (red meat, processed meats, refined grains and baked goods, sugar-sweetened beverages, desserts and ice cream, fried foods obtained away from home, and potatoes) (13, 46). We made the following adaptations to the score: 1) red and orange fruits and vegetables were included in the “other vitamin A−rich fruits and vegetables” category, in place of “carrots” as a food group; and 2) a “roots and tubers” group was used in place of the “potatoes” group from the original score. We categorized maize flour−based products as refined grains. Processed meat intake and liquid vegetable oil intake were not measured in the study and low-fat dairy intake could not be ascertained; therefore, all women were assigned low intakes for these groups, meaning 2 points for processed meats and 0 points for liquid vegetable oil and low-fat dairy intake. These adaptions were made in order to conform to local food consumption patterns. Points were assigned for consumption of healthy food groups as 0–1 serving/week (0 points), 2–3 servings/week (1 point), and ≥4 servings/week (2 points). Scoring for unhealthy food groups was assigned as: 0–1 serving/week (2 points), 2–3 servings/week (1 point), and ≥4 servings/week (0 points) (47). Points for each food group were summed to give an overall score. The PDQS has been used as measure of diet quality in studies in developed countries and has been associated with a higher risk of chronic diseases, including cardiovascular disease and diabetes (13, 46, 48, 49). Approval for the study was provided by the Ifakara Health Institute independent research board and the medical research council committee of the National Institutes of Medical Research in Tanzania and by the Harvard TH Chan School of Public Health (Boston) institutional review board. Written informed consent was obtained from all enrolled women. We described the prevalence of the household demographic characteristics and individual characteristics using means and standard deviations for continuous data, and frequencies for categorical data. We also described the frequency of household production of food crops, the prevalence of sales of food crops, and consumption of PDQS foods by women in the study population using frequencies. We used generalized estimating equation (GEE) linear models with exchangeable correlation (50), controlling for clustering by village pair, to evaluate the associations of food crop diversity and women's participation in nonfarm economic activities or employment with diet quality in the study. We adjusted for assignment to intervention (homestead garden/control) in the parent study and the cluster study design. In a sub-analysis, we further evaluated the association of production of specific food groups (grains, white roots and tubers, and plantains; legumes; nuts and seeds; vitamin A−rich dark green vegetables; other vitamin A−rich fruits and vegetables; other vegetables; and, other fruits) with the PDQS. In a secondary analysis, we also evaluated the association of alternative measures of on-farm diversity, crop species richness, and cash crop diversity with the PDQS. We evaluated for effect modification of associations by 1) MFD; 2) market access; and 3) sale of crops (sold at least 1 food crop in the previous year). We selected confounders based on associations with the outcome in univariate models at P < 0.20. Potential confounders included maternal education (no/primary, secondary, tertiary), wealth index (quintiles), parity (0, 1–2, ≥3 children), family size (continuous), women's age (18–24 years, 25–34 years, ≥35 years), BMI (<18.5, 18.5–24.9, ≥25 kg/m2), marital status (married/not married), food expenditure (continuous), weekly income (log, continous), farm size (continuous), and sale of food crops (sold at least 1 food crop in the previous year). We controlled for BMI as a proxy for energy intake, which could be a potential confounder. The missing-indicator method was used to account for missing covariate data. In a sensitivity analysis, we evaluated women's dietary diversity according to the MDD-W criteria outlined above. Food consumed by women was categorized into 10 food groups. We calculated a daily frequency of consumption for all food groups. If her frequency of consumption of a food group was 1 or more times daily, a woman was considered to have consumed the food group. Dietary diversity score (DDS) was computed as the total number of food groups consumed by a woman. We evaluated the association of food crop diversity with the DDS. The analyses were conducted using SAS version 9.4.

The study titled “Food Crop Diversity, Women’s Income-Earning Activities, and Distance to Markets in Relation to Maternal Dietary Quality in Tanzania” provides recommendations to improve access to maternal health. These recommendations are:

1. Promote food crop diversity: Encourage households to grow a variety of food crops by providing training and resources to farmers, promoting the cultivation of different crops, and diversifying agricultural practices.

2. Improve women’s access to income: Enhance women’s income-earning activities by supporting them in starting their own businesses, providing vocational training, and creating employment opportunities.

3. Enhance market access: Improve transportation infrastructure, establish local markets, and facilitate the sale and purchase of food crops to ensure that nutritious foods are available and accessible to women.

4. Empower women: Focus on empowering women by providing education, training, and resources to enhance their decision-making power and control over resources.

By implementing these recommendations, innovative solutions can be developed to address the challenges faced in accessing maternal health services and improve the overall well-being of women in low- and middle-income countries.
AI Innovations Description
The study titled “Food Crop Diversity, Women’s Income-Earning Activities, and Distance to Markets in Relation to Maternal Dietary Quality in Tanzania” provides recommendations that can be used to develop innovations to improve access to maternal health. The study suggests the following recommendations:

1. Promote food crop diversity: Encouraging households to grow a variety of food crops can improve maternal dietary quality. This can be achieved by providing training and resources to farmers, promoting the cultivation of different crops, and diversifying agricultural practices.

2. Improve women’s access to income: Enhancing women’s income-earning activities can contribute to better maternal health outcomes. This can be done by supporting women in starting their own businesses, providing vocational training, and creating opportunities for employment.

3. Enhance market access: Access to markets plays a crucial role in improving maternal dietary quality. Efforts should be made to improve transportation infrastructure, establish local markets, and facilitate the sale and purchase of food crops. This can help ensure that nutritious foods are available and accessible to women.

4. Empower women: Women’s empowerment is a key factor in improving maternal health. Programs should focus on empowering women by providing education, training, and resources to enhance their decision-making power and control over resources.

By implementing these recommendations, it is possible to develop innovative solutions that address the challenges faced in accessing maternal health services and improve the overall well-being of women in low- and middle-income countries.
AI Innovations Methodology
To simulate the impact of the main recommendations on improving access to maternal health, the following methodology can be used:

1. Promote food crop diversity: The simulation can involve providing training and resources to farmers in selected communities to encourage the cultivation of a variety of food crops. This can be done by organizing workshops and demonstration farms to educate farmers on the benefits of diversifying their crops. The impact can be measured by assessing changes in food crop diversity in the targeted communities over a specific period of time.

2. Improve women’s access to income: The simulation can involve implementing programs that support women in starting their own businesses or provide vocational training to enhance their income-earning activities. This can be done by partnering with local organizations or microfinance institutions to provide financial support and training to women entrepreneurs. The impact can be measured by tracking changes in women’s income levels and their ability to access maternal health services.

3. Enhance market access: The simulation can involve improving transportation infrastructure and establishing local markets in selected communities. Efforts can be made to facilitate the sale and purchase of food crops by connecting farmers with potential buyers and improving logistics. The impact can be measured by assessing changes in market access and availability of nutritious foods in the targeted communities.

4. Empower women: The simulation can involve implementing programs that focus on empowering women through education, training, and resource allocation. This can be done by providing scholarships or vocational training opportunities to women, as well as promoting gender equality and women’s rights. The impact can be measured by assessing changes in women’s decision-making power, control over resources, and access to maternal health services.

To evaluate the impact of these recommendations, data can be collected through surveys, interviews, and observations before and after the implementation of the simulated interventions. Key indicators to measure the impact can include changes in maternal dietary quality, women’s income levels, market accessibility, and women’s empowerment. Statistical analysis can be conducted to assess the significance of the observed changes and determine the effectiveness of the interventions in improving access to maternal health.

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