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