The relationship between climate change induced natural disasters and selected nutrition outcomes: a case of cyclone Idai, Zimbabwe | BMC Nutrition

Data collection

Study setting

The study was done in Eastern Zimbabwe, Manicaland Province, in the districts of Buhera, Chimanimani and Chipinge (Fig. 1). Manicaland is the second largest province in Zimbabwe with a population of 1,753,000 inhabitants [25]. The province is in the eastern most part of Zimbabwe (18.9216° S, 32.1746° E) and due to its proximity to Mozambique and the Indian Ocean it is prone to cyclones and other adverse weather events.

Map of Manicaland Province showing Buhera, Chimanimani and Chipinge Districts

Sample size and data collection

Using the Dobson formula [26], a sample size of 418 households was calculated. We anticipated a high non-response rate due to high prevalence of temporary shelters, therefore, we included a non-response rate of 28% giving our final sample size to be 535. The households were purposively recruited based on the impact of Cyclone Idai through consultations with chiefs, headmen and key stakeholder meetings.

Data collection tools

Data was collected using face-to-face interviews. Enumeration was done with the help of 10 [9] trained enumerators in each district fluent in the local language. A questionnaire adopted from the Zimbabwe Vulnerability Assessment Committee [27] was used to collect household quantitative data. This questionnaire consisted of the following sections: Household demographics, 24 hour and 1-week dietary recall section for individual and household. Data was captured on an android-based software called Kobo toolbox, a free platform for collecting humanitarian and research data.

Nutrition indicators like household dietary diversity, food consumption scores, minimum dietary diversity for women and for children were calculated as indicated below.

Household dietary diversity score (HDDS)

Household dietary diversity (HDDS) is used to measure the quality of diet especially macro- and micronutrients. It depicts household access to a variety of food groups. HDDS as an indicator gives a better reflection of food security at household and intra-household levels. Data was collected using a 24-hr recall method. There are 12 food groups used to calculate the household dietary diversity score namely, (1) Cereals, (2) Roots and tubers, (3) Vegetables, (4) Fruits, (5) Meat, poultry, and offals, (6) Eggs, (7) Fish and seafood, (8) Pulses, legumes, and nuts, (9) Milk and milk products, (10) Oils/ fats, (11) Sugar/ honey and (12) Miscellaneous. A household is given a score if it consumed food from a food group listed above. The HDDS variable was calculated for each household. The value of this variable ranges from zero (0) to twelve (12).

Minimum dietary diversity score women (MDD-W)

The minimum dietary diversity score for women was measured according to the FAO guidelines for measuring minimum dietary diversity score for women [28]. It measures micronutrient adequacy in the diets of women at the population level. All the foods consumed by women of reproductive age (15–49 years) at or outside the home during the previous day or night (last 24 hours) was recorded. To compute the score, the foods were assigned into the following 10 food groups: (1) Grains, roots, and tubers, (2) Pulses, (3) Nuts and seeds, (4) Dairy, (5) Meat, poultry, and fish, (6) Eggs, (7) Dark leafy greens and vegetables, (8) Other Vitamin A-rich fruits and Vegetables, (9) Other vegetables, (10) Other fruits. The threshold for adequacy is 5 or more food groups.

Minimum dietary diversity for children (MDD-C)

Minimum dietary diversity for children is defined as the proportion of children 6–23 months of age who receive foods from four or more food groups. It is calculated as:

The 7 foods groups used for determination of this indicator are: (1) grains, roots, and tubers, (2) legumes and nuts, (3) dairy products (milk, yogurt, cheese), (4) flesh foods (meat, fish, poultry, and liver/organ meats), (5) eggs, (6) vitamin-A rich fruits and vegetables, (7) other fruits and vegetables. A cut off of at least four food groups is associated with better quality of diets.

Food Consumption score (FCS)

Food consumption data was used to calculate food consumption scores consistent with the WFP methodology [29]. The food consumption score (FCS) was measured by collecting both consumption and frequency of different food groups by a household during the past 7 days before the survey. To calculate the FCS, standard weights were attached for each of the food groups that comprise the food consumption score. The food consumption groups include: starches, pulses, vegetables, fruit, meat, dairy, fats, and sugar. The consumption frequencies of the different foods in the groups were summed, with the maximum value for the groups capped at 7. The formula, based on these groups, with the standard weights, is: FCS = (starches*2) + (pulses*3) + vegetables + fruit + (meat*4) + (dairy*4) + (fats*.5) + (sugar*.5) (Oils*.5). The food consumption score therefore ranges from 0 to 112. FCS values from zero (0) to 28 indicates a poor FCS, 28.5 to 42 indicates a borderline FCS and from 35.5 to 112 indicates an acceptable FCS.

Severity of cyclone Idai

The severity of Cyclone Idai was grouped into five (5) categories which are: (i) not affected, (ii) moderately affected, (iii) extensively affected but still living in their homes, (iv) extensively affected, and relocated to camps, and (v) extensively affected and relocated to new houses. This categorization was based on the extent of damage to infrastructure (including shelter) and loss of human lives due to the cyclone. In the absence of a published scale, we developed our own severity scale basing on damage to infrastructure and loss of lives according to a similar scale by Caldera et al., 2016 and Caldera & Wirasinghe, 2015 whereby they categorized the severity of natural disasters based on the extent of damage to infrastructure, environment, fatalities and injuries sustained.

Data analysis

Data was downloaded from Kobo toolbox cloud storage and exported from Microsoft excel to SPSS v20 (Microsoft Inc., Chicago Illinois). Data cleaning and coding was done in SPSS v20.

Linearity of continuous variables was tested using QQ plots. For demographics, frequency tables were generated. Association between minimum dietary diversity for women, household dietary diversity, food consumption scores and severity were tested using Pearson Correlation test (continuous variables) and Chi square (categorical variables) where appropriate, with significance set at p < 0.05. Linear regression analysis was done to test for determinants of HDDS and FCS, and logistic regression for MDD-W.

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Marondera University of Agricultural Sciences and Technology (MUAST-26/22).