Impact of Climate Change Induced Natural Disasters on Nutrition Outcomes: A Case of Cyclone Idai, Zimbabwe

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This study was aimed at exploring the effects of Cyclone Idai on nutrition outcomes of women of child-bearing age and children under five years. Method The household-based cross-sectional study was conducted in Eastern Zimbabwe. Data were collected through face-to-face interviews to determine food consumption score (FCS) and household dietary diversity (HDDS), minimum dietary diversity for women (MDD-W) and minimum dietary diversity for children (MDD-C). Severity of Cyclone Idai was grouped into five categories based on the extent of damage to infrastructure and loss of human lives. Association between continuous and categorical variables was tested using Pearson correlation test and Chi square test, respectively. Linear and binary logistic regression was performed to investigate determinants of food security. Results A total of 535 households were interviewed. There was a significant correlation between severity of Cyclone Idai and MDD-W (p = 0.011), HDDS (p = 0.018) and FCS (p = 0.001). However, severity Cyclone Idai was not a determinant of any nutrition outcome, but gender of household head was a negative predictor of HDDS (β=-0.734, p = 0.040), and marital status of household head was a positive predictor (β = 0.093, p = 0.016) of FCS. Conclusion The findings provide a good baseline to inform future programming of food aid activities during disasters. More so, our findings call for evidence-based policies regarding composition of a food aid basket and targeting of beneficiaries. The main strength of this study is that it is the first to investigate the effects of cyclones on food and nutrition security indicators and is based on a large sample size thus making our results generalisable. Climate change Natural disasters Nutrition outcomes Cyclone Idai Zimbabwe Figures Figure 1 Figure 2 1. Introduction Climate induced natural disasters such as floods, cyclones, storms, and heat waves have been on the rise globally and have doubled since the early 1990s [1]. These disasters are now becoming more frequent and extreme due to an increase in greenhouse gases concentration in the atmosphere, rising temperatures and extreme rainfall [2]. These climate change induced disasters have exacerbated the risks of hunger and undernutrition [3] through the reduced yields of agricultural crops. Countries which already have food shortages, like low and middle income ones, are often the most vulnerable to climate change, and they have a low capacity to adapt [4]. Zimbabwe has experienced a number of unprecedented severe drought episodes in the last four decades; 1991–1992, 1994–1995, 2002–2003, 2015–2016, and 2018–2019 [5]. In addition to droughts, the incidences and frequencies of cyclones have increased. Cyclone Japhet occurred in 2003, resulting in widespread river flooding causing extensive crop damage in Mozambique, Zambia, and Zimbabwe [6]. Cyclone Dineo followed in 2017 and then Cyclone Idai in 2019. After Cyclone Idai hit Zimbabwe, six weeks later Cyclone Kenneth hit the same areas giving no room for recovery in the affected zones [7]. In Zimbabwe, Cyclone Idai affected approximately 270,000 people of which about 51,000 were displaced [8]. This disaster claimed more than 340 lives and some people are still missing. Areas in Chimanimani and Chipinge districts suffered the greatest impact [8]. With agricultural land rendered unusable and infrastructure e.g., storage facilities left inutile; this possibly affected dietary intake and diet quality but however, this was not assessed [9]. Research has shown that cyclones not only destroy infrastructure but also wash away food stocks, granaries, fields, gardens, and livestock [10]–[12]. Climate induced natural disasters usually result in food shortages, no access to safe and nutritious food, therefore, food consumption and dietary diversity are distorted. When a disaster occurs, women and children often suffer the most impact due to gender discriminatory cultural norms and inadequate access to resources [13]. Food shortages after a disaster render women more vulnerable to malnutrition because they have specific nutritional needs during adolescence, while pregnant and/or lactating women also tend to consume fewer calories to give priority to men and children [14]. Young children especially those below the age of five are often very sensitive to nutritional deficits. When children are exposed to such conditions of inadequacy especially during the first 1000 days, there are some irreversible damages that can occur to their cognitive development, health, and physical status. This negatively impacts on their full development potential [15], hence child malnutrition is a good indicator of poor household dietary diversity [16]. After Cyclone Idai hit Zimbabwe, approximately 3905 children between the ages of six and 59 months were admitted into treatment programmes for severe acute malnutrition, and this showed the negative impact that Cyclone Idai had on child nutrition and food security. Also and relatedly, in 2020, Cyclone Amphan hit Bangladesh’s south western coast, resulting in instability on food security as well as economic instability [17]. Cyclone Amphan left 40.8% of the adults with severe food insecurity due to job loss or loss of income, and a decrease in the living conditions. Disruption of livelihoods further paved way for increased prevalence of child malnutrition due to decreased dietary quality [18]. Most studies on the impact of climate change induced natural disasters mainly look at impact of natural disasters on the environment, agricultural productivity, livelihoods and water, sanitation and hygiene. Few studies [19] have researched on the impact of cyclones on food security outcomes. Most studies have studied effect of cyclones on agriculture output [20], livelihoods [21], [22], and poverty [23], [24]. Therefore, the main objective of this study was to assess the impact of Cyclone Idai on selected food and nutrition security outcomes (Food Consumption Score, Households Dietary Diversity Score, Minimum Dietary Diversity Score for Women and Minimum Dietary Diversity Score for Children) in the most cyclone affected region of Eastern Zimbabwe. 2. Methodology 2.1 Data collection 2.1.1 Study setting The study was done in Eastern Zimbabwe, Manicaland Province, in the districts of Buhera, Chimanimani and Chipinge (Figure 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. 2.1.2 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. 2.1.3 Data collection tools Data was collected using face-to-face interviews. Enumeration was done with the help of ten (10) 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 seven 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 and was determined by the researchers using literature and community interviews. 2.2 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). 3. Results 3.1 Background Characteristics A total of 535 households consisting of 171 women of reproductive age and 213 children were interviewed (Table 1 ). The mean age for the household head was 45.9 ± 40.14 years and the mean household size was 5.7 ± 2.9. The youngest head of household was 12 years, with the oldest at 92 years old. The number of persons per household varied from one (1) to 27 members. Of the 535 households interviewed, the majority (334 households) were married and living together, with 48.2% of these still living in their homes after extensive damage by Cyclone Idai. Most of the households were male headed (84.7%). Table 1 Household demographics Variable Characteristic Severity of cyclone Idai (%) P-value* Not affected Moderately affected Extensive but still living in their homes Extensive (relocated to camps Extensive (relocated to new houses) District Buhera (n = 231) 39.8 34.6 25.5 0 0 0.000 Chimanimani (n = 125) 0 0 77.6 8.0 14.4 Chipinge (n = 170) 0 40.6 59.5 0 0 Type of settlement Village (n = 461) 20 30.4 45.8 0 3.9 0.000 Camp (n = 10) 0 0 0 100 0 Other (n = 55) 0 16.4 83.6 0 0 Age of Household Head Mean (n = 526) 47.4 ± 16.4 49.9 ± 71.0 44 ± 15.9 49.3 ± 13.4 43.8 ± 17 0.690 Size of Household Mean (n = 526) 5.7 ± 3.3 6.2 ± 2.9 5.7 ± 2.7 5.5 ± 2.2 3.9 ± 1.8 0.030 Sex of Household head Male (n = 205) 87.5 80.6 86.1 100 75 0.675 Female (n = 37) 12.5 19.4 13.9 0 25 Marital Status Married living together (n = 334) 20.1 27.2 48.2 0.9 3.6 0.005 Married living apart (n = 35) 14.3 31.4 45.7 8.6 0 Divorced/Separated (n = 41) 7.3 31.7 53.7 4.9 2.4 Widow/Widower (n = 84) 19 35.7 41.7 1.2 2.4 Never married (n = 21) 0 9.5 71.4 4.8 14.3 Other (n = 11) 9.1 18.2 72.7 0 0 Highest level of education of household head None (n = 51) 3.9 39.2 54.9 0 2.0 0.007 Primary (n = 185) 18.4 30.8 44.3 3.2 3.2 ZJC (n = 84) 23.8 36.9 33.3 4.8 1.2 O’ level (n = 164) 17.1 18.3 60.4 0 4.3 A’ level (n = 6) 16.7 0 83.3 0 0 Diploma/certificate after primary (n = 1) 0 0 100 0 0 Diploma/certificate after secondary (n = 7) 0 42.9 42.9 0 14.3 Graduate/post-graduate (n = 1) 100 0 0 0 0 Other n = 27 22.6 29.6 40.7 0 7.4 * Chi Square for comparing categorical variables and ANOVA for comparing means There was a significant association between severity of cyclone and district ( χ 2 = 305.700; df = 8; p = 0.000). Chimanimani was the most affected district with 77.6% being extensively affected but still living in the same homes. In addition, there was a significant association between severity and type of settlement ( χ 2 = 557.201; df = 8; p = 0.000). Furthermore, the results in Table 1 show a significant association between severity of Cyclone Idai and marital status ( χ 2 = 40.274; df = 20; p = 0.005) and severity of Cyclone Idai and education level of household head ( χ 2 = 55.123; df = 32; p = 0.007). The results also reveal a significant difference in household size across severity category (p = 0.030). The households worst affected and requiring relocation were the smallest in size (3.9 ± 1.8). However, there was no significant difference in age of household head across severity (p = 0.690) as well as gender of household head (p = 0.675). [Insert Table 1 here] 3.2 Impact of Cyclone Idai on nutrition outcomes 3.2.1 Food consumption score (FCS) The overall median FCS was 32 [21.50, 45.80]. In addition, the median FCS for Category 1 (not affected) was 32 [22.00, 46.00], Category 2 was 30.5 [12.50, 40.50], Category 3 was 35 [22.25, 52.75], Category 4 was 10 [7.00, 24.00] and Category 5 was 27.75 [20.38, 37.88] (Table 2 ). The proportion with a poor FCS was 16%, 27.2%, 46.4%, 6.4% and 4.0% for category 1 to 5 respectively. There was a significant association between the severity of impact of Cyclone Idai and FCS ( χ 2 = 27.421; df = 8; p = 0.001). The highest proportion of households with a poor FCs were found in the first 3 categories (not affected, moderate and extensive but still living in their homes). 3.2.2 Household Dietary Diversity Score (HDDS) HDDS was generally high (≥ 5 food groups) in all 5 categories of severity with Category 1 households having a HDDS of 5.20 ± 1.4, Category 2 at 5.86 ± 2.0, Category 3 at 6.35 ± 2.0 and Category 4 and 5 at 5.22 ± 1.2 and 5.50 ± 2.0 respectively. There was a significant difference in HDDS across severity categories (p = 0.000). 3.2.3 Women Minimum Dietary Diversity Score (MDD-W) The proportion of women below the MDD-W cut-off was as follows; 34.3%, 22.5%, 40.2%, 2.0% and 1.0% for category 1–5 respectively. This proportion was highest for category 1 to 3. There was a significant association between MDD-W and severity of Cyclone Idai ( χ 2 = 12.220; df = 4; p = 0.016). 3.2.4 Children Minimum Dietary Diversity Score (MDD-C) A high proportion of children in overall were below MDD-C cut-off (that is 74.1% of the children were below the cut-off for child dietary diversity). The proportion below the MDD-C cut-off by severity of impact was 25.5%, 25.5%, 42.0%, 3.8% and 3.2% from category 1 to 5. There was however no significant difference in proportion below cut-off across severity category (χ 2 = 5.439; df = 4; p = 0.245). Table 2 Nutrition outcomes of study population by severity of Cyclone Idai 1 Nutrition outcome Characteristic Severity of Cyclone Idai (%) P-value* Not affected Moderately affected Extensive (Still living in their homes) Extensive (Relocated to camps) Extensive (Relocated to new houses) Food Consumption Score Poor (n = 125) 16.0 27.2 46.4 6.4 4.0 0.001 Borderline (n = 165) 18.8 33.3 41.8 1.2 4.8 Adequate (n = 217) 18.0 23.0 56.7 0 2.3 HDDS Mean (n = 500) 5.20 ± 1.4 5.86 ± 2.0 6.35 ± 2.0 5.22 ± 1.2 5.50 ± 2.0 0.000 MDD-W Inadequate (n = 102) 34.3 22.5 40.2 2.0 1.0 0.011 Adequate (n = 60) 16.7 15.0 58.3 3.3 6.7 MDD-C Below average (n = 157) 25.5 25.5 42.0 3.8 3.2 0.245 Above average (n = 49) 14.3 26.5 55.1 0 4.1 1 All results show % proportion of households/individuals *Pearson Chi square except where cells count was less than 5 Fisher’s exact was used 3.3 Determinants of nutrition outcomes of sampled households The results presented in Table 3 reveal that gender of household head was a negative predictor of HDDS (β= -0.734, p = 0.040) which shows that female headed households were found to be more food secure. Marital status was a significant positive predictor of FCS (β = 0.093, p = 0.016) showing that married couples who are living together had a higher FCS than others. However, severity of Cyclone Idai was not a predictor of any of the nutrition outcomes (HDDS, FCS and MDD-W). Table 3 Determinants of Nutrition outcomes 1 Variables Nutrition Outcomes HDDS FCS MDD-W Age of household head 0.004 0.003 0.033 Marital status 0.145 0.093* 0.109 Sex of household head -0.734* -0.264 -0.593 Household size 0.067 -0.034 -0.057 Highest level of education 0.006 0.057 0.140 Type of settlement -0.054 -0.087 0.699 Severity of Cyclone Idai -0.315 -0.100 0.613 Adjusted R 2 0.103 0.042 R 2 0.134 0.075 1 Simple linear regression with dependent variables, HDDS and FCS except for MDD-W where binary logistic regression was used. *Significant (p < 0.05) 3.4 Nutritional quality by severity of Cyclone Idai Consumption of Vitamin A-rich foods was in overall high in all settlements with only those living in camps (Category 4) having less than 50% households consuming Vitamin A-rich foods (Fig. 2 ). The highest proportion of households who never consumed these foods was also in camps. Households living in camps had the highest proportion (30%) of never consuming protein-rich foods; however, it also had the highest proportion of households who consumed protein-rich foods at least on 6 or less days prior to the survey. The proportion of households consuming Heme iron-rich foods daily is generally low across all the settlements. Camps had the highest proportion (40%) of households who never consumed Heme iron-rich foods seven (7) days before the survey and none of the households consumed Heme-iron foods daily. 4. Discussion This study sought to investigate the effect of Cyclone Idai on selected food security indicators namely FCS, HDDS, MDD-W and MDD-C. In general, the households worst affected by the cyclone were the smallest in size and headed by either divorced or never married household heads. Furthermore, households with poor socio-demographic profiles were more severely affected by the cyclone and this could be the reason they opted to be relocated since these households usually have poorer coping strategies and or support structures [ 30 ]. This is corroborated by findings from a study done in rural Sri Lanka [ 31 ] which showed that households that mostly depend on natural resources for their livelihood, and those with low incomes suffer greater losses from floods and droughts than other households. In terms of food security, contrary to our assertion that relocated households would have a poor FCS, the highest proportion of households with a poor FCs were found in the first 3 categories (not affected, moderate and extensive but still living in their homes). In general, these are food insecure districts, although relocated households had higher FCS possibly due to food aid. Data collection was conducted 31 months after the cyclone and by this time camps had been set up and there was relief activity by the Ministry of Health and Child Care alongside various Non-Governmental Organisations like the Adventist Development and Relief Agency International (ADRA), IOM, UNICEF, Nutrition Action Zimbabwe (NAZ), Save the Children, World Vision International, GOAL and WFP. IOM was working to support affected communities through technical assistance in shelter and information management. The diet quality for women in the severely affected categories appeared to have been better than that of women in moderately and non-affected households. The proportion of women below the MDD-W cut-off was highest for category 1 to 3 and lowest for relocated households (severely affected). This validates the important contribution of food aid as part of relief efforts. Though HDDS was adequate across all categories, the results revealed that a high proportion of children were below MDD-C cut-off (≥ 4 food groups). This reflects access by households to food but poor intrahousehold food distribution. However, the last 2 categories (relocated to camps or new homes) had the lowest proportion, meaning children living in relocated households (severely affected) had better access to diverse foods than those living in moderately and non-affected areas. This result contradicts with the types of food groups reportedly consumed. We found that the highest proportion of households who never consumed vitamin A rich foods were in the camps. Camps also had the highest proportion (40%) of households who never consumed Heme iron-rich foods. Further, households living in camps had the highest proportion (30%) of never consuming protein-rich foods. Households in camps were entirely dependent on food aid. The food aid ration consisted of 2.4kg pulses, 13.5kg cereal, 0.75kg vegetable oil, 6kg super cereal plus (Corn Soya Blend++) and 6kg super cereal (Corn Soya Blend) per person per month. It is evident that the food aid basket is lacking in animal source foods and perhaps the participants may not have known that the super cereal provided was fortified with Vitamin A hence it was a vitamin A source. Super cereal is prepared from heat treated maize, whole soya beans, vitamins and minerals to provide a highly nutritious meal [ 32 ]. A study done in Malawi showed that the provision of super cereal increased calorie density as well as enhancing the absorption of fat-soluble vitamins, thereby addressing the nutritional needs of children with moderate acute malnutrition [ 33 ]. 5. Conclusion The cyclone negatively affected most food security indicators with only the relocated households faring better possibly due to aid. There were variations in diet quality. Though at household level dietary diversity was adequate, this did not translate to adequate women and child dietary diversity. The findings presented in this paper provide a good baseline to inform future programming of food aid activities during disasters. More so, our findings call for evidence-based policies regarding composition of a food aid basket and targeting of beneficiaries. One limitation of our study was that we could not gain access to communities immediately after the cyclone, hence the relationships measured were confounded by relief activities. However, most food security indicators especially for children were below acceptable levels indicating inadequacy of relief activities for children and women dietary requirements, hence our results are still valid. Despite this limitation, our study had several strengths. It is the first study to investigate the effects of cyclones on food and nutrition security indicators. Moreover, we employed a large sample size thus making our results generalisable. Declarations Ethics approval and consent to participate 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). Informed consent was obtained from all subjects interviewed in the study and this was shown by signing the consent form. Informed consent was obtained from a parent and/or legal guardian for minors for study participation. Consent for publication Not applicable. Availability of data and materials The data presented in this study are available on request from the corresponding author, upon signing of data use agreement and Research Ethics Committee approval. Competing interests The authors declare that they have no competing interests. The sponsors had no role in the design, execution, interpretation, or writing of the study. Funding This paper is an output of a research project, Inventorying Intangible Cultural Heritage Assets Affected by Cyclone Idai in Chimanimani, Chipinge and Buhera districts in Zimbabwe, funded by the Arts and Humanities Research Council, UK. AHRC Reference: AH/V006436/1. However, we did not receive any funds to cover publication costs Author contributions Conceptualization was done by LM, VD, NH and GK. The methodology section was written by PC and VD. Formal analysis was done by VD, PC and JM. The original draft was written by VD, PC and LM. Review & Editing was done by all authors and they read and agreed to the published version of the manuscript. Acknowledgements The authors are grateful to Mr Mukozhiwa, Ms Siamena, Ms Mushangazhike and the enumerators for their contribution during data collection for this study. We acknowledge the financial support from the Arts and Humanities Research Council, UK. AHRC Reference: AH/V006436/1. References Thomas V, LLpez R. 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Nov. 2020;265:113298. doi: 10.1016/j.socscimed.2020.113298 . Khamis AG, Mwanri AW, Ntwenya JE, Kreppel K. The influence of dietary diversity on the nutritional status of children between 6 and 23 months of age in Tanzania. BMC Pediatr. Dec. 2019;19(1):518. doi: 10.1186/s12887-019-1897-5 . Hossain A, Ahmed B, Rahman T, et al, Household food insecurity, income loss, and symptoms of psychological distress among adults following the Cyclone Amphan in coastal Bangladesh, PLoS One, vol. 16, no. 11, p. e0259098, Nov. 2021, doi: 10.1371/journal.pone.0259098 . Hossain MN, Uddin MN, Rokanuzzaman M, Miah MA, Alauddin M. Effects of Flooding on Socio-Economic Status of Two Integrated Char Lands of Jamuna River, Bangladesh, J. Environ. Sci., p. 5, 2013. Paul SK, Paul BK, Routray JK. Post-Cyclone Sidr nutritional status of women and children in coastal Bangladesh: an empirical study. Nat Hazards. Oct. 2012;64(1):19–36. doi: 10.1007/s11069-012-0223-4 . Chikodzi D, Nhamo G, Chibvuma J. Impacts of Tropical Cyclone Idai on Cash Crops Agriculture in Zimbabwe in Cyclones in Southern Africa: Volume 3: Implications for the Sustainable Development Goals, Nhamo G, Chikodzi D, editors. Cham: Springer International Publishing, 2021, pp. 19–34. doi: 10.1007/978-3-030-74303-1_2 . Solayman H. Impacts of cyclone on livelihood: study on a coastal community, vol. 4, pp. 56–64, Dec. 2017. WFP. Threat to lives and livelihoods as cyclone Batsirai hurls towards Madagascar | World Food Programme. 2022. https://www.wfp.org/news/threat-lives-and-livelihoods-cyclone-batsirai-hurls-towards-madagascar (accessed Jun. 09, 2022). Ishizawa OA, Miranda JJ Weathering Storms: Understanding the Impact of Natural Disasters on the Poor in Central America, World Bank, Washington, DC, Working Paper, Jun. 2016. doi: 10.1596/1813-9450-7692 . Warr P, Aung LL. Poverty and inequality impact of a natural disaster: Myanmar’s 2008 cyclone Nargis, World Development. Vol. 122: no. C; 2019. pp. 446–61. ZimStat, Zimbabwe Population Census, p. 152, 2012. Naing L, Winn T, Rusli BN. Medical Statistics Practical Issues in Calculating the Sample Size for Prevalence Studies.”. ZimVAC, Zimbabwe Vulnerability Assessment Committee (ZimVAC), p. 97, 2020. FAO. Minimum dietary diversity for women: An updated guide to measurement - from collection to action. Rome: FAO; 2021. doi: 10.4060/cb3434en . WFP. Accessed. Mar. 28, 2022. [Online]. Available: https://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp197216.pdf . Hallegatte S, Vogt-Schilb A, Rozenberg J, Bangalore M, Beaudet C From Poverty to Disaster and Back: a Review of the Literature, EconDisCliCha, vol. 4, no. 1, pp. 223–247, Apr. 2020, doi: 10.1007/s41885-020-00060-5 . De Silva MMGT. and Kawasaki A. Socioeconomic Vulnerability to Disaster Risk: A Case Study of Flood and Drought Impact in a Rural Sri Lankan Community, Ecological Economics, vol. 152, pp. 131–140, Oct. 2018, doi: 10.1016/j.ecolecon.2018.05.010 . WFP. Accessed. Jun. 18, 2022. [Online]. Available: https://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp251114.pdf . Langlois B, Suri D, Wilner L, et al, Self-report vs. direct measures for assessing corn soy blend porridge preparation and feeding behavior in a moderate acute malnutrition treatment program in southern Malawi, Journal of Hunger & Environmental Nutrition, vol. 13, pp. 1–12, Nov. 2017, doi: 10.1080/19320248.2017.1374902 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2023 Read the published version in BMC Nutrition → Version 1 posted Editorial decision: Major revision 31 Oct, 2022 Reviews received at journal 06 Oct, 2022 Reviews received at journal 10 Sep, 2022 Reviewers agreed at journal 10 Sep, 2022 Reviewers agreed at journal 10 Sep, 2022 Reviewers invited by journal 10 Sep, 2022 Editor assigned by journal 08 Sep, 2022 Editor invited by journal 30 Aug, 2022 Submission checks completed at journal 30 Aug, 2022 First submitted to journal 22 Aug, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1986844","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132943702,"identity":"2db33b7a-14eb-417d-91d7-1497e384f40d","order_by":0,"name":"Vimbainashe Prisca Dembedza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYHACAwbGBhBiPvjgA5DLxk68FrZkwxkgLczEa+FRk+YB8QlpMbjdvPHRzR02svPbzzAb2/zaJs/HzMD44WMOHi13jhUb555JM27syT34OLfvtmEbMwOz5MxtuLVIzsgxk85tO5zYzJCXbJzbc5sRqIWNmRe/FvPfuW3/E9v435hJW/bctieohV8ix4w5t+1AYg+QIc3w43YiYS0yx4qlc88kG8+QeJZs2NtwO7mNmbEZr1/YpJs3fs7dYSc7vz/54IMff27bzm9vPvjhIx4tDBLIHMY2MNmARz26FoY/+BWPglEwCkbByAQAqeVT6g5vgMIAAAAASUVORK5CYII=","orcid":"","institution":"Marondera University of Agricultural Sciences and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vimbainashe","middleName":"Prisca","lastName":"Dembedza","suffix":""},{"id":132943703,"identity":"8e3586e6-24bd-4888-b9ab-bd6c6b78ea3c","order_by":1,"name":"Prosper Chopera","email":"","orcid":"","institution":"University of Zimbabwe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prosper","middleName":"","lastName":"Chopera","suffix":""},{"id":132943704,"identity":"de67d565-1d6d-4171-ab49-1e8f300a4c3a","order_by":2,"name":"Jacob Mapara","email":"","orcid":"","institution":"Chinhoyi University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Mapara","suffix":""},{"id":132943705,"identity":"1e58f4c0-07aa-4780-aab4-8d7951c8000e","order_by":3,"name":"Nomalanga Mpofu-Hamadziripi","email":"","orcid":"","institution":"Marondera University of Agricultural Sciences and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nomalanga","middleName":"","lastName":"Mpofu-Hamadziripi","suffix":""},{"id":132943706,"identity":"ea725f6d-574a-4525-8318-bd334cf4a724","order_by":4,"name":"George Kembo","email":"","orcid":"","institution":"Food and Nutrition Council, Zimbabwe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Kembo","suffix":""},{"id":132943707,"identity":"3044af24-35ba-4203-ae00-7af02e1002b0","order_by":5,"name":"Lesley Macheka","email":"","orcid":"","institution":"Marondera University of Agricultural Sciences and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lesley","middleName":"","lastName":"Macheka","suffix":""}],"badges":[],"createdAt":"2022-08-22 14:59:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1986844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1986844/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40795-023-00679-z","type":"published","date":"2023-01-27T18:33:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25994195,"identity":"527427fc-9f7f-49a2-bcdb-c21b119c1912","added_by":"auto","created_at":"2022-09-02 16:22:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003e\u003cstrong\u003eMap of Manicaland Province showing Chipinge, Buhera and Chimanimani\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1986844/v1/aeafd3a1eee8a95894efd0bb.jpg"},{"id":25993609,"identity":"174d9423-b4c4-417c-8e90-dc4e1c37a21f","added_by":"auto","created_at":"2022-09-02 16:17:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNutritional Quality analysis by severity of Cyclone Idai\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1986844/v1/811844dfab730bef4c517c2b.jpg"},{"id":44717799,"identity":"614725d4-9ee8-4b45-933f-473b62fb822d","added_by":"auto","created_at":"2023-10-16 18:39:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1986844/v1/e966f7c0-618e-449b-ae38-088e1878dd74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImpact of Climate Change Induced Natural Disasters on Nutrition Outcomes: A Case of Cyclone Idai, Zimbabwe\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate induced natural disasters such as floods, cyclones, storms, and heat waves have been on the rise globally and have doubled since the early 1990s\u0026nbsp;[1]. These disasters are now becoming more frequent and extreme due to an increase in greenhouse gases concentration in the atmosphere, rising temperatures and extreme rainfall\u0026nbsp;[2]. These climate change induced disasters have exacerbated the risks of hunger and undernutrition\u0026nbsp;[3]\u0026nbsp; through the reduced yields of agricultural crops. Countries which already have food shortages, like low and middle income ones, are often the most vulnerable to climate change, and they have a low capacity to adapt\u0026nbsp;[4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZimbabwe has experienced a number of unprecedented severe drought episodes in the last four decades; 1991\u0026ndash;1992, 1994\u0026ndash;1995, 2002\u0026ndash;2003, 2015\u0026ndash;2016, and 2018\u0026ndash;2019\u0026nbsp;[5]. In addition to droughts, the incidences and frequencies of cyclones have increased. Cyclone Japhet occurred in 2003, resulting in widespread river flooding causing extensive crop damage in Mozambique, Zambia, and Zimbabwe\u0026nbsp;[6].\u0026nbsp;Cyclone Dineo followed in 2017 and then Cyclone Idai in 2019.\u0026nbsp;After Cyclone Idai hit Zimbabwe, six weeks later Cyclone Kenneth hit the same areas giving no room for recovery in the affected zones\u0026nbsp;[7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Zimbabwe, Cyclone Idai affected approximately 270,000 people of which about 51,000 were displaced\u0026nbsp;[8]. This disaster claimed more than 340 lives and some people are still missing. \u0026nbsp;Areas in Chimanimani and Chipinge districts suffered the greatest impact\u0026nbsp;[8].\u0026nbsp;With agricultural land rendered unusable and infrastructure e.g., storage facilities left inutile; this possibly affected dietary intake and diet quality but however, this was not assessed\u0026nbsp;[9]. Research has shown that cyclones not only destroy infrastructure but also wash away food stocks, granaries, fields, gardens, and livestock\u0026nbsp;[10]\u0026ndash;[12]. Climate induced natural disasters usually result in food shortages, no access to safe and nutritious food, therefore, food consumption and dietary diversity are distorted. When a disaster occurs, women and children often suffer the most impact due to gender discriminatory cultural norms and inadequate access to resources\u0026nbsp;[13]. \u0026nbsp;Food shortages after a disaster render women more vulnerable to malnutrition because they have specific nutritional needs during adolescence, while pregnant and/or lactating women also tend to consume fewer calories to give priority to men and children\u0026nbsp;[14]. Young children especially those below the age of five are often very sensitive to nutritional deficits. When children are exposed to such conditions of inadequacy especially during the first 1000 days, there are some irreversible damages that can occur to their cognitive development, health, and physical status. This negatively impacts on their full development potential\u0026nbsp;[15], hence child malnutrition is a good indicator of poor household dietary diversity\u0026nbsp;[16].\u0026nbsp;After Cyclone Idai hit Zimbabwe, approximately 3905 children between the ages of six and 59 months were admitted into treatment programmes for severe acute malnutrition, and this showed the negative impact that Cyclone Idai had on child nutrition and food security. Also and relatedly, in 2020, Cyclone Amphan hit Bangladesh\u0026rsquo;s south western coast, resulting in instability on food security as well as \u0026nbsp;economic instability\u0026nbsp;[17]. Cyclone Amphan left 40.8% of the adults with severe food insecurity due to job loss or loss of income, and a decrease in the living conditions. Disruption of livelihoods further paved way for increased prevalence of child malnutrition due to decreased dietary quality\u0026nbsp;[18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies on the impact of climate change induced natural disasters mainly look at impact of natural disasters on the environment, agricultural productivity, livelihoods and water, sanitation and hygiene. Few studies [19] have researched on the impact of cyclones on food security outcomes. Most studies \u0026nbsp;have studied effect of cyclones on agriculture output [20], livelihoods [21], [22], and poverty [23], [24]. Therefore, the main objective of this study was to assess the impact of Cyclone Idai on selected food and nutrition security outcomes (Food Consumption Score, Households Dietary Diversity Score, Minimum Dietary Diversity Score for Women and Minimum Dietary Diversity Score for Children) in the most cyclone affected region of Eastern Zimbabwe. \u0026nbsp;\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1 Study setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was done in Eastern Zimbabwe, Manicaland Province, in the districts of Buhera, Chimanimani and Chipinge (Figure 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\u0026deg; S, 32.1746\u0026deg; E) and due to its proximity to Mozambique and the Indian Ocean it is prone to cyclones and other adverse weather events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2 Sample size and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.3 Data collection tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected using face-to-face interviews. Enumeration was done with the help of ten (10) 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.\u003c/p\u003e\n\u003cp\u003eNutrition indicators like household dietary diversity, food consumption scores, minimum dietary diversity for women and for children were calculated as indicated below.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHousehold dietary diversity score (HDDS)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHousehold 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMinimum dietary diversity score women (MDD-W)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMinimum dietary diversity for children (MDD-C)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMinimum dietary diversity for children is defined as the proportion of children 6\u0026ndash;23 months of age who receive foods from four or more food groups. It is calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFood Consumption score (FCS)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFood 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 seven 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.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSeverity of cyclone Idai\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 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 and was determined by the researchers using literature and community interviews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData 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.\u003c/p\u003e\n\u003cp\u003eLinearity 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\u0026lt;0.05. Linear regression analysis was done to test for determinants of HDDS and FCS, and logistic regression for MDD-W.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Background Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 535 households consisting of 171 women of reproductive age and 213 children were interviewed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age for the household head was 45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;40.14 years and the mean household size was 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9. The youngest head of household was 12 years, with the oldest at 92 years old. The number of persons per household varied from one (1) to 27 members. Of the 535 households interviewed, the majority (334 households) were married and living together, with 48.2% of these still living in their homes after extensive damage by Cyclone Idai. Most of the households were male headed (84.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHousehold demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eSeverity of cyclone Idai (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot affected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately affected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtensive but still living in their homes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtensive (relocated to camps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExtensive (relocated to new houses)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuhera (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChimanimani (n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChipinge (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillage (n\u0026thinsp;=\u0026thinsp;461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamp (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Household Head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (n\u0026thinsp;=\u0026thinsp;526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of Household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (n\u0026thinsp;=\u0026thinsp;526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of Household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried living together (n\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried living apart (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced/Separated (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidow/Widower (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever married (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eHighest level of education of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary (n\u0026thinsp;=\u0026thinsp;185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZJC (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u0026rsquo; level (n\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u0026rsquo; level (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma/certificate after primary (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma/certificate after secondary (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate/post-graduate (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther n\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* Chi Square for comparing categorical variables and ANOVA for comparing means\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was a significant association between severity of cyclone and district (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;305.700; df\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.000). Chimanimani was the most affected district with 77.6% being extensively affected but still living in the same homes. In addition, there was a significant association between severity and type of settlement (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;557.201; df\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.000). Furthermore, the results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show a significant association between severity of Cyclone Idai and marital status (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;40.274; df\u0026thinsp;=\u0026thinsp;20; p\u0026thinsp;=\u0026thinsp;0.005) and severity of Cyclone Idai and education level of household head (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;55.123; df\u0026thinsp;=\u0026thinsp;32; p\u0026thinsp;=\u0026thinsp;0.007). The results also reveal a significant difference in household size across severity category (p\u0026thinsp;=\u0026thinsp;0.030). The households worst affected and requiring relocation were the smallest in size (3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8). However, there was no significant difference in age of household head across severity (p\u0026thinsp;=\u0026thinsp;0.690) as well as gender of household head (p\u0026thinsp;=\u0026thinsp;0.675).\u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Impact of Cyclone Idai on nutrition outcomes\u003c/h2\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Food consumption score (FCS)\u003c/h2\u003e \u003cp\u003e The overall median FCS was 32 [21.50, 45.80]. In addition, the median FCS for Category 1 (not affected) was 32 [22.00, 46.00], Category 2 was 30.5 [12.50, 40.50], Category 3 was 35 [22.25, 52.75], Category 4 was 10 [7.00, 24.00] and Category 5 was 27.75 [20.38, 37.88] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The proportion with a poor FCS was 16%, 27.2%, 46.4%, 6.4% and 4.0% for category 1 to 5 respectively. There was a significant association between the severity of impact of Cyclone Idai and FCS (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;27.421; df\u0026thinsp;=\u0026thinsp;8; p\u0026thinsp;=\u0026thinsp;0.001). The highest proportion of households with a poor FCs were found in the first 3 categories (not affected, moderate and extensive but still living in their homes).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Household Dietary Diversity Score (HDDS)\u003c/h2\u003e \u003cp\u003eHDDS was generally high (\u0026ge;\u0026thinsp;5 food groups) in all 5 categories of severity with Category 1 households having a HDDS of 5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4, Category 2 at 5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0, Category 3 at 6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 and Category 4 and 5 at 5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 and 5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 respectively. There was a significant difference in HDDS across severity categories (p\u0026thinsp;=\u0026thinsp;0.000). \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Women Minimum Dietary Diversity Score (MDD-W)\u003c/h2\u003e \u003cp\u003eThe proportion of women below the MDD-W cut-off was as follows; 34.3%, 22.5%, 40.2%, 2.0% and 1.0% for category 1\u0026ndash;5 respectively. This proportion was highest for category 1 to 3. There was a significant association between MDD-W and severity of Cyclone Idai (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.220; df\u0026thinsp;=\u0026thinsp;4; p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Children Minimum Dietary Diversity Score (MDD-C)\u003c/h2\u003e \u003cp\u003eA high proportion of children in overall were below MDD-C cut-off (that is 74.1% of the children were below the cut-off for child dietary diversity). The proportion below the MDD-C cut-off by severity of impact was 25.5%, 25.5%, 42.0%, 3.8% and 3.2% from category 1 to 5. There was however no significant difference in proportion below cut-off across severity category (χ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.439; df\u0026thinsp;=\u0026thinsp;4; p\u0026thinsp;=\u0026thinsp;0.245).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrition outcomes of study population by severity of Cyclone Idai\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutrition outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eSeverity of Cyclone Idai (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot affected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately affected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtensive (Still living in their homes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003cp\u003e(Relocated to camps)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExtensive (Relocated to new houses)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFood Consumption Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor (n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorderline (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate (n\u0026thinsp;=\u0026thinsp;217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMDD-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInadequate (n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMDD-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow average (n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove average (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e All results show % proportion of households/individuals\u003c/p\u003e \u003cp\u003e*Pearson Chi square except where cells count was less than 5 Fisher\u0026rsquo;s exact was used\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determinants of nutrition outcomes of sampled households\u003c/h2\u003e \u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveal that gender of household head was a negative predictor of HDDS (β= -0.734, p\u0026thinsp;=\u0026thinsp;0.040) which shows that female headed households were found to be more food secure. Marital status was a significant positive predictor of FCS (β\u0026thinsp;=\u0026thinsp;0.093, p\u0026thinsp;=\u0026thinsp;0.016) showing that married couples who are living together had a higher FCS than others. However, severity of Cyclone Idai was not a predictor of any of the nutrition outcomes (HDDS, FCS and MDD-W).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of Nutrition outcomes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNutrition Outcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHDDS\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFCS\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMDD-W\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.734*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity of Cyclone Idai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003e Simple linear regression with dependent variables, HDDS and FCS except for MDD-W where binary logistic regression was used.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Nutritional quality by severity of Cyclone Idai\u003c/h2\u003e \u003cp\u003eConsumption of Vitamin A-rich foods was in overall high in all settlements with only those living in camps (Category 4) having less than 50% households consuming Vitamin A-rich foods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest proportion of households who never consumed these foods was also in camps. Households living in camps had the highest proportion (30%) of never consuming protein-rich foods; however, it also had the highest proportion of households who consumed protein-rich foods at least on 6 or less days prior to the survey. The proportion of households consuming Heme iron-rich foods daily is generally low across all the settlements. Camps had the highest proportion (40%) of households who never consumed Heme iron-rich foods seven (7) days before the survey and none of the households consumed Heme-iron foods daily.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study sought to investigate the effect of Cyclone Idai on selected food security indicators namely FCS, HDDS, MDD-W and MDD-C. In general, the households worst affected by the cyclone were the smallest in size and headed by either divorced or never married household heads. Furthermore, households with poor socio-demographic profiles were more severely affected by the cyclone and this could be the reason they opted to be relocated since these households usually have poorer coping strategies and or support structures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is corroborated by findings from a study done in rural Sri Lanka [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] which showed that households that mostly depend on natural resources for their livelihood, and those with low incomes suffer greater losses from floods and droughts than other households. In terms of food security, contrary to our assertion that relocated households would have a poor FCS, the highest proportion of households with a poor FCs were found in the first 3 categories (not affected, moderate and extensive but still living in their homes). In general, these are food insecure districts, although relocated households had higher FCS possibly due to food aid. Data collection was conducted 31 months after the cyclone and by this time camps had been set up and there was relief activity by the Ministry of Health and Child Care alongside various Non-Governmental Organisations like the Adventist Development and Relief Agency International (ADRA), IOM, UNICEF, Nutrition Action Zimbabwe (NAZ), Save the Children, World Vision International, GOAL and WFP. IOM was working to support affected communities through technical assistance in shelter and information management.\u003c/p\u003e \u003cp\u003eThe diet quality for women in the severely affected categories appeared to have been better than that of women in moderately and non-affected households. The proportion of women below the MDD-W cut-off was highest for category 1 to 3 and lowest for relocated households (severely affected). This validates the important contribution of food aid as part of relief efforts.\u003c/p\u003e \u003cp\u003eThough HDDS was adequate across all categories, the results revealed that a high proportion of children were below MDD-C cut-off (\u0026ge;\u0026thinsp;4 food groups). This reflects access by households to food but poor intrahousehold food distribution. However, the last 2 categories (relocated to camps or new homes) had the lowest proportion, meaning children living in relocated households (severely affected) had better access to diverse foods than those living in moderately and non-affected areas. This result contradicts with the types of food groups reportedly consumed. We found that the highest proportion of households who never consumed vitamin A rich foods were in the camps. Camps also had the highest proportion (40%) of households who never consumed Heme iron-rich foods. Further, households living in camps had the highest proportion (30%) of never consuming protein-rich foods. Households in camps were entirely dependent on food aid. The food aid ration consisted of 2.4kg pulses, 13.5kg cereal, 0.75kg vegetable oil, 6kg super cereal plus (Corn Soya Blend++) and 6kg super cereal (Corn Soya Blend) per person per month. It is evident that the food aid basket is lacking in animal source foods and perhaps the participants may not have known that the super cereal provided was fortified with Vitamin A hence it was a vitamin A source. Super cereal is prepared from heat treated maize, whole soya beans, vitamins and minerals to provide a highly nutritious meal [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A study done in Malawi showed that the provision of super cereal increased calorie density as well as enhancing the absorption of fat-soluble vitamins, thereby addressing the nutritional needs of children with moderate acute malnutrition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe cyclone negatively affected most food security indicators with only the relocated households faring better possibly due to aid. There were variations in diet quality. Though at household level dietary diversity was adequate, this did not translate to adequate women and child dietary diversity. The findings presented in this paper provide a good baseline to inform future programming of food aid activities during disasters. More so, our findings call for evidence-based policies regarding composition of a food aid basket and targeting of beneficiaries. One limitation of our study was that we could not gain access to communities immediately after the cyclone, hence the relationships measured were confounded by relief activities. However, most food security indicators especially for children were below acceptable levels indicating inadequacy of relief activities for children and women dietary requirements, hence our results are still valid. Despite this limitation, our study had several strengths. It is the first study to investigate the effects of cyclones on food and nutrition security indicators. Moreover, we employed a large sample size thus making our results generalisable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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). Informed consent was obtained from all subjects interviewed in the study and this was shown by signing the consent form. Informed consent was obtained from a parent and/or legal guardian for minors for study participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author, upon signing of data use agreement and Research Ethics Committee approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. The sponsors had no role in the design, execution, interpretation, or writing of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is an output of a research project, Inventorying Intangible Cultural Heritage Assets Affected by Cyclone Idai in Chimanimani, Chipinge and Buhera districts in Zimbabwe, funded by the Arts and Humanities Research Council, UK. AHRC Reference: AH/V006436/1. However, we did not receive any funds to cover publication costs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization was done by LM, VD, NH and GK. The methodology section was written by PC and VD. Formal analysis was done by VD, PC and JM. The original draft was written by VD, PC and LM. Review \u0026amp; Editing was done by all authors and they read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Mr Mukozhiwa, Ms Siamena, Ms Mushangazhike and the enumerators for their contribution during data collection for this study. We acknowledge the financial support from the Arts and Humanities Research Council, UK. AHRC Reference: AH/V006436/1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThomas V, LLpez R. Global Increase in Climate-Related Disasters. 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Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp251114.pdf\u003c/span\u003e\u003cspan address=\"https://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp251114.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanglois B, Suri D, Wilner L, et al, Self-report vs. direct measures for assessing corn soy blend porridge preparation and feeding behavior in a moderate acute malnutrition treatment program in southern Malawi, Journal of Hunger \u0026amp; Environmental Nutrition, vol.\u0026nbsp;13, pp.\u0026nbsp;1\u0026ndash;12, Nov. 2017, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19320248.2017.1374902\u003c/span\u003e\u003cspan address=\"10.1080/19320248.2017.1374902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Natural disasters, Nutrition outcomes, Cyclone Idai, Zimbabwe","lastPublishedDoi":"10.21203/rs.3.rs-1986844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1986844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe increased frequency of climate induced natural disasters has exacerbated the risks of malnutrition in the already vulnerable regions. This study was aimed at exploring the effects of Cyclone Idai on nutrition outcomes of women of child-bearing age and children under five years.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe household-based cross-sectional study was conducted in Eastern Zimbabwe. Data were collected through face-to-face interviews to determine food consumption score (FCS) and household dietary diversity (HDDS), minimum dietary diversity for women (MDD-W) and minimum dietary diversity for children (MDD-C). Severity of Cyclone Idai was grouped into five categories based on the extent of damage to infrastructure and loss of human lives. Association between continuous and categorical variables was tested using Pearson correlation test and Chi square test, respectively. Linear and binary logistic regression was performed to investigate determinants of food security.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 535 households were interviewed. There was a significant correlation between severity of Cyclone Idai and MDD-W (p\u0026thinsp;=\u0026thinsp;0.011), HDDS (p\u0026thinsp;=\u0026thinsp;0.018) and FCS (p\u0026thinsp;=\u0026thinsp;0.001). However, severity Cyclone Idai was not a determinant of any nutrition outcome, but gender of household head was a negative predictor of HDDS (β=-0.734, p\u0026thinsp;=\u0026thinsp;0.040), and marital status of household head was a positive predictor (β\u0026thinsp;=\u0026thinsp;0.093, p\u0026thinsp;=\u0026thinsp;0.016) of FCS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings provide a good baseline to inform future programming of food aid activities during disasters. More so, our findings call for evidence-based policies regarding composition of a food aid basket and targeting of beneficiaries. The main strength of this study is that it is the first to investigate the effects of cyclones on food and nutrition security indicators and is based on a large sample size thus making our results generalisable.\u003c/p\u003e","manuscriptTitle":"Impact of Climate Change Induced Natural Disasters on Nutrition Outcomes: A Case of Cyclone Idai, Zimbabwe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-02 16:17:15","doi":"10.21203/rs.3.rs-1986844/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-31T09:35:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-10-06T18:56:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-10T19:45:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c31920dc-2980-4fc0-86bb-b3de26585b98","date":"2022-09-10T06:15:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"01a8c0ac-b9f9-4a14-915b-ee4878f8f631","date":"2022-09-10T06:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-10T06:03:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-09T02:33:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-08-30T21:36:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-30T21:32:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2022-08-22T14:55:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57d03cd5-8d5e-42ab-8a5a-77d20ceef59e","owner":[],"postedDate":"September 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:37:05+00:00","versionOfRecord":{"articleIdentity":"rs-1986844","link":"https://doi.org/10.1186/s40795-023-00679-z","journal":{"identity":"bmc-nutrition","isVorOnly":false,"title":"BMC Nutrition"},"publishedOn":"2023-01-27 18:33:31","publishedOnDateReadable":"January 27th, 2023"},"versionCreatedAt":"2022-09-02 16:17:15","video":"","vorDoi":"10.1186/s40795-023-00679-z","vorDoiUrl":"https://doi.org/10.1186/s40795-023-00679-z","workflowStages":[]},"version":"v1","identity":"rs-1986844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1986844","identity":"rs-1986844","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}