| Literature DB >> 36230179 |
Md Salman Sohel1, Guoqing Shi2, Noshin Tasnim Zaman3, Babul Hossain4, Md Halimuzzaman5, Tosin Yinka Akintunde6, Huicong Liu6.
Abstract
This study examined the food insecurity and coping mechanisms among the indigenous Bangladeshi population of the Chittagong Hill Tracts (CHT) region to extract empirical evidence on the ongoing discussion on the COVID-19 pandemic-exacerbated food-insecurity situation. The study adopted a qualitative approach by interviewing 60 indigenous households. Data were collected in two phases between 15 June 2020, and 30 July 2021 in Bangladesh's Chittagong Hill Tracts (CHT) region. Thematic data analyses were performed using the Granheim approach and NVivo-12 software. The authors used Huston's social-ecological theory to explain the indigenous coping mechanisms. The research evidence revealed that most households experienced challenges over daily foods, manifesting in the decreasing consumption of them, the increased price of food items, a food crisis due to an income shock, malnutrition, the shifting to unhealthy food consumption, starvation and hunger, and food insufficiency, thereby leading to mental stress. This study further revealed that the indigenous population took crucial coping strategies to survive the pandemic. In response to COVID-19, they took loans and borrowed foods, reduced expenses, changed their food habits, avoided nutritional foods, relied on vegetables, sold domestic animals and properties, collected forest and hill foods, and depended on governmental and societal relief. This study also provides the in-depth policy actions for the urgent intervention of government, stakeholders, policymakers, NGOs, and development practitioners to take necessary initiatives to enhance the quality of life of the people that were affected by the post-pandemic recovery period.Entities:
Keywords: Bangladesh; CHT region; COVID-19; adaptation strategies; food; food consumption behavior; food insecurity; health; indigenous community; pandemic
Year: 2022 PMID: 36230179 PMCID: PMC9564301 DOI: 10.3390/foods11193103
Source DB: PubMed Journal: Foods ISSN: 2304-8158
Tabular literature review of COVID-19 researches on food security in Bangladesh.
| Reference | Methodology and Sample Size | Key Findings | Limitations |
|---|---|---|---|
| [ | Multivariate Multiple Ordinal Logit Regression, partial least squares path model with n = 540 | Higher COVID-19 severity correlating to starvation because of income shock and price hike, and food insecurity affecting purchasing and consuming patterns | Negative weights, multicollinearity, unavailability of global index for model validation, limited use of goodness-of-fit |
| [ | OLS approach with n = 50,000 | Estimation of the economic damage caused by the COVID-19-induced lockdown and proposed a minimal financial package to maintain food security for Bangladesh’s daily paid workers | Lack of theoretical discussion |
| [ | Multi-equation partial equilibrium with rice market data from FY96 to FY20 | Decrease in food security, higher import tariff limiting rice supply and stock enhancement strategy mitigating the negative impact | Absence of the influence of economy-wide imbalances on the Bangladeshi rice market, and the dynamics in relation to other agricultural and non-agricultural sectors |
| [ | Longitudinal study with 3544 persons Bangladesh, 3685 persons in Kenya, and 3582 | Unemployment, shutdown of business, disruption in agricultural activities, price hike, sickness/death, selling assets, extra earning, monetary support, decrease food/non-food intake, savings | No mention about most vulnerable demographic groups, and reasons of inequalities, no discussion whether job loss and business shutdown related to labor demand/supply, no theoretical framework |
| [ | Food Insecurity Experience Scale (FIES), linear probability model, 10,000 households | Food insecurity increased dramatically across families and began to impact groups that were in a better situation in the first survey | Phone interview, two study areas, no discussion on the issue of endogeneity under various data and interview constraints, lack of exogenous fluctuations, no theoretical linking |
| [ | Descriptive statistics, n = 397 | Income shock, unemployment, price hike, reducing grocery shopping, high-cost goods and unhealthy snacks, intaking nutrient food, adopted eggs and dried fish and stored rice, lentils and potatoes | Absence of theoretical framework, missing answers to some questions, the number of participants for different indicators was not the same |
| [ | Cross-sectional survey, n = 1876 | Decrease in consumption, job lost, or shut down businesses, income shock | Lack of theoretical discussion, short time frame—5 months, online participants, unable to assess any seasonal fluctuation in HFS and HDD, self-reported data by the participants |
| [ | Interpretive phenomenological analysis, 21 in-depth interviews and 4 FGDs | Skipping meals, rising prices, and a scarcity of fish, meat, potatoes, and vegetables, chronic nutritional scarcity, hunger, maternal and child malnutrition, and cheap food. | Limited to informal migrants in Dhaka city, small sample size |
| [ | Cross-sectional survey, 106 urban and 106 rural households | Selling or crediting property, lending food and money, reducing food quality and amount | Lack of theoretical dimension, short study timeline |
| [ | Gross margin analysis, n = 120 | Disruption of supply chain, income loss, limiting access to market, and productional capacity, reduction in vegetables’ cultivation, declined food consumption | No theoretical dimension, only focused on vegetables supply and food security, farmers as respondents |
| [ | Empirical work, n = 201 | Shift in food intake and diet, drop in household income, stockpiling food, skipping food or reducing consumption, raising the amount of budget allotted to food, getting food aid, borrowing | Limited to nine weeks lockdown, no discussion on the role of NGO or government, no theoretical discussion |
Tabular-based literature review about COVID-19 induced food security on indigenous communities worldwide.
| Reference/Sources | Study Area | Methodology and Sample Size | Key Findings | Limitations |
|---|---|---|---|---|
| [ | Indonesia | Cross-sectional study with n = 517 | Household Income loss, closure of work, high food insecurity in low-income families and houses comprising a younger member | Online interviews, less variation in socio- demographic profile, no theoretical implication, and inability to use conventional food consumption instruments |
| [ | USA | Longitudinal study and Food Security Survey Module (FSSM) with n = 167 | Women were more likely to have food insecurity, were less capable of affording proper meals, ended up eating smaller portions, and were more likely to starve than men | Lack of theoretical link, and missing discussion on Blackfeet tribal community’s coping strategies during COVID-19 |
| [ | USA | Cross-sectional study, n = 74,413 | Households headed by Asian, Black, Hispanic, or other racial minorities were not remarkably more food insecure than White households | Main focus on food access, no discussion on the nutritional consequences of food insecurity, cross-sectional research design and week-one HPS microdata, absence of theoretical dimension |
| [ | Arctic zone of Western Siberia | Multidisciplinary approach, n = 252 | Insufficient access to local food, vaccines and medicines, rise in production cost, reduction in selling reindeer products’ price, change in food diet, health risk | Missing theoretical dimension, limited numbers of participants in reindeer herding industry |
| [ | USA | Cross-sectional study, 3133 US counties | Infection rates were higher in Black, American Indian, or Alaska Native group with higher food scarcity and vast populations | Absence of theoretical framework, no explanation about food assistances or support services, and policy measures |
| [ | India | Cross-sectional study n = 211 | Barriers in getting ration, access to limited food items, starvation, lack of food supply | No theoretical framework, phone interview, short period |
Figure 1Indigenous household coping strategies through Huston’s social–ecological theory.
Figure 2Location of the study area.
Thematic data analysis procedure using Granheim and Lundman’s approach.
| Steps | Description |
|---|---|
| 1. Interview transcription | The interviews were taped and read again after hearing the recordings several times to comprehend their contents. |
| 2. Unit for the formation of meaning analysis | All interviews were analyzed as a single unit. Primary codes were created by abstracting the meaning units. |
| 3. Comprehensive sorting of similar codes | The grouping of similar fundamental codes into more comprehensive categories was conducted. |
| 4. Comparison of codes and establishment of subcategories | In contrast, all codes and data identified similarities and differences. This process resulted in the formation of categories and subcategories. |
| 5. Comparing subcategories and establishing primary categories | The initial interviews yielded an initial set of codes, categories, and subcategories, and the emerging codes were considered to be the results due to the thematic analysis approach. |
Figure 3Thematic issues of food insecurity during COVID-19.
Defined themes that were derived from the thematic analysis.
| Central Theme | Sub-Theme | Reference Code from NVivo-12 | Descriptive Coding |
|---|---|---|---|
| Food Insecurity | Decreased consumption | 88 |
|
| Increase the price of daily food items | 67 |
| |
| Income shock to the food crisis | 64 |
| |
| Poor nutrition | 41 |
| |
| Shifting to unhealthy and inexpensive food | 36 |
| |
| Starvation and hunger | 29 |
| |
| Food insufficiency leads to mental stress | 28 |
| |
| Coping Strategies | Taking loans and borrowing foods | 72 |
|
| Reduced expenses and savings | 60 |
| |
| Changing food habits | 49 |
| |
| Collecting forest and hill foods | 44 |
| |
| Selling domestic animals and property | 29 |
| |
| Social and governmental reliefs | 25 |
|
Distribution of the demographic profile of the interviewees.
| Category | Variable | N |
|---|---|---|
| Gender | Men | 39 |
| Women | 21 | |
| 20–30 | 17 | |
| 30–40 | 15 | |
| 40–50 | 11 | |
| 50–60 | 10 | |
| Education | Illiterate | 32 |
| Under Primary School | 13 | |
| Primary School | 9 | |
| High School | 6 | |
| Marital status | Married | 46 |
| Widow | 14 | |
| Ethnicity | Chakma | 24 |
| Marma | 18 | |
| Tripura | 9 | |
| Tanchangya | 9 | |
| Place of residence | Khagrachari | 20 |
| Rangamati | 20 | |
| Bandarban | 20 |
Figure 4Indigenous coping strategies.