Impact of the COVID-19 lockdown on household diet diversity in rural Bihar, India: a longitudinal survey
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic disrupted livelihoods and diets across the world. This study aimed to assess changes in household diet diversity and food consumption between the pre-COVID-19 period (December 2019–January 2020) and during the lockdown (March–May 2020), and to identify the socio-economic characteristics that determine these changes in rural Bihar, India. Methods Households that had provided their phone numbers in the pre-COVID-19 household survey (n = 1797) were contacted for interviews during the lockdown telephonic survey in a longitudinal survey in two districts (Gaya and Nalanda) in Bihar. In total, 939 households were interviewed. Using data on food consumption from both surveys, 876 households were included in the analysis. Food and Agriculture Organization’s household diet diversity score (HDDS) was used to compare diet diversity between the pre-COVID-19 period and during the lockdown. Logistic regression was used to identify factors affecting household diet diversity and food consumption in the study households. Results Low diet diversity increased from 51.6% (95% CI 48.3–54.9) to 75.8% (95% CI 73.0–78.6) from the pre-COVID-19 to the lockdown period. Reduced food consumption was reported across all foods with nearly a quarter of the households reporting reduced consumption of fruits (27%), pulses (25%) and cereals (21%). Nearly 60% and above reported stopping consumption of nutrient-rich foods such as chicken, fish and eggs although the population was predominantly non-vegetarian. Logistic regression analysis revealed that taking a loan from neighbours/relatives (OR = 1.8; 95% CI 1.3–2.5) and belonging to lower social groups (OR = 1.8; 95% CI 1.1–2.9) increased odds of low HDDS. While those possessing ration cards had lower odds of reduced consumption of all food items, it was not associated with stopping consumption of any food item. In an unadjusted analysis, receipt of cash transfer during lockdown was also not associated with diet diversity (OR = 1.2; 95% CI 0.9–1.7). Conclusions COVID-19 has impacted the consumption of nutrient-rich foods among already low-income rural households in India. Maintaining diet diversity among socio-economically vulnerable households during periods when food consumption is most threatened by shocks such as COVID-19 would need sustained government support in terms of social protection coverage and benefit transfers in rural communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".