Assessing the hidden burden and costs of COVID-19 pandemic in South Asia: Implications for health and well-being of women, children and adolescents
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately affected vulnerable populations. With its intensity expected to be cyclical over the foreseeable future, and much of the impact estimates still modeled, it is imperative that we accurately assess the impact to date, to help with the process of targeted rebuilding of services. We collected data from administrative health information systems in six South Asian countries (Afghanistan, Bangladesh, Nepal, India, Pakistan and Sri Lanka), to determine essential health services coverage disruptions between January-December 2020, and January-June 2021, compared to the same calendar months in 2019, and estimated the impact of this disruption on maternal and child mortality using the Lives Saved Tool. We also modelled impact of prolonged school closures on continued enrollment, as well as potential sequelae for the cohort of girls who have likely dropped out. Coverage of key maternal and child health interventions, including antenatal care and immunizations, decreased by up to 60%, with the largest disruptions observed between April and June 2020. This was followed by a period of recovery from July 2020 to March 2021, but a reversal of most of these gains in April/May 2021, likely due to the delta variant-fueled surge in South Asia at the same time. We estimated that disruption of essential health services between January 2020 and June 2021 potentially resulted in an additional 19,000 maternal and 317,000 child deaths, an increase of 19% and 13% respectively, compared to 2019. Extended school closures likely resulted in 9 million adolescents dropping out permanently, with 40% likely being from poorest households, resulting in decreased lifetime earnings. A projected increase in early marriages for girls who dropped out could result in an additional 500,000 adolescent pregnancies, 153,000 low birthweight births, and 27,000 additional children becoming stunted by age two years. To date, the increase in maternal and child mortality due to health services disruption has likely exceeded the overall number of COVID-19 deaths in South Asia. The indirect effects of the pandemic were disproportionately borne by the most vulnerable populations, and effects are likely to be long-lasting, permanent and in some cases inter-generational, unless policies aimed at alleviating these impacts are instituted at scale and targeted to reach the poorest of the poor. There are also implications for future pandemic preparedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".