COVID-19 and pregnancy: a comprehensive study of comorbidities and outcomes
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the impact of pregnancy and pre-existing comorbidities on COVID-19 infections and associated complications of hospitalisation and mortality in women of reproductive age (WRA). The study also compared the risk of severe COVID-19 complications between pregnant women (PW) and non-pregnant women (NPW) with and without pre-existing comorbidities. Special focus was placed on some understudied comorbidities of immunosuppression, chronic renal disease and chronic obstructive pulmonary disease (COPD). METHODS: The study utilized anonymized patient-related information for a population of 7,342,869 WRA from the Mexican Ministry of Health data repository on COVID-19. Descriptive variables were characterized using frequencies, percentages, means, and standard deviations. Adjusted odds ratios (aORs) were used to assess the associations between risk factors and outcomes of hospitalisation and mortality. The study covered the entire COVID-19 pandemic period from January 30, 2020, to May 5, 2023. RESULTS: The findings revealed that PW were not more likely to get COVID-19 infections than NPW. PW with COVID-19 infections were more likely to require hospital admission, intubation treatments, and ICU admission compared to NPW with COVID-19. PW with immunosuppression had an increased odds ratio (aOR) of getting COVID-19 infections compared to NPW (PW: aOR = 1.0396; NPW: aOR = 0.8373). NPW with immunosuppression had higher risk of mortality (all-cause death: aOR = 1.7084; COVID-19-associated death: aOR = 1.4079) and hospitalisation (all-cause hospitalisation: aOR = 4.1328; COVID-19-associated hospitalisation: aOR = 3.0451) than NPW without immunosuppression. Renal disease was identified as a concerning pre-existing condition that increased the risks of COVID-19 associated mortality/hospitalizations and all-cause mortality/hospitalizations for both PW and NPW. NPW with renal disease had much higher odds ratio (aOR) of either COVID-19-associated-hospitalisations (NPW: aOR = 8.639; PW: aOR = 1.7603) or all-cause hospitalisations (NPW: aOR = 8.8594; PW: aOR = 1.786) than PW with renal disease. CONCLUSIONS: This study provides valuable insights into the impact of pregnancy and pre-existing comorbidities on COVID-19 outcomes in WRA. The findings underscore the importance of considering demographic factors and pre-existing comorbidities in the management of PW with COVID-19. The study also highlights the need for further research to understand the unique impacts of different comorbidities, particularly immunosuppression and renal disease, on COVID-19 outcomes in WRA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".