Prevalence of diabetes in pregnancy among Indigenous women in Australia, Canada, New Zealand, and the USA: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples in countries with similar colonial histories have disproportionate burdens of disease compared with non-Indigenous peoples. We aimed to systematically identify and collate studies describing the prevalence of pre-existing diabetes and gestational diabetes, and compare the prevalence of these conditions between Indigenous and non-Indigenous pregnant women in Australia, Canada, New Zealand, and the USA. METHODS: For this systematic review and meta-analysis, an information specialist did a comprehensive search of eight databases (Ovid MEDLINE, Ovid Embase, Ovid Global Health, CINAHL [EBSCO], Scopus, ProQuest Dissertations and Theses Global, PROSPERO, and the Wiley Cochrane Library) in June, 2019, for studies published between inception and June 25, 2019, without restrictions on language, publication type, or year of publication. Database searches were supplemented by grey literature searches of the Bielefield Academic Search Engine and Google Scholar, and the reference lists of relevant articles were also manually searched. We included observational epidemiological studies comparing the prevalence of pre-existing diabetes or gestational diabetes in Indigenous and non-Indigenous pregnant women in Australia, Canada, New Zealand, and the USA. Two independent reviewers assessed study eligibility and risk of bias. We used a standardised data extraction form to collect information from the published reports of eligible studies, and, if needed, we contacted authors for further information. We did a Mantel-Haenszel random-effects meta-analysis to obtain the pooled unadjusted prevalence odds ratios (PORs) of pre-existing diabetes and gestational diabetes in Indigenous women compared with non-Indigenous women. We stratified meta-analyses by country and type of diabetes. The study is registered with PROSPERO, number CRD42018095971. FINDINGS: =48%); however, the magnitude and direction of the PORs from individual studies indicated an association between pre-existing diabetes or gestational diabetes and indigeneity among pregnant women. INTERPRETATION: The prevalence of pre-existing diabetes and gestational diabetes was higher in Indigenous pregnant women than in non-Indigenous pregnant women in four countries (Australia, Canada, New Zealand and the USA) with similar histories of colonialism. These findings have implications for prenatal care services and the monitoring of Indigenous women in industrialised countries. FUNDING: Canadian Institute of Health Research and the Women's and Children's Health Research Institute.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 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".