How systemic racism results in poorer outcomes for First Nations, and what First Nations are doing about it: the example of kidney health
Bibliographic record
Abstract
BACKGROUND: End-stage kidney disease continues to disproportionally impact the lives of First Nations peoples. Systemic racism is a key determinant, and manifests as differential access to determinants of health (housing, employment, access to care) and differential care. This paper discusses how different models of primary healthcare operating in rural and remote Manitoba communities results in different outcomes for patients identified as being at risk of kidney disease. METHODS: This study is a partnership between researchers from the First Nations Health and Social Secretariat of Manitoba and the University of Manitoba. We used health administrative data held at the Manitoba Centre for Health Policy for the period of 2006-2019, linked to the Manitoba First Nations Research File to identify First Nations. We compared rates of laboratory follow-up tests, nephrology consults, PHC visits, and hospitalizations between different models of care using a negative binomial regression model adjusted for age, sex, eGFR heat-map category, urine ACR heat-map category, and Elixhauser comorbidity index. RESULTS: We identified 12,613 First Nations people with chronic kidney disease (CKD) during the study period. First Nations individuals with CKD who reside in communities served by Nursing Stations (most remote communities) when supplemented by additional Indigenous programs were consistently more likely to receive follow-up serum creatinine (OR 1.37, 95% CI: 1.30-1.45, p<0.001), urine ACR (OR 1.22, 95% CI: 1.16-1.28, p<0.001), serum potassium (OR 1.40, 95% CI: 1.32-1.49, p<0.001) than individuals who lived in communities served by Nursing Stations alone, Health Centres, Health Offices, or Off Reserve. CONCLUSIONS: Our results show that addressing the rise in premature mortality experienced by First Nations from kidney diseases require greater investments in First Nations-centric primary healthcare, that is locally managed. Additionally, off-reserve primary healthcare services must be alerted to their need to better address the needs of First Nations at risk of CKD, with more consistent follow up, referrals, and in providing culturally safe care. Finally, First Nations-led research in kidney health and primary healthcare is leading to significant improvements in outcomes, and needs to be better supported and resourced, and imbedded in a context of greater investments to improve access to all determinants of health and counter systemic racism.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".