Undernutrition and Anemia Prevalence Among Indigenous San Women of Child-Bearing Age and Young Children in Rural Botswana
Bibliographic record
Abstract
Globally, Indigenous women and young children disproportionately face increased nutritional risks, which may have serious adverse health consequences. In Botswana, data is limited on the health and nutritional status of the San People, an Indigenous minority group primarily living in the Ghanzi District. This cross-sectional study aimed to assess the prevalence of anemia and undernutrition among San women and young children in Ghanzi District. We recruited 367 mother-child pairs (women 15–49 years and children 6–59 months) from San households from nine randomly selected areas. A capillary blood sample was collected, and weight and height were measured in both mothers and children. Hemoglobin (Hb) concentration was measured using a hemoglobinometer (HemoCue, AB). As per global recommendations, Hb concentrations were adjusted for altitude, smoking (in women), and ethnicity. Fifty-six % (n = 205/367) of women self-reported smoking in any form (rolled cigarettes or snuffing). Overall, adjusted anemia prevalence was 12% in non-pregnant women (Hb < 120 g/L), 26% in pregnant women (Hb < 110 g/L), and 42% in children (Hb < 110 g/L); but ranged widely based on the controversial factor of whether or not adjustments for ethnicity were applied (range of 6–26%, 22–30% and 35–68% prevalence, respectively). Thirty-nine % (n = 133/344) of non-pregnant women and 52% (n = 12/23) of pregnant women were underweight (BMI < 18.5 kg/m2). In children 6–23 months, 41% were underweight (weight-for-age z-score < -2SD), 13% were wasted (weight-for-height z-score < -2SD), and 65% were stunted (height-for-age z-score < -2SD); in children 24–59 months 57% were underweight, 13% were wasted and 66% were stunted. The high prevalence of smoking among women, underweight status among pregnant women, and anemia, stunting, and wasting among children were of the highest public health concern and should be addressed in future health and nutrition programming. These findings will inform and guide targeted nutrition and health policies for the San People and potentially motivate more research with other Indigenous groups. International Development Research Centre (Doctoral Research Award), Botswana International University of Science and Technology (Ph.D. Fellowship), and University of British Columbia (Public Scholar Initiative).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".