Undernutrition and Anemia Prevalence Among Indigenous San Women of Child-Bearing Age and Young Children in Rural Botswana
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».