Socio-demographic Characteristics of Tuberculosis Mortality in Grootfontein District, Namibia: analysis of hospital data between 2018 and 2022
Bibliographic record
Abstract
Context and objective: Tuberculosis (TB) is ranked first killer among infectious diseases and listed among the top 10 causes of death worldwide. One of the factors fuelling TB epidemic is the global rise of multidrug resistant TB. The aim of this study was to describe the sociodemographic characteristics of TB mortality. Methods: This was a documentary series of TB cases recorded (using TB registers, TB cards, and medical files) and attended at Grootfontein District Hospital in Namibia between 2018 and 2022. Descriptive analysis of deaths assigned as caused by TB using ICD 10 was performed. Results: Eighty-five deaths occurred from 2018 to 2022. The average number of deaths from TB per year was 17 (SD 6.8). There was a steady deaths increase from 2020 (9.4 %) to 2022 (31.8%). 44.7% of TB deaths were HIV co-infected. Majority of deaths from TB were in the 50 or above age categories (n = 26, 30.6 %). Males had 1.086 higher odds of dying from TB than females. Almost a quarter (24.7%) of all TB deaths was due to drug resistant TB. Conclusion: There has been an upward trend in TB deaths in recent years, particularly among HIV co-infected and men aged >50 years. Hence, the crucial need to enhance community awareness, TB surveillance and access to TB diagnosis and treatment to improve treatment outcomes and decrease TB mortalities. French title:Caractéristiques sociodémographiques de la mortalité par tuberculose dans le District de Grootfontein,Namibie : analyse des données hospitalières entre 2018 et 2022 Contexte & objectif: La tuberculose est la première cause de mortalité parmi les maladies infectieuses et figure parmi les dix premières causes de décès dans le monde. L'un des facteurs qui alimentent l'épidémie de tuberculose est l'augmentation mondiale de la tuberculose multirésistante. L'objectif de la présente étude a été de décrire les caractéristiques sociodémographiques de la mortalité due à la tuberculose. Méthodes: C’était une série documentaire des cas de tuberculose colligés (à l’aide de registres de tuberculose, cartes de tuberculose, dossiers médicaux) et admis à l'hôpital de district de Grootfontein en Namibie entre 2018 et 2022. Une analyse descriptive des décès attribués comme étant causés par la tuberculose à l'aide de la CIM 10 a été réalisée. Résultats: Quatrevingt cinq décès ont été déplorés entre 2018 et 2022. Le nombre moyen de décès dus à la tuberculose par an était de 17 (écart-type 6,8). Le nombre de décès a augmenté régulièrement entre 2020 (9,4 %) et 2022 (31,8 %). 44,7 % des personnes décédées de la tuberculose étaient coinfectées par le VIH. La majorité des décès dus à la tuberculose concernait les personnes âgées de 50 ans ou plus (n = 26, 30,6 %). Les hommes avaient 1,086 plus de chances de mourir de la tuberculose que les femmes. Près d'un quart (24,7 %) des décès dus à la tuberculose étaient dus à une tuberculose multirésistante. Conclusion: Une tendance à l’augmentation de décès dû à la tuberculose, en particulier (chez les PVV et les hommes agés > 50 ans) est observée au cours des dernières années. D’où le besoin crucial d'améliorer la sensibilisation de la communauté, la surveillance de la tuberculose et l'accès au diagnostic et au traitement de la tuberculose afin d'améliorer la mortalité due à la tuberculose.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".