Noncommunicable Diseases Household Survey Data Analysis, Sana’a City, Yemen, 2017
Notice bibliographique
Résumé
Background Noncommunicable diseases (NCDs) kill 41 million people each year, accounting for 71% of all deaths globally. The burden of NCDs is rising faster in developing countries of the Middle East than in other regions. The morbidity and mortality of NCDs are still not well-studied in Yemen. Objective The aim of this study was to describe the epidemiology of NCDs in Sana’a City, Yemen, for 2017. Methods Raw data of a house-to-house survey that was conducted by the Ministry of Public Health and Population in 2017 were analyzed. Data were collected from household heads who were asked if any household member had one of the following five NCDs: hypertension (HTN), diabetes (DM), bronchial asthma (BA), mental disorders (MD), and epilepsy. Data were entered and analyzed using Epi info 7.2. For calculations of prevalence, 2017 projections from the 2004 census were used. Results A total of 241,310 households were surveyed (1,592,646 household members), 59,061 (24.48%) of which included 70,178 members who had at least one NCD. The overall prevalence of NCDs was 4.4%. The disease-specific prevalence was as follows: HTN, 2.3%; DM, 2.2%; BA, 0.4%; MD, 0.27; and epilepsy, 0.19%. The overall NCD prevalence was significantly higher among females than males (5.1% vs 3.8%; odds ratio [OR] 1.35, 95% CI 1.33-1.35), which was also the case for the prevalence of HTN (3.1% vs 1.6%; OR 1.94, 95% CI 1.90-1.98), DM (2.3% vs 2.1%; OR 1.11, 95% CI 1.09-1.13), and BA (0.5% vs 0.3%; OR 1.56, 95% CI 1.49-1.65). In contrast, the prevalence of MD was significantly higher among males than females (0.35% vs 0.16%; OR 2.2, 95% CI 2.06-2.31). The prevalence of NCDs progressively increased with age. Nearly 18% of patients had more than one NCD; 35.2% of the patients with HTN also had DM. Conclusions One-quarter of the surveyed households had at least one member with one or more of the five NCDs and the overall prevalence of NCDs was 4.4%. These data reflect only the tip of the iceberg as the findings are based on self-reported diagnosed cases rather than standardized measures. More attention to NCDs, strengthened health care provision, the ability to obtain high-reliability data, an NCDs stepwise survey, and establishing an NCDs surveillance system are recommended.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».