Methodology for establishing demographic, development and environmental geospatial data surveillance platform in the context of a resource constrained environment: lessons from SOMAARTH DDESS, Palwal (India) (Preprint)
Notice bibliographique
Résumé
BACKGROUND Inadequate administrative health data, sub-optimal public health infrastructure, rapid and unplanned urbanization,environmental degradation and poor penetration of information technology make the tracking of health and well being of the populations within developing countries more challenging. This necessitates setting-up comprehensive surveillance platforms integrated with the information technologies that can cater to the full spectrum of the public health problems. OBJECTIVE This manuscript aims to provide methodological insights on establishing GIS integrated comprehensive surveillance platform in resource constrained rural settings. METHODS The INCLEN (International Clinical Epidemiology Network) Trust International established a comprehensive SOMAARTH Demographic, Development and Environmental Surveillance Site (DDESS) in a northern Indian rural setting. The surveillance platform evolved through adopting four major steps: 1) site preparation 2) data construction 3) data quality assurance 4) data update and maintenance system. Arc GIS 10.3 and QGIS 2.14 software were employed for geo-spatial data construction. Surveillance data architecture was built upon the geo-referenced land parcel data sets. The composition data pertaining to the land use (residential, non-residential, and vacant), water bodies, roads, railways, community trails, landmarks, water, sanitation and food environment, weather and air quality, demographic characteristics were constructed in relational manner within the surveillance platform. RESULTS A comprehensive surveillance platform encompassing 0.2 million population residing in 51 villages over a land mass of 251.7 sq. Km having 32,662 households and 19,260 nonresidential features (cattle shed, shops, health, education, banking, religious institutions etc.) is established. The processes adopted for subdivision of villages into sectors helped in developing geo-referenced location identification system in a setting where no postal addresses or postal codes system were in place. Also the socially and economically homogeneous community clusters (78% of 676 sectors) which usually hide within the village aggregates were disclosed. Characterization and storage of variety of data sets critical for health and epidemiology and generation of new information e.g. water, sanitation and hygiene through geo-analytics were demonstrated. Settlement pattern was compact to the extent that 80% of habitation was concentrated in 9% of the total village area. Community involvement proved helpful in the ground-truthing of the data sets for ascertaining the level of positional, temporal and attribute accuracies and identification of small habitations, missing in the official records. CONCLUSIONS SOMAARTH experience allowed characterization and monitoring of wide range of attributes from demography, development, and environmental domains and developed geospatial inter-phase to explore and explain their dynamic relationships, associations and pathways across multiple levels i.e. individual, household, neighborhood, and village. The methodology takes care of the common challenges faced while building information system in the developing countries. However generalizability and scalability needs to be tested in other resource constrained settings as well.
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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 | 0,017 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».