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)
Bibliographic record
Abstract
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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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".