- Loading...
- No images or files uploaded yet.
|
|
SpatialMapping
OverviewMapping of disease prevalence and healthcare utilization plays an important role in planning public health programs and medical services. Such maps, however, are typically not readily available at a local, community level in resource-denied settings where they are needed most. We will create a dynamic map that will improve access to and effective use of medical services in rural western Nepal where our organization operates. The main data points for this map will be continuously input by lay community health workers who serve as the frontline providers at the village level. The map will serve the following primary functions:
We will prototype this map in the district of Achham, Nepal, where we have been scaling up public health infrastructure. We employ the only physician in a district of 250,000 people whose healthcare services have been ravaged by a decade-long civil war, long-standing government neglect, and severe poverty. Our program will serve as a model for local monitoring of disease and healthcare utilization patterns in impoverished, rural areas with minimal health infrastructure. All maps, code, and protocol will be freely accessible and open-source. Current Status of our Mapping ProjectSee our current google earth map. The numbers correspond to the OPD visits in each village development committee during the first six months of clinical services. You may need the google earth plugin to view this
ProgramWe aim to construct a dynamic set of map layers to depict ongoing health services utilization in rural Achham. There is both a technical and human resource aspect to the program; we are developing a model for both data input (via our health worker network) and for data presentation (via our map prototype). The project is a practical undertaking for which we have already established much of the human capacity required to obtain the data inputs. Our data sources include:
This information is centralized in an Access database which we maintain through regular uploading from the handhelds carried by our mobile health workers. The map will consist of the following layers, with data inputs localized at the village level and tracked over time:
Our prototype will be based on data from 15 sub-districts known as village development committees (VDCs) that are located near to the heart of Nyaya Health’s clinical operations. Each of these VDCs are covered by 2 community health workers and in total consist of a population of approximately 50,000 people. We have already implemented each of these layers for data from our clinic; with funding from this grant, we will layer on the community health worker and government health post data sources, add a time-dynamic component, and improve the user interface to be useful to healthcare managers. Current Protocol for creating map of Nyaya Health Clinic OPD Visits DataResources: downloadable from http://www.sgrillo.net/googleearth/gegraph.htm SharedNyaya\Clinic\Grants\Google GeoChallenge Download and open SBMC.mdb Open the OPD query Export the OPD query to excel spreadsheet Cut and paste the contents of the exported query into the "query" sheet of "achham settlements GPS.xls" Go to the pivot table in the "pivot" sheet. Copy the relevant numbers to the "GE graph input.csv" file. Open GE Graph. Go to File>>Load Options>>nyaya_services.ggo File>>Open txt(csv)>>"GE graph input.csv" Click run and create the desired KML or KMZ file Upload new maps to maps.google.com via ID: nyayahealth These get embedded into the wiki via the google earth plug in: http://www.takitwithme.com/geembed.html
Current NeedsCorrect the SBMC.mdb file with the proper names found in the "achham settlements GPS.xls" file. [Have requested from Chhitij] Once that is done, create automated sheet to generate the .csv file. See "msupply_pharmoutput.xls" in SharedNyaya\Clinic\Data as one example. Determine how to represent these data month-to-month in a meaningful way on Google Earth. That is, incorporate time-dynamic element into the static layer described above. Add static items to the layers, such as location of Nyaya Health Clinic, Mangalsen Hospital, and area health posts. Once a good system is achieved for the above: Create similar strategy for pharmaceutical and HMIS disease code data, again using data solely from the clinic Create a more fine-grain geographical analysis by breaking down the VDCs by ward (i.e., "settlement") Incorporate the three layers (OPD visits, pharmaceutical utilization, and HMIS disease code) into a single, straightforward interface. Final steps: Incorporate similar data from CHWs and from government. Improve the interface to be useful to the Nyaya team for data analysis and program planning.
FundingApplied on 12/19/08 for: http://www.google.org/geochallenge.html Google response: "Our team will review your application within approximately 4 -6 weeks of the submission deadline and then contact you via email or phone." (will contact Sanjay) |
Comments (0)
You don't have permission to comment on this page.