{"doc_desc":{"title":"Exposure Pathways Between Climate and Health 2022","idno":"DDI.INSPIRE.LACUNA.2022.V1.0","producers":[{"name":"David Amadi","abbreviation":"DA","affiliation":"LSHTM","role":"DDI author"},{"name":"Dan  Kajungu","abbreviation":"DK","affiliation":"IMHDSS","role":"Technical assistance in data collection and processing"},{"name":"Agnes Kiragga","abbreviation":"AN","affiliation":"APHRC","role":"Documentation of Study and Review of the metadata"},{"name":"Maureen Ng'etich","abbreviation":"MN","affiliation":"APHRC","role":"Documentation of Study and Review of the metadata"},{"name":"Flavian Otieno","abbreviation":"FO","affiliation":"APHRC","role":"Documentation of Study and Review of the metadata"}]},"study_desc":{"title_statement":{"idno":"LACUNA.2024.V1.0","title":"Exposure Pathways Between Climate and Health 2022"},"authoring_entity":[{"name":"Agnes Kiragga","affiliation":"APHRC"},{"name":"Jim Todd","affiliation":"LSHTM"},{"name":"Dan Kajungu","affiliation":"IMHDSS"},{"name":"Marylene Wamukoya","affiliation":"APHRC"},{"name":"Mia Cramplin","affiliation":"LSHTM"},{"name":"Mark Urassa","affiliation":"NIMR"}],"oth_id":[{"name":"Iganga Mayuge Data Team","affiliation":"IMHDSS","email":"","role":"Providing Data"},{"name":"INSPIRE-Network","affiliation":"INSPIRE","email":"","role":"Providing IT Infrastucture for Data Processing"}],"production_statement":{"producers":[{"name":"David Amadi","affiliation":"LSHTM","role":"DDI author"},{"name":"Maureen Ngetich","affiliation":"APHRC","role":"Technical Assistance"},{"name":"Flavian Otieno","affiliation":"APHRC","role":"Technical Assistance"},{"name":"Agnes Karigga","affiliation":"APHRC","role":"Technical Assistance"}],"copyright":"This dataset documentation is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License. The dataset is shared in terms of the data-use agreement accepted at the time of data download.","funding_agencies":[{"name":"Lacuna Fund","abbreviation":"LF","role":"Current Funder"}]},"study_info":{"topics":[{"topic":"Verbal autopsy","vocab":"CIEL","uri":"https:\/\/openconceptlab.org\/"},{"topic":"Cause of Death","vocab":"MeSH","uri":"https:\/\/www.ncbi.nlm.nih.gov\/"},{"topic":"ICD-10","vocab":"MeSH","uri":"https:\/\/www.ncbi.nlm.nih.gov\/"},{"topic":"Climate","vocab":"MeSH","uri":"https:\/\/www.ncbi.nlm.nih.gov\/"}],"abstract":"Understanding climate change and the associated impacts on human health and wellbeing is one of the major intractable challenges facing planning departments, policy makers and health researchers in the coming decades. Novel approaches such as data science techniques which use routine and big data sources will be critical in supporting this area of enquiry to evaluate the effect of climate change in communities.\nThrough our Implementation Network for Sharing Population Information from Research Entities (INSPIRE), we have access to African population health data from 11 Health Demographic and Surveillance Sites (HDSS). These data contain demographic vital registration outcomes such as births, Death and mortality  collected over 10 years in rural and urban African communities. As part of the INSPIRE data standardization and governance, all HDSS data are transformed into the INSPIRE OMOP Common Data Model (CDM) to allow systematic analyses using common terminologies, vocabularies and coding schemes.\nWe plan to secure daily ground and remote sensor climate data from the respective national meteorological offices. Climate data will cover the areas\/countries in which the HDSS sites are located and transformed into the INSPIRE CDM and used to investigate the effect of climate change on mortality and morbidity outcomes. The linked and labelled datasets will be made FAIR using standard platforms\nincluding schema.org and governed through the INSPIRE secretariat. Project findings and outputs will be shared with national and sub-national policy-makers and researchers to inform effective data-driven decision making on mitigating the effects of climate change on health outcomes in Africa.","coll_dates":[{"start":"2007-01-01","end":"2022-12-31","cycle":"IMHDSS"}],"nation":[{"name":"Uganda","abbreviation":"UG"}],"geog_coverage":"The Iganga-Mayuge HDSS is located in the Iganga and Mayuge districts in Eastern Uganda. The demographic surveillance area consists of 65 villages spread over a 155 km square area with a population of 94,568 at the end of 2017. The average household size is five individuals, and the area is predominantly rural, with some peri-urban areas. Subsistence agriculture is the main occupation.","analysis_unit":"Individual, household interviews and climate data","universe":"This data file includes information on deaths that occurred among residents within the Health and Demographic Surveillance System (HDSS) study area from January 1, 2007, to December 31, 2022. Causes of death were classified using the Inter-VA classification algorithm","data_kind":"Verbal autopsy"},"method":{"data_collection":{"time_method":"Deaths occurring between January 1, 2007, and December 31, 2022","sampling_procedure":"All residents mortalities in the IMHDSS are included in the dataset","coll_mode":"Verbal autopsy procedures (Interviewing surviving relatives or caregivers of the deceased to gather information about the circumstances surrounding the death)"}},"data_access":{"dataset_use":{"cit_req":"D. Kajungu et al., \"Cohort Profile: The Iganga-Mayuge Health and Demographic Surveillance Site, Uganda (IMHDSS, Uganda),\" Int. J. Epidemiol., vol. 49, no. 4, pp. 1082-1082g, Aug. 2020, doi: 10.1093\/ije\/dyaa064.","disclaimer":"The user of the data acknowledges that the original collector of the data and the relevant funding agencies bear no responsibility for the data's use or interpretation or inferences based upon it."}}}}