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                                "en": "Aalto University",
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                            "en": "Terveystalo Healthcare",
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            "description": {
                "en": "Abstract Background COVID-19 has had wide-reaching effects on healthcare services beyond the direct treatment of the pandemic. Most current studies have reported changes in realised service usage, but the dynamics of how patients engage with healthcare services are less well understood. We analysed the effects of COVID-19 on healthcare bookings and cancellations for various service channels between January 2020 and July 2021. Methods Our data includes 7.3 million bookings, 11.0 million available appointments, and 405.1 thousand cancellations by 900.6 thousand individual patients between the ages of 18 and 65 years. The data were collected from electronic health record data, including laboratory and imaging services as well as inpatient stays, between January 2017 and July 2021. The patients were Finnish private and occupational healthcare customers in the capital region of Finland. We fitted an autoregressive moving average (ARIMA) model on data between 2017 and 2019 to predict the expected numbers of bookings, available appointments, and cancellations, which were compared to observed time series data between 2020 and 2021. Results Utilisation of physical, in-person primary care physician appointments decreased by up to 50% during the first 18 months of the pandemic. At the same time, digital care channels experienced a rapid, multi-fold increase in service usage. Simultaneously, the number of bookings for laboratory and imaging services decreased by 50% below the pre-pandemic projections. The number of specialist and hospital service bookings remained at the predicted level during the study period. Cancellations for most health services increased sharply by up to three times the pre-COVID levels during the first weeks of the pandemic but returned to the pre-pandemic levels for the rest of the study period. Conclusions The reduction in in-person appointments and the increase in the utilisation of digital services was likely a contributing factor in the decrease of the utilisation of diagnostic and imaging services throughout the study period. Utilisation of specialist care and hospital services were not affected. Cancellations contributed to the changes in service utilisation only during the first weeks of the pandemic."
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            "description": {
                "en": "This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022. \r\n\r\nThe dataset is presented in details in the following publication. Please cite this paper when using data. \r\n\r\nKummu et al. 2024. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990-2022. Under review in Scientific Data. \r\n\r\nPreprint is available at: https://doi.org/10.21203/rs.3.rs-4321741/v1\r\n\r\nCode is available at: https://github.com/mattikummu/griddedGDPpc \r\n\r\n\r\nThe following data is given (formats in brackets)\r\n\r\nGDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)\r\nGDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)\r\nGDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)\r\nTotal GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) \r\nInput data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc \r\nFiles are named as follows\r\nFormat: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. \r\nAdmin levels: adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level\r\nProduct type: GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_\r\n\r\n\r\nMetadata \r\n\r\nGrids for GDP per capita data:\r\n\r\nResolution: 5 arc-min (0.083333333 degrees)  (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided)\r\n\r\nSpatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) \r\n\r\nCoordinate ref system: EPSG:4326 - WGS 84 \r\n\r\nFormat: Multiband geotiff; each band for each year over 1990-2022 \r\n\r\nUnit: USD in 2017 international dollars\r\n\r\n\r\nGrids for total GDP:\r\n\r\nResolution: 30 arc-sec, 5 arc-min or 30 arc-min\r\n\r\nSpatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) \r\n\r\nCoordinate ref system: EPSG:4326 - WGS 84 \r\n\r\nFormat: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec)\r\n\r\nUnit: USD in 2017 international dollars\r\n \r\n\r\nGeospatial polygon (gpkg) files: \r\n\r\nSpatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) \r\n\r\nTemporal extent: annual over 1990-2022\r\n\r\nCoordinate ref system: EPSG:4326 - WGS 84 \r\n\r\nFormat: gkpk \r\n\r\nUnit:  USD in 2017 international dollars"
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