The "Utah 64 Small Health Statistics Areas" feature layer was developed by the Office of Public Health Assessment, Utah Department of Health using small area analysis methodology in 1997. Each feature was generated by combining a sufficient number of adjacent ZIP code area features to form a geographic area of approximately 33,500 persons (range 15,000 to 62,500). Criteria used for determining which ZIP code areas to combine together to form a Small Health Statistics Area included population size, local health district and county boundaries, similarity of ZIP code population's income level and community political boundaries. Input from local community representatives was used to refine area designations. The Utah 64 Small Health Statistics Areas provide a means of geographically analyzing and presenting health statistics at the community level. Producing information at the small area in Utah provides community planners and other with information that is specific to the populations living in their communities of concern. Small area analysis also allows an investigator to explore ecologic relationships between health status, lifestyle, the environment and the health system. In areas where a ZIP code crosses a county boundary, the 2008 and 2009 versions of Small Statistical Areas honor the ZIP code boundary leading to cases where a Small Statistical Areas can be in multiple counties. The 2012 and 2014 versions correct this issue by splitting ZIP code areas by county boundaries resulting in Small Statistical Areas that can only be found in one county. In the 2017 version, area 57 Grand/San Juan Counties was split into 2 areas, area 57.1 Grand county and 57.2 San Juan County.

The UK censuses took place on 29th April 2001. They were run by the Northern Ireland Statistics & Research Agency (NISRA), General Register Office for Scotland (GROS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics, and underpin funding allocation to provide public services.

The Census 2001: Small Area Microdata for Imputation Analysis (SAM) is a 5% sample of individuals for all countries of the UK, with 2.96 million cases. Local Authority is the lowest level of geography for England and Wales, Council Areas for Scotland and Parliamentary Constituencies for Northern Ireland. The Scilly Isles have been merged with Penwith and the City of London with Westminster. Orkney and Shetland are merged into one area. All other areas are identified. The median sample size for an authority is 5,600 records and nearly eighty authorities have more than 10,000 records. The amount of individual detail in the SAM is less than in the 2001 Individual Licenced Sample of Anonymised Records (I-SAR)(see under SNs 7205 and 7206) because of the greater geographical detail in the SAM.

Caveat - Students:
As with the Individual SAR, the SAM includes those enumerated in a communal establishment and also full-time students who were enumerated at an address that was not their usual term-time residence. For the latter there is only individual-level information on age, sex, marital status and full-time student status. It is recommended that these students are not included in any analyses as they do not form part of the usual residents population base.
This dataset contains 155 variables, including 67 imputation flag variables. The standard version, containing 88 SAM variables, is available under SN 7207.

These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

Small Area - National Statistical Boundaries - 2022 - UngeneralisedSmall Areas were designed as the lowest level of geography for the dissemination of statistics and generally comprise either complete or part of townlands or neighbourhoods. Small Areas were created by The National Institute of Regional and Spatial Analysis (NIRSA) on behalf of the Tailte Éireann (TE) in consultation with CSO.Small Areas generally comprise between 80 and 120 dwellings and nest within CSO Electoral Divisions.The Small Area boundaries have been amended based on Census 2022 population data.Update Notice: 4th August 2023: Attribution changed for ED and LEA attributes. An implication of this is CSO ED increase in count from 3419 to 3420 and CSO LEA boundary changes. ED and LEAs impacted areLEA 40aece0e-a19d-4e78-af9d-e129f5557496 DÚN LAOGHAIRE redrawnLEA d65ef6e7-75e6-49d9-bda9-d4690e8f68dc KILLINEY-SHANKILL redrawnED 2ae19629-1d37-13a3-e055-000000000001 renamed to DALKEY-COLIEMOREED 2ae19629-1e18-13a3-e055-000000000001 SHANKILL-RATHSALLAGH SA by GUIDS Impacted:('4c07d11e-166e-851d-e053-ca3ca8c0ca7f','4c07d11e-30f0-851d-e053-ca3ca8c0ca7f','4c07d11e-30b0-851d-e053-ca3ca8c0ca7f','4c07d11e-30a0-851d-e053-ca3ca8c0ca7f', '4c07d11e-30e2-851d-e053-ca3ca8c0ca7f','4c07d11e-30e3-851d-e053-ca3ca8c0ca7f','4c07d11e-309d-851d-e053-ca3ca8c0ca7f','4c07d11e-30bd-851d-e053-ca3ca8c0ca7f', '4c07d11e-34fc-851d-e053-ca3ca8c0ca7f','4c07d11e-353b-851d-e053-ca3ca8c0ca7f')('4c07d11e-2b05-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcd-851d-e053-ca3ca8c0ca7f','4c07d11e-3337-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcb-851d-e053-ca3ca8c0ca7f', '4c07d11e-16cf-851d-e053-ca3ca8c0ca7f') SA_PUB2022,SA_GEOGID_2022 updated for the following SA_GUID_2022 values4c07d11e-0aa3-851d-e053-ca3ca8c0ca7f4c07d11d-f918-851d-e053-ca3ca8c0ca7f4c07d11e-034c-851d-e053-ca3ca8c0ca7f4c07d11e-1042-851d-e053-ca3ca8c0ca7f4c07d11e-25c8-851d-e053-ca3ca8c0ca7f

CSO Small Areas - National Statistical Boundaries - 2022 - Generalised 20m. Published by Tailte Éireann. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Small Areas were designed as the lowest level of geography for the dissemination of statistics and generally comprise either complete or part of townlands or neighbourhoods. Small Areas were created by The National Institute of Regional and Spatial Analysis (NIRSA) on behalf of the Tailte Éireann (TE) in consultation with CSO.Small Areas generally comprise between 80 and 120 dwellings and nest within CSO Electoral Divisions.The Small Area boundaries have been amended based on Census 2022 population data.Generalised data: provided for information only.Update Notice: 4th August 2023: Attribution changed for ED and LEA attributes. An implication of this is CSO ED increase in count from 3419 to 3420 and CSO LEA boundary changes. ED and LEAs impacted are

LEA 40aece0e-a19d-4e78-af9d-e129f5557496 DÚN LAOGHAIRE redrawn

LEA d65ef6e7-75e6-49d9-bda9-d4690e8f68dc KILLINEY-SHANKILL redrawn

ED 2ae19629-1d37-13a3-e055-000000000001 renamed to DALKEY-COLIEMORE

ED 2ae19629-1e18-13a3-e055-000000000001 SHANKILL-RATHSALLAGH

SA by GUIDS Impacted:

('4c07d11e-166e-851d-e053-ca3ca8c0ca7f','4c07d11e-30f0-851d-e053-ca3ca8c0ca7f','4c07d11e-30b0-851d-e053-ca3ca8c0ca7f','4c07d11e-30a0-851d-e053-ca3ca8c0ca7f', '4c07d11e-30e2-851d-e053-ca3ca8c0ca7f','4c07d11e-30e3-851d-e053-ca3ca8c0ca7f','4c07d11e-309d-851d-e053-ca3ca8c0ca7f','4c07d11e-30bd-851d-e053-ca3ca8c0ca7f', '4c07d11e-34fc-851d-e053-ca3ca8c0ca7f','4c07d11e-353b-851d-e053-ca3ca8c0ca7f')

('4c07d11e-2b05-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcd-851d-e053-ca3ca8c0ca7f','4c07d11e-3337-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcb-851d-e053-ca3ca8c0ca7f', '4c07d11e-16cf-851d-e053-ca3ca8c0ca7f')

SA_PUB2022,SA_GEOGID_2022 updated for the following SA_GUID_2022 values

4c07d11e-0aa3-851d-e053-ca3ca8c0ca7f 4c07d11d-f918-851d-e053-ca3ca8c0ca7f 4c07d11e-034c-851d-e053-ca3ca8c0ca7f 4c07d11e-1042-851d-e053-ca3ca8c0ca7f 4c07d11e-25c8-851d-e053-ca3ca8c0ca7f

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

BackgroundThere has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.Methods and FindingsWe used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother's set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2–45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3–55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.ConclusionsThe new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival.Please see later in the article for the Editors' Summary

Small Areas are areas of population comprising between 50 and 200 dwellings created by The National Institute of Regional and Spatial Analysis(NIRSA) on behalf of the Ordnance Survey Ireland(OSi) in consultation with CSO. Small Areas were designed as the lowest level of geography for the compilation of statistics in line with data protection and generally comprise either complete or part of townlands or neighbourhoods. There is a constraint on Small Areas that they must nest within Electoral Division boundaries. The small area boundaries have been amended in line with population data from Census 2011

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

🇮🇪 아일랜드 English Small Areas were designed as the lowest level of geography for the dissemination of statistics and generally comprise either complete or part of townlands or neighbourhoods. Small Areas were created by The National Institute of Regional and Spatial Analysis (NIRSA) on behalf of the Tailte Éireann (TE) in consultation with CSO.Small Areas generally comprise between 80 and 120 dwellings and nest within CSO Electoral Divisions.The Small Area boundaries have been amended based on Census 2022 population data.Generalised data: provided for information only.Update Notice: 4th August 2023: Attribution changed for ED and LEA attributes. An implication of this is CSO ED increase in count from 3419 to 3420 and CSO LEA boundary changes. ED and LEAs impacted are

PLACES is a collaboration between CDC, the Robert Wood Johnson Foundation, and the CDC Foundation. PLACES provides health data for small areas across the country. This allows local health departments and jurisdictions, regardless of population size and rurality, to better understand the burden and geographic distribution of health measures in their areas and assist them in planning public health interventions.PLACES provides model-based, population-level analysis and community estimates of health measures to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTAs) across the United States.Although limited data are available at the county and metropolitan levels, PLACES represents a first-of-its-kind data analysis to release information for all US counties, places, census tracts, and ZCTAs. This system complements existing surveillance data by providing estimates necessary to understand the health issues affecting the residents of local areas of all sizes and regardless of urban or rural status; develop and implement effective and targeted prevention activities; identify health problems; and establish key health objectives. PLACES extends the original 500 Cities Project, reflects innovations in generating valid small area estimates for population health, and provides data uniformly across the urban-rural spectrum.PLACES is the first-ever project to provide data at multiple local area-levels, i.e., county-, place-, census tract-, and ZCTA-levels. These data can be viewed by geographic level as well as by measure and downloaded by users for their secondary analysis.PLACES enables retrieval, visualization, and exploration of uniformly-defined local area data in the 50 states and Washington, DC for conditions, behaviors, and risk factors that have a substantial effect on population health.

This dataset is for examples in the Ecography Software Note, FIESTA: A Forest Inventory Estimation and Analysis R package, by Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul, L.; Toney, Chris; White, Grayson W. The examples demonstrate how to generate estimates of forest attributes using three different FIESTA modules: Green Book (GB), Model-Assisted (MA), and Small Area (SA). Included in the dataset are: a geospatial vector shapefile (.shp) of the Middle Bear-Logan Watershed area of interest (AOI); an R sf object (.rds) defining an ecological extent encompassing the AOI, Ecomap Section M331D (Cleland et al. 2007) ; a SQLite database (.db) including FIA plot data downloaded from FIA's publicly available DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html) and subset to the M331D boundary; and five auxiliary spatially-explicit raster layers (.img) clipped to the M331D boundary.

To develop a new and unique geodatabase to identify locational disparities in population based breast cancer screening participation in South Australia according to place of residence. The small area geodatabase resulting from this project will provide a platform for advanced research into breast screening participation, geographic and spatial differentials in screening rates, and investigate possible predictors of these differentials. Specific analyses at the smallest geographic areas available will be undertaken where feasible to precisely identify social and demographic disparities and area level risk factors that warrant new service response.

Spatial analysis will be undertaken at the smallest area possible, ideally the statistical area 1 (SA1) level. In addition, we shall assess small area predictors of screening participation with measures that include age, education, socioeconomic status, ethnic composition, family status and housing, as well as screening service access (presence and distance to screening service). Changes in screening participation over time will be investigated and assessed in relation to variation in BreastScreen SA program delivery including service pathways and local availability and timing for mobile screening units.

Mean of the RMSEs obtained in the estimation of the relative risks for the small areas and 95% Bayesian prediction interval for the difference between the RMSEBYM and the RMSEmod that would be obtained after applying the convolution model and the proposed model to a new data set.

This analysis uses Welsh Index of Multiple Deprivation (WIMD) 2019 and Census 2021 data to estimate the proportions of population groups living in areas within each WIMD 2019 deprivation grouping. It identifies where people from various groups are most likely to live in terms of small area (Lower Super Output Area or LSOA) relative deprivation and whether this varies across groups. This analysis presents an overview of how different populations were distributed across Wales at the time of the 2021 Census. It does not take into account the interaction of different characteristics with each other or with deprivation. For example, older age groups have a smaller likelihood of living in the most deprived areas, which may affect populations with different age profiles such as certain ethnic groups, veterans or those in poor health. Results should be interpreted in simple terms of how likely the population was to live in the various deprivation areas of Wales at the time of the 2021 Census, rather than attempting to establish a relationship between specific characteristics and deprivation.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

SUMMARYTo be viewed in combination with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.This dataset shows where there was no data* relating to one of more of the following factors:Obesity/inactivity-related illnesses (recorded at the GP practice catchment area level*)Adult obesity (recorded at the GP practice catchment area level*)Inactivity in children (recorded at the district level)Excess weight in children (recorded at the Middle Layer Super Output Area level)* GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices.GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. This dataset identifies areas where data from 2019/20 was used, where one or more GPs did not submit data in either year (this could be because there are rural areas that aren’t officially covered by any GP practices), or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution.Results of the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ analysis in these areas should be interpreted with caution, particularly if the levels of obesity, inactivity and associated illnesses appear to be significantly lower than in their immediate surrounding areas.Really small areas with ‘missing’ data were deleted, where it was deemed that missing data will not have impacted the overall analysis (i.e. where GP data was missing from really small countryside areas where no people live).See also Health and wellbeing statistics (GP-level, England): Missing data and potential outliers dataDATA SOURCESThis dataset was produced using:- Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.- National Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. - Active Lives Survey 2019: Sport and Physical Activity Levels amongst children and young people in school years 1-11 (aged 5-16). © Sport England 2020.- Active Lives Survey 2019: Sport and Physical Activity Levels amongst adults aged 16+. © Sport England 2020.- GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.- Administrative boundaries: Boundary-LineTM: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.- MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Sport England 2020; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This shapefile includes geographic boundaries of the 2010-2012 National Survey on Drug Use and Health (NSDUH) substate regions along with small area estimation (SAE) values (prevalence rates, the map group, and the upper and lower bounds found in the map legends) related to each substate region. It can be used for analysis and data display with Geographic Information Systems (GIS) software.

Little is known about the contextual variability in osteoporosis medication utilization. Our aims were 1) to describe variations in utilization and spending on osteoporotic medication between the Primary Care Health Zones (PHZ) of the Valencia region, Spain, 2) to analyze observed variations using Small Area Variation Analysis methods, and 3) to quantify the influence of the specialized care level on variations in utilization. We conducted a population-based cross-sectional ecological study of expenditure and utilization of five therapeutic groups marketed as osteoporosis treatments in Spain in 2009. The unit of analysis was the PHZ (in total 240) nested in the 23 Hospital Healthcare Departments (HHD) of the region of Valencia, covering a population of about 4.9 million inhabitants. Drug utilization was measured by dispensed Defined Daily Dose per 1000 women aged 50 years old and over and day (DID) per PHZ and cost was measured by the annual osteoporosis drug cost per woman aged 50 and older as well as the average price of DDD (Defined Daily Dose) in each PHZ. We calculated Indirect Standardized Drug Utilization Ratios (ISR) and we used Spearman’s correlation to analyze associations between the ISRs of the different therapies. The average osteoporosis drug consumption was 119.1 DID, ranging from 77.6 to 171.3 DID (2.2 times higher) between PHZs in the 5th and 95th percentiles. Annual expenditure also showed a two-fold variation among PHZs. Average prices of the DDD by therapeutic group showed very low or no variation, although they differed substantially among therapeutic groups. Regarding the standardized consumption of osteoporotic drugs, HHDs explained a substantial part (39%) of the variance among PHZs. In conclusion, there is considerable variability in the volume and choice of anti-osteoporotic treatments between PHZs. with HHDs explaining an important proportion of the variation in utilization. Interventions aimed at reducing variation to improve appropriate care should take into account both the PHZ and HHD levels of care.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing