Mortality Statistics in US Cities

Deaths by Age and Cause of Death in 2016

By Health [source]

About this dataset

This dataset contains mortality statistics for 122 U.S. cities in 2016, providing detailed information about all deaths that occurred due to any cause, including pneumonia and influenza. The data is voluntarily reported from cities with populations of 100,000 or more, and it includes the place of death and the week during which the death certificate was filed. Data is provided broken down by age group and includes a flag indicating the reliability of each data set to help inform analysis. Each row also provides longitude and latitude information for each reporting area in order to make further analysis easier. These comprehensive mortality statistics are invaluable resources for tracking disease trends as well as making comparisons between different areas across the country in order to identify public health risks quickly and effectively

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How to use the dataset

This dataset contains mortality rates for 122 U.S. cities in 2016, including deaths by age group and cause of death. The data can be used to study various trends in mortality and contribute to the understanding of how different diseases impact different age groups across the country.

In order to use the data, firstly one has to identify which variables they would like to use from this dataset. These include: reporting area; MMWR week; All causes by age greater than 65 years; All causes by age 45-64 years; All causes by age 25-44 years; All causes by age 1-24 years; All causes less than 1 year old; Pneumonia and Influenza total fatalities; Location (1 & 2); flag indicating reliability of data.

Once you have identified the variables that you are interested in,you will need to filter the dataset so that it only includes relevant information for your analysis or research purposes. For example, if you are looking at trends between different ages, then all you would need is information on those 3 specific cause groups (greater than 65, 45-64 and 25-44). You can do this using a selection tool that allows you to pick only certain columns from your data set or an excel filter tool if your data is stored as a csv file type .

Next step is preparing your data - it’s important for efficient analysis also helpful when there are too many variables/columns which can confuse our analysis process – eliminate unnecessary columns, rename column labels where needed etc ... In addition , make sure we clean up any missing values / outliers / incorrect entries before further investigation .Remember , outliers or corrupt entries may lead us into incorrect conclusions upon analyzing our set ! Once we complete the cleaning steps , now its safe enough transit into drawing insights !

The last step involves using statistical methods such as linear regression with multiple predictors or descriptive statistical measures such as mean/median etc ..to draw key insights based on analysis done so far and generate some actionable points !

With these steps taken care off , now its easier for anyone who decides dive into another project involving this particular dataset with added advantage formulated out of existing work done over our previous investigations!

Research Ideas

• Creating population health profiles for cities in the U.S.
• Tracking public health trends across different age groups
• Analyzing correlations between mortality and geographical locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: rows.csv | Column name | Description | |:--------------------------------------------|:-----------------------------------...

The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is (Excel column A) ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (B) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; (C) ‘Region’, defining the region that each school is situated in the country; (D) School_name, the name assigned to each unique school; (E) ‘Language’ defining the medium-of-instruction used in each school (Kazakh or Russian); (F) ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling, see R code for recodes into numeric). (G) School location follows with schools defined as either (i) city or (ii) rural. Thereafter, the within-school variable, (H) ‘Gender’ is given for which each child is defined as either male or female. In addition, columns I to CN pertain to the item-response data for the project (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests. 2015 Test: Items OG1 to OG5.4 (columns I-S; link items for 2015 are OG2.2A, OG2.3A, OG3A; link items for 2016 are OG2.2B, OG2.3B, OG3B, columns T, U, and W). 2016 Test: Items OG2.2 to OG11.2 (columns T-AI; link items for 2016 are OG11.1A and OG11.2A; link items for 2017 are OG11.1B and OG11.2B, columns AJ and AK). 2017 Test: Items OG11.1 to K3.6 (columns AJ-AU; link items for 2017 are K3.3A and K3.6A; link items for 2018 are K3.3B and K3.6B, columns BP and BO). 2018 Test: Items K4.1 to K3.3 (AV-BP; link items for 2018 are K10.2A and K13.4A; link item for 2019 are K10.2B and K13.4B, columns BU and CC). 2019 Test: Items X19K1 to K6 (BQ-CN).

References: [1] Courtney, M. G. R., Rakhymbayeva, Z., Shilibekova, A., Ziyedenova, D., Soltangazina, S., Muratkyzy, A., Goodman, B., & Olzhayeva, A. (2022). Kazakh, Russian, and Uyghur child language literacy: The role of updated content of education on longitudinal growth trajectories in Kazakhstan. Studies in Educational Evaluation, 75(101189). doi:10.1016/j.stueduc.2022.101189

The dataset shows the statistical analysis organized in a spreadsheet (Microsoft Excel, 2016) with tabs linking to the Figures present in the manuscript. Differences in gene expression were assessed by analysis of variance (ANOVA). Equality of variances was checked by Levene’s test. For equal variances, one-way ANOVA was fitted to log2-transformed 2^(-ΔΔCq) with treatments as fixed term and biological repeats as random effects accounting for dependencies between treatments. To estimate and adjust for pairwise multiple comparisons, a non-parametric post-hoc Tukey HSD test was applied. For unequal variances, a Welch’s pairwise t-test with Holm’s correction was used. For the assay involving the interaction of two factors, a two-way ANOVA was fitted to the data with “treatments” as a fixed factor in the model. Biological repeats accounted for random effects nested under the interaction term. Statistics were calculated in RStudio (R studio team, 2017).

This release presents experimental statistics from the Assuring Transformation collection and considers data from the Learning Disability Census; Hospital Episodes Statistics; Quality Outcomes Framework; Mental Health and Learning Disabilities Data Set and Mental Health Services Data Set all published by the Health and Social Care Information Centre (HSCIC). The publication comprises: • This report which presents key measures at England level; • Excel reference data tables showing all annual analysis at England, CCG and provider level; • CSV file showing all annual analyses at England, CCG and provider level; • A metadata file to accompany the CSV file, which provides contextual information for each measure; • Derivations and constructions file detailing how measures were calculated. The majority of this publication considers data from the Assuring Transformation collection, analysing data from a different perspective to the regular monthly and quarterly releases. Data covers the year ending 29 February 2016. For analysis, a snapshot of the data was taken as at 31 March 2016.

The dataset supports spatiotemporal analysis of interactions between seasonal water, climate, land use, policy, and socioeconomic changes.

The seasonal water area data was extracted from The JRC Monthly Water History, V1.2. This entire history collection includes 430 monthly water detection images between March 1984 and December 2019. Each image contains 30m pixels with three possible cell values: 1 - not water (i.e., land), 2 - seasonal water, and 3 - permanent water. We aimed at examining the whole Hulun-Buir basin consisting of 12 banners. We counted the total number of cells with a 2 value each year over each banner from 2000 to 2014. We multiplied that number by 30m × 30m to get the yearly seasonal water areas by banners.

The data of precipitation and temperature were interpolated from the 50 weather stations in Inner Mongolia Autonomous Region (IMAR) of China (http://cdc.cma.gov.cn). The annual precipitation and the average temperature were spatially interpolated as two grid maps at 250m×250m using ArcGIS Inverse Distance Weighted (IDW). The cell values were resampled as the averaged values by counties. The data of land-use and land-cover changes (LULC) primarily came from The NASA MCD12Q1 Data Product (https://lpdaac.usgs.gov/products/mcd12q1v006/) at 500 m resolution. 16 out of the 17 International Geosphere-Biosphere Program LULC types were found in the study area, except "Evergreen Broadleaf Forest" (http://www.igbp.net/).

The socioeconomic variables were entered from the statistic yearbooks of the IMAR from 2000 to 2015 (IMAR Statistical Bureau, 2001 - 2016).

All data are stored in the excel file, titled as HulunBuir_Seasonal_Water_Change_Research_Data_Sources. The worksheet called ‘Data’ contains all data items; the worksheet of ‘Explanation’ provides full names and measurement units of the abbreviated variable names; and the worksheet of ‘Banner_Names’ includes the Chinese and English names along with the index ID for the counties involved with the dataset. Banner is the Mongolian term of the English word, County.

Abstract This dataset is a combined Excel spreadsheet dataset that integrates all available groundwater and surface water chemistry historical records. It includes field quality parameters, methane concentrations and major and minor ion concentrations. It is based on the following data sources: GA compiled hydrochemistry datasets: Surface water data from the Queensland water monitoring information portal (https://water-monitoring.information.qld.gov.au/), accessed and downloaded in January 2019; Data from EHS Support (2014) Water baseline assessment (ATP1087) prepared for Armour Energy. ## Attribution Geological and Bioregional Assessment Program ## History A hierarchical cluster analysis was conducted on groundwater and surface water datasets from the Isa GBA region. For this purpose, nine variables (Ca, Mg, Na, K, HCO3, Cl, SO4, electrical conductivity and pH) which were measured across most hydrochemical records were selected. Prior to the multivariate statistical analysis, all variables except for pH were log-transformed to ensure that each variable more closely follows a normal distribution. The multivariate statistical technique is described in more details by Raiber et al. (2012) and Raiber et al. (2016).

BackgroundTuberculosis remains a major public health threat worldwide, causing significant morbidity and mortality, particularly in low- and middle-income countries. In recent years, efforts to combat tuberculosis have focused on strengthening healthcare systems and increasing access to diagnostics and treatment services. There is scarcity of data on the prevalence of Mycobacterium tuberculosis and rifampicin-resistant tuberculosis in the Volta region of Ghana. Therefore, the aim of this study was to determine the trends of Mycobacterium tuberculosis and rifampicin resistance in a major teaching hospital in Ghana spanning a six-year period.MethodologyA retrospective cross-sectional hospital study was conducted at Ho Teaching Hospital, Ho, Ghana. Study data included archived results on tuberculosis testing using GeneXpert from 2016–2021. Archived data on tuberculosis testing were collected and entered using Microsoft Excel 2019. IBM SPSS (v26) was used for a statistical analysis of the prevalence of tuberculosis. P-value

Increased TB disease burden arises as a result of low treatment success rates stemming from the emergence of second-line drug (SLD) resistance. We aimed at determining the usefulness of SLD resistance markers as proxy indicators of time to sputum culture conversion; a renowned predictor of Tuberculosis (TB) treatment outcome, among SLD-resistant TB patients tested at the Uganda National TB Reference Laboratory (NTRL). A cross-sectional study was conducted on 72 bacteriologically confirmed SLDR TB patients with datasets including culture conversion time and second-line probe assay mutation profiles between 01/06/2017 and 31/12/2019. The data were then imported into STATA v15 for descriptive statistical analysis, Univariate cox proportional hazard model analysis and Kaplan-Meier survival curves at a 5% level of significance; p-value ≤ 0.05. Results indicate the median time was achieved at 3 (0–12) months across the studied patients. The rrs G1484T mutation associated with conferring drug ..., The TB patients whose drug resistance status was previously analyzed following the national diagnostic algorithm were re-evaluated using their respective line probe assay DNA strips as previously discussed in our paper (Mujuni et al., 2022). This was followed by the addition of resistance marker data which were manually curated respectively in a protected Microsoft Excel sheet [Research Resource Identifiers (RRIDs) RRID:SCR 016137]. This sheet contained the patient datasets with corresponding monthly culture conversion time prior to cleaning to standardize mutation curations and subsequently import these results into STATA v15 (RRID:SCR_012763) for analysis. The analysis included descriptive, Univariate cox proportional hazard model analyses with the use of the Kaplan-Meier survival curves. The level of significance was set at 5% and therefore a p-value ≤ 0.05 was considered statistically significant. The data were presented in the form of summary statistic tables and figures., Microsoft Excel 2016 (RRID:SCR 016137) STATA v15 (RRID:SCR_012763)

Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives. Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements. Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021. Resources in this dataset: Resource Title: Spreadsheet of data. File Name: GibbsMisFarm_Arthrop_Env_DepVar_201516_final.xlsxResource Description: This workbook contains all of the data used in this analysis. The first worksheet contains data dictionary information.Resource Software Recommended: Microsoft Excel, Office 365,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: MiscanthusXGiganteusGeoJSON.json

This annual report provides up-to-date statistics on the pre-European and current extent of the vegetation complexes of the south-west of Western Australia. The mapping does not extend to IBRA sub-region boundaries so it does not report by IBRA sub-region rather by the extent of the available mapping of the complex. The report also includes (1) statistics to assess the status of the CAR reserve system for a portion of the South West Region; (2) Region Scheme reports; (3) LGA Reports. This analysis is based on the Vegetation Complex mapping of the Swan Coastal Plain and South West Forests. This annual reporting was initiated in 2016 after the two vegetation complex mapping datasets were updated. The reports contains both external reports and the internal (DBCA and DWER) sub-reports. See the README worksheet in each excel file for more details. People external to DBCA can download the external version from DataWA at this link https://catalogue.data.wa.gov.au/dataset/dbca Note: to access the data, select the data source link located on the right-hand side.

1 DataSets for Paper-Patent comparison} Three datasets for paper/patent related data. The first three files are in Excel format the 4th in Python. The initial dataset is located first. Then selected subset and calculations are generated. 1.1 Article/patent ratio according to Global Innovation Index} World Bank declared this dataset is classified as Public under the Access to Information Classification Policy under Definition: Scientific and technical journal articles refer to the number of scientific and engineering articles published in a lot of fields. Scientific and technical article counts are from journals classified by the Institute for Scientific Information's Science Citation Index (SCI) and Social Sciences Citation Index (SSCI). We isolated two codes: 1. WIPO, Patent applications, residents indicator code 2012, 2. SCIMAGO, Scientific and technical journal articles indicator code 2015

Dutta, S., Lanvin, B., Wunsch-Vincent, S., 2020. Global innovation index 2020. WIPO, Johnson Cornell University. URL: https://www.globalinnovationindex.org/about-gii.

1.2 Article - patent ratio according to Global Innovation Index} All classifications and numbers are per country GDP. We focus on three items: 1. PCT patents by origin/bn PPP USD GDP, 2. Scientific and technical articles/bn PPP USD GDP and 3. Citable documents H-index OECD, 2014. Patents by main technology and by international patent classification (ipc) URL: https://www.oecd-ilibrary.org/content/data/data-00508-en , doi: https://doi.org/10.1787/data-00508-en

1.3 Article Patent Ratio according European Patent office Scientific and technical journal articles - tcdata360. URL: https://tcdata360.worldbank.org/indicators/IP.JRN.ARTC.SC?country=BRA&indicator=2015&viz=line_chart&years=2003,2016 . Download link https://tcdata360-backend.worldbank.org/api/v1/datasets/56/dump.csv

1.4 PYTHON sheet to solve min-max scaling data for PATENT/PAPER ratio To compare patents with papers, normalize my data with min-max scaling method The formula for calculating normalized score is the following X new = (X — X min)/ (X max — X min) 1 Python 1 PDF file

This annual report provides up-to-date statistics on the pre-European and current extent of the vegetation complexes of the south-west of Western Australia. The mapping does not extend to IBRA sub-region boundaries so it does not report by IBRA sub-region rather by the extent of the available mapping of the complex. The report also includes (1) statistics to assess the status of the CAR reserve system for a portion of the South West Region; (2) Region Scheme reports; (3) LGA Reports. This analysis is based on the Vegetation Complex mapping of the Swan Coastal Plain and South West Forests. This annual reporting was initiated in 2016 after the two vegetation complex mapping datasets were updated. The reports contains both external reports and the internal (DBCA and DWER) sub-reports. See the README worksheet in each excel file for more details. People external to DBCA can download the external version from DataWA at this link https://catalogue.data.wa.gov.au/dataset/dbca

ObjectiveNational interoperability is an agenda that has gained momentum in health care. Although several attempts to reach national interoperability, an alerting system through interconnected network of Health Information Exchange (HIE) organizations, Patient-Centered Data Home (PCDH), has seen preliminary success. The aim was to characterize the PCDH initiative through the Indiana Health Information Exchange's participation in the Heartland Region Pilot, which includes HIEs in Indiana, Ohio, Michigan, Kentucky, and Tennessee.Materials and MethodsAdmission, Discharge, and Transfer (ADT) transactions were collected between December 2016 and December 2017 among the seven HIEs in the Heartland Region. ADTs were parsed and summarized. Overlap analyses and patient matching software were used to characterize the PCDH patients. R software and Microsoft Excel were used to populate descriptive statistics and visualization.ResultsApproximately 1.5 million ADT transactions were captured. Majority of patients were female, ages 56–75 years, and were outpatient visits. Top noted reasons for visit were labs, screening, and abdominal pain. Based on the overlap analysis, Eastern Tennessee HIE was the only HIE with no duplicate service areas. An estimated 80 percent of the records were able to be matched with other records.DiscussionThe high volume of exchange in the Heartland Region Pilot established that PCDH is practical and feasible to exchange data. PCDH has the posture to build better comprehensive medical histories and continuity of care in real time.ConclusionThe value of the data gained extends beyond clinical practitioners to public health workforce for improved interventions, increased surveillance, and greater awareness of gaps in health for needs assessments. This existing interconnection of HIEs has an opportunity to be a sustainable path toward national interoperability.

BackgroundMalaria is a major cause of morbidity and mortality in children under the age of five worldwide. Although various malaria elimination measures have been implemented over the past decades, malaria remains a serious threat to public health, especially in tropical and subtropical areas. Ethiopia has set targets for eliminating malaria by 2030. No research has been conducted in the study area concerning malaria among children, who are the most malaria-prone segment of a community. The purpose of this study was to assess malaria prevalence and the factors associated with it among children under five years of age who attended the Sheko Health Center, Southwest Ethiopia, from June 1 to October 30, 2022.Materials and methodsAn institutional-based cross-sectional study was employed from June 1 to October 30, 2022, at the Sheko Health Center. Capillary blood samples were collected from 286 randomly selected symptomatic children. Data on socio-demographics and associated factors were collected using a pre-tested structured questionnaire, and data on parents’ and guardians’ knowledge about malaria was recorded on Excel 2016 Spreadsheets after interviewing them, and their responses were presented by a frequency table. Data were entered into Epi Data Manager (v4.0.2.101) and analyzed using the Statistical Package for Social Sciences (SPSS) version 25. Associated factors of malaria were analyzed using bivariate and multivariable logistic regression, and statistical significance was set at P < 0.05.ResultOverall, 23.4% (95% CI = 18.6–28.8%) malaria infection was recorded among the children whose blood samples were examined, with Plasmodium falciparum, Plasmodium vivax, and mixed infections (both species) representing 52.2%, 34.3%, and 13.4% of the cases, respectively. The majority of the parents or guardians believed that malaria is transmissible but could be prevented, and 80% of them considered mosquito bites to be the main mode of malaria transmission. Insecticide-treated net (ITN) was mentioned as a malaria prevention strategy by more than half of the respondents, while indoor residual spraying (IRS) was considered only by 19.6%. Based on multivariable logistic regression analysis, a significant association was found in children between the ages of 12 and 36 months (adjusted odds ratio = 5.050; 95% CI: 1.964–12.982), children who lived in rural areas (adjusted odds ratio = 2.901; 95% CI: 1.439–5.845), and children who did not use ITN the past two weeks before sample collection (adjusted odds ratio = 3.341; 95% CI: 1.646–6.781).ConclusionThis study revealed a high malaria prevalence among children aged under five years. Attention must be paid to improving the coverage of the ITN and its use in the study area, which could help reduce the risk of mosquito bites. Health education for the guardians of the children could also help to raise awareness about the prevention and control strategies for malaria transmission and further reduce the impact of the disease.

Factors associated with adherence to malaria treatment guidelines among health care workers in private health facilities in informal settlements, Kampala, Uganda.

Knowledge of the national guidelines for the diagnosis and treatment of malaria.