Group difference results from the voxel-wise analysis within anatomical ROIs.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

Mental Health Services for Children

Monthly Statistics from England

By data.world's Admin [source]

About this dataset

This dataset provides insight into the mental health services available to children and young people in England. The data includes all primary and secondary levels of care, as well as breakdowns by age group. Information is provided on the number of people in contact with mental health services; open ward stays; open referrals; referrals starting in reporting period; attended contacts; indirect activity; discharged from referral; missed care contacts by DNA reasons and more. With these statistics, analysts may be able to better understand the scope of mental health service usage across different age groups in England and make valuable conclusions about best practices for helping children & young people receive proper care

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How to use the dataset

This guide provides information on how to use this dataset effectively.

Understanding the Columns:

Each row represents data from a specific month within a reporting period. The first thing to do is to find out what each column represents - this is explained by their titles and descriptions included at the beginning of this dataset. Note that there are primary level columns (e.g., Reporting Period, Breakdown) which provide overall context while secondary level columns (e.g., CYP01 People in contact with children and young peoples' mentally health service…) provide more detail on specific indicators of interest related to that primary level column value pair (i.e., Reporting Period X).

Exploring Data Variables:

The next step is exploring which data variables could potentially be helpful when analyzing initiatives/programs related to mental health care for children & youth in England or developing policies related to them – look through all columns included here for ones you think would be most helpful such as ‘CYP21 – Open ward stays...’ or ‘MHS07a - People with an open hospital spell…’ and note down those that have been considered necessary/relevant based on your particular situation/needs before further analyzing using software packages like Excel or SPSS etc..

Analyzing Data Values:

Now comes the time for analyzing individual values provided under each respective column – take one single numerical data element such as ‘CYP02 – People… CPA end RP’ & run through it all looking at trends over time, averages across different sections by performing calculations via software packages available like tables provided above based upon sorted hierarchies needed.. Then you can then start looking into making meaningful correlations between different pieces of information given herein by cross-referencing contexts against each other resulting if any noticeable patterns found significant enough will make informative decisions towards policy implementations & program improvement opportunities both directly concerned

Research Ideas

• Using this dataset to identify key trends in mental health services usage among children and young people in England, such as the number of open ward stays and referrals received.
• Using the information to develop targeted solutions on areas of need identified from the data by geographical area or age group, i.e creating campaigns or programs specifically targeting specific groups at a higher risk of experiencing mental health difficulties or engaging with specialist services.
• Tracking how well these initiatives are working over time by monitoring relevant metrics such as attendance at appointments, open referrals etc to evaluate their effectiveness in improving access and engagement with mental health services for those most in need

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. - ...

Household net worth statistics aims to provide a picture of the net worth (wealth) of New Zealanders, by looking at their household assets and liabilities – financial and non-financial.

The Adjusted Wealth Parity Index (WPIA) is calculated by dividing the poorest quintile value for the indicator by the richest quintile value for the indicator. If the resulting value exceeds 1, the ratio is inverted and subtracted from 2. The adjusted wealth parity index is symmetrical around 1 and lies in the range 0-2. An adjusted WPI equal to 1 indicates parity between the richest and poorest quintiles. In general, a value less than 1 indicates disparity in favor of the richest quintile and a value greater than 1 indicates disparity in favor of the poorest quintile. For more information, consult the UNESCO Institute for Statistics: http://uis.unesco.org/

P-value of the comparison between normotensive and hypertensive groups presenting the same risk factor.

IntroductionRoot canal sealing materials play a crucial role in an endodontic procedure by forming a bond between the dentinal walls and the gutta-percha. The current study aims to analyse the dentinal tubule penetration and adhesive pattern, including the push-out bond strength of six commercially available root canal sealers.MethodologyEighty-four mandibular first premolars were split into seven groups (and n = 12), Group 1: Dia-Root, Group 2: One-Fil, Group 3: BioRoot RCS, Group 4: AH Plus, Group 5: CeraSeal, Group 6: iRoot SP, Group 7: GP without sealer (control). Two groups were made, one for dentinal tubule penetration and the other for push-out bond strength; the total sample size was one hundred sixty-eight. Root canal treatment was performed using a method called the crown down technique, and for obturation, the single cone technique was used. A confocal laser scanning microscope (Leica, Microsystem Heidel GmbH, Version 2.00 build 0585, Germany) was used to evaluate dentinal tubule penetration, and Universal Testing Machine was utilised to measure the push-out bond strength (Shimadzu, Japan) using a plunger size of 0.4 mm and speed of 1mm/min. Finally, the adhesive pattern of the sealers was analysed by HIROX digital microscope (KH-7700). Statistical analysis was carried out by a one-way Anova test, Dunnet’s T3 test, and Chi-square test.ResultsHighest dentinal tubule penetration was noticed with One-Fil (p

a Values are mean (±SD) of the gap of the scores between independence and frequency, and the values in bold present positive gaps.b The domains of school setting are only applicable for children with the occupation of student, therefore the number of participants (n = 13,520) of this domains would be less than the other domains (n = 13,906).c The values of the gaps were compared across setting using repeated measured ANOVA.d The post hoc analyses across settings were performed using Scheffé's method.e The values of the gaps were compared across diagnostic/severity groups using one-way ANOVA.f The values of the gaps were compared across diagnostic/severity groups using Welch ANOVA because the data fail to meet the equal variance assumption by significant Levene's test for homogeneity of variancesg The post hoc analyses across diagnostic groups were performed across diagnostic groups with Scheffé's method.h The post hoc analyses across diagnostic/severity groups were performed using Games-Howell post hoc analysis because the data fail to meet the equal variance assumption by significant Levene's test for homogeneity of variances.HCLA = home and community living activities; ID = Intellectual disability; ASD = Autistic spectrum disorder; LH = language/hearing impairment; CP = Cerebral palsy; Mil = mild; Mod = moderate; S = severe; P = profoundComparisons of the independence-frequency gaps across setting, severity and diagnosis for children with only one diagnosis of all severity levels in body function.