The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaThe NIHR Public Health Overview project team worked with colleagues across NIHR public health research to define the inclusion criteria for NIHR public health research awards. NIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

Health Service Research (HSR) PubMed Queries contains preformulated specialized PubMed searches on healthcare quality and costs.

This research intended to analyze the current usage of health research evidence in health planning, determinants, and readiness to use knowledge translation tools among planning teams in Tanzania. Specifically, the study aims to 1) analyze the current usage of health research evidence among planning team members at the regional and council levels, 2) analyze the capability for the use of health research evidence among planning team members at regional and council levels, 3) analyze the opportunities for the use of health research evidence among health planning members at regional and council levels, 4) to identify the motivations for the use of health research evidence among health planning team members at regional and council levels, and 5) to assess the readiness of the planning team members on the use of knowledge translation tools. The study employed an exploratory mixed-method study design. It was conducted in nine (9) regions and eighteen (18) Councils of Tanzania Mainland involving the health planning team members.

Poster presented at eResearch Australasia in Melbourne, October 2016.AbstractIn 2016 the Australian National Data Service (ANDS) ran the highly valued 23 (research data) Things program. The self-directed program, was designed for anyone who wanted to learn more about research data - what it is, why it is a global hot topic, finding it, re-using it, managing it, and more. ANDS has also been engaging with the medical and health community, through a series of ‘Sharing Health-y Data: Challenges and Solutions’ workshops across Australia, a ‘virtual’ health data community group within the 23 Things program, and their resources relevant to medical and health data collated on the ‘medical and health data’ page of their website.

As the active phase of running the 23 (research data) Things program throughout 2016 came to a close, ANDS produced a re-purpose toolkit. The 10 medical and health research data Things a part of this toolkit. It was developed from the 23 (research data) Things materials; adapting the Things that were most relevant to medical and health data, and also substituting medical and health examples in many of the activities. Each Thing has two or three activities. Some of the activities are intended as an introduction to a topic, and some delve a little deeper, to allow for a range of previous experience and knowledge. The program is intended to be reused and adapted as required, and accordingly it has a CC-BY licence. 10 medical and health research data Things is a flexible, adaptable resource for anyone working with medical, clinical or health data.

Health Services Research Projects in Progress (HSRProj) was a database of health services research and public health projects in progress, related to research in quality, cost, and access to health care. It included behavioral health research and public health research, with over 38,000 projects and information back to the 1990s.

This resource was retired on September 14, 2021 and is no longer updated.

Total Union Budget allocation for the Department of Health Research under the Ministry of Health and Family Welfare. It contains budgetary allocations for expenditures related to Indian Council of Medical Research, Matters Relating to Epidemics, Natural Calamities and Development of Tools to Prevent Outbreaks, Promotion, Co-ordination and development of basic, applied and clinical research etc.

"The purpose of the Agency for Healthcare Research and Quality is to enhance the quality, appropriateness, and effectiveness of health services, and access to such services through the establishment of a broad base of scientific research and through the promotion of improvements in clinical and health system practices, including the prevention of diseases and other health conditions."

Indian Journal of Public Health Research And Development CiteScore 2024-2025 - ResearchHelpDesk - Indian Journal of Public Health Research & Development is a double blind peer-reviewed. The frequency is monthly. It deals with all aspects of Public Health including Community Medicine, Clinical Research, Epidemiology, Occupational Health, Public Health, Environmental Hazards, and Public Health Laws and covers all medical specialties concerned with research and development for the masses. The journal strongly encourages reports of research carried out within Indian continent and South East Asia. ISSN No of Indian "Journal of Public Health Research & Development" Print ISSN: 0976-0245 Online ISSN: 0976-5506 and is indexed with Index Copernicus (Poland). It is also brought to notice that the journal is being covered by many international databases. The journal is covered by EBSCO (USA), Embase, EMCare & Scopus database. The journal is now part of DST, CSIR, and UGC consortia. We have pleasure to inform you that our journal is covered for faculty promotion in relation to MCI letter dated 3-9-2015 regarding indexing of journals. This journal qualifies the criteria as it is indexed in EMBASE Scopus till date. (Proof can be submitted on request) This journal was covered by Index Copernicus till 2010. It has been again submitted to Index Copernicus and likely to cover soon.

Institute of Gender and Children's Health Research Organisation File

African Population and Health Research Center Activity File

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

The FAIR Database:Facilitating Access to Public Health Research Literature

https://doi.org/10.5061/dryad.wdbrv15zn

Description of the data and file structure

The file is in .tsv format with the following headers:

PaperId: the Microsoft Academic number for the paper

PaperTitle: the title of the paper

Citations: a list of Microsoft Academic numbers for the papers in the paper's bibliography

coFoS: Microsoft Academic 'fields of study' / Topics

Authors: the authors of the paper

Abstract: the abstract of the paper

PublicationDate: the date of publication

DocType: the type of document

FamilyId: not used

RecordId: not used

CN: not used

Incl: not used

Lang: not used

V: volume

I: issue

DOI: DOI

JN: journal title

PG: pages

Y: year of publication

URLs: URLs where the full text paper may be downloaded

umls: not used

Place: PROGRESS-Plus category

Race: PROGRESS-Plus category

Occupation: PROGRESS-Plus category

Gender: PROG...

2011 Census data for England and Wales, linked to Mortality Data, Hospital Episode Statistics (HES) data, and GP Extraction Service (GPES) data for Pandemic Planning and Research Data.

In 2018, approximately ** percent of the total health research expenditure in the United Kingdom (UK) came from the business sector, the highest of the research sectors despite the share slightly dropping since 2014. The university sector increased its share of health research expenditure in the UK from **** percent in 2014 to **** percent in 2018.

Summary of logistic regression (for the complete dataset refer to data in S1 Appendix).

Details of health research initiatives. (XLSX)

This statistic is based on a survey conducted in January 2022. It displays the opinions of surveyed Americans if federal investment in mental health research is enough. The survey shows that 58 percent of respondents believed that federal investment in mental health research is not adequate.

Apportionment file 11218315 retrieved from OMB public records

BackgroundIn medical practice, clinically unexpected measurements might be quite properly handled by the remeasurement, removal, or reclassification of patients. If these habits are not prevented during clinical research, how much of each is needed to sway an entire study?Methods and ResultsBelieving there is a difference between groups, a well-intentioned clinician researcher addresses unexpected values. We tested how much removal, remeasurement, or reclassification of patients would be needed in most cases to turn an otherwise-neutral study positive. Remeasurement of 19 patients out of 200 per group was required to make most studies positive. Removal was more powerful: just 9 out of 200 was enough. Reclassification was most powerful, with 5 out of 200 enough. The larger the study, the smaller the proportion of patients needing to be manipulated to make the study positive: the percentages needed to be remeasured, removed, or reclassified fell from 45%, 20%, and 10% respectively for a 20 patient-per-group study, to 4%, 2%, and 1% for an 800 patient-per-group study. Dot-plots, but not bar-charts, make the perhaps-inadvertent manipulations visible. Detection is possible using statistical methods such as the Tadpole test.ConclusionsBehaviours necessary for clinical practice are destructive to clinical research. Even small amounts of selective remeasurement, removal, or reclassification can produce false positive results. Size matters: larger studies are proportionately more vulnerable. If observational studies permit selective unblinded enrolment, malleable classification, or selective remeasurement, then results are not credible. Clinical research is very vulnerable to “remeasurement, removal, and reclassification”, the 3 evil R's.