In order to assess the possible effects of lifestyle on fertility and pregnancy outcome, the PALS (Pregnancy and Lifestyle study) collected extensive data on a broad range of parameters termed ‘lifestyle’ from couples who were planning a natural (non-assisted) pregnancy in the coming months. There was no intervention. Participants were recruited over a six year period from 1988 to 1993 in response to extensive promotion in the local media. Male and female partners were interviewed independently and all interviews were conducted prospectively before the couple attempted to conceive. The result of each month of ‘trying’ was recorded and pregnancies were confirmed by urine tests and by ultrasound. The length of gestation of each pregnancy was recorded and pregnancies at term were classified with respect to weight. Multiple pregnancies and/or babies with congenital abnormalities have been excluded from the dataset. The data is stored as an xls file and each variable has a codename. For each of 582 couples there are 355 variables, the codes for which are described in a separate metadata file. The questionnaire based data includes information about households, occupation, chemical exposures at work and home, diet, smoking, alcohol use, hobbies, exercise and health. Recorded observations include monthly pregnancy tests and pregnancy outcomes.
{"title":"Data from the PALS (Pregnancy and Lifestyle Study), a Community-Based Study of Lifestyle on Fertility and Reproductive Outcome","authors":"J. Ford","doi":"10.5334/OHD.AP","DOIUrl":"https://doi.org/10.5334/OHD.AP","url":null,"abstract":"In order to assess the possible effects of lifestyle on fertility and pregnancy outcome, the PALS (Pregnancy and Lifestyle study) collected extensive data on a broad range of parameters termed ‘lifestyle’ from couples who were planning a natural (non-assisted) pregnancy in the coming months. There was no intervention. Participants were recruited over a six year period from 1988 to 1993 in response to extensive promotion in the local media. Male and female partners were interviewed independently and all interviews were conducted prospectively before the couple attempted to conceive. The result of each month of ‘trying’ was recorded and pregnancies were confirmed by urine tests and by ultrasound. The length of gestation of each pregnancy was recorded and pregnancies at term were classified with respect to weight. Multiple pregnancies and/or babies with congenital abnormalities have been excluded from the dataset. The data is stored as an xls file and each variable has a codename. For each of 582 couples there are 355 variables, the codes for which are described in a separate metadata file. The questionnaire based data includes information about households, occupation, chemical exposures at work and home, diet, smoking, alcohol use, hobbies, exercise and health. Recorded observations include monthly pregnancy tests and pregnancy outcomes.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70694348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detailed world mortality data such as collected by the World Health Organization gives a wealth of information about causes of death worldwide over a time span of 60 year. However, the raw mortality data in text format as provided by the WHO is not directly suitable for systematic research and data mining. In this Data Paper, a relational database is presented that is created from the raw WHO mortality data set and includes mortality rates, an ICD-code table and country reference data. This enriched database, as a corpus of global mortality data, can be readily imported in relational databases but can also function as the data source for other types of databases. The use of this database can therefore greatly facilitate global epidemiological research that may provide new clues to genetic or environmental factors in the origins of diseases.
{"title":"A Relational Database of WHO Mortality Data Prepared to Facilitate Global Mortality Research","authors":"A. Roos","doi":"10.5334/OHD.AO","DOIUrl":"https://doi.org/10.5334/OHD.AO","url":null,"abstract":"Detailed world mortality data such as collected by the World Health Organization gives a wealth of information about causes of death worldwide over a time span of 60 year. However, the raw mortality data in text format as provided by the WHO is not directly suitable for systematic research and data mining. In this Data Paper, a relational database is presented that is created from the raw WHO mortality data set and includes mortality rates, an ICD-code table and country reference data. This enriched database, as a corpus of global mortality data, can be readily imported in relational databases but can also function as the data source for other types of databases. The use of this database can therefore greatly facilitate global epidemiological research that may provide new clues to genetic or environmental factors in the origins of diseases.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70694274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Health and Retirement Study (HRS) is a nationally representative longitudinal survey of more than 37,000 individuals in 23,000 households over age 50 in the United States. Fielded biennially since 1992, it was established to provide a national resource for data on the changing health and economic circumstances associated with aging. HRS covers four broad topic areas—income and wealth; health, cognition, and use of health care services; work and retirement; and family connections. HRS data are also linked at the individual level to administrative records from Social Security and Medicare, Veteran’s Administration, the National Death Index, and employer-provided pension plan information. In 2006, data collection expanded to include biomarkers and genetics and greater depth in psychosocial well-being and social context. This blend of economic, health, and psychosocial information provides unprecedented potential to study increasingly complex questions about aging and retirement. HRS prioritizes rapid release of data while simultaneously protecting the confidentiality of respondents. Three categories of data—public, sensitive, and restricted—can be accessed through procedures described on the HRS website (hrsonline.isr.umich.edu).
{"title":"The Health and Retirement Study: A Public Data Resource for Research on Aging","authors":"A. Sonnega, D. Weir","doi":"10.5334/OHD.AM","DOIUrl":"https://doi.org/10.5334/OHD.AM","url":null,"abstract":"The Health and Retirement Study (HRS) is a nationally representative longitudinal survey of more than 37,000 individuals in 23,000 households over age 50 in the United States. Fielded biennially since 1992, it was established to provide a national resource for data on the changing health and economic circumstances associated with aging. HRS covers four broad topic areas—income and wealth; health, cognition, and use of health care services; work and retirement; and family connections. HRS data are also linked at the individual level to administrative records from Social Security and Medicare, Veteran’s Administration, the National Death Index, and employer-provided pension plan information. In 2006, data collection expanded to include biomarkers and genetics and greater depth in psychosocial well-being and social context. This blend of economic, health, and psychosocial information provides unprecedented potential to study increasingly complex questions about aging and retirement. HRS prioritizes rapid release of data while simultaneously protecting the confidentiality of respondents. Three categories of data—public, sensitive, and restricted—can be accessed through procedures described on the HRS website (hrsonline.isr.umich.edu).","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70693724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 1970 British Cohort Study (BCS70) is one of Britain’s world famous national longitudinal birth cohort studies, three of which are run by the Centre for Longitudinal Studies at the Institute of Education, University of London. BCS70 follows the lives of more than 17,000 people born in England, Scotland and Wales in a single week of 1970. Over the course of cohort members lives, the BCS70 has collected information on health, physical, educational and social development, and economic circumstances among other factors. Since the birth survey in 1970, there have been nine ‘sweeps’ of all cohort members at ages 5, 10, 16, 26, 30, 34, 38 and most recently at 42. Data has been collected from a number of different sources (the midwife present at birth, parents of the cohort members, head and class teachers, school health service personnel and the cohort members themselves). The data has been collected in a variety of ways including via paper and electronic questionnaires, clinical records, medical examinations, physical measurements, tests of ability, educational assessments and diaries. The majority of BCS70 survey data can be accessed by bona fide researchers through the UK Data Service at the University of Essex.
{"title":"1970 British Cohort Study","authors":"Matt Brown","doi":"10.5334/OHD.AL","DOIUrl":"https://doi.org/10.5334/OHD.AL","url":null,"abstract":"The 1970 British Cohort Study (BCS70) is one of Britain’s world famous national longitudinal birth cohort studies, three of which are run by the Centre for Longitudinal Studies at the Institute of Education, University of London. BCS70 follows the lives of more than 17,000 people born in England, Scotland and Wales in a single week of 1970. Over the course of cohort members lives, the BCS70 has collected information on health, physical, educational and social development, and economic circumstances among other factors. Since the birth survey in 1970, there have been nine ‘sweeps’ of all cohort members at ages 5, 10, 16, 26, 30, 34, 38 and most recently at 42. Data has been collected from a number of different sources (the midwife present at birth, parents of the cohort members, head and class teachers, school health service personnel and the cohort members themselves). The data has been collected in a variety of ways including via paper and electronic questionnaires, clinical records, medical examinations, physical measurements, tests of ability, educational assessments and diaries. The majority of BCS70 survey data can be accessed by bona fide researchers through the UK Data Service at the University of Essex.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70693510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Morgan, G. Valuri, M. Croft, Sonal Shah, P. D. Prinzio, Jennifer A. Griffith, T. Mcneil, A. Jablensky
This database has been constructed to support a program of work designed to untangle genetic and environmental contributions to the risk for schizophrenia and other adverse outcomes in the offspring of mothers with schizophrenia and other severe mental illness. To do this, it utilises Western Australian whole-population health and social services databases. Records on the Western Australian psychiatric case register have been linked to Midwives’ Notification of Birth records and to Registrations of Births (for paternal links) as well as to other data sets. Maternal links identify women with psychosis who gave birth in Western Australia between 1980 and 2001. Comparison mothers are those with no record of psychiatric illness who gave birth in Western Australia over the same period. The study database comprises 246,873 mothers and 467,945 children: 889 mothers with schizophrenia (1,672 children); 1,644 mothers with bipolar disorder (3,358 children); 4,200 mothers with unipolar major depression (8,864 children); 775 mothers with other psychoses (1,592 children); and 239,365 comparison mothers (452,459 children). Full psychiatric histories for mothers, fathers and children have been extracted. At the time of the most recent update to the psychiatric data on children, 33,363 children had a history of psychiatric illness; 5,500 of these had had at least one contact with mental health services at which a diagnosis of a psychotic disorder had been recorded. Data have also been collected on obstetric complications and a range of infant and childhood morbidities including birth defects, intellectual disability, educational achievement, childhood abuse, criminal offending. The program aims are to: (i) determine the frequency and distribution of obstetric complications in women with schizophrenia compared to a non-psychiatric comparison group of mothers; (ii) explore the spectrum of outcomes for the children born to women with schizophrenia compared to comparison children, and to assess the specificity of these findings to maternal schizophrenia compared to maternal bipolar disorder, unipolar major depression and other psychotic illness; and (iii) examine the relationship between familial psychiatric status, obstetric complications and mental health outcomes for children. The data sit in an Ingres relational database. A full description of the database and its elements has been published [1]. Numbers in this article differ from previously published numbers as a result of new linkages and updates to the database.
{"title":"Longitudinal, Whole-population Data Examining Pathways of Risk from Conception to Disease: The Western Australian Schizophrenia High-risk e-Cohort","authors":"V. Morgan, G. Valuri, M. Croft, Sonal Shah, P. D. Prinzio, Jennifer A. Griffith, T. Mcneil, A. Jablensky","doi":"10.5334/OHD.AJ","DOIUrl":"https://doi.org/10.5334/OHD.AJ","url":null,"abstract":"This database has been constructed to support a program of work designed to untangle genetic and environmental contributions to the risk for schizophrenia and other adverse outcomes in the offspring of mothers with schizophrenia and other severe mental illness. To do this, it utilises Western Australian whole-population health and social services databases. Records on the Western Australian psychiatric case register have been linked to Midwives’ Notification of Birth records and to Registrations of Births (for paternal links) as well as to other data sets. Maternal links identify women with psychosis who gave birth in Western Australia between 1980 and 2001. Comparison mothers are those with no record of psychiatric illness who gave birth in Western Australia over the same period. The study database comprises 246,873 mothers and 467,945 children: 889 mothers with schizophrenia (1,672 children); 1,644 mothers with bipolar disorder (3,358 children); 4,200 mothers with unipolar major depression (8,864 children); 775 mothers with other psychoses (1,592 children); and 239,365 comparison mothers (452,459 children). Full psychiatric histories for mothers, fathers and children have been extracted. At the time of the most recent update to the psychiatric data on children, 33,363 children had a history of psychiatric illness; 5,500 of these had had at least one contact with mental health services at which a diagnosis of a psychotic disorder had been recorded. Data have also been collected on obstetric complications and a range of infant and childhood morbidities including birth defects, intellectual disability, educational achievement, childhood abuse, criminal offending. The program aims are to: (i) determine the frequency and distribution of obstetric complications in women with schizophrenia compared to a non-psychiatric comparison group of mothers; (ii) explore the spectrum of outcomes for the children born to women with schizophrenia compared to comparison children, and to assess the specificity of these findings to maternal schizophrenia compared to maternal bipolar disorder, unipolar major depression and other psychotic illness; and (iii) examine the relationship between familial psychiatric status, obstetric complications and mental health outcomes for children. The data sit in an Ingres relational database. A full description of the database and its elements has been published [1]. Numbers in this article differ from previously published numbers as a result of new linkages and updates to the database.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70692484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The National Child Development Study (NCDS) is one of Britain’s world-renowned national longitudinal birth cohort studies, three of which are run by the Centre for Longitudinal Studies at the Institute of Education, University of London. The study is an ongoing multi-disciplinary longitudinal study which follows the lives of around 17,000 people born in England, Scotland and Wales in a single week of 1958. Over the course of cohort members lives, the NCDS has collected information on health, physical, educational and social development, and economic circumstances among other factors. The broad aim of the study is to examine the impact that circumstances and experiences at one stage of life have on outcomes and achievements in later life. Since the birth survey in 1958, there have been ten ‘sweeps’ of all cohort members at ages 7, 11, 16, 23, 33, 42, 44/5 (a biomedical collection) 46, 50 and most recently at 55. Data has been collected from a number of different sources (the midwife present at birth, parents of the cohort members, teachers, doctors and the cohort members themselves). The data has been collected in a variety of ways including via paper and electronic questionnaires, clinical records, medical examinations, physical measurements, tests of ability and educational assessments. The information collected forms a high quality data resource for scientific investigations across a full range of domains of individuals’ lives and across different points in time in them. The study has been designed so as to ensure comparability with other major cohort studies so as permit the examination of links between social change and the changing experiences of different cohorts. The majority of NCDS survey data can be accessed by bona fide researchers through the UK Data Service at the University of Essex.
{"title":"National Child Development Study (or 1958 Birth Cohort)","authors":"Matt Brown, A. Goodman","doi":"10.5334/OHD.AK","DOIUrl":"https://doi.org/10.5334/OHD.AK","url":null,"abstract":"The National Child Development Study (NCDS) is one of Britain’s world-renowned national longitudinal birth cohort studies, three of which are run by the Centre for Longitudinal Studies at the Institute of Education, University of London. The study is an ongoing multi-disciplinary longitudinal study which follows the lives of around 17,000 people born in England, Scotland and Wales in a single week of 1958. Over the course of cohort members lives, the NCDS has collected information on health, physical, educational and social development, and economic circumstances among other factors. The broad aim of the study is to examine the impact that circumstances and experiences at one stage of life have on outcomes and achievements in later life. Since the birth survey in 1958, there have been ten ‘sweeps’ of all cohort members at ages 7, 11, 16, 23, 33, 42, 44/5 (a biomedical collection) 46, 50 and most recently at 55. Data has been collected from a number of different sources (the midwife present at birth, parents of the cohort members, teachers, doctors and the cohort members themselves). The data has been collected in a variety of ways including via paper and electronic questionnaires, clinical records, medical examinations, physical measurements, tests of ability and educational assessments. The information collected forms a high quality data resource for scientific investigations across a full range of domains of individuals’ lives and across different points in time in them. The study has been designed so as to ensure comparability with other major cohort studies so as permit the examination of links between social change and the changing experiences of different cohorts. The majority of NCDS survey data can be accessed by bona fide researchers through the UK Data Service at the University of Essex.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70692689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Alexander, D. Morley, J. Medlock, K. Searle, W. Wint
The presence of roe deer can be an important component within ecological and epidemiological systems contributing to the risk and spread of a range of vector-borne diseases. Deer are important hosts for many vectors, and may therefore serve as a focal point or attractant for vectors or may themselves act as a reservoir for vector-borne disease. Three spatial modelling techniques were used to generate an ensemble model describing the proportion of suitable roe deer habitat within recorded distributions for Europe as identified from diverse sources. The resulting model is therefore an index of presence, which may be useful in supporting the modelling of vector-borne disease across Europe.
{"title":"A First Attempt at Modelling Roe Deer (Capreolus capreolus) Distributions Over Europe","authors":"N. Alexander, D. Morley, J. Medlock, K. Searle, W. Wint","doi":"10.5334/OHD.AH","DOIUrl":"https://doi.org/10.5334/OHD.AH","url":null,"abstract":"The presence of roe deer can be an important component within ecological and epidemiological systems contributing to the risk and spread of a range of vector-borne diseases. Deer are important hosts for many vectors, and may therefore serve as a focal point or attractant for vectors or may themselves act as a reservoir for vector-borne disease. Three spatial modelling techniques were used to generate an ensemble model describing the proportion of suitable roe deer habitat within recorded distributions for Europe as identified from diverse sources. The resulting model is therefore an index of presence, which may be useful in supporting the modelling of vector-borne disease across Europe.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70692310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The presence of red deer may be a contributing factor within the ecological and epidemiological systems contributing to the risk and spread of a range of vector-borne diseases. Deer are important hosts for many vectors, and may therefore serve as a focal point or attractant for vectors or may themselves become a reservoir for vector-borne disease. Three spatial modelling techniques were used to generate an ensemble model describing the proportion of suitable red deer habitat within recorded distributions for Europe as identified from diverse sources. The resulting model is therefore an index of presence, which may be useful in supporting the modelling of vector-borne disease across Europe.
{"title":"A First Attempt at Modelling Red Deer (Cervus elaphus) Distributions Over Europe","authors":"W. Wint, D. Morley, J. Medlock, N. Alexander","doi":"10.5334/OHD.AG","DOIUrl":"https://doi.org/10.5334/OHD.AG","url":null,"abstract":"The presence of red deer may be a contributing factor within the ecological and epidemiological systems contributing to the risk and spread of a range of vector-borne diseases. Deer are important hosts for many vectors, and may therefore serve as a focal point or attractant for vectors or may themselves become a reservoir for vector-borne disease. Three spatial modelling techniques were used to generate an ensemble model describing the proportion of suitable red deer habitat within recorded distributions for Europe as identified from diverse sources. The resulting model is therefore an index of presence, which may be useful in supporting the modelling of vector-borne disease across Europe.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70692160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Midlife in the United States (MIDUS) is a national longitudinal study of health and well-being (http://midus.wisc.edu/). It was conceived by a multidisciplinary team of scholars interested in understanding aging as an integrated bio-psycho-social process, and as such it includes data collected in a wide array of research protocols using a variety of survey and non-survey instruments. The data captured by these different protocols (comprising around 20,000 variables) represent survey measures, cognitive assessments, daily stress diaries, clinical, biomarker and neuroscience data which are contained in separate flat or stacked data files with a common ID system that allows easy data merges among them. All MIDUS datasets and documentation are archived at the ICPSR (http://www.icpsr.umich.edu/) repository at the University of Michigan and are publicly available in a variety of formats and statistical packages. Special attention is given to providing clear user-friendly documentation; the study has embraced the Data Documentation Initiative (DDI) metadata standard and produces DDI-Lifecycle compliant codebooks. Potential for secondary use of MIDUS is high and actively encouraged. The study has become very popular with the research public as measured by data downloads and citation counts (see Reuse Potential below).
{"title":"The Midlife in the United States (MIDUS) Series: A National Longitudinal Study of Health and Well-being.","authors":"Barry T Radler","doi":"10.5334/ohd.ai","DOIUrl":"https://doi.org/10.5334/ohd.ai","url":null,"abstract":"<p><p>Midlife in the United States (MIDUS) is a national longitudinal study of health and well-being (http://midus.wisc.edu/). It was conceived by a multidisciplinary team of scholars interested in understanding aging as an integrated bio-psycho-social process, and as such it includes data collected in a wide array of research protocols using a variety of survey and non-survey instruments. The data captured by these different protocols (comprising around 20,000 variables) represent survey measures, cognitive assessments, daily stress diaries, clinical, biomarker and neuroscience data which are contained in separate flat or stacked data files with a common ID system that allows easy data merges among them. All MIDUS datasets and documentation are archived at the ICPSR (http://www.icpsr.umich.edu/) repository at the University of Michigan and are publicly available in a variety of formats and statistical packages. Special attention is given to providing clear user-friendly documentation; the study has embraced the Data Documentation Initiative (DDI) metadata standard and produces DDI-Lifecycle compliant codebooks. Potential for secondary use of MIDUS is high and actively encouraged. The study has become very popular with the research public as measured by data downloads and citation counts (see Reuse Potential below).</p>","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4280664/pdf/nihms633639.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32948530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dataset is of a health survey amongst the 21.5 million poor families of the Indian state of Andhra Pradesh conducted during April and May 2013. The dataset captures individual characteristics and household characteristics of the past 365 days. Data was collected by 2022 trained field staff of Aarogyasri Health Care Trust (AHCT) of Government of Andhra Pradesh using a questionnaire modelled after that used for the health surveys by National Sample Survey Organisation of India.
{"title":"Data from \"Social determinants of unmet hospitalisation need amongst the poor in Andhra Pradesh, India: A cross- sectional study.\"","authors":"Srikant Nagulapalli","doi":"10.5334/JOPHD.AF","DOIUrl":"https://doi.org/10.5334/JOPHD.AF","url":null,"abstract":"The dataset is of a health survey amongst the 21.5 million poor families of the Indian state of Andhra Pradesh conducted during April and May 2013. The dataset captures individual characteristics and household characteristics of the past 365 days. Data was collected by 2022 trained field staff of Aarogyasri Health Care Trust (AHCT) of Government of Andhra Pradesh using a questionnaire modelled after that used for the health surveys by National Sample Survey Organisation of India.","PeriodicalId":74349,"journal":{"name":"Open health data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70678713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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