scholarly journals Standard multiple imputation of survey data didn’t perform better than simple substitution in enhancing an administrative dataset: the example of self-rated health in England

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Frank Popham ◽  
Elise Whitley ◽  
Oarabile Molaodi ◽  
Linsay Gray
Keyword(s):  
Multiple Imputation ◽  
Survey Data ◽  
Census Data ◽  
Household Survey ◽  
Population Data ◽  
Geographic Region ◽  
Housing Tenure ◽  
Population Census ◽  
Self Rated Health ◽  
Multiply Imputed

Abstract Background Health surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are becoming less representative as response levels fall. Routinely collected administrative data offer more extensive population coverage but typically comprise fewer health topics. We explore whether data combination and multiple imputation of health variables from survey data is a simple and robust way of generating these variables in the general population. Methods We use the UK Integrated Household Survey and the English 2011 population census both of which included self-rated general health. Setting aside the census self-rated health data we multiply imputed self-rated health responses for the census using the survey data and compared these with the actual census results in 576 unique groups defined by age, sex, housing tenure and geographic region. Results Compared with original census data across the groups, multiply imputed proportions of bad or very bad self-rated health were not a markedly better fit than those simply derived from the survey proportions. Conclusion While multiple imputation may have the potential to augment population data with information from surveys, further testing and refinement is required.

PERANCANGAN APLIKASI SENSUS PENDUDUK PADA BADAN PUSAT STATISTIK KOTA MAKASSAR BERBASIS ANDROID

2021 ◽  
Vol 6 (1) ◽  
pp. 48
Author(s):  
Nurkhalik Wahdanial Asbara
Keyword(s):  
Data Collection ◽  
Census Data ◽  
Population Data ◽  
Accurate Information ◽  
Population Census ◽  
Male Population ◽  
Female Population ◽  
Technological Developments ◽  
The Government ◽  
Population Ratio

Technological developments and changes in government systems are developing rapidly. Both of these lead to efforts to carry out duties, protect functions and serve the community. This encourages the government to take various adjustment steps quickly in line with the dynamics of development that occur. One of them is through a population census. The population census is an important issue that must be handled properly. The population census in this study takes population data in an area based on the number of male population, female population, ratio, and population density. The data was taken and submitted to the Makassar City Statistics Agency. Population Census is a presentation of information that has the ability to present accurate information, and helps facilitate the search for a population census data. The population census is carried out every 5 years which is carried out by census officers to carry out data collection to each resident's house, the data collection process is carried out by conventional recording and submitting it to the central statistics agency for database entry. With this application, it is expected to provide convenience to Population census officers to perform the process of inputting population data and the data is directly stored in the database without having to return to the office to input again.

scholarly journals Demographic Losses Due To Alcohol-Related Mortality in Lithuania: Results Obtained Through Combining Death Records with the 2011 Population Census Data

2014 ◽  
Vol 53 (1) ◽  
pp. 15-23
Author(s):  
Daumantas Stumbrys ◽  
Domantas Jasilionis ◽  
Dalia Ambrozaitienė ◽  
Vlada Stankūnienė
Keyword(s):  
Marital Status ◽  
Mortality Risk ◽  
Census Data ◽  
Population Data ◽  
Mortality Data ◽  
Population Census ◽  
The Republic ◽  
The Individual ◽  
Education Groups ◽  
Related Mortality

This paper presents the results of a study on sociodemographic mortality differentials in Lithuania based on censuslinked mortality data. Population data come from the individual records of the 2011 Population and Housing Census of the Republic of Lithuania. The results of the research demonstrate that education and marital status are very strong predictors of alcohol-related mortality. Among males aged 30 and older, the alcohol-related mortality risk in non-married groups is up to 3.4 times as high as in the group of married males. The alcohol-related mortality risk in lower-education groups is up to 3.7 times as high as in the group of those with higher education. The findings of the study suggest that the elimination of educational differences would allow avoiding 55.7 %, the elimination of marital status differences – 40.2 %, the elimination of ethnic group differences – 11.1 % of alcohol-related deaths.

scholarly journals Incidence of drug-treated chronic diseases using administrative pharmaceutical data

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Shaun Purkiss ◽  
Tessa Keegel ◽  
Hassan Vally ◽  
Dennis Wollersheim
Keyword(s):  
Chronic Disease ◽  
Chronic Diseases ◽  
Survey Data ◽  
Census Data ◽  
Respiratory Illness ◽  
Self Report ◽  
World Health ◽  
Population Census ◽  
Incidence Data ◽  
Anatomic Therapeutic Chemical

Background Pharmaceutical data can be used to identify the presence of drug-treated chronic diseases (CD) in individuals using assigned World Health Organization Anatomic Therapeutic Chemical (ATC) classifications of medicines prescribed. ATC codes define treatment domains and provides a method to case define CD that has previously been used to estimate CD prevalence within populations. Main Aim We determined selected CD incidence from an administrative pharmaceutical dataset, and compared them with published CD incidence results. Approach An Australian Pharmaceutical Benefits Scheme (PBS) database covering the period 2003-14 was used for this study. The earliest prescriptions exchanged by individuals for an ATC defined CD were identified and the annual count recorded. These values were combined with Australian population census data to calculate the annual incidence of ATC defined CD. Australian PBS derived incidence estimates (PDI) were compared with published Australian and world incidence data. Results The PDI of 16 chronic diseases were compared with incidence estimates using self-report surveys from the literature. Mean percentage differences between PDI estimates varied greatly when compared to survey data (mean 33% (SD ±79%). Diabetes (-29%), gout (4%), glaucoma (69%) and tuberculosis (14%) showed closer associations. In contrast, PDI estimates (n/1000/year) showed particularly high incidence levels as compared with self-report data for dyspepsia (16.9 v 4.5), dyslipidaemia (11.6 v 5.6) and respiratory illness (17.6 v 2.6). Conclusion Incidence estimates of drug treated chronic disease can be obtained using pharmaceutical data and may be a useful source for a number of conditions. Some PDI differ considerably from survey data. The interpretation of PDI requires context on how a particular CD presents. Accuracy and relevance are likely to depend upon how drug treatments relate to the initial management of the chronic disease.

scholarly journals Advances in mapping population and demographic characteristics at small-area levels

2020 ◽  
Vol 49 (Supplement_1) ◽  
pp. i15-i25
Author(s):  
Daniela Fecht ◽  
Samantha Cockings ◽  
Susan Hodgson ◽  
Frédéric B Piel ◽  
David Martin ◽  
...  
Keyword(s):  
Big Data ◽  
Small Area ◽  
Census Data ◽  
Economic Status ◽  
Demographic Data ◽  
Population Data ◽  
Demographic Characteristics ◽  
Population Characteristics ◽  
Population Census ◽  
The Us

Abstract Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

Antebellum Urbanization in the American Northeast

1965 ◽  
Vol 25 (4) ◽  
pp. 592-608 ◽  
Author(s):  
Jeffrey G. Williamson
Keyword(s):  
Economic History ◽  
Census Data ◽  
Growth Models ◽  
Empirical Studies ◽  
Population Data ◽  
Quantitative Information ◽  
City Size ◽  
Population Census ◽  
High Productivity ◽  
Sector Model

No one seriously questions the familiar association either between levels of urban development and degrees of industrial maturity or between rates of change in these two indices. It has even become commonplace in macroeconomic growth theory to simplify the complexities of structural change into some variation of the urban-rural two-sector model although, in the real world, shifts from commercial to industrial urban employment, let alone more complex intersectoral shifts, are of prime importance. Indeed, many of these growth models place great emphasis not upon changes in sector productivity but upon resource shifts between low- and high-productivity employment, while in empirical studies urban and rural population data very often appear as explicit substitutes for sectoral employment. But given the paucity of macroeconomic data for the antebellum period, especially prior to 1839, one is left puzzled by our relative inattention to the wealth of population census data by residence. American economic history textbooks are stuffed with quantitative information on the number of cities, their spectacular growth, and the percentage of population urbanized, but these may not be the most effective uses of this great data pool. The plethora of urban histories and the abundant attention to urban rivalry may be useful complements, but they are not very effective substitutes for quantitative analysis of overall American urbanization and experience with city size distribution. The very attention which the topic is currently receiving by economists, geographers, and historians suggests that much remains undone in the study of early American urbanization.

scholarly journals Prevalence estimation of intellectual disability using national administrative and household survey data: The importance of survey question specificity

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Orla McBride ◽  
Pauline Heslop ◽  
Gyles Glover ◽  
Laurence Taggart ◽  
Lisa Hanna-Trainor ◽  
...  
Keyword(s):  
Intellectual Disability ◽  
Northern Ireland ◽  
Survey Data ◽  
Census Data ◽  
Household Survey ◽  
Survey Question ◽  
Prevalence Estimation ◽  
Prevalence Estimates ◽  
Disability Prevalence ◽  
Household Survey Data

BackgroundVariability in prevalence estimation of intellectual disability has been attributed to heterogeneity in study settings, methodologies, and intellectual disability case definitions. Among studies based on national household survey data specifically, variability in prevalence estimation has partly been attributed to the level of specificity of the survey questions employed to determine the presence of intellectual disability. Specific aims & methodUsing standardised difference scoring, and ‘intellectual disability’ survey data from the 2007 Northern Ireland Survey on Activity Limitation and Disability (NISALD) (N=23,689) and the 2011 Northern Ireland Census (N=1,770,217) the following study had two aims. First, we aimed to demonstrate the effects of survey question specificity on intellectual disability prevalence estimation. Second, we aimed to produce reliable estimates of the geographic variation of intellectual disability within private households in Northern Ireland while also assessing the socio-demographic, health-related and disability characteristics of this population. FindingsPrevalence estimates generated using the more crudely classified intellectual disability Census data indicated a prevalence of 2% for the overall population, 3.8% for children aged between 0 and 15 years, and 1.5% for citizens aged 16 years or older. Intellectual disability prevalence estimates generated using the more explicitly defined 2007 NISALD data indicated a population prevalence of 0.5% for the overall population, 1.3% for children aged between 0 and 15 years, and 0.3% for citizens aged 16 years or older. The NISALD estimates were consistent with most recent international meta-analysis prevalence estimates. According to the NISALD data, the majority of those with an intellectual disability were male, lived outside Belfast, and experienced severe intellectual disability, with multiple comorbid health conditions. DiscussionThe current findings highlight the importance of survey question specificity in the estimation of intellectual disability prevalence and provide reliable prevalence estimates of intellectual disability in Northern Ireland. The findings also demonstrate the utility of administrative data for detecting and understanding intellectual disability, and inform recommendations on how to maximise use of future intellectual disability Census data.

Queens of the Desert: Convictism and Marital Attitudes across Australia

2020 ◽  
Vol 110 ◽  
pp. 457-462
Author(s):  
Victoria Baranov ◽  
Ralph De Haas ◽  
Pauline Grosjean
Keyword(s):  
Nineteenth Century ◽  
Social Norms ◽  
Spatial Variation ◽  
Survey Data ◽  
Household Survey ◽  
Same Sex ◽  
Same Sex Marriage ◽  
Founder Populations ◽  
Household Survey Data ◽  
Combined Data

We merge data on spatial variation in the presence of convicts across eighteenth and nineteenth century Australia with results from the country's 2017 poll on same-sex marriage and with household survey data. These combined data allow us to identify the lasting impact of convict colonization on social norms about marriage. We find that in areas with higher historical convict concentrations, more Australians recently voted in favor of same-sex marriage and hold liberal views about marriage more generally. Our results highlight how founder populations can have lasting effects on locally held social norms.

scholarly journals How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001–2011

2021 ◽  
Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth
Keyword(s):  
Administrative Data ◽  
Internal Migration ◽  
Census Data ◽  
Local Population ◽  
Population Data ◽  
Population Estimates ◽  
Demographic Groups ◽  
Small Areas ◽  
Intercensal Period ◽  
Statistical Agencies

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

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