scholarly journals 817Official small area health statistics using the stratified reweighting method

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Ian Rayson ◽  
Sean Buttsworth
Keyword(s):  
Health Survey ◽  
Small Area ◽  
Census Data ◽  
Local Level ◽  
Health Data ◽  
Local Health ◽  
Health Statistics ◽  
Data Set ◽  
Acceptable Quality ◽  
Generalised Linear Mixed Model

Abstract Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.

scholarly journals 610Official statistics on health outcomes and risk factors for small geographic areas

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Ian Rayson
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Health Outcomes ◽  
Survey Data ◽  
Health Survey ◽  
Mixed Model ◽  
Local Level ◽  
Outcome Variable ◽  
Local Health ◽  
Health Statistics

Abstract Background The 2017-18 National Health Survey (NHS) is an Australia-wide detailed health survey conducted by the Australian Bureau of Statistics (ABS). Although the survey enables reliable National and State official health statistics, the sample size is too small to produce reliable data for smaller population areas. To produce such data the ABS applies an innovative Small Area Estimation (SAE) approach, combining the survey data and several population data sources. Methods We predict prevalence of each health outcome variable by fitting a logistic mixed model. The modelled NHS data are enhanced by data from the ABS 2016 Census, Estimated Resident Population, and several administrative sources including Medical and Pharmaceutical transactions. Models are selected using a bespoke stepwise selection process; where the predictor variables have a strong association with the health outcome, whilst also ensuring that the estimated rates maintain consistency with published national data for that health outcome. Results Health statistics were produced for over 25 health outcomes and risk factors for 1134 Population Health Areas (PHAs) across Australia. The data show significant variation in rates between areas that are not evident in National and State level data. For example, the prevalence of adult current smokers in PHAs ranged from 4.4% to 34.6%, compared to 15.1% nationally. Conclusions The ABS SAE approach is an innovative method that enables production of reliable official health statistics, meeting a known data gap of local level health data. Key messages The ABS SAE approach delivers reliable official local health statistics, meeting an important data need not met using survey data alone.

scholarly journals Understanding Local Regulation of Fracking: A Spatial Econometric Approach

2015 ◽  
Vol 44 (2) ◽  
pp. 138-163 ◽  
Author(s):  
Patrick J. Walsh ◽  
Stephen Bird ◽  
Martin D. Heintzelman
Keyword(s):  
New York ◽  
Oil And Gas ◽  
Census Data ◽  
Negative Impact ◽  
Local Level ◽  
Land Development ◽  
Spatial Econometric ◽  
Data Set ◽  
Utica Shale ◽  
Level Increase

Fracking is a controversial practice but is thriving in many areas. We combine a comprehensive data set on local bans and moratoria in the state of New York with local-level census data and spatial characteristics in a spatial econometric analysis of local fracking policies. Some factors, including location in the Utica shale, proportion of registered Democrats, and education level, increase the probability of restrictions on fracking. Extent of local land development, location in highly productive petroleum areas, and number of extant oil and gas wells are among factors that have a negative impact on the likelihood of a ban or moratorium.

scholarly journals Integrating national surveys to estimate small area variations in poor health and limiting long-term illness in Great Britain

BMJ Open ◽  
2017 ◽  
Vol 7 (8) ◽  
pp. e016936 ◽  
Author(s):  
Graham Moon ◽  
Grant Aitken ◽  
Joanna Taylor ◽  
Liz Twigg
Keyword(s):  
Health Status ◽  
Health Survey ◽  
Small Area ◽  
Census Data ◽  
Small Area Estimation ◽  
Secondary Outcome ◽  
Area Estimation ◽  
National Surveys ◽  
Self Rated Health

ObjectivesThis study aims to address, for the first time, the challenges of constructing small area estimates of health status using linked national surveys. The study also seeks to assess the concordance of these small area estimates with data from national censuses.SettingPopulation level health status in England, Scotland and Wales.ParticipantsA linked integrated dataset of 23 374 survey respondents (16+ years) from the 2011 waves of the Health Survey for England (n=8603), the Scottish Health Survey (n=7537) and the Welsh Health Survey (n=7234).Primary and secondary outcome measuresPopulation prevalence of poorer self-rated health and limiting long-term illness. A multilevel small area estimation modelling approach was used to estimate prevalence of these outcomes for middle super output areas in England and Wales and intermediate zones in Scotland. The estimates were then compared with matched measures from the contemporaneous 2011 UK Census.ResultsThere was a strong positive association between the small area estimates and matched census measures for all three countries for both poorer self-rated health (r=0.828, 95% CI 0.821 to 0.834) and limiting long-term illness (r=0.831, 95% CI 0.824 to 0.837), although systematic differences were evident, and small area estimation tended to indicate higher prevalences than census data.ConclusionsDespite strong concordance, variations in the small area prevalences of poorer self-rated health and limiting long-term illness evident in census data cannot be replicated perfectly using small area estimation with linked national surveys. This reflects a lack of harmonisation between surveys over question wording and design. The nature of small area estimates as ‘expected values’ also needs to be better understood.

Bringing the Missing Million Home: Correcting the 1991 Small Area Statistics for Undercount

2002 ◽  
Vol 34 (6) ◽  
pp. 1021-1035 ◽  
Author(s):  
Richard Mitchell ◽  
Danny Dorling ◽  
David Martin ◽  
Ludi Simpson
Keyword(s):  
Small Area ◽  
Census Data ◽  
Decennial Census ◽  
Data Set ◽  
Web Based ◽  
Marginalised Groups ◽  
Small Area Statistics ◽  
Web Based System

The 1991 UK Decennial Census missed about 1.2 million people. These missing individuals present a serious challenge to any census user interested in measuring intercensal change, particularly amongst the most marginalised groups in society who were prominent amongst the missing population. Recently, a web-based system for accessing census data from the 1971, 1981, and 1991 censuses was launched ( www.census.ac.uk/cdu/lct ). The ‘LCT’ package also provides access to a set of 1991 small area statistics (SAS) which have been corrected to compensate for the missing million. The authors explain the methods used for adjusting the SAS counts, provide examples of the differences between analysis with the adjusted and unadjusted data, and recommend the use of the new data set to all those interested in intercensal change.

scholarly journals Rescaling and Small Area Estimation of Health Survey Data as applied to Smoking Rates in Allegheny County, Pennsylvania

2021 ◽  
Author(s):  
Shaina L Stacy ◽  
Hukum Chandra ◽  
Raanan Gurewitsch ◽  
LuAnn L. Brink ◽  
Linda B. Robertson ◽  
...  
Keyword(s):  
Survey Data ◽  
Health Survey ◽  
Small Area ◽  
Small Area Estimation ◽  
Local Health ◽  
Smoking Rate ◽  
Allegheny County ◽  
Census Tracts ◽  
Area Estimation ◽  
Health Survey Data

We propose a novel, two-step method for rescaling health survey data and creating small area estimates of smoking rates using a Behavioral Risk Factor Surveillance System (BRFSS) survey administered in 2015 to participants living in Allegheny County, in the state of Pennsylvania, USA. The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize and select from available census tract specific ancillary data on social vulnerability for small area estimation (SAE) of local health risk using an area level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. The ever-smoking rate was slightly above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (>65%) ever-smoking rates. These small area estimates may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations, and other health-related behaviors and outcomes.

scholarly journals Institutional Facilitators and Barriers to Local Public Health Preparedness Planning for Vulnerable and At-Risk Populations

2014 ◽  
Vol 129 (6_suppl4) ◽  
pp. 35-41 ◽  
Author(s):  
Christine A. Bevc ◽  
Matthew C. Simon ◽  
Tanya A. Montoya ◽  
Jennifer A. Horney
Keyword(s):  
Public Health ◽  
At Risk ◽  
Census Data ◽  
Local Level ◽  
Evaluation Study ◽  
Local Health ◽  
Public Health Preparedness ◽  
Preparedness Planning ◽  
At Risk Populations ◽  
Facilitators And Barriers

Objective. Numerous institutional facilitators and barriers to preparedness planning exist at the local level for vulnerable and at-risk populations. Findings of this evaluation study contribute to ongoing practice-based efforts to improve response services and address public health preparedness planning and training as they relate to vulnerable and at-risk populations. Methods. From January 2012 through June 2013, we conducted a multilevel, mixed-methods evaluation study of the North Carolina Preparedness and Emergency Response Research Center's Vulnerable & At-Risk Populations Resource Guide, an online tool to aid local health departments' (LHDs') preparedness planning efforts. We examined planning practices across multiple local, regional, and state jurisdictions utilizing user data, follow-up surveys, and secondary data. To identify potential incongruities in planning, we compared respondents' reported populations of interest with corresponding census data to determine whether or not there were differences in planning priorities. Results. We used data collected from evaluation surveys to identify key institutional facilitators and barriers associated with planning for at-risk populations, including challenges to conducting assessments and lack of resources. Results identified both barriers within institutional culture and disconnects between planning priorities and evidence-based identification of vulnerable and at-risk populations, including variation in the planning process, partnerships, and perceptions. Conclusions. Our results highlight the important role of LHDs in preparedness planning and the potential implications associated with organizational and bureaucratic impediments to planning implementation. A more in-depth understanding of the relationships among public institutions and the levels of preparedness that contribute to the conditions and processes that generate vulnerability is needed.

scholarly journals Barriers State Agencies Face with the Dissemination of Local Health Data through Web-Based Data Query Systems (Preprint)

2019 ◽  
Author(s):  
Manik Ahuja ◽  
Robert Aseltine Jr
Keyword(s):  
Surveillance System ◽  
Contextual Factors ◽  
Local Level ◽  
Health Data ◽  
Behavioral Risk ◽  
Local Health ◽  
Data Systems ◽  
Web Based ◽  
Data Query ◽  
Query Systems

BACKGROUND Web Based Data Query Systems (WDQS) make health data at the local level easily accessible to the public health. Despite their benefits Many state and local health agencies face significant challenges with their dissemination. OBJECTIVE The purpose of this study is to identify the most significant challenges they face from the perspective of Behavioral Risk Factor Surveillance System (BRFSS) coordinators. We also seek to find an association between perceived system aspects, challenges faced, contextual factors, and overall satisfaction with state level health data systems. METHODS We surveyed Behavioral Risk Surveillance System (BRFSS) coordinators from 43 states. We surveyed participants about contextual factors and asked them to rate system aspects and challenges they face with their health data system on a Likert scale. We used two sample t-tests to compare means on participant ratings for states with and without Web Based Data Query Systems (WDQS). RESULTS Overall, 95.4% of states make health data available over the internet, while 65.1% employ a WDQS. States reported the challenge of cost of hardware/software as a greater challenge between states with WDQS than without WDQS. States rated standardization of vocabulary more favorably in states with WDQS (n=3.32; 95% CI, 2.94-3.69) versus states without WDQS (n=2.85, 95% CI, 2.47-3.22). CONCLUSIONS Securing adequate resources, and commitment to standardization are vital in the dissemination of local level health data. Factors such a receiving data in a timely manner, privacy, and political opposition are less significant of a barrier than anticipated.

scholarly journals Measuring Trends in Health Inequalities across Urban Cities in Canada: A Focus on Health System Outcomes

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Meredith Nichols ◽  
Junior Chuang ◽  
Sara Grimwood ◽  
Geoff Hynes ◽  
Jean Harvey ◽  
...  
Keyword(s):  
Public Policy ◽  
Health System ◽  
Health Inequalities ◽  
Census Data ◽  
Local Level ◽  
Relative Concentration ◽  
Income Quintile ◽  
Postal Code ◽  
Local Health ◽  
Over Time

IntroductionThe majority of Canadians live in cities, which have experienced rising income inequality. This study examines how socio-economic inequalities in health system outcomes vary across and within Canada’s major cities over time to better understand these differences and to support informed decision-making and public policy planning to reduce inequalities. Objectives and ApproachThis study links a range of hospitalization indicators with neighbourhood income quintile and city geography data using patient postal codes and Statistics Canada’s Postal Code Conversion File Plus (PCCF+). Age-standardized indicator rates were calculated and income-related health inequalities were summarized using disparity rate ratio (DRR), disparity rate difference (DRD) and relative concentration index (RCI). Data were pooled across five-year intervals and linked to Census data years (2006, 2011, and 2016). City (Census Metropolitan Areas (CMAs)) and sub-city (Census Subdivisions (CSDs)) results enabled comparisons within and across cities and provided local level information to strengthen measuring and monitoring of health inequalities. ResultsAnalysis of the age-standardized rates for the hospitalization indicators (Hospitalizations for COPD (less than 75 years), Heart Attacks, Injury, Stroke, Self-Injury, Opioid Poisoning, Ambulatory Care Sensitive Conditions, and Hospitalizations Entirely Caused by Alcohol), overall and by neighborhood income quintile revealed an income gradient and significant variations within and across the CMAs and over time. Variations in DRR, DRD and RCI results were also observed across the CMAs over time, and between the CSDs within a CMA. Income-related inequalities in some hospitalization indicators persisted in Canada’s major cities with trends showing that people from lower income neighbourhoods experienced increased rates of hospitalization compared to people from higher income neighbourhoods. Conclusion/ImplicationsThis is the first study examining socio-economic health inequalities at city and sub-city levels across Canada. The methods used are relevant to others interested in local health inequality measurement. Our analysis provides evidence for developing and targeting public policy and health interventions to improve outcomes for vulnerable populations within cities.

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