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 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.

Harmonising Local Health Survey Data

2003 ◽  
pp. 133-154
Author(s):  
Alfredo Morabia ◽  
◽  
Mary E. Northridge ◽  
Sigrid Beer-Borst ◽  
Serge Hercberg
Keyword(s):  
Survey Data ◽  
Health Survey ◽  
Local Health ◽  
Health Survey Data

scholarly journals Time-Varying Associations Between an Exposure History and a Subsequent Health Outcome: A landmark Approach to Identify Critical Windows.

2021 ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background: Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology.Methods: We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model.Results: A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease).Conclusions: This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

scholarly journals Spatial Distribution and Analysis of Risk Factors Associated with Anemia Among Women of Reproductive Age: Case of 2014 Rwanda Demographic and Health Survey Data

2018 ◽  
Vol 11 (1) ◽  
pp. 425-437
Author(s):  
Faustin Habyarimana ◽  
Temesgen Zewotir ◽  
Shaun Ramroop
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Survey Data ◽  
Health Survey ◽  
Demographic And Health Survey ◽  
Reproductive Age ◽  
Bed Net ◽  
Women Of Reproductive Age ◽  
Factors Associated ◽  
Health Survey Data

Background:Anemia is an important public health problem affecting all age groups of the population. The objective of this study was to identify the risk factors associated with anemia among women of childbearing age in Rwanda and map their spatial variation.Methods:The 2014/15 Rwanda Demographic and Health survey data was used and the structured logistic regression model was fitted to the data, where fixed effects were modeled parametrically, non-linear effects were modeled non-parametrically using second order random walk priors and spatial effects were modeled using Markov Random field priors.Results:The prevalence of anemia among non-pregnant women of reproductive age was 18.9%. Women from the households which use water from the unprotected well had a higher risk of having anemia than a woman from the household where they use water piped into dwelling or yard. The risk of anemia was higher among underweight women and women living in households without toilet facilities. The anemia was less pronounced among the women using contraception, literate women, women from the households which use a bed net and living in rich households.Conclusion:The findings from this study highlighted the districts with the highest number of anemic women and this can help the policymakers and other public health institutions to design a specific programme targeting these districts in order to improve the health status and living conditions of these women. The findings also suggest an improvement of toilet facilities, bed net use and source of drinking water in affected households.

Prevalence and risk factors of overweight among adolescents: an analysis of Bhutan Global School-based Student Health Survey data

2021 ◽  
pp. 1-14
Author(s):  
Tashi Dendup ◽  
I Gusti Ngurah Edi Putra ◽  
Tashi Tobgay ◽  
Gampo Dorji ◽  
Sonam Phuntsho ◽  
...  
Keyword(s):  
Risk Factors ◽  
Survey Data ◽  
Health Survey ◽  
Student Health ◽  
School Based ◽  
Health Survey Data

scholarly journals Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. Methods We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. Results A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease). Conclusions This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

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 Health outcomes as an organisation-wide quality initiative

1998 ◽  
Vol 21 (4) ◽  
pp. 251
Author(s):  
Bronwyn Healy ◽  
Dianne Kelleher ◽  
Alex Bennie
Keyword(s):  
Health Outcome ◽  
Health Outcomes ◽  
Local Level ◽  
Continuum Of Care ◽  
Local Implementation ◽  
Policy Debates ◽  
Comprehensive Health ◽  
The Continuum

Since the burgeoning of the ?health outcomes? movement there has been an ever-increasingbody of literature on health outcomes policy debates, directions, frameworksand tools for implementing health outcome-directed initiatives. There is a significantgap in the literature, however, in regard to translating a comprehensive healthoutcomes policy into practice at a local level. This paper addresses that gap. It describesthe local implementation of a comprehensive health outcomes approach which worksacross the continuum of care. It identifies those organisation-wide structures andprocesses that support successful progress, thereby providing a useful guide to otherorganisations wishing to institutionalise the health outcomes approach.

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