scholarly journals Calculation of smoking rates by dong/eup/myeon unit using small-area estimation in the Community Health Survey

2015 ◽  
Vol 37 ◽  
pp. e2015013
Author(s):  
Kay O Lee ◽  
Jong Seok Byun ◽  
Yang Wha Kang ◽  
Yun Sil Ko ◽  
Hyo Jin Kim
Keyword(s):  
Community Health ◽  
Health Survey ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Community Health Survey

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.

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.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level

scholarly journals Association between alcohol consumption status and obesity-related comorbidities in men: data from the 2016 Korean community health survey

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo-Yeon Kim ◽  
Hyewon Nam ◽  
Jeong-Ju Yoo ◽  
Yoon-Young Cho ◽  
Dug-Hyun Choi ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Binge Drinking ◽  
Community Health ◽  
Health Survey ◽  
High Alcohol ◽  
High Alcohol Consumption ◽  
Community Health Survey ◽  
Alcoholic Drinks ◽  
Binge Drinkers ◽  
Obesity Hypertension

Abstract Background This study was performed to investigate the association between the amount of alcohol consumption or binge drinking and obesity-related comorbidities in Korean men. Methods A total of 103,048 men aged 19 years or older were investigated in the 2016 Korean Community Health Survey. The participants were divided into five groups according to the standard number of alcoholic drinks consumed per week. Results Of the total participants, 20.7% were in the high alcohol consumption group, consuming more than 28 drinks per week. After adjustment for clinical factors, high alcohol consumption was significantly associated with higher odds ratios (ORs) of obesity (OR, 1.449; 95% confidence interval [CI], 1.412 to 1.591; P < 0.0001), hypertension (OR, 1.76; 95% CI, 1.636 to 1.894; P < 0.0001), and dyslipidemia (OR, 1.356; 95% CI, 1.247 to 1.474; P < 0.0001). In contrast, mild to moderate alcohol consumption was associated with a lower risk of diabetes (OR, 0.799; 95% CI, 0.726 to 0.88; P = 0.0015) and high alcohol consumption was not associated with a higher risk of diabetes (OR, 0.945; 95% CI, 0.86 to 1.039; P = 0.0662). Among drinkers, except for social drinkers, binge drinking was significantly associated with higher risks of obesity, hypertension, diabetes, and dyslipidemia. Conclusions High alcohol consumption was associated with higher risks of obesity, hypertension, and dyslipidemia in Korean men. In contrast, high consumption was not associated with a higher risk of diabetes. In particular, binge drinkers were associated with higher risks of obesity, hypertension, diabetes, and dyslipidemia compared to non-binge drinkers.

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