We propose a new summary measure of population health (SMPH), the well-being-adjusted health expectancy (WAHE). WAHE belongs to a subgroup of health-adjusted life expectancy indicators and gives the ...number of life years equivalent to full health. WAHE combines health and mortality information into a single indicator with weights that quantify the reduction in well-being associated with decreased health. WAHE's advantage over other SMPHs lies in its ability to differentiate between the consequences of health limitations at various levels of severity and its transparent, simple valuation function. Following the guidelines of a Committee on Summary Measures of Population Health, we discuss WAHE's validity, universality, feasibility sensitivity and ensure its reproducibility. We evaluate WAHE's performance compared to life expectancy, the most commonly used indicators of health expectancy (HE) and disability-adjusted life expectancy (DALE) in an empirical application for 29 European countries. Data on health and well-being are taken from the 2018 EU-SILC, and the life tables are from Eurostat. DALE is taken from the database of the Global Burden of Disease Programme. WAHE's sensitivity to univariate and multivariate state specifications is studied using the three Minimum European Health Module health dimensions: chronic morbidity, limitations in activities of daily living, and self-rated health. The empirical tests of the indicators’ correspondence reveal that WAHE has the strongest correlation with the other SMPHs. Moreover, WAHE estimates are in agreement with all other SMPHs. Additionally, WAHE and all other SMPHs form a group of reliable indicators for studying population health in European countries. Finally, WAHE estimates are robust, regardless of whether health is defined across one or multiple simultaneous dimensions of health. We conclude that WAHE is a useful and reliable indicator of population health and performs at least as well as other commonly used SMPHs.
Arsenic (As) and breast cancer risk Muszynska, Magdalena; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna ...
Hereditary cancer in clinical practice,
01/2012, Letnik:
10, Številka:
Suppl 4
Journal Article
Recenzirano
Odprti dostop
Doc number: A8 Abstract: The study was conducted to determine the correlations between serum concentration of arsenic (As) with increased or decreased predisposition to breast and ovarian cancer.
Iron and breast cancer risk Sukiennicki, Grzegorz; Muszyńska, Magdalena; Huzarski, Tomasz ...
Hereditary cancer in clinical practice,
12/2012, Letnik:
10, Številka:
Suppl 4
Journal Article
Selenium (Se) and breast cancer risk Jaworska - Bieniek, Katarzyna; Jakubowska, Anna; Durda, Katarzyna ...
Hereditary cancer in clinical practice,
12/2012, Letnik:
10, Številka:
Suppl 4
Journal Article
Zinc and breast cancer risk Kaczmarek, Katarzyna; Jakubowska, Anna; Sukiennicki, Grzegorz ...
Hereditary cancer in clinical practice,
12/2012, Letnik:
10, Številka:
Suppl 4
Journal Article
Abstract
Aims
We assess the effect of smoking on regional disparities in mortality in
P
oland and its contribution to the change in regional disparities during the last two decades.
Design, Setting ...and Participants
We used population‐level mortality data from the population registry for 379 Nomenclature of Territorial Units for Statistics (NUTS
)
‐4
P
olish regions for 1991–93 and 2008–10.
Measurements
The importance of smoking was assessed by smoking‐attributable mortality (
SAM
) derived using a simplified indirect
P
eto‐
L
opez method. Regional differences in age‐standardized all‐cause, smoking‐ and non‐smoking‐attributable mortality (
NSAM
) rates at ages 35 years and over were mapped, and spatial clustering (
M
oran's
I
) and coefficients of variation (
CV
) were estimated. The contribution of
SAM
to variation in all‐cause mortality was assessed by variance decomposition and compared over time.
Findings
In 2008–10, all‐cause and
SAM
rates were characterized by a similar pattern of spatial clustering (
M
oran's
I
> 0.44,
P
< 0.0001). For
NSAM
, a more random pattern with less regional clustering showed (
M
oran's
I
= 0.34,
P
< 0.0001). The contribution of smoking to regional variation was substantial 54%, 95% confidence interval (
CI)
= 44.9, 62.5 among men; 24.9%, 95% CI = 20.9, 29.1 among women, and compared with 1991–93, 27.5 percentage points lower for men and 6.3 percentage points higher for women. Smoking contributed to the divergence between the regions in all‐cause mortality between 1991–93 and 2008–10 for men increase in
CV
of
SAM
by 2% (0, 4%), but not for women decrease in
CV
of
SAM
by 15% (22, 10%).
Conclusions
Differences in past smoking behaviour may largely explain the regional differences in all‐cause mortality existing in 2008–10 in
P
oland, and its trends since 1991–1993.
We propose a new summary measure of population health (SMPH), the well-being-adjusted health expectancy (WAHE). WAHE belongs to a subgroup of health-adjusted life expectancy indicators and gives the ...number of life years equivalent to full health. WAHE combines health and mortality information into a single indicator with weights that quantify the reduction in well-being associated with decreased health. WAHE's advantage over other SMPHs lies in its ability to differentiate between the consequences of health limitations at various levels of severity and its transparent, simple valuation function. Following the guidelines of a Committee on Summary Measures of Population Health, we discuss WAHE's validity, universality, feasibility sensitivity and ensure its reproducibility. We evaluate WAHE's performance compared to life expectancy, the most commonly used indicators of health expectancy (HE) and disability-adjusted life expectancy (DALE) in an empirical application for 29 European countries. Data on health and well-being are taken from the 2018 EU-SILC, and the life tables are from Eurostat. DALE is taken from the database of the Global Burden of Disease Programme. WAHE's sensitivity to univariate and multivariate state specifications is studied using the three Minimum European Health Module health dimensions: chronic morbidity, limitations in activities of daily living, and self-rated health. The empirical tests of the indicators' correspondence reveal that WAHE has the strongest correlation with the other SMPHs. Moreover, WAHE estimates are in agreement with all other SMPHs. Additionally, WAHE and all other SMPHs form a group of reliable indicators for studying population health in European countries. Finally, WAHE estimates are robust, regardless of whether health is defined across one or multiple simultaneous dimensions of health. We conclude that WAHE is a useful and reliable indicator of population health and performs at least as well as other commonly used SMPHs.
The robotic test stations of the considered design, operated at industrial plants, must first perform the processes and tasks they have been intended for as required by manufacturing cost reduction. ...It is important that these processes are completed at minimum power consumption. The paper presents the process of system parameter selection for minimised power consumption with the example of an actual robotic test stand built for manufacturing quality control of stators. The developed solutions were tested on a real-life object and deployed on the measurement test stand.
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