Blood-Pressure Targets in Comatose Survivors of Cardiac Arrest.

BACKGROUND: Evidence to support the choice of blood-pressure targets for the treatment of comatose survivors of out-of-hospital cardiac arrest who are receiving intensive care is limited. METHODS: In a double-blind, randomized trial with a 2-by-2 factorial design, we evaluated a mean arterial blood-pressure target of 63 mm Hg as compared with 77 mm Hg in comatose adults who had been resuscitated after an out-of-hospital cardiac arrest of presumed cardiac cause; patients were also assigned to one of two oxygen targets (reported separately). The primary outcome was a composite of death from any cause or hospital discharge with a Cerebral Performance Category (CPC) of 3 or 4 within 90 days (range, 0 to 5, with higher categories indicating more severe disability; a category of 3 or 4 indicates severe disability or coma). Secondary outcomes included neuron-specific enolase levels at 48 hours, death from any cause, scores on the Montreal Cognitive Assessment (range, 0 to 30, with higher scores indicating better cognitive ability) and the modified Rankin scale (range, 0 to 6, with higher scores indicating greater disability) at 3 months, and the CPC at 3 months. RESULTS: A total of 789 patients were included in the analysis (393 in the high-target group and 396 in the low-target group). A primary-outcome event occurred in 133 patients (34%) in the high-target group and in 127 patients (32%) in the low-target group (hazard ratio, 1.08; 95% confidence interval [CI], 0.84 to 1.37; P = 0.56). At 90 days, 122 patients (31%) in the high-target group and 114 patients (29%) in the low-target group had died (hazard ratio, 1.13; 95% CI, 0.88 to 1.46). The median CPC was 1 (interquartile range, 1 to 5) in both the high-target group and the low-target group; the corresponding median modified Rankin scale scores were 1 (interquartile range, 0 to 6) and 1 (interquartile range, 0 to 6), and the corresponding median Montreal Cognitive Assessment scores were 27 (interquartile range, 24 to 29) and 26 (interquartile range, 24 to 29). The median neuron-specific enolase level at 48 hours was also similar in the two groups. The percentages of patients with adverse events did not differ significantly between the groups. CONCLUSIONS: Targeting a mean arterial blood pressure of 77 mm Hg or 63 mm Hg in patients who had been resuscitated from cardiac arrest did not result in significantly different percentages of patients dying or having severe disability or coma. (Funded by the Novo Nordisk Foundation; BOX ClinicalTrials.gov number, NCT03141099.).

Changes in Health Indicators Among Caregivers - United States, 2015-2016 to 2021-2022.

Caregivers provide support to persons who might otherwise require placement in long-term care facilities. Approximately one in five U.S. adults provides care to family members or friends who have a chronic health condition or disability. Promoting the well-being of this large segment of the population is a public health priority as recognized by the 2022 National Strategy to Support Family Caregivers. Although negative associations between caregiving and caregiver health are known, changes in the health status of caregivers over time are not. Data from the 2015-2016 and 2021-2022 Behavioral Risk Factor Surveillance System were analyzed to compare changes in the prevalence of 19 health indicators among cross-sectional samples of caregivers and noncaregivers at different time points. Caregivers experienced improvements in prevalence of four health indicators, whereas six worsened. Some health indicators, such as cigarette smoking, improved for both caregivers and noncaregivers, although smoking prevalence remained higher for caregivers (16.6% versus 11.7%). Prevalence of lifetime depression increased for both groups and remained higher among caregivers (25.6%) than among noncaregivers (18.6%). During 2021-2022, age-adjusted estimates for caregivers were unfavorable for 13 of the 19 health indicators when compared with noncaregivers. Strategies for supporting caregivers are available, and integrating these with existing programs to address mental health and chronic diseases among this population might improve caregiver well-being. For example, many community organizations support caregivers by offering interventions designed to relieve caregiver strain, including skills training, support groups, and care coordination.

Association between low cognitive performance and diabetes-related health indicators across racial and ethnic groups in adults with diabetes.

AIM: To examine the associations between low cognitive performance (LCP) and diabetes-related health indicators (including body mass index [BMI], HbA1c, systolic blood pressure [SBP], low-density lipoprotein [LDL] and self-reported poor physical health) and whether these associations vary across racial/ethnic subgroups. METHODS: We identified adults aged 60 years or older with self-reported diabetes from the 2011-2014 National Health and Nutrition Examination Survey. Individuals with cognitive test scores in the lowest quartile were defined as having LCP. We used regression models to measure the associations of LCP with diabetes-related biometrics (BMI, HbA1c, SBP and LDL); and self-reported poor physical health. Moreover, we explored potential variations in these associations across racial/ethnic subgroups. RESULTS: Of 873 (261 with LCP) adults with diabetes, LCP was associated with higher HbA1c, SBP and LDL (adjusted difference: 0.41%, 5.01 mmHg and 5.08 mg/dL, respectively; P < .05), and greater odds of reporting poor physical health (adjusted odds ratio: 1.59, P < .05). The association between LCP and HbA1c was consistent across racial/ethnic groups, and notably pronounced in Hispanic and Other. BMI worsened with LCP, except for non-Hispanic Black. Excluding the Other group, elevated SBP was observed in people with LCP, with Hispanic showing the most significant association. LDL levels were elevated with LCP for Hispanic and Other. Physical health worsened with LCP for both non-Hispanic Black and Hispanic. CONCLUSIONS: We quantified the association between LCP and diabetes-related health indicators. These associations were more pronounced in Hispanic and Other racial/ethnic groups.

Bayesian modeling of spatial ordinal data from health surveys.

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual-level model for small-area estimation of survey-based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive distribution. Post-stratification of the results of the proposed individual-level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to describe the geographical distribution of a self-perceived health indicator from the Health Survey of the Region of Valencia (Spain) for the year 2016.

Association of Life's essential 8 score with the risk of all-cause mortality and cardio-cerebrovascular mortality in patients with stroke.

BACKGROUND: A higher Life's Essential 8 (LE8)-based cardiovascular health (CVH) has been reported to be associated with a lower risk of both all-cause mortality and cardio-cerebrovascular diseases (CCVDs) related mortality in adults in the United States. At the same time, multiple studies have shown a significant negative association of CVH with the risk of stroke and CCVDs. Since no research has investigated the applicability of the LE8 in stroke patients, this study aimed to explore the association of LE8 with all-cause mortality and cardio-cerebrovascular mortality in stroke patients. METHODS: Data of patients were extracted from the National Health and Nutrition Examination Surveys (NHANES) database in 2007-2018 in this retrospective cohort study. Weighted univariate and multivariate COX regression analyses were utilized to investigate the associations of LE8 with all-cause mortality and cardio-cerebrovascular mortality. We further explored these relationships in subgroups of age, gender, body mass index (BMI), cancer, congestive heart failure (CHF), and coronary heart disease (CHD). The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Among the eligible patients, 278 died from all-cause and 89 (8.38%) of them died due to CCVDs. After adjusting for covariates, patients with LE8 score ≥ 58.75 seemed to have both lower risk of all-cause mortality (HR = 0.46, 95%CI: 0.31-0.69) and cardio-cerebrovascular mortality (HR = 0.51, 95%CI: 0.26-0.98), comparing to those with LE8 score < 48.123. Higher LE8 scores were associated with lower risk of all-cause mortality in patients aged < 65 years old, without cancer, and whatever the gender, BMI, CHF or CHD conditions (all P < 0.05). The relationships between high LE8 scores and low cardio-cerebrovascular mortality risk were only found in age < 65 years old and non-cancer subgroups (all P < 0.05). CONCLUSION: A higher LE8 score was associated with lower risk of both all-cause mortality and cardio-cerebrovascular mortality in patients with stroke, which may provide some reference for risk management and prognosis improvement in stoke. However, more evidences are needed to verify this beneficial role of high LE8 score in stroke prognosis.

Trends in cardiovascular health metrics and associations with long-term mortality among US adults with coronary heart disease.

BACKGROUND AND AIMS: Our study examined the trends of cardiovascular health metrics in individuals with coronary heart disease (CHD) and their associations with all-cause and cardiovascular disease mortality in the US. METHODS AND RESULTS: The cohort study was conducted based on the National Health and Nutrition Examination Survey 1999-2018 and their linked mortality files (through 2019). Baseline CHD was defined as a composite of self-reported doctor-diagnosed coronary heart disease, myocardial infarction, and angina pectoris. Cardiovascular health metrics were assessed according to the American Heart Association recommendations. Long-term all-cause and cardiovascular disease mortality were the primary outcomes. Survey-adjusted Cox regression models were used to estimate hazard ratios and corresponding 95% confidence intervals for the associations between cardiovascular health metrics and all-cause and cardiovascular disease mortality. The prevalence of one or fewer ideal cardiovascular health metrics increased from 14.15% to 22.79% (P < 0.001) in CHD, while the prevalence of more than four ideal cardiovascular health metrics decreased from 21.65% to 15.70 % (P < 0.001) from 1999 to 2018, respectively. Compared with CHD participants with one or fewer ideal cardiovascular health metrics, those with four or more ideal cardiovascular health metrics had a 35% lower risk (hazard ratio, 0.65; 95% confidence interval: 0.51, 0.82) and a 44% lower risk (0.56; 0.38, 0.84) in all-cause and cardiovascular disease mortality, respectively. CONCLUSION: Substantial declines were noted in ideal cardiovascular health metrics in US adults with CHD. A higher number of cardiovascular health metrics was associated with lower all-cause and cardiovascular disease mortality in them.

The paradox of growing technical capacities with low global governance: a review of Voluntary National Reviews' SDG health-related indicators.

BACKGROUND: This study delves into the States' accountability for health-related Sustainable Development Goal (SDG) indicators from 2016 to 2020. An analysis of Voluntary National Reviews (VNR) is employed as an instrument to scrutinize the alignment of States' indicators with the global indicator framework, shedding light on global health governance within the context of the 2030 Agenda and States' strategic prioritization. A curation of 60 health-related indicators from 195 VNRs, produced during the aforementioned period, is organized into thematic groups. RESULTS: Our results highlight a concerning discrepancy in the reporting frequency of various health-related themes. The findings reveal a paradoxical coexistence characterized by the concurrent strengthening and diminution of the global health governance articulated in the Agenda's global health governance. This manifests in the increased utilization and consistency of health-related indicators over the study years, coupled with an emphasis on infectious diseases and child and maternal health indicators. Conversely, a discernible governance decline is evidenced by the inadequate representation of health-related indicators in VNRs, notably within the domains of universal health coverage and health system indicators. Furthermore, High-Income States exhibit diminished accountability. CONCLUSIONS: The VNRs unveil a paradox wherein burgeoning technical capacity coexists with governance deficits, a phenomenon attributable to both statistical capabilities and political preferences. The prevalent use of proxy indicators in VNRs oversimplifies the presentation of official indicators, thereby compromising the aspirational goal of pioneering statistical innovations for measuring intricate issues in the SDGs. In light of our conceptualization of the 2030 Agenda's global health as a regime complex governance, we advocate for comprehensive investigations into each health regime cluster. This approach aims to unravel disputes, discern patterns, and elucidate States' preferences concerning specific thematic areas. Functioning as an accountability mechanism for the Agenda's governance, VNRs underscore States' adaptability and short-term learning capabilities, offering valuable insights for identifying harmful goal prioritization. The discretionary nature of indicator selection by States in the VNRs, enabled by the Agenda's proposition of a contextual adaptation of the SDGs and a blind eye to the guideline's request to review all SDG indicators, highlights a critical flaw in the VNR as an accountability mechanism.

A resource-oriented perspective on the aging workforce - exploring job resource profiles and their associations with various health indicators.

BACKGROUND: Promoting older workers' health in the context of increasing labor force participation and skill shortages is crucial. Examining job resource profiles offers a promising approach to understanding how to promote and maintain the health of older workers within the workplace. However, it is unclear how different job resources interact within distinct worker subgroups. Thus, this study explores the association between the job resource profiles of distinct subgroups and various health indicators among older workers in Europe. METHODS: Data from 4,079 older workers (age range: 50-60 years, 57% female) from waves 6 and 8 of the Survey of Health, Ageing and Retirement in Europe (SHARE) were analyzed. Latent profile analysis was employed to identify distinct job resource profiles using social support, recognition, job promotion, autonomy, and development opportunities. Associations between these profiles and various health indicators were examined, alongside the sociodemographic and socioeconomic characteristics associated with each profile. RESULTS: Four distinct job resource profiles emerged: (I) average job resource workers (n = 2170, 53%), (II) high social job resource workers (n = 983, 24%), (III) low job resource workers (n = 538, 13%), and (IV) autonomous decision-making workers (n = 388, 10%). Workers in the (II) high social job resource profile had the highest socioeconomic status and reported the best self-perceived health, lowest depressive symptoms, and fewest limitations and chronic diseases. Conversely, workers in the (III) low job resource profile had the second-lowest socioeconomic status and reported the poorest health outcomes. Surprisingly, older workers with high autonomy (profile IV) had the lowest socioeconomic status and the second worst self-perceived health. This may be because they perceive themselves as autonomous while lacking support and recognition. CONCLUSION: There is wide variation in the level and composition of resources available to older workers in the workplace. The most vulnerable subgroups, such as low job resource workers (profile III) and autonomous decision-making workers (profile IV), could benefit from tailored workplace health promotion interventions, such as support from supervisors or peers. Strengthening older workers' job resources, including social support and recognition, can improve their health and contribute to them remaining in the workforce.

Metrological performances of the global chronic morbidity indicator of the Minimum European Health Module and implications for chronic disease prevalence and socioeconomic gradient estimations.

BACKGROUND: Although the global chronic morbidity indicator (GCMI) of the Minimum European Health Module (MEHM) was not specifically designed to monitor chronic disease in the population, it is increasingly used for this purpose in Europe and elsewhere. However, its metrological characteristics have seldom been examined, with various sensitivity issues being raised. The present study investigated the metrological performances of the GCMI and analyzed its implications in terms of prevalence and demographic and socioeconomic gradients of chronic conditions in the population. METHODS: We used data from two large French nationwide representative surveys with cross-sectional and longitudinal data conducted between 2010 and 2021. The surveys used MEHM and collected data on numerous chronic conditions and socioeconomic indicators. Criterion and predictive validity of the GCMI regarding chronic conditions and the resultant socioeconomic gradients were compared with indicators based on reports of individual chronic conditions. RESULTS: GCMI sensitivity to capture chronic conditions varied from <20 to 80% depending on the chronic condition. Number of chronic conditions, gender, age and education were also associated with GCMI endorsement. However, the GCMI was predictive of mortality and activity limitations independently of individual conditions. CONCLUSION: The varying lack of sensitivity depending on the chronic condition and the respondent's sociodemographic status may bias estimates of demographic and socioeconomic gradients compared with indicators based on reports of individual chronic conditions. Differences between GCMI and list-based approaches should be more cautiously considered when monitoring chronic conditions in the population. These approaches should be viewed as complementary rather than contradictory or interchangeable.

Identification of methodological issues regarding direct impact indicators of COVID-19: a rapid scoping review on morbidity, severity and mortality.

BACKGROUND: During the first epidemic wave, COVID-19 surveillance focused on quantifying the magnitude and the escalation of a growing global health crisis. The scientific community first assessed risk through basic indicators, such as the number of cases or rates of new cases and deaths, and later began using other direct impact indicators to conduct more detailed analyses. We aimed at synthesizing the scientific community's contribution to assessing the direct impact of the COVID-19 pandemic on population health through indicators reported in research papers. METHODS: We conducted a rapid scoping review to identify and describe health indicators included in articles published between January 2020 and June 2021, using one strategy to search PubMed, EMBASE and WHO COVID-19 databases. Sixteen experts from European public health institutions screened papers and retrieved indicator characteristics. We also asked in an online survey how the health indicators were added to and used in policy documents in Europe. RESULTS: After reviewing 3891 records, we selected a final sample of 67 articles and 233 indicators. We identified 52 (22.3%) morbidity indicators from 33 articles, 105 severity indicators (45.1%, 27 articles) and 68 mortality indicators (29.2%, 51). Respondents from 22 countries completed 31 questionnaires, and the majority reported morbidity indicators (29, 93.5%), followed by mortality indicators (26, 83.9%). CONCLUSIONS: The indicators collated here might be useful to assess the impact of future pandemics. Therefore, their measurement should be standardized to allow for comparisons between settings, countries and different populations.

Years of good life is a well-being indicator designed to serve research on sustainability.

Sustainable development (SD) as popularized by the Brundtland Commission and politically enshrined in the Sustainable Development Goals has been the explicit focus of sustainability science. While there is broad agreement that the trend of human well-being (W) over time should serve as a sustainability criterion, the literature so far has mostly addressed this in terms of its determinants rather than focusing on W itself. There is broad agreement that an indicator for W should have multiple constituents, clearly going beyond gross domestic product. Here, we propose a tailor-made indicator to serve precisely this purpose following a set of specified desiderata, including its applicability to flexibly defined subnational populations by gender, place of residence, ethnicity, and other relevant characteristics. The indicator, years of good life (YoGL), reflects the evident fact that in order to be able to enjoy any quality of life, one has to be alive and thus is primarily based on life expectancy. However, since mere survival is not considered good enough, life years are counted conditional on meeting minimum standards in two dimensions: the objective dimension of capable longevity (consisting of being out of absolute poverty and enjoying minimal levels of physical and cognitive health) and the subjective dimension of overall life satisfaction. We illustrate the calculation of this indicator for countries and subpopulations at different stages of development and with different degrees of data availability.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Chronic Disease Indicators: 2022-2024 Refresh and Modernization of the Web Tool.

Easy access and display of state-level estimates of the prevalence of chronic diseases and their risk factors can guide evidence-based decision-making, policy development, and tailored efforts to improve population health outcomes; however, these estimates are often presented across multiple websites and reports. The Chronic Disease Indicators (CDI) web tool (www.cdc.gov/cdi) disseminates state-level data compiled from various data sources, including surveys, vital records, and administrative data, and applies standardized definitions to estimate and track a wide range of key indicators of chronic diseases and their risk factors. In 2022-2024, the indicators were refreshed to include 113 measures across 21 topic areas, and the web tool was modernized to enhance its key features and functionalities, including standardized indicator definitions; interactive charts, graphs, and maps that present data in a visually appealing format; an easy-to-use web-based interface for users to query and extract the data they need; and state comparison reports to identify geographic variations in disease and risk factor prevalence. National and state-level estimates are provided for the overall population and, where applicable, by sex, race and ethnicity, and age. We review the history of CDIs, describe the 2022-2024 refresh process, and explore the interactive features of the CDI web tool with the goal of demonstrating how practitioners, policymakers, and other users can easily examine and track a wide range of key indicators of chronic diseases and their risk factors to support state-level public health action.

Predictive value of the Global Activity Limitation Indicator (GALI) on all-cause mortality.

OBJECTIVES: The Global Activity Limitation Indicator (GALI) is an instrument that measures long-term overall disability. The objective of this study was to evaluate GALI's predictive value on mortality while examining variations according to sex, age, and educational level. STUDY DESIGN: Longitudinal study. METHODS: This longitudinal study was based on 42,991 individuals aged ≥15 years who participated in the 2011-2012 National Health Survey and the 2014 European Health Survey in Spain. These records were linked to mortality data up to December 2021. GALI assessed self-reported functional limitation in the past 6 months and classified individuals into three categories: severely limited, limited but not severely, and not limited. Incidence rate ratios (IRR) were calculated using Poisson regression models, adjusting for sociodemographic, lifestyle, and health status variables. RESULTS: Compared to individuals with no limitations, those with non-severe limitations had an IRR for mortality of 1.27 (95% CI: 1.16-1.38), and 2.04 (95% CI: 1.81-2.31) in those with severe limitations. Women with severe limitations exhibited a higher IRR (2.32; 95% CI: 1.98-2.71) compared to men (1.73; 95% CI: 1.45-2.08) (P for interaction = 0.005). Individuals <65 years with severe limitations showed a greater association (2.22; 95% CI: 1.58-3.10) than those ≥65 (1.49; 95% CI: 1.32-1.69) (P for interaction <0.001). Among individuals with lower educational attainment, the IRR was 2.08 (95% CI: 1.83-2.37), and 1.87 (95% CI: 1.37-2.56) for the higher education group (P for interaction = 0.017). CONCLUSIONS: GALI is a robust predictor of all-cause mortality in the general population and subgroups. The association is stronger in women, individuals <65 years, and those with lower educational levels.

Sustainable Development Goals' health-related indicators for Brazil and Ecuador: an analysis for the period of 1990-2019.

OBJECTIVE: This article aims to analyse the evolution of 40 Sustainable Development Goals' (SDGs) health-related indicators in Brazil and Ecuador from 1990 to 2019. STUDY DESIGN: Epidemiological study of long-term trends in 40 SDGs' health-related indicators for Brazil and Ecuador from 1990 to 2019, using estimates from the Global Burden of Disease Study. METHODS: Forty SDGs' health-related indicators and an index from 1990 to 2017 for Brazil and Ecuador, and their projections up to 2030 were extracted from the Institute for Health Metrics and Evaluation's Global Burden of Disease website and analysed. The percent annual change (PC) between 1990 and 2019 was calculated for both countries. RESULTS: Both countries have made progress on child stunting (Brazil: PC = -38%; Ecuador: PC = -43%) and child wasting prevalences (Brazil: PC = -42%; Ecuador: PC = -41%), percent of vaccine coverage (Brazil: PC = +215%; Ecuador: PC = +175%), under-5 (Brazil: PC = -75%; Ecuador: PC = -60%) and neonatal mortality rates (Brazil: PC = -69%; Ecuador: PC = -51%), health worker density per 1000 population (Brazil: PC = +153%; Ecuador: PC = +175%), reduction of neglected diseases prevalences (Brazil: PC = -40%; Ecuador: PC = -58%), tuberculosis (Brazil: PC = -27%; Ecuador: PC = -55%) and malaria incidences (Brazil: PC = -97%; Ecuador: PC = -100%), water, sanitation and hygiene mortality rates (Brazil and Ecuador: PC = -89%). However, both countries did not show sufficient improvement in maternal mortality ratio to meet SDGs targets (Brazil: PC = -37%; Ecuador: PC = -40%). Worsening of indicators were found for violence, such as non-intimate partner violence for both countries (Brazil: PC = +26%; Ecuador: PC = +18%) and suicide mortality rate for Ecuador (PC = +66%), child overweight indicator for Brazil (PC = -67%), disaster mortality rates (Brazil: PC = +100%; Ecuador: PC = +325%) and alcohol consumption (Brazil: PC = +46%; Ecuador: PC = +35%). CONCLUSIONS: Significant improvements are necessary in both countries requiring the strengthening of health and other policies, particularly concerning the prevention and management of violence and alcohol consumption, and preparedness for dealing with environmental disasters.

Health status in selected post-communist European countries: a comparative study between Poland and Albania.

OBJECTIVES: The aim of this analysis was to compare selected health status indicators of the Albanian and Polish populations, pertinent to two former communist countries in Central and Eastern Europe (CEE). METHODS: This analysis was based on the estimates related to the Global Burden of Disease (GBD) 2019 study, reported by the Institute for Health Metrics and Evaluation (IHME). For Poland, IHME uses data mainly from the Central Statistical Office of Poland, whereas for Albania the information is based on the reports from the National Institute of Statistics. RESULTS: In 2019, life expectancy at birth was slightly higher in Albania compared to Poland (78.5 years vs. 78.1 years, respectively). Mortality rate from noncommunicable diseases was similar in both countries (about 520 deaths per 100,000 population). In 2019, the main risk factor for the overall mortality in both countries was the high systolic blood pressure. In Albania, high systolic blood pressure accounted for almost 32% of deaths from all causes, whereas in Poland it accounted for only 21% of all deaths. The second main risk factor in Albania concerned the dietary factors which were responsible for almost one in four deaths. In Poland, the second main risk factor for all-cause mortality concerned tobacco which was responsible for one in five deaths. The third leading risk factor in Albania was tobacco (responsible for one in five deaths), whereas in Poland it concerned the dietary risks (responsible for about 19% of the all-cause mortality). CONCLUSIONS: This analysis provides useful information about the current health status of two populations pertinent to the former Communist Bloc in CEE. While health indicators can provide important information about the differences in health status between populations, it is important to interpret these indicators in the context of the specific challenges and limitations facing each country.

[Healthy life expectancy: a critical look at the benefits and potential of the demographic health indicator]. / Gesunde Lebenserwartung: Ein kritischer Blick auf Nutzen und Potenziale des demographischen Gesundheitsindikators.

Demographic ageing yields many societal consequences that depend strongly on the health status of the population. Special indicators have been developed for tracking and assessing population health, which are referred to with the overarching term healthy life expectancy (HLE). The derivation of HLE is intuitive and easily comprehensible. However, an overly simplistic interpretation hides the extreme complexity inherent in adding the health dimension to the life table. This makes HLE compared to classic life expectancy (LE) extremely sensitive to certain conceptual and methodological features. In the article, this is presented in more detail for three aspects: the underlying definition of health, the choice of survey data as a basis for estimating health status, and the reporting behavior of survey participants. It is shown that the impact on HLE can be enormous, leading to considerable bias in the interpretation of levels and trends, but also in the analysis of differences between populations. Nevertheless, the extension of classical LE to HLE is an important achievement that must not be abandoned. Therefore, the article also discusses ways in which the HLE indicator could be made more robust and reliable. Until this is achieved, however, the high methodological sensitivity of HLE must not be ignored if it is used to assess the health status of populations and as a basis for health policy measures.

Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study.

PURPOSE: This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study. METHODS: Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years). RESULTS: For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with PT = 0.05 and R2 = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586). CONCLUSION: This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.