DIAGNOSTICS OF THE CURRENT STATE OF HEALTHCARE IN RUSSIA

2020 ◽  
Vol 15 (5) ◽  
pp. 35-55
Author(s):  
N.P. STARYKH ◽  
◽  
A.V. EGOROVA ◽  
Keyword(s):  
Health Care ◽  
Life Expectancy ◽  
Statistical Data ◽  
Secondary Analysis ◽  
World Health ◽  
Healthy Life Expectancy ◽  
Federal State ◽  
Sociological Research ◽  
Healthy Life ◽  
Current State

The purpose of the article is to analyze the current state of healthcare in Russia. Scientific novelty of the study: the authors suggest that the efficiency of the health care system depends on the state of such indicators of public health as life expectancy and healthy life expectancy. Life expectancy is an integrated demographic indicator that characterizes the number of years that a person would live on average, provided that the age-specific mortality rate of a generation would be at the level for which the indicator was calculated throughout life. The indicator ‘healthy life expectancy’ is formed by subtracting the number of years of unhealthy life (due to chronic diseases, disabilities, mental and behavioral disorders, etc.) from the life expectancy indicator. Results: the article presents an analysis of the current state of Russian healthcare based on statistical data provided by the Federal State Statistics Service, the World Health Organization, and world rankings. Attention is focused on the perceptions of Russians about the quality of medical services and Russian healthcare. Conclusions about the current state of health care in Russia are formulated by the authors, based on a secondary analysis of statistical data, as well as data from sociological research presented by leading Russian sociological centers.

scholarly journals Health Outcomes And The Cost-Quality Trade-Off In Health Care: Empirical Study Of OECD Countries

2011 ◽  
Vol 3 (7) ◽  
Author(s):  
Adora D. Holstein
Keyword(s):  
Health Care ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Health Outcomes ◽  
Health System ◽  
Oecd Countries ◽  
World Health ◽  
Healthy Life Expectancy ◽  
Healthy Life ◽  
Trade Off

This study applies multivariate regression analysis to cross-section data of 30 OECD countries to determine if there is a trade-off between health care cost and the quality of the health system on one hand, and better health outcomes on the other. It also investigates whether a higher quality health system leads to superior health outcomes. The empirical results provide positive answers to the above two questions. Indices of responsiveness, fairness or accessibility, and overall efficiency of the health system developed by the World Health Organization were used in this study to measure health system quality. The rate of infant mortality and a disability-free or healthy life expectancy measure developed by the WHO are used as indicators of health outcomes. The empirical models control for the effects of cross-country differences in literacy level and health-risk or lifestyle. The study finds evidence that the more responsive and accessible the countrys health system is, the longer is the healthy life expectancy of its people. Moreover, the more accessible and efficient the countrys health system is, the lower is the rate of infant mortality.

scholarly journals Healthy life expectancy in Brazil: applying the Sullivan method

2005 ◽  
Vol 21 (suppl 1) ◽  
pp. S7-S18 ◽  
Author(s):  
Dalia Elena Romero ◽  
Iúri da Costa Leite ◽  
Célia Landmann Szwarcwald
Keyword(s):  
Life Expectancy ◽  
Functional Limitation ◽  
Good Health ◽  
World Health ◽  
Healthy Life Expectancy ◽  
State Of Health ◽  
World Health Survey ◽  
Healthy Life ◽  
Mortality And Morbidity ◽  
Sullivan Method

The objective of this study is to present the method proposed by Sullivan and to estimate the healthy life expectancy using different measures of state of health, based on information from the World Health Survey carried out in Brazil in 2003. By combining information on mortality and morbidity into a unique indicator, simple to calculate and easy to interpret, the Sullivan method is currently the one most commonly used for estimating healthy life expectancy. The results show higher number of healthy years lost if there is a long-term disease or disability that limits daily activities, regardless of the difficulty in performing such activities or the severity of the functional limitations. The two measures of healthy life expectancy adjusted by the severity of functional limitation show results very similar to estimates based on the perception of state of health, especially in advanced age. It was also observed, for all measures used, that the proportion of healthy years lost increases significantly with age and that, although females have higher life expectancy than males, they live proportionally less years in good health.

scholarly journals Nutritional Eating; What Do You Know and When Did You Know It? (P04-098-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Deborah Calhoun-Parker
Keyword(s):  
Heart Disease ◽  
Life Expectancy ◽  
Chronic Diseases ◽  
The United States ◽  
Time Frame ◽  
Poor Health ◽  
World Health ◽  
Healthy Life Expectancy ◽  
Healthy Life ◽  
Nutritional Information

Abstract Objectives The World Health Organization (W.H.O.) projects by 2020 chronic disease will account for 73% of deaths worldwide (W.H.O., 2010). In the United States (U.S.) minorities are high risk for chronic diseases. U.S. census projects by 2050 American minorities as the majority (Census, 2000). Purposes of pilot study 1) identify individual knowledge of chronic diseases; 2) when known (time frame); and 3) knowledge implemented to improve health. Important because if projections are correct health of the majority of people worldwide and U.S. society in particular, (Americas’ minority/majority) forecast as: poor health with short healthy life expectancy. Leading chronic diseases causing mortality in America: heart disease, cancer and lower respiratory diseases (Center for Disease Control, 2016). Hispanics are 16% of U.S. population. Leading cause of mortality: cancer. African Americans are 13.6% of U.S. population. Leading cause of mortality: heart disease. Societal challenge: mitigating health issues of a minority/majority. Methods A convenience sample adults (N = 15) utilized; most minorities. They completed 32 item questionnaire. Some items were Likert scale 5 strongly agree and 1 strongly disagree. Results Ninety-nine % have family member(s) with health challenges. More than 50% indicate being, “Healthy”. Half indicate being overweight. The majority response to frequency questions: 2–3 weekly. Example, most consume 9 servings of fruits/vegetables (F/V) 2–3 weekly. USDA recommend 9 servings of F/V daily. Time frame questions: ‘when known’. Example, half indicate meat and dairy as a diet necessity. When known, majority indicate over a year ago. Meat/dairy linked with chronic diseases. Majority misidentifies nutrient dense foods. Example, majority indicate white potatoes and iceberg lettuce as nutrient dense. Nutrient dense foods mitigate chronic diseases. Response to Likert type scale items, example, “I work hard to improve my dietary lifestyle”, most indicate ‘agree’. Conclusions Current nutritional information limited. Outdated nutritional information implemented. Nutrient dense diet lacking. The trajectory forecast of a minority/majority with poor health and short healthy life expectancy is on target. Funding Sources N/A.

scholarly journals Life Expectancy and Healthy Life Expectancy of Adults in Oman: Does Women’s Longer Life Expectancy than Men Mean Success or Burden for Women?

2021 ◽  
Vol 10 ◽  
pp. 136-145
Author(s):  
M. Mazharul Islam ◽  
Keyword(s):  
Life Expectancy ◽  
Life Table ◽  
Gender Gap ◽  
Poor Health ◽  
World Health ◽  
Healthy Life Expectancy ◽  
Population Census ◽  
Multiple Sources ◽  
World Health Survey ◽  
Healthy Life

Objectives: The objective of this study was to examine the life expectancy (LE) and healthy life expectancy (HLE) of Omani adults with age and gender differentials, focusing on whether the higher LE of women than men is a gain or burden for women. Method: Data for the study come from multiple sources such as the 2010 population census, the 2008 World Health Survey in Oman, and secondary data published in the Statistical Yearbook of Oman. The life table and the modified life table proposed by Sullivan were used for estimating the LE and HLE of adult people of age 20 and above, respectively. Results: LE in Oman reached 76 years for both sexes in recent times. However, since 2010 LE has been stalled in the vicinity of 76 years in Oman. Women had higher LE than men (79 years versus 74 years). In terms of HLE, men outweighed women in Oman. At the age of 20, the gap between male-female LE was found to be 4.7 years in favor of females, whereas the gap between male-female HLE was found to be 5.8 years in favor of males. Females spent a relatively long time in poor health status than males (20.8 years versus 10.8 years) and the proportion of life spent in poor health was greater for females than males (35.0% vs. 19.3%). This revealed the paradox of less mortality but higher morbidity among women, supporting the “Failure of Success” hypothesis. Conclusion: Appropriate health policy and strategy need to be taken to reduce the gender gap in LE and HLE in Oman.

scholarly journals How the unsolved problem of finding the Healthy Life Expectancy (HLE) in the far past was resolved: The case of Sweden (1751-2016) with forecasts to 2060 and comparisons with HALE

2020 ◽  
Author(s):  
Christos H Skiadas ◽  
Charilaos Skiadas
Keyword(s):  
Life Expectancy ◽  
Population Analysis ◽  
Life Tables ◽  
World Health ◽  
Healthy Life Expectancy ◽  
Estimation Methods ◽  
Healthy Life ◽  
Healthy Life Years ◽  
Life Years ◽  
The World

Healthy Life Expectancy (HLE) estimates are achieved after systematic work of a large group of researchers all over the world during last decades. The most successful estimate was termed as HALE and is provided by the World Health Organization (WHO) in the related website. Having established a methodology of data collection and handling the HLE can be estimated and provided to researchers and policy makers.However, it remains an unexplored period of the last few centuries where, LE data exists along with the appropriate life tables, but not enough information for HLE estimates is collected and stored. The problem is now solved following a methodology of estimating the HLE from the life tables after the Healthy Life Years Lost (HLYL) estimation.Our methodology on a Direct HLYL estimation from Life Tables, is tested and verified via a series of additional methods including a Weibull parameter test, a Gompertz parameter alternative and of course a comparison with HALE estimates from WHO. The complete methodology and estimation methods are published in the book on “Demography of Population Health, Aging and Health Expenditures” of Volume 50 of the Springer Series on Demographic Methods and Population Analysis. https://www.springer.com/gp/book/9783030446949, https://doi.org/10.1007/978-3-030-44695-6

scholarly journals Direct Healthy Life Expectancy Estimates from Life Tables with a Sullivan Extension: The case of Brazil 2003

2021 ◽  
Author(s):  
YIANNIS DIMOTIKALIS ◽  
Christos H Skiadas
Keyword(s):  
Life Expectancy ◽  
Direct Method ◽  
World Health ◽  
Healthy Life Expectancy ◽  
World Health Survey ◽  
Healthy Life ◽  
Life Years ◽  
The World ◽  
Life Years Lost ◽  
Sullivan Method

The Healthy Life Expectancy (HLE) in Brazil 2003 was estimated by Romero et al (2005) by using the Sullivan method and data from the World Health Survey carried out in Brazil in 2003. Here we use a Direct method to estimate the Healthy Life Years Lost (HLYL) and then the HLE. This is done after the analytic derivation of a more general model of survival-mortality and the estimation of a parameter bx related to the HLYL is followed by the formulation of a computer program providing results similar to those of the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. This program is an extension of classical life table including more columns to estimate the cumulative mortality, the average mortality, the person life years lost, and finally the HLYL parameter bx. Even more, a further extension of the Excel program based on the Sullivan method provides estimates of the Healthy Life Expectancy at every year of the lifespan.

scholarly journals Changing Educational Gradients in Life Expectancy With and Without Disease Among U.S. Older Adults From 2000 to 2010

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 505-505
Author(s):  
Matthew Farina ◽  
Phillip Cantu ◽  
Mark Hayward
Keyword(s):  
Older Adults ◽  
Life Expectancy ◽  
Chronic Diseases ◽  
Population Aging ◽  
Good Health ◽  
Vital Statistics ◽  
Healthy Life Expectancy ◽  
Healthy Life ◽  
Education Group ◽  
Education Groups

Abstract Recent research has documented increasing education inequality in life expectancy among U.S. adults; however, much is unknown about other health status changes. The objective of study is to assess how healthy and unhealthy life expectancies, as classified by common chronic diseases, has changed for older adults across education groups. Data come from the Health and Retirement Study and National Vital Statistics. We created prevalence-based life tables using the Sullivan method to assess sex-specific life expectancies for stroke, heart disease, cancer, and arthritis by education group. In general, unhealthy life expectancy increased with each condition across education groups. However, the increases in unhealthy life expectancy varied greatly. While stroke increased by half a year across education groups, life expectancy with diabetes increased by 3 to 4 years. In contrast, the evidence for healthy life expectancy provides mixed results. Across chronic diseases, healthy life expectancy decreased by 1 to 3 years for respondents without a 4-year degree. Conversely, healthy life expectancy increased for the college educated by .5 to 3 years. While previous research shows increases in life expectancy for the most educated, trends in life expectancy with chronic conditions is less positive: not all additional years are in lived in good health. In addition to documenting life expectancy changes across education groups, research assessing health of older adults should consider the changing inequality across a variety of health conditions, which will have broad implications for population aging and policy intervention.

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