scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2019 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Urban Areas ◽  
Small Area ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual age-specific numbers of population and deaths for all neighborhood-level areas in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all neighborhoods. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all neighborhoods, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in neighborhoods in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each neighborhood.

scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2020 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Small Area ◽  
Standardized Mortality Ratio ◽  
Small Areas ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Small Area Level ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small-areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small-areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small-areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small-area.

USE OF CODED MORTALITY DATA TO ASSESS AREA CANCER RATES: IMPACT OF RESIDENCE REPORTING AND CODING ERRORS

1990 ◽  
Vol 132 (supp1) ◽  
pp. 178-182 ◽  
Author(s):  
ALLAN N. WILLIAMS ◽  
REBECCA A. JOHNSON ◽  
ALAN P. BENDER
Keyword(s):  
Rural Areas ◽  
Cancer Mortality ◽  
Mortality Rates ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Death Certificates ◽  
Total Cancer ◽  
Mortality Ratio ◽  
Cancer Rates ◽  
Standardized Mortality Ratios

Abstract In spite of their limitations, mortality data are used in many epidemiologic and public health settings. In this investigation, the authors examined the extent to which community cancer mortality rates were affected by incorrect reporting or coding of residence on death certificates. Observed and expected cancer mortality for two adjacent communities in northern rural Minnesota for the periods 1970–1974 and 1980–1984 were obtained from computerized state mortality data. Using statewide rates to obtain expected values, standardized mortality ratios for total cancers for both periods combined were 138 for men (101 observed deaths) and 148 for women (86 observed deaths). These excesses were statistically significant (p < 0.05). However, after review of data from the actual death certificates, city maps, and information from city officials, 44 of the 187 total cancer deaths (24%) were found to have had an incorrectly reported or coded residence status. After removal of these cases, the standardized mortality ratio for total cancers for males went from 138 to 107, and for females the standardized mortality ratio went from 148 to 111. No standardized mortality ratios remained statistically significant These findings may have implications for those who use mortality data for assessing cancer rates in communities in rural areas.

Loss of life expectancy derived from a standardized mortality ratio in Denmark, Finland, Norway and Sweden

2018 ◽  
Vol 46 (7) ◽  
pp. 767-773 ◽  
Author(s):  
Mette Vinther Skriver ◽  
Michael Væth ◽  
Henrik Støvring
Keyword(s):  
Life Expectancy ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Loss Of Life ◽  
Theoretical Relationship ◽  
Life Expectancy At Birth ◽  
Linear Association ◽  
Approximate Relationship ◽  
Mortality Ratio ◽  
The Relationship

Aims: The standardized mortality ratio (SMR) is a widely used measure. A recent methodological study provided an accurate approximate relationship between an SMR and difference in lifetime expectancies. This study examines the usefulness of the theoretical relationship, when comparing historic mortality data in four Scandinavian populations. Methods: For Denmark, Finland, Norway and Sweden, data on mortality every fifth year in the period 1950 to 2010 were obtained. Using 1980 as the reference year, SMRs and difference in life expectancy were calculated. The assumptions behind the theoretical relationship were examined graphically. The theoretical relationship predicts a linear association with a slope, [Formula: see text], between log(SMR) and difference in life expectancies, and the theoretical prediction and calculated differences in lifetime expectancies were compared. We examined the linear association both for life expectancy at birth and at age 30. All analyses were done for females, males and the total population. Results: The approximate relationship provided accurate predictions of actual differences in lifetime expectancies. The accuracy of the predictions was better when age was restricted to above 30, and improved if the changes in mortality rate were close to a proportional change. Slopes of the linear relationship were generally around 9 for females and 10 for males. Conclusions: The theoretically derived relationship between SMR and difference in life expectancies provides an accurate prediction for comparing populations with approximately proportional differences in mortality, and was relatively robust. The relationship may provide a useful prediction of differences in lifetime expectancies, which can be more readily communicated and understood.

scholarly journals A cross-sectional study to assess knowledge of women about cervical cancer: an urban and rural comparison

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Małgorzata Lesińska-Sawicka
Keyword(s):  
Cervical Cancer ◽  
Life Expectancy ◽  
Correct Answer ◽  
Rural Areas ◽  
Urban Areas ◽  
Statistical Significance ◽  
Average Life Expectancy ◽  
Cross Sectional ◽  
Awareness Campaigns ◽  
The City

Abstract Introduction Cervical cancer and its etiopathogenesis, the age of women in whom it is diagnosed, average life expectancy, and prognosis are information widely covered in scientific reports. However, there is no coherent information regarding which regions—urban or rural—it may occur more often. This is important because the literature on the subject reports that people living in rural areas have a worse prognosis when it comes to detection, treatment, and life expectancy than city dwellers. Material and methods The subjects of the study were women and their knowledge about cervical cancer. The research was carried out using a survey directly distributed among respondents and via the Internet, portals, and discussion groups for women from Poland. Three hundred twenty-nine women took part in the study, including 164 from rural and 165 from urban areas. The collected data enabled the following: (1) an analysis of the studied groups, (2) assessment of the respondents’ knowledge about cervical cancer, and (3) comparison of women’s knowledge depending on where they live. Results The average assessment of all respondents’ knowledge was 3.59, with women living in rural areas scoring 3.18 and respondents from the city—4.01. Statistical significance (p < 0.001) between the level of knowledge and place of residence was determined. The results indicate that an increase in the level of education in the subjects significantly increases the chance of getting the correct answer. In the case of age analysis, the coefficients indicate a decrease in the chance of obtaining the correct answer in older subjects despite the fact that a statistically significant level was reached in individual questions. Conclusions Women living in rural areas have less knowledge of cervical cancer than female respondents from the city. There is a need for more awareness campaigns to provide comprehensive information about cervical cancer to women in rural areas. A holistic approach to the presented issue can solve existing difficulties and barriers to maintaining health regardless of the place of life and residence. Implication for cancer survivors They need intensive care for women’s groups most burdened with risk factors.

The Standardized Mortality Ratio and Life Expectancy

1992 ◽  
Vol 135 (7) ◽  
pp. 824-831 ◽  
Author(s):  
Shan P. Tsai ◽  
Robert J. Hardy ◽  
C. P. Wen
Keyword(s):  
Life Expectancy ◽  
Standardized Mortality Ratio ◽  
Mortality Ratio

scholarly journals Hospital mortality under surgical care

2011 ◽  
Vol 93 (3) ◽  
pp. 193-200 ◽  
Author(s):  
O Aziz ◽  
D Fink ◽  
L Hobbs L ◽  
G Williams ◽  
TC Holme
Keyword(s):  
Surgical Care ◽  
Hospital Performance ◽  
General Surgeon ◽  
Mortality Data ◽  
Co Morbidity ◽  
Specific Mortality ◽  
Mortality Ratio ◽  
Case Mix Adjustment ◽  
Surrogate Measure ◽  
Morbidity Index

INTRODUCTION The ‘hospital standardised mortality ratio’ (HSMR) has been used in England since 1999 to measure NHS hospital performance. Large variations in reported HSMR between English hospitals have recently led to heavy criticism of their use as a surrogate measure of hospital performance. This paper aims to review the mortality data for a consultant general surgeon contributed by his NHS trust over a 3-year period as part of the trust's HSMR calculation and evaluate the accuracy of coding the diagnoses and covariates for case mix adjustment. SUBJECTS AND METHODS The Dr Foster Intelligence database was interrogated to extract the NHS trust's HSMR benchmark data on inpatient mortality for the surgeon from 1 April 2006 to 31 March 2009 and compared to the hospital notes. RESULTS 30 patients were identified of whom 12 had no evidence of being managed by the surgeon. This represents a potential 40% inaccuracy rate in designating consultant responsibility. The remaining 18 patients could be separated into ‘operative’ (11 patients) and ‘non-operative’ (7 patients) groups. Only 27% in the operative group and 43% of the non-operative mortality group respectively had a Charlson co-morbidity index recorded despite 94% of the cases having significant co-morbidities CONCLUSIONS Highlighting crude and inaccurate clinician-specific mortality data when only 1-5% of deaths under surgical care may be associated with avoidable adverse events seems potentially irresponsible.

scholarly journals ‘Trend in premature mortality from four major NCDs in Nanjing, China, 2007–2018’

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huafeng Yang ◽  
Yali Fu ◽  
Xin Hong ◽  
Hao Yu ◽  
Weiwei Wang ◽  
...  
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Respiratory Diseases ◽  
Premature Mortality ◽  
Population Data ◽  
Integrated Approach ◽  
Mortality Data ◽  
Relative Reduction ◽  
Mortality Trends ◽  
Chronic Respiratory Diseases

Abstract Background This study aims to analyze the trends of premature mortality caused from four major non-communicable diseases (NCDs), namely cardiovascular disease (CVD), cancer, chronic respiratory diseases, and diabetes in Nanjing between 2007 and 2018 and project the ability to achieve the “Healthy China 2030” reduction target. Methods Mortality data of four major NCDs for the period 2007–2018 were extracted from the Death Information Registration and Management System of Chinese Center for Disease Control and Prevention. Population data for Nanjing were provided by the Nanjing Bureau of Public Security. The premature mortality was calculated using the life table method. Joinpoint regression model was used to estimate the average annual percent changes (AAPC) in mortality trends. Results From 2007 to 2018, the premature mortality from four major NCDs combined in Nanjing decreased from 15.5 to 9.5%, with the AAPC value at − 4.3% (95% CI [− 5.2% to − 3.4%]). Overall, it can potentially achieve the target, with a relative reduction 28.6%. The premature mortality from cancer, CVD, chronic respiratory diseases and diabetes all decreased, with AAPC values at − 4.2, − 5.0%, − 5.9% and − 1.6% respectively. A relative reduction of 40.6 and 41.2% in females and in rural areas, but only 21.0 and 12.8% in males and in urban areas were projected. Conclusion An integrated approach should be taken focusing on the modifiable risk factors across different sectors and disciplines in Nanjing. The prevention and treatment of cancers, diabetes, male and rural areas NCDs should be enhanced.

scholarly journals Geografias da Diabetes Mellitus em Portugal: Como as Condições do Contexto Influenciam o Risco de Morrer

2014 ◽  
Vol 27 (3) ◽  
pp. 309 ◽  
Author(s):  
Paula Santana ◽  
Cláudia Costa ◽  
Adriana Loureiro ◽  
João Raposo ◽  
José Manuel Boavida
Keyword(s):  
Diabetes Mellitus ◽  
Relative Risk ◽  
Rural Areas ◽  
Public Health Problem ◽  
Standardized Mortality Ratio ◽  
Geographic Pattern ◽  
Bayesian Hierarchical ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
Relative Risk Of Death

<strong>Introduction:</strong> Diabetes Mellitus is a public health problem that is on the increase throughout the world, including in Portugal. This paper aims to identify the changing geographic pattern of this cause of death in Portugal and its association with sociomaterial deprivation.<br /><strong>Material and Methods:</strong> This is a transversal ecological study of the deaths by Diabetes Mellitus in Portuguese municipalities in three periods (1989-1993, 1999-2003 and 2006-2010). It uses a Bayesian hierarchical model in order to obtain a smooth standardized mortality ratio and the relative risk of death by Diabetes Mellitus associated to sociomaterial deprivation.<br /><strong>Results:</strong> In 1989-1993, the highest smooth standardized mortality ratio values were found in coastal urban municipalities (80% of municipalities with smooth standardized mortality ratio ≥ 161, of which 60% are urban); in 2006-2010, the opposite was found, with the highest smooth standardized mortality ratio values occurring in rural areas in southern inland regions (76.9% of municipalities with smooth standardized mortality ratio ≥ 161, of which 69.2% are rural), particularly the Alentejo. The relative risk of death by Diabetes Mellitus increases with vulnerability associated to social and economic conditions in the area of residence, and is significant in the last two periods (relative risk: 1.00; IC95%: 0.98-1.02).<br /><strong>Discussion:</strong> Diabetes Mellitus presents a geographic pattern marked by coastal-inland and urban-rural asymmetry. However, this has been altering over the last twenty years. 48% of the population reside in municipalities where the smooth standardized mortality ratio has increased in the last twenty years, particularly in the rural areas of inland Portugal.<br /><strong>Conclusion: </strong>The highest smooth standardized mortality ratio are currently found in rural municipalities with the highest index of sociomaterial deprivation.<br /><strong>Keywords:</strong> Demography; Diabetes Mellitus/epidemiology; Diabetes Mellitus/mortality; Portugal; Socioeconomic Factors.

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