As Prof. Young-Ho Khang points out, numerator-denominator bias may affect the estimation of mortality for the Korean and Japanese populations, because we used a cross-sectional unlinked design.[1] We mentioned the possibility of this bias in our paper, citing a study from Lithuania, which suggests that the mortality of persons with high socioeconomic status may be underestimated as a result of this bias.[2] However, based on a national validation study Prof. Khang suggests that the direction of this bias may work the other way around in the Korean population.[3] Furthermore, because – according to his information – the registration of occupation has changed in South Korea, Prof. Khang also claims that the deterioration of the mortality rates among upper non-manual workers observed in our paper is likely to be an artefact.
While we agree with Prof. Khang that the direction of the numerator-denominator bias may be different in South Korea as compared to Lithuania, we do not agree that the ‘reverse’ manual/non-manual mortality rate ratio that we found in South Korea can be explained by this bias, or that the unfavourable mortality trends among upper non-manual workers that we observed in South Korea can be explained by a change in registering occupation. Our findings prior to 2005 are similar to those of a longitudinal study that followed participants between 1995 and 2008 and reported low mortality among male managers and professional workers in South Korea.[4] Our stu...
As Prof. Young-Ho Khang points out, numerator-denominator bias may affect the estimation of mortality for the Korean and Japanese populations, because we used a cross-sectional unlinked design.[1] We mentioned the possibility of this bias in our paper, citing a study from Lithuania, which suggests that the mortality of persons with high socioeconomic status may be underestimated as a result of this bias.[2] However, based on a national validation study Prof. Khang suggests that the direction of this bias may work the other way around in the Korean population.[3] Furthermore, because – according to his information – the registration of occupation has changed in South Korea, Prof. Khang also claims that the deterioration of the mortality rates among upper non-manual workers observed in our paper is likely to be an artefact.
While we agree with Prof. Khang that the direction of the numerator-denominator bias may be different in South Korea as compared to Lithuania, we do not agree that the ‘reverse’ manual/non-manual mortality rate ratio that we found in South Korea can be explained by this bias, or that the unfavourable mortality trends among upper non-manual workers that we observed in South Korea can be explained by a change in registering occupation. Our findings prior to 2005 are similar to those of a longitudinal study that followed participants between 1995 and 2008 and reported low mortality among male managers and professional workers in South Korea.[4] Our study, however, also finds that after 2010 mortality among upper non-manual workers rapidly increased, causing a reversal of the upper non-manual/manual rate ratio. It is unlikely that this is due to a change in the recording of occupation. According to our information, there was a slight change in the method of recording occupation on Korean death certificates during the 1990s, but there has been no further changes since the year 2000.[5-6] Therefore, we believe that the discrepancy between some Korean studies and our results mentioned by Prof. Khang is due to a difference in study period.
Prof. Khang’s letter implicitly also suggests the possibility that numerator-denominator bias explains our findings on Japan. In contrast to South Korea, there are no longitudinal studies in Japan in which occupational class at baseline can be related to mortality during follow-up. We therefore can only speculate about the magnitude and direction of this bias. However, the method of recording occupation on death certificates or the census has not changed during the study period in Japan, and the deterioration of the mortality rate among upper non-manual workers is therefore unlikely to be an artefact.
Reference
1. Tanaka H, Nusselder WJ, Bopp M, et al. Mortality inequalities by occupational class among men in Japan, South Korea and eight European countries: a national register-based study, 1990–2015. J Epidemiol Community Health 2019;73:750-758. doi:10.1136/jech-2018-211715
2. Shkolnikov VM, Jasilionis D, Andreev EM, et al. Linked versus unlinked estimates of mortality and length of life by education and marital status: evidence from the first record linkage study in Lithuania. Soc Sci Med 2007;64:1392–406. doi:10.1016/j.socscimed.2006.11.014
3. Kim HR, Khang YH. [Reliability of education and occupational class: a comparison of health survey and death certificate data]. J Prev Med Public Health. 2005;38:443-448. (in Korean)
4. Lee H-E, Kim H-R, Chung YK, et al. Mortality rates by occupation in Korea: a nationwide, 13-year follow-up study. Occup Environ Med 2016;73:329–35. doi:10.1136/oemed-2015-103192
5. Vital Statistics Division, Social Statistics Bureau, Statistics Korea (KOSTAT) [Internet]. Available: http://kostat.go.kr/portal/eng/aboutUs/3/2/9/2/index.static (Accessed 23 Sep 2019)
6. Supreme Court of Korea. Regulation on Document Form of Family Related Registration (in Korean) [Internet]. Available: https://glaw.scourt.go.kr/wsjo/gchick/sjo330.do?contId=2258861&q=%EA%B0%... (Accessed 24 Sep 2019)
In their article (1) Allik et al. proposes a very interesting contribution on the principles and options for the construction of deprivation indices. About weighting indicators, they referred to the European deprivation index (EDI), an index aiming at using a unique methodology for all European Union, and advised to rather be “guided by theory and the specific context of each country” than data-driven. We totally agree that deprivation indices need to be theory driven. The construction of EDI is then guided by this approach. EDI is indeed based on the fundamental concept of relative poverty defined by the material impossibility of accessing basic needs that correspond to the average standard of living in a given country. This theoretical development was proposed by Townsend and Gordon in various publications at the end of the 20th century. In order to propose a measure of relative poverty that should be as comparable as possible between European countries, these basic needs have been defined specifically in each country from the same European database (EUSILC) with the same methodology.
This country–specific basics needs were then tested through regression analyzes to make sure that they were well correlated with objective and subjective poverty, here again specifically in each country, and that additivity, validity and reliability were preserved. Finally, we selected by regression analysis the country-specific combination of features the most correlated to these bas...
In their article (1) Allik et al. proposes a very interesting contribution on the principles and options for the construction of deprivation indices. About weighting indicators, they referred to the European deprivation index (EDI), an index aiming at using a unique methodology for all European Union, and advised to rather be “guided by theory and the specific context of each country” than data-driven. We totally agree that deprivation indices need to be theory driven. The construction of EDI is then guided by this approach. EDI is indeed based on the fundamental concept of relative poverty defined by the material impossibility of accessing basic needs that correspond to the average standard of living in a given country. This theoretical development was proposed by Townsend and Gordon in various publications at the end of the 20th century. In order to propose a measure of relative poverty that should be as comparable as possible between European countries, these basic needs have been defined specifically in each country from the same European database (EUSILC) with the same methodology.
This country–specific basics needs were then tested through regression analyzes to make sure that they were well correlated with objective and subjective poverty, here again specifically in each country, and that additivity, validity and reliability were preserved. Finally, we selected by regression analysis the country-specific combination of features the most correlated to these basic needs among the common variables in EUSILC and the national census of the country concerned. Detailed construction was described previously (2)
Conversely to what Allik et al. interpretation might suggest, we have never thought of proposing to all European countries to use the French version of EDI, but proposed to each country included in EUSILC the same theory–based methodology to allow comparability while accounting for each specific context.
Even if national policies is capable to some extent to curb rising inequality, we think Europe is the most relevant level to analyze and tackle health inequalities. Thanks to the extension of the construction of trans-cultural deprivation index in a growing number of European countries (3), several studies have been carried out in recent years on different aspects of social inequalities of health in a comparative way between different European countries (4,5)
1- Allik M, Leyland A, Ichiara MYT, Dundas R. Creating small-area deprivation indices: a guide for stage and options. J Epidemiol Comm Health. 2019;0 1-6. Doi:10.1136/jech.2019-213255
2- Pornet C, Delpierre C, Dejardin O, Grosclaude P, Launay L, Guittet L,Lang T, Launoy G. Construction of an adaptable European transnational ecological deprivation index: the French version J Epidemiol Comm Health . 2012 Nov;66(11):982-9.
3- Guillaume E, Pornet C, Dejardin O, Launay L, Lillini R, Vercelli M, Marí-Dell’Olmo M, Fernández- Fontelo A, Borrell C, Ribeiro AI, Fatima de Pina M, Mayer A, Delpierre C, Rachet B, Launoy G. Development of a cross-cultural deprivation index in five European countries. J Epidemiol Comm Health 2016 May;70(5):493-9
4- Ribeiro AI, Fraga S, Kelly-Irving M, Delpierre C, Stringhini S, Kivimaki M, Joost S, Guessous I, Gandini M, Vineis P, Barros H.Neighbourhood socioeconomic deprivation and allostatic load: a multi-cohort study. Sci Rep. 2019 Jun 19;9(1):8790.
5- Robinson O, Tamayo I, de Castro M et al. The Urban Exposome during Pregnancy and Its Socioeconomic Determinants. Env Health Perspectives 2018 ; 126 ;7.
I read with great interest the article by Tanaka and colleagues [1], which examined occupational inequalities in mortality in Korea and reported the surprising result that manual workers in Korea enjoyed lower mortality than non-manual workers. The authors employed unlinked data from Japan and Korea, with population denominators from census data and mortality numerators from death certificates. This type of unlinked data is prone to numerator-denominator bias. A prior Korean study examined the reliability of occupational class between survey and death certificate data using individually linked data from the Korea National Health and Nutrition Examination Survey (KNHANES), clearly showing this possibility [2]. Among 104 deaths of KNHANES participants aged 30-64, the number of deaths among non-manual workers increased from 8 in the survey data to 12 in the death certificate data, while the number of deaths among manual workers decreased from 59 in the survey data to 41 in the death certificate data [2]. The number of deaths in other groups (corresponding to ‘inactive or class unknown’) increased from 37 to 51. Therefore, using unlinked data may result in increased mortality estimates among non-manual workers and other groups and reduced mortality estimates among manual workers [2]. It should be noted that, in Appendix Table 1-2 of the article by Tanaka and colleagues [1], the ‘inactive or class unknown’ group accounted for 44%-51% of total deaths in the most recent 10 years...
I read with great interest the article by Tanaka and colleagues [1], which examined occupational inequalities in mortality in Korea and reported the surprising result that manual workers in Korea enjoyed lower mortality than non-manual workers. The authors employed unlinked data from Japan and Korea, with population denominators from census data and mortality numerators from death certificates. This type of unlinked data is prone to numerator-denominator bias. A prior Korean study examined the reliability of occupational class between survey and death certificate data using individually linked data from the Korea National Health and Nutrition Examination Survey (KNHANES), clearly showing this possibility [2]. Among 104 deaths of KNHANES participants aged 30-64, the number of deaths among non-manual workers increased from 8 in the survey data to 12 in the death certificate data, while the number of deaths among manual workers decreased from 59 in the survey data to 41 in the death certificate data [2]. The number of deaths in other groups (corresponding to ‘inactive or class unknown’) increased from 37 to 51. Therefore, using unlinked data may result in increased mortality estimates among non-manual workers and other groups and reduced mortality estimates among manual workers [2]. It should be noted that, in Appendix Table 1-2 of the article by Tanaka and colleagues [1], the ‘inactive or class unknown’ group accounted for 44%-51% of total deaths in the most recent 10 years in Korea. The percentage in Japan was even greater. Moreover, the methods of recording occupation in Korean death certificates have changed during recent decades. In the 1990s, ‘occupation before death’ was recorded, which was later changed to ‘occupation at the time of occurrence of disease or accident’. This change might have influenced the trends reported by Tanaka and colleagues [1] in mortality according to occupational class between 1990 and 2015. In fact, several national studies in Korea employing individual mortality follow-up have shown clear mortality inequalities unfavorable to manual workers compared with non-manual workers [3, 4, 5]. A recent KNHANES long-term mortality follow-up study revealed that male manual workers aged 30-64 had 3.85 times higher (95% confidence interval: 2.25–6.60) mortality risks than their non-manual counterparts [3]. In conclusion, the surprising reverse pattern in the relationship between occupational class and mortality in Korea reported by Tanaka and colleagues [1] is likely due to numerator-denominator bias, and therefore may not reflect the true situation in Korea.
1. Tanaka H, Nusselder WJ, Bopp M, Brønnum-Hansen H, Kalediene R, Lee JS, Leinsalu M, Martikainen P, Menvielle G, Kobayashi Y, Mackenbach JP. Mortality inequalities by occupational class among men in Japan, South Korea and eight European countries: a national register-based study, 1990–2015. J Epidemiol Community Health doi: 10.1136/jech-2018-211715
2. Kim HR, Khang YH. [Reliability of education and occupational class: a comparison of health survey and death certificate data]. J Prev Med Public Health. 2005;38:443-8. (in Korean)
3. Khang YH, Kim HR. Socioeconomic Inequality in mortality using 12-year follow-up data from nationally representative surveys in South Korea. Int J Equity Health 2016;15:51.
4. Khang YH, Lee SI, Lee MS, Jo MW. Socioeconomic mortality inequalities in Korea Labor & Income Panel Study. Korean J Health Policy Admin 2004;14:1-20. (in Korean)
5. Lee HE, Kim HR, Chung YK, Kang SK, Kim EA. Mortality rates by occupation in Korea: a nationwide, 13-year follow-up study. Occup Environ Med 2016;73:329-35.
We read with interest the paper ‘Prevalence and sociodemographic determinants of adult obesity: a large representative household survey in a resource-constrained African setting with double burden of undernutrition and overnutrition’(1). Chigbu et al., (2018) provide valuable data on obesity prevalence among adults in Enugu State in Nigeria and recommend using their information for the development of Nigerian obesity prevention policy (1). However, the authors do not explore the limitations of their data when recommending its use for development of health policy. We focus our discussion on the limitations of this data.
Firstly, Chigbu et al collected data in Enugu State, which is only one of 36 states in Nigeria and the obesity prevalence is likely to differ in other states (2). Kandala and Stranges (2017) reported obesity prevalence among women in Nigeria varies considerably between states (2). South-eastern states of Nigeria generally have higher female obesity rates than northern and western states (2). We recommend that the differences in obesity prevalence across Nigeria be considered when using the data in Enugu State to inform obesity prevention policy.
Secondly, they have collected anthropometric measurements and sociodemographic information, but not nutrition and physical activity data. Overnutrition and physical activity data is important for obesity prevention and research on this is limited in Nigeria. The Demographic Health S...
We read with interest the paper ‘Prevalence and sociodemographic determinants of adult obesity: a large representative household survey in a resource-constrained African setting with double burden of undernutrition and overnutrition’(1). Chigbu et al., (2018) provide valuable data on obesity prevalence among adults in Enugu State in Nigeria and recommend using their information for the development of Nigerian obesity prevention policy (1). However, the authors do not explore the limitations of their data when recommending its use for development of health policy. We focus our discussion on the limitations of this data.
Firstly, Chigbu et al collected data in Enugu State, which is only one of 36 states in Nigeria and the obesity prevalence is likely to differ in other states (2). Kandala and Stranges (2017) reported obesity prevalence among women in Nigeria varies considerably between states (2). South-eastern states of Nigeria generally have higher female obesity rates than northern and western states (2). We recommend that the differences in obesity prevalence across Nigeria be considered when using the data in Enugu State to inform obesity prevention policy.
Secondly, they have collected anthropometric measurements and sociodemographic information, but not nutrition and physical activity data. Overnutrition and physical activity data is important for obesity prevention and research on this is limited in Nigeria. The Demographic Health Survey 2014 only measured women for body mass index (BMI) and the most recent STEPwise surveillance was 16 years ago in Lagos state only (1, 3). Oyeyemi et al. (2018) reported major gaps in physical activity research in Nigeria (4). Overnutrition and physical activity data, in our view, should be a research priority when developing obesity prevention policy.
Thirdly, Chigbu and colleagues measured body mass index (BMI), waist circumference and tricep skinfold thickness, yet only BMI was used for obesity classification (1). BMI does not measure fat tissue percentage or where fat tissue is located, and these are both important health indicators (5). There are more accurate measures of fat mass than BMI, such as bioelectrical impedance methods (5), although these may not be appropriate for population studies in resource-constrained settings. However, waist circumference can be used with BMI as a more accurate, yet cost effective, measure of health risk (5).
Finally, although Chigbu and associates have provided valuable information, we suggest that identification of limitations in their research would be helpful especially when using information for the development of obesity prevention policy in Nigeria.
References:
1. Chigbu CO, Parhofer KG, Aniebue UU, et al. Prevalence and sociodemographic determinants of adult obesity: a large representative household survey in a resource-constrained African setting with double burden of undernutrition and overnutrition. J Epidemiol Community Health. 2018;72(8):702-7.
2. Kandala NB, Stranges S. Geographic variation of overweight and obesity among women in Nigeria: a case for nutritional transition in sub-Saharan Africa. PLoS One. 2014;9(6):e101103.
3. Nigerian Heart Foundation and Federal Minstry of Health and Social Services. Health Behaviour Monitor Among Nigerian Adult Popultaion. 2003 [Date accessed: March 2019]. Available from: https://www.who.int/ncds/surveillance/steps/2003_STEPS_Report_Nigeria.pdf
4. Oyeyemi AL, Oyeyemi AY, Omotara BA, et al. Physical activity profile of Nigeria: implications for research, surveillance and policy. Pan Afr Med J. 2018;30:175.
5. Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutri Today. 2015;50(3):117-28.
We all know eating together as a family can boost conversation, foster closeness and encourage healthy ways with food. However, a 2011 survey of 1354 people for the insurance firm Cornish Mutual found 48% of British households do not share a meal every day. [1]
This study shows that by having a family dinner together it can increase children's daily fruit and vegetable intake to reach the 5 A Day target. It reinforces the view that children learn more from what adults do than what they say, therefore it is the parental role modelling that helps shape their future habits.
The strengths of this study are its large sample size (2383 children) and reliable methods of assessing dietary intake through a validated food intake tool. However, there are limitations which have not been noted by the researchers.
This is a single sample of London schoolchildren taking part in trials assessing school gardening and diet. We do not know whether the children who were taking part in this trial may have particular characteristics that make them different from, for example, children selected from a completely ra...
We all know eating together as a family can boost conversation, foster closeness and encourage healthy ways with food. However, a 2011 survey of 1354 people for the insurance firm Cornish Mutual found 48% of British households do not share a meal every day. [1]
This study shows that by having a family dinner together it can increase children's daily fruit and vegetable intake to reach the 5 A Day target. It reinforces the view that children learn more from what adults do than what they say, therefore it is the parental role modelling that helps shape their future habits.
The strengths of this study are its large sample size (2383 children) and reliable methods of assessing dietary intake through a validated food intake tool. However, there are limitations which have not been noted by the researchers.
This is a single sample of London schoolchildren taking part in trials assessing school gardening and diet. We do not know whether the children who were taking part in this trial may have particular characteristics that make them different from, for example, children selected from a completely random primary school sample. Also, the children in this London area may not be representative of the entire UK population in terms of culture and ethnicity, which may be related to family eating patterns.
While home environment and parental food attitudes are likely to influence the child's food intake, there may be other factors such as children's preference, social factors or peer pressure. One or a combination of these factors could directly influence the child's food intake.
In the United States, the month of October is the national "Eat Better, Eat Together Month". [2] A tool kit has been developed to promote family meal time. [3]
If your family isn't already making dining together a priority, now is the perfect time to start!
REFERENCES
1. Deborah Clark Associates. Press release: Half of UK families are not eating together. 24 February 2011.
As Prof. Young-Ho Khang points out, numerator-denominator bias may affect the estimation of mortality for the Korean and Japanese populations, because we used a cross-sectional unlinked design.[1] We mentioned the possibility of this bias in our paper, citing a study from Lithuania, which suggests that the mortality of persons with high socioeconomic status may be underestimated as a result of this bias.[2] However, based on a national validation study Prof. Khang suggests that the direction of this bias may work the other way around in the Korean population.[3] Furthermore, because – according to his information – the registration of occupation has changed in South Korea, Prof. Khang also claims that the deterioration of the mortality rates among upper non-manual workers observed in our paper is likely to be an artefact.
Show MoreWhile we agree with Prof. Khang that the direction of the numerator-denominator bias may be different in South Korea as compared to Lithuania, we do not agree that the ‘reverse’ manual/non-manual mortality rate ratio that we found in South Korea can be explained by this bias, or that the unfavourable mortality trends among upper non-manual workers that we observed in South Korea can be explained by a change in registering occupation. Our findings prior to 2005 are similar to those of a longitudinal study that followed participants between 1995 and 2008 and reported low mortality among male managers and professional workers in South Korea.[4] Our stu...
In their article (1) Allik et al. proposes a very interesting contribution on the principles and options for the construction of deprivation indices. About weighting indicators, they referred to the European deprivation index (EDI), an index aiming at using a unique methodology for all European Union, and advised to rather be “guided by theory and the specific context of each country” than data-driven. We totally agree that deprivation indices need to be theory driven. The construction of EDI is then guided by this approach. EDI is indeed based on the fundamental concept of relative poverty defined by the material impossibility of accessing basic needs that correspond to the average standard of living in a given country. This theoretical development was proposed by Townsend and Gordon in various publications at the end of the 20th century. In order to propose a measure of relative poverty that should be as comparable as possible between European countries, these basic needs have been defined specifically in each country from the same European database (EUSILC) with the same methodology.
Show MoreThis country–specific basics needs were then tested through regression analyzes to make sure that they were well correlated with objective and subjective poverty, here again specifically in each country, and that additivity, validity and reliability were preserved. Finally, we selected by regression analysis the country-specific combination of features the most correlated to these bas...
I read with great interest the article by Tanaka and colleagues [1], which examined occupational inequalities in mortality in Korea and reported the surprising result that manual workers in Korea enjoyed lower mortality than non-manual workers. The authors employed unlinked data from Japan and Korea, with population denominators from census data and mortality numerators from death certificates. This type of unlinked data is prone to numerator-denominator bias. A prior Korean study examined the reliability of occupational class between survey and death certificate data using individually linked data from the Korea National Health and Nutrition Examination Survey (KNHANES), clearly showing this possibility [2]. Among 104 deaths of KNHANES participants aged 30-64, the number of deaths among non-manual workers increased from 8 in the survey data to 12 in the death certificate data, while the number of deaths among manual workers decreased from 59 in the survey data to 41 in the death certificate data [2]. The number of deaths in other groups (corresponding to ‘inactive or class unknown’) increased from 37 to 51. Therefore, using unlinked data may result in increased mortality estimates among non-manual workers and other groups and reduced mortality estimates among manual workers [2]. It should be noted that, in Appendix Table 1-2 of the article by Tanaka and colleagues [1], the ‘inactive or class unknown’ group accounted for 44%-51% of total deaths in the most recent 10 years...
Show MoreDear Editor,
We read with interest the paper ‘Prevalence and sociodemographic determinants of adult obesity: a large representative household survey in a resource-constrained African setting with double burden of undernutrition and overnutrition’(1). Chigbu et al., (2018) provide valuable data on obesity prevalence among adults in Enugu State in Nigeria and recommend using their information for the development of Nigerian obesity prevention policy (1). However, the authors do not explore the limitations of their data when recommending its use for development of health policy. We focus our discussion on the limitations of this data.
Firstly, Chigbu et al collected data in Enugu State, which is only one of 36 states in Nigeria and the obesity prevalence is likely to differ in other states (2). Kandala and Stranges (2017) reported obesity prevalence among women in Nigeria varies considerably between states (2). South-eastern states of Nigeria generally have higher female obesity rates than northern and western states (2). We recommend that the differences in obesity prevalence across Nigeria be considered when using the data in Enugu State to inform obesity prevention policy.
Secondly, they have collected anthropometric measurements and sociodemographic information, but not nutrition and physical activity data. Overnutrition and physical activity data is important for obesity prevention and research on this is limited in Nigeria. The Demographic Health S...
Show MoreWe all know eating together as a family can boost conversation, foster closeness and encourage healthy ways with food. However, a 2011 survey of 1354 people for the insurance firm Cornish Mutual found 48% of British households do not share a meal every day. [1]
This study shows that by having a family dinner together it can increase children's daily fruit and vegetable intake to reach the 5 A Day target. It reinforces the view that children learn more from what adults do than what they say, therefore it is the parental role modelling that helps shape their future habits.
The strengths of this study are its large sample size (2383 children) and reliable methods of assessing dietary intake through a validated food intake tool. However, there are limitations which have not been noted by the researchers.
This is a single sample of London schoolchildren taking part in trials assessing school gardening and diet. We do not know whether the children who were taking part in this trial may have particular characteristics that make them different from, for example, children selected from a completely ra...
Show MorePages