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.
We thank Professors Bartick and Tomori for their comments on our paper. [1] We entirely agree that unexplained death in infancy (UDI) in the (mainly White British) general population of England and Wales is strongly associated with deprivation, as shown by many previous studies. Clearly, any factor that is associated with deprivation among the White British group will be a risk factor for UDI in the general population.
However, our paper is about ethnic, not socio-economic, variation. [2] The finding of a nearly five-fold disparity in risk across ethnic groups in England and Wales is both striking and novel. Moreover, we demonstrate that this disparity is not explained by deprivation. Formal adjustment for deprivation (IMD quintiles) does not even slightly reduce the ethnic variation (see Table 2). A simple scatter plot of ethnic groups illustrates the lack of a relationship between deprivation and risk, with a virtually horizontal overall trend line (see Figure at https://doi.org/10.5287/bodleian:XmE4XBaoZ). For example, Black Caribbean babies have nearly triple the UDI risk of Black African babies, but similar levels of deprivation. The Indian, Pakistani and Bangladeshi ethnic groups each have around half the UDI risk of White British babies; the White British and Indian groups have similar (relatively low) levels of deprivation, and the Pakistani and Bangladeshi groups are the most deprived in England and...
We thank Professors Bartick and Tomori for their comments on our paper. [1] We entirely agree that unexplained death in infancy (UDI) in the (mainly White British) general population of England and Wales is strongly associated with deprivation, as shown by many previous studies. Clearly, any factor that is associated with deprivation among the White British group will be a risk factor for UDI in the general population.
However, our paper is about ethnic, not socio-economic, variation. [2] The finding of a nearly five-fold disparity in risk across ethnic groups in England and Wales is both striking and novel. Moreover, we demonstrate that this disparity is not explained by deprivation. Formal adjustment for deprivation (IMD quintiles) does not even slightly reduce the ethnic variation (see Table 2). A simple scatter plot of ethnic groups illustrates the lack of a relationship between deprivation and risk, with a virtually horizontal overall trend line (see Figure at https://doi.org/10.5287/bodleian:XmE4XBaoZ). For example, Black Caribbean babies have nearly triple the UDI risk of Black African babies, but similar levels of deprivation. The Indian, Pakistani and Bangladeshi ethnic groups each have around half the UDI risk of White British babies; the White British and Indian groups have similar (relatively low) levels of deprivation, and the Pakistani and Bangladeshi groups are the most deprived in England and Wales.
In the paper we discuss various potential mediators of the ethnic differences, including sleep practices [3] breastfeeding [4,5] and tobacco use [6], based on the ethnic-specific prevalence of these factors in prior survey data. We suggest that careful comparison of ethnic patterns of exposure and outcome might lead to a better understanding of the aetiology of these very distressing deaths.
1. Bartick M, Tomori C. Deprivation is the most striking finding of this study; other known risk factors must be explored to explain ethnic variation. 2018. https://jech.bmj.com/content/early/2018/07/04/jech-2018-210453.responses....
2. Kroll ME, Quigley MA, Kurinczuk JJ, et al. Ethnic variation in unexplained deaths in infancy, including sudden infant death syndrome (SIDS), England and Wales 2006-2012: national birth cohort study using routine data. J Epidemiol Community Health 2018;72(10):911-18. doi: 10.1136/jech-2018-210453
3. Farooqi S, Perry IJ, Beevers DG. Ethnic differences in infant-rearing practices and their possible relationship to the incidence of sudden infant death syndrome (SIDS). Paediatr Perinat Epidemiol 1993;7(3):245-52.
4. Griffiths LJ, Tate AR, Dezateux C. The contribution of parental and community ethnicity to breastfeeding practices: evidence from the Millennium Cohort Study. Int J Epidemiol 2005;34(6):1378-86. doi: 10.1093/ije/dyi162
5. Griffiths LJ, Tate AR, Dezateux C, et al. Do early infant feeding practices vary by maternal ethnic group? Public Health Nutr 2007;10(9):957-64. doi: 10.1017/S1368980007665513
6. Wardle H. Use of tobacco products. Health Survey for England 2004: the health of minority ethnic groups: The Information Centre 2006:93-129.
We congratulate the authors on this timely and interesting study: ‘Political views of doctors in the UK: a cross-sectional study’ [1]. We address Question 12, asking whether doctors agree that ‘Patients should be charged for non-urgent care if they are not eligible for free NHS treatment’.
The authors correctly state that agreement does not mean support for current NHS charging regulations, not least because the most recent amendments in England were introduced after this questionnaire (October 2017 [2]), however we remain concerned about potential misinterpretation, and suggest aspects of charging regulations where doctors’ opinions could be further explored.
Firstly, the question, which understandably echoes government policy language on charging, is similar to asking ‘Should people have to pay for things that are not free?’ without addressing complexity of eligibility, and the fact that some people living in the UK have lost their eligibility with recent regulations. An assessment of opinions would require measuring knowledge of charging and its relationship with immigration enforcement, as well as evaluating acceptance of the immigration system itself, as this now determines eligibility. Windrush patients being denied NHS treatment highlighted the complexity of this issue [3].
Secondly, doctors’ opinions on measures which penalise and threaten patients if they seek care, such as linking unpaid NHS debt to immigration enforcement [4], and NHS data shar...
We congratulate the authors on this timely and interesting study: ‘Political views of doctors in the UK: a cross-sectional study’ [1]. We address Question 12, asking whether doctors agree that ‘Patients should be charged for non-urgent care if they are not eligible for free NHS treatment’.
The authors correctly state that agreement does not mean support for current NHS charging regulations, not least because the most recent amendments in England were introduced after this questionnaire (October 2017 [2]), however we remain concerned about potential misinterpretation, and suggest aspects of charging regulations where doctors’ opinions could be further explored.
Firstly, the question, which understandably echoes government policy language on charging, is similar to asking ‘Should people have to pay for things that are not free?’ without addressing complexity of eligibility, and the fact that some people living in the UK have lost their eligibility with recent regulations. An assessment of opinions would require measuring knowledge of charging and its relationship with immigration enforcement, as well as evaluating acceptance of the immigration system itself, as this now determines eligibility. Windrush patients being denied NHS treatment highlighted the complexity of this issue [3].
Secondly, doctors’ opinions on measures which penalise and threaten patients if they seek care, such as linking unpaid NHS debt to immigration enforcement [4], and NHS data sharing with the Home office in general, should also be explored.
Thirdly, assessing acceptability of charging for non-urgent treatment should address pricing, and whether treatment should be denied without payment. The current system enforces denial of non-urgent treatment unless 150% is paid upfront in secondary care and some community services, and urgent care is chargeable retrospectively. Furthermore, ‘urgency’ should be defined, given that life-saving chemotherapy has been denied in the current system.
Lastly, an assessment should explore attitudes to charging vulnerable groups including children, pregnant women, refused asylum seekers and undocumented migrants. Discussions often focus on ‘health tourism’, overlooking the population of undocumented migrants living in the UK, including an estimated 120,000 children [5]. By denying care to these groups the UK’s approach is inconsistent with many comparable countries, and breeches international human rights law [6].
The authors showed that public health specialists had greater disagreement with the statement, perhaps due to greater awareness of current regulations. We suggest that greater awareness raising amongst heath-workers is crucial, and would change opinions. As more patients are denied or deterred from care with new changes, doctors will be instrumental in collecting evidence of harm, and as advocates for our patients.
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Dr Robert Verrecchia, Public Health Registrar, Imperial College Healthcare NHS Trust
Dr Neal Russell, Clinical Research Fellow and Paediatric Registrar, St George’s University, London
Dr Jessica Potter, Clinical Research Fellow and Respiratory Registrar, Queen Mary University, London
1. Mandeville KL, Satherley R-M, Hall JA, et al (2018) Political views of doctors in the UK: a cross-sectional study. Journal of Epidemiology and Community Health Published Online First: 30 July 2018. doi: 10.1136/jech-2018-210801
2. GOV.UK. (2018) Guidance on implementing the overseas visitor charging regulations. UK Government: Department of Health and Social Care. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploa... Accessed on 12.08.2018
3. Gentleman A. (2018) Londoner denied NHS care: 'It's like I'm being left to die' The Guardian Available at: https://wwwtheguardiancom/uk-news/2018/mar/10/denied-free-nhs-cancer-car.... Accessed on 12.08.2018
4. Corbett J. (2018) Response to the Independent Chief Inspector of Borders and Immigration's call for evidence: Home Office partnership working with other govenment departments https://www.doctorsoftheworld.org.uk/Handlers/Download.ashx?IDMF=0ab1fbf..., Accessed on 12.08.2018
5. Sigona N, Hughes, V. . (2012) No Way Out, No Way In. Irregular Migrant Children and Families in the UK https://www.compas.ox.ac.uk/wp-content/uploads/PR-2012-Undocumented_Migr... ESRC Centre on Migration, Policy and Society Accessed on 12.08.2018
6. CESCR. (2000)Committee on Economic, Social and Cultural Rights: The Right to the Highest Attainable Standard of Health (Article 12) http://www.refworld.org/pdfid/4538838d0.pdf: Office of the High Commissioner for Human Rights, Accessed on 12.08.2018
We read with interest the article by Kroll et al., “Ethnic variation in unexplained death in infancy, including sudden infant death syndrome (SIDS), England and Wales 2006-12: national birth cohort study using routine data”[1]. While the five-fold disparity in death rates across ethnic groups is notable, the most striking finding was marked association of infant death with deprivation seen in Table 1, with an OR of 3.45 (95% CI 2.82-4.23) between the most deprived group and the least deprived group. Indeed, 69% of deaths were found in the two most deprived quintiles. The analytical attention on ethnic variation in the paper overshadows the central finding that the majority of risk is driven by poverty.
Furthermore, unmarried status is a potent indicator of socioeconomic status that may cluster with poverty, lack of social support and experiences of racial discrimination. The remaining variation that the paper attributes to possible cultural variation must be broken down into specific known risk factors, such as tobacco exposure, sleep position, preterm birth, alcohol and substance abuse, lack of prenatal care, formula feeding, sofa sharing, and the combination of bedsharing with these other risk factors[2].These known risk factors are also largely clustered around poverty. Even sleep position is indirectly associated with poverty via formula feeding, as videographic data show that bedsharing formula feeding infants are more likely to assume hazardous sleep position...
We read with interest the article by Kroll et al., “Ethnic variation in unexplained death in infancy, including sudden infant death syndrome (SIDS), England and Wales 2006-12: national birth cohort study using routine data”[1]. While the five-fold disparity in death rates across ethnic groups is notable, the most striking finding was marked association of infant death with deprivation seen in Table 1, with an OR of 3.45 (95% CI 2.82-4.23) between the most deprived group and the least deprived group. Indeed, 69% of deaths were found in the two most deprived quintiles. The analytical attention on ethnic variation in the paper overshadows the central finding that the majority of risk is driven by poverty.
Furthermore, unmarried status is a potent indicator of socioeconomic status that may cluster with poverty, lack of social support and experiences of racial discrimination. The remaining variation that the paper attributes to possible cultural variation must be broken down into specific known risk factors, such as tobacco exposure, sleep position, preterm birth, alcohol and substance abuse, lack of prenatal care, formula feeding, sofa sharing, and the combination of bedsharing with these other risk factors[2].These known risk factors are also largely clustered around poverty. Even sleep position is indirectly associated with poverty via formula feeding, as videographic data show that bedsharing formula feeding infants are more likely to assume hazardous sleep positions than breastfeeding infants[3], and prone sleep is generally not seen in breastfeeding infants sleeping with their mothers[4].
While some of the ethnic variation may be driven by genuine cultural traditions, without data on other major known risk factors of SUID associated with poverty, this analysis could lead to a mistaken overemphasis on ethnicity.
1 Kroll ME, Quigley MA, Kurinczuk JJ, et al. Ethnic variation in unexplained deaths in infancy, including sudden infant death syndrome (SIDS), England and Wales 2006-2012: national birth cohort study using routine data. J Epidemiol Community Health 2018.
2 Bartick M, Tomori C. Sudden infant death and social justice: A syndemics approach. Matern Child Nutr 2018 Aug 23:e12652.{Epub ahead of print].
3 Ball HL. Parent-infant bed-sharing behavior : Effects of feeding type and presence of father. Hum Nat 2006;17:301-18.
4 Richard C, Mosko S, McKenna J, et al. Sleeping position, orientation, and proximity in bedsharing infants and mothers. Sleep 1996;19:685-90.
It is bittersweet to see one’s predictions of a fall in life expectancy coming into being.
I work on statistics, but also talk to patients regularly about their diets, lifestyles and
environments. The medical service struggles to deal with the results of poor diet and
pollution. Perhaps it is time for a health service to deal with the causes of illness.
Ancel Keys crusaded against fats. He cherry picked data from only 6 of the available
22 countries. Sugar was then used to make low fat food palatable. Fructose and
galactose, in sugar, milk, corn syrup and fruit, are implicated in cancer, heart disease,
dementia and diabetes.
Are the NHS and social care the priorities? Perhaps money to buy good food is more
important, and maybe we have too much medicine, not too little. I have seen patients
taking up to 29 different drugs. No pharmacologist can work out how they interact.
One patient took 5 drugs for his asthma. People complain of drug side effects and are
just given more drugs to deal with these symptoms.
The Depression was forgotten, and it was assumed we could keep becoming richer,
until 2008. Similarly we cannot just extrapolate the increasing life expectancy figures.
Public health improved after building reservoirs, chlorinating water, installing sewage
systems, reducing overcrowding and setting up smokeless zones.
However, chemical production has increased greatly...
It is bittersweet to see one’s predictions of a fall in life expectancy coming into being.
I work on statistics, but also talk to patients regularly about their diets, lifestyles and
environments. The medical service struggles to deal with the results of poor diet and
pollution. Perhaps it is time for a health service to deal with the causes of illness.
Ancel Keys crusaded against fats. He cherry picked data from only 6 of the available
22 countries. Sugar was then used to make low fat food palatable. Fructose and
galactose, in sugar, milk, corn syrup and fruit, are implicated in cancer, heart disease,
dementia and diabetes.
Are the NHS and social care the priorities? Perhaps money to buy good food is more
important, and maybe we have too much medicine, not too little. I have seen patients
taking up to 29 different drugs. No pharmacologist can work out how they interact.
One patient took 5 drugs for his asthma. People complain of drug side effects and are
just given more drugs to deal with these symptoms.
The Depression was forgotten, and it was assumed we could keep becoming richer,
until 2008. Similarly we cannot just extrapolate the increasing life expectancy figures.
Public health improved after building reservoirs, chlorinating water, installing sewage
systems, reducing overcrowding and setting up smokeless zones.
However, chemical production has increased greatly since 1945, polluting air and
water. Many chemicals are in household products. Animal feed is often from poorly
tested GM crops. One food can contain a variety of pesticides, herbicides and food
additives. Heavy traffic pollutes the towns. Cars and machines have made exercise
optional. Antibiotics have been abused in agriculture and medicine, damaging gut
bacteria and producing drug resistant infections. Amalgam fillings, fluorescent lights
and some vaccines contain mercury. Vaccines and some water supplies contain
aluminium. People are exposed to cordless phones, microwaves, phone masts and
wifi. Over the counter, prescription, alcohol and illegal drugs interact. Many drugs
cause nutritional deficiencies, for example statin drugs cause coenzyme Q10
deficiency. Food banks cannot provide fresh food. People have had to move away
from their families to find work, and are no longer nearby to help relatives. These
factors interact.
The increases in, autism, asthma, dementia, diabetes and cancer should warn us to
make life healthier for the population, rather than dealing with damage already done.
We should maximise healthy life expectancy, not mere existence.
References:
1. Moss M, Freed D. The Cow and the Coronary: Epidemiology,
Biochemistry and Immunology. Int J Cardiol 2003; 87: 203-216.
2. Moss M. Drugs as Anti-nutrients. J Nutr Env Med 2007; 16(2):149-
166. DOI: 10.1080/13590840701352740.
In “Years of life lost due to encounters with law enforcement in the USA, 2015–2016,” Bui et al. estimate the public health impact of police use of force by a simple computation of the years of life lost by the people killed by police.[1] Unnecessary use of force by police is a problem demanding serious attention, and leadership in policing has responded with interventions and training in recent years to improve de-escalation techniques and reduce the incidence of unnecessary or unlawful use of force. Bui et al.’s analysis, however, fails to consider three key factors in these analyses: first, the distinction between necessary and unnecessary/unlawful uses of force; second, the potential impacts on years of life lost had the police not have intervened in these specific scenarios; and third, the broader impacts of police intervention on public health.
Police may use lethal force when they have sufficient reason to believe that a person poses a risk of serious physical injury or death to another person. A reporter for The Washington Post concludes that “the vast majority of individuals shot and killed by police officers… were armed with guns and killed after attacking police officers or civilians or making other direct threats.”[2] Unnecessary or unjustified use of force by police are thought to account for about five percent of the total number of incidents of use of force,[2] with great skeptics acknowledging they are certainly fewer than 50%.[3] Including sensitivit...
In “Years of life lost due to encounters with law enforcement in the USA, 2015–2016,” Bui et al. estimate the public health impact of police use of force by a simple computation of the years of life lost by the people killed by police.[1] Unnecessary use of force by police is a problem demanding serious attention, and leadership in policing has responded with interventions and training in recent years to improve de-escalation techniques and reduce the incidence of unnecessary or unlawful use of force. Bui et al.’s analysis, however, fails to consider three key factors in these analyses: first, the distinction between necessary and unnecessary/unlawful uses of force; second, the potential impacts on years of life lost had the police not have intervened in these specific scenarios; and third, the broader impacts of police intervention on public health.
Police may use lethal force when they have sufficient reason to believe that a person poses a risk of serious physical injury or death to another person. A reporter for The Washington Post concludes that “the vast majority of individuals shot and killed by police officers… were armed with guns and killed after attacking police officers or civilians or making other direct threats.”[2] Unnecessary or unjustified use of force by police are thought to account for about five percent of the total number of incidents of use of force,[2] with great skeptics acknowledging they are certainly fewer than 50%.[3] Including sensitivity analyses to account for necessary versus unnecessary/unlawful use of force, including the range of reasonable estimates of the proportion of incidents in which use of force was unjustified, would allow for more meaningful insights from Bui et al.’s estimates.
Additionally, as noted above, police use of lethal force often occurs in the context of imminent threats to the safety of others. So one of the counterfactuals of police use of force is the years of life that would have been lost were it not for police intervention. These are not merely co-occurring outcomes; one specifically seeks to bring about the other. In the language of public health, police use of force is intended to reduce imminent risks of morbidity and mortality. Estimates for years of potential life lost by other potential victims, had the police not intervened, are entirely missing from Bui et al.’s model.
Finally, there are broader impacts of policing at the individual and population levels that are not captured in these analyses. The substantial reduction in homicide in America over the past two and a half decades has been characterized as a “public health triumph,” and police work—including the use of force in the face of imminent morbidity and mortality risks—has played a non-negligible part in it. It has added hundreds of thousands of years of life to the population, since, for example, “the impact of the decline in homicide on the life expectancy of black men is roughly equivalent to the impact of eliminating obesity altogether.”[4]
To compare the deaths caused by policing to those caused meningitis or bicycle crashes is facile because unlike diseases or accidents, policing is an intervention. When police interventions are done well, they also stand to reduce their own incidence: for example, as the violent crime rate in New York City decreased precipitously, so did the need for its police to make forceful interventions.[5] As a result, shootings by police in New York City have fallen to record lows along with violent crime.[6]
In that same way public health officials seek to reduce the iatrogenic effects of interventions (think of the prescribing of opioid analgesics), police seek ways to reduce the use of unnecessary and unlawful force, and to make force less necessary to begin with. Practitioners have made strides in this regard. The police killing of unarmed people has shown significant declines in the US in the last three years,[7] and innovative use of force curricula are being introduced across the nation.[8]
The victims whose lives were saved by police interventions may have counterfactual stories to tell, but they are not just anecdotal. The interventionist nature of policing should shape the structure of research questions. Future models estimating the impact of use of police force on public health outcomes should explicitly account for justified versus unjustified use of force, the counterfactual of lack of police intervention, and the broader context of policing efforts on the outcomes of interest to more precisely estimate the magnitude of the impact of unlawful police intervention on public health. In a model with these additional inputs, the 57,754 years of life lost due to use of force by police in 2016 would likely decline to a level well below the threshold of a public health emergency from a population-level perspective.
REFERENCES
[1] Bui AL, Coates MM, Matthay EC. Years of life lost due to encounters with law enforcement in the USA, 2015-2016. J Epidemiol Community Health 2018;72:715-8
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 MoreWe thank Professors Bartick and Tomori for their comments on our paper. [1] We entirely agree that unexplained death in infancy (UDI) in the (mainly White British) general population of England and Wales is strongly associated with deprivation, as shown by many previous studies. Clearly, any factor that is associated with deprivation among the White British group will be a risk factor for UDI in the general population.
However, our paper is about ethnic, not socio-economic, variation. [2] The finding of a nearly five-fold disparity in risk across ethnic groups in England and Wales is both striking and novel. Moreover, we demonstrate that this disparity is not explained by deprivation. Formal adjustment for deprivation (IMD quintiles) does not even slightly reduce the ethnic variation (see Table 2). A simple scatter plot of ethnic groups illustrates the lack of a relationship between deprivation and risk, with a virtually horizontal overall trend line (see Figure at https://doi.org/10.5287/bodleian:XmE4XBaoZ). For example, Black Caribbean babies have nearly triple the UDI risk of Black African babies, but similar levels of deprivation. The Indian, Pakistani and Bangladeshi ethnic groups each have around half the UDI risk of White British babies; the White British and Indian groups have similar (relatively low) levels of deprivation, and the Pakistani and Bangladeshi groups are the most deprived in England and...
Show MoreWe congratulate the authors on this timely and interesting study: ‘Political views of doctors in the UK: a cross-sectional study’ [1]. We address Question 12, asking whether doctors agree that ‘Patients should be charged for non-urgent care if they are not eligible for free NHS treatment’.
Show MoreThe authors correctly state that agreement does not mean support for current NHS charging regulations, not least because the most recent amendments in England were introduced after this questionnaire (October 2017 [2]), however we remain concerned about potential misinterpretation, and suggest aspects of charging regulations where doctors’ opinions could be further explored.
Firstly, the question, which understandably echoes government policy language on charging, is similar to asking ‘Should people have to pay for things that are not free?’ without addressing complexity of eligibility, and the fact that some people living in the UK have lost their eligibility with recent regulations. An assessment of opinions would require measuring knowledge of charging and its relationship with immigration enforcement, as well as evaluating acceptance of the immigration system itself, as this now determines eligibility. Windrush patients being denied NHS treatment highlighted the complexity of this issue [3].
Secondly, doctors’ opinions on measures which penalise and threaten patients if they seek care, such as linking unpaid NHS debt to immigration enforcement [4], and NHS data shar...
We read with interest the article by Kroll et al., “Ethnic variation in unexplained death in infancy, including sudden infant death syndrome (SIDS), England and Wales 2006-12: national birth cohort study using routine data”[1]. While the five-fold disparity in death rates across ethnic groups is notable, the most striking finding was marked association of infant death with deprivation seen in Table 1, with an OR of 3.45 (95% CI 2.82-4.23) between the most deprived group and the least deprived group. Indeed, 69% of deaths were found in the two most deprived quintiles. The analytical attention on ethnic variation in the paper overshadows the central finding that the majority of risk is driven by poverty.
Furthermore, unmarried status is a potent indicator of socioeconomic status that may cluster with poverty, lack of social support and experiences of racial discrimination. The remaining variation that the paper attributes to possible cultural variation must be broken down into specific known risk factors, such as tobacco exposure, sleep position, preterm birth, alcohol and substance abuse, lack of prenatal care, formula feeding, sofa sharing, and the combination of bedsharing with these other risk factors[2].These known risk factors are also largely clustered around poverty. Even sleep position is indirectly associated with poverty via formula feeding, as videographic data show that bedsharing formula feeding infants are more likely to assume hazardous sleep position...
Show MoreIt is bittersweet to see one’s predictions of a fall in life expectancy coming into being.
I work on statistics, but also talk to patients regularly about their diets, lifestyles and
environments. The medical service struggles to deal with the results of poor diet and
pollution. Perhaps it is time for a health service to deal with the causes of illness.
Ancel Keys crusaded against fats. He cherry picked data from only 6 of the available
22 countries. Sugar was then used to make low fat food palatable. Fructose and
galactose, in sugar, milk, corn syrup and fruit, are implicated in cancer, heart disease,
dementia and diabetes.
Are the NHS and social care the priorities? Perhaps money to buy good food is more
important, and maybe we have too much medicine, not too little. I have seen patients
taking up to 29 different drugs. No pharmacologist can work out how they interact.
One patient took 5 drugs for his asthma. People complain of drug side effects and are
just given more drugs to deal with these symptoms.
The Depression was forgotten, and it was assumed we could keep becoming richer,
until 2008. Similarly we cannot just extrapolate the increasing life expectancy figures.
Public health improved after building reservoirs, chlorinating water, installing sewage
systems, reducing overcrowding and setting up smokeless zones.
However, chemical production has increased greatly...
Show MoreIn “Years of life lost due to encounters with law enforcement in the USA, 2015–2016,” Bui et al. estimate the public health impact of police use of force by a simple computation of the years of life lost by the people killed by police.[1] Unnecessary use of force by police is a problem demanding serious attention, and leadership in policing has responded with interventions and training in recent years to improve de-escalation techniques and reduce the incidence of unnecessary or unlawful use of force. Bui et al.’s analysis, however, fails to consider three key factors in these analyses: first, the distinction between necessary and unnecessary/unlawful uses of force; second, the potential impacts on years of life lost had the police not have intervened in these specific scenarios; and third, the broader impacts of police intervention on public health.
Show MorePolice may use lethal force when they have sufficient reason to believe that a person poses a risk of serious physical injury or death to another person. A reporter for The Washington Post concludes that “the vast majority of individuals shot and killed by police officers… were armed with guns and killed after attacking police officers or civilians or making other direct threats.”[2] Unnecessary or unjustified use of force by police are thought to account for about five percent of the total number of incidents of use of force,[2] with great skeptics acknowledging they are certainly fewer than 50%.[3] Including sensitivit...
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