Aside form many of the concerns about the imputed causality of the conclusions in this paper, there are some simple issues with the data. It would be helpful to clarify them.
The biggest issue is the disparity between the age standardised death rates (ASDR) used in the paper (calculated by the authors) and the ASDR as published by the ONS. The paper claims to use the ONS template to perform their own calculations, but the numbers are very different from the actual numbers published by the ONS. The ASDR for England and Wales in the ONS stats is a little over 1,000 per 100,000 in 2016 but the figures used in the paper seem to be around 500.
At first glance this looks like the paper has used the 1976 standard European population instead of the more recent and more reliable 2013 population (see a comparison of the two here https://www.nrscotland.gov.uk/files/statistics/age-standardised-death-ra... ). It is unclear whether this makes a huge difference to the results, but the reason for the disparity should have been noticed and mentioned or it casts a serious shadow over the results. And, why do your own calculations when the results of that calculation are actually available from a reliable source like the ONS? This is a strange choice.
Also, in assuming that the key relevant causes are primarily related to health and soci...
Aside form many of the concerns about the imputed causality of the conclusions in this paper, there are some simple issues with the data. It would be helpful to clarify them.
The biggest issue is the disparity between the age standardised death rates (ASDR) used in the paper (calculated by the authors) and the ASDR as published by the ONS. The paper claims to use the ONS template to perform their own calculations, but the numbers are very different from the actual numbers published by the ONS. The ASDR for England and Wales in the ONS stats is a little over 1,000 per 100,000 in 2016 but the figures used in the paper seem to be around 500.
At first glance this looks like the paper has used the 1976 standard European population instead of the more recent and more reliable 2013 population (see a comparison of the two here https://www.nrscotland.gov.uk/files/statistics/age-standardised-death-ra... ). It is unclear whether this makes a huge difference to the results, but the reason for the disparity should have been noticed and mentioned or it casts a serious shadow over the results. And, why do your own calculations when the results of that calculation are actually available from a reliable source like the ONS? This is a strange choice.
Also, in assuming that the key relevant causes are primarily related to health and social care resources without some simple comparisons to other data that is also available seems a little premature. Mortality trends are also available for many other European countries in this format. PHE's analysis pointed out this in commenting on some recent changes in mortality: "The increase in mortality rates in 2015 was not limited to England alone. It was seen across Europe on a comparable scale. The six biggest countries in the European Union (France, Germany, Italy, Poland, Spain, UK), all saw a fall in their life expectancies for both sexes." Whether this is also explains the earlier increases from 2011 to 2014 is unclear, but the comparisons with other datasets in other countries should surely have been done. If some external cause (a severe flu strain for example), common across many countries, were impacting the data, surely this should be a relevant confounding factor?
I can't be sure these factors are relevant to the paper's conclusions, but the fact they have not been considered or discussed is a cause for some due skepticism.
It is to be commended that the article on the effects of economic crises on population health outcomes in Latin America, by. Callum Williams et al., clearly explains the methods the authors used for the analysis. For that reason, the paper is a very good example of how not to use a specific type of research tool, the panel regression. In a panel regression, as in any time-series investigation of causality, a key issue is to adjust for time trends, so that variables are stationary series (1, 2). If this adjustment is missing, results are biased by trends in the variables. For example, the paper says that “after removing inflation and unemployment as controls from our regression analysis, GDP per capita increases were found to be associated with improvements in all mortality metrics.” This is just an spurious result, as in every country the trend in GDP per capita is a rising one and the trend in mortality is a declining one. If you put the number of Starbucks coffee-shops in the country rather than GDP per capita, it will be also associated with “improvements in all mortality metrics” as Starbucks are also increasing in number.
Lack of adjustment for time trends in the variables in more than sufficient to make the results of the regression spurious, but the models in this paper have another major flaw: both unemployment and GDP per capita are included at the same time as explanatory variables in the models. Callum Williams and coauthors seem unaware that these two var...
It is to be commended that the article on the effects of economic crises on population health outcomes in Latin America, by. Callum Williams et al., clearly explains the methods the authors used for the analysis. For that reason, the paper is a very good example of how not to use a specific type of research tool, the panel regression. In a panel regression, as in any time-series investigation of causality, a key issue is to adjust for time trends, so that variables are stationary series (1, 2). If this adjustment is missing, results are biased by trends in the variables. For example, the paper says that “after removing inflation and unemployment as controls from our regression analysis, GDP per capita increases were found to be associated with improvements in all mortality metrics.” This is just an spurious result, as in every country the trend in GDP per capita is a rising one and the trend in mortality is a declining one. If you put the number of Starbucks coffee-shops in the country rather than GDP per capita, it will be also associated with “improvements in all mortality metrics” as Starbucks are also increasing in number.
Lack of adjustment for time trends in the variables in more than sufficient to make the results of the regression spurious, but the models in this paper have another major flaw: both unemployment and GDP per capita are included at the same time as explanatory variables in the models. Callum Williams and coauthors seem unaware that these two variables are both indexing economic conditions, so that they have a very strong correlation. This generates what statisticians call co-linearity, which creates major uncertainties in interpreting the results of any regression model. But since the correlation is negative because GDP per capita rises when unemployment declines, that co-linearity is, say, on steroids. Of course, these reasons make the conclusions of the study fully unsupported. The idea of Callum Williams et at that you can put in a regression annual data of unemployment rates and GDP per capita reveals they have a very fuzzy idea of how the economy works. Of course, as the paper says, this is the first investigation to put unemployment, GDP per capita and inflation as explanatory variables in a regression modelling mortality as dependent variable. This is a quite an accomplishment!
A number of previous investigations on Latin American countries (3, 4) have shown that in these countries, as in high-income countries (2, 5-12), mortality tends to rise during economic expansions and decline during recessions.
Since the confusing studies published by Harvey Brenner in the 1970s, which later were considered as mostly flawed (13, 14), investigations on the macroeconomic effects on mortality have been plagued by many faulty studies (11, 15). The publication of this study by Callum Williams et al. confirms that, regretfully, it is to be expected that poor statistical methods will continue plaguing this field and producing spurious results on which nothing can be inferred (16).
References
1. Diggle PJ. Time series: A biostatistical introduction. Oxford ; New York: Oxford University Press; 1989.
2. Ionides E et al. Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends. Annals of Applied Statistics. 2013;7(3):1362-85.
3. Tapinos GP, Mason A, Bravo J. Demographic responses to economic adjustment in Latin America. Oxford; New York: Clarendon Press; Oxford University Press; 1997.
4. Gonzalez F, Quast T. Mortality and business cycles by level of development: Evidence from Mexico. Soc Sci Med. 2010 12;71(12):2066-73.
5. Ruhm CJ. Are recessions good for your health? Q J Econ. 2000;115(2):617-50.
6. Lindo JM. Aggregation and the estimated effects of economic conditions on health. J Health Econ. 2015 3;40(0):83-96.
7. Tapia Granados JA. Macroeconomic fluctuations and mortality in postwar Japan. Demography. 2008;45(2):323-43.
8. Tapia Granados JA. Recessions and mortality in Spain, 1980-1997. European Journal of Population. 2005 Dec;21:393-422.
9. Tapia Granados JA. The economic crisis and health in Spain and Europe: Is mortality increasing? / La crisis y la salud en España y en Europa: ¿está aumentando la mortalidad? Salud Colectiva (Buenos Aires). 2014;10(1):81-91.
10. Tapia Granados JA, House JS, Ionides EL, Burgard SA, Schoeni RF. Individual joblessness, contextual unemployment, and mortality risk. American Journal of Epidemiology. 2014;180(3):280-7.
11. Tapia Granados JA, Ionides EL. Mortality and macroeconomic fluctuations in contemporary Sweden. Eur J Popul. 2011;27(2):157-84.
12. Lin S. Economic fluctuations and health outcome: A panel analysis of Asian-Pacific countries. Applied Economics. 2009;41:519-30.
13. Ruhm CJ. Macroeconomic conditions, health, and government policy. In: Schoeni RF, House JS, Kaplan G, Pollack H, editors. Making Americans healthier: Social and economic policy as health policy. New York: Russell Sage; 2008.
14. Kasl SV, Jones BA. The impact of job loss and retirement on health. In: Berkman LF, Kawachi I, editors. Social epidemiology. Oxford; New York: Oxford University Press; 2000. p. 118-36.
15. Tapia Granados JA, Ionides EL. Statistical evidence shows that mortality tends to fall during recessions: a rebuttal to Catalano and Bruckner. International Journal of Epidemiology. 2016 September 15;45(5):1683-5.
16. Tapia Granados JA. Macroeconomic Effects on Mortality: Issues, Controversies, and Directions for Research. In: Scott R, Buchmann M, editors. Emerging Trends in the Social and Behavioral Sciences.2017. New York: John Wiley; 2017. p. 1-16.
We thank Prof. Helio S. A. Camargo Jr, a respected author of a handbook on breast image exams, for his letter, which presents an opportunity to make our points clearer. We agree that “having a mammogram is not the same thing as being screened with mammography”. According to Tomazelli et al (2017), based on the National Breast Cancer Control Information System (Sismama), 96.2% of the mammograms in Brazil were for screening (performed in asymptomatic women) and 3.8% were diagnostic (in patients with suspicious breast cancer signs and/or symptoms), in the period they analyzed (2010-2011) (1).
That means that less than 1 in 25 mammograms in Brazil were diagnostic, which must be one of the lowest rates in the world. The proportion of screening over diagnostic mammography must have further increased, with the expansion in coverage of breast screening in the last five years (2). The distribution of the mammographies for reasons other than screening are, therefore, diluted in the municipalities, without forming specific clusters.
We also agree that “death certificates in Brazil do not always reflect the actual cause of death” and we recognized this limitation in our study. But is noteworthy the Brazilian health information system has improved dramatically in last decades since the creation of SUS (Public Health System) in 1988, in terms of quality and completeness. The analysis of data quality collected by the Mortality Information System indicates t...
We thank Prof. Helio S. A. Camargo Jr, a respected author of a handbook on breast image exams, for his letter, which presents an opportunity to make our points clearer. We agree that “having a mammogram is not the same thing as being screened with mammography”. According to Tomazelli et al (2017), based on the National Breast Cancer Control Information System (Sismama), 96.2% of the mammograms in Brazil were for screening (performed in asymptomatic women) and 3.8% were diagnostic (in patients with suspicious breast cancer signs and/or symptoms), in the period they analyzed (2010-2011) (1).
That means that less than 1 in 25 mammograms in Brazil were diagnostic, which must be one of the lowest rates in the world. The proportion of screening over diagnostic mammography must have further increased, with the expansion in coverage of breast screening in the last five years (2). The distribution of the mammographies for reasons other than screening are, therefore, diluted in the municipalities, without forming specific clusters.
We also agree that “death certificates in Brazil do not always reflect the actual cause of death” and we recognized this limitation in our study. But is noteworthy the Brazilian health information system has improved dramatically in last decades since the creation of SUS (Public Health System) in 1988, in terms of quality and completeness. The analysis of data quality collected by the Mortality Information System indicates that, between 2000 and 2009, there was an improvement in coverage and the completeness of variables collected by this system. The causes of death presented a significant improvement in their definition throughout the decade (3). In 2010, the proportion of deaths due to ill-defined causes in the Southeast region of Brazil was 8.1%, decreasing to 7.1% after the investigation and reclassification proposed by the Ministry of Health (4). Additionally, the availability of large computerized databases on health turned the record linkage technique into an alternative for different study designs, particularly spatial analysis (5).
We agree that lack of access to data of the private sector is a serious problem, in terms of transparency and accountability of processes and outcomes in Brazil. Beyond diseases with mandatory notification and vital statistics (births and deaths), information about morbidity and hospitalization in the private sector is not publicly available on a regular basis, and usually accessed only from population-based household surveys, such as Demographic and Health Surveys (6). While SUS data is publicly available in open databases (except for issues related to patient confidentiality), information of private sector is treated as a black box, which limits comparison with public sector.
We would be glad to collaborate on joint analysis of the private sector regarding breast cancer, and other relevant public health problems.
Warmest regards,
Carmen Simone Grilo Diniz
Alessandra Cristina Guedes Pellini
Adeylston Guimarães Ribeiro
Marcello Vannucci Tedardi
Marina Jorge de Miranda
Michelle Mosna Touso
Oswaldo Santos Baquero
Patrícia Carla dos Santos
Francisco Chiaravalloti-Neto
References
1 - Tomazelli JG, Migowski A, Ribeiro CM, Assis M, Abreu DMF. Avaliação das ações de detecção precoce do câncer de mama no Brasil por meio de indicadores de processo: estudo descritivo com dados do Sismama, 2010-2011. Epidemiol Serv Saúde 2017;26(1):61-70.
2 - Vigitel Brasil 2016: Vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Ministério da Saúde, Secretaria de Vigilância em Saúde, Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde. Brasília: Ministério da Saúde, 2017. [cited 2017 Nov 6] Available from: http://portalarquivos.saude.gov.br/images/pdf/2017/junho/07/vigitel_2016...
3 - Maranhão AGK, Vasconcelos AMN, Aly CMC, Rabello Neto DL, Porto DL, Oliveira H, et al. Como morrem os brasileiros: caracterização e distribuição geográfica dos óbitos no Brasil, 2000, 2005 e 2009. Ministério da Saúde, organizador. Saúde Brasil 2010: uma análise da situação de saúde e evidências selecionadas de impacto de ações de vigilância em saúde. Brasília: Ministério da Saúde; 2011. v. 1. p. 51-78. [cited 2017 Nov 21] Available from: http://www.repositorio.unb.br/bitstream/10482/12475/1/CAPITULO_ComoMorre...
4 - França E, Teixeira R, Ishitani L, Duncan BB, Cortez-Escalante JJ, Morais Neto OL, Szwarcwald CL. Ill-defined causes of death in Brazil: a redistribution method based on the investigation of such causes. Rev Saúde Pública 2014;48(4):671-681. [cited 2017 Nov 21] Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102014000....
5 - Peres SV, Latorre MRDO, Tanaka LF, Michels FAZ, Teixeira MLP, Coeli CM, Almeida MF.. Melhora na qualidade e completitude da base de dados do Registro de Câncer de Base Populacional do município de São Paulo: uso das técnicas de linkage. Rev bras Epidemiol 2016;19(4):753-765.
6 - Diniz CSG, d'Oliveira AFPL, Lansky S. Equity and women's health services for contraception, abortion and childbirth in Brazil. Reprod Health Matters 2012;20(40): 94-101.
I do not refute the hypothesis that spending constraints had adverse health impacts. However, the authors do not provide convincing evidence to support their hypothesis. For example, it does not seems sensible to investigate separately the association between spending and number of deaths by place of deaths. Surely, what we care about is the total number of deaths? If we find more deaths at home and in care homes and fewer deaths in hospital, this could be a good thing, since hospital is not most people's preferred place of death. Since the authors do not present results for all deaths, we do not know if the main effect is shifting deaths from hospitals to other places.
Table 1 reports the number of observations as 28. So there are 14 data points for male mortality and 14 data points for female mortality. But the explanatory variables, expenditure on health and social care, are not reported separately for males and females. So the same values of these variables are used twice!
The associations between spending and mortality reported in the paper are clearly not causal relationships. Nevertheless, the authors claim that around £25 to £30 billion additional spending are required to close the gap.
The description of the methods are misleading. The authors describe their models as fixed effects regression models but what they actually do is a long way from a fixed effects model traditionally used by economists to control for area-specific unobserved e...
I do not refute the hypothesis that spending constraints had adverse health impacts. However, the authors do not provide convincing evidence to support their hypothesis. For example, it does not seems sensible to investigate separately the association between spending and number of deaths by place of deaths. Surely, what we care about is the total number of deaths? If we find more deaths at home and in care homes and fewer deaths in hospital, this could be a good thing, since hospital is not most people's preferred place of death. Since the authors do not present results for all deaths, we do not know if the main effect is shifting deaths from hospitals to other places.
Table 1 reports the number of observations as 28. So there are 14 data points for male mortality and 14 data points for female mortality. But the explanatory variables, expenditure on health and social care, are not reported separately for males and females. So the same values of these variables are used twice!
The associations between spending and mortality reported in the paper are clearly not causal relationships. Nevertheless, the authors claim that around £25 to £30 billion additional spending are required to close the gap.
The description of the methods are misleading. The authors describe their models as fixed effects regression models but what they actually do is a long way from a fixed effects model traditionally used by economists to control for area-specific unobserved effects. In fact, the authors have only one area - England - and their fixed effect seems to be gender, which as described above, does not make much sense since there are no separate figures for female and male health and social care expenditure.
The authors goal of demonstrating the negative impacts of austerity is laudable, but they need to do so using valid scientific methods.
Further to my earlier response to this article, it is probably appropriate to add some further clarifying detail. The principal problem lies in the fact that the detailed trends in deaths do not conform to the assumed calendar year breaks assumed in this study. The international evidence indicates that deaths (and medical admissions) have for many years shown on/off switching along with single-year-of-age specific changes.
Indeed deaths and medical admissions are not the only health factors to be affected and the gender ratio at birth along with admissions for certain conditions during pregnancy and childbirth also simultaneously change. The ratio of female to male admissions also show unexplained and simultaneous changes (and have done so for many years). It is difficult to pin these changes on a simple spending explanation.
Hospital bed occupancy likewise undergoes unexplained changes. It has also been my experience from a 25-year career in healthcare analysis that delayed discharges of care always increase during these unexplained periods of higher deaths and medical admissions.
Rather than citing all the individual studies can I refer the reader to over 200 studies on this topic published over the past 9 years. These can be found at http://www.hcaf.biz/2010/Publications_Full.pdf
I hope this will lead to the further fruitful investigation of this enigmatic and recurring phenomena....
Further to my earlier response to this article, it is probably appropriate to add some further clarifying detail. The principal problem lies in the fact that the detailed trends in deaths do not conform to the assumed calendar year breaks assumed in this study. The international evidence indicates that deaths (and medical admissions) have for many years shown on/off switching along with single-year-of-age specific changes.
Indeed deaths and medical admissions are not the only health factors to be affected and the gender ratio at birth along with admissions for certain conditions during pregnancy and childbirth also simultaneously change. The ratio of female to male admissions also show unexplained and simultaneous changes (and have done so for many years). It is difficult to pin these changes on a simple spending explanation.
Hospital bed occupancy likewise undergoes unexplained changes. It has also been my experience from a 25-year career in healthcare analysis that delayed discharges of care always increase during these unexplained periods of higher deaths and medical admissions.
Rather than citing all the individual studies can I refer the reader to over 200 studies on this topic published over the past 9 years. These can be found at http://www.hcaf.biz/2010/Publications_Full.pdf
I hope this will lead to the further fruitful investigation of this enigmatic and recurring phenomena.
A very nice study with focused vision for future. Read it and appreciate with acknowledgement to bring this entire study to us. Would like to highlight a follow up of the said subjects as per their genetic makeup in this era of personalised medicine. Hypoxia and level of venous hypoxia as a key factor is missing to be aligned with calories intake and other factors which will define change the entire scope of study beside its implementation. The genes associated with obesity and involved in energy hemostasis must be considered at least as per study performed.
The increase in mortality since 2011 has been an intriguing area of inquiry. I have already published several papers on this topic which suggest that social care spending is not the major contributory factor [1-18]. Several other papers are in press [19-24]. The issues raised in these papers have sadly been missed in this study. It would appear that further research is required on this important topic to disentangle cause and effect.
References
1. Jones R (2014) Infectious-like Spread of an Agent Leading to Increased Medical Admissions and Deaths in Wigan (England), during 2011 and 2012. British Journal of Medicine and Medical Research 4(28): 4723-4741. doi: 10.9734/BJMMR/2014/10807
2. Jones R, Beauchant S (2015) Spread of a new type of infectious condition across Berkshire in England between June 2011 and March 2013: Effect on medical emergency admissions. British Journal of Medicine and Medical Research 6(1): 126-148. doi: 10.9734/BJMMR/2015/14223
3. Jones R (2015) Unexpected and Disruptive Changes in Admissions Associated with an Infectious-like Event Experienced at a Hospital in Berkshire, England around May of 2012. British Journal of Medicine and Medical Research 6(1): 56-76. doi: 10.9734/BJMMR/2015/13938
4. Jones R (2015) A previously uncharacterized infectious-like event leading to spatial spread of deaths across England and Wales: Characteristics of the most recent event and a time series for past events. Brit J Medicine and...
The increase in mortality since 2011 has been an intriguing area of inquiry. I have already published several papers on this topic which suggest that social care spending is not the major contributory factor [1-18]. Several other papers are in press [19-24]. The issues raised in these papers have sadly been missed in this study. It would appear that further research is required on this important topic to disentangle cause and effect.
References
1. Jones R (2014) Infectious-like Spread of an Agent Leading to Increased Medical Admissions and Deaths in Wigan (England), during 2011 and 2012. British Journal of Medicine and Medical Research 4(28): 4723-4741. doi: 10.9734/BJMMR/2014/10807
2. Jones R, Beauchant S (2015) Spread of a new type of infectious condition across Berkshire in England between June 2011 and March 2013: Effect on medical emergency admissions. British Journal of Medicine and Medical Research 6(1): 126-148. doi: 10.9734/BJMMR/2015/14223
3. Jones R (2015) Unexpected and Disruptive Changes in Admissions Associated with an Infectious-like Event Experienced at a Hospital in Berkshire, England around May of 2012. British Journal of Medicine and Medical Research 6(1): 56-76. doi: 10.9734/BJMMR/2015/13938
4. Jones R (2015) A previously uncharacterized infectious-like event leading to spatial spread of deaths across England and Wales: Characteristics of the most recent event and a time series for past events. Brit J Medicine and Medical Research 5(11): 1361-1380. doi: 10.9734/BJMMR/2015/14285
5. Jones R (2015) Are emergency admissions contagious? Brit J Healthcare Management 21(5): 227-235.
6. Jones R (2015) Recurring Outbreaks of an Infection Apparently Targeting Immune Function, and Consequent Unprecedented Growth in Medical Admission and Costs in the United Kingdom: A Review. British Journal of Medicine and Medical Research 6(8): 735-770. doi: 10.9734/BJMMR/2015/14845
7. Jones R (2015) A new type of infectious outbreak? SMU Medical Journal 2(1): 19-25. http://smu.edu.in/content/dam/manipal/smu/documents/Journal%20Issue%203/...
8. Jones R (2015) Small area spread and step-like changes in emergency medical admissions in response to an apparently new type of infectious event. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 1(2): 42-54. doi: 10.15761/FGNAMB.1000110
9. Jones R (2015) Infectious-like spread of an agent leading to increased medical hospital admission in the North East Essex area of the East of England. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 1(3): 98-111. doi: 10.15761/FGNAMB.1000117
10. Jones R (2015) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. FGNAMB 1(3): 78-79. doi: 10.15761/FGNAMB.1000114
11. Jones R (2015) A time series of infectious-like events in Australia between 2000 and 2013 leading to extended periods of increased deaths (all-cause mortality) with possible links to increased hospital medical admissions. International Journal of Epidemiologic Research 2(2): 53-67. http://ijer.skums.ac.ir/article_12869_2023.html
12. Jones R (2016) Deaths in English Lower Super Output Areas (LSOA) show patterns of very large shifts indicative of a novel recurring infectious event. SMU Medical Journal 3(2): 23-36. https://pdfs.semanticscholar.org/c3aa/71a1b78e053cba4a871093dd43aa896d9e...
13. Jones R (2016) A presumed infectious event in England and Wales during 2014 and 2015 leading to higher deaths in those with
neurological and other disorders. Journal of Neuroinfectious Diseases 7(1): 1000213 doi: 10.4172/2314-7326.1000213
14. Jones R (2016) Unusual trends in NHS staff sickness absence. BJHCM 22(4): 239-240.
15. Jones R (2016) A regular series of unexpected and large increases in total deaths (all-cause mortality) for male and female residents of mid super output areas (MSOA) in England and Wales: How high level analysis can miss the contribution from complex small-area spatial spread of a presumed infectious agent. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 2(2): 1-13. doi: 10.15761/FGNAMB.1000129
16. Jones R (2017) Outbreaks of a Presumed Infectious Agent Associated with Changes in Fertility, Stillbirth, Congenital Abnormalities and the Gender Ratio at Birth. British Journal of Medicine and Medical Research 20(8): 1-36. doi: 10.9734/BJMMR/2017/32372
17. Jones R (2017) Outbreaks of a presumed infectious pathogen creating on/off switching in deaths. SDRP Journal of Infectious Diseases Treatment and Therapy 1(1): 1-6. http://www.openaccessjournals.siftdesk.org/articles/pdf/Outbreaks-of-a-p...
18. Jones R (2017) Year-to-year variation in deaths in English Output Areas (OA), and the interaction between a presumed infectious agent and influenza in 2015. SMU Medical Journal 4(2): 37-69. http://smu.edu.in/content/dam/manipal/smu/smims/Volume4No2July2017/SMU%2...(July%202017)%20-%204.pdf
19. Jones R (2017) A reduction in acute thrombotic admissions during a period of unexplained increased deaths and medical admissions in the UK. European Journal of Internal Medicine doi: http://dx.doi.org/10.1016/j.ejim.2017.09.007
20. Jones R (2017) Deaths and medical admissions in the UK show an unexplained and sustained peak after 2011. European Journal of Internal Medicine (in press). http://www.ejinme.com/article/S0953-6205(17)30370-9/fulltext
21. Jones R (2017) Periods of unexplained higher deaths and medical admissions have occurred previously – but were apparently ignored, misinterpreted or not investigated. European Journal of Internal Medicine (in press)
22. Jones R (2017) Age-specific and year of birth changes in hospital admissions during a period of unexplained higher deaths in England. European Journal of Internal Medicine (in press) http://www.sciencedirect.com/science/article/pii/S0953620517304053
23. Jones R (2017) Role of social group and gender in outbreaks of a novel agent leading to increased deaths, with insights into higher international deaths in 2015. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 3(1): in press.
24. Jones R (2017) Different patterns of male and female deaths in 2015 in English and Welsh local authorities question the role of austerity as the primary force behind higher deaths. Fractal Geometry and Nonlinear Analysis in Medicine and Biology 3(2): in press.
This study appears to be flawed. This is due to the fact that although spending may have gone down, the number of nurses and care workers may have gone up. The rate of care may also have increased within a year that had less spending, factors which do not appear to have been addressed.
There can be no doubt that constraints on healthcare spending has an adverse effect upon mortality.
If we analyse several key areas required for the safe and effective functioning of a hospital then it is clear to see that the reduction in real term funding has had a multifactorial effect upon some of the following:
• Staffing: There are now record numbers of rota gaps. Shortages of doctors across all medical specialties is the norm. Trusts are routinely staffing rota gaps with internal locums or leaving posts vacant, resulting in certain services being dangerously understaffed or closing down. Rota gaps save trusts thousands of pounds, relying on the goodwill of the remaining staff to fill the void.
• Equipment: Essential equipment is frequently defective, out of date or unsafe. Operating theatres have to contend with instruments that are ill maintained (owing to outsourcing) leading to increased operating time and putting lives at risk.
• Medications: Health care authorities are rationing oncological medications despite NICE guidelines. We have a post code lottery for cancer and reproductive services.
• Buildings and maintenance: Hospitals are ill maintained. Heating and ventilation failures are common in theatre. Money spent on PFI repayments could be used for building maintenance.
• Study budgets: Cuts in study budgets have a negative impact upon training and education. Maintaining up to date skills is essential. Cutting study budget...
There can be no doubt that constraints on healthcare spending has an adverse effect upon mortality.
If we analyse several key areas required for the safe and effective functioning of a hospital then it is clear to see that the reduction in real term funding has had a multifactorial effect upon some of the following:
• Staffing: There are now record numbers of rota gaps. Shortages of doctors across all medical specialties is the norm. Trusts are routinely staffing rota gaps with internal locums or leaving posts vacant, resulting in certain services being dangerously understaffed or closing down. Rota gaps save trusts thousands of pounds, relying on the goodwill of the remaining staff to fill the void.
• Equipment: Essential equipment is frequently defective, out of date or unsafe. Operating theatres have to contend with instruments that are ill maintained (owing to outsourcing) leading to increased operating time and putting lives at risk.
• Medications: Health care authorities are rationing oncological medications despite NICE guidelines. We have a post code lottery for cancer and reproductive services.
• Buildings and maintenance: Hospitals are ill maintained. Heating and ventilation failures are common in theatre. Money spent on PFI repayments could be used for building maintenance.
• Study budgets: Cuts in study budgets have a negative impact upon training and education. Maintaining up to date skills is essential. Cutting study budgets prevents the updating of evidence based practices.
• Morale: Although difficult to quantify, the over burdening of staff caused by an erosion of pay, facilities, pharmacological and investigative armamentarium has led to a decrease in staff morale. Trainees are no longer applying for run through training whilst they analyse their options, resulting in the loss of enthusiastic middle grade staff that were once essential for the delivery of first class health care.
We therefore have the perfect storm and with it the adverse effect upon mortality is clear. Funding must be increased if we are to avoid the unnecessary and preventable loss of life..
Physicians would happily spend more time with patients, just as restaurants happily serve appetizers, sides, and desserts, IF they were reimbursed for the extra time, but the insurance system was set up to deal with big, unexpected, single-diagnoses events, so doesn't address the complexity and time of a non-procedural primary-care visit.
Direct-pay environments, where the physician can make $20/hr after expenses, encourage proper allocation of time, but the 'co-pay' environment, where the insurer caps everything at a 99214 (which one can perform in 4 minutes) so the patient with 9 interacting problems who you spend 40 minutes with and try to bill a 99215 (which may pay $100/40min versus $50/4 minutes, so you don't even meet overhead), you get a kangaroo-court "audit" where your services are deemed 'not medically necessary' and you are threatened with fines (or jail, in the case of Anthem/Medicare).
So doctors do what they are paid to do, which is 4 minute visits.
Aside form many of the concerns about the imputed causality of the conclusions in this paper, there are some simple issues with the data. It would be helpful to clarify them.
The biggest issue is the disparity between the age standardised death rates (ASDR) used in the paper (calculated by the authors) and the ASDR as published by the ONS. The paper claims to use the ONS template to perform their own calculations, but the numbers are very different from the actual numbers published by the ONS. The ASDR for England and Wales in the ONS stats is a little over 1,000 per 100,000 in 2016 but the figures used in the paper seem to be around 500.
At first glance this looks like the paper has used the 1976 standard European population instead of the more recent and more reliable 2013 population (see a comparison of the two here https://www.nrscotland.gov.uk/files/statistics/age-standardised-death-ra... ). It is unclear whether this makes a huge difference to the results, but the reason for the disparity should have been noticed and mentioned or it casts a serious shadow over the results. And, why do your own calculations when the results of that calculation are actually available from a reliable source like the ONS? This is a strange choice.
Also, in assuming that the key relevant causes are primarily related to health and soci...
Show MoreIt is to be commended that the article on the effects of economic crises on population health outcomes in Latin America, by. Callum Williams et al., clearly explains the methods the authors used for the analysis. For that reason, the paper is a very good example of how not to use a specific type of research tool, the panel regression. In a panel regression, as in any time-series investigation of causality, a key issue is to adjust for time trends, so that variables are stationary series (1, 2). If this adjustment is missing, results are biased by trends in the variables. For example, the paper says that “after removing inflation and unemployment as controls from our regression analysis, GDP per capita increases were found to be associated with improvements in all mortality metrics.” This is just an spurious result, as in every country the trend in GDP per capita is a rising one and the trend in mortality is a declining one. If you put the number of Starbucks coffee-shops in the country rather than GDP per capita, it will be also associated with “improvements in all mortality metrics” as Starbucks are also increasing in number.
Show MoreLack of adjustment for time trends in the variables in more than sufficient to make the results of the regression spurious, but the models in this paper have another major flaw: both unemployment and GDP per capita are included at the same time as explanatory variables in the models. Callum Williams and coauthors seem unaware that these two var...
Dear Editor
We thank Prof. Helio S. A. Camargo Jr, a respected author of a handbook on breast image exams, for his letter, which presents an opportunity to make our points clearer. We agree that “having a mammogram is not the same thing as being screened with mammography”. According to Tomazelli et al (2017), based on the National Breast Cancer Control Information System (Sismama), 96.2% of the mammograms in Brazil were for screening (performed in asymptomatic women) and 3.8% were diagnostic (in patients with suspicious breast cancer signs and/or symptoms), in the period they analyzed (2010-2011) (1).
Show MoreThat means that less than 1 in 25 mammograms in Brazil were diagnostic, which must be one of the lowest rates in the world. The proportion of screening over diagnostic mammography must have further increased, with the expansion in coverage of breast screening in the last five years (2). The distribution of the mammographies for reasons other than screening are, therefore, diluted in the municipalities, without forming specific clusters.
We also agree that “death certificates in Brazil do not always reflect the actual cause of death” and we recognized this limitation in our study. But is noteworthy the Brazilian health information system has improved dramatically in last decades since the creation of SUS (Public Health System) in 1988, in terms of quality and completeness. The analysis of data quality collected by the Mortality Information System indicates t...
I do not refute the hypothesis that spending constraints had adverse health impacts. However, the authors do not provide convincing evidence to support their hypothesis. For example, it does not seems sensible to investigate separately the association between spending and number of deaths by place of deaths. Surely, what we care about is the total number of deaths? If we find more deaths at home and in care homes and fewer deaths in hospital, this could be a good thing, since hospital is not most people's preferred place of death. Since the authors do not present results for all deaths, we do not know if the main effect is shifting deaths from hospitals to other places.
Table 1 reports the number of observations as 28. So there are 14 data points for male mortality and 14 data points for female mortality. But the explanatory variables, expenditure on health and social care, are not reported separately for males and females. So the same values of these variables are used twice!
The associations between spending and mortality reported in the paper are clearly not causal relationships. Nevertheless, the authors claim that around £25 to £30 billion additional spending are required to close the gap.
The description of the methods are misleading. The authors describe their models as fixed effects regression models but what they actually do is a long way from a fixed effects model traditionally used by economists to control for area-specific unobserved e...
Show MoreFurther to my earlier response to this article, it is probably appropriate to add some further clarifying detail. The principal problem lies in the fact that the detailed trends in deaths do not conform to the assumed calendar year breaks assumed in this study. The international evidence indicates that deaths (and medical admissions) have for many years shown on/off switching along with single-year-of-age specific changes.
Indeed deaths and medical admissions are not the only health factors to be affected and the gender ratio at birth along with admissions for certain conditions during pregnancy and childbirth also simultaneously change. The ratio of female to male admissions also show unexplained and simultaneous changes (and have done so for many years). It is difficult to pin these changes on a simple spending explanation.
Hospital bed occupancy likewise undergoes unexplained changes. It has also been my experience from a 25-year career in healthcare analysis that delayed discharges of care always increase during these unexplained periods of higher deaths and medical admissions.
Rather than citing all the individual studies can I refer the reader to over 200 studies on this topic published over the past 9 years. These can be found at http://www.hcaf.biz/2010/Publications_Full.pdf
I hope this will lead to the further fruitful investigation of this enigmatic and recurring phenomena....
Show MoreA very nice study with focused vision for future. Read it and appreciate with acknowledgement to bring this entire study to us. Would like to highlight a follow up of the said subjects as per their genetic makeup in this era of personalised medicine. Hypoxia and level of venous hypoxia as a key factor is missing to be aligned with calories intake and other factors which will define change the entire scope of study beside its implementation. The genes associated with obesity and involved in energy hemostasis must be considered at least as per study performed.
The increase in mortality since 2011 has been an intriguing area of inquiry. I have already published several papers on this topic which suggest that social care spending is not the major contributory factor [1-18]. Several other papers are in press [19-24]. The issues raised in these papers have sadly been missed in this study. It would appear that further research is required on this important topic to disentangle cause and effect.
References
1. Jones R (2014) Infectious-like Spread of an Agent Leading to Increased Medical Admissions and Deaths in Wigan (England), during 2011 and 2012. British Journal of Medicine and Medical Research 4(28): 4723-4741. doi: 10.9734/BJMMR/2014/10807
Show More2. Jones R, Beauchant S (2015) Spread of a new type of infectious condition across Berkshire in England between June 2011 and March 2013: Effect on medical emergency admissions. British Journal of Medicine and Medical Research 6(1): 126-148. doi: 10.9734/BJMMR/2015/14223
3. Jones R (2015) Unexpected and Disruptive Changes in Admissions Associated with an Infectious-like Event Experienced at a Hospital in Berkshire, England around May of 2012. British Journal of Medicine and Medical Research 6(1): 56-76. doi: 10.9734/BJMMR/2015/13938
4. Jones R (2015) A previously uncharacterized infectious-like event leading to spatial spread of deaths across England and Wales: Characteristics of the most recent event and a time series for past events. Brit J Medicine and...
This study appears to be flawed. This is due to the fact that although spending may have gone down, the number of nurses and care workers may have gone up. The rate of care may also have increased within a year that had less spending, factors which do not appear to have been addressed.
The government ONS also predicted in 2004 that due to the ageing population and steadily declining mortality rate, this would lead to an increase, expected to start within 2010/2011.
http://webarchive.nationalarchives.gov.uk/20160108034023/http://www.ons....
Change in population also doesn't appear to have been taken into consideration as well as reasons for death.
There can be no doubt that constraints on healthcare spending has an adverse effect upon mortality.
Show MoreIf we analyse several key areas required for the safe and effective functioning of a hospital then it is clear to see that the reduction in real term funding has had a multifactorial effect upon some of the following:
• Staffing: There are now record numbers of rota gaps. Shortages of doctors across all medical specialties is the norm. Trusts are routinely staffing rota gaps with internal locums or leaving posts vacant, resulting in certain services being dangerously understaffed or closing down. Rota gaps save trusts thousands of pounds, relying on the goodwill of the remaining staff to fill the void.
• Equipment: Essential equipment is frequently defective, out of date or unsafe. Operating theatres have to contend with instruments that are ill maintained (owing to outsourcing) leading to increased operating time and putting lives at risk.
• Medications: Health care authorities are rationing oncological medications despite NICE guidelines. We have a post code lottery for cancer and reproductive services.
• Buildings and maintenance: Hospitals are ill maintained. Heating and ventilation failures are common in theatre. Money spent on PFI repayments could be used for building maintenance.
• Study budgets: Cuts in study budgets have a negative impact upon training and education. Maintaining up to date skills is essential. Cutting study budget...
Physicians would happily spend more time with patients, just as restaurants happily serve appetizers, sides, and desserts, IF they were reimbursed for the extra time, but the insurance system was set up to deal with big, unexpected, single-diagnoses events, so doesn't address the complexity and time of a non-procedural primary-care visit.
Direct-pay environments, where the physician can make $20/hr after expenses, encourage proper allocation of time, but the 'co-pay' environment, where the insurer caps everything at a 99214 (which one can perform in 4 minutes) so the patient with 9 interacting problems who you spend 40 minutes with and try to bill a 99215 (which may pay $100/40min versus $50/4 minutes, so you don't even meet overhead), you get a kangaroo-court "audit" where your services are deemed 'not medically necessary' and you are threatened with fines (or jail, in the case of Anthem/Medicare).
So doctors do what they are paid to do, which is 4 minute visits.
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