Phantoms never die: living with unreliable population data

Link: https://www.macs.hw.ac.uk/~andrewc/papers/JRSS2016B.pdf

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Summary:

The analysis of national mortality trends is critically dependent on the quality of the population, exposures and deaths data that underpin death rates. We develop a framework that allows us to assess data reliability and to identify anomalies, illustrated, by way of example, using England and Wales population data. First, we propose a set of graphical diagnostics that help to pinpoint anomalies. Second, we develop a simple Bayesian model that allows us to quantify objectively the size of any anomalies. Two-dimensional graphical diagnostics and modelling techniques are shown to improve significantly our ability to identify and quantify anomalies. An important conclusion is that significant anomalies in population data can often be linked to uneven patterns of births of people in cohorts born in the distant past. In the case of England and Wales, errors of more than 9% in the estimated size of some birth cohorts can be attributed to an uneven pattern of births. We propose methods that can use births data to improve estimates of the underlying population exposures. Finally, we consider the effect of anomalies on mortality forecasts and annuity values, and we find significant effects for some cohorts. Our methodology has general applicability to other sources of population data, such as the Human Mortality Database.

Keywords: Baby boom;Cohort–births–deaths exposures methodology; Convexity adjustment ratio; Deaths; Graphical diagnostics; Population data

Author(s): Andrew J.G.Cairns, Heriot-Watt University, Edinburgh, UK David Blake, Cass Business School, London, UK Kevin Dowd Durham University Business School, UK and Amy R. Kessler Prudential Retirement, Newark, USA

Publication Date: 2016

Publication Site: Journal of the Royal Statistical Society

J. R. Statist. Soc. A (2016) 179, Part 4, pp. 975–1005

Drug Overdose Mortality by Usual Occupation and Industry: 46 U.S. States and New York City, 2020

Link: https://www.cdc.gov/nchs/data/nvsr/nvsr72/nvsr72-07.pdf

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Objective—This report describes deaths from drug overdoses in 2020 in U.S. residents in 46 states and New York City by usual occupation and industry. August 22, 2023

Conclusions—Variation in drug overdose death rates and PMRs by usual occupation and industry in 2020 demonstrates the disproportionate burden of the ongoing drug overdose crisis on certain sectors of the U.S. workforce.

Methods—Frequencies, death rates, and proportionate mortality ratios (PMRs) are presented using the 2020 National Vital Statistics System mortality data file. Data were restricted to decedents aged 16–64 for rates and 15–64 for PMRs with usual occupations and industries in the paid civilian workforce. Age-standardized drug overdose death rates were estimated for usual occupation and industry groups overall, and age-adjusted drug overdose PMRs were estimated for each usual occupation and industry group overall and by sex, race and Hispanic-origin group, type of drug, and drug overdose intent. Age-adjusted drug overdose PMRs were also estimated for individual occupations and industries.

Results—Drug overdose mortality varied by usual occupation and industry. Workers in the construction and extraction occupation group (162.6 deaths per 100,000 workers, 95% confidence interval: 155.8–169.4) and construction industry group (130.9, 126.0–135.8) had the highest drug overdose death rates. The highest group-level drug overdose PMRs were observed in decedents in the construction and extraction occupation group and the construction industry group (145.4, 143.6–147.1 and 144.9, 143.2–146.5, respectively). Differences in drug overdose PMRs by usual occupation and industry group were observed within each sex, within each race and Hispanicorigin group, by drug type, and by drug overdose intent. Among individual occupations and industries, the highest drug overdose PMRs were observed in decedents who worked as fishers and related fishing occupations and in fishing, hunting, and trapping industries (193.1, 166.8–222.4 and 186.5, 161.7–214.1, respectively).

Author(s): Billock RM, Steege AL, Miniño A.

Publication Date: August 22, 2023

Publication Site: CDC, National Vital Statistics System

Unhelpful, inflammatory Jama Network Open paper suggests that people in Red states dream up vaccine injuries

Link:https://www.drvinayprasad.com/p/unhelpful-inflammatory-jama-network?utm_source=post-email-title&publication_id=231792&post_id=143191018&utm_campaign=email-post-title&isFreemail=true&r=9bg2k&triedRedirect=true&utm_medium=email

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Now let’s turn to the paper. Here is what the authors find (weak correlation btw voting and vaccine injuries) , and here are the issues.

  1. These data are ecological. It doesn’t prove that republicans themselves are more likely to report vaccine injuries. It would not be difficult to pair voting records with vaccine records at an individual patient level if the authors wished to do it right— another example of research laziness.
  2. What if republicans actually DO have more vaccine injuries? The authors try to correct for the fact by adjusting for influenza adverse events.

Let me explain why this is a poor choice. The factors that predict whether someone has an adverse event to influenza vaccine may not be the same as those that predict adverse events from covid shots. It could be that there are actually more covid vaccine injuries in one group than another— even though both had equal rates of influenza injuries.

Another way to think of it is, there can be two groups of people and you can balance them by the rate with which they get headaches from drinking wine, but one group can be more likely to get headaches from reading without glasses because more people in that group wear glasses. In other words, states with more republicans might be states with specific co-morbidities that predict COVID vaccine adverse side effects but not influenza vaccine side effects. We already know that COVID vaccine injuries do affect different groups (young men, for e.g.).

Author(s): Vinay Prasad

Publication Date: 2 Apr 2024

Publication Site: Vinay Prasad’s Thoughts and Observations at substack

Links Between Early Retirement and Mortality

Link: https://www.ssa.gov/policy/docs/workingpapers/wp93.html#:~:text=Relative%20to%20those%20retiring%20at,odds%20of%20dying%20by%200.1089

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In this paper I use the 1973 cross-sectional Current Population Survey (CPS) matched to longitudinal Social Security administrative data (through 1998) to examine the relationship between retirement age and mortality for men who have lived to at least age 65 by year 1997 or earlier.1 Logistic regression results indicate that controlling for current age, year of birth, education, marital status in 1973, and race, men who retire early die sooner than men who retire at age 65 or older. A positive correlation between age of retirement and life expectancy may suggest that retirement age is correlated with health in the 1973 CPS; however, the 1973 CPS data do not provide the ability to test that hypothesis directly.

Regression results also indicate that the composition of the early retirement variable matters. I represent early retirees by four dummy variables representing age of entitlement to Social Security benefits—exactly age 62 to less than 62 years and 3 months (referred to as exactly age 62 in this paper), age 62 and 3 months to 62 and 11 months, age 63, and age 64. The reference variable is men taking benefits at age 65 or older. I find that men taking benefits at exactly age 62 have higher mortality risk than men taking benefits in any of the other four age groups. I also find that men taking benefits at age 62 and 3 months to 62 and 11 months, age 63, and age 64 have higher mortality risk than men taking benefits at age 65 or older. Estimates of mortality risk for “early” retirees are lowered when higher-risk age 62 retirees are combined with age 63 and age 64 retirees and when age 62 retirees are compared with a reference variable of age 63 and older retirees. Econometric models may benefit by classifying early retirees by single year of retirement age—or at least separating age 62 retirees from age 63 and age 64 retirees and age 63 and age 64 retirees from age 65 and older retirees—if single-year breakdowns are not possible.

The differential mortality literature clearly indicates that mortality risk is higher for low-educated males relative to high-educated males. If low-educated males tend to retire early in relatively greater numbers than high-educated males, higher mortality risk for such individuals due to low educational attainment would be added to the higher mortality risk I find for early retirees relative to that for normal retirees. Descriptive statistics for the 1973 CPS show that a greater proportion of age 65 retirees are college educated than age 62 retirees. In addition, a greater proportion of age 64 retirees are college educated than age 62 retirees, and a lesser proportion of age 64 retirees are college educated than age 65 or older retirees. Age 63 retirees are only slightly more educated than age 62 retirees.

Despite a trend toward early retirement over the birth cohorts in the 1973 CPS, I do not find a change in retirement age differentials over time. However, I do find a change in mortality risk by education over time. Such a change may result from the changing proportion of individuals in each education category over time, a trend toward increasing mortality differentials by socioeconomic status, or a combination of the two.

This paper does not directly explore why a positive correlation between retirement age and survival probability exists. One possibility is that men who retire early are relatively less healthy than men who retire later and that these poorer health characteristics lead to earlier deaths. One can interpret this hypothesis with a “quasidisability” explanation and a benefit optimization explanation. Links between these interpretations and my analysis of the 1973 CPS are fairly speculative because I do not have the appropriate variables needed to test these interpretations.

A quasi-disability explanation, following Kingson (1982), Packard (1985), and Leonesio, Vaughan, and Wixon (2000), could be that a subgroup of workers who choose to take retired-worker benefits at age 62 is significantly less healthy than other workers but unable to qualify for disabled-worker benefits. An econometric model with a mix of both these borderline individuals and healthy individuals retiring at age 62 and with almost no borderline individuals retiring at age 65 could lead to a positive correlation between retirement and mortality, even if a greater percentage of individuals who retire at age 62 are healthy than unhealthy. Evidence for this hypothesis can be inferred from the finding that retiring at exactly age 62 increases the odds of dying in a unit age interval by 12 percent relative to men retiring at 62 and 3 months to 62 and 11 months for men in the 1973 CPS. In addition, retiring exactly at age 62 increases the odds of dying by 23 percent relative to men retiring at age 63 and by 24 percent relative to men retiring at age 64. A group with relatively severe health problems waiting for their 62nd birthday to take benefits could create this result.

An explanation based on benefit optimization follows Hurd and McGarry’s research (1995, 1997) in which they find that individuals’ subjective survival probabilities roughly predict actual survival. If men in the 1973 CPS choose age of benefit receipt based on expectations of their own life expectancy, then perhaps a positive correlation between age of retirement and life expectancy implies that their expectations are correct on average. If actuarial reductions for retirement before the normal retirement age are linked to average life expectancy and an individual’s life expectancy is below average, it may be rational for that individual to retire before the normal retirement age. Evidence for this hypothesis can be inferred from the fact that men retiring at age 62 and 3 months to age 62 and 11 months, age 63, and age 64 all experience greater mortality risk than men retiring at age 65 or older. If only men with severe health problems who are unable to qualify for disability benefits are driving the results, we probably would not expect to see this result. We might expect most of these individuals to retire at the earliest opportunity (exactly age 62).2

Author(s): Hilary Waldron

Publication Date: August 2001

Publication Site: Social Security Office of Policy, ORES Working Paper No 93

Supercentenarian and remarkable age records exhibit patterns indicative of clerical errors and pension fraud

Link: https://www.biorxiv.org/content/10.1101/704080v3

PDF: https://www.biorxiv.org/content/10.1101/704080v3.full.pdf

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Abstract

The observation of individuals attaining remarkable ages, and their concentration into geographic sub-regions or ‘blue zones’, has generated considerable scientific interest. Proposed drivers of remarkable longevity include high vegetable intake, strong social connections, and genetic markers. Here, we reveal new predictors of remarkable longevity and ‘supercentenarian’ status. In the United States, supercentenarian status is predicted by the absence of vital registration. The state-specific introduction of birth certificates is associated with a 69-82% fall in the number of supercentenarian records. In Italy, England, and France, which have more uniform vital registration, remarkable longevity is instead predicted by poverty, low per capita incomes, shorter life expectancy, higher crime rates, worse health, higher deprivation, fewer 90+ year olds, and residence in remote, overseas, and colonial territories. In England and France, higher old-age poverty rates alone predict more than half of the regional variation in attaining a remarkable age. Only 18% of ‘exhaustively’ validated supercentenarians have a birth certificate, falling to zero percent in the USA, and supercentenarian birthdates are concentrated on days divisible by five: a pattern indicative of widespread fraud and error. Finally, the designated ‘blue zones’ of Sardinia, Okinawa, and Ikaria corresponded to regions with low incomes, low literacy, high crime rate and short life expectancy relative to their national average. As such, relative poverty and short lifespan constitute unexpected predictors of centenarian and supercentenarian status and support a primary role of fraud and error in generating remarkable human age records.

Author(s): Saul Justin Newman

https://doi.org/10.1101/704080

Publication Date: 14 Mar 2024

Publication Site: bioRXiV

#20: Many great things you missed this year

Link: https://www.scientificdiscovery.dev/p/20-so-many-great-things-you-missed

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You can see that, in childhood, in the US, the most common causes of death are ‘external causes’. This is a broad category that includes accidents, falls, violence, and overdoses, and is shown in red. But there’s also a notable contribution from birth disorders (in muted green), childhood cancers (in blue), and respiratory diseases (in cyan).

The share of deaths in childhood from cancers stood out to me. We’ve seen lots of progress against many childhood cancers over the last 50 years — notably in treating leukemia, brain cancers, kidney cancers, lymphomas, and retinoblastoma — but this is a reminder that there’s still further to go.

From adolescence until middle-age, ‘external causes’ are now the overwhelming cause of death. Around 80% of deaths at the age of 20 in the US are due to external causes. These result from causes such as accidents, violence, and overdoses.

At older ages, diseases rise in importance. Causes of death also become more varied, although cardiovascular diseases and cancers are the most common.

You might also be wondering about the brown category at the bottom, called ‘special ICD codes’. That’s a placeholder category in the system for deaths caused by new diseases — predominantly Covid-19, since the data spans 2018 to 2021.3

Author(s): SALONI DATTANI

Publication Date: 16 Mar 2022

Publication Site: Scientific Discovery on substack

The mystery of the ‘golden cohort’

Link:https://www.bbc.com/news/uk-15024436

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The life experience of British people born between the years 1925 and 1934 has long had demographers and insurance companies scratching their heads.

For reasons which remain unclear, individuals within this slice of the UK population have been living longer and healthier lives than groups both older and younger.

Today the Office for National Statistics returns to the mystery of the so-called “golden cohort”, trying to understand better why the members of the generation born in the midst of the Great Depression have been enjoying higher rates of mortality improvement throughout their adult lives.

One tool used to track the golden cohort is a heat chart which, in this case, looks at annual mortality improvements for men and women. It takes a bit of explaining, but the diagrams reflect the social history of Britain over the last century or so.

Starting with men (Figure 1a), the most obvious feature of the heat chart are the vertical bands of blue and brown in the bottom left corner. Blue represents worsening mortality and brown improving, so the blue slice closest furthest to the left is the cohort decimated by World War I and the influenza pandemic.

Author(s):

Publication Date: 23 Sep 2011

Publication Site: BBC

Why do Swiss people die?

Link: https://blog.datawrapper.de/why-do-swiss-people-die/

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Looking at the evolution of premature deaths, we can celebrate the progress made in medical research. Years lost to infectious diseases like tuberculosis have reduced dramatically, and deaths due to AIDS in particular are nowadays close to zero, a drastic decline since the height of the pandemic in the 1990s. Cancer and cardiovascular diseases have followed a similar path, though they still cause a high number of premature deaths. We can observe that years lost to suicide before age 70 have also declined significantly. In a country where assisted suicide is legal, there is maybe something empowering in the prospect of dying healthy of old age. Years lost to alcoholism and car accidents have also declined — it may be that prevention and overall security have reduced these types of more behavioral deaths.

Author(s): Luc Guillemot

Publication Date: 26 Oct 2023

Publication Site: datawrapper

Lessons Learned During the Pandemic Can Help Improve Care in Nursing Homes

Link: https://oig.hhs.gov/documents/evaluation/9808/OEI-02-20-00492.pdf

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OIG recommends that the Centers for Medicare & Medicaid Services (CMS):

1. Implement and expand upon its policies and programs to strengthen the nursing home workforce.

2. Reassess nurse aide training and certification requirements.

3. Update the nursing home requirements for infection control to incorporate lessons learned from the pandemic.

4. Provide effective guidance and assistance to nursing homes on how to comply with updated infection control requirements.

5. Facilitate sharing of strategies and information to help nursing homes overcome challenges and improve care.

CMS did not explicitly state its concurrence or nonconcurrence for the five recommendations.

Author: Christi A. Grimm

Publication Date: February 2024

Publication Site: Office of the Inspector General, HHS

Staffing shortages, poor infection control plague nursing homes

Link: https://www.upi.com/Health_News/2024/03/01/nursing-home-staffing-shortage/8751709302182/

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Although the pandemic has ended, staffing shortages and employee burnout still plague U.S. nursing homes, a new government report finds.

But the problems didn’t end there: The report, issued Thursday by the Inspector General’s Office at the U.S. Department of Health and Human Services, showed that infection-control procedures were still sorely lacking at many facilities.

Not only that, COVID-19 booster vaccination rates remain far lower than they should be, with only 38% of residents and 15% of staff up-to-date on their shots, according to a recent KFF report.

Author(s): Robin Foster

Publication Date: 1 Mae 2024

Publication Site: UPI

COVID-19 Had a Devastating Impact on Medicare Beneficiaries in Nursing Homes During 2020

Link: https://oig.hhs.gov/oei/reports/OEI-02-20-00490.pdf

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The overall mortality rate in nursing homes rose 32 percent in 2020. The pandemic had far-reaching implications for all nursing home beneficiaries, beyond those who had or likely had COVID-19. Among all Medicare beneficiaries in nursing homes, 22.5 percent died in 2020, which is an increase of one-third from 2019 when 17.0 percent of Medicare beneficiaries in nursing homes died. This 32-percent increase amounts to 169,291 more deaths in 2020 than if the mortality rate had remained the same as in 2019. Each month of 2020 had a higher mortality rate than the corresponding month a year earlier.

Almost 1,000 more beneficiaries died per day in April 2020 than in the previous year. In April 2020 alone, a total of 81,484 Medicare beneficiaries in nursing homes died. This is almost 30,000 more deaths—an average of about 1,000 per day—compared to the previous year. This increase in number occurred even though the nursing home population was smaller in April 2020. Overall, Medicare beneficiaries in nursing homes were almost twice as likely to die in April 2020 than in April 2019. In April 2020, 6.3 percent of all Medicare beneficiaries in nursing homes died, whereas 3.5 percent died in April 2019.

The mortality rates also rose at the end of 2020. In November, 5.1 percent of all Medicare beneficiaries in nursing homes died, and in December that increased to 6.2 percent. Again, these rates are markedly higher than the previous year. In November 2019, 3.6 percent of all Medicare beneficiaries in nursing homes died, and, in December 2019, 3.8 percent did.

Author(s): Jenell Clarke-Whyte and team

Publication Date: June 2021

Publication Site: Office of Inspector General, HHS

‘Fourth Wave’ of Opioid Epidemic Crashes Ashore, Propelled by Fentanyl and Meth

Link:https://kffhealthnews.org/news/article/fourth-wave-opioid-epidemic-fentanyl-millennium-health-report/

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The United States is knee-deep in what some experts call the opioid epidemic’s “fourth wave,” which is not only placing drug users at greater risk but is also complicating efforts to address the nation’s drug problem.

These waves, according to a report out today from Millennium Health, began with the crisis in prescription opioid use, followed by a significant jump in heroin use, then an increase in the use of synthetic opioids like fentanyl.

The latest wave involves using multiple substances at the same time, combining fentanyl mainly with either methamphetamine or cocaine, the report found. “And I’ve yet to see a peak,” said one of the co-authors, Eric Dawson, vice president of clinical affairs at Millennium Health, a specialty laboratory that provides drug testing services to monitor use of prescription medications and illicit drugs.

The report, which takes a deep dive into the nation’s drug trends and breaks usage patterns down by region, is based on 4.1 million urine samples collected from January 2013 to December 2023 from people receiving some kind of drug addiction care.

Its findings offer staggering statistics and insights. Its major finding: how common polysubstance use has become. According to the report, an overwhelming majority of fentanyl-positive urine samples — nearly 93% — contained additional substances. “And that is huge,” said Nora Volkow, director of the National Institute on Drug Abuse at the National Institutes of Health.

Author(s): Colleen DeGuzman

Publication Date: 21 Feb 2024

Publication Site: KFF Health News