Phantoms never die: living with unreliable population data

Mary Pat Campbell

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

Mary Pat Campbell

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

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Excerpt:

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

Reports of COVID-19 Vaccine Adverse Events in Predominantly Republican vs Democratic States

Mary Pat Campbell

Link: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2816958?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=032924

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Abstract:

Importance  Antivaccine sentiment is increasingly associated with conservative political positions. Republican-inclined states exhibit lower COVID-19 vaccination rates, but the association between political inclination and reported vaccine adverse events (AEs) is unexplored.

Objective  To assess whether there is an association between state political inclination and the reporting rates of COVID-19 vaccine AEs.

Design, Setting, and Participants  This cross-sectional study used the AE reports after COVID-19 vaccination from the Vaccine Adverse Event Reporting System (VAERS) database from 2020 to 2022, with reports after influenza vaccines from 2019 to 2022 used as a reference. These reports were examined against state-level percentage of Republican votes in the 2020 US presidential election.

Exposure  State-level percentage of Republican votes in the 2020 US presidential election.

Main Outcomes and Measures  Rates of any AE among COVID-19 vaccine recipients, rates of any severe AE among vaccine recipients, and the proportion of AEs reported as severe.

Results  A total of 620 456 AE reports (mean [SD] age of vaccine recipients, 51.8 [17.6] years; 435 797 reports from women [70.2%]; a vaccine recipient could potentially file more than 1 report, so reports are not necessarily from unique individuals) for COVID-19 vaccination were identified from the VAERS database. Significant associations between state political inclination and state AE reporting were observed for all 3 outcomes: a 10% increase in Republican voting was associated with increased odds of AE reports (odds ratio [OR], 1.05; 95% CI, 1.05-1.05; P < .001), severe AE reports (OR, 1.25; 95% CI, 1.24-1.26; P < .001), and the proportion of AEs reported as severe (OR, 1.21; 95% CI, 1.20-1.22; P < .001). These associations were seen across all age strata in stratified analyses and were more pronounced among older subpopulations.

Conclusions and Relevance  This cross-sectional study found that the more states were inclined to vote Republican, the more likely their vaccine recipients or their clinicians reported COVID-19 vaccine AEs. These results suggest that either the perception of vaccine AEs or the motivation to report them was associated with political inclination.

Author(s):David A. Asch, MD, MBA1,2; Chongliang Luo, PhD3; Yong Chen, PhD2,4,5Author(s):

Publication Date: 29 Mar 2024

Publication Site: JAMA Network Open

Ominous Technical Trends for US Treasury Bulls, Three Durations

Mary Pat Campbell

Link:https://mishtalk.com/economics/ominous-technical-trends-for-us-treasury-bulls-three-durations/

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Image courtesy of Stockcharts.Com, inset by Centerpoint Securities, annotations by Mish.

Excerpt:

Technical patterns on 2-year, 10-year, and 30-year US treasuries all suggest yields are heading higher. Let’s also discuss the supporting fundamental case.

Centerpoint explains “An ascending triangle chart pattern is a bullish technical pattern that typically signals the continuation of an uptrend. They can signal a coming bullish breakout above an area of resistance after it has been tested several times.”

Many people do not believe in technical patterns, others believe in nothing else. Certainly, technical patterns fail often enough.

My take is they work best as entry and exit point strategies, especially when fundamentals align.

Author(s): Mike Shedlock

Publication Date: 3 Apr 2024

Publication Site: MishTalk

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

Mary Pat Campbell

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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Excerpt:

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

Mary Pat Campbell

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