Changes in Cause-of-Death Attribution During the Covid-19 Pandemic: Association with Hospital Quality Metrics and Implications for Future Research

Kathleen A. Fairman
Kellie J. Goodlet
James D. Rucker

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Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is often comorbid with conditions subject to quality metrics (QM) used for hospital performance assessment and rate-setting. Although diagnostic coding change in response to financial incentives is well documented, no study has examined the association of QM with SARS-CoV-2 cause-of-death attribution (CODA). Calculations of excess all-cause deaths overlook the importance of accurate CODA and of distinguishing policy-related from virus-related mortality.

Objective

Examine CODA, overall and for QM and non-QM diagnoses, in 3 pandemic periods: awareness (January 19-March 14), height (March 15-May 16), and late (May 17-June 20).

Methods

Retrospective analysis of publicly available national weekly COD data, adjusted for population growth and reporting lags, October 2014-June 20, 2020. CODA in 5 pre-pandemic influenza seasons was compared with 2019-20. Suitability of the data to distinguish policy-related from virus-related effects was assessed.

Results

Following federal guidance permitting SARS-CoV-2 CODA without laboratory testing, mortality from the QM diagnoses cancer and chronic lower respiratory disease declined steadily relative to prior-season means, reaching 4.4% less and 12.1% less, respectively, in late pandemic. Deaths for non-QM diagnoses increased, by 21.0% for Alzheimer’s disease and 29.0% for diabetes during pandemic height. Increases in competing CODs over historical experience, suggesting SARS-CoV-2 underreporting, more than offset declines during pandemic height. However, in the late-pandemic period, declines slightly numerically exceeded increases, suggesting SARS-CoV-2 overreporting. In pandemic-height and late-pandemic periods, respectively, only 83.5% and 69.7% of increases in all-cause deaths were explained by changes in the reported CODs, including SARS-CoV-2, preventing assessment of policy-related mortality or of factors contributing to increased all-cause deaths.

Conclusions

Substitution of SARS-CoV-2 for competing CODs may have occurred, particularly for QM diagnoses and late in the pandemic. Continued monitoring of these trends, qualitative research on pandemic CODA, and the addition of place-of-death data and psychiatric CODs to the file would facilitate assessment of policy-related and virus-related effects on mortality.

Ascertainment of the number of deaths from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is foundational to understanding the severity, scope, and spread of the infection. Despite its importance, estimation of SARS-CoV-2 deaths is challenging because advanced age, genetic polymorphisms, and obesity-related comorbidities that predispose to inflammatory states increase the likelihood of dysregulated immunological function, severe respiratory distress, and mortality from infectious respiratory illness. ^1,2 These host factors represent competing potential causes of death (COD). For example, 98.8% of Italy’s SARS-CoV-2 deaths occurred in persons with > 1 comorbidity, 48.6% with > 3 comorbidities, and median decedent age was 80 years. ³ Similarly, of U.S. SARS-CoV-2 deaths reported as of May 28, 2020, 93% involved other CODs (mean 2.5 additional causes), and 60% occurred in persons aged > 75 years. ⁴ This pattern of multiple contributing CODs is common in respiratory infection-related mortality. ⁵

In death certificate issuance during the pandemic, methods to account for this pattern varied, as no single standard for SARS-CoV-2-attributable death exists. In Italy, all deaths in patients testing positive for SARS-CoV-2 were attributed to the infection despite high prevalence rates for comorbid conditions, measured in early deaths: ischemic heart disease (30%), diabetes (36%), cancer (20%), and atrial fibrillation (25%). ⁶ The U.S. National Center for Health Statistics (NCHS) issued death-certification guidance on March 4, 2020, indicating that SARS-CoV-2 should be reported if “the disease caused or is assumed to have caused or contributed to death.” ⁷ Follow-up guidance issued on April 3 indicated that it was “acceptable to report COVID-19 on a death certificate without [laboratory test] confirmation” if circumstances indicating likely infection were “compelling within a reasonable degree of certainty.” ⁸

This nonspecific guidance should be interpreted in light of previous research findings that COD attribution (CODA) errors are common on death certificates, particularly in infectious disease and septic shock. ^9,10 In one survey of New York City (NYC) resident physicians in 2010, 49% indicated they had knowingly reported an inaccurate COD on one or more certificates, often (54%) at the behest of hospital staff, and 70% reported they had at least once been unable to report septic shock “as an accepted cause of death” and had been “forced to list an alternate cause.” ⁹ In an audit of NYC data from 2010-2014, 67% of pneumonia death certificates contained > 1 error, compared with 46% for cancer and 32% for diabetes. ¹⁰

Such CODA ambiguities are often addressed by calculating “excess deaths,” defined as all-cause deaths exceeding those projected from historical experience. ¹¹ This method, recently used to estimate that official tallies of SARS-CoV-2 deaths represented only about 66%-78% of the disease’s true mortality impact, ^12,13 is potentially advantageous in estimating SARS-CoV-2 impact by accounting for deaths that may not have been explicitly coded as infection-related. ¹⁴ Examples include deaths from cardiac events to which undetected SARS-CoV-2 may have contributed ¹⁵ or out-of-hospital deaths occurring without medical care because of health-system overcrowding. ¹⁶ Despite these advantages, the method is compromised by 3 considerations when applied to SARS-CoV-2 CODA, which should be quantified to inform future policy.

First, the method should distinguish natural from societal causes to account for possible consequences of policy decisions and fears that, although prompted by anticipated effects of SARS-CoV-2, were not direct or inevitable viral sequelae. Examples include suicides from stay-at-home order-related labor market contraction ¹⁷ and social isolation, ¹⁸ increases in domestic violence, ¹⁹ overdoses due to interruptions in substance use disorder treatment, ²⁰ and delays in emergency care for life-threatening conditions ^21–23 in geographic areas where health-system overcrowding was expected but not realized. ^24,25 To promote evidence-based public health policy, population-level disease-mitigation strategies that go beyond traditional practices of isolating the sick and quarantining those exposed to disease merit empirical investigation. ^26,27

Second, the method should reflect the effects that financial incentives around hospital quality metrics (QM), which are commonly associated with provider coding practices, may have on CODA. ^28–30 For example, in United Kingdom hospitals, increases in coding for palliative-care admissions produced a severity-adjusted mortality-rate decline of 50% over the 5-year period ending in 2009, while the crude death rate remained unchanged. ³¹ Although we are not aware of studies linking QMs to CODA, it is known that CODA errors are more likely to occur in hospitals than elsewhere, ³² with an 85% error rate reported in comparisons of death certificates with autopsy findings at one regional academic institution. ³³ The potential effect of financial incentives on CODA is particularly important for SARS-CoV-2 because several competing CODs, including chronic lower respiratory disease (CLRD), acute myocardial infarction, heart failure, pneumonia, and stroke, are included in Medicare 30-day mortality measures used to calculate prospective payment rates. ³⁴ All but one of these (CLRD) is included in Agency for Healthcare Research and Quality inpatient quality indicators. ³⁵ Sepsis and cancer, other competing causes of death, are also the target of QM. ^36–38 Although not affecting all-cause death counts, the incentive to substitute SARS-CoV-2 for another COD could affect the accuracy of the SARS-CoV-2-attributed count.

Third, the method should account for baseline life expectancies among those whose deaths were reported as caused by SARS-CoV-2. For example, at age 80 years, the 1-year probability of death is 5.8% for males and 4.3% for females, higher in those with cardiovascular comorbidities. ^39,40 In that age group, the population-level risk of a SARS-CoV-2 death in New York City, a pandemic epicenter, was 1.5% in about 3 pandemic months through June 17, 2020. ⁴¹ Thus, deaths from competing CODs would be expected to decline late in the pandemic and in subsequent months. From a policy perspective, quantifying this effect is consistent with the quality-adjusted life year approach in evidence-based medicine, which considers future life expectancy in assessing the effects of disease and disease-mitigation interventions. ⁴²

To permit assessments of SARS-CoV-2-related mortality, publicly available NCHS data include weekly aggregated totals for all-cause deaths, natural-cause deaths, and selected categories of CODs, reported as final data for 2014-2018 and provisional data for 2019-2020. ⁴ These data, which are updated weekly, have important limitations. First, International Classification of Diseases (ICD)-10 diagnosis codes are grouped into broad categories, rather than the individual ICD-10 codes available in full COD files (Appendix 1). Second, only 11 selected diagnostic categories are reported. Third, although 63% of deaths are reported within 10 days, reporting lags vary by state. ⁴ Reporting delays for injurious deaths are greater because they require investigation (e.g., forensic toxicology). ⁴³ Pending investigation, these deaths are often assigned ICD-10 code R99, “ill-defined and unknown cause of mortality.” ⁴³

In this exploratory study, we used these files to provide preliminary evidence on the following: (1) change in CODA compared with historical experience; (2) association of CODA with QM; and (3) suitability of the files to distinguish policy-related from virus-related effects. All analyses were adjusted for population and reporting lags and based on comparisons of 2020 with equivalent weeks in the 5 most recent years. We hypothesized that if substitution of SARS-CoV-2 for alternative CODs occurred, death counts for competing diagnoses would decline relative to historical experience during the pandemic, especially after issuance of the NCHS death-certification guidance; these declines would be greater for QM than for other conditions; and they would accelerate late in the pandemic as earlier SARS-CoV-2 deaths offset later deaths from competing causes.

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Jul 30, 2020
SciScore for 10.1101/2020.07.25.20162198: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected.
Randomization not detected.
Blinding not detected.
Power Analysis not detected.
Sex as a biological variable not detected.
Table 2: Resources
Software and Algorithms
Sentences Resources
All analyses were performed using IBM SPSS v25.0 (Armonk, NY) with a priori alpha=0.05.
SPSS
suggested: (SPSS, RRID:SCR_002865)
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Important limitations of this exploratory work should be noted. Foremost, findings of …
SciScore for 10.1101/2020.07.25.20162198: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected.
Randomization not detected.
Blinding not detected.
Power Analysis not detected.
Sex as a biological variable not detected.
Table 2: Resources
Software and Algorithms
Sentences Resources
All analyses were performed using IBM SPSS v25.0 (Armonk, NY) with a priori alpha=0.05.
SPSS
suggested: (SPSS, RRID:SCR_002865)
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Important limitations of this exploratory work should be noted. Foremost, findings of this study should be reassessed using data from later weeks to verify or refute the preliminary trends described here. Additional limitations suggest improvements that might be made to the COD files pending availability of the full, final COD data. These full COD data, which are currently available only through 2018, likely will not be posted until 2022. First, a large number of deaths had no COD or a nonspecific COD recorded in the file. Compared with prior years, NEC deaths more than doubled in a progression that began early in 2020, as pandemic awareness was growing but before SARS-CoV-2 would have been expected to have any biological effect. Moreover, of increases in all-cause death during the pandemic, 16.5% during pandemic height and 30.3% in the late-pandemic period could not be linked to change in the specific CODs included in the file. Based on national evidence of increases in prescriptions for psychotropic drugs beginning on February 16, 2020,54 reported tripling of depression or anxiety comparing April and May 2019 to 2020,55 and unprecedented media coverage of SARS-CoV-2 compared with other pandemics,48 measurement of the mental health effects of growing pandemic awareness and stay-at-home orders on mortality would likely be helpful. This measurement is not possible with the NCHS COD files as currently constructed but could be facilitated with the addition of a new category for ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:
Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
No protocol registration statement was detected.
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Software and Algorithms
Sentences	Resources
All analyses were performed using IBM SPSS v25.0 (Armonk, NY) with a priori alpha=0.05.	SPSS suggested: (SPSS, RRID:SCR_002865)

Software and Algorithms
Sentences	Resources
All analyses were performed using IBM SPSS v25.0 (Armonk, NY) with a priori alpha=0.05.	SPSS suggested: (SPSS, RRID:SCR_002865)

SciScore for 10.1101/2020.07.25.20162198: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Software and Algorithms
Sentences	Resources
All analyses were performed using IBM SPSS v25.0 (Armonk, NY) with a priori alpha=0.05.	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Important limitations of this exploratory work should be noted. Foremost, findings of this study should be reassessed using data from later weeks to verify or refute the preliminary trends described here. Additional limitations suggest improvements that might be made to the COD files pending availability of the full, final COD data. These full COD data, which are currently available only through 2018, likely will not be posted until 2022. First, a large number of deaths had no COD or a nonspecific COD recorded in the file. Compared with prior years, NEC deaths more than doubled in a progression that began early in 2020, as pandemic awareness was growing but before SARS-CoV-2 would have been expected to have any biological effect. Moreover, of increases in all-cause death during the pandemic, 16.5% during pandemic height and 30.3% in the latepandemic period could not be linked to change in the specific CODs included in the file. Based on national evidence of increases in prescriptions for psychotropic drugs beginning on February 16, 2020,54 reported tripling of depression or anxiety comparing April and May 2019 to 2020,55 and unprecedented media coverage of SARS-CoV-2 compared with other pandemics,48 measurement of the mental health effects of growing pandemic awareness and stay-at-home orders on mortality would likely be helpful. This measurement is not possible with the NCHS COD files as currently constructed but could be facilitated with the addition of a new category for psychiatric UCODs (i.e., ICD10 category F, excluding intellectual disabilities and developmental disorders). Similarly, disaggregation of R99, which is used pending forensic investigation of injurious deaths,43 from the rest of the NEC category would facilitate assessments of potential deaths from suicide or overdose. Second, we imputed possible effects of financial incentives on CODA but did not measure incentives directly. To support or refute our findings, the addition of place of death to the COD files would allow for assessment of whether the QM associations we observed were greater in hospital than in nonhospital settings. Additionally, this change would facilitate assessment of changes in out-of-hospital deaths from heart disease or cerebrovascular disease,21-23 informing the question of whether stay-at-home orders exacerbated deaths from these causes in geographic areas where hospital bed supply was not limited during the pandemic.24-25 Third, neither the study outcome measure nor recommended changes to the COD files would account for long-term physiological effects of SARS-CoV-2, which are the subject of an ongoing cohort study;56 of suboptimal policy decisions that could have increased SARS-CoV-2 deaths, (e.g., mandatory posthospitalization release of infected patients to nursing facilities);57 or of long-term effects of stay-at-home order-related declines in preventive care (e.g., pediatric immunizations).58 Finally, it should be noted that although recent comparisons of infection-fatality and case-fatality rates for influenza and SARS-CoV-2 have adopted an implicit assumption that the respective infections’ numerators are equivalent,59 the 2 estimates are based on markedly different methods that make direct comparison problematic. National estimates of influenza hospitalizations and deaths are based on a statistical model incorporating ambulatory and inpatient data, in addition to the raw death certificate counts reported here.60 In contrast, SARS-CoV-2 death counts rely solely on the judgment of the reporter and are, at present, not clinically validated. Pending assessment of the reliability and validity of SARS-CoV-2 death reporting, policy making death-certificate counts should be circumspect, recognizing both the possible vulnerability of the SARS-CoV-2 data to financial incentives and detection bias and the absence of important COD information from the currently available NCHS files. Conclusion SARS-CoV-2 deaths appear underreported with some comorbidities, such as Alzheimer’s disease and diabetes, and overreported with others including cancer and CLRD, the second and third leading causes of natural death in the United States. Possible overreporting slightly exceeded underreporting for competing CODs beginning late in the pandemic and merits further investigation. Relatively small additions to available NCHS COD data—including place of death, disaggregation of R99 from other NEC deaths, and psychiatric UCODs—could facilitate preliminary analyses of important questions about direct effects of SARS-CoV-2, as well as the effects of stay-at-home orders and media-driven pandemic awareness, on mortality in the pandemic period. Figure 1. Population-Adjusted and Lag-Adjusted Weekly Death Counts, by Week of Season, 5-Year and 3-Year Prior Season Means and 2019-20 Panel 1. Overview Panel 2. Hospital Quality Metric Diagnoses Figure 1 (continued). Population-Adjusted and Lag-Adjusted Weekly Death Counts, by Week of Season, 2014-15 to 2018-19 (Mean) and 2019-2020 Panel 3. Non Hospital Quality-Metric Diagnoses Panel 4. Other Competing Causes of Death Notes: Season week 1 begins September 29-October 5, depending on year. Five-year means are seasons 2014-15 through 201819, and 3-year means are 2016-17 through 2018-19. Season week 17 (January 19) is the start of pandemic awareness (beginning of intense media coverage of the pandemic).48 Season week 25 (March 15) is the approximate start of the pandemic height period. Season week 28 (April 5) corresponds to the issuance of NCHS guidance stating laboratory testing was recommended but not required to name SARS-CoV-2 as the underlying cause of death. Season week 34 (May 17, 2020) is the start of the late pandemic period. Acute respiratory (Panel 1) is the sum of SARS-CoV-2, influenza/pneumonia, and other respiratory illnesses, nearly all of which are acute (e.g., pharyngitis). Not elsewhere classified (Panel 1) includes R99, the code used for non natural-cause deaths pending forensic investigation. QM diagnoses include cancer, chronic lower respiratory disease, and septicemia. Non-QM diagnoses include Alzheimer’s disease, diabetes, kidney disease, and other respiratory illnesses. QM=quality metric; SARS-CoV2=severe acute respiratory syndrome coronavirus 2. Table 1. Population-Adjusted and Reporting Lag-Adjusted Mortality Counts, 5-Year Means and Selected Causes of Death, by Pandemic Time Period AllHeart Cancer CLRD Cause Disease Awareness (January 19-March 14) 429,578 101,925 87,088 26,642 2020 431,021 99,822 86,076 25,171 Change 1,443 -2,103 -1,012 -1,471 0.3% -2.1% -1.2% -5.5% Height (March 15-May 16) 450,736 105,741 95,845 27,144 2020 573,984 115,451 94,761 26,633 Change 123,247 9,710 -1,084 -511 27.3% 9.2% -1.1% -1.9% Late pandemic (May 17-June 20) 238,360 55,267 52,653 13,499 2020 266,453 55,905 50,336 11,869 Change 28,094 639 -2,318 -1,630 11.8% 1.2% -4.4% -12.1% Reporting guidance through observation end (June 20) 534,420 124,589 116,327 31,089 2020 661,614 132,794 112,677 28,802 Change 127,194 8,205 -3,650 -2,287 23.8% 6.6% -3.1% -7.4% Sensitivity Analyses Excluding Final Week Late pandemic (May 17-June 13)b 190,926 44,345 42,045 10,858 2020 217,464 45,399 40,709 9,619 Change 26,538 1,054 -1,336 -1,239 13.9% 2.4% -3.2% -11.4% Reporting guidance through observation end (June 13)b 486,986 113,668 105,718 28,448 2020 612,625 122,289 103,050 26,552 Change 125,639 8,621 -2,669 -1,897 25.8% 7.6% -2.5% -6.7% a CBVD Alzheimers Disease 22,286 22,826 540 2.4% 18,356 18,164 -192 -1.0% 23,516 25,571 2,055 8.7% 18,533 22,425 3,892 21.0% 12,333 12,996 663 5.4% 9,579 10,596 1,016 10.6% 27,786 29,993 2,207 7.9% 21,627 25,603 3,976 18.4% 9,870 10,497 627 6.4% 7,680 8,526 846 11.0% 25,322 27,493 2,171 8.6% 19,728 23,533 3,805 19.3% Diabe Sum of influenza/pneumonia, SARS-CoV-2, and other respiratory, which consists mostly of acute infectious respiratory illness (e.g., pharyngitis). b Final w account for possible underreporting even after adjustment. CBVD=cerebrovascular disease; CLRD=chronic lower respiratory disease; SARS-CoV-2=seve Appendix 1. ICD-10 Code Groupings in NCHS Preliminary Cause-of-Death Files and Quality NCHS-Reported ICD-10 ICD-10 Code Description Measure Category Codes Bolded Diagnosis Codes Indica Septicemia A40-A41 Sepsis A40 Streptococcal sepsis A41 Other sepsis Malignant neoplasms Diabetes C00-C97 Alzheimer’s disease Influenza and pneumonia G30 J10-J18 Pneumonia J10 Influenza due to other identified influenza J11 Influenza due to unidentified influenza viru J12 Viral pneumonia, not elsewhere classif J13 Pneumonia due to Streptococcus pneu J14 Pneumonia due to Hemophilus influen J15 Bacterial pneumonia, not elsewhere cl J16 Pneumonia due to other infectious org classified J17 Pneumonia in diseases classified elsew J18 Pneumonia, unspecified organism Chronic LRD J40-J47 COPD Quality metric was 100% of all deaths in ca Code range includes malignant neoplasms of a E10-E14 E10 Type 1 diabetes mellitus E11 Type 2 diabetes mellitus E13 Other specified diabetes mellitus E14 is an old code for unspecified diabetes me G30 Alzheimer's disease Quality metric was 81% of all deaths in cate J40 Bronchitis, not specified as acute or chron J41 Simple and mucopurulent chronic bron J42 Unspecified chronic bronchitis J43 Emphysema J44 Other chronic obstructive pulmonary d J45 Asthma J46 Status asthmaticus J47 Bronchiectasis Quality metric was 97% of all deaths in cate Other respiratory J00-J06 J30-J39 J67 J70-J98 J00 Acute nasopharyngitis [common cold] J01 Acute sinusitis J02 Acute pharyngitis J03 Acute tonsillitis J04 Acute laryngitis and tracheitis J05 Acute obstructive laryngitis [croup] and ep J06 Acute upper respiratory infections of mult J30 Vasomotor and allergic rhinitis J31 Chronic rhinitis, nasopharyngitis and phar J32 Chronic sinusitis J33 Nasal polyp J34 Other and unspecified disorders of nose a J35 Chronic diseases of tonsils and adenoids J36 Peritonsillar abscess J37 Chronic laryngitis and laryngotracheitis J38 Diseases of vocal cords and larynx, not e J39 Other diseases of upper respiratory tract J67 Hypersensitivity pneumonitis due to organ J70 Respiratory conditions due to other extern J80 Acute respiratory distress syndrome J81 Pulmonary edema J82 Pulmonary eosinophilia, not elsewhere cla J84 Other interstitial pulmonary diseases J85 Abscess of lung and mediastinum J86 Pyothorax J90 Pleural effusion, not elsewhere classified J91 Pleural effusion in conditions classified el J92 Pleural plaque J93 Pneumothorax and air leak J94 Other pleural conditions J95-J95 Intraoperative and postprocedural co respiratory system, not elsewhere classified J96 Respiratory failure, not elsewhere classifi J98 Other respiratory disorders Note: The above code range excludes J99, “R classified elsewhere.” Nephrotic syndrome, nephrosis N00-N07 N17-N19 N25-N27 Symptoms, signs, and abnormal clinical and laboratory findings NEC R00-R99 Diseases of the heart I00-I09 I11 Myocardial infarction N00 Acute nephritic syndrome N01 Rapidly progressive nephritic syndrome N02 Recurrent and persistent hematuria N03 Chronic nephritic syndrome N04 Nephrotic syndrome N05 Unspecified nephritic syndrome N06 Isolated proteinuria with specified morpho N07 Hereditary nephropathy, not elsewhere c N17 Acute kidney failure N18 Chronic kidney disease (CKD) N19 Unspecified kidney failure N25 Disorders resulting from impaired renal tu N26 Unspecified contracted kidney N27 Small kidney of unknown cause R00-R09 Symptoms and signs involving the c R10-R19 Symptoms and signs involving the d R20-R23 Symptoms and signs involving the s R25-R29 Symptoms and signs involving the n systems R30-R39 Symptoms and signs involving the g R40-R46 Symptoms and signs involving cogn and behavior R47-R49 Symptoms and signs involving spee R50-R69 General symptoms and signs R70-R79 Abnormal findings on examination o R80-R82 Abnormal findings on examination o R83-R89 Abnormal findings on examination o and tissues, without diagnosis R90-R94 Abnormal findings on diagnostic ima without diagnosis R97-R97 Abnormal tumor markers R99-R99 Ill-defined and unknown cause of mo Note: R99 is the code often used for injury-rela death investigation. I00-I02 Acute rheumatic fever I05-I09 Chronic rheumatic heart diseases I13 I20-I51 CBVD I60-I69 Stroke I11 Hypertensive heart disease I13 Hypertensive heart and chronic kidney dis I20 Angina pectoris I21 Acute myocardial infarction I22 Subsequent ST elevation (STEMI) and n myocardial infarction I23 Certain complications following ST elev elevation (NSTEMI) myocardial infarction (w I24 Other acute ischemic heart diseases I25 Chronic ischemic heart disease I26-I28 Pulmonary heart disease and disease I30 Acute pericarditis I31 Other diseases of pericardium I32 Pericarditis in diseases classified elsewhe I33 Acute and subacute endocarditis I34 Nonrheumatic mitral valve disorders I35 Nonrheumatic aortic valve disorders I36 Nonrheumatic tricuspid valve disorders I37 Nonrheumatic pulmonary valve disorders I38 Endocarditis, valve unspecified I39 Endocarditis and heart valve disorders in d I40 Acute myocarditis I41 Myocarditis in diseases classified elsewhe I42 Cardiomyopathy I43 Cardiomyopathy in diseases classified els I44 Atrioventricular and left bundle-branch blo I45 Other conduction disorders I46 Cardiac arrest I47 Paroxysmal tachycardia I48 Atrial fibrillation and flutter I49 Other cardiac arrhythmias I50 Heart failure I51 Complications and ill-defined descriptions Quality metric was 69% of all deaths in cate I60 Nontraumatic subarachnoid hemorrhag I61 Nontraumatic intracerebral hemorrhage I62 Other and unspecified nontraumatic int I63 Cerebral infarction I64 Stroke, not specified if hemorrhagic or I65 Occlusion and stenosis of precerebral arte infarction I66 Occlusion and stenosis of cerebral arterie infarction I67 Other cerebrovascular diseases I68 Cerebrovascular disorders in diseases cla I69 Sequelae of cerebrovascular disease Quality metric was 72% of all deaths in cate a Based on analysis of 2018 full COD file; deaths from UCOD with the bolded diagnoses e deaths in the category. Appendix 2. Populations and Adjustment Factorsa Population Adjustment factorb a 2014 298,359,744 1.03116 2015 2016 2017 2018 300,580,055 302,749,939 304,658,917 306,225,413 1.02355 1.01621 1.00984 1.00468 Population counts are as of July 1 each year and exclude the District of Columbia and three states with incomplete reporting: Connec used to adjust all death counts to the July 1, 2019, population of 307,657,969. Appendix 3. Example Reporting Lag Calculations by Categorya Mean (SD) Percentage Change in Reported Deaths from Earlier to Later One-Week Reporting Increment Weeks lagb All-Causes Natural Heart Cancer Chronic LRD Causes Disease 1.6 32.0 (8.2) 30.6 (8.2) 29.2 (8.6) 27.3 (8.6) 27.3 (8.2) 2.6 8.9 (2.6) 8.4 (2.6) 8.8 (2.7) 6.8 (2.2) 8.2 (2.7) 3.6 3.3 (0.6) 2.9 (0.5) 3.7 (0.6) 2.3 (0.5) 3.1 (0.4) 4.6 1.7 (0.3) 1.3 (0.2) 2.0 (0.3) 1.1 (0.2) 1.7 (0.5) 5.6 1.0 (0.2) 0.7 (0.2) 1.4 (0.2) 0.6 (0.1) 1.0 (0.3) 6.6 0.6 (0.1) 0.3 (0.1) 0.9 (0.2) 0.3 (0.1) 0.5 (0.2) 7.6 0.4 (0.1) 0.2 (0.1) 0.7 (0.1) 0.3 (0.1) 0.3 (0.1) 8.6 0.3 (0.1) 0.1 (0.1) 0.5 (0.1) 0.2 (0.0) 0.2 (0.1) 9.6 0.2 (0.1) 0.1 (0.1) 0.4 (0.1) 0.2 (0.1) 0.2 (0.1) 10.6 0.2 (0.0) 0.0 (0.1) 0.3 (0.1) 0.2 (0.1) 0.2 (0.1) 11.6 0.1 (0.0) 0.0 (0.1) 0.3 (0.1) 0.1 (0.0) 0.2 (0.1) 12.6 0.1 (0.1) 0.0 (0.1) 0.3 (0.1) 0.1 (0.1) 0.2 (0.1) 13.6 0.1 (0.2) 0.0 (0.2) 0.3 (0.2) 0.1 (0.2) 0.1 (0.2) 14.6 0.2 (0.3) 0.1 (0.3) 0.3 (0.3) 0.1 (0.3) 0.2 (0.4) 15.6 0.1 (0.1) 0.0 (0.2) 0.2 (0.2) 0.1 (0.1) 0.1 (0.2) 16.6 0.1 (0.1) 0.0 (0.1) 0.2 (0.1) 0.1 (0.0) 0.1 (0.1) Compounded adjustment multipliers using mean values (shown through 8.6 weeks)c 2.6b 1.183 1.146 1.217 1.128 1.172 3.6 1.086 1.058 1.119 1.057 1.083 4.6 1.051 1.028 1.078 1.033 1.051 5.6 1.034 1.014 1.057 1.022 1.034 6.6 1.024 1.008 1.043 1.016 1.024 7.6 1.018 1.005 1.034 1.013 1.018 8.6 1.014 1.003 1.027 1.010 1.015 Compounded adjustment multipliers using maximum values (shown through 8.6 weeks)c 2.6b 1.261 1.230 1.238 1.202 1.279 3.6 1.118 1.094 1.113 1.089 1.128 4.6 1.075 1.055 1.074 1.056 1.088 5.6 1.054 1.038 1.054 1.042 1.064 6.6 1.042 1.029 1.046 1.034 1.051 7.6 1.035 1.025 1.040 1.029 1.044 8.6 1.030 1.022 1.035 1.025 1.038 a Although only all-cause, natural-cause, and top 3 causes of death are shown here, all calculations were performed separately for all diagnostic categories. Each lag calculation is based on 6-7 combinations of reporting week/update week combinations beginning with reporting week ending February 1, 2020 and ending June 20, 2020, for 7 weekly updates beginning May 27, 2020, and ending July 8, 2020. b Weeks elapsed from end of reporting week to date of update. Because one state reported no deaths during the week ending June 27, its data could not be used; therefore, the smallest lag time in the file was 2.6 weeks (from week end June 20 to reporting date July 8). c Although multipliers are shown only through 8.6 weeks, adjustments were made through 16.6-week lags. SD-standard deviation. June 20, 2020, Reported as of July 8, 2020 June 20, 2020, Reported as of July 8, 2020, continued June 20, 2020, Reported as of July 8, 2020, continued June 20, 2020, Reported as of July 8, 2020, continued June 20, 2020, Reported as of July 8, 2020, continued Notes: Week 1 begins September 29-October 5, depending on year. Week 285 (March 15, 2020) is the approximate pandemic start. Week 288 (April 5, 2020) corresponds to the issuance of NCHS guidance stating laboratory test was recommended but not required to name SARS-CoV-2 as the underlying cause of death. NCHS=National Center for Health Statistics; SARS-CoV-2=severe acute Appendix 5. Time Series Model-Predicted Versus Actual Weekly Deaths, Calibration Period, October 2014 through September 2019, Deaths from All Causes and Top 3 Specific Natural Causes All Causes Heart Disease Cancer Chronic Lower Respiratory Disease Appendix 6. Summary of Projected Versus Actual Population-Adjusted Death Counts by Pandemic June 20 as of July 8, 2020, Time Series Analysis Projec Projected, Using First-Order Autoregression Nonau Point 95% CI 95% CI Point Estimate Lower Upper Difference Estimate Pandemic awareness (Jan 19-Mar 14) All-cause 431,021 433,187 413,231 454,356 -2,166 440,203 Heart disease 99,822 100,860 92,064 110,497 -1,038 103,615 Cancer 86,076 86,098 71,228 104,066 -22 86,076 CLRD 25,171 25,707 22,790 28,839 -536 26,839 NECa 7,344 4,892 4,310 5,551 2,452 4,892 Total QMb 117,018 117,984 104,259 133,520 -966 118,862 Total some QMb 133,600 134,296 126,033 143,168 -696 137,916 Total non-QMb 46,432 47,055 41,806 53,226 -623 48,478 Pandemic height (Mar 15-May 16) All-cause 453,098 431,265 410,994 452,831 21,833 365,155 Heart disease 91,663 89,345 81,864 97,511 2,318 84,971 Cancer 74,190 74,053 61,644 88,954 137 74,038 CLRD 21,420 21,503 19,278 23,845 -83 21,796 NECa 7,377 3,869 3,477 4,309 3,508 7,377 Total QMb 100,937 100,822 89,609 113,449 115 100,673 Total some QMb 121,649 118,886 111,974 126,223 2,763 111,679 Total non-QMb 44,462 42,426 37,874 47,784 2,036 39,258 Late pandemic (May 17-June 20) All-cause 387,338 387,489 370,653 405,326 -151 344,374 Heart disease 79,692 80,005 73,870 86,650 -313 79,143 Cancer 70,906 72,890 61,003 87,089 -1,984 73,160 CLRD 17,082 17,915 16,282 19,606 -833 19,097 NECa 8,160 3,828 3,468 4,231 4,332 3,828 Total QMb 92,602 94,880 84,852 106,084 -2,278 96,700 Total some QMb 103,039 103,526 98,119 109,254 -487 102,376 Total non-QMb 37,464 37,286 33,645 41,510 178 36,095 a QM diagnoses are cancer, CLRD, and septicemia. Some QM (56%-81% of reported category) diagnoses a disease, and influenza/pneumonia. No QM diagnoses are Alzheimer’s disease, diabetes, and other (mostly CI=confidence interval; CLRD=chronic lower respiratory disease; NEC=not elsewhere classified; QM=quality Actual

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

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