ASA Connect

During the COVID-19 pandemic, counts of cases and deaths were the primary surveillance data, aka "metrics," used to inform policymakers and the public about the current situation.  Case counts depend on individuals' concerns about exposure and symptoms, their decisions to be tested, test availability and requirements, and other factors.  These factors cause drop-offs in diagnosis and reporting at every step in the reporting process, so the numbers of actual cases, as well as deaths and hospitalizations are far higher than reported, varying over time and among population subgroups.

At the beginning of the pandemic, it was necessary to make use of the available data, but for too long we relied on outdated surveillance systems.  Their widely recognized flaws no doubt contributed to the sense that public health didn't know what it was doing.  To build back trust, we need to develop new data systems that complement case counts.  Population sampling, excess mortality estimation, syndromic and wastewater surveillance, can provide a more comprehensive and accurate assessment of the pandemic's impact on different populations and as it changes over time. 

Expanding surveillance data types beyond case reports will require the Centers for Disease Control and Prevention (CDC) to work with state health departments, hospitals, and other data providers to standardize definitions, presentation methods, and so on.  CDC has traditionally played an important leadership role by developing standard surveillance case definitions, and since the pandemic has worked to develop systems to share data with healthcare providers.  Sadly, these efforts were set aside when Robert F. Kennedy, Jr. came into office.  So an ASA effort would be very welcome!

Population sampling

During the COVID-19 pandemic, for instance, British health authorities implemented the REal-time Assessment of Community Transmission-1 (REACT-1) study to monitor the prevalence of SARS-CoV-2 infection.  REACT-1 obtains self-administered throat and nose swabs from a random sample of the 100,000 or more participants at ages 5 years and over every month.  Swabs are tested for SARS-CoV-2 infection and samples testing positive sent for viral genome sequencing.  This survey identified the rapid spread of the Omicron variant in areas of London and southern England in November and December 2021.  In the U.S. blood samples obtained for other clinical assessments were analyzed to track infections.

Questionnaire-based survey's Census Bureau's Pulse Survey can help us understand the psychological, social, educational, and economic consequences of COVID-19, both in general and in different socio-demographic groups.

Surveys of "sentinel" populations can also be helpful.  For example, testing representative samples of dairy and poultry farm workers would more effectively monitor the Avian influenza that emerged in American farms in 2024 than relying on voluntary case reports.  This is especially important where immigrant workers are concerned about coming forward and farmers fear the costs of control measures.

Syndromic and wastewater surveillance

Syndromic surveillance methods include tracking patients seeking care at physicians' offices and emergency department.  For instance, CDC and Force of Infection track outpatient influenza-like illness cases and emergency department visits for influenza, COVID-19 and Respiratory Syncytial Virus (RSV).  The key point is that these data are not influenced by decisions about whether to send samples for lab testing, and not subject to delays associated with testing. 

 Regular testing of wastewater samples can be very effective in tracking COVID-19 and many other pathogens because there is no bias in who uses the toilet.  Wastewater surveillance can detect the emergence of new pathogens, but does not identify specific individuals.  Similarly, bulk testing of milk at the farm level can identify the presence of Avian influenza without the expense or bias of testing individual cattle.

Excess mortality estimation

Excess mortality estimates are based on comparing the observed number of deaths in the pandemic period to an earlier time, include both direct (caused by a documented COVID-19 infection) and indirect deaths (for example, a heart attack victim unable to get care due to overcrowded emergency rooms).  They provided the most complete estimates of COVID-19's impact, showing that through April 2022, there were 37% more deaths associated with COVID-19 than reported as such, especially in COVID-19 deaths were less likely to be classified as such, with greater differences in rural areas, the South, and in counties that supported Trump.

System approach needed

Public health emergencies are complex phenomenon that cannot be summarized in a single indicator, so CDC should develop a balanced portfolio of metrics that together describe the epidemiologic situation and provide information to guide decision-making.  Metrics are intended to inform – not decide –policy decisions that balance epidemiologic benefits and social and economic costs, taking into account the current state of the pandemic. 

The need for different types of data means that surveillance data are the product of a complex data enterprise with diverse stakeholders.  Healthcare providers, test centers, labs, hospital administrators, funeral directors send reports to health departments where data are compiled, processed, analyzed and published by local, state, federal agencies, the media, and others.  Each has its own procedures and interests.  In addition, the many formats in which health departments, data publishers, and the media presented data during the pandemic created confusion.  The different definitions and formats create opportunities for manipulation.  Recognizing this reality, the World Health Organization working to develop "collaborative surveillance" in countries around the globe.

Surveillance systems are familiar for early detection of outbreaks, but the pandemic reminds us that policymakers need for metrics that track both health outcomes as well as social and economic factors over time.  These metrics compare among population groups and over time, so consistency in data systems is more important than complete counts.  This requires careful coordination among the many entities in generating public health data. 

I agree totally with you Michael.  Another way to track deaths is through timely autopsy reporting.  Florida fired their excellent statistician, Ms. Rebeka Jones, and restricted autopsy reports where deaths by covid (counted) and deaths were with covid (not counted).  If a covid patient died with another possible cause, e.g. heart disease, it was not counted. The integrity of the Florida surveillance system became corrupted when she was let go.   Of interested, despite Florida's cheating, the elderly death rate at 65 and older attributed to covid was 3-fold higher per capita in Florida than in Canada.  Canada came very close to what Fauci wanted in the US.  We should stop harassing him.

Best wishes,

Jon

During the COVID-19 pandemic, counts of cases and deaths were the primary surveillance data, aka "metrics," used to inform policymakers and the public about the current situation.  Case counts depend on individuals' concerns about exposure and symptoms, their decisions to be tested, test availability and requirements, and other factors.  These factors cause drop-offs in diagnosis and reporting at every step in the reporting process, so the numbers of actual cases, as well as deaths and hospitalizations are far higher than reported, varying over time and among population subgroups.

At the beginning of the pandemic, it was necessary to make use of the available data, but for too long we relied on outdated surveillance systems.  Their widely recognized flaws no doubt contributed to the sense that public health didn't know what it was doing.  To build back trust, we need to develop new data systems that complement case counts.  Population sampling, excess mortality estimation, syndromic and wastewater surveillance, can provide a more comprehensive and accurate assessment of the pandemic's impact on different populations and as it changes over time. 

Expanding surveillance data types beyond case reports will require the Centers for Disease Control and Prevention (CDC) to work with state health departments, hospitals, and other data providers to standardize definitions, presentation methods, and so on.  CDC has traditionally played an important leadership role by developing standard surveillance case definitions, and since the pandemic has worked to develop systems to share data with healthcare providers.  Sadly, these efforts were set aside when Robert F. Kennedy, Jr. came into office.  So an ASA effort would be very welcome!

Population sampling

During the COVID-19 pandemic, for instance, British health authorities implemented the REal-time Assessment of Community Transmission-1 (REACT-1) study to monitor the prevalence of SARS-CoV-2 infection.  REACT-1 obtains self-administered throat and nose swabs from a random sample of the 100,000 or more participants at ages 5 years and over every month.  Swabs are tested for SARS-CoV-2 infection and samples testing positive sent for viral genome sequencing.  This survey identified the rapid spread of the Omicron variant in areas of London and southern England in November and December 2021.  In the U.S. blood samples obtained for other clinical assessments were analyzed to track infections.

Questionnaire-based survey's Census Bureau's Pulse Survey can help us understand the psychological, social, educational, and economic consequences of COVID-19, both in general and in different socio-demographic groups.

Surveys of "sentinel" populations can also be helpful.  For example, testing representative samples of dairy and poultry farm workers would more effectively monitor the Avian influenza that emerged in American farms in 2024 than relying on voluntary case reports.  This is especially important where immigrant workers are concerned about coming forward and farmers fear the costs of control measures.

Syndromic and wastewater surveillance

Syndromic surveillance methods include tracking patients seeking care at physicians' offices and emergency department.  For instance, CDC and Force of Infection track outpatient influenza-like illness cases and emergency department visits for influenza, COVID-19 and Respiratory Syncytial Virus (RSV).  The key point is that these data are not influenced by decisions about whether to send samples for lab testing, and not subject to delays associated with testing. 

 Regular testing of wastewater samples can be very effective in tracking COVID-19 and many other pathogens because there is no bias in who uses the toilet.  Wastewater surveillance can detect the emergence of new pathogens, but does not identify specific individuals.  Similarly, bulk testing of milk at the farm level can identify the presence of Avian influenza without the expense or bias of testing individual cattle.

Excess mortality estimation

Excess mortality estimates are based on comparing the observed number of deaths in the pandemic period to an earlier time, include both direct (caused by a documented COVID-19 infection) and indirect deaths (for example, a heart attack victim unable to get care due to overcrowded emergency rooms).  They provided the most complete estimates of COVID-19's impact, showing that through April 2022, there were 37% more deaths associated with COVID-19 than reported as such, especially in COVID-19 deaths were less likely to be classified as such, with greater differences in rural areas, the South, and in counties that supported Trump.

System approach needed

Public health emergencies are complex phenomenon that cannot be summarized in a single indicator, so CDC should develop a balanced portfolio of metrics that together describe the epidemiologic situation and provide information to guide decision-making.  Metrics are intended to inform – not decide –policy decisions that balance epidemiologic benefits and social and economic costs, taking into account the current state of the pandemic. 

The need for different types of data means that surveillance data are the product of a complex data enterprise with diverse stakeholders.  Healthcare providers, test centers, labs, hospital administrators, funeral directors send reports to health departments where data are compiled, processed, analyzed and published by local, state, federal agencies, the media, and others.  Each has its own procedures and interests.  In addition, the many formats in which health departments, data publishers, and the media presented data during the pandemic created confusion.  The different definitions and formats create opportunities for manipulation.  Recognizing this reality, the World Health Organization working to develop "collaborative surveillance" in countries around the globe.

Surveillance systems are familiar for early detection of outbreaks, but the pandemic reminds us that policymakers need for metrics that track both health outcomes as well as social and economic factors over time.  These metrics compare among population groups and over time, so consistency in data systems is more important than complete counts.  This requires careful coordination among the many entities in generating public health data.