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The Impact of COVID-19 on the Use of Preventive Health Care

Updated 11/09/2020 to include data through 8/31/2020. Since the original post on 9/9/2020, the following updates have been incorporated:

  • Data on the administration of HIV tests and on obesity counselling have been added. 
  • A note on claim lags has been added, see Methods Note below.

COVID-19 has had an extraordinary impact on the US health care system since its emergence in early 2020. One of the largest and most immediate impacts has been the death toll, with the pandemic having claimed more than 238,000 lives as of November 9, 2020, but the pandemic has also brought a set of (seemingly endless) new trade-offs and choices for people to make as they navigate their daily lives and the health care system. Among them, whether, when, and how to resume their pre-pandemic health care life. How do the risks of leaving their homes and going to medical facilities stack up against the well-documented benefits of preventive care? Whether annual mammograms or other screenings or children's well-child visits and immunizations, each venture into non-emergency health care sparks a calculus of risk and reward without a clear answer.

Several studies have identified a substantial drop in health care utilization in March and April as most medical offices closed or dramatically scaled back operations, and people generally avoided interactions with the health system in the hopes of not contracting the virus, including reductions in outpatient visits, emergency department visits, and elective surgeries like lower joint replacement. However, these studies were often limited in scale and scope. Curious about the effect the pandemic is having on which health care services people receive, the Health Care Cost Institute (HCCI) looked at a sample of health claims clearinghouse records from 18 states containing 184 million claims from 30 million patients in 2019 and 94 million claims from 20 million patients for the first 6 months in 2020. Specifically, we examined women's preventative health services, select services provided during pregnancy and delivery, childhood immunizations, and other sentinel preventive medical services including colonoscopies, and prostate-specific antigen (PSA) tests. Overall, we found that the pandemic is having a significant dampening effect on the use of certain health care services.

The dashboard below compares use of the select services between 2020 and 2019 using a 7-day rolling average. Note that due to potential delays in providers' submission of claims, the count of more recent claims may change as more claims become available. See methods note for more information.

Submitted claims for most preventive services we examined, such as mammography and childhood immunizations exhibited significant declines in 2020 compared to 2019, particularly mid-March through mid-April. Even by the end of August 2020, utilization of many preventive services appeared to be running below 2019 levels. Trends from the data through August 31, 2020 show:

  • Childhood immunizations were, on aggregate, down about 60 percent in mid-April in 2020 compared to 2019. This ranged from 75 percent for Meningococcal and HPV vaccines to 33 percent for Rotavirus and the diphtheria, tetanus toxoids, acellular pertussis family of vaccines.
  • Mammograms and Pap smears were down nearly 80 percent in April 2020 compared to 2019, and in August were still down roughly 20 and 10 percent from 2019, respectively.
  • Colonoscopies, down almost 90% at one point in mid-April 2020 compared to 2019, are as of August 2020 still down about 30% compared to last year, representing a substantial but incomplete rebound in care delivered.
  • PSA tests, which are used for prostate cancer screening, while down approximately 22% for the year, have seen a strong rebound, with delivery of PSA tests returning to 2019 levels starting in June.
  • Use of services that cannot be deferred or forgone, like childbirth, have tracked more closely to 2019 levels. All deliveries declined by about 8 percent on average from 2019 to 2020. Cesarean section deliveries declined slightly more than vaginal deliveries (approximately 11 percent and 6 percent, respectively), perhaps reflecting changes in preferences towards early elective deliveries, which tend to have longer lengths of stay than vaginal deliveries, though additional research is needed to understand what is leading to the difference in utilization.

This analysis is merely a preliminary glimpse at the impact of COVID-19 on health care utilization in 2020 and is not intended to provide definitive answers about the ways in which the pandemic is affecting people's health care. We expect HCCI's new national dataset, with more than 1 billion claims for approximately 55 million people with employer-sponsored health insurance coverage, which will be launched in later 2020, will facilitate a much more comprehensive assessment of those questions. These data suggest, though, that for now, people have chosen to forego care they would otherwise have received with potential implications for their long-term health and well-being.

Methods Note

Methods Note

This study uses data made available by the COVID-19 Research Database, a cross-industry collaborative contributing real world, de-identified data to researchers wishing to study issues related to COVID-19. For this analysis we used a clearinghouse database which spanned 184 million claims from almost 30 million patients in 2019 and 94 million claims from nearly 20 million patients in the first 6 months in 2020. The claims were all from members who resided in a consistent set of 18 states, with roughly half living in California in both study years. Lastly, while the claims were associated with all payer types, they were predominately from the commercially insured: 86% commercial (ESI and MA), 12% Medicare, and 2% Medicaid, on average across both years.

Claims clearinghouses are responsible for scrubbing and transmitting medical claims to insurance carriers. Once a response is received from the issuer, the clearinghouse transmits the denial or acceptance of the claim back to the provider. While some clearinghouses may transmit payment information back to the provider, typically only charge data are reliably available from the clearinghouse. Additionally, accurate enrollment files may not be available since they are maintained by the issuer, which presents a limitation for this type of analysis.

We defined our services of interest from physician and other professional service charges (i.e. non-facility charges) as the count of unique claims from the charge submissions for the following HCPCS/CPT codes:

Women’s Preventative Health Services


Diagnostic pap smear (cytopathology)

88141, 88142, 88143, 88147, 88148, 88150, 88152, 88153, 88155, 88164, 88165, 88166, 88167, 88174, 88175

Screening pap smear (cytopathology)

P3000, G0123, G0143, G0145, G0146, G0147, G0148

Diagnostic mammography

77055, 77056, 77066, 77065

Screening mammography

77057, 77067

Birth control, IUD

J7297, J7298

Ultrasound, pelvic (non-obstetric)

76858, 76857, 76830

Human papilloma virus (HPV) test

87623, 87624

Pregnancy and Delivery


Ultrasound, pregnancy

76801, 76802, 76805, 76810, 76811, 76812, 76817

Childbirth, Caesarian section

59510, 59514, 59515, 59618, 59620, 59622, 59409, 59612

Childbirth, vaginal delivery

59400, 59409, 59410, 59610, 59614

Childhood Immunizations


Measles, mumps, rubella, varicella

90707, 90710, 90716

Hepatitis A


Hepatitis B


Haemophilus influenza type b (Hib)

90647, 90648

Human papilloma virus (HPV)



90680, 90681

Pneumococcal conjugate



90620, 90621, 90734

Poliovirus vaccine, inactivated (IPV)


Diphtheria, tetanus toxoids, acellular pertussis (Td/DTaP/Tdap)

90700, 90714, 90715

Diphtheria, tetanus toxoids, and acellular pertussis vaccine and inactivated poliovirus vaccine (DTaP-IPV)


Diphtheria, tetanus toxoids, acellular pertussis vaccine, haemophilus influenza type b, and inactivated poliovirus vaccine (DTaP-IPV/Hib)


Diphtheria, tetanus toxoids, acellular pertussis vaccine, Hepatitis B, and inactivated poliovirus vaccine (DTaP-Hep BIPV)


Other Health Services


Diagnostic colonoscopy

45379, 45380, 45381, 45382, 45383, 45384, 45385, 45386, 45387, 45388, 45389, 45390, 45391, 45392, 45393, 45394, 45395, 45396, 45397, 45398

Screening colonoscopy

45378, G0105, G0121

Diagnostic prostate-specific antigen (PSA) test


Screening prostate-specific antigen (PSA) test


HIV test

86689, 86701, 86702, 86703, 87534, 57535, 87536, 87390

Influenza vaccine

90653, 90694, 90662, 90672, 90674, 90682, 90685, 90686, 90687, 90688, 90756, 90656, 90662, Q2035

Obesity Counselling

G0447, G0473

The table below shows the total count of claims used for this analysis by service and by year, from January 1 to August 31 in each year.

Service Name 2019 Count 2020 Count
All Childbirths 111,048 101,751
All Colonoscopies 296,755 196,999
All Mammograms 619,401 452,365
All Pap Smears 809,356 606,843
All PSA Tests 669,577 563,579
All Vaccinations 3,389,420 2,433,094
C-Section Deliveries 47,148 41,899
Diagnostic Colonoscopies 186,638 126,524
Diagnostic Mammograms 123,982 108,290
Diagnostic Pap Smears 787,082 591,009
Diagnostic PSA Tests 646,754 541,398
DTaP-Hep B-IPV Vaccine 102,885 85,401
DTaP-IPV/Hib_Vaccine 173,300 146,627
Flu Vaccines 372,897 427,919
Hemophilus Influenza B (Hib) Vaccine 216,585 171,246
Hepatitis A Vaccine 277,679 198,631
Hepatitis B Vaccine 166,626 124,941
HIV Tests 112,821 88,790
HPV Tests 427,511 333,490
HPV Vaccine 268,365 181,405
Intrauterine Devices 17,216 14,433
Meningococcal Vaccines 328,341 212,650
MMRV Vaccine 374,840 233,833
Obesity Counselling 251,755 202,770
Pelvic Ultrasounds 321,158 257,294
Pneumococcal Conjugate Vaccine 457,756 360,333
Poliovirus Vaccine (IPV) 111,803 70,387
Pregnancy Ultrasounds 265,791 248,941
Rotavirus Vaccine 227,243 193,001
Screening Colonoscopies 110,117 70,475
Screening Mammograms 495,419 344,075
Screening Pap Smears 22,274 15,834
Screening PSA test 22,823 22,181
TdaP-DTaP Vaccine 586,928 388,352
TdaP-IPV Vaccine 97,069 66,287
Vaginal Deliveries 63,900 59,852

Percent Change for each service on a given day of the year is calculated as the difference between the 2020 and 2019 values divided by the 2019 value, then multiplied by 100. That is, Percent Change = (([2020]-[2019])/[2019])×100. 

We attempted to stratify the data by gender and age, however, demographic data for claims submitted in 2020 was significantly more incomplete than the demographic information on claims from 2019. While more specificity in the demographic profile of these services is important, the preventative screenings, tests, and procedures, selected (with the exception of colonoscopies and HIV tests), are utilized primality by a single sex, and immunizations are primarily utilized by children.

Trends were calculated using a 7-day rolling average, and we include a 60-day run out window from the date of data extract to allow for sufficient claims maturity. Note that inferences made comparing 2019 utilization to 2020 utilization may be influenced by factors not related to patients delaying or foregoing care. These factors include but are not limited to claims lag beyond our 60-day window, and the possible change in the mix of providers utilizing the clearinghouse from which these data are derived or a change in the mix of the types of patients being served.

We differentiate diagnostic services from screenings using their respective HCPCS/CPT codes. There may be limitations to our results related to how payers reimburse these services as some payers may require different or additional mechanisms for documenting diagnostic or screening services other than the HCPCS/CPT code.

We also do not discuss impacts of the pandemic on the utilization of telehealth services or other service substitutions, however, with the exception of HIV testing and pregnancy testing, none of these services are customarily performed without direct care from a health professional. While we can’t fully attribute the dips in preventative care to patient’s decisions to delay/forego care (e.g. many providers may have been closed during shutdown orders), this does represent a meaningful shift in the delivery of services.

Finally, the data from these findings are overly representative of the western U.S. and not generalizable to the entire population of insured persons.

The data, technology, and services used in the generation of these research findings were generously supplied pro bono by the COVID-19 Research Database partners, who are acknowledged at


Note on claim lags:

Research using claims data must contend with the fact that there is a lag between when a service is performed and when the provider submits a claim. This lag is highly variable, with some claims requiring a full year to fully mature.  For this reason, we allow for a minimum sixty-day runout period for claims to be submitted before we analyze the data.  However, since claims sometimes take longer than sixty days to be submitted, our data is still incomplete, especially towards the more recent service dates.

To try to understand how much data may be missing, and how that may affect our results, we compared data with service dates between 1/1/2020 and 6/27/2020 that were pulled on 8/26/2020 with those which were pulled on 9/29/2020, 34 days later.  We found that across all service categories, there were claims submitted during the additional month of run-out, with the most recent dates showing the largest change. The graph below shows the discrepancy in our percent change calculations from the data pulled on 8/26/2020 compared to data pulled on 9/29/2020. 


The most recent dates (claims from 60 days before the data was pulled on 8/26/2020) had about five percentage points fewer claims than data pulled the following month.  This discrepancy shrinks as the data becomes less recent, with an average difference of less than 1 percent for claim service dates in January (e.g. over 200 days of data run-out). The immediate needs for health services information during the pandemic must be balanced against a slow claims data submission process. Although traditional research in health services will use claims data with at least a year of maturity, the goal of this analysis is to provide data in real time, necessitating this trade-off. This note is meant to caution readers in their interpretation of the more recent data, and help illustrate that although some claims have not yet been submitted, these trends still hold true.


Adjusting for claims lag:

In order to get a better sense of what the data may look like after all the claims are completed, we attempted to make predictions for the additional number of claims that will be recorded, based on the number of claims that were added between 8/26/20 and 9/29/20 (the dates on which we extracted the data).  Across services, the number of claims that appear in the second data pull but are missing in the first data pull consistently rose as the service date neared the date of the first data pull, and we observed that this trend more resembled exponential decay as opposed to a linear trend.


We built a simple regression model to predict the claims on any given day based on the time relative to the data pull. Because of the trend in claims relative to the date, we log-transformed the outcome variable.  We also allowed this trend to vary by service. To do so, we included an interaction term with the time relative to the data pull and service-specific dummy variables. The model can be written as:

ln(Yit)β0 + β1X1β2X2,i + β3X1X2,i + εit


Yit  = Completion factor for service i on day t, i.e. the number of additional claims outstanding, expressed as a percentage of recorded claims
X1  = The number of days between the service date t and the date the data is pulled
X2,i  = Indicator variable for each specific service i

The model was fitted to our data from 8/26/20, with the additional claims that were added by 9/29/20 serving as the outcome variable. The model had an adjusted R2 of 0.88, and will be validated against and re-fitted with future rounds of data.  

In order to get a prediction of the total claims lag (not just the additional claims that show up after 34 days), we added together the predicted percentage of additional claims for each day t with those for the day 34 days prior or a multiple thereof  (i.e. YtYt-34Yt-68 + Yt-102 +...), until we get back to the beginning of the year. For example, for the most recent service date, if a provider submitted a claim within 0-60 days, that claim is already recorded in the data. To adjust for the number of claims from that date that take longer than 60 days to be submitted, we added together the predicted number of claims with a 61-94 day lag, 95-128 day lag, 129-162 day lag, etc., creating an estimate of the claim count after practically all the claims are recorded. Finally, we added the predicted number of additional claims to the recorded claims from the second round of data (extracted 9/29/2020) to create lag-adjusted claim counts and to calculate the percent change in service utilization from 2019 to 2020. The chart below shows how some of our adjusted numbers compare to the unadjusted ones.  An interactive version of this chart with all services can be found here.



The table below shows a subset of our calculated completion factors (the predicted additional claims as a fraction of recorded claims) and adjusted claim counts for total vaccinations on selected dates.


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