Playing defense? Health care in the era of Covid

Abstract

Health workers have to balance their own welfare vs. that of their patients particularly when patients have a readily transmissible disease. These risks become more consequential during an outbreak, and especially so when the chance of severe illness or mortality is non-negligible. One way to reduce risk is by reducing contact with patients. Such changes could be along the intensive or extensive margins. Using data on primary care outpatient encounters during the early months of the Covid-19 pandemic, I document important changes in the intensity of provider-patient interactions. Significantly, I find that adherence to clinical guidelines, the probability that routine procedures such as physical examinations were completed, and even the quality of information given by health providers, all declined sharply. I present evidence that these effects likely reflect risk mitigation behavior by health providers.

Keywords: Covid-19; Health workers; Quality; Risk compensation.

Copyright © 2022 Elsevier B.V. All rights reserved.

Figures

Fig. 1

Effect of the pandemic on quality of primary care encounters. Note: The outcome variables are noted in the captions. History-taking denotes the fraction of recommended history-taking questions asked by the attending health provider. Procedures Index is an average of the following indicators: a physical examination, a blood pressure check, a temperature check, and an examination with a stethoscope. Communication Index is an average of the following indicators: whether the health worker explained their diagnosis to the patient, provided health education, and discussed when the patient should return for a follow-up. Figure shows raw (unadjusted) trends in these variables by month of the encounter. The number of observations per month is shown in Table A.2. Months with fewer than 50 observations are omitted from the figure. Dashed line denotes the start month of the epidemic in 2020. Source of data is the Patient Data.

Fig. 2

Correlation between health worker perceptions of Covid-19 mortality and level of worry about getting infected. Note: To measure perceptions of Covid-19 mortality health workers were asked how many people (out of 100) they thought would die if all 100 got infected with the virus. This is grouped into the categories shown. The Y-axis shows responses to the following question: “On a scale of 0–10 where 0 is not worried at all and 10 is extremely worried, how worried are you about getting Coronavirus or Covid-19?” Mean scores are computed for various levels of perceived infection fatality (X-axis). Source of data is the Health Worker Data.

Fig. 3

Health worker knowledge of Covid-19 symptoms. Note: Health workers were asked to mention all the symptoms of Covid-19 that they knew. The proportion of health workers mentioning each symptom is shown. Source of data is the Health Worker Data.

Fig. 4

Heterogeneity in the effect of the pandemic by fraction of patients with Covid-19 symptoms. Note: The dependent variables are noted in the captions. History-taking denotes the fraction of recommended history-taking questions asked by the attending health provider. Procedures Index is an average of the following indicators: a physical examination, a blood pressure check, a temperature check, and an examination with a stethoscope. Communication Index is an average of the following indicators: whether the health worker explained their diagnosis to the patient, provided health education, and discussed when the patient should return for a follow-up. This figure exploits heterogeneity in the fraction of patients with Covid-19 symptoms (fever or cough). To plot this figure I collapsed the data to the health center level and then regressed each dependent variable on an interaction between the pandemic exposure indicator and the fraction of patients with Covid-19 symptoms. To capture non-linearity this is specified as a quadratic. The model includes health center dummies. The figure shows coefficients and 95% confidence intervals from this regression. Standard errors are clustered at the health center level. Source of data is the Patient Data.

Fig. 5

Effect heterogeneity by level of worry about getting infected. Note: The dependent variables are noted in the captions. History-taking denotes the fraction of recommended history-taking questions asked by the attending health provider. Procedures Index is an average of the following indicators: a physical examination, a blood pressure check, a temperature check, and an examination with a stethoscope. Communication Index is an average of the following indicators: whether the health worker explained their diagnosis to the patient, provided health education, and discussed when the patient should return for a follow-up. Figure shows coefficients and 95% confidence intervals from a health center fixed effects regression model in which the pandemic exposure variable During is interacted with a categorical variable constructed by averaging health worker ratings – when asked how worried they were about getting infected with the virus on a scale from 0–10 – for all health workers in a health center. This is categorized as follows: [0-2), [2-4), [4-6), [6-8), [8-10]. The regression estimates are in Table A.9. The models control for patient’s age, sex, number of household assets owned, presenting symptoms, illness duration, the day of the week of the visit, and calendar month fixed effects. Standard errors in parentheses are clustered at the level of the health center. Source of data is the Patient Data.