Identifying areas of potential critical healthcare shortages: A case study of spatial accessibility to ICU beds during the COVID-19 pandemic in Florida

Diabetes Care and Mental Health During the COVID-19 Pandemic: Perspectives of Adolescents with Diabetes, Parents, and Providers

This study explored ways in which the COVID-19 pandemic impacted adolescents' diabetes management and psychosocial functioning, and how adolescents, parents, and providers viewed telemedicine. We present data from three studies: (1) a comparison of psychosocial functioning and glycemic levels before and after pandemic onset (n = 120 adolescents; 89% with type 1 diabetes), (2) an online survey of parents about pandemic-related stressors (n = 141), and (3) qualitative interviews with adolescents, parents, and medical providers about the pandemic's impacts on adolescents' diabetes care and mental health (n = 13 parent-adolescent dyads; 7 medical providers). Results suggested some adverse effects, including disrupting routines related to health behaviors and psychosocial functioning and impairing adolescents' quality of life. Despite these challenges, most participants did not endorse significant impacts. Some even noted benefits, such as increased parental supervision of diabetes management that can be leveraged beyond the pandemic. Furthermore, telemedicine offers benefits to continuity of diabetes care but presents challenges to care quality. These findings underscore the varied and unique impacts of the COVID-19 pandemic on adolescents with diabetes.

COVID-19 preparedness and response in rural and remote areas: A scoping review

This scoping review used the Arksey and O'Malley approach to explore COVID-19 preparedness and response in rural and remote areas to identify lessons to inform future health preparedness and response planning. A search of scientific and grey literature for rural COVID-19 preparedness and responses identified 5 668 articles published between 2019 and early 2022. A total of 293 articles were included, of which 160 (54.5%) were from high income countries and 106 (36.2%) from middle income countries. Studies focused mostly on the Maintenance of Essential Health Services (63; 21.5%), Surveillance, epidemiological investigation, contact tracing and adjustment of public health and social measures (60; 20.5%), Coordination and Planning (32; 10.9%); Case Management (30; 10.2%), Social Determinants of Health (29; 10%) and Risk Communication (22; 7.5%). Rural health systems were less prepared and national COVID-19 responses were often not adequately tailored to rural areas. Promising COVID-19 responses involved local leaders and communities, were collaborative and multisectoral, and engaged local cultures. Non-pharmaceutical interventions were applied less, support for access to water and sanitation at scale was weak, and more targeted approaches to the isolation of cases and quarantine of contacts were preferable to blanket lockdowns. Rural pharmacists, community health workers and agricultural extension workers assisted in overcoming shortages of health professionals. Vaccination coverage was hindered by weaker rural health systems. Digital technology enabled better coordination, communication, and access to health services, yet for some was inaccessible. Rural livelihoods and food security were affected through disruptions to local labour markets, farm produce markets and input supply chains. Important lessons include the need for rural proofing national health preparedness and response and optimizing synergies between top-down planning with localised planning and coordination. Equity-oriented rural health systems strengthening and action on rural social determinants is essential to better prepare for and respond to future outbreaks.

Quantitative analysis of early apparent diffusion coefficient values from MRIs for predicting neurological prognosis in survivors of out-of-hospital cardiac arrest: an observational study

BACKGROUND

This study aimed to quantitatively analyse ultra-early brain diffusion-weighted magnetic resonance imaging (DW-MRI) findings to determine the apparent diffusion coefficient (ADC) threshold associated with neurological outcomes in comatose survivors of out-of-hospital cardiac arrest (OHCA).

METHODS

This retrospective study included adult survivors of comatose OHCA who underwent DW-MRI imaging scans using a 3-T MRI scanner within 6 h of the return of spontaneous circulation (ROSC). We investigated the association between neurological outcomes and ADC values obtained through voxel-based analysis on DW-MRI. Additionally, we constructed multivariable logistic regression models with pupillary light reflex (PLR), serum neuron-specific enolase (NSE), and ADC values as independent variables to predict poor neurological outcomes. The primary outcome was poor neurological outcome 6 months after ROSC, determined by the Cerebral Performance Category 3-5.

RESULTS

Overall, 131 patients (26% female) were analysed, of whom 74 (57%) showed poor neurological outcomes. The group with a poor neurological outcome had lower mean whole brain ADC values (739.1 vs. 787.1 × 10-6 mm/s) and higher percentages of voxels with ADC below threshold in all ranges (250-1150) (all P < 0.001). The mean whole brain ADC values (area under the receiver operating characteristic curve [AUC] 0.83) and the percentage of voxels with ADC below 600 (AUC 0.81) had the highest sensitivity of 51% (95% confidence interval [CI] 39.4-63.1; cut-off value ≤ 739.2 × 10-6 mm2/s and > 17.2%, respectively) when the false positive rate (FPR) was 0%. In the multivariable model, which also included PLR, NSE, and mean whole brain ADC values, poor neurological outcome was predicted with the highest accuracy (AUC 0.91; 51% sensitivity). This model showed more accurate prediction and sensitivity at an FPR of 0% than did the combination of PLR and NSE (AUC 0.86; 30% sensitivity; P = 0.03).

CONCLUSIONS

In this cohort study, early voxel-based quantitative ADC analysis after ROSC was associated with poor neurological outcomes 6 months after cardiac arrest. The mean whole brain ADC value demonstrated the highest sensitivity when the FPR was 0%, and including it in the multivariable model improved the prediction of poor neurological outcomes.

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Daily changes in spatial accessibility to ICU beds and their relationship with the case-fatality ratio of COVID-19 in the state of Texas, USA

During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios.

Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Association of ultra-early diffusion-weighted magnetic resonance imaging with neurological outcomes after out-of-hospital cardiac arrest

BACKGROUND

This study aimed to investigate the association between ultra-early (within 6 h after return of spontaneous circulation [ROSC]) brain diffusion-weighted magnetic resonance imaging (DW-MRI) and neurological outcomes in comatose survivors after out-of-hospital cardiac arrest.

METHODS

We conducted a registry-based observational study from May 2018 to February 2022 at a Chungnam national university hospital in Daejeon, Korea. Presence of high-signal intensity (HSI) (P_HSI) was defined as a HSI on DW-MRI with corresponding hypoattenuation on the apparent diffusion coefficient map irrespective of volume after hypoxic ischemic brain injury; absence of HSI was defined as A_HSI. The primary outcome was the dichotomized cerebral performance category (CPC) at 6 months, defined as good (CPC 1-2) or poor (CPC 3-5).

RESULTS

Of the 110 patients (30 women [27.3%]; median (interquartile range [IQR]) age, 58 [38-69] years), 48 (43.6%) had a good neurological outcome, time from ROSC to MRI scan was 2.8 h (IQR 2.0-4.0 h), and the P_HSI on DW-MRI was observed in 46 (41.8%) patients. No patients in the P_HSI group had a good neurological outcome compared with 48 (75%) patients in the A_HSI group. In the A_HSI group, cerebrospinal fluid (CSF) neuron-specific enolase (NSE) levels were significantly lower in the group with good neurological outcome compared to the group with poor neurological outcome (20.1 [14.4-30.7] ng/mL vs. 84.3 [32.4-167.0] ng/mL, P < 0.001). The area under the curve for P_HSI on DW-MRI was 0.87 (95% confidence interval [CI] 0.80-0.93), and the specificity and sensitivity for predicting a poor neurological outcome were 100% (95% CI 91.2%-100%) and 74.2% (95% CI 62.0-83.5%), respectively. A higher sensitivity was observed when CSF NSE levels were combined (88.7% [95% CI 77.1-95.1%]; 100% specificity).

CONCLUSIONS

In this cohort study, P_HSI findings on ultra-early DW-MRI were associated with poor neurological outcomes 6 months following the cardiac arrest. The combined CSF NSE levels showed higher sensitivity at 100% specificity than on DW-MRI alone. Prospective multicenter studies are required to confirm these results.

Geographic disparities in COVID-19 testing and outcomes in Florida

BACKGROUND

Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS.

RESULTS

Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.

CONCLUSIONS

Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

The Contributions of Population Distribution, Healthcare Resourcing, and Transportation Infrastructure to Spatial Accessibility of Health Care

Population demand, healthcare resourcing, and transportation linkage are considered as major determinants of spatial access to health care. Temporal changes of the 3 determinants would result in gain or loss of spatial access to health care. As a remarkable milestone achieved by Targeted Poverty Reduction Project launched in China, the significant improvements in spatial access to health care served as an ideal context for investigating the relative contributions of these 3 determinants to the changes in spatial access to health care in a rural county. A national level poverty-stricken county, Chishui county from Guizhou province, China, was chosen as our study area. The enhanced two-step floating catchment area model and the chain substitution method were employed for analysis. The relative contributions of the 3 determinants demonstrated variations with villages. The relative contributions of healthcare resourcing were positive in all villages as indicated by sharp increases in healthcare resources. Population changes and transportation infrastructure expansion had both negative and positive effects on spatial access to health care for different villages. Decisionmakers should take into account the duration of travel time spent between where people live, where transport hubs are located, and where healthcare services are delivered in the process of formulating policies toward rural healthcare planning. For villages with poorly-established infrastructure, the optimization of population distribution and healthcare resourcing should be considered as the priority. A stronger marginal effect would be induced by transportation infrastructure expansion with increased spatial accessibility. This study provides empirical evidences to inform healthcare planning in low- and middle-income countries.

Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces

The relationship between the social structure of urban spaces and the evolution of the COVID-19 pandemic is becoming increasingly evident. Analyzing the socio-spatial structure in relation to cases may be one of the keys to explaining the ways in which this contagious disease and its variants spread. The aim of this study is to propose a set of variables selected from the social context and the spatial structure and to evaluate the temporal spread of infections and their different degrees of intensity according to social areas. We define a model to represent the relationship between the socio-spatial structure of the urban space and the spatial distribution of pandemic cases. We draw on the theory of social area analysis and apply multivariate analysis techniques to check the results in the urban space of the city of Malaga (Spain). The proposed model should be considered capable of explaining the functioning of the relationships between societal structure, socio-spatial segregation, and the spread of the pandemic. In this paper, the study of the origins and consequences of COVID-19 from different scientific perspectives is considered a necessary approach to understanding this phenomenon. The personal and social consequences of the pandemic have been exceptional and have changed many aspects of social life in urban spaces, where it has also had a greater impact. We propose a geostatistical analysis model that can explain the functioning of the relationships between societal structure, socio-spatial segregation, and the temporal evolution of the pandemic. Rather than an aprioristic theory, this paper is a study by the authors to interpret the disparity in the spread of the pandemic as shown by the infection data.

Accessibility calculation and equality evaluation of medical facilities for COVID-19 pandemic treatment: A case study of the Wuhan metropolitan development zone

Medical facility equality is a critical metric for determining equal access to medical care. Their spatial distribution is important for effective pandemic treatment and daily prevention in cities. This paper used the Kernel Density Two-Step Floating Catchment Area (KD2SFCA) and shortest distance methods to calculate the accessibility of designated COVID-19 Fangcang hospitals and fever clinics in the Wuhan Metropolitan Development Zone. Their equality was evaluated by the Gini coefficient and Lorentz curve. Several results were obtained: (1) The facilities’ accessibility declines radial from the central to peripheral areas. (2) Most of the demand points in the study area can reach the medical facilities for COVID-19 pandemic treatment within 60 minutes. (3) For the spatial distribution of these facilities, the equality evaluated for different time thresholds differed significantly, with long time thresholds having better equality than for short time thresholds. (4) While the distances distribution of fever clinics is balanced, the equality gap in various areas remains enormous when considering population distribution. Suggestions for optimizing the spatial distribution of pandemic treatment medical facilities in Wuhan are proposed, and which will serve as references for the planning of Wuhan’s pandemic medical facilities in the future.

Examining the impact of COVID-19 vaccination rates on differential access to critical care

The measurement of potential access to health care has focused primarily on what might be called "place-based" access or the differential access among geographic locations rather than between different populations. The vaccination program to inoculate the population against the effects of the COVID-19 virus has created two different at-risk populations. This research examines the impact of COVID-19 vaccination rates on access to critical care for persons fully-vaccinated versus those not fully-vaccinated. In this situation, additional tools are necessary to understand: 1) if there is a significant difference in accessibility between different populations, 2) the magnitude of this difference and how it is distributed across accessibility levels, and 3) how the differences between groups are distributed across the state. A study of access to intensive care unit (ICU) beds by these two populations for the state of Illinois found that although there was a statistically significant difference in access, the magnitude of differences was small. A more important difference was being located in the Chicago Area of the state. The not-fully vaccinated in the Chicago Area had higher than expected spatial access due to the lower need for ICU beds by a higher percentage of fully vaccinated people.

The risk profile of patients with COVID-19 as predictors of lung lesions severity and mortality—Development and validation of a prediction model

Objective

We developed and validated a prediction model based on individuals' risk profiles to predict the severity of lung involvement and death in patients hospitalized with coronavirus disease 2019 (COVID-19) infection.

Methods

In this retrospective study, we studied hospitalized COVID-19 patients with data on chest CT scans performed during hospital stay (February 2020-April 2021) in a training dataset (TD) (n = 2,251) and an external validation dataset (eVD) (n = 993). We used the most relevant demographical, clinical, and laboratory variables (n = 25) as potential predictors of COVID-19-related outcomes. The primary and secondary endpoints were the severity of lung involvement quantified as mild (≤25%), moderate (26–50%), severe (>50%), and in-hospital death, respectively. We applied random forest (RF) classifier, a machine learning technique, and multivariable logistic regression analysis to study our objectives.

Results

In the TD and the eVD, respectively, the mean [standard deviation (SD)] age was 57.9 (18.0) and 52.4 (17.6) years; patients with severe lung involvement [n (%):185 (8.2) and 116 (11.7)] were significantly older [mean (SD) age: 64.2 (16.9), and 56.2 (18.9)] than the other two groups (mild and moderate). The mortality rate was higher in patients with severe (64.9 and 38.8%) compared to moderate (5.5 and 12.4%) and mild (2.3 and 7.1%) lung involvement. The RF analysis showed age, C reactive protein (CRP) levels, and duration of hospitalizations as the three most important predictors of lung involvement severity at the time of the first CT examination. Multivariable logistic regression analysis showed a significant strong association between the extent of the severity of lung involvement (continuous variable) and death; adjusted odds ratio (OR): 9.3; 95% CI: 7.1–12.1 in the TD and 2.6 (1.8–3.5) in the eVD.

Conclusion

In hospitalized patients with COVID-19, the severity of lung involvement is a strong predictor of death. Age, CRP levels, and duration of hospitalizations are the most important predictors of severe lung involvement. A simple prediction model based on available clinical and imaging data provides a validated tool that predicts the severity of lung involvement and death probability among hospitalized patients with COVID-19.

An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas

Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant concern during the coronavirus disease (COVID-19) pandemic. Our study examined the stochastic distribution of spatial accessibility under the uncertainties underlying the availability of intensive care unit (ICU) beds and ease of mobility in the Greater Houston area of Texas. Based on the randomized supply and mobility from their historical changes, we employed Monte Carlo simulation to measure ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method. We then conducted hierarchical clustering to classify regions of adequate (sufficient and reliable) accessibility and inadequate (insufficient and unreliable) accessibility. Lastly, we investigated the relationship between the accessibility measures and the case fatality ratio of COVID-19. As result, locations of sufficient access also had reliable accessibility; downtown and outer counties, respectively, had adequate and inadequate accessibility. We also raised the possibility that inadequate health care accessibility may cause higher COVID-19 fatality ratios.

Establishment of a Rapid Response, 100-Bedded COVID-19 Hospital With General Wards and Intensive Care Unit Within 35 Days in North Karnataka, India: Report From the Field

In the state of Karnataka, India, the first case of coronavirus disease (COVID-19) was diagnosed on March 9, 2020. As stated by the WHO, around 15% of COVID-19 patients require treatment in the Intensive Care Unit (ICU). Keeping this in mind, along with the increase in COVID-19 patients in Karnataka, it was predicted that the prevailing general wards and ICUs would be overburdened with the added non-COVID-19 diseases and infirmities. Hence, it was decided to set up a separate infrastructure to reduce the chances of transmission among the patients within the hospital. Thus, the board at SDM College of Medical Sciences & Hospital, Dharwad, Karnataka, took the responsibility to establish a fully equipped 100-bedded hospital in its premise as part of the national and state services to combat the outbreak. The aim of the study was to establish an isolated, fully functional hospital, equipped with all necessary diagnostic and critical care facilities to treat patients diagnosed with COVID-19 in North Karnataka, India.

Access to the COVID-19 services during the pandemic - a scoping review

Appropriate accessibility to coronavirus disease 2019 (COVID-19) services is essential in the efficient management of the pandemic. Different geospatial methods and approaches have been used to measure accessibility to COVID-19 health-related services. This scoping review aimed to summarize and synthesize the geospatial studies conducted to measure accessibility to COVID-19 healthcare services. Web of Science, Scopus, and PubMed were searched to find relevant studies. From 1113 retrieved unique citations, 26 articles were selected to be reviewed. Most of the studies were conducted in the USA and floating catchment area methods were mostly used to measure the spatial accessibility to COVID-19 services including vaccination centres, Intensive Care Unit beds, hospitals and test sites. More attention is needed to measure the accessibility of COVID-19 services to different types of users especially with combining different non-spatial factors which could lead to better allocation of resources especially in populations with limited resources.

The Spatial Distribution and Influencing Factors of Urban Cultural and Entertainment Facilities in Beijing

Cultural and entertainment facilities are an important mainstay for urban development and the well-being of urban residents. Studying their spatial distribution is thus of great significance for improving urban functions and shaping urban characteristics. This paper uses the Simpson index, grid method, kernel density, nearest neighbor analysis and hierarchical clustering analysis to present in detail the spatial pattern, hotspot distribution and clustering characteristics of urban cultural and entertainment facilities in Beijing. With the help of the spatial lag model, the main factors affecting the spatial distribution of the facilities are explored. The results are as follows: Different types of cultural and entertainment facilities have different spatial agglomeration effects, which are closely related to the historical background of Beijing, industrial distribution, and the living needs of residents; the facilities generally present a spatial distribution with prominent centrality, strong clustering and significant heterogeneity; and financial insurance institution density, building density, securities company density, housing rent and distance to nearest scenic spot are the main factors affecting the distribution of the facilities. Analyzing the distribution characteristics and influencing factors of urban cultural and entertainment facilities in Beijing will provide typical cases and decision-making references that can underpin the informed layout and planning of urban cultural and entertainment industries and facilities.

How Did School Meal Access Change during the COVID-19 Pandemic? A Two-Step Floating Catchment Area Analysis of a Large Metropolitan Area

SARS-CoV-2 (COVID-19) resulted in school closures and contingencies across the U.S. that limited access to school meals for students. While some schools attempted to provide alternative meal access points where students or parents could pick up meals, many students—especially those in low-income households—lacked adequate transportation to these access points. Thus, physical proximity to meal access points was particularly important during the pandemic. In this study, we explore how school meal access changed during the COVID-19 pandemic, especially as it relates to race/ethnicity and socio-economic status. Taking into account both the “supply” (meal access points) and the “demand” (low-income students) for free meals, we employed a two-step floating catchment area analysis to compare meal accessibility in St. Louis, Missouri before and during the pandemic in the spring and summer of 2019 and 2020. Overall, while school meal access decreased during the spring of 2020 during the early months of the pandemic, it increased during the summer of 2020. Moreover, increased access was greatest in low-income areas and areas with a higher proportion of Black residents. Thus, continuing new policies that expanded access to school meals—especially for summer meal programs—could lead to positive long-term impacts on children’s health and well-being.