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Objectives: We aimed to describe conditions of confinement among people incarcerated in the United States during the coronavirus disease 2019 (COVID-19) pandemic and assess the feasibility of a community science data collection approach. Methods: We developed a web-based survey with community partners to collect information on confinement conditions (COVID-19 safety, basic needs, support). Formerly incarcerated adults released after March 1, 2020, or non-incarcerated adults in communication with an incarcerated person (proxy) were recruited through social media from July 25, 2020, through March 27, 2021. Descriptive statistics were estimated in aggregate and separately by proxy or formerly incarcerated status. Additionally, we compared responses between proxy and formerly incarcerated respondents using chi-square or Fisher's exact tests as appropriate based on alpha=0.05. Results: Of 378 responses, 94% were by proxy, and 76% reflected state prison conditions. Participants reported inability to physically distance (>6ft at all times) (92%), inadequate access to soap (89%), water (46%), toilet paper (49%) and showers (68%). Among people who received mental healthcare before the pandemic, 75% reported reduced care. We found that responses were consistent between formerly incarcerated people and proxy-respondents. Conclusions: Our findings suggest that a community-science approach to data collection is feasible. Based on these findings, COVID-19 safety and basic needs were not sufficiently addressed within some carceral settings. Thus, we recommend the lived experiences of incarcerated individuals should be included to make informed and equitable policy decisions.

Objectives: We aimed to describe conditions of confinement among people incarcerated in the United States during the coronavirus disease 2019 (COVID-19) pandemic using a community-science data collection approach. Methods: We developed a web-based survey with community partners to collect information on confinement conditions (COVID-19 safety, basic needs, support). Formerly incarcerated adults released after March 1, 2020, or nonincarcerated adults in communication with an incarcerated person (proxy) were recruited through social media from July 25, 2020 to March 27, 2021. Descriptive statistics were estimated in aggregate and separately by proxy or formerly incarcerated status. Responses between proxy and formerly incarcerated respondents were compared using Chi-square or Fisher's exact tests based on α=0.05. Results: Of 378 responses, 94% were by proxy, and 76% reflected state prison conditions. Participants reported inability to physically distance (≥6 ft at all times; 92%), inadequate access to soap (89%), water (46%), toilet paper (49%), and showers (68%) for incarcerated people. Among those receiving prepandemic mental health care, 75% reported reduced care for incarcerated people. Responses were consistent between formerly incarcerated and proxy respondents, although responses by formerly incarcerated people were limited. Conclusions: Our findings suggest that a web-based community-science data collection approach through nonincarcerated community members is feasible; however, recruitment of recently released individuals may require additional resources. Our data obtained primarily through individuals in communication with an incarcerated person suggest COVID-19 safety and basic needs were not sufficiently addressed within some carceral settings in 2020–2021. The perspectives of incarcerated individuals should be leveraged in assessing crisis–response strategies.

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.