The impact of the COVID-19 epidemic on mental health of undergraduate students in New Jersey, cross-sectional study

Picture one hundred sixty-two college students in Northern New Jersey in April 2020, filling out a questionnaire while the worst COVID-19 outbreak in the country rages around them. They are not recalling it or reflecting on it afterward. They are measuring it in real time, from the inside. The first number that comes back from that measurement is a mean depression T-score of sixty-four point four. For context, a T-score of fifty is the population average. These students were sitting well above it. That is the study that Kecojevic and colleagues conducted — a cross-sectional survey of undergraduates at a suburban public university in Northern New Jersey, a region the authors describe as among the hardest hit in the early U.S. epidemic. As of April twenty-seventh, 2020, New Jersey had reported over one hundred eleven thousand confirmed cases and more than six thousand deaths, with the counties surrounding the university among the highest in the state. The survey went to all four hundred fifty students enrolled in a single introductory core curriculum course on personal health. One hundred sixty-two completed it, resulting in a response rate of thirty-six percent. The sample skewed young and female — with a median age of nineteen, about seventy-one percent women, nearly two-thirds non-White, and two-thirds freshmen. Roughly thirty-one percent were health majors. These details matter because they shape how you interpret the mental health findings that follow. Here is something the study establishes first, and it is important: these students were not uninformed. Kecojevic and colleagues assessed COVID-19 knowledge with ten items drawn from CDC fact sheets. Nearly two-thirds — sixty-four percent — answered every single item correctly. Ninety-eight percent correctly identified fever, fatigue, and dry cough as main symptoms. Ninety-three percent knew respiratory droplets were the primary mode of spread. When it came to behavior, the numbers were just as high. Every student who answered the behavioral questions reported increasing hand washing, ninety-eight point seven percent limited going out, and ninety-seven point five percent started wearing masks. Students weren't just informed; they acted on it. They also trusted the right sources. Eighty-seven percent said they trusted official sources, while only twelve percent trusted social media. They used health professionals, government websites, and news organizations — not Facebook. And yet, being informed, trusting the right channels, and doing the right things — none of that protected these students from what the mental health data shows. So what did the data show? The survey measured four domains of psychological burden. Depression, anxiety, and somatization were captured with the Brief Symptom Inventory-eighteen — a validated instrument that converts raw symptom scores to T-scores using gender-specific community norms, where fifty is average. Perceived stress was measured with the ten-item Perceived Stress Scale, which tracks how often students felt overwhelmed or out of control over the past month. The scales held together well, with Cronbach's alpha — a measure of internal consistency — at zero point ninety-four for the overall Brief Symptom Inventory-eighteen. The mean depression T-score was sixty-four point four. Anxiety came in at fifty-eight point two. Somatization — which refers to physical symptoms like headaches or stomach pain that are rooted in psychological distress — was fifty-three point five. The Perceived Stress Scale mean was twenty point six out of a possible forty. Across all four measures, the picture was the same: elevated distress. And the distress was not evenly distributed. Seventy-three percent of students reported difficulty focusing on academic work. Nearly fifty-seven percent had lost a job or seen their work hours cut. Almost sixty percent had trouble obtaining hygiene supplies or medications. These weren't peripheral inconveniences — they were the direct predictors that the regression models identified. The multivariable regression analysis is where the study specifies who suffered most and why. The models identified distinct patterns for each outcome. For depression, two predictors stood out. Employment loss — meaning losing a job or having hours reduced — had a standardized coefficient of zero point twenty-five, with a confidence interval running from zero point ten to zero point forty. Inability to focus on academic work had a coefficient of zero point twenty-four, with a confidence interval from zero point nine to zero point forty-two. Together, those two variables, along with a couple of others, explained about twenty-four percent of the variance in depression scores. That's a meaningful chunk for a model this simple. Anxiety had a different signature. Students beyond freshman year — sophomores, juniors, and seniors — reported higher anxiety than freshmen, with a coefficient of zero point sixteen. Students who spent more than one hour per day searching news websites for COVID-19 information had higher anxiety, with a coefficient of zero point twenty-one. Again, inability to focus contributed, with a coefficient of zero point seventeen. That's worth pausing on. Time spent on news sites searching for information predicted worse anxiety, not better. Searching social media for COVID information was not a significant predictor in the adjusted model — it was specifically the news-site searching that was associated with anxiety. The authors offer two explanations: people who feel more vulnerable may seek out more information, and anxious individuals may use information-seeking as a coping mechanism. Either way, the relationship between consuming information and feeling calmer did not hold here. Somatization told a related but subtly different story. Inability to focus on academic work predicted higher somatic symptoms, with a coefficient of zero point twenty-one. Being very to extremely concerned about COVID-19 was also a predictor, with a coefficient of zero point twenty-one. However, the finding that stands out is this: trusting news media was associated with lower somatization, with a coefficient of negative zero point seventeen. So the quantity of information-seeking predicted more anxiety, while the quality of trust in sources predicted fewer physical symptoms of distress. Those two findings together suggest that it's not simply about how much information students consumed, but about their relationship to that information — whether they found it credible and whether it settled uncertainty or amplified it. Perceived stress followed yet another pattern. Females reported higher stress than males, with male sex associated with lower stress, having a coefficient of negative zero point seventeen. Inability to focus on academic work predicted higher stress, with a coefficient of zero point eighteen. Difficulty obtaining medications and cleaning supplies was associated with elevated stress, also at zero point eighteen. The stress model explained about sixteen percent of variance — somewhat less than depression's twenty-four percent, but still statistically meaningful. One thread runs through all four outcomes: academic focus. Difficulty concentrating on coursework was a significant predictor of depression, anxiety, somatization, and perceived stress. It was not just associated with one — but all four. The authors flag this as a signal for intervention because it is one of the few targets that shows up across every mental health domain they measured. What should universities do with this? Kecojevic and colleagues point to three priorities. First, proactive mental health services — not waiting for students to seek help, but reaching out, particularly to students signaling academic difficulties. Second, support for academic continuity — the online transition created real structural disruption, and students who couldn't focus were bearing a mental health cost that showed up in the regression coefficients. Third, material support. Employment loss predicted depression as strongly as academic difficulty did. Difficulty getting medications and cleaning supplies predicted stress. These are not soft variables; they are concrete circumstances that drove measurable psychological outcomes. Now for the honest accounting of what this study cannot tell us. The design is cross-sectional — a single snapshot in time. That means the associations are real, but the causal arrows are unknown. Does inability to focus cause depression? Does depression make it harder to focus? The data cannot answer that. The sample is also one course at one institution in one region during an extreme local outbreak, so generalizing requires caution. The mental health measures were self-reported and not clinician-verified. The questionnaire captured a limited slice of relevant experiences — with no direct measure of COVID-19 exposure in the household, and no assessment of prior mental health history. What this study does offer is something genuinely valuable: real-time measurement of a population under acute stress, with enough analytical specificity to identify which experiences drove the psychological burden. This is not just exposure to the pandemic generally, but specifically the inability to focus, the job loss, the scramble for basic supplies, and the hours spent scrolling through news sites. That granularity is what makes the findings actionable. The consistency of academic focus as a predictor across all four outcomes points directly at where campus intervention should land. This lecture was created by ennepō. Go to https://ennepo.ai to Discover, Create and Follow the latest research in your field. Read when you can. Listen when you want to.