01 / ContextThe question
behind the work.
Hospital occupancy is commonly treated as an efficiency measure, but its relationship with mortality is confounded by hospital type, case mix, and the difference between average utilization and true surge pressure.
My role
I defined the research question, prepared the hospital-level measures, built simple and adjusted regression models, checked diagnostics, compared rural and urban patterns, and wrote the report.
03 / In detailTreating utilization as a systems signal
I built a hospital-level analysis around capacity utilization, bed size, teaching status, rural context, and risk-adjusted heart-failure mortality. The goal was to see what occupancy reveals once it is placed beside the hospital characteristics that shape how capacity is used.
The workflow starts with a simple linear relationship, then adds controls, a quadratic occupancy term, rural interaction, and diagnostic checks. That sequence makes the change in the story visible as the model becomes more realistic.
The result was more interesting than the expected story
The simple model estimated a negative occupancy slope of about -2.8731 with p below 0.001. The stronger quadratic specification kept the negative direction and found meaningful curvature, with a centered linear term near -1.749 and a quadratic term near -4.4503.
Rural hospitals remained at a higher modeled mortality level than similar urban hospitals. The interaction analysis separated that baseline difference from the shape of the occupancy relationship itself.
Making the finding useful
The report includes relationship plots, rural-versus-urban prediction curves, residual and influence diagnostics, and a full self-contained analysis. It turns the headline result into an operating question about capacity, service mix, referral patterns, staffing, and hospital capability.
That is the part I cared about most: showing how an apparently simple throughput measure can carry a deeper systems story once the model and context are built around it.