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Hospital temperature-risk reports

This tool was Groundswell's submission to the FortyGuard Hackathon 2026. It estimates how recent and forecast temperatures may increase deaths among people living near a selected hospital. This page explains the calculation, its sources, and where it should not be used.

Methodology

A report estimates temperature-attributable all-cause mortality during the next 14 days for residents inside the selected radius. It is a population estimate, not a count of hospital patients or deaths at the facility.

Temperature inputs

Open-Meteo supplies hourly temperature at the hospital for the previous 21 days and the next 14 days. Groundswell converts these hourly values into daily means. The recent values are archived weather-model estimates, not weather-station readings.

FortyGuard supplies a 100 m map of mean, minimum, and maximum temperature for the previous 21 days. Groundswell uses the difference between each grid cell and the hospital grid cell to localize the Open-Meteo series. This approach carries the recent spatial pattern into the forecast. It does not create an independent weather forecast for each grid cell.

Residents in the catchment

The selected radius defines a circular catchment around the hospital. The app assigns WorldPop's 2026 population pixels to the exact FortyGuard grid polygons, then checks that population is neither lost nor assigned twice. Every temperature and mortality total is weighted by this estimated resident population. The result describes people who live nearby, not the hospital's service population.

How temperature affects mortality

Groundswell fitted state mortality models with CDC death records and ERA5-Land temperature data from 2014 through 2019. Each model estimates how mortality changes as daily mean temperature moves away from the temperature associated with the lowest mortality.

Temperature can affect mortality on the day of exposure or during the following 21 days. The model accounts for this delay. Each date in the 14-day forecast combines the recent and forecast temperatures that can affect mortality on that date. The report does not include effects expected after the forecast ends.

A 2019 state mortality rate supplies the expected daily deaths for the catchment. Groundswell applies the modeled temperature effect in each populated grid cell, then sums the cells and days. A positive estimate means more deaths than at the lowest-risk temperature. A negative estimate means fewer. These are modeled estimates, not counts of identifiable deaths.

The Time chart expresses each daily total per 100,000 catchment residents. The Temperature chart separates that total into heat and cold contributions according to whether each lagged exposure is above or below the model's minimum-mortality temperature. When heat and cold effects interact, the calculation divides the shared effect equally so the two contributions add exactly to the daily total.

Risk concentration divides the range of cell-level 14-day mortality rates into five equal-width bands, then sums the resident population in the cells within each band. The bands describe relative variation inside this catchment. They are not clinical thresholds and should not be compared as fixed categories between reports.

Age and cause estimates

The five age and seven cause rows use the same localized temperature series and their own age- and cause-specific models. Cause estimates use the whole catchment population. WorldPop does not supply local age structure for this report, so age rows apply each group's share of the 2019 state population to the catchment. "Who is most affected" ranks rows by temperature-attributable deaths per 100,000 people during the 14-day outlook. The percentage change compares each row with its own baseline mortality at its own minimum-mortality temperature, so it is not used to compare burden between rows. Intervals shown for some rows reflect ranges created by suppressed source death counts. The all-age headline is calculated separately and is not the sum of these rows.

Confidence and state support

High
The release checks found no material anomaly in the observed hot-tail curve structure. This does not mean the model has been externally validated.
Medium
The observed hot-tail curve has a limited structural concern. Reports retain the estimate and show a medium-confidence notice.
Low
Reports retain the estimate and show a low-confidence notice in the request form and report. Low-confidence age or cause rows also retain their rating.

This tier rates observed hot-tail shape. It does not include uncertainty in the minimum-mortality temperature, effect significance, external validation, or the certainty of a row's rank.

Reports cover the 48 contiguous states and District of Columbia when a state has a rated all-age model. Alaska, Hawaii, and US territories are outside the current hospital and model coverage. Groundswell checks availability when each report starts because model coverage can change between releases.

Limits

  • The Open-Meteo forecast can be wrong, and the app does not publish a forecast uncertainty interval.
  • Downscaling carries one recent FortyGuard spatial pattern across all 35 daily temperatures. Local conditions may change during the forecast.
  • WorldPop is a modeled population estimate, and the R2025A release is an alpha product. It does not measure who is present on a given day.
  • Mortality models and baseline rates are state-level. Applying them to a small area assumes the catchment resembles the state in ways the report cannot observe.
  • If a localized temperature is outside the model's supported range, the report flags the affected population share and names affected age or cause rows.
  • The report does not estimate emergency visits, admissions, patient demand, staffing need, or outcomes for an individual. Do not use it as a clinical tool or as the sole basis for emergency decisions.