Design a regional health study that takes weather and season seriously

1. Choose the exposure and outcome before naming a season

Write a proposed question at a scale that the sources could support. For example, a daily service-count question needs an explanation of what a counted encounter means, which population could generate it and which day receives the event. A patient-level exposure question needs a different plan. A citywide weather observation is not automatically an individual person’s exposure, and a service count is not automatically a population disease rate.

Distinguish the study’s motivation from its variables. “Summer pressure” could refer to temperature, humidity, travel, working hours, a changing population or several of these together. Select the question first, then list which observations would help answer it. Keep the initial plan open to revision if the required data are unavailable; a familiar seasonal label is not a substitute for an operational definition.

2. Read regional studies for design choices, not portable thresholds

Yezli and colleagues’ 2023 study examined daily temperature and non-accidental mortality in Mecca across nine Hajj seasons during 2006–2014. It analysed residents and pilgrims separately and considered delayed associations. These design details make population definition and the observation period visible. They do not supply a current forecast or a temperature threshold that another service can adopt without its own justification.

Alahmad and colleagues’ Kuwait study used 2010–2016 daily mortality and 24-hour mean ambient temperature in a time-series analysis with lagged associations. The measured exposure matters: a daily mean is not a daily maximum or an index combining temperature with humidity. These are historical observational studies. This guide uses their stated designs as examples; it does not reproduce their models, appraise every source limitation or transfer their effect estimates to a new population.

Reference: Yezli and colleagues, 2023: Mecca resident and pilgrim temperature–mortality study · Alahmad and colleagues, 2019: temperature and mortality in Kuwait

3. Build a calendar that represents the setting

Start an alignment table with the actual dates in both sources. Record time zone, day boundaries, whether the health record uses event or reporting date, and the weather observation’s location and aggregation window. In a study around a recurring event, record the dates for each occurrence rather than treating the event name as an unchanged part of the calendar. Check that the proposed reference periods are actually present.

Then list other changes that could matter to the question: weekday patterns, service opening hours, attendance, travel, an outbreak or changes in the way encounters were coded. Treat this as a design discussion, not a command to add every possible variable to a model. A variable’s role depends on the question and causal assumptions. Have the relevant methodological and local-service expertise review the plan before selecting adjustment terms.

On small screens, scroll the table horizontally, or focus it and use the arrow keys.

4. Inspect missing exposure days as missing observations

The National Center for Meteorology lists a historical weather-data request service. Before planning around it, confirm the requested station, period, variables, format, availability and applicable terms. A service listing is not evidence that a particular file is obtainable or that a request has been made. Retain the source description and the date of any actual response alongside the data dictionary.

In a wholly synthetic exercise, a calendar contains 31 days and the health file has a daily entry for each. Five daily mean-temperature observations are absent, leaving 26 dates with both a health entry and the specified weather observation. The five missing temperatures are not zero degrees. No health-count values, completed model or actual data request are supplied. The exercise tests a join and its missingness states, not an exposure–outcome association.

Reference: Saudi National Center for Meteorology: historical weather data service

On small screens, scroll the table horizontally, or focus it and use the arrow keys.

5. Set analysis choices before treating the join as evidence

Investigate the missing dates and document the proposed handling rule. Were observations not collected, not supplied, or lost during parsing? Those possibilities are questions, not established explanations. Preserve the first audit and record any corrected import separately. Compare the coverage of the sources before excluding dates; the retained period may differ from the intended period in ways relevant to the question.

The 26 paired dates do not establish that the dataset is large enough to study seasonality. The appropriate observation period, lag structure, functional form, outcome distribution and uncertainty assessment remain unresolved here. Decide them with suitable statistical input and a study-specific rationale. Copying a regional paper’s model name or lag window would not provide that rationale, especially when the setting, population or data frequency differs.

6. Write a methods-readiness note with explicit limits

A useful readiness note states the proposed question, source versions, exposure definition, calendar alignment, missingness counts and outstanding design choices. Include a small list of discrepancies with a named owner for each. If a source changes, retain the earlier specification so that differences in later counts can be explained. Describe a planned sensitivity check as planned until it has actually been run and inspected.

When presenting a future analysis, distinguish association from a causal claim and identify the population and period to which the observations relate. Do not turn a research estimate into advice for an individual’s clinical care. For the current fictional exercise, the defensible result is much smaller: the date join works for 26 days, five exposure observations remain absent, and no association has been estimated. That is a useful preparation finding with a clear next step.

Questions for a practical final check

• Exposure, outcome, population and unit are explicit.
• Historical regional designs are not presented as local thresholds.
• Dates, day boundaries and event periods are aligned.
• Missing temperature is not coded as zero.
• The paired-date count is not evidence of an adequate study duration.
• The note separates inspected records from proposed analyses.