Patient demographics#

The parameters age_gender_probs, bin_size, lower_cutoff, upper_cutoff, and truncated define the demographic composition of the synthetic patient cohort.
They determine how age and sex are distributed, how patients are grouped into age intervals, and how outliers are handled during population sampling.

age_gender_probs#

Specifies the baseline distribution of patient age and sex.
By default, these probabilities are derived from NHS England Hospital Outpatient Activity 2023–24 (Summary Report 3) [1], which reports outpatient attendances by age and sex.

Format#

Type: list or tuple of dictionaries
Default: DEFAULT_AGE_GENDER_PROBS stored in constants.py
Each record contains the following keys:

{
    "age_yrs": "15-19",
    "total_female": 0.01738,
    "total_male": 0.01348
}

Validation rules#

  • Must contain both "total_female" and "total_male" proportions for each age range.

  • Probabilities must be finite and non-negative.

  • Values are normalized internally to ensure that total female + total male = 1.0 across all age groups.

  • If unspecified, the NHS-derived default is applied.

How it works#

During patient generation, ages are sampled proportionally to these probabilities, ensuring that the simulated cohort mirrors real-world demographic patterns observed in NHS outpatient services.

The distribution reflects adult outpatient activity (ages 15+) and is truncated according to the lower and upper cutoffs defined below.
These values serve as statistical weights, not deterministic constraints, allowing for reproducible yet probabilistic cohort creation.

Note on gender representation:
The NHS source data aggregates attendances by binary sex (“Male” and “Female”).
The simulator inherits this binary structure for reproducibility.
Expanding to more inclusive gender representations would require access to disaggregated datasets not currently available in the NHS open data.

bin_size#

Defines the size of age intervals (in years) used for cohort grouping and reporting.

Format#

Type: int
Default: 5
Typical range: 1–10

Validation rules#

  • Must be a positive integer.

  • Determines the resolution of derived age groups (e.g., 15–19, 20–24, etc.).

  • Does not affect raw sampling but controls how aggregated outputs (e.g., charts or summaries) are labeled.

How it works#

The bin_size parameter is applied during post-processing to group patient ages into standard reporting intervals.
It supports downstream analytics and visualization without modifying the underlying patient-level data.

lower_cutoff and upper_cutoff#

Define the minimum and maximum age limits of the simulated cohort.
These parameters constrain age sampling and ensure consistency with the outpatient focus of the dataset.

Format#

Type: int
Defaults:

  • lower_cutoff = 15

  • upper_cutoff = 90

Validation rules#

  • Both values must be integers.

  • lower_cutoff must be strictly less than upper_cutoff.

  • Typical adult outpatient settings exclude children under 15.

  • Patients above 90 years are grouped into a “90+” category.

How it works#

  • Ages below the lower cutoff are excluded entirely when truncated=True.

  • Ages above the upper cutoff are included but reported as part of a capped “90+” group for demographic aggregation.

  • This configuration ensures that the simulated dataset remains focused on adult populations, as per NHS reporting conventions.

truncated#

Controls whether ages outside the defined range are excluded or capped.

Format#

Type: bool
Default: True

Validation rules#

  • Must be a boolean (True or False).

  • When True, age sampling strictly enforces the lower and upper cutoffs.

  • When False, rare outliers may be included but adjusted statistically.

How it works#

When truncation is enabled, the generator filters out ages below 15 before sampling and redistributes their probability mass among valid bins.
This behavior ensures that pediatric cases are not simulated unless explicitly allowed by the user.

Demographic attributes in generated tables#

  • The fields sex and dob (date of birth) are stored in the patients table.

  • The fields age and age_group are calculated dynamically in the appointments table based on appointment date and dob.

  • This design preserves normalization while supporting age-based analysis directly from appointment-level data.

Note on date of birth:
Each patient’s dob is reverse-engineered from their sampled age at the first appointment, with a random offset (0–364 days) to avoid clustering.
This allows age to evolve consistently across appointments, maintaining temporal coherence.

References#

[1] NHS England (2024). Hospital Outpatient Activity 2023–24: Summary Report 3.
https://files.digital.nhs.uk/34/18846B/hosp-epis-stat-outp-rep-tabs-2023-24-tab.xlsx

Next steps#

  • Explore Appointment timing to learn how punctuality and durations vary across demographics.

  • Review Patients table to examine how demographic attributes are embedded.

  • See Patient flow for how demographics interact with visit frequency and first attendances.

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