diff --git a/pyproject.toml b/pyproject.toml
index b7501fda..02f493db 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -94,7 +94,7 @@ combine-as-imports = true
 convention = "google"
 
 [tool.ruff.lint.pylint]
-max-args = 7
+max-args = 8
 
 [tool.ruff.format]
 quote-style = "double"
diff --git a/src/nhp/model/aae.py b/src/nhp/model/aae.py
index 9dba1f41..68e0656c 100644
--- a/src/nhp/model/aae.py
+++ b/src/nhp/model/aae.py
@@ -26,6 +26,7 @@ class AaEModel(Model):
         run_params: The parameters to use for each model run. Generated automatically if left as
             None. Defaults to None.
         save_full_model_results: Whether to save the full model results or not. Defaults to False.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
     """
 
     def __init__(
@@ -35,6 +36,7 @@ def __init__(
         hsa: Any = None,
         run_params: dict | None = None,
         save_full_model_results: bool = False,
+        aggregation_columns: list[str] | None = None,
     ) -> None:
         """Initialise the A&E Model.
 
@@ -44,6 +46,7 @@ def __init__(
             hsa: Health Status Adjustment object. Defaults to None.
             run_params: The run parameters to use. Defaults to None.
             save_full_model_results: Whether to save full model results. Defaults to False.
+            aggregation_columns: The columns to use for aggregation. Defaults to None.
         """
         # call the parent init function
         super().__init__(
@@ -54,6 +57,7 @@ def __init__(
             hsa,
             run_params,
             save_full_model_results,
+            aggregation_columns,
         )
 
     def _get_data(self, data_loader: Data) -> pd.DataFrame:
diff --git a/src/nhp/model/inpatients.py b/src/nhp/model/inpatients.py
index 1cf04e15..4df3d937 100644
--- a/src/nhp/model/inpatients.py
+++ b/src/nhp/model/inpatients.py
@@ -27,6 +27,7 @@ class InpatientsModel(Model):
         run_params: The parameters to use for each model run. Generated automatically if left as
             None. Defaults to None.
         save_full_model_results: Whether to save the full model results or not. Defaults to False.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
     """
 
     def __init__(
@@ -36,6 +37,7 @@ def __init__(
         hsa: Any = None,
         run_params: dict | None = None,
         save_full_model_results: bool = False,
+        aggregation_columns: list[str] | None = None,
     ) -> None:
         """Initialise the Inpatients Model.
 
@@ -45,6 +47,7 @@ def __init__(
             hsa: Health Status Adjustment object. Defaults to None.
             run_params: The run parameters to use. Defaults to None.
             save_full_model_results: Whether to save full model results. Defaults to False.
+            aggregation_columns: The columns to use for aggregation. Defaults to None.
         """
         # call the parent init function
         super().__init__(
@@ -55,6 +58,7 @@ def __init__(
             hsa,
             run_params,
             save_full_model_results,
+            aggregation_columns,
         )
 
     def _get_data(self, data_loader: Data) -> pd.DataFrame:
diff --git a/src/nhp/model/model.py b/src/nhp/model/model.py
index 204f158b..56070b17 100644
--- a/src/nhp/model/model.py
+++ b/src/nhp/model/model.py
@@ -43,6 +43,7 @@ class Model:
         run_params: The parameters to use for each model run. Generated automatically if left as
             None. Defaults to None.
         save_full_model_results: Whether to save the full model results or not. Defaults to False.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
 
     Attributes:
         model_type: A string describing the type of model, must be one of "aae", "ip", or "op".
@@ -67,6 +68,7 @@ def __init__(
         hsa: Any | None = None,
         run_params: dict | None = None,
         save_full_model_results: bool = False,
+        aggregation_columns: List[str] | None = None,
     ) -> None:
         """Initialise the Model.
 
@@ -78,6 +80,8 @@ def __init__(
             hsa: Health Status Adjustment object. Defaults to None.
             run_params: The run parameters to use. Defaults to None.
             save_full_model_results: Whether to save full model results. Defaults to False.
+            aggregation_columns: The columns to use for aggregation. If left None (Default), then
+              ["pod", "sitetret"] is used.
         """
         valid_model_types = ["aae", "ip", "op"]
         assert model_type in valid_model_types, "Model type must be one of 'aae', 'ip', or 'op'"
@@ -107,6 +111,8 @@ def __init__(
         self.run_params = run_params or self.generate_run_params(params)
         self.save_full_model_results = save_full_model_results
 
+        self._aggregation_columns = aggregation_columns or ["pod", "sitetret"]
+
     @property
     def measures(self) -> List[str]:
         """The names of the measure columns.
@@ -408,8 +414,7 @@ def path_fn(f):
 
         return mr.get_aggregate_results()
 
-    @staticmethod
-    def get_agg(results: pd.DataFrame, *args: str) -> pd.Series:
+    def get_agg(self, results: pd.DataFrame, *args: str) -> pd.Series:
         """Get aggregation from model results.
 
         Args:
@@ -419,7 +424,7 @@ def get_agg(results: pd.DataFrame, *args: str) -> pd.Series:
         Returns:
             Aggregated results.
         """
-        return results.groupby(["pod", "sitetret", *args, "measure"])["value"].sum()
+        return results.groupby([*self._aggregation_columns, *args, "measure"])["value"].sum()
 
     def save_results(self, model_iteration: ModelIteration, path_fn: Callable[[str], str]) -> None:
         """Save the results of running the model.
diff --git a/src/nhp/model/outpatients.py b/src/nhp/model/outpatients.py
index 38bfa357..1916bdfb 100644
--- a/src/nhp/model/outpatients.py
+++ b/src/nhp/model/outpatients.py
@@ -26,6 +26,7 @@ class OutpatientsModel(Model):
         run_params: The parameters to use for each model run. Generated automatically if left as
             None. Defaults to None.
         save_full_model_results: Whether to save the full model results or not. Defaults to False.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
     """
 
     def __init__(
@@ -35,6 +36,7 @@ def __init__(
         hsa: Any = None,
         run_params: dict | None = None,
         save_full_model_results: bool = False,
+        aggregation_columns: list[str] | None = None,
     ) -> None:
         """Initialise the Outpatients Model.
 
@@ -44,6 +46,7 @@ def __init__(
             hsa: Health Status Adjustment object. Defaults to None.
             run_params: The run parameters to use. Defaults to None.
             save_full_model_results: Whether to save full model results. Defaults to False.
+            aggregation_columns: The columns to use for aggregation. Defaults to None.
         """
         # call the parent init function
         super().__init__(
@@ -54,6 +57,7 @@ def __init__(
             hsa,
             run_params,
             save_full_model_results,
+            aggregation_columns,
         )
 
     def _get_data(self, data_loader: Data) -> pd.DataFrame:
diff --git a/src/nhp/model/run.py b/src/nhp/model/run.py
index e8690649..3b804ca2 100644
--- a/src/nhp/model/run.py
+++ b/src/nhp/model/run.py
@@ -34,10 +34,10 @@ def update(self, n=1):
             tqdm.progress_callback(self.n)
 
 
-def timeit(func: Callable, *args) -> Any:
+def timeit(func: Callable, *args, **kwargs) -> Any:
     """Time how long it takes to evaluate function `f` with arguments `*args`."""
     start = time.time()
-    results = func(*args)
+    results = func(*args, **kwargs)
     print(f"elapsed: {time.time() - start:.3f}s")
     return results
 
@@ -50,6 +50,7 @@ def _run_model(
     run_params: dict,
     progress_callback: Callable[[Any], None],
     save_full_model_results: bool,
+    aggregation_columns: list[str] | None = None,
 ) -> list[ModelRunResult]:
     """Run the model iterations.
 
@@ -63,6 +64,7 @@ def _run_model(
         run_params: The generated run parameters for the model run.
         progress_callback: A callback function for progress updates.
         save_full_model_results: Whether to save full model results.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
 
     Returns:
         A list containing the aggregated results for all model runs.
@@ -70,8 +72,10 @@ def _run_model(
     model_class = model_type.__name__[:-5]
     logging.info("%s", model_class)
     logging.info(" * instantiating")
-    # ignore type issues here: Model has different arguments to Inpatients/Outpatients/A&E
-    model = model_type(params, data, hsa, run_params, save_full_model_results)  # type: ignore
+    # ignore type issues here: model_type is Type[Model] so ty checks against Model.__init__,
+    # which has extra leading positional args (model_type, measures) that the concrete subclasses
+    # don't expose — the positional mapping is correct at runtime.
+    model = model_type(params, data, hsa, run_params, save_full_model_results, aggregation_columns)  # ty: ignore[invalid-argument-type]
     logging.info(" * running")
 
     # set the progress callback for this run
@@ -115,6 +119,7 @@ def run_all(
     nhp_data: Callable[[int, str], Data],
     progress_callback: Callable[[Any], Callable[[Any], None]] = noop_progress_callback,
     save_full_model_results: bool = False,
+    aggregation_columns: list[str] | None = None,
 ) -> tuple[dict[str, pd.DataFrame], list[str]]:
     """Run the model.
 
@@ -126,6 +131,7 @@ def run_all(
         progress_callback: A callback function for updating progress.
             Defaults to noop_progress_callback.
         save_full_model_results: Whether to save full model results. Defaults to False.
+        aggregation_columns: The columns to use for aggregation. Defaults to None.
 
     Returns:
         A dictionary containing the results dataframes, and a list of the variants that were run.
@@ -152,6 +158,7 @@ def run_all(
                 run_params,
                 progress_callback(m.__name__[:-5]),
                 save_full_model_results,
+                aggregation_columns,
             )
             for m in model_types
         ]
@@ -161,13 +168,17 @@ def run_all(
 
 
 def run_single_model_run(
-    params: dict, data_path: str, model_type: Type[Model], model_run: int
+    params: dict,
+    data_path: str,
+    model_type: Type[Model],
+    model_run: int,
+    aggregation_columns: list[str] | None = None,
 ) -> None:
     """Runs a single model iteration for easier debugging in vscode."""
     data = Local.create(data_path)
 
     print("initialising model...  ", end="")
-    model = timeit(model_type, params, data)
+    model = timeit(model_type, params, data, aggregation_columns=aggregation_columns)
     print("running model...       ", end="")
     m_run = timeit(ModelIteration, model, model_run)
     print("aggregating results... ", end="")
diff --git a/tests/unit/nhp/model/test_model.py b/tests/unit/nhp/model/test_model.py
index f884db99..f1867464 100644
--- a/tests/unit/nhp/model/test_model.py
+++ b/tests/unit/nhp/model/test_model.py
@@ -14,9 +14,10 @@
 @pytest.fixture
 def mock_model():
     """Create a mock Model instance."""
-    with patch.object(Model, "__init__", lambda s, m, p, d, c: None):
+    with patch.object(Model, "__init__", lambda *args, **kwargs: None):
         mdl = Model(None, None, None, None)  # type: ignore
     mdl.model_type = "aae"
+    mdl._aggregation_columns = ["pod", "sitetret"]
     mdl.params = {
         "input_data": "synthetic",
         "model_runs": 3,
diff --git a/tests/unit/nhp/model/test_run.py b/tests/unit/nhp/model/test_run.py
index 06a56f35..a276be2a 100644
--- a/tests/unit/nhp/model/test_run.py
+++ b/tests/unit/nhp/model/test_run.py
@@ -139,6 +139,7 @@ def test_run_all(mocker):
             {"variant": "variants"},
             pc_m(),
             False,
+            None,
         )
         for m in [InpatientsModel, OutpatientsModel, AaEModel]
     ]
@@ -184,7 +185,9 @@ def test_run_single_model_run(mocker, capsys):
     ndl_mock.create.assert_called_once_with("data")
 
     assert timeit_mock.call_count == 3
-    assert timeit_mock.call_args_list[0] == call("model_type", params, "nhp_data")
+    assert timeit_mock.call_args_list[0] == call(
+        "model_type", params, "nhp_data", aggregation_columns=None
+    )
     assert timeit_mock.call_args_list[2] == call(mr_mock.get_aggregate_results)
 
     assert capsys.readouterr().out == "\n".join(