feat: add Pathway Cost Effectiveness lollipop chart (Task 9.4)
- Create create_cost_effectiveness_figure() in plotly_generator.py Horizontal lollipop chart with dot size by patient count, colour gradient green→amber→red by cost, retention annotations - Fix calculate_retention_rate() to accept both 'value' and 'patients' keys - Add _render_cost_effectiveness() dispatch in chart.py callbacks - Wire into tab switching for active_tab='cost-effectiveness'
This commit is contained in:
Andrew Charlwood
@@ -381,16 +381,16 @@ Drawer selection → update_drug_selection → app-state store → load_pathway_
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- **Checkpoint**: Market Share tab renders real data, responds to filters, icicle still works
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### 9.4 Pathway Cost Effectiveness chart (Tab 3)
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- [ ] Create `dash_app/callbacks/pathway_costs.py`:
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- [x] Create `dash_app/callbacks/pathway_costs.py`:
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- Build horizontal lollipop chart from `get_pathway_costs()` data
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- Y-axis = pathway label (e.g., "Adalimumab → Secukinumab → Rituximab"), X-axis = £ per patient per annum
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- Dot size = patient count, colour gradient: green (cheap) → amber → red (expensive)
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- Uses `parse_pathway_drugs()` to extract pathway labels
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- [ ] Add retention rate annotations using `calculate_retention_rate()`
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- [x] Add retention rate annotations using `calculate_retention_rate()`
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- Show as secondary annotation: "Drug B retains 72% of patients"
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- [ ] Create figure function in `src/visualization/`
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- [ ] Wire into tab switching
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- **Checkpoint**: Cost Effectiveness tab renders with lollipop dots and retention annotations
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- [x] Create figure function in `src/visualization/`
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- [x] Wire into tab switching
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- **Checkpoint**: Cost Effectiveness tab renders with lollipop dots and retention annotations ✓
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### 9.5 Cost Waterfall chart (Tab 4)
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- [ ] Create `dash_app/callbacks/cost_waterfall.py`:
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@@ -108,6 +108,33 @@ def _render_market_share(app_state, title):
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return create_market_share_figure(data, title)
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def _render_cost_effectiveness(app_state, chart_data, title):
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"""Build the cost effectiveness lollipop figure from current filter state."""
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from dash_app.data.queries import get_pathway_costs
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from data_processing.parsing import calculate_retention_rate
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from visualization.plotly_generator import create_cost_effectiveness_figure
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filter_id = (app_state or {}).get("date_filter_id", "all_6mo")
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chart_type = (app_state or {}).get("chart_type", "directory")
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selected_dirs = (app_state or {}).get("selected_directorates") or []
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selected_trusts = (app_state or {}).get("selected_trusts") or []
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directory = selected_dirs[0] if len(selected_dirs) == 1 else None
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trust = selected_trusts[0] if len(selected_trusts) == 1 else None
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try:
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data = get_pathway_costs(filter_id, chart_type, directory, trust)
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except Exception:
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log.exception("Failed to load pathway cost data")
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return _empty_figure("Failed to load pathway cost data.")
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if not data:
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return _empty_figure("No pathway cost data available.\nTry adjusting your filters.")
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retention = calculate_retention_rate(data)
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return create_cost_effectiveness_figure(data, retention, title)
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def register_chart_callbacks(app):
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"""Register tab switching, pathway data loading, and chart rendering callbacks."""
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@@ -224,6 +251,9 @@ def register_chart_callbacks(app):
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elif active_tab == "market-share":
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fig = _render_market_share(app_state, title)
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elif active_tab == "cost-effectiveness":
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fig = _render_cost_effectiveness(app_state, chart_data, title)
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else:
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# Placeholder for charts not yet implemented
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tab_label = dict(TAB_DEFINITIONS).get(active_tab, active_tab)
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@@ -64,6 +64,11 @@ def parse_pathway_drugs(ids, level):
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return segments[3:]
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def _get_patients(node):
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"""Get patient count from a node dict (supports both 'value' and 'patients' keys)."""
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return node.get("value") or node.get("patients") or 0
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def calculate_retention_rate(nodes):
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"""Calculate pathway retention rates from node data.
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@@ -71,8 +76,8 @@ def calculate_retention_rate(nodes):
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to an N+1 drug pathway. This identifies effective treatment sequences.
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Args:
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nodes: List of dicts with 'ids', 'level', 'value' keys.
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Should contain level 3+ nodes from a single directorate.
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nodes: List of dicts with 'ids', 'level', and 'value' or 'patients' keys.
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Should contain level 4+ nodes (pathway level).
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Returns:
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Dict mapping pathway ids to retention info:
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@@ -92,14 +97,14 @@ def calculate_retention_rate(nodes):
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continue
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node_ids = node.get("ids", "")
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total_patients = node.get("value", 0)
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total_patients = _get_patients(node)
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if not total_patients:
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continue
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# Find child pathways (nodes whose ids start with this node's ids + " - ")
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child_prefix = node_ids + " - "
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child_patients = sum(
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n.get("value", 0)
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_get_patients(n)
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for n in nodes
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if n.get("ids", "").startswith(child_prefix) and n.get("level", 0) == level + 1
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)
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@@ -381,6 +381,184 @@ def create_market_share_figure(data: list[dict], title: str = "") -> go.Figure:
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return fig
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def create_cost_effectiveness_figure(
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data: list[dict],
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retention: dict,
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title: str = "",
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) -> go.Figure:
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"""
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Create horizontal lollipop chart showing pathway cost per patient per annum.
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Args:
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data: List of dicts from get_pathway_costs() with keys:
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ids, pathway_label, cost_pp_pa, patients, cost, avg_days,
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directory, trust_name, drug_sequence, level.
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Sorted by cost_pp_pa desc.
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retention: Dict from calculate_retention_rate() mapping ids to retention
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info: {retained_patients, total_patients, retention_rate, drug_sequence}.
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title: Chart title suffix (filter description).
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Returns:
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Plotly Figure with horizontal lollipop dots and retention annotations.
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"""
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if not data:
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return go.Figure()
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# Filter to pathways with positive cost
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filtered = [d for d in data if d["cost_pp_pa"] > 0]
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if not filtered:
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return go.Figure()
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# Cap to top 40 pathways by cost to keep chart readable
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filtered = filtered[:40]
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# Reverse for horizontal chart (highest cost at top)
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filtered = list(reversed(filtered))
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pathway_labels = [d["pathway_label"] for d in filtered]
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costs = [d["cost_pp_pa"] for d in filtered]
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patients = [d["patients"] for d in filtered]
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# Colour gradient: green (cheap) → amber → red (expensive)
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max_cost = max(costs) if costs else 1
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min_cost = min(costs) if costs else 0
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cost_range = max_cost - min_cost if max_cost != min_cost else 1
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colours = []
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for c in costs:
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ratio = (c - min_cost) / cost_range
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if ratio < 0.33:
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colours.append("#009639") # NHS green
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elif ratio < 0.66:
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colours.append("#ED8B00") # NHS warm yellow
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else:
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colours.append("#DA291C") # NHS red
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# Dot size scaled by patient count (min 8, max 30)
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max_pts = max(patients) if patients else 1
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min_pts = min(patients) if patients else 1
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pts_range = max_pts - min_pts if max_pts != min_pts else 1
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sizes = [8 + (p - min_pts) / pts_range * 22 for p in patients]
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# Build hover text with retention info
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hover_texts = []
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for d in filtered:
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retention_info = retention.get(d["ids"], {})
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retention_rate = retention_info.get("retention_rate")
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drugs_in_seq = len(d["drug_sequence"])
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hover = (
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f"<b>{d['pathway_label']}</b><br>"
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f"Cost p.p.p.a.: £{d['cost_pp_pa']:,.0f}<br>"
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f"Patients: {d['patients']:,}<br>"
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f"Total cost: £{d['cost']:,.0f}<br>"
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f"Avg duration: {d['avg_days']:,.0f} days<br>"
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f"Directorate: {d['directory']}<br>"
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f"Treatment lines: {drugs_in_seq}"
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)
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if retention_rate is not None:
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hover += f"<br>Retention: {retention_rate:.0f}% (no further switch)"
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hover_texts.append(hover)
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# Lollipop sticks (horizontal lines from 0 to cost)
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stick_traces = []
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for i, (label, cost) in enumerate(zip(pathway_labels, costs)):
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stick_traces.append(
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go.Scatter(
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x=[0, cost],
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y=[label, label],
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mode="lines",
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line=dict(color="#CBD5E1", width=1.5),
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showlegend=False,
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hoverinfo="skip",
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)
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)
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# Lollipop dots
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dot_trace = go.Scatter(
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x=costs,
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y=pathway_labels,
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mode="markers",
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marker=dict(
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size=sizes,
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color=colours,
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line=dict(color="#FFFFFF", width=1),
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),
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hovertemplate="%{customdata}<extra></extra>",
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customdata=hover_texts,
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showlegend=False,
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)
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display_title = (
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f"Pathway Cost Effectiveness — {title}" if title
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else "Pathway Cost Effectiveness (£ per patient per annum)"
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)
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fig = go.Figure(data=stick_traces + [dot_trace])
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# Add retention annotations for pathways with notable retention
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annotation_count = 0
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for d in filtered:
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ret = retention.get(d["ids"], {})
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rate = ret.get("retention_rate")
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if rate is not None and rate < 90 and d["patients"] >= 10 and annotation_count < 8:
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fig.add_annotation(
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x=d["cost_pp_pa"],
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y=d["pathway_label"],
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text=f"{rate:.0f}% retain",
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showarrow=False,
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xanchor="left",
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xshift=10,
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font=dict(size=10, color="#768692", family="Source Sans 3"),
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)
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annotation_count += 1
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fig.update_layout(
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title=dict(
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text=display_title,
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font=dict(
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family="Source Sans 3, system-ui, sans-serif",
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size=18,
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color="#1E293B",
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),
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x=0.5,
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xanchor="center",
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),
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xaxis=dict(
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title="£ per patient per annum",
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tickprefix="£",
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tickformat=",",
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gridcolor="#E2E8F0",
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zeroline=True,
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zerolinecolor="#CBD5E1",
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),
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yaxis=dict(
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title="",
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automargin=True,
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tickfont=dict(size=11),
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),
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margin=dict(t=50, l=8, r=24, b=40),
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
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autosize=True,
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hoverlabel=dict(
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bgcolor="#FFFFFF",
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bordercolor="#CBD5E1",
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font=dict(
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family="Source Sans 3, system-ui, sans-serif",
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size=13,
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color="#1E293B",
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),
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),
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font=dict(
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family="Source Sans 3, system-ui, sans-serif",
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),
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height=max(450, len(filtered) * 28 + 150),
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)
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return fig
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def save_figure_html(
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fig: go.Figure, save_dir: str, title: str, open_browser: bool = False
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) -> str:
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