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docs: update progress.txt with iteration 26 (Task 9.4 complete — cost effectiveness chart)

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Andrew Charlwood
2026-02-06 19:39:48 +00:00
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@@ -1445,3 +1445,67 @@ Console error: `WARN: Multiple implied roots, cannot build icicle hierarchy of t
- Read `get_pathway_costs()` data shape
### Blocked items:
- None
## Iteration 26 — 2026-02-06
### Task: Phase 9 — Task 9.4 (Pathway Cost Effectiveness chart — Tab 3)
### Why this task:
- Tasks 9.1 (tab infra), 9.2 (queries), 9.3 (market share) complete. Task 9.4 is next in sequence.
- Progress.txt from iteration 25 explicitly recommended this task.
- This is the user's key insight chart — shows cost effectiveness of different treatment pathways with retention annotations.
### Status: COMPLETE
### What was done:
- **Fixed `calculate_retention_rate()` in `src/data_processing/parsing.py`**:
- Added `_get_patients()` helper that accepts either `"value"` or `"patients"` key
- Bug: function returned 0 entries because `get_pathway_costs()` returns data with `"patients"` key, not `"value"`. The original function only checked `node.get("value", 0)`.
- Now works with both chart data nodes (`value`) and pathway cost data (`patients`).
- **Created `create_cost_effectiveness_figure(data, retention, title)` in `src/visualization/plotly_generator.py`**:
- Horizontal lollipop chart: stick lines from 0 to cost, dot at cost value
- Y-axis = pathway label (e.g., "Adalimumab → Secukinumab → Rituximab"), X-axis = £ per patient per annum
- Dot size scaled by patient count (min 8px, max 30px)
- Colour gradient: green (#009639) for cheap, amber (#ED8B00) for mid, red (#DA291C) for expensive
- Hover shows: pathway name, cost p.p.p.a., patients, total cost, avg duration, directorate, treatment lines, retention rate
- Retention annotations for pathways with <90% retention and ≥10 patients (up to 8 annotations)
- Caps at top 40 pathways by cost for readability
- Dynamic height based on pathway count
- NHS design aesthetic: Source Sans 3, transparent backgrounds, clean gridlines
- **Added `_render_cost_effectiveness(app_state, chart_data, title)` helper in `dash_app/callbacks/chart.py`**:
- Extracts filter params (date_filter_id, chart_type, single directory/trust)
- Calls `get_pathway_costs()` → `calculate_retention_rate()` → `create_cost_effectiveness_figure()`
- Handles empty data and exceptions gracefully
- **Wired into `update_chart` dispatch**: `active_tab == "cost-effectiveness"` → `_render_cost_effectiveness()`
- **Architecture note**: Like market share, uses the `_render_X()` helper pattern within the single `update_chart` callback. No separate callback file needed.
### Validation results:
- Tier 1 (Code): `from dash_app.app import app` — OK, 11 callbacks registered
- Tier 1 (App starts): `python run_dash.py` → "Dash is running on http://127.0.0.1:8050/" — no errors
- Tier 3 (Functional):
- Directory chart: 111 pathways, 111 retention entries, 41 traces — renders correctly
- Indication chart: 92 pathways, 92 retention entries — renders correctly
- RHEUMATOLOGY filter: 38 pathways, 39 traces — filters correctly
- NNUH trust filter: 50 pathways — filters correctly
- Empty filter (nonexistent directory): 0 pathways — returns empty figure
- Date filter (2yr_12mo): 17 pathways — filters correctly
- Icicle still works: 293 nodes, 11,118 patients — no regression
- Market share still works — no regression
### Files changed:
- `src/data_processing/parsing.py` — Fixed: calculate_retention_rate() accepts both 'value' and 'patients' keys
- `src/visualization/plotly_generator.py` — Added: create_cost_effectiveness_figure() (~170 lines)
- `dash_app/callbacks/chart.py` — Added: _render_cost_effectiveness() helper + dispatch branch
- `IMPLEMENTATION_PLAN.md` — Task 9.4 marked [x]
### Committed: 4ef7239 "feat: add Pathway Cost Effectiveness lollipop chart (Task 9.4)"
### Patterns discovered:
- `get_pathway_costs()` returns `"patients"` as the key while chart data nodes use `"value"`. Shared parsing functions that operate on either data format need to handle both keys.
- Lollipop chart pattern: separate `go.Scatter` with `mode="lines"` for sticks + one `go.Scatter` with `mode="markers"` for dots. Each stick is its own trace (40 pathways = 41 total traces).
- Retention annotation threshold: `rate < 90% AND patients >= 10` avoids annotating trivial pathways. Cap of 8 annotations prevents visual clutter.
- The `_render_X(app_state, chart_data, title)` pattern differs slightly from market share (`_render_market_share(app_state, title)`) by accepting `chart_data` — this is because cost effectiveness queries directly from SQLite rather than using chart-data nodes. Future charts may also use this pattern.
### Next iteration should:
- Start Task 9.5 — Cost Waterfall chart (Tab 4)
- Sub-steps:
1. Create figure function in `src/visualization/plotly_generator.py` — `create_cost_waterfall_figure(data, title)`
2. Build Plotly waterfall chart from `get_cost_waterfall()` data
3. Each bar = one directorate's average cost_pp_pa, sorted highest to lowest
4. NHS colours, responds to chart_type toggle, date filter, trust filter
5. Wire into `update_chart` via `_render_cost_waterfall()` helper
- The query `get_cost_waterfall()` returns [{directory, cost_pp, patients, total_cost}] sorted by cost_pp desc
- Read `get_cost_waterfall()` in pathway_queries.py for exact data shape
### Blocked items:
- None