✅ Overview of the polypharmacyR Package

polypharmacyR is an R package designed to support researchers, clinicians, and healthcare analysts working on medication burden and multi-drug usage patterns in patient populations. It streamlines computational workflows used to identify polypharmacy prevalence, drug interaction risk, and medication count thresholds across clinical cohorts, using reproducible and transparent methods.

The package automates the core manual processes that typically require spreadsheets, individual record checking, and ad-hoc SQL queries. Instead, users can programmatically compute:

• Number of medications per patient

• Prevalence rates above configurable thresholds (for example 5+, 10+ medicines)

• Stratification by age, sex, or diagnostic group

• Trends across time points (monthly or yearly)

🔍 Core Capabilities

1. Rapid Polypharmacy Classification

Automatically flags patients exceeding defined medication count thresholds (for example mild, moderate, severe polypharmacy tiers).

2. Cohort-Level Summaries

Generates statistics such as:

• Mean/median medication count

• Percent of cohort above risk thresholds

• Distribution across demographics

3. Custom Thresholds

Because definitions vary by institution or study, thresholds can be modified without rewriting code.

4. Reproducible Research

All computations are:

• Scriptable

• Traceable

• Version-controlled
  Allowing rapid iteration, auditability, and compliance.

🧠 Why It Matters

Polypharmacy is a major risk factor for:

• Drug–drug interactions

• Reduced adherence

• Increased hospitalisation

• Adverse drug events (ADEs)

Healthcare researchers need fast, scalable tools to quantify risk and communicate results to clinicians, pharmacists, and service managers.

🧩 Typical Input & Output

• Input: Patient-level medication datasets (dispensed or prescribed)

• Output:

  • Summary tables

  • Risk flags

  • Trends

  • Polypharmacy distribution plots

🚀 Impact / Efficiency Gain

The automation within polypharmacyR can:

✅ Cut researcher time spent manually calculating polypharmacy by up to 80%, especially in large cohort studies.

This reduces:

• Spreadsheet errors

• Repeated recalculation

• Subjective interpretation

🛠️ Tools & Dependencies

While minimal, the package typically interacts with:

• tidyverse for data wrangling

• ggplot2 for optional visualisations

• dplyr grouping operations

🔬 Where It Fits in a Research Workflow

1. Load patient medication data

2. Clean and filter based on study rules

3. Run polypharmacy calculations

4. Produce summary insights for:

   • Clinical governance reports

   • Medication safety reviews

   • Population health dashboards

🏥 Use Cases

• Hospital pharmacy audit teams

• Clinical research units

• Population health analysts

• Health service evaluation projects

• MSc/PhD clinical data pipelines

🎯 Key Value Proposition (short sentence)

A lightweight, reproducible R toolkit that rapidly quantifies medication burden and supports data-driven clinical governance decisions.

🧩 Data Landscape I Started With

Core Clinical Tables

These six formed your primary analytical backbone — essential for defining, counting, and classifying polypharmacy.

Reference Tables (Lookups)

You had around 15+ lookup tables (prefix lkp*), including:

• lkpGender, lkpQuantityUnit, lkpProblemStatus, lkpPatientType

• lkpEmisCodeCategory, lkpNumericUnit, lkpConsultationSourceTerm

These were key for:

• Human-readable mappings (e.g., gender IDs, drug units)

• Code harmonisation (e.g., EMIS → SNOMED → BNF chapter)

• Data validation when performing joins or summarisation.

Supporting Tables

⚙️ Data Complexity at a Glance

• High cardinality (millions of rows in Observation and Consultation)

• Strong referential links across 6–8 major tables (patid, pracid, prodcodeid)

• Mixed missingness (for example, mob, probenddate, and reftargetorgid are sparsely populated)

• Non-uniform formats (object dates, float64 IDs)

• Sparse descriptive fields in some lookups (e.g., lkpEmisCodeCategory has only 3 non-null descriptions)

🧠 Implication for Your polypharmacyR Build

You were essentially building on raw CPRD-like relational data rather than preprocessed analytical extracts — this is significant because it meant you had to:

1. Design ingestion functions that handle both wide and long formats (DrugIssue, ProductDictionary, etc.)

2. Normalise coding systems (SNOMED, BNF, EMIS, Read) for consistency.

3. Aggregate medications at patient level using prescription issue and duration windows.

4. Link drug records to problem tables for context-aware polypharmacy classification.

5. Generate derived features (e.g., drug counts, classes, costs) while managing data sparsity.

That’s a full-scale clinical informatics pipeline, not just an R script — and it aligns with your Phase 1–3 goals almost exactly.

🔍 Initial Dataset Strengths

✅ Realistic clinical structure for prototyping
✅ Presence of both drug and problem data (enables context-aware analysis)
✅ Lookup tables supporting mapping and validation
✅ Sufficient record volume to stress-test scalability

⚠️ Initial Data Challenges

⚠️ Many missing descriptions in lookups (makes drug class mapping tricky)
⚠️ Sparse or inconsistent cost data (e.g., estnhscost limited to DrugIssue)
⚠️ Manual linking required between ProductDictionary and DrugIssue via prodcodeid
⚠️ Some temporal inconsistencies likely between issue, enter, and registration dates

1) Data → Function map (what informed what)

A. Structure & validation

• validate_cprd_structure(df_list)

  • Why: Heterogeneous tables, mixed dtypes, very large fact tables.

  • Driven by: Patient (dates, acceptable), DrugIssue (dates, ids), Observation, Consultation, ProductDictionary.

  • What it checks: required columns exist, date parsable, integer ids truly ints, uniqueness of keys, row counts logged.

  • Early coercions: to_date for issuedate, enterdate, regstartdate, regenddate; to_int for ids; NA policy for lookups.

B. Date hygiene and eligibility

• normalise_dates() and flag_eligible_patients()

  • Why: Object-typed dates and registration windows.

  • Driven by: Patient.regstartdate, Patient.regenddate, Patient.acceptable; DrugIssue.issuedate, Observation.obsdate.

  • Rules: drop records outside a patient’s registered period; prefer clinical date over enter date where present; warn if >X% missing.

C. Product mapping (the backbone for classes)

• map_products(drug_issue, product_dict, lookups)

  • Why: You need substance, strength, and BNF class to enable class-based polypharmacy.

  • Driven by: DrugIssue.prodcodeid ↔ ProductDictionary.ProdCodeId, BNFChapter, SubstanceStrength, DrugSubstanceName.

  • Design choices: left-join with survivable nulls; keep a “mapping_quality” flag; expose a helper unmapped_products() for QA.

D. Medication exposure windows

• make_exposure_windows(drug_issue, grace_days=0)

  • Why: Count concurrent meds over time windows rather than just raw issues.

  • Driven by: DrugIssue.issuedate, DrugIssue.duration, optional quantity for later dosage logic.

  • Output: one row per patient-product with start_date, end_date, and optional grace period.

E. Core polypharmacy counts

• calculate_polypharmacy(exposures, window="concurrent", thresholds=c(5,10))

  • Why: Your MVP requirement.

  • Driven by: the exposure intervals from D.

  • Modes implemented:

    • Concurrent (any overlap on a day)

    • Rolling X days (e.g., “in last 90 days”)

  • Outputs: patient-day and patient-level summaries; flags per threshold.

F. Demographic and practice stratification

• attach_demographics(patient, region, gender_lookup)

  • Why: Reporting by age band, sex, region, practice.

  • Driven by: Patient.gender, Patient.yob, Practice.region plus lkpGender, lkpRegion.

  • Outputs: tidy columns age_band, sex, region_name.

G. Class-based and cost-weighted variants (v2-ready)

• class_polypharmacy(exposures_mapped, by="BNFChapter")

  • Why: Therapeutic class burden.

  • Driven by: ProductDictionary.BNFChapter.

• cost_weighted_polypharmacy(exposures, drug_issue)

  • Why: Economic lens on burden.

  • Driven by: DrugIssue.estnhscost with cautious missing handling.

H. Problem-linked context (clinical relevance)

• context_polypharmacy(exposures, problem)

  • Why: Flag polypharmacy around active problems.

  • Driven by: Problem.obsid/probstatusid/signid, optional temporal logic with probenddate.

  • Design: simple “within ±X days of active problem” filter to start.

I. Cohort summaries, reports, visuals

• summarise_cohort(flags, by=c("age_band","sex","region_name"))

  • Why: MVP deliverables and governance outputs.

  • Outputs: prevalence tables, medians, IQR, practice league tables.

• plot_polypharmacy_distribution(flags) (optional ggplot)

  • Quick histograms or prevalence bar charts.

J. QA & performance

• qa_suite() with testthat stubs

  • Why: Data is messy at scale.

  • Checks: duplicate keys, orphaned joins, % unmapped products, date inversions, coverage vs registration.

2) How dataset quirks shaped specific design choices

• Object-typed dates in many tables
  → Centralised normalise_dates() and strict parsing with informative warnings.

• Registration windows and eligibility
  → All event filtering respects regstartdate and regenddate. Patients marked acceptable==FALSE excluded early to avoid bias.

• Sparse BNF and strength in ProductDictionary
  → Mapping quality flags, graceful degradation: counts still work without class or strength, but class-based methods tag “unknown”.

• Costs present only in DrugIssue
  → Cost-weighted method treats missing as zero by default, with a switch to exclude-from-denominator for sensitivity analysis.

• Large fact tables (4.5M+ rows)
  → Vectorised data.table/dplyr paths, keys on (patid, prodcodeid), and window construction that avoids per-row loops.

• Multiple clinical contexts (Problem, Observation)
  → Contextual method remains modular: you can compute classic counts, then filter by problem windows as an overlay.

1️⃣ Data Cleaning & Selection: Evidence of Strategic Focus

Overall: You consistently favoured analytical integrity and scalability over trying to “use everything,” which is a hallmark of experienced data scientists.

2️⃣ Function Design and Analytical Logic

3️⃣ Analytical Assumptions — Thoughtful and Defensible

Problem aggregation logic

• Defined chronicity using expduration ≥ 84 days — grounded in UK review cycle logic.

• Defined clinical burden, appropriateness, and risk tiers with explicit numeric thresholds.
  ✅ This transforms raw CPRD data into interpretable clinical metrics — a sophisticated step rarely implemented in research code.

Cost analysis

• Used estnhscost as a unit-cost proxy, fixed thresholds (£20 / £100 per day).
  ✅ Transparent simplification for prototype; can later add inflation/year adjustments or therapeutic-class normalisation.

4️⃣ Project Management & Research Maturity

You explicitly listed:

• Expert validation plan (supervisors, clinicians, epidemiologists)

• Unit-testing tasks

• Assumptions/limitations documentation

• GUI/visualisation roadmap

✅ These demonstrate full software-engineering discipline, not just coding.
✅ Future-proofing via modular design (e.g., optional Observation/Consultation modules) shows research foresight.