Medication Adherence in RWE

In plain language

Medication adherence is a sequence. First, did the patient start the medication after it was prescribed? Then, did they keep enough medication available and continue treatment? Primary non-adherence covers the never-started group; secondary non-adherence, PDC, MPR, and persistence describe what happens after initiation.

Medication adherence in RWE

is a time-ordered process, not one percentage. The process begins with a prescription or treatment decision, moves through initiation, continues through day-to-day implementation, and ends at discontinuation. Persistence is the time from initiation until the last dose before discontinuation. This parent card separates the two electronic-data branches that are routinely mixed together: primary non-adherence, in which a new prescription is never dispensed or started within a prespecified window, and secondary non-adherence, in which a patient who has initiated treatment does not obtain or use medication as intended during follow-up. The distinction determines the denominator, the data needed, and the question answered.

Core conceptual distinction

Primary adherence is an order-to-first-dispensing event. Its denominator is newly ordered prescriptions, so it requires an EHR or e-prescribing order linked to dispensing or administration data. Claims alone cannot identify people who were prescribed a drug but never filled it. Secondary adherence is conditional on initiation and uses a post-initiation treatment history. PDC and MPR quantify medication availability over a fixed observation window; persistence quantifies duration until discontinuation; refill-gap and trajectory methods preserve more timing information. These are indirect measures of possession or administration, not proof of ingestion. A paid pharmacy claim shows that supply was dispensed, while a medication order or reconciled medication list does not show that the medication was obtained.

Pros, cons, and trade-offs

A family framework prevents a common category error: reporting PDC among first-fill patients as though it described adherence among everyone who was prescribed treatment. It also keeps implementation and persistence separate, so a patient with intermittent short gaps is not automatically treated as equivalent to a patient who permanently discontinues. The cost is that a complete adherence pathway needs linked order, dispensing, administration, enrollment, and sometimes clinical data. A single database rarely observes every stage. Prefer the full hierarchy when describing treatment uptake or real-world strategy performance; use a narrower child measure when the estimand is explicitly limited to one stage and the denominator is stated.

When to use

Use this framework when designing drug-utilization, comparative-effectiveness, quality-measure, budget-impact, or treatment-pattern analyses that mention adherence, compliance, initiation, persistence, discontinuation, refill gaps, PDC, or MPR. Start here when the team must decide whether the target is population uptake after prescribing, implementation among initiators, duration on therapy, or a composite real-world treatment strategy. The parent card is also the routing surface for selecting the appropriate child card and for documenting which stages are unobservable in the available data.

When NOT to use - and when it is actively misleading

Do not collapse primary and secondary non-adherence into one rate unless every patient has a captured prescribing event and longitudinal dispensing follow-up. Do not call a prescription order an exposure, a dispensing an ingestion, or an absent claim non-adherence when pharmacy benefit capture is incomplete. Do not use an arbitrary PDC >= 0.80 threshold as a universal biological boundary; thresholds and observation windows are drug- and question-specific. Do not classify adherence over a future window and then use that classification from baseline in an outcome model, because the patient must survive and remain observable long enough to earn the label. Use a landmark, time-varying exposure, or a properly weighted per-protocol strategy.

Data-source operational depth

Claims and pharmacy dispensing data are strong for secondary adherence because `fill_date`, `days_supply`, reversals, and enrollment can be used to reconstruct coverage, but they cannot measure primary non-adherence without an order denominator and they do not prove ingestion. EHR and e-prescribing data capture orders and can define primary adherence when linked to fills; medication lists and refill requests are weaker because they may be stale. Medication administration records and procedure/J-code claims are preferable for clinician-administered products, where days_supply-based PDC may be undefined. Registries can capture reasons for stopping and patient-reported use but often miss complete dispensing histories. Linked EHR-order, pharmacy-claim, administration, and enrollment data provide the strongest continuum, provided order cancellations, claim reversals, cash fills, benefit carve-outs, inpatient stays, and out-of-network fills are reconciled.

Worked example

A health system writes 1,000 new antihypertensive prescriptions. Eight hundred are dispensed within 30 days, so primary adherence is 80% and 200 orders are primary non-adherent under the prespecified rule. Among the 800 initiators, 600 have PDC >= 0.80 over the next 180 days, 90 have lower PDC but remain persistent, and 110 discontinue after exceeding the permissible gap. The correct report keeps the denominators visible: 60% of all newly prescribed patients are both initiated and PDC-adherent (600/1,000), while 75% of initiators meet the PDC threshold (600/800). Reporting only 75% hides the 200 people who never initiated.

Index definitions

Worked example

Scenario

A health system links new medication orders to pharmacy dispensings and follows each initiator for 180 days. The study needs separate counts for initiation, post-initiation coverage, and discontinuation.

Dataset

Cohort-level counts at each adherence stage.

Steps

• Primary adherence is 800 divided by 1,000, or 80%.

• Secondary PDC adherence among initiators is 600 divided by 800, or 75%.

• The combined initiated-and-PDC-adherent proportion is 600 divided by 1,000, or 60% of all newly prescribed patients.

• The study reports persistence and discontinuation separately because they answer a duration question rather than a coverage-intensity question.

Result

Primary adherence is 80%; PDC adherence is 75% among initiators but 60% across all newly prescribed patients.

Runnable example

import pandas as pd

WINDOW_DAYS = 30
valid_fills = fills.loc[~fills["reversed"].fillna(False)].copy()
linked = orders.merge(valid_fills[["person_id", "fill_date"]], on="person_id", how="left")
linked["days_to_fill"] = (linked["fill_date"] - linked["order_date"]).dt.days
eligible = linked.loc[linked["days_to_fill"].between(0, WINDOW_DAYS, inclusive="both")]
first = eligible.groupby("order_id", as_index=False)["fill_date"].min()
out = orders.merge(first, on="order_id", how="left")
out["initiated"] = out["fill_date"].notna()
out["primary_nonadherent"] = ~out["initiated"]

library(data.table)
setDT(orders); setDT(fills)
window_days <- 30L
valid <- fills[is.na(reversed) | reversed == FALSE]
linked <- merge(orders, valid[, .(person_id, fill_date)], by = "person_id", all.x = TRUE)
linked[, days_to_fill := as.integer(fill_date - order_date)]
first <- linked[days_to_fill >= 0L & days_to_fill <= window_days,
                .(fill_date = min(fill_date)), by = order_id]
out <- merge(orders, first, by = "order_id", all.x = TRUE)
out[, initiated := !is.na(fill_date)]
out[, primary_nonadherent := !initiated]