Clinical effectiveness and cost-effectiveness of issuing longer versus shorter duration (3-month vs. 28-day) prescriptions in patients with chronic conditions: systematic review and economic modelling

Volume and cost of repeat prescriptions in primary care

In England, the NHS spends > £9B on prescription medicines dispensed in the community each year. 1 An increasing number of NHS patients receive prescriptions for chronic conditions, which can be generated without the need for a consultation in primary care. These are known as repeat prescriptions. 2 It is estimated that repeat prescriptions account for two-thirds of prescriptions generated in primary care. 3 Among the 20 most prescribed medicines dispensed in the community in England during 2015, all but one (amoxicillin, an antibiotic) are commonly prescribed on a repeat basis for chronic conditions (Table 1). Such conditions include diabetes, asthma and hypertension.

The majority of prescription costs in primary care are for chronic conditions. For the last 9 years, drugs used in the treatment of diabetes [classified under section 6.1 of the British National Formulary (BNF)] have accounted for the largest net ingredient costs (NICs) (i.e. the cost of the drug not including dispensing costs, fees or discounts) of prescriptions dispensed in primary care in England. Costs increased for diabetes drugs by £87.6M (10.3%) since 2014 to reach £936.7M in 2015. 5

The total NIC of all prescriptions dispensed in the community has increased by 16.8% since 2005, despite a fall in the average NIC per prescription. 5 It has been estimated that between £100M and £300M is wasted in the form of unused or partially used medications each year. 6,7 Ensuring that prescriptions are issued for a duration that minimises the waste of medicines is an important factor in reducing financial loss to the NHS.

Guidance and policy on repeat prescription length

With regard to repeat prescriptions, there is some ambiguity in the Department of Health’s (DH’s) guidance on prescription length. Guidance issued by commissioners (Primary Care Trusts until 2011 and now Clinical Commissioning Groups) in some areas, as well as that from the Pharmaceutical Services Negotiating Committee (PSNC),8 has encouraged general practitioners (GPs) to issue shorter prescriptions, typically of 28 days in length. 9–11 This guidance was based on evidence that limiting prescription length to 28 days reduces medicine waste and thus results in cost savings,12,13 and on the reported success of local prescribing schemes, for example in Surrey and Grampian,13 and Brighton and Hove. 14 This was stated to be in line with the DH’s policy to strike a ‘balance between patient convenience, good medical practice and drug wastage’. 11 Shorter prescription lengths have also been shown to benefit patients by providing better signalling to GPs for treatment discontinuations due to adverse events. 15

The guidance issued by local commissioning organisations and by the PSNC8 encouraging shorter prescriptions has tended to advocate a blanket 28-day prescribing policy. In contrast, the National Institute for Health and Care Excellence (NICE), through the BNF, recommends blanket 28-day prescribing for certain classes of controlled drugs only. 16 In addition, recent evidence argues for a more informed use of 28-day intervals for repeat prescriptions,7,17,18 closer to the DH’s more general principle that prescription duration should be consistent with medically appropriate patient needs while also considering NHS resources, patient convenience and the dangers of having excess quantities of prescription medications in the home. 19 Similarly, the British Medical Association and the General Medical Council do not recommend a specific prescription duration but instead encourage safe and appropriate repeat prescription intervals adapted to the needs of individual patients. 19,20

Indeed, evidence shows that there may be some disadvantages to shorter prescriptions. Shorter prescriptions may (1) increase the costs to the health system through increased GP administrative workload and dispensing fees to pharmacists,21 (2) increase costs incurred by the patient,22 (3) have a negative impact on patient satisfaction23 and (4) have a negative impact on adherence. 24 Whether or not the most commonly used prescription length should be changed was identified as a key area for research in the DH’s 2011 roundtable, Making Best Use of Medicines. 25

Inclusion and exclusion criteria

To address the above research questions, the inclusion and exclusion criteria of the populations, interventions, comparisons, outcomes and study types of interest are defined in the following sections and summarised in Table 3.

Search strategy

To identify relevant primary studies, we searched a number of databases:

• MEDLINE (PubMed)

• EMBASE

• Cumulative Index to Nursing and Allied Health Literature (CINAHL)

• Web of Science

• Cochrane Central Register of Controlled Trials, which includes the Database of Abstracts of Reviews of Effects (DARE), the HTA database and the NHS Economic Evaluation Database (NHS EED).

We also performed searches in grey literature databases:

• Open Archives Initiative harvester (OAIster)

• OpenGrey

• The New York Academy of Medicine (NYAM)’s Grey Literature Report.

Searches were conducted in all of the databases between 13 October 2015 and 21 October 2015. The search was rerun in PubMed in June 2016 to check that no additional relevant studies had been published in the interim period. Given that no additional studies of relevance were identified, further searching of other databases was deemed unnecessary.

All of the search terms used were in English, but the searches were not otherwise restricted by language. Search terms included (but were not limited to) ‘prescription length’, ‘prescription duration’, ‘medication duration’, ‘medication length’, ‘length of prescription’, ‘duration of prescription’, ‘prescribing pattern’, ‘prescription pattern’, ‘repeat dispensing’, ‘prescription interval’, ‘dispensing trends’, ‘prescription trends’, ‘prescribing trends’, ‘standardised prescribing’, ‘standardised prescription’, ‘one month prescription’, ‘one month supply’, ‘three month prescription’, ‘three month supply’, ‘90 day supply’, ‘28 day supply’, ‘30 day supply’, ‘long prescription’ and ‘short prescription’.

Details of the full search strategy are presented in Appendix 1.

Additional searching techniques to identify relevant studies were applied. These included:

• searching for systematic reviews and health technology assessments that could yield additional primary studies. We searched the Cochrane Database of Systematic Reviews (CDSR), NIHR HTA, DARE and the NICE website between 13 October 2015 and 21 October 2015

• checking the references within included papers and other reviews

• searching for additional studies carried out by the first authors of relevant studies

• carrying out citation searches of key publications to identify subsequent publications that have cited those key publications [using the ‘cited by’ option in Google Scholar™ (Google Inc., Mountain View, CA, USA)].

Study selection

The study selection involved four stages, shown in Table 4.

Data extraction

To facilitate data extraction, a form was developed in Microsoft Excel^® (Microsoft Corporation, Redmond, WA, USA) and piloted using several studies. Eligible studies were extracted twice, once each by two reviewers working independently (two of AK, AM, CMiani, JE and SK). When the data were incomplete [e.g. sample sizes not reported, standard deviations (SDs) not reported], we attempted to contact the study authors.

In some cases, studies were found to be ineligible during this process. These studies were discussed among the wider research team to ensure that there was agreement regarding their ineligibility before they were added to the table of excluded studies. The two data extraction forms were then combined and compared by a third reviewer. Any discrepancies were resolved through discussion among all of the reviewers.

Risk-of-bias assessment

As no RCTs were included, to assess risk of bias in observational cohort and cross-sectional studies we used the Risk Of Bias in Non-Randomized Studies – of Interventions (ROBINS-I) assessment tool. 30 The tool assesses seven domains of bias (see Table 24, Appendix 4, for an overview of the tool). For each domain, the ‘signalling questions’ were completed by three reviewers independently (two of CMiani, JE and SK in each case) to determine the domain-level risk of bias, with any discrepancies resolved through discussion or by consulting a fourth reviewer (CMeads). The overall risk of bias was determined by all four reviewers based on the domain-level risk of bias and reviewers’ judgement of both the severity of the bias in a particular domain and the relative consequences of bias in different domains. 30 Studies were considered to be at a ‘serious risk’ of bias if one or more of the domains assessed was at serious risk of bias. When insufficient data were reported to allow a judgement, the risk of bias was classified as ‘no information’.

Grading of Recommendations Assessment, Development and Evaluation assessment

We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria31 to assess the quality of the body of evidence for each outcome. As no RCTs were eligible for inclusion, only GRADE methodology applicable to non-RCTs is presented here. Using the standard four GRADE levels of quality (high, moderate, low and very low), non-RCTs were considered to have an initial rating of low. This rating was then up- or downgraded using the criteria (1) risk of bias, (2) imprecision, (3) inconsistency, (4) indirectness and (5) publication bias.

Synthesising the evidence

The evidence included in this review was largely summarised using a narrative synthesis, with data presented in tables, in the text, and in forest plots for visual purposes.

For each eligible outcome presented within a study, we calculated effect sizes [odds ratios (ORs) with 95% confidence intervals (CIs) for dichotomous outcomes, and mean difference (MD) with 95% CIs for continuous outcomes]. In some of the studies, SDs were imputed based on p-values (in cases where we could not obtain SDs from the study authors). We have noted when this was done in the tables.

The studies classified data by general therapeutic area (e.g. lipid-lowering drugs, antidiabetics) and by more specific chemical classification [e.g. angiotensin-converting enzyme inhibitors (ACEIs), statins]. We refer to these henceforth as the therapeutic class and the chemical class, respectively. We presented data for both the therapeutic class and the chemical class when these were available. When a study reported data by chemical class only,32 we combined these data using a meta-analysis in order to derive data for one of the corresponding broader therapeutic classes as evaluated in other studies (i.e. lipid-lowering agents, antidiabetics, etc.). We did not compare any effect size differences between the different therapeutic classes, as this review was not designed to consider these differences.

Exploratory meta-analyses were conducted for medication adherence and wastage. In each of these meta-analyses, we combined continuous and dichotomous data. The first step of this process was to calculate the standardised mean differences (SMDs) with 95% CIs for each dichotomous or continuous outcome as presented within each study. Dichotomous outcomes were converted to continuous data using the methods recommended in the Cochrane Handbook section 9.4.6. 33 We then calculated a standard error from these CIs. The last step was to pool the SMD and standard errors using a random-effects model in RevMan version 5.3 (RevMan, The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen, Denmark). Evidence of the extent of statistical heterogeneity was assessed by visually examining the extent to which the CIs overlapped. Additionally, the I² value, automatically calculated by the RevMan software, was reported, and an interpretation of the levels of heterogeneity was made based on the recommendations of Deeks et al. 34

Studies identified

Our search identified a total of 24,876 records across the databases searched. After the removal of duplicates and the initial screening of titles and abstracts, we considered 47 references for full-text evaluation. Of these, nine studies were identified as eligible for inclusion in the review, along with seven additional studies retrieved through backwards and forwards citation checking (Figure 1). Appendix 3 provides reference details of excluded studies and reasons for their exclusion based on our full-text review.

FIGURE 1.

The PRISMA flow chart.

Included studies

Outcomes

One study presented findings of a risk factor for health outcomes,39 nine studies presented data on medication adherence,35–37,39,40,43–45,47 medication wastage was reported in six studies,32,36,38,42,45,48 costs were reported in five studies42,44–46,49 and three studies reported on ‘other’ outcomes. These ‘other’ outcomes were medication persistency,45 number of clinical encounters,37 and number of prescriptions and prescription duration. 41 No evidence was found related to other outcomes of interest. Findings for each outcome are presented in turn below.

Adherence

Medication adherence was reported in nine of the included studies (six retrospective cohort studies,35–37,39,40,45 one pre–post controlled study44 and two cross-sectional studies). 43,47 All measures used to assess adherence were indirect estimates based on pharmacy claims refill data. The most common measures of adherence were the proportion of days covered (PDC) or the medication possession ratio (MPR) (Box 1). When the MPR is used, patients who routinely refill their medications early can have a MPR of > 100% (i.e. the numerator can be greater than the denominator).

BOX 1 - Definitions of PDC and MPR

PDC is calculated as:

MPR is calculated as:

Measures of adherence were presented as dichotomous (i.e. the number of patients with ≥ 80% or < 80% PDC or MPR) or continuous outcomes (i.e. mean PDC or MPR). One study reported both dichotomous and continuous outcomes. 44 It was possible to calculate effect sizes for six cohort studies based on the information reported in the papers (see Table 8 and Figures 2 and 3). 35–37,39,40,45 The findings are presented for dichotomous and continuous outcomes in turn below and summarised in Table 8.

TABLE 8 - Studies that evaluated claims-based medication adherence

ARB, angiotensin II receptor blocker; MD, mean difference; NR, not reported; ns, not significant; SSRI, selective serotonin reuptake inhibitor.

The authors reported an adjusted risk ratio of 1.41 (95% CI 1.28 to 1.55), p < 0.01, controlling for age, gender, race, co-payment, comorbidities and insurance status. Given that this is a retrospective cohort study, we have presented the effect size as an OR.

The Domino 2011 study is included twice in this table because this study reports outcomes in two ways.

Difference-in-difference-in-differences measures = differences from baseline to follow-up between two states (North Carolina and Georgia).

Abstract.

Numerators were calculated from data presented in the abstract.

The authors reported the following effect sizes: cholesterol OR 0.60 (95% CI 0.57 to 0.64), p < 0.001; hypertension OR 0.60 (95% CI 0.56 to 0.63), p < 0.001; diabetes OR 0.61 (95% CI 0.53 to 0.70), p < 0.001. Our calculated ORs, as presented in Figure 2, are similar.

Numerators were calculated from data presented in the text.

Regression models were adjusted for site and for whether or not the patient was enrolled in a health plan.

The authors were contacted, but SDs were not available.

We combined the means from the mandatory and voluntary 90-day groups. SDs were imputed in order to calculate an effect size (based on p < 0.01).

In the multivariate model for mean adherence, each day supply had a parameter estimate of 0.19, so that for 30 days, adherence was estimated to be 5.7%.

Presented as rate in Figure 3.

Means and SDs were obtained from the study authors.

Note

All information in this table is taken directly from the studies indicated in column 1.

Reference, study type	Condition(s)/medication(s) evaluated	Adherence measurement (as reported by the study authors)	Duration of study	90-day supply (unless otherwise stated)	30-day supply (unless otherwise stated)	Effect size
Dichotomous outcomes (≥ 80% adherence)
Batal et al. 2007,39 retrospective cohort	Lipid-lowering agents (statins)	Pharmacy refills; each patient’s adherence score was calculated as their days of drug acquired divided by their days in the study (days from first prescription fill to last prescription fill). The primary outcome was proportion with ≥ 80% adherence	3 years	60-day supply: 1307 out of 2553 (51%)	303 out of 833 (36%)	OR 0.53 (95% CI 0.45 to 0.62)a
bDomino et al. 2011,44 pre–post controlled study with a cost–consequences analysis	Antidepressants, antipsychotics, antihypertensives, antidiabetics, seizure disorder medications, lipid-lowering agents (statins)	PDC measure; calculates daily indicators of medication used divided by the number of days in the quarter. The outcome was reported as difference-in-difference-in-differencesc in the percentage of quarters in which individuals had PDC ≥ 80%	18 months	NR	NR	Statins: –0.132 (0.025), p < 0.01; diabetes: –0.053 (0.017), p < 0.01; antihypertensives: –0.083 (0.006), p < 0.01; seizure disorder: –0.022 (0.014), p = ns; antidepressants: –0.027 (0.021), p = ns; antipsychotics 0.004 (0.018), p = ns
Hermes et al. 2010,35 retrospective cohortd	Antihypertensives, antidiabetics, cholesterol-lowering agents	PDC (no further details reported). The primary outcome was the proportion with a PDC ≥ 80%	540 days	Cholesterol-lowering: 5414e out of 7219 (74.9%); antihypertensives: 7928 out of 9405 (84.3%); antidiabetics: 1221 out of 1578 (77.4%)	Cholesterol-lowering: 20,820 out of 31,982 (65.1%); antihypertensives: 41,064 out of 53,192 (77.2%); antidiabetics: 6094 out of 8844 (68.9%)	Cholesterol-lowering:f OR 0.62 (95% CI 0.59 to 0.66); antihypertensives: OR 0.63 (95% CI 0.59 to 0.67); antidiabetics: OR 0.65 (95% CI 0.57 to 0.74)
Pfeiffer et al. 2012,37 retrospective cohort	Antidepressants	Proportion of patients who received ≥ 180 days of an antidepressant treatment during the 231-day period following the index prescription. The primary outcome was the proportion with > 80% adherence	7 years	67,077g out of 87,000 (77.1%)	‘Less than a 90-day supply’: 123,993 out of 296,634 (41.8%)	OR 0.21 (95% CI 0.21 to 0.22)
Schmittdiel et al. 2015,47 cross-sectional study	Antihypertensives (ACEIs; ARBs), antidiabetics (oral antihyperglycaemics), lipid-lowering agents (statins)	PDC: the percentage of days in the measurement period ‘covered’ by prescription fills for the same medication or medications in the same therapeutic category. The primary outcome was predictors of adherence modelled as a dichotomous outcome in a Poisson regression model	Up to 1 year	NR	NR	Estimated risk ratio of being adherent (PDC ≥ 0.8) (reference group with < 31 days’ supply):h ACEI/ARB: 61–90 days 1.35; > 90 days 1.61; oral diabetes medications: 61–90 days 1.48; > 90 days 1.61; statins: 61–90 days 1.47; > 90 days 1.61 (p < 0.001 for all)
Continuous (mean PDC or MPR)
bDomino et al. 2011,44 pre–post controlled study with a cost–consequences analysis	Antidepressants, antipsychotics, antihypertensives, antidiabetics, seizure disorder medications, lipid-lowering agents (statins)	PDC measure; as above	18 months	NR	NR	Statins: –0.080 (0.012), p < 0.01; diabetes: –0.034 (0.008), p< 0.01; antihypertensives: –0.045 (0.002), p < 0.01; seizure disorder: –0.009 (0.006), p = ns; antidepressants: –0.030 (0.010), p < 0.01; antipsychotics: –0.010 (0.008), p = ns
Jiang et al. 2007,36 retrospective cohortd	Antidepressants (SSRIs), antihypertensives (ACEIs), lipid-lowering agents (statins)	MPR (no further details reported)	1 year	Mandatory 90 days: 0.7543 (SD not reported)i (n = 148), voluntary 90 days: 0.6895 (SD not reported) (n = 582)	0.3999 (SD not reported) (n = 955)	MD –0.30 (95% CI –0.58 to –0.03)j
Schectman et al. 2002,43 cross-sectional study	42 medications: antihypertensives, antidiabetics, lipid-lowering agents	Number of days of therapy dispensed between first and last refills divided by interval between first and last refills. The primary outcome was predictors of adherence modelled in a multivariable linear regression model. No details were reported as to whether or not MPR was capped at 100%	9 months	NR	NR	Based on multivariate analysis, each 30-day increment in prescription drug supply (maximum supply was 90 days) was associated with a 5.7% increase in mean adherence (p < 0.0001)k
Steiner et al. 1993,40 retrospective cohort	Digoxin	The proportion of prescribed dose of maintenance medication obtained. Calculated as the total days’ supply divided by the number of days between the first and last fills	14 months	31 to 89 days: 103.6%l (SD 26.6) (n = 41) ≥ 90 days: 113.0%l (SD 21.4) (n = 46)	89.7% (SD 34.9) (n = 27)	≥ 90 days vs. ≤ 30 days: MD –0.23 (95% CI –0.38 to –0.09)
Taitel et al. 2012,45 retrospective cohort with a cost–consequences analysis	Antidepressants (SSRIs), antidiabetics (oral hypoglycaemics), antihypertensives, lipid-lowering agents (statins)	MPR: sum of the days’ supply for each therapeutic class divided by 365, the number of days in the follow-up period	1 year	Antihypertensives: 0.910 (SD 0.174)m (n = 5835); statins: 0.819 (SD 0.194) (n = 2162); SSRIs: 0.817 (SD 0.196) (n = 266); hypoglycaemics: 0.875 (SD 0.190) (n = 1511)	Antihypertensives: 0.774 (SD 0.292) (n = 33,009); statins: 0.671 (SD 0.278) (n = 12,136); SSRIs: 0.611 (SD 0.295) (n = 7017); hypoglycaemics: 0.775 (SD 0.289) (n = 11,842)	Antihypertensives: MD –0.14 (95% CI –0.14 to –0.13); statins: MD –0.15 (95% CI –0.16 to –0.14); SSRIs: MD –0.21 (95% CI –0.23 to –0.18); hypoglycaemics: MD –0.12 (95% CI –0.13 to –0.11)

FIGURE 2.

Studies that assessed the percentage of patients with ≥ 80% medication adherence. An ‘event’ is defined as ≥ 80% adherent. Batal et al. 39 compared 30-day medication supply with 60-day supply, and Pfeiffer et al. 37 compared 90-days’ supply with < 90 days’ supply. M–H, Mantel–Haenszel.

FIGURE 3.

Studies that assessed mean adherence using the MPR or the PDC. a, Steiner et al. 40 compared a 30-day medication supply with a ≥ 90-day supply. IV, instrumental variable.

Proportion of days covered and medication possession ratio measured as a dichotomous outcome

Five studies (three cohort studies, one pre–post study and one cross-sectional study) compared the proportion of patients with ≥ 80% or < 80% medication adherence using PDC35,37,44,47 or MPR. 39

Effect sizes (ORs) could be calculated for three of the cohort studies because they reported adherence rates of individuals who received longer prescription lengths and those who received shorter prescription lengths (see Table 8). 35,37,39 These studies presented data for lipid-lowering agents, antihypertensives, antidiabetics and antidepressants. The effect sizes consistently show that longer prescriptions increase medication adherence compared with shorter prescriptions for all four clinical classes (see Figure 2).

Domino et al. 44 compared changes in adherence both before and after the introduction of a policy in one US state (North Carolina) to reduce prescription length from 100 days to 34 days, and another state (Georgia) where the maximum duration of supply remained constant at 31 days. Adherence was expressed as the proportion of quarters per year in which individuals were at least 80% adherent to their target medications. This study found that shortening the prescription length was associated with a reduction in adherence for five of the six clinical classes studied, but the difference was statistically significant only for statins, diabetes and antihypertensive medications (see Table 8). The policy change in North Carolina was stated to have increased the rate at which chronic medication users were non-adherent to their prescribed medication. 44

In a cross-sectional study by Schmittdiel et al. ,47 associations between adherence and health system characteristics, including mean days’ supply of medication (as categorical variables 31–60 days, 61–90 days and > 90 days), were analysed using regression analysis. The authors considered various cardiovascular medications including ACEIs and angiotensin II receptor blockers (ARBs), statins and oral diabetes medications. 47 Across all medications, the strongest health system predictor of achieving greater medication adherence was a longer prescription length (a mean supply of > 90 days) [the relative risk (RR) was 1.61 for each of these medications; p < 0.001] (see Table 8). 47

Proportion of days covered and medication possession ratio measured as a continuous outcome

Five studies (three cohort studies,36,40,45 one pre–post controlled study44 and one cross-sectional study43) evaluated medication adherence using mean values of PDC44 or MPR. 36,40,43,45

Effect sizes (mean differences) could be calculated for the three cohort studies that reported the mean adherence rates of individuals who received longer prescription lengths and of those who received shorter prescription lengths (see Table 8). 36,40,45 These studies presented data for lipid-lowering agents, antihypertensives, antidiabetics, antidepressants and digoxin. The effect sizes consistently show that longer prescriptions increase medication adherence compared with shorter prescriptions for all four clinical classes, and for digoxin (see Figure 3).

The pre–post controlled study by Domino et al. 3 found that shortening the supply of medication from 100 days to 34 days was associated with decreased adherence by 1.5–4.6 percentage points across the six therapeutic classes studied. However, the results for seizure medication and antipsychotics were not statistically significant (see Table 8).

Finally, a cross-sectional study by Schectman et al. 43 examined associations between demographics and prescription factors, including length of medication supply and adherence to medications for diabetes, hypertension and hypercholesterolaemia. Based on a multivariate regression model, the authors reported that each 30-day increment in prescription length (the maximum supply was 90 days) was associated with a 5.7% increase in mean adherence (p < 0.0001) (see Table 8). 43

Pooled meta-analysis

As an exploratory analysis, we combined dichotomous and continuous measures of adherence from six retrospective cohort studies35–37,39,40,45 by pooling SMDs for each therapeutic class and all classifications combined (Figure 4). As ORs or mean differences with 95% CIs (and thus SMDs) could not be calculated for three of the nine studies reporting on adherence, their results are not included in this analysis. 43,44,47 The overall effect size shows that medication adherence was significantly increased in patients who received a 90-day medication supply compared with patients who received a 30-day supply (SMD –0.45, 95% CI –0.65 to –0.26; p < 0.00001), but there was significant statistical heterogeneity between the different studies/comparisons [τ² = 0.10; χ² = 2611.51, degrees of freedom (df) = 10; p < 0.00001; I² = 100%]. Given this heterogeneity, the effect size estimate may not be meaningful, and should, as intended, be considered exploratory.

FIGURE 4.

Combined meta-analysis of studies/comparisons that assessed claims-based medication adherence. The results for different therapeutic classes presented by the same author did not share the same control groups, so they could be pooled as separate studies in the meta-analysis. Batal et al. 39 compared 30 vs. 60 days’ medication supply, Pfeiffer37 compared 90 vs. < 90 days’ supply and Steiner40 compared 30 vs. ≥ 90 days’ medication supply. The analysis is based on six retrospective cohort studies. IV, instrumental variable.

Quality assessment

Based on a GRADE assessment, the overall quality of the evidence for adherence is moderate. This assessment was made because most of the studies that evaluated this outcome were considered to be at only a moderate risk of bias, there was consistency in the direction of effect, the CIs were generally narrow and we did not find any evidence (such as small study effects) that could indicate publication bias. However, there was concern regarding the extent to which the evidence presented is applicable to the UK setting, as all of the studies were conducted in the USA, which has a very different health service structure from that of the UK.

In summary, there is consistent evidence that longer prescriptions increase medication adherence compared with shorter prescriptions. The overall quality of the evidence for this outcome is moderate.

Wastage

Medication wastage was reported in six of the included studies (five retrospective cohort studies32,36,38,45,48 and one cross-sectional cost analysis42). All measures of wastage were indirectly estimated based on pharmacy claims refill data. The majority of these studies defined wastage in a similar manner, such as a ‘switch in medication type within the same clinical class or to the same medication but with a different strength, occurring before the expected refill date’. 32 One study also included discontinuation within its definition. 48

It was possible to calculate effect sizes for four cohort studies,32,36,42,45 based either on information reported in the papers or on data obtained from the study authors. Three different ‘wastage’ outcomes were reported in the included studies: the percentage of days’ supply wasted, the mean number of days’ supply wasted and the percentage of patients who wasted medication. Two studies reported multiple outcomes. 38,42 The findings related to each of these outcomes are presented in turn below and summarised in Table 9.

TABLE 9 - Studies that evaluated medication wastage

HMG CoA, 3-hydroxy-3-methylglutaryl coenzyme; MD, mean difference; PPI, proton pump inhibitor; SSRI, selective serotonin reuptake inhibitor.

Abstract.

We combined the means from the mandatory and voluntary 90-day groups. SDs were imputed in order to calculate an effect size (based on p > 0.05).

Numerators have been calculated based on overall sample sizes and percentages.

SDs were imputed in order to calculate an effect size (p-values were reported for each comparison).

Means and SDs were obtained from the study authors.

Note

All information in this table is taken directly from the studies indicated in column 1.

Reference, study type	Condition(s)/medication(s) evaluated	Wastage definition and calculation (as reported by the study authors)	Duration of study	90 days’ supply (unless otherwise stated)	30 days’ supply (unless otherwise stated)	Effect size
Percentage of days’ supply wasted
Faris et al. 2010,48 retrospective cohorta	Medications for multiple sclerosis, rheumatoid arthritis, oncology and growth hormone	Wastage occurred when patients switched medication or stopped taking therapy (drop-off waste). The outcome measured was the proportion of days’ supply wasted, calculated as the sum of switch waste and drop-off waste divided by total days supplied	21 months	Multiple sclerosis: 2.44%; rheumatoid arthritis: 2.73%; oncology: 1.90%; growth hormone: 4.28% (sample sizes and SDs were not reported)	Multiple sclerosis: 2.55%; rheumatoid arthritis: 3.97%; oncology: 3.42% growth hormone: 3.54% (sample sizes and SDs were not reported)	An effect size was not reported and could not be calculated
Ryvkin and Garavaglia 2009,38 retrospective cohorta	Antihypertensives, lipid-lowering agents, antiulcers (PPIs)	Defined as a switch within therapeutic class. The outcome measured was the proportion of days’ supply wasted	Not reported	Antihypertensives: 2.0% (sample sizes and SDs not reported); lipid-lowering agents: 1.2% (sample sizes and SDs not reported); PPIs: 0.7% (sample sizes and SDs not reported)	Antihypertensives: 2.1% (sample sizes and SDs not reported); lipid-lowering agents: 0.4% (sample sizes and SDs not reported); PPIs: 0.7% days (sample sizes and SDs not reported)	An effect size was not reported and could not be calculated
Percentage of patients who wasted medication
Ryvkin and Garavaglia 2009,38 retrospective cohorta	Antihypertensives, lipid-lowering agents, antiulcers (PPIs)b	Defined as a switch within therapeutic class. The outcome measured was the proportion of patients who wasted medication	Not reported	Antihypertensives: 5.2% (sample sizes not reported); lipid-lowering agents: 2.9% (sample sizes not reported); PPIs: 1.8% (sample sizes not reported)	Antihypertensives: 6.3% (sample sizes not reported); lipid-lowering agents: 1.2% (sample sizes not reported); PPIs: 1.9% (sample sizes not reported)	An effect size was not reported and could not be calculated
Taitel et al. 2012,45 retrospective cohort with a cost–consequences analysis	Antidepressants (SSRIs), antidiabetics (oral hypoglycaemics), antihypertensives, lipid-lowering agents (statins)	Defined as a switch of drug type or strength within the same therapeutic class that occurred before the expected refill date. The outcome measured was the percentage of patients who wasted medication	1 year	Antihypertensives: 712c out of 5835 (12.2%); statins: 212 out of 2162 (9.8%); SSRIs: 39 out of 266 (14.7%); hypoglycaemics: 175 out of 1511 (11.6%)	Antihypertensives: 3928 out of 33,009 (11.9%); statins: 1104 out of 12,136 (9.1%); SSRIs: 975 out of 7017 (13.9%) hypoglycaemics: 1255 out of 11,842 (10.6%)	Antihypertensives: OR 0.97 (95% CI 0.89 to 1.06); statins: OR 0.84 (95% CI 0.72 to 0.98); SSRIs: OR 0.94 (95% CI 0.66 to 1.33); hypoglycaemics: OR 0.90 (95% CI 0.76 to 1.07)
Walton 2001,42 cross-sectional study	Lipid-lowering agents (HMG CoA reductase inhibitors)	Defined as a switch within therapeutic class. The outcome measured was the proportion of patients that switched who	1 year	545 out of 3635 (15.0%)	1909 out of 13,355 (14.3%)	OR 0.95 (95% CI 0.85 to 1.05)
Mean number of days’ supply wasted
Jiang et al. 2007,36 retrospective cohorta	Antidepressants (SSRIs), antihypertensives (ACEIs), lipid-lowering agents (statins)	Wastage occurred either when patients switched to different medication within the same class or to similar medication of a different strength, so that the patients’ actual days’ supply was less than the dispensed days’ supply. The outcome measured was the total days’ supply wasted among a normalised 30-day period	1 year	All classes: mandatory 90 days: 2.5 days per 30-day period (n = 148); voluntary 90 days: 2.2 days per 30-day period (n = 582)	All classes: 2.3 days per 30-day period (n = 955)	MD –0.10 (95% CI –0.20 to 0.00)b
Murphy et al. 2012,32 retrospective cohort	Antidepressants (SSRIs, tricyclics), antidiabetics (biguanides, insulin), antihypertensives (alpha-beta blockers, ACEIs, angiotensin II receptor antagonists, calcium channel blockers, cardioselective beta-blockers, loop diuretics, thiazides), lipid-lowering agents (HMG CoA reductase inhibitors, fibric acid derivatives), thyroid hormones	Wastage was defined as an excess days’ supply of medication resulting from a switch in medication within the same therapeutic class or to the same medication but of a different strength occurring before the expected refill date. The outcome measured was the mean number of days wasted calculated as the sum of the excess days divided by the total number of fills, converted to 30-day equivalents	1 year	Antidepressants: SSRIs: 0.142 days per 30-day period (SDs not reported) (n = 3337); tricyclics: 0.147 days per 30-day period (n = 456). Antidiabetics: insulins: 0.512 days per 30-day period (n = 546); biguanides: 0.131 days per 30-day period (n = 1938). Antihypertensives: cardioselective beta-blockers: 0.144 days per 30-day period (n = 4353); alpha-beta blockers: 0.202 days per 30-day period (n = 554); calcium channel blockers: 0.156 days per 30-day period (n = 3246); ACEIs: 0.134 days per 30-day period (n = 4786); angiotensin II receptor antagonists: 0.247 days per 30-day period (n = 2224); loop diuretics: 0.100 days per 30-day period (n = 556); thiazides: 0.062 days per 30-day period (n = 1778). Lipid-lowering agents: HMG CoA reductase inhibitors: 0.118 days per 30-day period (n = 10,674); fibric acid derivatives: 0.079 days per 30-day period (n = 1236); thyroid hormones: 0.383 days per 30-day period (n = 4846)	Antidepressants: SSRIs: 0.157 days per 30-day period (SDs not reported) (n = 7969); tricyclics: 0.131 days per 30-day period (n = 1091). Antidiabetics: insulins: 0.281 days per 30-day period (n = 1545); biguanides: 0.082 days per 30-day period (n = 3429). Antihypertensives: cardioselective beta-blockers: 0.087 days per 30-day period (n = 5458); alpha-beta blockers: 0.114 days per 30-day period (n = 934); calcium channel blockers: 0.127 days per 30-day period (n = 4249); ACEIs: 0.102 days per 30-day period (n = 6371); angiotensin II receptor antagonists: 0.117 days per 30-day period (n = 2451); loop diuretics: 0.151 days per 30-day period (n = 1179); thiazides: 0.024 days per 30-day period (n = 2335). Lipid-lowering agents: HMG CoA reductase inhibitors: 0.086 days per 30-day period (n = 10,410); fibric acid derivatives: 0.079 days per 30-day period (n = 1710); thyroid hormones: 0.252 days per 30-day period (n = 6725)	Antidepressants: SSRIs: MD 0.02 (95% CI –0.03 to 0.06);d tricyclics: MD –0.02 (95% CI –0.06 to 0.03). Antidiabetics: insulins: MD –0.23 (95% CI –0.43 to –0.04); biguanides: MD –0.02 (95% CI –0.06 to 0.03). Antihypertensives: cardioselective beta-blockers: MD –0.06 (95% CI –0.11 to –0.01); alpha-beta blockers: MD –0.09 (95% CI –0.20 to 0.03); calcium channel blockers: MD –0.03 (95% CI –0.08 to 0.02); ACEIs: MD –0.03 (95% CI –0.07 to 0.00); angiotensin II receptor antagonists: MD –0.13 (95% CI –0.24 to –0.02); loop diuretics: MD 0.05 (95% CI –0.06 to 0.16); thiazides: MD –0.04 (95% CI –0.07 to –0.00). Lipid-lowering agents: HMG CoA reductase inhibitors: MD –0.03 (95% CI –0.06 to 0.00); fibric acid derivatives: MD 0.0 (95% CI 0.00 to 0.00); thyroid hormones: MD –0.13 (95% CI –0.24 to –0.02)
Taitel et al. 2012,45 retrospective cohort with a cost–consequences analysis	Antidepressants (SSRIs), antidiabetics (oral hypoglycaemics), antihypertensives, lipid-lowering agents (statins)	Defined as a switch of drug type or strength within the same therapeutic class that occurred before the expected refill date. The outcome measured was the average number of waste days	1 year	Antihypertensives: 9.211 days (SD 30.284)e (n = 5835); statins: 5.757 days (SD 22.205) (n = 2162); SSRIs: 10.425 days (SD 32.463) (n = 266); hypoglycaemics: 7.899 days per 90-day period (SD 25.385) (n = 1511)	Antihypertensives: 4.037 days (SD 16.236) (n = 33,009); statins: 2.251 days (SD 10.673) (n = 12,136); SSRIs: 3.501 days per 30-day period (SD 12.941) (n = 7017); hypoglycaemics: 3.289 days per 30-day period (SD 13.441) (n = 11,842)	Antihypertensives: MD –5.17 (95% CI –5.97 to –4.38); statins: MD –3.51 (95% CI –4.46 to –2.55); SSRIs: MD –6.92 (95% CI –10.84 to –3.01); hypoglycaemics: MD –4.61 (95% CI –5.91 to –3.31)
Walton 2001,42 cross-sectional study	Lipid-lowering agents (HMG CoA reductase inhibitors)	Defined as a switch within therapeutic class. The outcome measured was the mean number of days wasted, calculated as the difference between the average quantity dispensed and the average quantity used for each group. If a switch occurred, the quantity used was calculated as the difference between the date that the first prescription was dispensed and the date that the second, different, prescription was dispensed	1 year	5.33 days (SD not reported) (n = 3635)	1.06 days (SD not reported) ‘for each 30-day fill period’ (n = 13.355)	An effect size was not reported and could not be calculated. It is not clear whether or not these results are standardised for the same time period; therefore, it is not clear whether or not the results are directly comparable

Percentage of days’ supply wasted

Two retrospective cohort studies reported on the percentage of days’ supply wasted. 38,48 Both studies were reported in conference abstracts, and insufficient data were reported to enable an effect size to be calculated. An attempt was made to contact the authors for further information, but no additional data were obtained. Both studies found only small differences in the proportion of days’ supply wasted between patients with different prescription lengths, although neither study reported raw data or statistical comparisons (see Table 9).

Percentage of patients who wasted medication

Three studies (two retrospective cohort studies38,45 and one cross-sectional cost analysis42) reported on the percentage of patients who wasted medication. As none of these three studies stated how wastage per patient was defined, it is not clear whether these patients wasted a small or a large amount of medication.

Effect sizes (ORs) could be calculated for lipid-lowering agents, antihypertensives, antidiabetics and antidepressants based on data presented in two of the studies. 42,45 These data consistently show no statistical differences in the proportion of patients who wasted medication between longer and shorter prescriptions for each of the clinical classes evaluated (Figure 5).

FIGURE 5.

Studies that assessed the percentage of patients with wasted medication. M–H, Mantel–Haenszel.

The third study38 reported insufficient data to calculate an effect size; however, similarly to the other studies, the authors found only small differences in wastage between study participants, although no statistical comparisons were reported.

Mean number of days’ supply wasted

Four studies (three retrospective cohort studies32,36,45 and one cross-sectional cost analysis42) measured the mean number of days’ supply wasted over 1 year. Two of the studies used wastage data to inform their cost analysis. 42,45 As such, these two studies appear to present unstandardised means (i.e. the actual mean number of days’ supply wasted over the study period) for each comparison group. The other two studies32,36 investigated wastage as a primary outcome and reported a standardised mean days’ supply (i.e. the mean number of days’ supply wasted per 30 days).

Effect sizes (mean difference) for lipid-lowering agents, antihypertensives, antidiabetics, antidepressants and thyroid hormones could be calculated based on data presented in three of the studies (see Table 9 and Figures 6 and 7). 32,36,45 As Murphy et al. 32 and Jiang et al. 36 report rate data per 30 days, we have presented mean differences from these studies in a separate forest plot (Figure 7) from Taitel et al. 45 (Figure 6).

FIGURE 6.

Studies that assessed mean days with wasted medication. IV, instrumental variable.

FIGURE 7.

Studies that assessed mean days with wasted medication per 30 days (rate data). For Jiang et al. ,36 the means from the mandatory and voluntary 90-day groups were combined. IV, instrumental variable.

The data from Taitel et al. 45 show that longer prescriptions significantly increase medication wastage compared with shorter prescriptions for all of the clinical classes evaluated (see Figure 6). Jiang et al. 36 reported that the mean number of days with wasted medication was highest for patients who had a mandatory 90-day supply of (any) medication (2.5 days per 30-day period), followed by a 30-day supply (2.3 days per 30-day period), and that the mean number of days with wasted medication was lowest for patients who had a voluntary 90-day supply (2.2 days per 30-day period). However, there was no significant difference between the groups. 36 To estimate the effect size, the two 90-day supply groups were combined, and a SD was imputed based on a p-value of > 0.05. The data show that wastage was higher in patients who received a 90-day supply of (any) medication than in those who received a 30-day supply. 36 The results from Murphy et al. 32 are less consistent: looking across 14 therapeutic classes, the results do not significantly favour one prescription length over the other (see Figure 7). 32

The data presented by Walton42 on wastage are less clear. It appears that wastage of lipid-lowering medication was higher in the 90-day supply group than in the 30-day supply group (5.33 days vs. 1.06 days). 42 However, based on our reading of the data, it is not completely clear whether or not these two sets of numbers are standardised to the same time period and, therefore, it is unclear whether or not they are directly comparable.

Pooled analysis

We conducted an exploratory meta-analysis combining data from three retrospective cohort studies that measured days of medication supply wasted. 32,36,45 The SMDs were pooled for each therapeutic class and for all classifications (Figure 8). This first required pooling data reported by Murphy et al. 32 for each chemical class, and then pooling these results with those from the other studies that reported data by therapeutic class. Mean differences with 95% CIs (and SMDs) could not be calculated for three of the above studies,38,42,48 including two that reported dichotomous data (i.e. percentage of days that medication was wasted), and so the results of these studies are not included in this analysis.

FIGURE 8.

Meta-analysis of studies/comparisons that assessed medication waste days. For Jiang et al. 36 the means from the mandatory and voluntary 90-day groups were combined. The analysis is based on three retrospective cohort studies. 32,36,45 IV, instrumental variable.

The overall effect size demonstrates that longer prescriptions are associated with a small but significant increase in the mean number of days’ supply wasted (SMD –0.15 days, 95% CI –0.23 to –0.08 days; p < 0.0001), but there was significant statistical heterogeneity between the different studies/comparisons (τ² = 0.01; χ² = 382.90, df = 9; p < 0.00001; I² = 98%). Given this heterogeneity, the effect size estimate may not be meaningful, and should, as intended by the original authors, be considered as exploratory.

Quality assessment

Based on a GRADE assessment, the overall quality of the evidence for ‘wastage’ is very low. This assessment was made because most of the studies that evaluated this outcome did not report enough information to assess quality, although there was consistency in the direction of effect (where calculable), and the CIs were generally narrow. There was some evidence of small study effects, which could be indicative of publication bias. There was also concern regarding the extent to which the evidence presented is applicable to the UK setting, as all of the studies were conducted in the USA, which has a very different health-care system from that of the UK. However, we note that two of the studies used public-funded payers (Medicaid, Veterans Affairs) as their data source. 42,45

In summary, current evidence suggests that the proportion of patients with wasted medication does not differ according to prescription length. However, a longer prescription period (90 days’ supply) shows small increases in the mean number of days’ supply wasted compared with a shorter prescription period (30 days’ supply), although the results were not always significant. The overall quality of this evidence is very low.

Limitations of the evidence

All of the studies were conducted in the USA, which has a distinctly different health-care system from that of the UK, with very different (generally higher) costs. Unlike in the UK, where people receiving repeat prescriptions do not pay for their medication and where many people are exempt from prescription fees, in the USA the majority of patients are required by insurance companies to make co-payments. Differences in prescribing systems could also differentially affect adherence and wastage outcomes. For example, in the USA, a 3-month supply of medication can be dispensed through mail order, which may reduce dispensing costs more significantly than in a less automated system,49 although the impact that this has on wastage is not clear. Murphy et al. 32 found less wastage among patients filling retail rather than mail-order prescriptions, although the difference was not statistically significant. Schmittdiel et al. 47 also found greater adherence among individuals who made greater use of mail order to deliver medications.

Furthermore, the study populations evaluated in these US studies may not be directly generalisable to the UK population. Two studies were conducted among Medicaid patients,44,45 who, on average, have lower socioeconomic status than the general population, and four studies were conducted among members of Veterans health-care systems,37,40,42,46 which serve a population with distinctly different demographics and health challenges from those of the general population.

Only one of the included studies39 considered the impact of different prescription lengths on clinical outcomes. Further research is needed on the impact of prescription length on clinical outcomes before any definite conclusions can be drawn.

There is a lack of evidence on patients’ perspectives of different prescription lengths to inform this review, and this aspect needs further research. In a study on patients’ perceptions of, and satisfaction with, a 28-day prescribing policy for thyroid hormone, Mitchell et al. 23 found widespread dissatisfaction. The most common reasons cited for being dissatisfied were the inconvenience of having to pick up the prescription/tablets more frequently, as it interferes with the working day. 23 A qualitative study61 that investigated patients with chronic conditions’ experiences of repeat prescriptions in the NHS found that patients often described shorter prescriptions as a hassle. 61 Respondents reported that managing a repeat prescription was a time-consuming task. In a response to a call for evidence for the DH’s review of prescription charges for those with chronic conditions, Katherine White (chair of the Addison’s Disease Self-Help Group) describes the 28-day prescription policy as disempowering, claiming that it causes anxiety for patients when they are running low on a prescription, and constrains their ability to travel. 62

None of the included studies considered whether or not changing prescription lengths may affect non-pharmacological interventions, for example a physician’s ability to monitor a condition and explain non-pharmacological management issues. The impact of prescription length on clinician–patient relationships warrants further investigation.

All of the studies used indirect measures to assess adherence. The two key measures used were PDC and MPR. These proxy measures may introduce bias in favour of longer prescriptions. For example, if one patient receives a 90-day supply of medication and another is prescribed a 30-day supply, but both stop taking their medication after 14 days, at 90 days pharmacy refill adherence may be calculated as 100% for the first patient and 33% for the second patient. It is also possible that results may differ by the type of measure used, as the PDC has been found to provide a more conservative estimate of adherence than the MPR, and thus may underestimate effectiveness compared with the MPR. 63

Furthermore, refill adherence may not reflect actual medication adherence, as it assumes that medication is being taken exactly as prescribed. 64,65 For example, a patient who takes half of the recommended daily dose of their medication for all days in a month will have the same PDC as a patient who takes the correct daily dose for only half the days in the month (PDC = 0.5 in both cases). 44 In addition, refill data do not capture instances of patients self-discontinuing by not refilling their prescriptions. A review of methods for assessing refill compliance, however, has determined that indirect measures can nevertheless be ‘a useful source of compliance information’. 66

None of the studies explored reasons why adherence may differ between prescription lengths. Reasons for medication non-adherence are often complex. Non-adherence can be either intentional (e.g. when a patient actively deviates from the treatment regimen stemming from their health beliefs) or non-intentional (e.g. passive deviation as a result of carelessness or forgetfulness). 67 Some study authors have suggested that longer prescription lengths may facilitate greater adherence, as longer prescriptions overcome barriers to non-intentional adherence such as enabling patients to follow a regular medicine regimen39,40 and reducing logistical barriers such as trips to the pharmacy. 43 However, all of the included studies were observational, and no participants were randomly assigned to the different prescription durations. As such, the majority of studies are at risk of selection bias owing to systematic differences between patients who received a longer or shorter prescription. Pfeiffer et al. 37 reported that patients who received a 90-day medication supply were more likely to be treated in primary care settings and to have fewer prior or subsequent outpatient visits, suggesting that patients who receive longer prescriptions may have more stable illnesses. Rabbani and Alexander49 also found that men were more likely to receive longer prescriptions than women, and that white patients were more likely to receive longer prescriptions than patients from other ethnic backgrounds.

It was not clear from the evidence if differences in drug item prices had an impact on the relationship between prescription lengths and costs to the health system. Parikh et al. 46 restricted their analysis to the less expensive medications (no more than US$1.50 per day). The authors comment that, for this reason, cost savings might not be generalisable to more expensive medications the costs of wastage of which would have been more substantial. This resonates with a UK commentary in which Hickey et al. 68 argue that for lower-cost medication such as levothyroxine tablets (which are commonly used to treat hypothyroidism) with a standard dispensing fee of £0.90 per item, the NHS could save > £7M per year if 28-day prescribing were replaced with 4-monthly prescriptions.

None of the studies considered time spent and cost borne by the prescriber, administrator or pharmacist to write, process and deliver the prescription, although dispensing fees, which may be seen as paying for that time/cost, were sometimes taken into account. More research is needed to understand the impact of prescribing policy on professionals’ workloads and incomes.

Six out of 16 included studies were commissioned or funded by private providers, pharmacies or insurers (i.e. Walgreens,32,36,45 Accredo Health Group,48 Prime Therapeutics LLC35 and Kaiser Permanente47). It is possible that such funding may have had an influence on the formulation of the research questions, and, to some extent, on the findings. For example, it may have introduced a publication bias and had an impact on the choice of outcomes in favour of wastage or costs over clinical outcomes.

The majority of studies were retrospective cohort studies based on secondary analysis of administrative data. The use of administrative data limits the quality of the analysis, as there is no control over the types of patients included in the dataset, and we cannot validate how the data were collected and recorded. 69 As such, underlying confounding as a result of selection bias limits the strength of the conclusions that can be drawn. The negative consequences of reliance on cohort data are highlighted by research into hormone replacement therapy, with early evidence based on cohort studies later disproved; the study population was healthier, better educated and from higher socioeconomic backgrounds and had better access to health care than the control group. 70

Limitations of the review

Although we followed rigorous systematic review methodology, it is possible that some bias may have been introduced during the review process. For example, some changes to the protocol were made, including the decision to exclude studies that did not evaluate patients with non-chronic diseases, such as patients receiving repeat prescriptions for contraception. This decision was made because of the higher than expected number of studies that evaluated patients with chronic conditions, which were directly applicable to our systematic review research questions. It is possible, however, that some valuable data may have been missed through making this decision. In addition, we stated in the protocol that we would include only patients treated in primary care settings, but then decided to include some studies in which the setting was unclear, or in which some patients may have received treatment in an outpatient setting. We made this decision to be inclusive but, as a result, some of these studies may not be directly applicable to primary care settings. We note, however, that the results from all of the studies appear to be consistent regardless of setting.

It is also possible that we may have missed evidence by restricting the inclusion/exclusion criteria to studies that evaluated 28-day versus 3-month prescriptions, or close to these prescription lengths. Studies were identified in the literature that evaluated other prescription lengths (e.g. Wong et al. 24), and data from different prescription lengths could have helped to inform a model that evaluates an optimal prescription length.

We decided to use the ROBINS-I quality assessment tool, a very recent tool that was published in 2016. This tool did not capture some biases specific to retrospective studies, such as whether or not there was bias in the selection of the study cohort, and for this reason we used the accompanying guidance to critically appraise the risk of bias in each category, but did not always apply the full list of questions. However, in our study, the results obtained using this tool were similar to those obtained with other quality assessment measures (e.g. the Newcastle–Ottawa scale52). It appears that the GRADE quality assessment, in any case, appropriately reflected the overall evidence, and we are confident in these assessments.

While conducting this review, we also found that some studies included only patients who were new to treatment, or presented subgroup results for patients who were new to treatment compared with those who had existing prescriptions. Other studies did not report this information. We did not distinguish between these population groups in our analyses because this distinction was not considered at the protocol stage, and because there were not enough studies that reported this information to carry out a post hoc subgroup analysis. It would be helpful if future studies reported this type of information to facilitate analysis.

In this review, a number of the included studies presented data by type of chemical class or by therapeutic class. We did not attempt to compare across therapeutic class, as this was not the remit of the review and direct comparisons between the effect sizes for different therapeutic classes cannot be made. It does appear that there may be some differences between therapeutic classes, and this merits further investigation.

Finally, for six35,36,38,41,46,48 out of the 16 included studies, only an abstract or extended abstract was available and, despite our attempts to contact the authors, no further publications were available. Full peer review of the quality of the studies was not possible for five of the abstracts,35,36,38,41,48 and the lack of detailed findings from these six studies limits the overall conclusions that can be drawn. The decision to include abstracts was based, in discussion with our expert advisors, on the paucity of high-quality data identified and our decision to be as inclusive as possible.

Treatment patterns evaluated

Treatment patterns were evaluated in each of the five cohorts in a similar manner. Data for each patient were first ordered in sequence from earliest to latest prescription date. To identify treatment patterns, three main variables were used: (1) product code (CPRD unique code for treatment selected by the GP; this is unique to individual brands, formulations, strengths, etc.); (2) drug substance code (derived from ‘drugsubstance’ variable and used to identify different dosages and/or formulations of the same drug chemical substance); and (3) drug class code [derived from ‘bnfcode’ variable and used to identify drugs with different chemical compositions, but a similar mechanism of action based on their categorisation in the BNF (e.g. SSRIs)]. Next, four different types of treatment patterns that may occur in a sequence of prescriptions were identified for each patient over the course of their available follow-up, namely:

1. refills of the same product

   1. product, drug substance and drug class codes are the same for two prescriptions in sequence, for example two prescriptions for metformin 500-mg tablets

2. substitutions between different dosages or formulations of the same drug substance

   1. product codes are different, but drug substance and drug class codes are the same for two prescriptions in sequence, for example a switch in dosage for the same statin (atorvastatin 10-mg tablets to atorvastatin 40-mg tablets)

3. substitutions between drugs that are in the same class

   1. product and drug substance codes are different, but drug class codes are the same for two prescriptions in sequence, for example a switch from one statin (atorvastatin 40-mg tablets) to another statin (simvastatin 40-mg tablets)

4. substitutions between drugs that have similar clinical indications from different classes (e.g. different drugs used in the secondary prevention of myocardial infarction)

   1. product, drug substance and drug class codes are different for two prescriptions in sequence, for example a switch from an ACEI (captopril 25-mg tablets) to calcium channel blocker (amlodipine 10-mg tablets).

The volume of medication wastage from early refills and treatment switches (defined as a repeat prescription or new prescription based on the mapped substitutions outlined above, respectively, being issued prior to the expiry of the previously prescribed quantity) was estimated for prescriptions within the 11-year study period (i.e. any prescriptions not falling within this time period were excluded from the analyses). This was done by dividing the total qty entered by the GP for the prescribed product by the ndd prescribed for the event and comparing this with the difference in the two dates associated with the event and the next prescription in the sequence. Prescriptions issued on the same day for drugs in the same class and with different product codes (e.g. two different statins) were considered prescriber error and dropped from the analysis (see column 9 in Table 12). The one exception to this was for antiplatelet drugs in the secondary prevention of myocardial infarction cohort, as it was assumed that two different antiplatelet drugs could be prescribed at the same time. In addition, prescriptions for medications with similar clinical indications from different classes issued on the same day were counted not as a switch, but rather as an add-on to existing therapy or concomitant therapy (see column 11 in Table 12).

Analysis of medication wastage

As treatment patterns were assessed in sequence, there was the potential to overstate the amount of wastage that occurred. For example, counting every overlap of prescription days associated with refills of the same product as wastage (i.e. even prescriptions issued 1 day before the expiry of the previous prescription would be counted as 1 day’s worth of medication wastage, and two prescriptions issued on the same day would count as one prescription as entirely wasted) may overstate actual wastage. A threshold of 1 year after the initial prescription in a particular series was, therefore, used to estimate wastage for early refills (i.e. repeat prescriptions), in that any product prescribed over and above the expected quantity to be consumed within the 1-year time period was considered waste. This is to account for the fact that patients may fill their prescriptions before their existing supply is exhausted, but still consume all of the previous prescription before using the new supply.

In addition, although excessive switching of drugs could appear to be wastage, consistent patterns could suggest a valid, prescribed treatment regimen. To avoid the overestimation of wastage, additional effort was made to differentiate between add-ons/concomitant therapy and switches for medications with similar clinical indications from different classes (i.e. product, drug substance and drug class codes are different for two prescriptions in sequence). Generally, if the difference between the number of changes between medications with similar clinical indications from different classes and the number of unique drug classes within an annual period was ≥ 1, then any overlap in prescription dates was not considered wastage as a result of a switch, but rather as an add-on or concomitant therapy. For example, within an annual period, a patient may have six changes between clinically related drugs from different classes, but these changes are only between two different drugs from different classes (i.e. two unique drug classes). As six minus two is greater than one, these changes were considered not switches, but rather add-on/concomitant prescriptions. The rationale for this was that, if the number of changes was large, but the number of unique drugs involved in the changes was low, an add-on or concomitant therapy was being prescribed, whereas if the number of changes was large and the number of unique drugs involved in the changes was also large, switches in therapies were occurring and therefore there was the potential for wastage to occur. A similar approach was applied by Taitel et al. ,45 and theirs is the only previous study that has attempted to differentiate between add-on/concomitant prescriptions and actual switches in therapy. Additional constraints in counting overlaps in dates for two prescriptions in sequence for medications with similar clinical indications from different classes were also applied in three of the case study conditions owing to the potential for a number of the included therapies to be given concomitantly. For the glucose control in T2DM cohort, overlaps in prescription dates involving metformin with drugs from other classes were not counted as switches (and therefore wastage), as metformin is usually administered in combination with other classes of oral antidiabetics. 79 For the treatment of hypertension in T2DM cohort, overlaps in prescription dates involving ACEIs and angiotensin II receptor antagonists with either calcium-channel blockers or thiazide-like diuretics were not counted as switches, as these therapies are commonly administered together as second-line therapy. 76 Finally, for the secondary prevention of myocardial infarction cohort, only overlapping prescription dates involving beta-blockers and angiotensin II receptor antagonists were counted as switches, as patients are likely to receive the other classes of drugs included in the analysis (ACEIs, antiplatelet drugs and statins) continuously over the course of treatment. 81

Costs

To estimate the costs of wastage, defined daily doses (DDDs) associated with each drug substance code in the five cohorts were first obtained from the World Health Organization’s Anatomical Therapeutic Chemical (ATC)/DDD Index 2016. 83 The NHS Business Services Authority’s prescription cost analysis (PCA) 2015,84 which provides details of the quantity of individual doses and NICs of all the prescriptions in England, was then used to determine a NIC/quantity value of a specific strength of the medication associated with each drug substance code. This value was then standardised using the associated DDD to obtain a cost per day for each drug substance code in all five of the cohorts. Details of these calculations are provided in Appendix 6.

The dispensing fees from the Drug Tariff [£0.90 per standard prescription and 2% of the cost per prescription (cost per day multiplied by prescription length) for prescriptions over £100]84 and the estimated cost for a physician to issue a prescription based on the literature were then determined for each prescription. A targeted literature review was designed (see Appendix 7 for search strategy and study selection details) to determine the time involved for a physician to issue a prescription (i.e. how long it takes a GP to complete the technical process of producing a prescription; note that this does not include clinical decision-making time or administrative staff time). It should be noted that none of the identified studies from the targeted literature review reported the time involved for a physician to issue a prescription from a UK-specific primary care context. It was therefore necessary to prioritise the use of the available evidence for this parameter based on studies with the largest sample sizes and those studies reporting prescriber time for different types of prescriptions (e.g. new vs. renewals). Refills were assigned a shorter time than changes in dose/formulation, within drug classes and between drug classes (48.7 seconds vs. 61.2 seconds). 85 Per minute costs related to GPs’ time (£3.80/minute), derived from the Personal Social Services Research Unit’s (PSSRU’s) Unit Costs of Health and Social Care, were then applied. 86 All costs are reported in 2015 Great British pounds (GBP).

Statistical analysis

Descriptive analyses of trends in treatment switching and early refills were used to assess medication wastage. The proportion of days’ supply wasted, the mean number of days’ supply wasted and the mean costs of wastage per prescription were determined for two prescription lengths (< 60 and ≥ 60 days, used to proxy 28-day and 3-month prescriptions, respectively) over the 11-year period (2004–14). The mean cost of wastage per prescription was reported for each of the four treatment patterns individually over the 11-year study period and combined for each annual period. Two-sample t-tests using groups (prescription lengths < 60 days and ≥ 60 days) assuming unequal variance were used to compare the differences between the < 60 days and ≥ 60 days groups. This statistical test was chosen despite the potential for skewed data (i.e. costs) because our interest was in testing the arithmetic means and not other characteristics of the distribution (e.g. log transforming data tests for equality of geometric means).

To determine and compare the total unnecessary costs (TUCs) (costs of medication wastage, dispensing fees and prescriber time) associated with prescription lengths < 60 days and ≥ 60 days, a model originally used by Walton42 was adapted and applied to the prescription data from the five cohorts. The model incorporates the quantity (Q) of medication wastage (where Q_{days wasted} = Q_{days supplied} – Q_{days used}) and the cost (C) of medication wastage (where C_wastage = C_drug/day × Q_{days wasted}) as well as the cost of dispensing a prescription (C_dispensing) and the cost of prescriber time associated with issuing a prescription (C_{prescriber time}) to estimate the TUC. To compare the TUCs between different prescription lengths (< 60 and ≥ 60 days), it was necessary to standardise the time period over 90 days. The TUCs for the two prescription lengths were then calculated using the two equations below:

The terms (90/Q_{days used < 60}) and (90/Q_{days used ≥ 60}) standardise the total costs over a period of days (90 days) and C_dispensing and C_{prescriber time} are subtracted to reflect that one dispensing fee and prescriber time costs for one prescription will be incurred for each set period of days (90 days). Each value used in the equations represents a mean calculated for the entire group of patients for each prescription length in each of the five cohorts over the 11-year study period. Mean values for the base case analysis are reported in Table 13. An example scenario is provided in Box 2 to highlight how the model compares the TUCs for prescription lengths < 60 days and ≥ 60 days over a standardised period of 90 days.

One-way sensitivity analyses were conducted to examine differences in TUC under a variety of different scenarios, including exclusion of prescriber time costs, ± 50% mean days wasted, ± 50% of the mean cost of drugs per day, dispensing fees and prescriber time.

All statistical analyses were preformed using Stata^®/MP 13.1 (Stata Corp LP, College Station, TX, USA). The protocol (16_117R) for this study was approved on 21 June 2016 by the Independent Scientific Advisory Committee (ISAC), the independent body that approves the use of CPRD data.

Overall cohort selection

Full samples for the five cohorts ranged in size, with four of the cohorts having a similar number of patients (range 208,682–310,391) and the depression cohort being much larger (1,207,523 patients) (see Table 12, column 1). The number of observations varied from 13,388,759 in the lipid management cohort to 44,151,527 in the secondary prevention of myocardial infarction cohort (see Table 12, column 2). The proportion of patients dropped from the full sample to create the limited sample owing to missing or observations equal to zero in either the ndd or qty variables was quite substantial, ranging from 15% for the lipid management cohort to 55% for the glucose control in T2DM cohort. The impact on the proportion of observations was, however, much smaller, ranging from 6% in both the lipid management and hypertension cohorts to 21% in the glucose control in T2DM cohort. Taking random samples from the limited samples resulted in the number of observations from each cohort ranging from 1,010,463 for the depression cohort to 10,131,377 for the secondary prevention of myocardial infarction cohort (see Table 12, column 8). The numbers of observations were slightly reduced after accounting for prescription error (i.e. prescriptions issued on the same day for medications in the same class with different product codes were dropped from the sample) (see Table 12, column 9). A number of prescriptions for medications with similar clinical indications from different classes issued on the same day were also identified, ranging from 12,401 for the depression cohort to 5,856,361 in the secondary prevention of myocardial infarction cohort; these were assumed to represent add-ons or concomitant therapy, and therefore were not counted as wastage despite overlapping dates (see Table 12, column 11).

Medication wastage

Over the 11-year study period, there was a statistically significant difference in the proportion of days’ supply wasted, the mean number of days’ supplied wasted and the mean cost of wastage per prescription between the shorter (< 60 days) and longer (≥ 60 days) prescription groups for all five of the case study conditions (Table 14). The proportion of days’ supply wasted was consistently larger for the ≥ 60 days groups across the case study conditions, with the exception of the depression cohort, in which the < 60 days group wasted 6.3% of days’ supply wasted, compared with 3.7% in the ≥ 60 days group. The mean number of days’ supply wasted was also consistently larger for the ≥ 60 days groups, but the difference between the two prescription length groups was much smaller for the depression cohort than for the other four cohorts. The mean cost of wastage per prescription was the largest for the ≥ 60 days groups for the glucose control and lipid management cohorts and largest for the < 60 days groups for the glucose control and depression cohorts. Similar mean costs of wastage per prescription were observed for the ≥ 60 days groups (range 0.43–0.51) and < 60 days groups (range 0.05–0.09) in the other cohorts.

Medication wastage by treatment pattern

In four out of the five case study conditions, all four treatment patterns resulted in a statistically significant difference in the mean cost of wastage per prescription between the two prescription lengths (Table 15). The one exception was for the depression cohort, in which the mean cost of wastage per prescription for both dosage/formulation and within-class treatment switches did not show statistically significant differences between the two prescription length groups. The refill treatment pattern consistently had the largest mean cost of wastage per prescription across the case study conditions, particularly for the ≥ 60 days groups. The one exception was for the depression cohort that also had similarly large mean costs of wastage per prescription for the dosage/formulation switch treatment pattern compared with the refill treatment pattern. The lowest mean cost of wastage per prescription was consistently observed for the between-class switch treatment (switches between medications with similar clinical indications from different classes) pattern across all case study conditions reporting these values. Note that the lipid management cohort did not report any between-class treatment switches, as all medications included in the analysis were from the same class of statins. Low mean costs of wastage per prescription were also reported for the within-class switch treatment pattern for the hypertension, lipid management, and depression cohorts.

Medication wastage over time

On an annual basis, a statistically significant difference in the mean cost of wastage per prescription was observed between the two prescription lengths for each of the 11 years across the case study conditions (Table 16). The one exception was for the years 2012 and 2013 for the depression cohort, in which no statistically significant differences in the mean costs between the two prescription lengths were observed, despite the magnitude of the mean costs remaining relatively consistent over the 11 years for both groups. For the most part, the magnitude of the mean costs also remained relatively consistent over the 11-year study period across the other four case study conditions. There were, however, a few trends in the mean costs that should be noted.

In the glucose control in T2DM cohort, the mean costs for the ≥ 60 days group in 2004 and 2011 were slightly higher (range £1.81–3.14) than the other nine annual means (range £0.87–1.40). In the hypertension cohort, there was a slight trend of decreasing magnitude of the mean cost over the 11 years for the < 60 days group; a decrease in mean cost was limited to the years 2013 and 2014 in the ≥ 60 days group. For both the lipid management and the secondary prevention of myocardial infarction cohorts, the magnitude of the mean costs remained relatively consistent over the 11 years for the prescription length < 60 days groups, whereas there was a slightly decreasing trend in the magnitude of the mean costs for the prescription length ≥ 60 days groups.

Differences in total unnecessary costs for short and long prescription lengths

After standardising the TUCs (wastage, dispensing fees and prescriber time) to a 90-day period, shorter (< 60 days) and longer (≥ 60 days) prescription lengths in each of the five case study conditions were compared (see Appendix 8). Using the mean values reported in Table 13 resulted in the base case TUCs and differences reported in Appendix 8. The differences in TUCs reported between the two prescription length groups resulted in the average TUC for prescriptions ≥ 60 days being lower (range £6.33–9.07 per prescription) than for prescriptions < 60 days.

The results of the sensitivity analyses for different scenarios are also shown in Appendix 8. The cost savings reported in the base case for prescriptions ≥ 60 days remained for all tested scenarios. The magnitude of the savings, however, reduced considerably across all five of the case study conditions when prescriber time costs were excluded from the models (range £0.72–2.12). The other scenarios tested had relatively little impact on the magnitude of the savings, with the exception of increases and decreases of 50% in the cost of prescriber time.

Comparison with previous studies

Six previous studies have estimated costs differences associated with long (3-month) and short (1-month) prescriptions. 22,42,44–46,49 All of the existing studies are from various perspectives in the USA (e.g. Veterans Affairs, Medicaid and non-institutionalised civilian populations). Only one of the studies accounted for differences in prescriber time (indirectly by assessing differences in all Medicaid expenditures, i.e. outpatient, inpatient, emergency and pharmacy costs),44 and only four of the studies accounted for the impact of differences in drug wastage. 42,44–46 Prescriptions for a number of chronic diseases were assessed,44,45,49 but some studies limited their analyses to specific drugs (e.g. simvastatin and lovastatin)42 or did not limit their analyses to any particular therapeutic class of medication. 46

Walton42 applied a similar model to the one used in our study to estimate the TUC (wastage and dispensing fees only) associated with either 30-day or 90-day supplies of two statin medications. When compared with the results of our analysis for the lipid management cohort and excluding prescriber time costs, the difference in the mean TUC between long and short prescription lengths was greater in the US study (US$2.45 vs. £0.79). It should be noted that the dispensing fee used by Walton42 was also slightly larger (US$1.79 vs. £0.90), which may partly account for the slightly larger savings reported.

Taitel et al. 45 estimated the per-patient savings of switching from 30-day prescriptions to 90-day prescriptions for four therapeutic classes of medication (statins, antihypertensives, SSRIs and oral hypoglycaemics). Similarly to our analysis, all four therapeutic classes reported a greater number of wastage days for 90-day prescriptions than for 30-day prescriptions. However, after adding dispensing fees and medication costs to the costs of wastage and standardising to per-patient per-year values, savings were reported of US$7.70 for statins, US$10.80 for antihypertensives, US$18.52 for SSRIs and US$26.86 for oral hypoglycaemics. 45 Multiplying our reported savings for longer prescriptions standardised to 90 days by four, so as to encompass a year, results in estimated savings of £3.16 per prescription per year for the lipid management cohort, £5.00 for the hypertension cohort, £8.48 for the depression cohort and £2.88 for the glucose control in T2DM cohort. Thus, in all cases, the estimated savings were somewhat larger in the Taitel et al. 45 US study than our estimates for the UK, although this is to be expected based on the estimated savings in our study being on a per-prescription per-year basis rather than on a per-patient per-year basis as reported by Taitel et al. 45 Any further differences may be attributed to the inclusion of volume discounts for medication costs in the study by Taitel et al. ,45 and the fact that the drugs included in our study were not limited to single classes of medications (with the exception of the lipid management cohort), and were selected to be representative of all of the treatments available for a particular case study condition.

Rabbani et al. 49 assessed differences in third-party expenditures for 3-month and 1-month supplies of 395 unique medications for common chronic conditions, including high cholesterol, hypertension, hypothyroidism and depression. 49 Only two of the six payers in their study achieved statistically significant savings with a 3-month supply (range US$0.34M to US$1.74M), as the majority of savings were accrued for individuals through reductions in out-of-pocket costs, which were not assessed in our analysis. 49 Another major difference between our study and that conducted by Rabbani et al. 49 is the latter’s lack of consideration of the cost of medication wastage, making it difficult to compare the results of this study with the results of our analysis in a meaningful manner.

Parikh et al. 46 estimated a theoretical saving of US$6.17 if 90-day prescriptions were issued instead of 30-day prescriptions. 46 In addition to medication and wastage costs, Parikh et al. 46 also included mailing costs for mail-order prescriptions that were not considered in our analysis. The mean cost saving in our analysis over the five case study conditions when excluding prescriber time costs was £1.30 per prescription, but it is difficult to compare our results with those presented by Parikh et al. 46 because their analysis was not specific to any particular chronic condition and included only a very small number of prescriptions (346 unique prescriptions) over a short time period (3 months). The standardisation approach used by Parikh et al. 46 was also different from the models used in our study. Costs of wastage, dispensing fees (US$1.573–4.275 vs. £0.90) and mailing costs were simply multiplied by three for the 30-day prescriptions to determine the total cost, then subtracted from the actual total costs associated with 90-day prescriptions. This difference in methodology and dispensing fees might account for the difference in the magnitude of savings reported in our study.

The first study by Domino et al. 22 simulated the effect of a policy change from a maximum of a 100-day supply for prescriptions to one in which only a 34-day supply was allowed for six specific therapeutic categories of medications. 22 Only three therapeutic categories (sulfonylureas, SSRIs and ACEIs), all of which were associated with relatively small amounts of wastage in comparison with the other categories (antiulcers, antipsychotics, non-steroidal anti-inflammatory drugs), were included as treatments for one or more of the five case study conditions analysed in our study. A comparison of our results is therefore difficult, but a specific pattern should be noted. When simulating 34-day supplies for all six categories, any savings in wastage as a result of the shorter prescription length were offset by increases in dispensing fees (US$5.60 per prescription). 22 Reducing the dispensing fee to US$2.40 resulted in the wastage savings exceeding dispensing costs for three of the categories (antiulcers, antipsychotics and non-steroidal anti-inflammatory drugs), none of which was assessed in our analysis. For the remaining three categories, the reduction in dispensing fees required to achieve savings for 34-day supplies was not noted, although Domino et al. 22 stated that it would have to be much lower, which is consistent with the findings of our analyses.

The second study by Domino et al. 44 provides the most comprehensive study to date using a pre–post controlled partial difference-in-difference-in-differences design to assess the impact of a prescription length policy change in the North Carolina Medicaid programme (from 100 to 34 days’ supply) on total Medicaid expenditures (outpatient, inpatient, emergency and pharmacy costs). 44 Total Medicaid expenditures were shown to decrease for patients initially receiving 100-day prescriptions after the implementation of the shorter prescription length policy (range US$245–440 per person per quarter in all six classes of medications assessed (antihypertensives, antidiabetic medications, lipid-lowering drugs, seizure disorder medications, antidepressants and antipsychotics). The results, however, are not stratified by expenditure category (with the exception of also reporting decreases in expenditures for the targeted prescriptions across all six medication classes); therefore, it is not clear to what extent changes in outpatient, inpatient or emergency visits are individually responsible for the decreases in expenditures. In addition to the difference in study design, a number of methodological contrasts with our study should also be noted. To be included in the analysis, medical diagnoses were required for four of the six medication classes (antihypertensives, antidepressants, antipsychotics and seizure disorder medications),44 which is in contrast to our reliance on product codes to identify our five cohorts of interest. Our analysis could therefore be capturing a broader range of patients (i.e. those receiving the medications of interest off-label or for other conditions). The study by Domino et al. 44 also accounted for differences in adherence, and indicated that reduced adherence for shorter prescriptions led to the decreases in expenditures, but this was not assessed in our study owing to differences in study design and available data. This somewhat counterintuitive finding may potentially be explained by small adverse health effects as a result of changes in adherence, patients absorbing any health effects through informal care or tolerating greater disease burden, and the 18-month post-policy period in the study being too short to capture the spillover effects of decreased medication adherence on other Medicaid services. Furthermore, NHS expenditures in the UK for the treatment of chronic conditions are likely to differ based on the organisation of primary care and the existence of a referral system to specialty care compared with those incurred in a US Medicaid patient population; therefore, the generalisability of the conclusions presented by Domino et al. 44 to the UK context are unclear. It will, however, be important to incorporate a more comprehensive measurement of the potential differences in NHS expenditures associated with different prescription lengths in any future UK study.

Limitations of the cost analysis

Although our study, to the best of the authors’ knowledge, provides the only available evidence of the unnecessary costs associated with different prescription lengths from the perspective of the NHS in the UK, and builds on existing methodological approaches available in the literature (accounts for prescriber time costs and potential wastage between medications with similar clinical indications from different classes, rather than single classes of drugs), there are a few limitations that warrant discussion. First, a limitation of CPRD prescription data is that they do not indicate whether or not a medication has been dispensed, or if patients took their prescribed medications as recommended (i.e. they indicate only when a prescription has been issued). Our estimates may, therefore, either over- or understate the amount of wastage that actually occurred, depending on patient behaviour not captured by CPRD. Assuming that all prescriptions issued were actually dispensed places an upper bound on the potential savings that would occur if drug wastage from premature medication switches could be eliminated entirely. In terms of medication adherence, longer prescription lengths (≈ 3 months) consistently show improved adherence compared with shorter prescription lengths (≈ 1 month). 39,44,45 One of these studies, however, also indicated that reduced adherence related to shorter prescription lengths led to lower total expenditure from a Medicaid perspective (outpatient, inpatient, emergency and pharmacy costs). 44 In contrast, our study was not designed to estimate the impact of different prescription lengths on total NHS expenditures, as this would require additional linkages to secondary care datasets (e.g. Hospital Episode Statistics) and more comprehensive analysis of CPRD through consideration of additional data files (e.g. clinical, consultations and laboratory test files). Chapter 5 provides a model-based analysis that attempts to capture these ‘downstream’ costs as well as health outcomes. The main objective of our study was, however, to estimate differences in TUCs (medication wastage, dispensing fees and prescriber time costs) associated with different prescription lengths, therefore it may be pertinent to conduct more comprehensive analyses using linked data in future research.

Second, the five case study conditions were purposively rather than randomly selected to represent the impact of medication refill and switching behaviour on wastage; they may not be representative of prescribing behaviour in other chronic conditions. However, those selected do represent some of the most common chronic conditions treated with prescribed medications. Nine of the top 20 prescribed medications within NHS England were included in at least one of the case study conditions in our analyses, and, combined, they accounted for around £378M drug expenditure in NHS England in 2015 (4% of the total), and are, therefore, highly policy relevant. In common with other studies,22,45 our analyses have shown that the amount of wastage, and therefore its associated cost, can differ depending on the therapeutic category of the medications being analysed. Our analysis also excluded patients in whom one or more observations were missing or had zero values for either the ndd and/or qty variables. This approach resulted in substantial proportions (15–55%) of patients from the full samples being dropped and therefore limits the generalisability of the results from our original random samples. Appropriate methods to impute these variables are, however, limited, and our approach was similar to other studies using CPRD data. 77,78

Third, the identification of patients within CPRD with the five case study conditions (see Table 11) was based solely on product codes, rather than on a specific medical diagnosis (identified using Read codes in CPRD). This was straightforward for four of the five conditions, but required additional assumptions for the secondary prevention of myocardial infarction cohort. To avoid the additional complexity of using Read codes, patients were identified according to whether or not they had prescriptions for drugs as per clinical guidelines, for example patients receiving concurrent prescriptions for an ACEI, antiplatelet and statin for a duration of ≥ 1 year. 81 As the main aim of our study was to estimate drug wastage, the possible inclusion of patients without a previous myocardial infarction event, but still receiving at least four of the prescriptions of interest for ≥ 1 year, provided our analysis with relevant information concerning drug wastage, dispensing fees and prescriber time.

Fourth, an overlap of dates between prescriptions does not necessarily mean wastage has occurred, as consumption of early refills may be delayed until the initial supply is exhausted, and treatment changes might actually be add-ons to existing prescriptions or concomitant therapy rather than switches in therapy. To ensure that wastage was not overestimated, a threshold of 1 year after the initial prescription in a particular series was used to estimate wastage for early refills and a threshold of < 1 in the difference between the number of drug changes between medications with similar clinical indications from different classes and the number of unique drug classes within an annual period was used to identify wastage from between-class treatment switches. There is, however, a possibility that our analysis approach could have overestimated the amount of medication wastage.

Fifth, for pragmatic purposes, we dichotomised prescription lengths into ‘short’ versus ‘long’, with a cut-off point of 60 days. This will have classified 56-day prescriptions as ‘short’. Although this will have resulted in a loss of sensitivity (there may be differences in TUC between 1- and 2-month prescriptions), the overall conclusions are not affected: 3-month prescriptions are associated with higher drug wastage than 1- or 2-month prescriptions, but the reduction in administration costs (e.g. dispensing fees and prescriber time) more than compensates for this, leading to overall lower TUC with the longer prescription lengths.

Finally, a number of assumptions were required to assign unit costs to the estimated proportions of wastage. Mean cost per day values derived using DDDs, NICs and quantities at the drug substance level were calculated and then applied to any prescription categorised under that particular drug substance. This approach is not ideal; given the inability to link CPRD data to individual unit costs specific to each prescription, it was necessary, but it means that the direction and magnitude of any resulting bias is difficult to predict.

Furthermore, NICs do not include any discounts that may be applied or include any adjustment for revenue received by the NHS if a prescription charge is paid at the time the prescription is dispensed, or if the patient has purchased a pre-payment certificate, and therefore may be different from the net cost incurred specifically by the NHS. Patients with T2DM are exempt from the prescription charge,88 and, overall, almost 90% of prescriptions dispensed in the NHS in England are exempt. 5 Given a current prescription charge of £8.40 (as in August 2016), and with only 10% of prescriptions attracting a patient charge, revenue to the NHS is, on average, £0.84 per prescription. If this average revenue per prescription is incorporated into our TUC models, the predicted net savings to the NHS from issuing longer prescriptions decreases on average by £1.56 (range £1.45–1.69) from the base case values across all five of the case study conditions. Applying the adjusted cost savings accounting for losses in revenue from prescription charges to the NHS from switching to longer prescriptions for antidepressants and the two statins (simvastatin and atorvastatin) would reduce projected savings from £305M to £246M and from £424M to £323M, respectively.

All of these limitations risk biasing the results. The projected savings should, therefore, be interpreted with caution, especially if they are to be extended to other types of medications that are not prescribed at relatively low unit costs to a large population of patients. Proportional savings for high-costs drugs used to treat relatively small patient groups may not be similarly observed.

Case study 1: primary prevention of cardiovascular events in patients with recent onset type 2 diabetes mellitus

The results for each case study are structured as per the methods overview presented in Table 17.

Case study 2: selective serotonin reuptake inhibitors

Stage 1: identify relevant data and decision models

1a

The systematic review (see Chapter 2) identified two studies that had examined the relationship between prescription length and adherence in antidepressant medications. 37,45 No studies examined the relationship between prescription length (or adherence) and health outcomes, and no decision models were identified.

1b

We reviewed relevant NICE guidance on depression in adults. The most recent NICE guidance (CG90, published October 2009 and updated April 2016) included a decision model that examined the cost-effectiveness of alternative pharmacological interventions (10 different groups of antidepressants were assessed overall) with or without cognitive–behavioural therapy (CBT). 95 Table 18 provides an overview of the model, which is a decision tree with a 15-month time horizon. The model is operated in Microsoft Excel.

As Figure 11 shows, in an example with two treatment arms, there are 12 possible pathways through the model. Each pathway has probability nodes attached to three discrete treatment phases. These treatment phases are a three-month acute treatment phase, a 6-month maintenance period (which together form a 9-month ‘treatment phase’) and a 6-month follow-up phase. Probability nodes in the model reflect the likelihood of dropout (during the acute phase), no remission (during the maintenance period) and relapse (during the follow-up phase). Costs (from the NHS perspective; see Table 29, Appendix 9) and QALYs (derived from another study93) are assigned to each of the pathways through the model. The results for each analysis were reported for two separate cohorts of 100 patients with moderate and severe depression. Patients with severe depression faced higher patient monitoring costs than those with moderate depression because they had hospital outpatient mental health consultations. Deterministic sensitivity analysis was carried out on the upper and lower 95% credible intervals around some of the input parameters (e.g. response and dropout probabilities). The guideline states that probabilistic sensitivity analysis was not possible because of limited access to data from a network meta-analysis from which many of the input parameters were derived. 95

FIGURE 11.

Case study 2: adapted decision tree.

1c

Although the original decision model did not include a placebo arm, the full NICE guidance did include a separate clinical evidence review, which included data on the health consequences of specific pharmacological interventions when compared with placebo. These data were used to inform our model adaptations.

As the decision model did not include dispensing fees, we identified these from the NHS Drug Tariff. 60 The costs of prescriber time (see Table 13) and wastage (see Table 14) were identified in the CPRD analysis of depression (see Chapter 3).

Case study 3: secondary prevention of cardiovascular events

Limitations of the decision modelling

The three case studies are based on existing, good-quality decision models, all of which have been used to inform policy in the past. However, there are a number of limitations to our analyses.

First, our assumption of a positive relationship between prescription length and adherence was based on a small number of studies (n = 2 in each case study) identified in the systematic review. The limitations of these studies are fully assessed in the systematic review section, but, crucially, they were observational, not randomised experimental studies, and so are potentially subject to bias. Other potential sources of bias include those common to all decision modelling, namely validity of all input data on cost and effectiveness, whether or not the structure of the model is plausible and whether or not the model is ‘consistent’ (Philips et al. 98). In contrast to other studies that have used decision modelling, including the evaluation of the NMS by Boyd et al. ,90 which assessed the longer-term cost and health impacts of a policy change in pharmacy to support increased adherence, our study was limited because it was not based on evidence from a robust RCT.

Second, we assumed that the accrued costs and QALYs in the treatment arms of the models equated to ‘perfect adherence’, or at least to ‘best adherence’, and that the placebo or no treatment arms represented zero adherence. Given prior belief (informed by the systematic review) that longer prescriptions are associated with better adherence, we set the 3-month prescription costs and QALYs equal to the treatment arm costs and QALYs. The 28-day prescription costs and QALYs were assumed to be a weighted average of the treatment and placebo arm according to the RR of being adherent (as described in the Methods section of this chapter, the RR of being adherent in those studies was based on two standard measures of adherence: a dichotomised measure, based on the percentage of patients with ≥ 80% medication adherence, and the MPR, i.e. days covered per measurement period). These were necessary assumptions owing to the lack of better-quality data.

There are several points to note. First, except for the additional transaction and wastage costs, the ICER is insensitive to the ‘starting point’ for the analysis. For example, the opposite extreme would have been to assume that the 28-day prescription arm costs and QALYs were equal to those in the placebo/no treatment arm, and to define the 3-month prescription arm as a weighted average of 1 – 1/RR of the treatment arm costs and QALYs and 1/RR of the no treatment arm. This would yield the same ICER as illustrated in Figure 10 and Equation 5:

where λ = willingness to pay for a unit of outcome and E = QALYs (NICE assigns a value of £20,000–30,000 to λ).

Second, as stated in the Methods section of this chapter, we have used ‘no treatment’ and ‘placebo’ interchangeably in the analysis and, as a result, we may have underestimated the health gain from increased adherence as a result of longer prescription lengths.

Other limitations were that we assumed adherence did not change over the period of the analysis, and, although we took a NHS perspective, we excluded any revenue gained by the NHS from prescription charges. Patients with diabetes are exempt from the prescription charge, and almost 90% of prescriptions dispensed in the community in England do not attract any charge; therefore, any revenue would be minimal and thus unlikely to change the results of our analysis. 88 Costs of drugs were based on the lowest price brand and pack size listed in the BNF, and we did not consider any combination preparations. If more expensive versions were used, then the cost savings through reduced wastage would be higher than those suggested here.

MEDLINE (PubMed)

Prescription length*[title/abstract] OR prescription duration*[title/abstract] OR medication duration*[title/abstract] OR “medication length”[title/abstract] OR “length of prescription”[title/abstract] OR “length of prescriptions”[title/abstract] OR “duration of prescription”[title/abstract] OR “duration of prescriptions”[title/abstract] OR “durations of prescriptions”[title/abstract] OR “drug prescribing”[title/abstract] OR “multiple drug prescriptions”[title/abstract] OR prescribing pattern*[title/abstract] OR prescription pattern*[title/abstract] OR prescribing behavior*[title/abstract] OR prescribing behaviour*[title/abstract] OR prescribing practice*[title/abstract] OR prescribing standard*[title/abstract] OR (installment[title/abstract] AND dispensing[title/abstract]) OR repeat prescri*[title/abstract] OR “repeat dispensing”[title/abstract] OR prescribing interval*[title/abstract] OR prescription interval*[title/abstract] OR “28 day supply”[title/abstract] OR “34 day supply”[title/abstract] OR (“28 day"[title/abstract] AND (“drug supply”[title/abstract] OR prescribing[title/abstract] OR prescription[title/abstract])) OR “56 day supply”[title/abstract] OR (“56 day"[title/abstract] AND (“drug supply"[title/abstract] OR prescribing[title/abstract] OR prescription[title/abstract])) OR “28 day drug limit”[title/abstract] OR “56 day drug limit”[title/abstract] OR “one month prescription”[title/abstract] OR “one month prescriptions”[title/abstract] OR “1 month prescription”[title/abstract] OR “1 month supply”[title/abstract] OR “one month supply”[title/abstract] OR “3 month prescriptions”[title/abstract] OR “three month prescription”[title/abstract] OR “three month prescriptions”[title/abstract] OR “3 month prescription”[title/abstract] OR “3 month supply”[title/abstract] OR “three month supply”[title/abstract] OR “90 day supply”[title/abstract] OR “30 day supply”[title/abstract] OR “60 day supply”[title/abstract] OR dosage unit*[title/abstract] OR “prescription standardization”[title/abstract] OR “prescription standardisation”[title/abstract] OR prescription restriction*[title/abstract] OR prescribing restriction*[title/abstract] OR “restricting prescriptions”[title/abstract] OR “restricting medication”[title/abstract] OR medication restriction*[title/abstract] OR dispensing restriction*[title/abstract] OR prescribing trend*[title/abstract] OR prescription trend*[title/abstract] OR dispensing trend*[title/abstract] OR “trends in dispensing”[title/abstract] OR “trends in prescribing"[title/abstract] OR prescription suppl*[title/abstract] OR medication suppl*[title/abstract] OR term prescription*[title/abstract] OR ((short course*[title/abstract] OR long course*[title/abstract]) AND (prescription*[title/abstract] OR medication*[title/abstract])) OR “short prescription”[title/abstract] OR “long prescription”[title/abstract] OR “short prescriptions”[title/abstract] OR “long prescriptions”[title/abstract] OR standardized prescri*[title/abstract] OR “standardised prescription”[title/abstract] OR “standardised prescribing”[title/abstract] OR (Standardization[title/abstract] AND (prescribing[title/abstract] OR prescription*[title/abstract])) OR (Standardisation[title/abstract] AND (prescribing[title/abstract] OR prescription*[title/abstract])) OR individualized prescri*[title/abstract] OR individualised prescri*[title/abstract] OR (individualization[title/abstract] AND prescrib*[title/abstract]) OR (individualisation[title/abstract] AND prescrib*[title/abstract]) OR Drug Prescriptions/trends OR Drug Prescriptions/supply and distribution

Results: 8242 – animal = 8207.

EMBASE

(prescription* NEAR/2 length*):ti,ab OR (prescription* NEAR/2 duration*):ti,ab OR (medication* NEAR/2 duration*):ti,ab OR (medication* NEAR/2 length*):ti,ab OR “drug prescribing”:ti,ab OR “multiple drug prescriptions”:ti,ab OR (prescri* NEXT/1 pattern*):ti,ab OR (prescribing NEXT/1 behaviour*):ab,ti (prescribing NEXT/1 behavior*):ab,ti OR (prescribing NEXT/1 practice*):ab,ti OR (prescribing NEXT/1 standard*):ab,ti OR “installment dispensing”:ti,ab OR (repeat NEXT/1 prescri*):ti,ab OR (repeat NEXT/1 dispens*):ti,ab OR (prescri* NEXT/1 interval*):ti,ab OR (prescri* NEXT/1 interval*):ti,ab OR ((28 OR 30 OR 34 OR 56 OR 60 OR 90) NEXT/1 day NEXT/1 supply):ti,ab OR ((28 OR 30 OR 34 OR 56 OR 60 OR 90) NEXT/1 day NEXT/1 drug NEXT/1 supply):ti,ab OR ((28 OR 30 OR 34 OR 56 OR 60 OR 90) NEXT/1 day NEXT/1 prescri*):ti,ab OR ((28 OR 30 OR 56 OR 60 OR 90) NEXT/1 day NEXT/1 drug NEXT/1 limit):ti,ab OR “one month prescription”:ti,ab OR “1 month prescription”:ti,ab OR “one month supply”:ti,ab OR “1 month supply”:ti,ab OR (3 NEXT/1 month NEXT/1 prescription*):ti,ab OR (three NEXT/1 month NEXT/1 prescription*):ti,ab OR “3 month supply”:ti,ab OR “three month supply”:ti,ab OR (dosage NEXT/1 unit*) OR “prescription standardization”:ti,ab OR “prescription standarisation”:ti,ab OR (prescri* NEAR/1 restrict*):ti,ab OR (medication* NEAR/1 restrict*):ti,ab OR (dispensing NEXT/1 restrict*):ti,ab OR (prescri* NEAR/2 trends):ti,ab OR (dispensing NEAR/2 trends):ti,ab OR ((prescription OR medication) NEXT/1 suppl*):ti,ab OR ((short OR long) NEXT/1 (course OR term) NEXT/1 (prescription* OR medication*)):ti,ab OR ((short OR long) NEXT/1 prescription*):ti,ab OR ((standardised OR standardized OR standardization OR standardisation) NEXT/2 prescri*):ti,ab OR ((individualized OR individualized OR individualization OR individualisation) NEXT/1 prescri*):ti,ab

Results: 6266 – duplicates/animal = 3600.

Cumulative Index to Nursing and Allied Health Literature

TI “prescription length*” OR AB “prescription length*” OR TI “prescription duration*” OR AB “prescription duration*” OR TI “length* of prescription*” OR AB “length* of prescription*” OR TI “duration* of prescription*” OR AB “duration* of prescription*” OR TI “drug prescribing” OR AB “drug prescribing” OR TI “multiple drug prescriptions” OR AB “multiple drug prescriptions” OR TI “prescri* pattern*” OR AB “prescri* pattern*” OR TI “prescribing behavior*” OR AB “prescribing behavior*” OR TI “prescribing behaviour*” OR AB “prescribing behaviour*” OR TI “prescribing practice*” OR AB “prescribing practice*” OR TI “prescribing standard*” OR AB “prescribing standard*” OR TI “installment dispensing” OR AB “installment dispensing” OR TI “repeat prescri*” OR AB “repeat prescri*” OR TI “28 day supply” OR AB “28 day supply” OR TI “30 day supply” OR AB “30 day supply” OR TI “30 day drug supply” OR AB “30 day drug supply” OR TI “28 day drug supply” OR AB “28 day drug supply” OR TI “34 day drug supply” OR AB “34 day drug supply” OR TI “34 day supply” OR AB “34 day supply OR TI “28 day prescri*” OR AB “28 day prescri*” OR TI “30 day prescri*” OR AB “30 day prescri*” OR TI “34 day prescri*” OR AB “34 day prescri*” OR TI “28 day drug limit*” OR AB “28 day drug limit*” OR TI “30 day drug limit*” OR AB “30 day drug limit*” OR TI “34 day drug limit*” OR AB “34 day drug limit*” OR TI “56 day supply” OR AB “56 day supply” OR TI “56 day drug supply” OR AB “56 day drug supply” OR TI “56 day prescri*” OR AB “56 day prescri*” OR TI “56 day drug limit*” OR AB “56 day drug limit*” OR TI “60 day supply” OR AB “60 day supply” OR TI “60 day drug supply” OR AB “60 day drug supply” OR TI “60 day prescri*” OR AB “60 day prescri*” OR TI “60 day drug limit*” OR AB “60 day drug limit*” OR TI “90 day supply” OR AB “90 day supply” OR TI “90 day drug supply” OR AB “90 day drug supply” OR TI “90 day prescri*” OR AB “90 day prescri*” OR TI “90 day drug limit*” OR AB “90 day drug limit*” TI “one month prescription*” OR AB “one month prescription*” OR TI “1 month prescription*” OR AB “1 month prescription*” OR TI “1 month supply” OR AB “1 month supply” OR TI “one month supply” OR AB “one month supply” OR TI “three month prescription*” OR AB “three month prescription*” OR TI “3 month prescription*” OR AB “3 month prescription*” OR TI “3 month supply” OR AB “3 month supply” OR TI “three month supply” OR AB “three month supply” OR TI “dosage unit*” AND AB “dosage unit*” OR TI “prescription standardization*” OR AB “prescription standardization*” OR TI “prescription standardisation*” OR AB “prescription standardisation*” OR TI “prescri* restriction*” OR AB “prescri* restriction*” OR TI “restricting prescription*” OR AB “restricting prescription*” OR TI “restricting medication*” OR AB “restricting medication*” OR TI “medication restriction*” OR AB “medication restriction*” OR TI “dispensing restriction*” OR AB “dispensing restriction*” OR TI “prescri* trend*” OR AB “prescri* trend*” OR TI “dispensing trend*” OR AB “dispensing trend*” OR TI “trends in dispensing” OR AB “trends in dispensing” OR TI “trends in prescribing” OR AB “trends in prescribing” OR TI “prescription suppl*” OR AB “prescription suppl*” OR TI “medication suppl*” OR AB “medication suppl*” OR TI “term prescription*” OR AB “term prescription*” OR TI “short course prescription*” OR AB “short course prescription*” OR TI “long course prescription*” OR AB “long course prescription*” OR TI “short course medication*” OR AB “short course medication*” OR TI “long course medication*” OR AB “long course medication*” OR TI “short prescription*” OR AB “short prescription*” OR TI “long prescription*” OR AB “long prescription*” OR TI “standardized perscri*” OR AB “standardized perscri*” OR TI “standardised perscri*” OR AB “standardised perscri*” OR TI “standarization of prescri*” OR AB “standarization of prescri*” OR TI “standarisation of prescri*” OR AB “standarisation of prescri*” OR TI “individualized prescri*” OR AB “individualized prescri*” OR TI “individualised prescri*” OR AB “individualised prescri*” OR TI “individualization prescri*” OR AB “individualization prescri*” OR TI “individualisation prescri*” OR AB “individualisation prescri*”

Results: 1737 – duplicates = 367.

Web of Science

Refined by: [excluding] DOCUMENT TYPES: ( LETTER OR NEWS ITEM OR EDITORIAL MATERIAL OR BOOK CHAPTER OR NOTE OR BOOK REVIEW OR DISCUSSION ) AND [excluding] WEB OF SCIENCE CATEGORIES: ( OPERATIONS RESEARCH MANAGEMENT SCIENCE OR VETERINARY SCIENCES OR COMPUTER SCIENCE ARTIFICIAL INTELLIGENCE OR MATHEMATICAL COMPUTATIONAL BIOLOGY OR COMPUTER SCIENCE THEORY METHODS OR METEOROLOGY ATMOSPHERIC SCIENCES OR MECHANICS OR FORESTRY OR TELECOMMUNICATIONS OR MATHEMATICS OR MATERIALS SCIENCE MULTIDISCIPLINARY OR ECOLOGY OR FOOD SCIENCE TECHNOLOGY OR AUTOMATION CONTROL SYSTEMS OR ASTRONOMY ASTROPHYSICS ) AND [excluding] WEB OF SCIENCE CATEGORIES: ( STATISTICS PROBABILITY OR POLYMER SCIENCE OR MATHEMATICS INTERDISCIPLINARY APPLICATIONS OR AGRICULTURE DAIRY ANIMAL SCIENCE OR PLANT SCIENCES OR PHYSICS PARTICLES FIELDS OR BIOCHEMISTRY MOLECULAR BIOLOGY OR OCEANOGRAPHY OR ENGINEERING MULTIDISCIPLINARY ) AND [excluding] RESEARCH AREAS: ( WATER RESOURCES OR MATERIALS SCIENCE OR MINING MINERAL PROCESSING OR METALLURGY METALLURGICAL ENGINEERING OR MATHEMATICS ) AND [excluding] WEB OF SCIENCE CATEGORIES: ( PHYSICS FLUIDS PLASMAS OR ENERGY FUELS OR AGRICULTURE MULTIDISCIPLINARY ) Indexes=SCI-EXPANDED, CPCI-S Timespan=All years

TS=(prescription* NEAR/2 length*) OR TS=(prescription* NEAR/2 duration*) OR TS=(“duration of medication*”) OR TS=(“length* of medication*”) OR TS=(“drug prescribing”) OR TS=(“multiple drug prescriptions”) OR TS=(“prescription pattern*”) OR TS=(“prescribing pattern*”) OR TS=(“prescri* pattern*”) OR TS=(“prescribing behavior*”) OR TS=(“prescribing behaviour*”) OR TS=(“prescription behavior*”) OR TS=(“prescription behaviour*”) OR TS=(“prescri* practice*”) OR TS=(“prescription standard*”) OR TS=(“prescribing standard*”) OR TS=(“installment dispensing”) OR TS=(“repeat dispens*”) OR TS=(“repeat* prescription*”) OR TS=(“repeat* prescribing*”) OR TS=(“prescribing interval*”) OR TS=(“prescription interval*”) OR TS=(“28 day supply”) OR TS=(“30 day supply”) OR TS=(“34 day supply”) OR TS=(“56 day supply”) OR TS=(“60 day supply”) OR TS=(“90 day supply”) OR TS=(“28 day drug supply”) OR TS=(“30 day drug supply”) OR TS=(“34 day drug supply”) OR TS=(“56 day drug supply”) OR TS=(“60 day drug supply”) OR TS=(“90 day drug supply”) OR TS=(“28 day prescri*”) OR TS=(“30 day prescri*”) OR TS=(“34 day prescri*”) OR TS=(“56 day prescri*”) OR TS=(“60 day prescri*”) OR TS=(“90 day prescri*”) OR TS=(“28 day drug limit*”) OR TS=(“30 day drug limit*”) OR TS=(“34 day drug limit*”) OR TS=(“56 day drug limit*”) OR TS=(“60 day drug limit*”) OR TS=(“90 day drug limit*”) OR TS=(“one month prescription”) OR TS=(“1 month prescription”) OR TS=(“one month supply”) OR TS=(“1 month supply”) OR TS=(“three month prescription*”) OR TS=(“3 month prescription*”) OR TS=(“three month supply”) OR TS=(“3 month supply”) OR TS=(“dosage unit*”) OR TS=(“prescription standardization”) OR TS=(“prescription standardisation”) OR TS=(prescri* NEAR/1 restrict*) OR TS=(medication* NEAR/1 restrict*) OR TS=(“dispensing restrict*”) OR TS=(dispensing NEAR/2 trends) OR TS=(prescri* NEAR/2 trends) OR TS=(“prescription suppl*”) OR TS=(“medication suppl*”) OR TS=(“short term prescription*”) OR TS=(“short term medication*”) OR TS=(“short course prescription*”) OR TS=(“short course medication*”) OR TS=(“long term prescription*”) OR TS=(“long term medication*”) OR TS=(“long course prescription*”) OR TS=(“long course medication*”) OR TS=(“short prescription*”) OR TS=(“long prescription*”) OR TS=((standardized OR standardised OR standardization OR standardisation) NEAR/2 prescri*) OR TS=((individualized OR indvidualised OR individualization OR individualization) NEAR/1 (prescription* OR prescribing))

8592 – duplicates/animal = 3002.

Cochrane

“length of prescription”:ti,ab OR “prescription length”:ti,ab OR “prescription duration”:ti,ab OR “drug prescribing”:ti,ab OR “multiple drug prescription*”:ti,ab OR “prescri* pattern”:ti,ab OR “prescribing behavior”:ti,ab OR “prescri practice”:ti,ab OR “prescri* standard*”:ti,ab OR “repeat dispens*”:ti,ab OR “repeat prescri*”:ti,ab OR “prescri* interval*”:ti,ab OR “28 day supply":ti,ab OR “30 day supply":ti,ab or “34 day supply":ti,ab OR “60 day supply":ti,ab or “90 day supply":ti,ab OR “28 day prescri*”:ti,ab OR “30 day prescri*”:ti,ab or “34 day prescri*":ti,ab OR “60 day prescri*”:ti,ab or “90 day prescri*”:ti,ab or “56 day prescri*”:ti,ab OR “one month prescription”:ti,ab OR “1 month prescription”:ti,ab OR “one month supply”:ti,ab OR “1 month supply”:ti,ab OR “three month prescription”:ti,ab OR “3 month prescription”:ti,ab OR “3 month supply”:ti,ab OR “three month supply”:ti,ab OR “dosage unit*”:ti,ab OR “prescription standardization”:ti,ab OR “prescri* restriction*”:ti,ab OR “medication restrict*”:ti,ab OR “dispensing restriction*”:ti,ab OR “dispensing NEAR/2 trend*”:ti,ab OR “prescription NEAR/2 trend*” OR “medication supply”:ti,ab OR “medication supplies”:ti,ab OR “short term prescription*”:ti,ab OR “long term prescription*”:ti,ab OR “prescription suppl*”:ti,ab OR ((standardized OR standardised OR standardization OR standardisation) NEAR/2 prescri*) OR ((individualized OR indvidualised OR individualization OR individualization) NEAR/1 (prescription* OR prescribing))

After duplicates: 69.

National Institute for Health and Care Excellence

“length of prescription” OR “prescription length” OR “medication length” OR “prescription trends” OR “medication trends” OR “multiple prescriptions” OR “30 day supply” OR “60 day supply” OR “90 day supply” OR “one month supply” OR “three month supply” OR “prescription supply” OR “medication supply” OR “short term prescription” OR “long term prescription” OR “standardised prescription” OR “individualised prescription” OR “prescribing behaviour”

**added 5 records.

Total

15,250 (No year limits).

(Limiting to 1995 ≈ 13,470/Limiting to 2000 ≈ 12,150/Limiting to 2005 ≈ 9950).

The New York Academy of Medicine

prescribing patterns; prescription length; one month supply; 28 day supply; length of prescription; multiple drug prescription; dispensing restriction; prescribing trends; prescribing behavior; individualized prescribing; individualized prescription; month supply;

Open Archives Initiative harvester

Ti: Prescribing patterns; ti: length of prescription; ti: prescription standardization; ti: dispensing regulation; ti: 30 day supply; ti: repeat dispensing; ti: medication prescription; ti: individualized prescri*; ti: multiple drug prescri*; ti: short term prescri*; ti: long term prescri*; ti: prescription trends; ti: prescribing trends;

OpenGrey

“prescribing patterns”; “prescription length”; “prescription standardization”; “dispensing regulations”; “prescription trends”; “prescription patterns”; “individualized prescri*”; “prescribing trends”; “28 day supply”; “multiple prescriptions”;

Total

14.

Study selection details

The targeted literature search identified a total of 227 citations. After titles and abstracts were screened, 216 citations were excluded and 11 citations were reviewed in full-text. Four studies contained relevant information and the most appropriate evidence was selected from the four studies by prioritising evidence from larger sample sizes and studies that reported prescriber time for different types of prescriptions (e.g. new vs. renewals) and/or different types of prescribers (GP vs. nurse). 85,100–102 It should be noted that one of the four studies identified was a systematic review and led to the identification of two additional studies with relevant information based on their reporting of mean consultation times based on large sample sizes and in different types of prescribers. 103,104