Research

Impact of issuing longer- versus shorter-duration prescriptions: a systematic review

Sarah King, Céline Miani, Josephine Exley, Jody Larkin, Anne Kirtley and Rupert A Payne
British Journal of General Practice 2018; 68 (669): e286-e292. DOI: https://doi.org/10.3399/bjgp18X695501

Abstract

Background Long-term conditions place a substantial burden on primary care services, with drug therapy being a core aspect of clinical management. However, the ideal frequency for issuing repeat prescriptions for these medications is unknown.

Aim To examine the impact of longer-duration (2–4 months) versus shorter-duration (28-day) prescriptions.

Design and setting Systematic review of primary care studies.

Method Scientific and grey literature databases were searched from inception until 21 October 2015. Eligible studies were randomised controlled trials and observational studies that examined longer prescriptions (2–4 months) compared with shorter prescriptions (28 days) in patients with stable, chronic conditions being treated in primary care. Outcomes of interest were: health outcomes, adverse events, medication adherence, medication wastage, professional administration time, pharmacists’ time and/or costs, patient experience, and patient out-of-pocket costs.

Results From a search total of 24 876 records across all databases, 13 studies were eligible for review. Evidence of moderate quality from nine studies suggested that longer prescriptions are associated with increased medication adherence. Evidence from six studies suggested that longer prescriptions may increase medication waste, but results were not always statistically significant and were of very low quality. No eligible studies were identified that measured any of the other outcomes of interest, including health outcomes and adverse events.

Conclusion There is insufficient evidence relating to the overall impact of differing prescription lengths on clinical and health service outcomes, although studies do suggest medication adherence may improve with longer prescriptions. UK recommendations to provide shorter prescriptions are not substantiated by the current evidence base.

INTRODUCTION

Long-term conditions place a substantial burden on health services, particularly in the primary care setting where they are commonly managed.1 For those patients with relatively stable conditions, drug therapy is usually managed using repeat prescriptions, which allow patients to request an additional prescription for a long-term medication without requiring a further consultation with a clinician.

The UK Department of Health advises that the frequency of repeat prescriptions should ‘... balance patient convenience with clinical appropriateness, cost-effectiveness and patient safety.’, but does not specify a recommended time period.2 However, local guidance from many health service commissioners, as well as the UK’s Pharmaceutical Services Negotiating Committee, encourages GPs to issue shorter prescriptions, typically 28 days in length.3–6 This guidance is based on non-systematic review evidence of reductions in medicines waste and consequent cost savings.7,8 One study has reported that shorter prescription lengths may benefit patients by providing better signalling to GPs for treatment discontinuations due to adverse events.9 However, other work does not support the use of shorter prescriptions, with some studies suggesting they may increase health service costs by:

  • increasing GP administrative workload and pharmacy dispensing costs;10,11

  • increasing patient-incurred costs through more frequent trips to the pharmacist;11 and

  • adversely affecting medication adherence and patient satisfaction.12–14

Prescription lengths also vary considerably between, and within, countries. For example, the duration of thyroid prescriptions has been found to range from 28 days in France to 6 months in Australia,15 and prescription durations across all therapeutic areas in the Canadian province of Quebec were approximately half the length of those in the rest of Canada.16

Given the disparity in evidence and practice, a systematic review was undertaken to examine the impact of primary care physicians issuing longer- (2–4 months) versus shorter- (28-day) duration prescriptions in patients with stable chronic conditions. The results of a cost analysis and decision analysis model are reported separately.17,18

METHOD

A systematic review was conducted following a standardised methodology and consistent with PRISMA guidance.19,20 The protocol is published on the PROSPERO database (registration number CRD42015027042). The protocol and choice of outcomes was drawn up in consultation with lay patient representatives.21

How this fits in

Local guidance from many health service commissioners, as well as the UK’s Pharmaceutical Services Negotiating Committee, encourages GPs to issue shorter-duration prescriptions, typically 28 days in length. This guidance is based on non-systematic review evidence, which was not substantiated by this systematic review. Longer prescription lengths for people with stable, chronic conditions could be potentially important to GPs by reducing their workload. It also has the potential to have a positive impact for patients, by improving adherence and thus medication effectiveness, and reducing time, cost, and inconvenience of frequently collecting prescriptions.

Data sources

The authors searched 12 major scientific and grey literature databases for entries dated from inception until 21 October 2015, with no country or language restrictions. Search terms included combinations of the terms ‘prescription’, ‘length’, and ‘duration’, as well as specific time periods. Backward and forward citation searches were conducted. Details of the databases searched and the full search terms are available from the authors.

An updated search of PubMed in July 2017 identified no further articles.

Eligibility criteria

To be eligible, studies had to be randomised controlled trials (RCTs) or observational studies that compared longer-duration prescriptions (2–4 months) with 28-day prescriptions (or those lasting around 1 month) in specific patients. Participants had chronic conditions (such as hypothyroidism, diabetes, cardiovascular disease, and depression) that were relatively stable. Studies were restricted to primary care settings in middle- and high-income countries; those conducted exclusively in secondary or tertiary care settings were excluded. The studies had to report on one or more of the following outcomes:

  • health outcomes;

  • adverse events;

  • medication adherence;

  • medication wastage;

  • professional administration time;

  • pharmacists’ time and/or costs;

  • patient experience; and

  • patient out-of-pocket costs.

Data extraction and synthesis

Two independent reviewers screened titles and abstracts identified by the searches, and screened full papers of potentially relevant studies. A third reviewer resolved disagreements. Relevant studies’ characteristics were independently extracted by two reviewers, with a third reviewer checking and comparing the data extraction. An attempt was made to contact study authors for data missing from the identified papers.

Studies were analysed by outcome and by therapeutic area (for example, lipid-lowering medication or diabetic medication) as most of the included studies reported their results in this way. Studies varied in the nature and detail of the drug classification used; where necessary, medication categories (for example, statins) were grouped into the corresponding therapeutic area (for example, lipid lowering) to improve consistency across studies.

Within each study, effect sizes were calculated as odds ratios (ORs) with 95% confidence intervals (CIs) for dichotomous outcomes, and the mean difference (MD) with 95% CIs for continuous outcomes. Where appropriate, standard deviations (SDs) were imputed based on P-values.19 Forest plots were generated using RevMan version 5.3. Meta-analyses were not conducted due to clinical heterogeneity between studies. The review was not designed to consider differences between therapeutic areas.

Risk of bias and quality of evidence

As only observational studies were identified, the authors assessed risk of bias using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool, although additional sources of bias (for example, sample size) were also considered.22 Risk of bias was assessed by two reviewers independently, with discrepancies resolved through discussion.

The GRADE criteria were used to assess the quality of evidence for each outcome.23

RESULTS

The initial search identified 24 876 records across all databases. After duplicate removal, screening of titles and abstracts, and searching citations, 53 references were considered for full-text evaluation. Thirteen references representing 13 studies met the inclusion criteria; four were only reported in abstract form but were included because they presented clear outcome data.24–27 Study characteristics are available from the authors.

All 13 studies were conducted in the US; they comprised nine retrospective cohorts,24–32 three cross-sectional analyses,33–35 and one retrospective before-and-after study.36 Three provided details of the healthcare setting, which included:

  • a primary care clinic;28

  • patients seen in primary care, mental health clinic, inpatient services, and primary care mental health;30 and

  • an internal medicine practice.33

Other studies did not explicitly report being conducted in primary care although the authors considered them unlikely to have been conducted exclusively in secondary or tertiary care settings (for example, because they included claims data from community pharmacies).

When reported, study populations included patients new to treatment,25–27,30 patients receiving ongoing care,28,29,31 or both.35 Comparisons between prescription lengths were assessed for various therapeutic medication groups, including, most commonly, medications to lower lipids, and those for hypertension, diabetes, and depression.25–30,32–36 Most studies compared a 30-day medication supply with that for a longer period, as follows:

  • a 90-day supply;24–26,29,32,35

  • a 60-day supply;28 or

  • both 31-to-89-day or >90-day supplies.27,31,34

Other studies compared 100-day versus 34-day supplies,36 <90-day supplies versus a 90-day supply,30 and a range of prescription lengths ≤90 days.33

No eligible studies were identified that measured health outcomes or adverse events. Only one retrospective cohort study measured a risk factor for a health outcome: serum cholesterol was lower in the 60-day prescription group compared with its 30-day counterpart at 3 years (mean 4.8 mmol/l [SD 1.2 mmol/l] versus 5.0 mmol/l [SD 1.4 mmol/l] respectively; P = 0.003).28

No eligible studies reported professional administration time, pharmacists’ time and/or costs, patient experience, or out-of-pocket costs other than prescription costs. The most common reported outcomes were medication adherence and wastage.

Medication adherence

Nine studies reported medication adherence, indirectly estimated using pharmacy claims refill data (available from the authors).25,26,28,30–34,36 Commonly used measures of adherence were the:

  • proportion of days covered (PDC) — number of days in a given time period ‘covered’ by prescription claims for a particular drug, divided by the number of days in the time period; or

  • medication possession ratio (MPR) — total number of days supplied for all refills of a particular drug in a given time period, divided by the number of days in the time period.

The review authors elected not to separate these measures in their analyses (although PDC has been found to provide a more conservative estimate of adherence than the MPR).37 PDC and MPR were expressed either as the proportion of patients achieving a particular threshold (generally ≥80%) or the average (mean) value.

Consistent findings were found across all studies. Three cohort studies found that prescription lengths of <90 days were associated with poorer adherence across a range of therapeutic areas (including lipid-lowering therapy, antihypertensives, diabetes medication, and antidepressants) based on adherence using a <80% threshold (OR range: 0.21–0.65, Table 1).25,28,30 A further three cohort studies found similar associations based on mean reduction in adherence (mean decrease range: 0.12–0.30, Table 2).26,31,32

View this table:
Table 1.

Patients with ≥80% medication adherence, by prescription length

View this table:
Table 2.

Mean medication adherence, by prescription length

A controlled before-and-after study36 found that the shortening of antihypertensive, diabetic, and lipid-lowering prescription length from 100 days to 34 days was statistically significantly associated (P<0.01) with a 5.3–13.2% reduction in time periods where PDC was ≥80%, and a mean decrease in PDC of 0.034–0.080. No differences were observed for seizure medication or antipsychotics (data not shown).36

In a further cross-sectional study, prescriptions of >90 days were associated with greater medication adherence (PDC >80%) compared with prescriptions of ≤30 days for drugs affecting the renin-angiotensin system, statins, and oral diabetes medications (relative risk 1.61, P<0.001 for each).34 A second cross-sectional study found each 30-day increment in prescription length (up to a maximum of 90 days) was associated with a 5.7% increase in mean adherence (P<0.0001) to diabetes, antihypertensive, and lipid-lowering medications (data not shown).33

Medication wastage

Medication wastage was reported in six of the included studies (available from the authors).24,26,27,29,32,35 All measures of wastage were indirect, estimated based on pharmacy claims refill data. The majority of these studies defined wastage in a similar manner, such as a: ‘… switch in medication within the same therapeutic class or to the same medication but different strength occurring before the expected refill date.’ 29

One study also included discontinuation within its definition.24

Waste was expressed as:

  • percentage of days’ supply wasted;

  • mean number of days’ supply wasted; or

  • percentage of patients with wasted medication.

Two retrospective cohort studies assessed percentage of days’ supply wasted and found only small differences (≤1.5%) between different prescription lengths.24,27 However, neither study reported raw data or statistical comparisons, and additional information could not be obtained from the authors.

Three studies evaluated the percentage of patients who wasted medication.27,32,35 ORs could be calculated for one retrospective cohort and one cross-sectional study.32,35 In general, there was a non-statistically significant trend for longer prescriptions (90 days versus 30 days) to be associated with higher proportions of patients with wasted medication; this was statistically significant for lipid-lowering drugs in the study by Taitel et al only (OR 0.84, 95% CI = 0.72 to 0.98, Table 3).32 A third cohort study reported varying patterns across therapeutic areas, but with no statistical analysis and insufficient data to calculate effect sizes.27

View this table:
Table 3.

Patients with wasted medication, by prescription length

Four studies reported the mean number of days’ supply wasted over 1 year.26,29,32,35 Effect sizes could not be calculated for one study in which it was unclear whether days wasted was standardised between the two prescription groups.35 The remaining studies found evidence that shorter (30-day versus 90-day) prescriptions were statistically significantly associated with a mean reduction in waste days. Across a range of therapeutic areas, Taitel et al reported a reduction of 3.51–6.92 days over a 1-year study period (Table 4),32 and Murphy et al found a reduction of 0.03–0.13 days over a 30-day period (Table 5);29 Jiang et al found a mean reduction of 0.10 days averaged for all therapeutic areas (Table 5).26

View this table:
Table 4.

Mean days with wasted medication over the study period, by prescription length

View this table:
Table 5.

Mean days with wasted medication per 30 days (rate data), by prescription length

Risk of bias and quality of evidence

Lack of methodological detail prevented assessment of risk of bias for the four studies presented as abstracts.24–27 One study was classified as having a serious risk of bias due to its small sample size31 and another was similarly classified because a cut-off point of 84 days was used, with no justification provided for the decision.32 The remaining seven studies were considered to have a moderate risk of bias (further details available from the authors).28–30,33–36 In nine studies, the authors did not explicitly report taking measures to control for selection bias.11,28–35

In terms of GRADE assessment, the evidence was determined to be of very low quality for all outcomes except adherence; the evidence relating to adherence was considered to be of moderate quality.

DISCUSSION

Summary

This is the first systematic review of evidence comparing the impact of shorter and longer prescriptions on clinical and health service outcomes. The authors found some evidence from six studies that longer prescriptions are associated with increased medication waste, but the results were not always statistically significant and are of very low quality. Evidence of moderate quality was found that suggested that longer prescriptions are associated with better adherence.

If medication adherence is positively correlated with health outcomes, as seems to be suggested by the wider literature,38,39 there may be benefits to increasing the length of repeat prescriptions for patients with chronic conditions. However, the authors found no direct evidence assessing the association between different prescription lengths and health outcomes (including adverse events). Furthermore, although it is important to minimise medication waste, this needs to be balanced against the needs of patients and clinicians’ workloads; however, the authors found no direct evidence comparing different prescription lengths with differences in health professionals’ administrative time, pharmacists’ time, or patient experience.

Strengths and limitations

Although the authors followed rigorous methodology, there are limitations to this systematic review. It is possible that some of the studies are not truly representative of primary care, although the findings are generally consistent, regardless of setting. Moreover, all of the eligible studies were conducted in the US and their applicability to UK settings could be limited given differences in healthcare systems.

The authors may also have missed evidence where prescription lengths were considerably different from the inclusion criteria for this review. Some of the studies differentiated patients receiving new, versus existing, prescriptions, but the authors did not consider this in the protocol and insufficient studies reported this information to allow a post-hoc subgroup analysis.

Finally, it was not possible to make comparisons of effect sizes between different therapeutic areas. One of the authors recently conducted an analysis within routine UK primary care health records, not included in this systematic review, which addresses some of these concerns.17

A key issue with all of the studies was their use of indirect, proxy measures for both adherence and waste, based on administrative prescription refill data. The two key adherence measures used were PDC and MPR, which may introduce bias in favour of longer prescriptions, as well as underestimating true adherence.40,41 Similar concerns can be raised about the estimation of waste. Nevertheless, a review of such approaches has determined that indirect measures still have value.42

None of the studies explored why adherence may differ between prescription lengths. Reasons for medication non-adherence are often complex, and can be both intentional and unintentional.43 Longer prescription lengths may overcome barriers to unintentional adherence, including enabling patients to follow a regular medicine regimen or reducing logistical barriers, such as visits to the pharmacy.28,31,33 However, given the observational nature of the studies, there is a risk of systematic differences, with longer prescriptions issued to those patients considered to be more adherent by the prescriber, those thought to have greater stability of their illness,30 or those of non-white ethnicity.44

The authors identified only one study that showed a beneficial association between longer prescriptions and improved clinical outcome.28 There was a lack of research examining the association between prescription duration and other outcomes, although some non-comparative evidence exists for shorter prescriptions being considered inconvenient and disempowering, and causing patient dissatisfaction and anxiety.13,14,45

Implications for research and practice

This review has found that medication adherence may be associated with longer-duration prescriptions, which, in theory, may translate into clinical benefit. The evidence that such prescriptions also lead to increased waste is weak. Current UK policy recommending the provision of shorter prescriptions is not substantiated by the current evidence base, but further research is required to evaluate the clinical, health service, and economic impact of differing prescription lengths.

Acknowledgments

The authors thank the following people who made invaluable suggestions: Anthony Avery, Molly Morgan Jones, and Adam Martin.

Notes

Funding

This research was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) contract number: 14/159/07. All work was conducted and analysed independently of the funder. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

Ethical approval

Ethical approval was not required for this research.

Provenance

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

Discuss this article

Contribute and read comments about this article: bjgp.org/letters

  • Received October 3, 2017.
  • Revision requested November 17, 2017.
  • Accepted December 12, 2017.

REFERENCES

  1. 1.↵
    1. Baird B,
    2. Charles A,
    3. Honeyman M,
    4. et al.
    (2016) Understanding pressures in general practice. (King’s Fund, London) https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/Understanding-GP-pressures-Kings-Fund-May-2016.pdf (accessed 21 Feb 2018).
  2. 2.↵
  3. 3.↵
    1. Pharmaceutical Services Negotiating Committee.
    (2007) Medicines wastage and 28-day prescribing guidance. (Pharmaceutical Services Negotiating Committee, London).
  4. 4.
    1. NHS Cambridgeshire and Peterborough Clinical Commissioning Group.
    (2009) Repeat medication for 28 days. (Cambridgeshire Primary Care Trust, Cambridge).
  5. 5.
    1. North East Essex Clinical Commissioning Group
    (2011) 28 day prescribing policy. (North East Essex Clinical Commissioning Group, Colchester).
  6. 6.↵
    1. NHS Dorset Clinical Commissioning Group
    (2013) Medicines code chapter 15: policy for repeat prescribing and medication review. (Dorset Clinical Commissioning Group).
  7. 7.↵
    1. Hawksworth GM,
    2. Wright DJ,
    3. Chrystyn H
    (1996) A day to day analysis of the unwanted medicinal products returned to community pharmacies for disposal. J Social Administrative Pharmacy 13(4):215–222.
    OpenUrl
  8. 8.↵
    1. Pharmaceutical Services Negotiating Committee.
    (2013) PSNC briefing 086/13: medicines wastage and prescription duration. (PSNC, London).
  9. 9.↵
    1. Sun AP,
    2. Kirby B,
    3. Black C,
    4. et al.
    (2014) Unplanned medication discontinuation as a potential pharmacovigilance signal: a nested young person cohort study. BMC Pharmacol Toxocol 15(1):11.
    OpenUrl
  10. 10.↵
    1. White KG
    (2010) UK interventions to control medicines wastage: a critical review. Int J Pharm Pract 18(3):131–140.
    OpenUrlPubMed
  11. 11.↵
    1. Domino ME,
    2. Olinick J,
    3. Sleath B,
    4. et al.
    (2004) Restricting patients’ medication supply to one month: saving or wasting money? Am J Health Syst Pharm 61(13):1375–1379.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Wong MC,
    2. Tam WW,
    3. Wang HH,
    4. et al.
    (2015) Duration of initial antihypertensive prescription and medication adherence: a cohort study among 203 259 newly diagnosed hypertensive patients. Int J Cardiol 182:503–508.
    OpenUrl
  13. 13.↵
    1. Mitchell AL,
    2. Hickey B,
    3. Hickey JL,
    4. Pearce SH
    (2009) Trends in thyroid hormone prescribing and consumption in the UK. BMC Public Health 9:132.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Wilson PM,
    2. Kataria N,
    3. McNeilly E
    (2013) Patient and carer experience of obtaining regular prescribed medication for chronic disease in the English National Health Service: a qualitative study. BMC Health Serv Res 13(1):192.
    OpenUrlPubMed
  15. 15.↵
    1. British Thyroid Foundation
    (2009) Prescription lengths: prescribing trends around the world, http://www.btf-thyroid.org/projects/prescription-lengths/227-prescribing-trends-around-the-world (accessed 21 Feb 2018).
  16. 16.↵
    1. Smolina K,
    2. Morgan S
    (2014) The drivers of overspending on prescription drugs in Quebec. Healthc Policy 10(2):19–26.
    OpenUrl
  17. 17.↵
    1. Doble B,
    2. Payne R,
    3. Harshfield A,
    4. Wilson ECF
    (2017) Retrospective, multicohort analysis of the Clinical Practice Research Datalink (CPRD) to determine differences in the cost of medication wastage, dispensing fees and prescriber time of issuing either short (<60 days) or long (≥60 days) prescription lengths in primary care for common, chronic conditions in the UK. BMJ Open 7(12):e019382.
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Martin A,
    2. Payne R,
    3. Wilson ECF
    (2018) Long term costs and health consequences of issuing shorter duration prescriptions for patients with chronic health conditions in the English NHS. Appl Health Econ Health Policy, in press.
  19. 19.↵
    1. Higgins JPT,
    2. Green S
    , eds (2017) Cochrane handbook for systematic reviews of interventions, version 510, http://handbook.cochrane.org (accessed 21 Feb 2018).
  20. 20.↵
    1. Liberati A,
    2. Altman D,
    3. Tetzlaff J,
    4. et al.
    (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med 151(4):W65–W94.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. University of Cambridge, Primary Care Unit.
    (2018) Patient and public involvement (PPI). (University of Cambridge), http://www.phpc.cam.ac.uk/pcu/research/ppi/ (accessed 3 Feb 2018).
  22. 22.↵
    1. Sterne JAC,
    2. Hernán MA,
    3. Reeves B,
    4. et al.
    (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919.
    OpenUrlFREE Full Text
  23. 23.↵
    1. Balshem H,
    2. Helfand M,
    3. Schünemann HJ,
    4. et al.
    (2011) GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 64(4):401–406.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Faris RJ,
    2. Filipek TM,
    3. Tang J,
    4. et al.
    (2010) A retrospective comparative analysis of medication waste from day supply plan design in specialty pharmacy. J Manag Care Pharm 16(7):516.
    OpenUrl
  25. 25.↵
    1. Hermes M,
    2. Gleason PP,
    3. Starner CI
    (2010) Adherence to chronic medication therapy associated with 90-day supplies compared with 30-day supplies. J Manag Care Pharm 16(2):141–142.
    OpenUrl
  26. 26.↵
    1. Jiang JZ,
    2. Khandelwal NG,
    3. Lee KY
    (2007) Comparing medication adherence and wastage among three different retail programs. Value Health 10(3):A29.
    OpenUrl
  27. 27.↵
    1. Ryvkin M,
    2. Garavaglia S
    (2009) Wasted medication: how big is the problem? Value Health 12(3):A82.
    OpenUrl
  28. 28.↵
    1. Batal HA,
    2. Krantz MJ,
    3. Dale RA,
    4. et al.
    (2007) Impact of prescription size on statin adherence and cholesterol levels. BMC Health Serv Res 7:175.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Murphy P,
    2. Kahndelwal N,
    3. Duncan I
    (2012) Comparing medication wastage by fill quantity and fulfillment channel. Am J Pharm Benefits 4(5):e166–e171.
    OpenUrl
  30. 30.↵
    1. Pfeiffer PN,
    2. Szymanski BR,
    3. Valenstein M,
    4. et al.
    (2012) Trends in antidepressant prescribing for new episodes of depression and implications for health system quality measures. Med Care 50(1):86–90.
    OpenUrlPubMed
  31. 31.↵
    1. Steiner JF,
    2. Robbins LJ,
    3. Roth SC,
    4. Hammond WS
    (1993) The effect of prescription size on acquisition of maintenance medications. J Gen Intern Med 8(3):306–310.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Taitel M,
    2. Fensterheim L,
    3. Kirkham H,
    4. et al.
    (2012) Medication days’ supply, adherence, wastage, and cost among chronic patients in Medicaid. Medicare Medicaid Res Rev 2(3):E1–E13.
    OpenUrl
  33. 33.↵
    1. Schectman JM,
    2. Bovbjerg VE,
    3. Voss JD
    (2002) Predictors of medication-refill adherence in an indigent rural population. Med Care 40(12):1294–1300.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Schmittdiel JA,
    2. Nichols GA,
    3. Dyer W,
    4. et al.
    (2015) Health care system-level factors associated with performance on Medicare STAR adherence metrics in a large, integrated delivery system. Med Care 53(4):332–337.
    OpenUrl
  35. 35.↵
    1. Walton SM,
    2. Arondekar BV,
    3. Johnson NE,
    4. Schumock GT
    (2001) A model for comparing unnecessary costs associated with various prescription fill-quantity policies: illustration using VA data. J Managed Care Pharm 7(5):386–390.
    OpenUrl
  36. 36.↵
    1. Domino ME,
    2. Martin BC,
    3. Wiley-Exley E,
    4. et al.
    (2011) Increasing time costs and copayments for prescription drugs: an analysis of policy changes in a complex environment. Health Serv Res 46(3):900–919.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Martin BC,
    2. Wiley-Exley EK,
    3. Richards S,
    4. et al.
    (2009) Contrasting measures of adherence with simple drug use, medication switching, and therapeutic duplication. Ann Pharmacother 43(1):36–44.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Dragomir A,
    2. Côté R,
    3. White M,
    4. et al.
    (2010) Relationship between adherence level to statins, clinical issues and health-care costs in real-life clinical setting. Value Health 13(1):87–94.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Perreault S,
    2. Ellia L,
    3. Dragomir A,
    4. et al.
    (2009) Effect of statin adherence on cerebrovascular disease in primary prevention. Am J Med 122(7):647–655.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Christensen DB,
    2. Williams B,
    3. Goldberg HI,
    4. et al.
    (1997) Assessing compliance to antihypertensive medications using computer-based pharmacy records. Med Care 35(11):1164–1170.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Lam WY,
    2. Fresco P
    (2015) Medication adherence measures: an overview. Biomed Res Int 2015:217047.
    OpenUrl
  42. 42.↵
    1. Steiner JF,
    2. Prochazka AV
    (1997) The assessment of refill compliance using pharmacy records: methods, validity, and applications. J Clin Epidemiol 50(1):105–116.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Payne R
    (2014) Understanding can lead to a solution for non-adherence. Prescriber 25(22):27–28.
    OpenUrl
  44. 44.↵
    1. Rabbani A,
    2. Alexander GC
    (2009) Cost savings associated with filling a 3-month supply of prescription medicines. Appl Health Econ Health Policy 7(4):255–264.
    OpenUrlPubMed
  45. 45.↵
    1. Addison’s Disease Self Help Group
    (2009) Letter to Professor Gilmore on review of prescription charges for those with long-term conditions, https://www.addisons.org.uk/files/file/124-2009-review-of-prescription-charges/ (accessed 1 Mar 2018).
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British Journal of General Practice: 68 (669)
British Journal of General Practice
Vol. 68, Issue 669
April 2018
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Impact of issuing longer- versus shorter-duration prescriptions: a systematic review
Sarah King, Céline Miani, Josephine Exley, Jody Larkin, Anne Kirtley, Rupert A Payne
British Journal of General Practice 2018; 68 (669): e286-e292. DOI: 10.3399/bjgp18X695501
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