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Meta-Analysis
. 2016 Nov 22;11(11):CD011227.
doi: 10.1002/14651858.CD011227.pub2.

Non-medical prescribing versus medical prescribing for acute and chronic disease management in primary and secondary care

Greg Weeks  1 , Johnson George, Katie Maclure, Derek Stewart
Affiliations
Meta-Analysis

Non-medical prescribing versus medical prescribing for acute and chronic disease management in primary and secondary care

Greg Weeks et al. Cochrane Database Syst Rev. .

Abstract

Background: A range of health workforce strategies are needed to address health service demands in low-, middle- and high-income countries. Non-medical prescribing involves nurses, pharmacists, allied health professionals, and physician assistants substituting for doctors in a prescribing role, and this is one approach to improve access to medicines.

Objectives: To assess clinical, patient-reported, and resource use outcomes of non-medical prescribing for managing acute and chronic health conditions in primary and secondary care settings compared with medical prescribing (usual care).

Search methods: We searched databases including CENTRAL, MEDLINE, Embase, and five other databases on 19 July 2016. We also searched the grey literature and handsearched bibliographies of relevant papers and publications.

Selection criteria: Randomised controlled trials (RCTs), cluster-RCTs, controlled before-and-after (CBA) studies (with at least two intervention and two control sites) and interrupted time series analysis (with at least three observations before and after the intervention) comparing: 1. non-medical prescribing versus medical prescribing in acute care; 2. non-medical prescribing versus medical prescribing in chronic care; 3. non-medical prescribing versus medical prescribing in secondary care; 4 non-medical prescribing versus medical prescribing in primary care; 5. comparisons between different non-medical prescriber groups; and 6. non-medical healthcare providers with formal prescribing training versus those without formal prescribing training.

Data collection and analysis: We used standard methodological procedures expected by Cochrane. Two review authors independently reviewed studies for inclusion, extracted data, and assessed study quality with discrepancies resolved by discussion. Two review authors independently assessed risk of bias for the included studies according to EPOC criteria. We undertook meta-analyses using the fixed-effect model where studies were examining the same treatment effect and to account for small sample sizes. We compared outcomes to a random-effects model where clinical or statistical heterogeneity existed.

Main results: We included 46 studies (37,337 participants); non-medical prescribing was undertaken by nurses in 26 studies and pharmacists in 20 studies. In 45 studies non-medical prescribing as a component of care was compared with usual care medical prescribing. A further study compared nurse prescribing supported by guidelines with usual nurse prescribing care. No studies were found with non-medical prescribing being undertaken by other health professionals. The education requirement for non-medical prescribing varied with country and location.A meta-analysis of surrogate markers of chronic disease (systolic blood pressure, glycated haemoglobin, and low-density lipoprotein) showed positive intervention group effects. There was a moderate-certainty of evidence for studies of blood pressure at 12 months (mean difference (MD) -5.31 mmHg, 95% confidence interval (CI) -6.46 to -4.16; 12 studies, 4229 participants) and low-density lipoprotein (MD -0.21, 95% CI -0.29 to -0.14; 7 studies, 1469 participants); we downgraded the certainty of evidence from high due to considerations of serious inconsistency (considerable heterogeneity), multifaceted interventions, and variable prescribing autonomy. A high-certainty of evidence existed for comparative studies of glycated haemoglobin management at 12 months (MD -0.62, 95% CI -0.85 to -0.38; 6 studies, 775 participants). While there appeared little difference in medication adherence across studies, a meta-analysis of continuous outcome data from four studies showed an effect favouring patient adherence in the non-medical prescribing group (MD 0.15, 95% CI 0.00 to 0.30; 4 studies, 700 participants). We downgraded the certainty of evidence for adherence to moderate due to the serious risk of performance bias. While little difference was seen in patient-related adverse events between treatment groups, we downgraded the certainty of evidence to low due to indirectness, as the range of adverse events may not be related to the intervention and selective reporting failed to adequately report adverse events in many studies.Patients were generally satisfied with non-medical prescriber care (14 studies, 7514 participants). We downgraded the certainty of evidence from high to moderate due to indirectness, in that satisfaction with the prescribing component of care was only addressed in one study, and there was variability of satisfaction measures with little use of validated tools. A meta-analysis of health-related quality of life scores (SF-12 and SF-36) found a difference favouring usual care for the physical component score (MD 1.17, 95% CI 0.16 to 2.17), but not the mental component score (MD 0.58, 95% CI -0.40 to 1.55). However, the quality of life measurement may more appropriately reflect composite care rather than the prescribing component of care, and for this reason we downgraded the certainty of evidence to moderate due to indirectness of the measure of effect. A wide variety of resource use measures were reported across studies with little difference between groups for hospitalisations, emergency department visits, and outpatient visits. In the majority of studies reporting medication use, non-medical prescribers prescribed more drugs, intensified drug doses, and used a greater variety of drugs compared to usual care medical prescribers.The risk of bias across studies was generally low for selection bias (random sequence generation), detection bias (blinding of outcome assessment), attrition bias (incomplete outcome data), and reporting bias (selective reporting). There was an unclear risk of selection bias (allocation concealment) and for other biases. A high risk of performance bias (blinding of participants and personnel) existed.

Authors' conclusions: The findings suggest that non-medical prescribers, practising with varying but high levels of prescribing autonomy, in a range of settings, were as effective as usual care medical prescribers. Non-medical prescribers can deliver comparable outcomes for systolic blood pressure, glycated haemoglobin, low-density lipoprotein, medication adherence, patient satisfaction, and health-related quality of life. It was difficult to determine the impact of non-medical prescribing compared to medical prescribing for adverse events and resource use outcomes due to the inconsistency and variability in reporting across studies. Future efforts should be directed towards more rigorous studies that can clearly identify the clinical, patient-reported, resource use, and economic outcomes of non-medical prescribing, in both high-income and low-income countries.

PubMed Disclaimer

Conflict of interest statement

The authors are researchers in the area of non‐medical prescribing. While their studies may be referenced in the review Background, it is unlikely they will meet inclusion criteria for studies to be included in the review.

GW: none known.

JG: Dr George is a chief investigator on investigator‐initiated research grants or grant applications supported by Pfizer Australia, Boehringer‐Ingelheim, and Australian Lung Foundation. These organisations had no involvement in the design of those studies, analysis of data, or publications resulting from those studies.

KM: none known.

DS: none known.

Figures

1
1
Study flow diagram.
2
2
'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.
3
3
Forest plot of comparison: 1 Non‐medical prescribing group versus usual care, Outcome: 1.2 Systolic blood pressure mmHg.
4
4
Forest plot of comparison: 1 Non‐medical prescribing group versus usual care, Outcome: 1.1 HbA1c (%).
5
5
Forest plot of comparison: 1 Non‐medical prescribing group versus usual care, Outcome: 1.3 Low‐density lipoprotein (LDL) mmol/L.
1.1
1.1. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 1 Systolic blood pressure mmHg.
1.2
1.2. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 2 HbA1c (%).
1.3
1.3. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 3 Low‐density lipoprotein (LDL) mmol/L.
1.4
1.4. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 4 Low‐density lipoprotein pharmacist vs nurse 6 mths.
1.5
1.5. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 5 Adherence (continuous).
1.6
1.6. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 6 Adherence (dichotomous).
1.7
1.7. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 7 Health‐related quality of life.
1.8
1.8. Analysis
Comparison 1 Non‐medical prescribing group versus usual care, Outcome 8 Health facility resource use.
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Ginson 2000 {published data only}
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McGhan 1983 {published data only}
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Meulepas 2008 {published data only}
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Monyatsi 2012 {published data only}
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Murphy 2010 {published data only}
    1. Murphy G, Daly M, Ryan J, Shanahan F, Harney S, Michael M. Comparison of physician‐and nurse prescriber‐led dose adjustment of DMARD therapy in RA. American College of Rheumatology/Association of Rheumatology Health Professionals Annual Scientific Meeting ACR/ARHP 10; 2009 Oct 16‐21; Philadelphia (PA) United States. 2010:62.
Neto 2011 {published data only}
    1. Neto PR, Marusic S, Lyra Junior DP, Pilger D, Cruciol‐Souza JM, et al. Effect of a 36‐month pharmaceutical care program on the coronary heart disease risk in elderly diabetic and hypertensive patients. Journal of Pharmacy & Pharmaceutical Sciences 2011;14:249‐63. - PubMed
Norman 2010 {published data only}
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O'Hare 2004 {published data only}
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Obreli‐Neto 2011 {published data only}
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Omran 2013 {published data only}
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Omran 2015 {published data only}
    1. Omran D, Majumdar SR, Johnson JA, Tsuyuki RT, Lewanczuk RZ, Guirguis LM, et al. Pharmacists on primary care team: effect on antihypertensive medication management in patients with type 2 diabetes. Journal of the American Pharmacists Association 2015;55:265‐8. - PubMed
Pape 2011 {published data only}
    1. Pape GA, Hunt JS, Butler KL, Siemienczuk J, LeBlanc BH, Gillanders W, et al. Team‐based care approach to cholesterol management in diabetes mellitus: 2‐Year cluster randomized controlled trial. Archives of Internal Medicine 2011;171:1480‐6. - PubMed
Payton 2011 {published data only}
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Reid 2005 {published data only}
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Sanne 2010 {published data only}
    1. Sanne I, Orrell C, Fox MP, Conradie F, Ive P, Zeinecker J, et al. Nurse versus doctor management of HIV‐infected patients receiving antiretroviral therapy (CIPRA‐SA): a randomised non‐inferiority trial. Lancet 2010;376:33‐40. - PMC - PubMed
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Seng 2011 {published data only}
    1. Seng Tan P, Thomas PT, Chua SS. Clinical outcomes of pharmacist‐managed diabetes clinic in Malaysia. Seventy‐first Scientific Sessions of the American Diabetes Association; 2011 June 24‐28; San Diego (CA), United States. 2011:60.
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Sonnex 2014 {published data only}
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Stafford 2011 {published data only}
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Stone 2010 {published data only}
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Stromberg 2003 {published data only}
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Wilson 2003 {published data only}
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References to studies awaiting assessment

Barton 2013 {published data only}
    1. Barton GR, Fairall L, Bachmann MO, Uebel K, Timmerman V, Lombard C, et al. Cost‐effectiveness of nurse‐led versus doctor‐led antiretroviral treatment in South Africa: pragmatic cluster randomised trial. Tropical Medicine and International Health 2013;18(6):769‐77. - PubMed
Neilson 2015 {published data only}
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Tsuyuki 2014 {published data only}
    1. Tsuyuki RT, Rosenthal MM, Pearson GJ. Improving dyslipidemia management in the community: A randomized trial of pharmacist prescribing, The RxACT study. Canadian Journal of Cardiology 2014;30(10):S118‐9. - PMC - PubMed

References to ongoing studies

Mikuls 2015 {published data only}
    1. Mikuls TR, Cheetham TC, Rashid N, Levy GD, Kerimian A, Low KJ, et al. A pragmatic cluster‐randomized controlled trial of an automated, pharmacy‐based intervention to optimize allopurinol therapy in gout. Arthritis and Rheumatology 2015;67:Suppl 10.

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