Aim:In this paper, we aim to provide an updated source of information for nonmedical prescribing (NMP) in New Zealand (NZ). Methods: A variety of NZ sources were used to collect data: legislation, policy documents and information from professional and regulatory organizations, and education providers. Results: In NZ, the legal categories for prescribers include authorized, designated, and delegated prescribers. Authorized prescribers include dentists, midwives, nurse practitioners, and optometrist prescribers. Designated prescribers include pharmacist prescribers, registered nurse prescribers, and dietitian prescribers. There are no delegated prescribers in NZ at this time. There is variation in the regulation, educational programmes and prescribing competencies used by the different prescribing health professionals involved in NMP in NZ. Conclusion: This update collates relevant information relating to NMP in NZ into one consolidated document and provides policy makers with a current overview of prescribing rights, service delivery models, training requirements, and prescribing competencies used for NMP in NZ. As NMP in NZ continues to expand and evolve, this paper will form a baseline for future NMP research in NZ. NZ needs to develop overarching NMP policy to enable consistency in the various aspects of NMP, thereby delivering a safe and sustainable NMP service in NZ.
Background and Objectives Valuing children’s health states for use in economic evaluations is globally relevant and is of particular relevance in jurisdictions where a cost-utility analysis is the preferred form of analysis for decision making. Despite this, the challenges with valuing child health mean that there are many remaining questions for debate about the approach to elicitation of values. The aim of this paper was to identify and describe the methods used to value children’s health states and the specific issues that arise in the use of these methods. Methods We conducted a systematic search of electronic databases to identify studies published in English since 1990 that used preference elicitation methods to value child and adolescent (under 18 years of age) health states. Eligibility criteria comprised valuation studies concerning both child-specific patient-reported outcome measures and child health states defined in other ways, and methodological studies of valuation approaches that may or may not have yielded a value set algorithm. Results A total of 77 eligible studies were identified from which data on country setting, aims, condition (general population or clinically specific), sample size, age of respondents, the perspective that participants were asked to adopt, source of values (respondents who completed the preference elicitation tasks) and methods questions asked were extracted. Extracted data were classified and evaluated using narrative synthesis methods. The studies were classified into three groups: (1) studies comparing elicitation methods ( n = 30); (2) studies comparing perspectives ( n = 23); and (3) studies where no comparisons were presented ( n = 26); selected studies could fall into more than one group. Overall, the studies varied considerably both in methods used and in reporting. The preference elicitation tasks included time trade-off, standard gamble, visual analogue scaling, rating/ranking, discrete choice experiments, best-worst scaling and willingness to pay elicited through a contingent valuation. Perspectives included adults’ considering the health states from their own perspective, adults taking the perspective of a child (own, other, hypothetical) and a child/adolescent taking their own or the perspective of another child. There was some evidence that children gave lower values for comparable health states than did adults that adopted their own perspective or adult/parents that adopted the perspective of children. Conclusions Differences in reporting limited the conclusions that can be formed about which methods are most suitable for eliciting preferences for children’s health and the influence of differing perspectives and values. Difficulties encountered in drawing conclusions from the data (such as lack of consensus and poor reporting making it difficult for users to choose and inter...
Background Population growth and general practitioner workforce constraints are creating increasing demand for health services in New Zealand (NZ) and internationally. Non-medical prescribing (NMP) is one strategy that has been introduced to help manage this. Little is known about the NMP practice trends in NZ. The aim of this study was to provide a current overview of the scale, scope, and trends of NMP practice in NZ. Methods All claims for community dispensed medicines prescribed by a non-medical prescriber were extracted from the NZ Pharmaceutical Collection for the period 2016–2020. Patient demographics were retrieved from the Primary Health Organisation enrolment collection. These national databases contain prescription information for all subsidised community pharmacy medicines dispensed and healthcare enrolment data for 96% of New Zealanders. Results The proportion of prescriptions written by all NMP providers and patients receiving NMP prescriptions increased each year from 1.8% (2016) to 3.6% (2019) and 8.4% (2016) to 14.4% (2019) respectively. From 2016 to 2019, the proportion of NMP patients who had at least one NMP prescription increased from 26% to 39% for nurse prescribers, from 1% to 9% for pharmacist prescribers, from 2% to 3% for dietitian prescribers, and decreased from 47% to 22% for dentists, and from 20% to 12% for midwives. The most commonly prescribed medicines were antibiotics (amoxicillin, amoxicillin with clavulanic acid, and metronidazole), and analgesics (paracetamol, and codeine phosphate). While some NMP providers were prescribing for patients with greater health needs, all NMP providers could be better utilised to reach more of these patients. Conclusions This study highlights that although the NMP service has been implemented in NZ, it has yet to become mainstream healthcare practice. This work provides a baseline to evaluate the NMP service moving forward and enable policy development. Improved implementation and integration of primary care NMP services can ensure continued access to prescribing services and medicines for our communities.
Background Childhood multi-attribute utility instruments (MAUIs) can be used to measure health utilities in children (aged ≤ 18 years) for economic evaluation. Systematic review methods can generate a psychometric evidence base that informs their selection for application. Previous reviews focused on limited sets of MAUIs and psychometric properties, and only on evidence from studies that directly aimed to conduct psychometric assessments. Objective This study aimed to conduct a systematic review of psychometric evidence for generic childhood MAUIs and to meet three objectives: (1) create a comprehensive catalogue of evaluated psychometric evidence; (2) identify psychometric evidence gaps; and (3) summarise the psychometric assessment methods and performance by property. Methods A review protocol was registered with the Prospective Register of Systematic Reviews (PROSPERO; CRD42021295959); reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. The searches covered seven academic databases, and included studies that provided psychometric evidence for one or more of the following generic childhood MAUIs designed to be accompanied by a preference-based value set (any language version): 16D, 17D, AHUM, AQoL-6D, CH-6D, CHSCS-PS, CHU9D, EQ-5D-Y-3L, EQ-5D-Y-5L, HUI2, HUI3, IQI, QWB, and TANDI; used data derived from general and/or clinical childhood populations and from children and/or proxy respondents; and were published in English. The review included ‘direct studies’ that aimed to assess psychometric properties and ‘indirect studies’ that generated psychometric evidence without this explicit aim. Eighteen properties were evaluated using a four-part criteria rating developed from established standards in the literature. Data syntheses identified psychometric evidence gaps and summarised the psychometric assessment methods/results by property. Results Overall, 372 studies were included, generating a catalogue of 2153 criteria rating outputs across 14 instruments covering all properties except predictive validity. The number of outputs varied markedly by instrument and property, ranging from 1 for IQI to 623 for HUI3, and from zero for predictive validity to 500 for known-group validity. The more recently developed instruments targeting preschool children (CHSCS-PS, IQI, TANDI) have greater evidence gaps (lack of any evidence) than longer established instruments such as EQ-5D-Y, HUI2/3, and CHU9D. The gaps were prominent for reliability (test–retest, inter-proxy-rater, inter-modal, internal consistency) and proxy-child agreement. The inclusion of indirect studies ( n = 209 studies; n = 900 outputs) increased the number of properties with at least one output of acceptable performance. Common methodological issues in psychometric assessment were identified, e.g., lack of reference measures to help interpre...
Background The Paediatric Quality of life Inventory (PedsQLTM) Generic Core Scales and the Child Health Utilities 9 Dimensions (CHU9D) are two paediatric health-related quality of life (HRQoL) measures commonly used in overweight and obesity research. However, no studies have comprehensively established the psychometric properties of these instruments in the context of paediatric overweight and obesity. The aim of this study was to assess the reliability, acceptability, validity and responsiveness of the PedsQL and the CHU9D in the measurement of HRQoL among children and adolescents living with overweight and obesity. Subjects/Methods Subjects were 6544 child participants of the Longitudinal Study of Australian Children, with up to 3 repeated measures of PedsQL and CHU9D and aged between 10 and 17 years. Weight and height were measured objectively by trained operators, and weight status determined using World Health Organisation growth standards. We examined reliability, acceptability, known group and convergent validity and responsiveness, using recognised methods. Results Both PedsQL and CHU9D demonstrated good internal consistency reliability, and high acceptability. Neither instrument showed strong convergent validity, but PedsQL appears to be superior to the CHU9D in known groups validity and responsiveness. Compared with healthy weight, mean (95%CI) differences in PedsQL scores for children with obesity were: boys −5.6 (−6.2, −4.4); girls −6.7 (−8.1, −5.4) and differences in CHU9D utility were: boys −0.02 (−0.034, −0.006); girls −0.035 (−0.054, −0.015). Differences in scores for overweight compared with healthy weight were: PedsQL boys −2.2 (−3.0, −1.4) and girls −1.3 (−2.0, −0.6) and CHU9D boys: no significant difference; girls −0.014 (−0.026, −0.003). Conclusion PedsQL and CHU9D overall demonstrated good psychometric properties, supporting their use in measuring HRQoL in paediatric overweight and obesity. CHU9D had poorer responsiveness and did not discriminate between overweight and healthy weight in boys, which may limit its use in economic evaluation.
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