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BackgroundObesity is an important risk factor for many chronic diseases. Mobile health interventions such as smartphone apps can potentially provide a convenient low-cost addition to other obesity reduction strategies. ObjectiveThis study aimed to estimate the impacts on quality-adjusted life-years (QALYs) gained and health system costs over the remainder of the life span of the New Zealand population (N=4.4 million) for a smartphone app promotion intervention in 1 calendar year (2011) using currently available apps for weight loss. MethodsThe intervention was a national mass media promotion of selected smartphone apps for weight loss compared with no dedicated promotion. A multistate life table model including 14 body mass index–related diseases was used to estimate QALYs gained and health systems costs. A lifetime horizon, 3% discount rate, and health system perspective were used. The proportion of the target population receiving the intervention (1.36%) was calculated using the best evidence for the proportion who have access to smartphones, are likely to see the mass media campaign promoting the app, are likely to download a weight loss app, and are likely to continue using this app. ResultsIn the base-case model, the smartphone app promotion intervention generated 29 QALYs (95% uncertainty interval, UI: 14-52) and cost the health system US $1.6 million (95% UI: 1.1-2.0 million) with the standard download rate. Under plausible assumptions, QALYs increased to 59 (95% UI: 27-107) and costs decreased to US $1.2 million (95% UI: 0.5-1.8) when standard download rates were doubled. Costs per QALY gained were US $53,600 for the standard download rate and US $20,100 when download rates were doubled. On the basis of a threshold of US $30,000 per QALY, this intervention was cost-effective for Māori when the standard download rates were increased by 50% and also for the total population when download rates were doubled. ConclusionsIn this modeling study, the mass media promotion of a smartphone app for weight loss produced relatively small health gains on a population level and was of borderline cost-effectiveness for the total population. Nevertheless, the scope for this type of intervention may expand with increasing smartphone use, more easy-to-use and effective apps becoming available, and with recommendations to use such apps being integrated into dietary counseling by health workers.

The work proposes a mathematical model of the process of COVID-19 epidemic as it evolved in New Zealand. The model uses a system of differential equations which emanate from natural assumptions on some probability measure and evolution of this measure on evolving family of simplexes. The authors tried to create the model which, at one hand, is simple and easy to follow. and, at the other hand, reflects the observed epidemic process correctly. The practical aim was to come to justifiable estimations of important parameters like the rate of infection as function of time, thus quantifying effectiveness of the Government measures. Another parameters estimated were the probability distribution of detection times and recovery times. [ABSTRACT FROM AUTHOR]

Background There is little systematic assessment of how total health expenditure is distributed across diseases and comorbidities. The objective of this study was to use statistical methods to disaggregate all publicly funded health expenditure by disease and comorbidities in order to answer three research questions: (1) What is health expenditure by disease phase for noncommunicable diseases (NCDs) in New Zealand? (2) Is the cost of having two NCDs more or less than that expected given the independent costs of each NCD? (3) How is total health spending disaggregated by NCDs across age and by sex? Methods and findings We used linked data for all adult New Zealanders for publicly funded events, including hospitalisation, outpatient, pharmaceutical, laboratory testing, and primary care from 1 July 2007 to 30 June 2014. These data include 18.9 million person-years and $26.4 billion in spending (US$ 2016). We used case definition algorithms to identify if a person had any of six NCDs (cancer, cardiovascular disease [CVD], diabetes, musculoskeletal, neurological, and a chronic lung/liver/kidney [LLK] disease). Indicator variables were used to identify the presence of any of the 15 possible comorbidity pairings of these six NCDs. Regression was used to estimate excess annual health expenditure per person. Cause deletion methods were used to estimate total population expenditure by disease. A majority (59%) of health expenditure was attributable to NCDs. Expenditure due to diseases was generally highest in the year of diagnosis and year of death. A person having two diseases simultaneously generally had greater health expenditure than the expected sum of having the diseases separately, for all 15 comorbidity pairs except the CVD-cancer pair. For example, a 60-64-year-old female with none of the six NCDs had $633 per annum expenditure. If she had both CVD and chronic LLK, additional expenditure for CVD separately was $6,443/$839/$9,225 for the first year of diagnosis/prevalent years/last year of life if dying of CVD; additional expenditure for chronic LLK separately was $6,443/$1,291/$9,051; and the additional comorbidity expenditure of having both CVD and LLK was $2,456 (95% confidence interval [CI] $2,238-$2,674). The pattern was similar for males (e.g., additional comorbidity expenditure for a 60-64-year-old male with CVD and chronic LLK was $2,498 [95% CI $2,264-$2,632]). In addition to this, the excess comorbidity costs for a person with two diseases was greater at younger ages, e.g., excess expenditure for 45-49-year-old males with CVD and chronic LLK was 10 times higher than for 75-79-year-old males and six times higher for females. At the population level, 23.8% of total health expenditure was attributable to higher costs of having one of the 15 comorbidity pairs over and above the six NCDs separately; of the remaining expenditure, CVD accounted for 18.7%, followed by musculoskeletal (16.2%), neurological (14.4%), cancer (14.1%), chronic LLK disease (7.4%), and diabetes (5.5%). Major limitations included incomplete linkage to all costed events (although these were largely non-NCD events) and missing private expenditure. Conclusions The costs of having two NCDs simultaneously is typically superadditive, and more so for younger adults. Neurological and musculoskeletal diseases contributed the largest health system costs, in accord with burden of disease studies finding that they contribute large morbidity. Just as burden of disease methodology has advanced the understanding of disease burden, there is a need to create disease-based costing studies that facilitate the disaggregation of health budgets at a national level., Author(s): Tony Blakely 1,2,*, Giorgi Kvizhinadze 1, June Atkinson 1, Joseph Dieleman 3, Philip Clarke 2,4 Introduction The Global Burden of Disease (GBD) has achieved the vision of a coherent [...]

Background The anti-human epidermal growth factor receptor 2 (HER2) monoclonal antibody trastuzumab improves outcomes in patients with node-positive HER2+ early breast cancer. Given trastuzumab's high cost, we aimed to estimate its cost-effectiveness by heterogeneity in age and estrogen receptor (ER) and progesterone receptor (PR) status, which has previously been unexplored, to assist prioritisation. Methods and Findings A cost-utility analysis was performed using a Markov macro-simulation model, with a lifetime horizon, comparing a 12-mo regimen of trastuzumab with chemotherapy alone using the latest (2014) effectiveness measures from landmark randomised trials. A New Zealand (NZ) health system perspective was adopted, employing high-quality national administrative data. Incremental quality-adjusted life-years for trastuzumab versus chemotherapy alone are two times higher (2.33 times for the age group 50-54 y; 95% CI 2.29-2.37) for the worst prognosis (ER-/PR-) subtype compared to the best prognosis (ER+/PR+) subtype, causing incremental cost-effectiveness ratios (ICERs) for the former to be less than half those of the latter for the age groups from 25-29 to 90-94 y (0.44 times for the age group 50-54 y; 95% CI 0.43-0.45). If we were to strictly apply an arbitrary cost-effectiveness threshold equal to the NZ gross domestic product per capita (2011 purchasing power parity [PPP]-adjusted: US$30,300; [euro]23,700; £21,200), our study suggests that trastuzumab (2011 PPP-adjusted US$45,400/[euro]35,900/£21,900 for 1 y at formulary prices) may not be cost-effective for ER+ (which are 61% of all) node-positive HER2+ early breast cancer patients but cost-effective for ER-/PR- subtypes (37% of all cases) to age 69 y. Market entry of trastuzumab biosimilars will likely reduce the ICER to below this threshold for premenopausal ER+/PR- cancer but not for ER+/PR+ cancer. Sensitivity analysis using the best-case effectiveness measure for ER+ cancer had the same result. A key limitation was a lack of treatment-effect data by hormone receptor subtype. Heterogeneity was restricted to age and hormone receptor status; tumour size/grade heterogeneity could be explored in future work. Conclusions This study highlights how cost-effectiveness can vary greatly by heterogeneity in age and hormone receptor subtype. Resource allocation and licensing of subsidised therapies such as trastuzumab should consider demographic and clinical heterogeneity; there is currently a profound disconnect between how funding decisions are made (largely agnostic to heterogeneity) and the principles of personalised medicine., Author(s): William Leung 1,2,*, Giorgi Kvizhinadze 1, Nisha Nair 1, Tony Blakely 1 Introduction Between 12% and 25% of breast cancer cases are human epidermal growth factor receptor 2 (HER2) [...]

Objective: We aimed to estimate the cost-effectiveness of brief weight-loss counselling by dietitian-trained practice nurses, in a high-income-country case study.Design: A literature search of the impact of dietary counselling on BMI was performed to source the 'best' effect size for use in modelling. This was combined with multiple other input parameters (e.g. epidemiological and cost parameters for obesity-related diseases, likely uptake of counselling) in an established multistate life-table model with fourteen parallel BMI-related disease life tables using a 3 % discount rate.Setting: New Zealand (NZ).Participants: We calculated quality-adjusted life-years (QALY) gained and health-system costs over the remainder of the lifespan of the NZ population alive in 2011 (n 4·4 million).Results: Counselling was estimated to result in an increase of 250 QALY (95 % uncertainty interval -70, 560 QALY) over the population's lifetime. The incremental cost-effectiveness ratio was 2011 $NZ 138 200 per QALY gained (2018 $US 102 700). Per capita QALY gains were higher for Māori (Indigenous population) than for non-Māori, but were still not cost-effective. If willingness-to-pay was set to the level of gross domestic product per capita per QALY gained (i.e. 2011 $NZ 45 000 or 2018 $US 33 400), the probability that the intervention would be cost-effective was 2 %.Conclusions: The study provides modelling-level evidence that brief dietary counselling for weight loss in primary care generates relatively small health gains at the population level and is unlikely to be cost-effective. [ABSTRACT FROM AUTHOR]

Objective: We aimed to estimate the maximum intervention cost (EMIC) a society could invest in a life-saving intervention at different ages while remaining cost-effective according to a user-specified cost-effectiveness threshold. Methods: New Zealand (NZ) was used as a case study, and a health system perspective was taken. Data from NZ life tables and morbidity data from a burden of disease study were used to estimate health-adjusted life-years (HALYs) gained by a life-saving intervention. Health system costs were estimated from a national database of all publicly funded health events (hospitalizations, outpatient events, pharmaceuticals, etc.). For illustrative purposes we followed the WHO-CHOICE approach and used a cost-effectiveness threshold of the gross domestic product (GDP) per capita (NZ$45,000 or US$30,000 per HALY). We then calculated EMICs for an “ideal” life-saving intervention that fully returned survivors to the same average morbidity, mortality, and cost trajectories as the rest of their cohort. Findings: The EMIC of the “ideal” life-saving intervention varied markedly by age: NZ$1.3 million (US$880,000) for an intervention to save the life of a child, NZ$0.8 million (US$540,000) for a 50-year-old, and NZ$0.235 million (US$158,000) for an 80-year-old. These results were predictably very sensitive to the choice of discount rate and to the selected cost-effectiveness threshold. Using WHO data, we produced an online calculator to allow the performance of similar calculations for all other countries. Conclusions: We present an approach to estimating maximal cost-effective investment in life-saving health interventions, under various assumptions. Our online calculator allows this approach to be applied in other countries. Policymakers could use these estimates as a rapid screening tool to determine if more detailed cost-effectiveness analyses of potential life-saving interventions might be worthwhile or which proposed life-saving interventions are very unlikely to benefit from such additional research. [ABSTRACT FROM AUTHOR]