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Background: The World Health Organization recommends all countries consider screening for H. pylori to prevent gastric cancer. We therefore aimed to estimate the cost-effectiveness of a H. pylori serology-based screening program in New Zealand, a country that includes population groups with relatively high gastric cancer rates.Methods: A Markov model was developed using life-tables and morbidity data from a national burden of disease study. The modelled screening program reduced the incidence of non-cardia gastric cancer attributable to H. pylori, if infection was identified by serology screening, and for the population expected to be reached by the screening program. A health system perspective was taken and detailed individual-level costing data was used.Results: For adults aged 25-69 years old, nation-wide screening for H. pylori was found to have an incremental cost of US$196 million (95% uncertainty interval [95% UI]: $182-$211 million) with health gains of 14,200 QALYs (95% UI: 5,100-26,300). Cost per QALY gained was US$16,500 ($7,600-$38,400) in the total population and 17% (6%-29%) of future gastric cancer cases could be averted with lifetime follow-up. A targeted screening program for Māori only (indigenous population), was more cost-effective at US$8,000 ($3,800-$18,500) per QALY.Conclusions: This modeling study found that H. pylori screening was likely to be cost-effective in this high-income country, particularly for the indigenous population. While further research is needed to help clarify the precise benefits, costs and adverse effects of such screening programs, there seems a reasonable case for policy-makers to give pilot programs consideration, particularly for any population groups with relatively elevated rates of gastric cancer. [ABSTRACT FROM AUTHOR]

Background: There is momentum internationally to improve coordination of complex care pathways. Robust evaluations of such interventions are scarce. This paper evaluates the cost-utility of cancer care coordinators for stage III colon cancer patients, who generally require surgery followed by chemotherapy. Methods: We compared a hospital-based nurse cancer care coordinator (CCC) with 'business-as-usual' (no dedicated coordination service) in stage III colon cancer patients in New Zealand. A discrete event microsimulation model was constructed to estimate quality-adjusted life-years (QALYs) and costs from a health system perspective. We used New Zealand data on colon cancer incidence, survival, and mortality as baseline input parameters for the model. We specified intervention input parameters using available literature and expert estimates. For example, that a CCC would improve the coverage of chemotherapy by 33 % (ranging from 9 to 65 %), reduce the time to surgery by 20 % (3 to 48 %), reduce the time to chemotherapy by 20 % (3 to 48 %), and reduce patient anxiety (reduction in disability weight of 33 %, ranging from 0 to 55 %). Results: Much of the direct cost of a nurse CCC was balanced by savings in business-as-usual care coordination. Much of the health gain was through increased coverage of chemotherapy with a CCC (especially older patients), and reduced time to chemotherapy. Compared to 'business-as-usual', the cost per QALY of the CCC programme was $NZ 18,900 (≈ $US 15,600; 95 % UI: $NZ 13,400 to 24,600). By age, the CCC intervention was more cost-effective for colon cancer patients < 65 years ($NZ 9,400 per QALY). By ethnicity, the health gains were larger for Māori, but so too were the costs, meaning the cost-effectiveness was roughly comparable between ethnic groups. Conclusions: Such a nurse-led CCC intervention in New Zealand has acceptable cost-effectiveness for stage III colon cancer, meaning it probably merits funding. Each CCC programme will differ in its likely health gains and costs, making generalisation from this evaluation to other CCC interventions difficult. However, this evaluation suggests that CCC interventions that increase coverage of, and reduce time to, effective treatments may 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]

Background Similar to many developed countries, vaccination against human papillomavirus (HPV) is provided only to girls in New Zealand and coverage is relatively low (47% in school-aged girls for dose 3). Some jurisdictions have already extended HPV vaccination to school-aged boys. Thus, exploration of the cost-utility of adding boys' vaccination is relevant. We modeled the incremental health gain and costs for extending the current girls-only program to boys, intensifying the current girls-only program to achieve 73% coverage, and extension of the intensive program to boys. Methods A Markov macro-simulation model, which accounted for herd immunity, was developed for an annual cohort of 12-year-olds in 2011 and included the future health states of: cervical cancer, pre-cancer (CIN I to III), genital warts, and three other HPV-related cancers. In each state, health sector costs, including additional health costs from extra life, and quality-adjusted life-years (QALYs) were accumulated. The model included New Zealand data on cancer incidence and survival, and other cause mortality (all by sex, age, ethnicity and deprivation). Results At an assumed local willingness-to-pay threshold of US$29,600, vaccination of 12-year-old boys to achieve the current coverage for girls would not be cost-effective, at US$61,400/QALY gained (95% UI $29,700 to $112,000; OECD purchasing power parities) compared to the current girls-only program, with an assumed vaccine cost of US$59 (NZ$113). This was dominated though by the intensified girls-only program; US$17,400/QALY gained (95% UI: dominant to $46,100). Adding boys to this intensified program was also not cost-effective; US$128,000/QALY gained, 95% UI: $61,900 to $247,000). Vaccination of boys was not found to be cost-effective, even for additional scenarios with very low vaccine or program administration costs - only when combined vaccine and administration costs were NZ$125 or lower per dose was vaccination of boys cost-effective. Conclusions These results suggest that adding boys to the girls-only HPV vaccination program in New Zealand is highly unlikely to be cost-effective. In order for vaccination of males to become cost-effective in New Zealand, vaccine would need to be supplied at very low prices and administration costs would need to be minimised. [ABSTRACT FROM AUTHOR]

Background A critical first step toward incorporating equity into cost-effectiveness analyses is to appropriately model interventions by population subgroups. In this paper we use a standardized treatment intervention to examine the impact of using ethnic-specific (Māori and non-Māori) data in cost-utility analyses for three cancers. Methods We estimate gains in health-adjusted life years (HALYs) for a simple intervention (20% reduction in excess cancer mortality) for lung, female breast, and colon cancers, using Markov modeling. Base models include ethnic-specific cancer incidence with other parameters either turned off or set to non-Māori levels for both groups. Subsequent models add ethnic-specific cancer survival, morbidity, and life expectancy. Costs include intervention and downstream health system costs. Results For the three cancers, including existing inequalities in background parameters (population mortality and comorbidities) for Māori attributes less value to a year of life saved compared to non-Māori and lowers the relative health gains for Māori. In contrast, ethnic inequalities in cancer parameters have less predictable effects. Despite Māori having higher excess mortality from all three cancers, modeled health gains for Māori were less from the lung cancer intervention than for non-Māori but higher for the breast and colon interventions. Conclusions Cost-effectiveness modeling is a useful tool in the prioritization of health services. But there are important (and sometimes counterintuitive) implications of including ethnic-specific background and disease parameters. In order to avoid perpetuating existing ethnic inequalities in health, such analyses should be undertaken with care. [ABSTRACT FROM AUTHOR]