Geographic health inequalities in Norway: a Gini analysis of cross-county differences in mortality from 1980 to 2014.

BACKGROUND: This study aims at quantifying the level and changes over time of inequality in age-specific mortality and life expectancy between the 19 Norwegian counties from 1980 to 2014. METHODS: Data on population and mortality by county was obtained from Statistics Norway for 1980-2014. Life expectancy and age-specific mortality rates (0-4, 5-49 and 50-69 age groups) were estimated by year and county. Geographic inequality was described by the absolute Gini index annually. RESULTS: Life expectancy in Norway has increased from 75.6 to 82.0 years, and the risk of death before the age of 70 has decreased from 26 to 14% from 1980 to 2014. The absolute Gini index decreased over the period 1980 to 2014 from 0.43 to 0.32 for life expectancy, from 0.012 to 0.0057 for the age group 50-69 years, from 0.0038 to 0.0022 for the age group 5-49 years, and from 0.0009 to 0.0006 for the age group 0-4 years. It will take between 2 and 32 years (national average 7 years) until the counties catch up with the life expectancy in the best performing county if their annual rates of increase remain unchanged. CONCLUSION: Using the absolute Gini index as a metric for monitoring changes in geographic inequality over time may be a valuable tool for informing public health policies. The absolute inequality in mortality and life expectancy between Norwegian counties has decreased from 1980 to 2014.

Evidence for scaling up HIV treatment in sub-Saharan Africa: A call for incorporating health system constraints.

Jan Hontelez and colleagues argue that the cost-effectiveness studies of HIV treatment scale-up need to include health system constraints to be more informative.

Costs and expected gain in lifetime health from intensive care versus general ward care of 30,712 individual patients: a distribution-weighted cost-effectiveness analysis.

BACKGROUND: Clinicians, hospital managers, policy makers, and researchers are concerned about high costs, increased demand, and variation in priorities in the intensive care unit (ICU). The objectives of this modelling study are to describe the extra costs and expected health gains associated with admission to the ICU versus the general ward for 30,712 patients and the variation in cost-effectiveness estimates among subgroups and individuals, and to perform a distribution-weighted economic evaluation incorporating extra weighting to patients with high severity of disease. METHODS: We used a decision-analytic model that estimates the incremental cost per quality-adjusted life year (QALY) gained (ICER) from ICU admission compared with general ward care using Norwegian registry data from 2008 to 2010. We assigned increasing weights to health gains for those with higher severity of disease, defined as less expected lifetime health if not admitted. The study has inherent uncertainty of findings because a randomized clinical trial comparing patients admitted or rejected to the ICU has never been performed. Uncertainty is explored in probabilistic sensitivity analysis. RESULTS: The mean cost-effectiveness of ICU admission versus ward care was €11,600/QALY, with 1.6 QALYs gained and an incremental cost of €18,700 per patient. The probability (p) of cost-effectiveness was 95% at a threshold of €22,000/QALY. The mean ICER for medical admissions was €10,700/QALY (p = 97%), €12,300/QALY (p = 93%) for admissions after acute surgery, and €14,700/QALY (p = 84%) after planned surgery. For individualized ICERs, there was a 50% probability that ICU admission was cost-effective for 85% of the patients at a threshold of €64,000/QALY, leaving 15% of the admissions not cost-effective. In the distributional evaluation, 8% of all patients had distribution-weighted ICERs (higher weights to gains for more severe conditions) above €64,000/QALY. High-severity admissions gained the most, and were more cost-effective. CONCLUSIONS: On average, ICU admission versus general ward care was cost-effective at a threshold of €22,000/QALY (p = 95%). According to the individualized cost-effectiveness information, one in six ICU admissions was not cost-effective at a threshold of €64,000/QALY. Almost half of these admissions that were not cost-effective can be regarded as acceptable when weighted by severity of disease in terms of expected lifetime health. Overall, existing ICU services represent reasonable resource use, but considerable uncertainty becomes evident when disaggregating into individualized results.

Guidance on priority setting in health care (GPS-Health): the inclusion of equity criteria not captured by cost-effectiveness analysis.

This Guidance for Priority Setting in Health Care (GPS-Health), initiated by the World Health Organization, offers a comprehensive map of equity criteria that are relevant to health care priority setting and should be considered in addition to cost-effectiveness analysis. The guidance, in the form of a checklist, is especially targeted at decision makers who set priorities at national and sub-national levels, and those who interpret findings from cost-effectiveness analysis. It is also targeted at researchers conducting cost-effectiveness analysis to improve reporting of their results in the light of these other criteria. THE GUIDANCE WAS DEVELOP THROUGH A SERIES OF EXPERT CONSULTATION MEETINGS AND INVOLVED THREE STEPS: i) methods and normative concepts were identified through a systematic review; ii) the review findings were critically assessed in the expert consultation meetings which resulted in a draft checklist of normative criteria; iii) the checklist was validated though an extensive hearing process with input from a range of relevant stakeholders. The GPS-Health incorporates criteria related to the disease an intervention targets (severity of disease, capacity to benefit, and past health loss); characteristics of social groups an intervention targets (socioeconomic status, area of living, gender; race, ethnicity, religion and sexual orientation); and non-health consequences of an intervention (financial protection, economic productivity, and care for others).

Distribution-Weighted Cost-Effectiveness Analysis Using Lifetime Health Loss.

BACKGROUND: It is widely acknowledged that concerns for the worse off need to be integrated with the concern for cost effectiveness in priority setting, and several countries are seeking to do so. In Norway, a comprehensive framework for priority setting was recently proposed to specify the worse off in terms of lifetime loss of quality-adjusted life-years (QALYs). However, few studies have shown how to calculate such health losses, how to integrate health loss into cost-effectiveness analyses (CEAs) and how such integration impacts the incremental cost-effectiveness ratios (ICERs). The aim of this study was to do so. METHODS: The proposed framework was applied to data from 15 recent economic evaluations of drugs. Available data were used to calculate the lifetime health loss of the target groups, and the proposed marginal weighting function was employed to adjust standard ICERs according to the size of this loss. Standard and weighted ICERs were compared to a threshold of US$35,000 per QALY gained. RESULTS: Lifetime health loss can be calculated with the use of available data and integrated by a marginal weighting function with CEAs. Such integration affected standard ICERs to a varying degree and changed the number of interventions considered cost effective from three to eight. CONCLUSION: Calculation of lifetime health loss and its integration with CEA is feasible and can influence the reimbursement and ranking of interventions. To facilitate regular integration, guidelines for economic evaluations could require (i) adjustment according to distributional concerns and (ii) that data on health loss are extracted directly from the models and reported. Generic databases on health loss could be developed alongside such efforts.

Age, risk, and life expectancy in Norwegian intensive care: a registry-based population modelling study.

BACKGROUND: Knowledge about the expected life years gained from intensive care unit (ICU) admission could inform priority-setting decisions across groups of ICU patients and across medical specialties. The aim of this study was to estimate expected remaining lifetime for patients admitted to ICUs during 2008-2010 and to estimate the gain in life years from ICU admission. METHODS: This is a descriptive, population modelling study of 30,712 adult mixed ICU admissions from the Norwegian Intensive Care Registry. The expected remaining lifetime for each patient was estimated using a decision-analytical model. Transition probabilities were based on registered Simplified Acute Physiology Score (SAPS) II, and standard and adjusted Norwegian life-tables. RESULTS: The hospital mortality was 19.4% (n = 5,958 deaths). 24% of the patients were estimated to die within the first year after ICU admission in our model. Under an intermediate (base case), optimistic (O), and pessimistic (P) scenario with respect to long-term mortality, the average expected remaining lifetime was 19.4, 19.9, and 12.7 years. The majority of patients had a life expectancy of more than five years (84.8% in the base case, 89.4% in scenario O, and 55.6% in scenario P), and few had a life expectancy of less than one year (0.7%, 0.1%, and 12.7%). The incremental gain from ICU admission compared to counterfactual general ward care was estimated to be 0.04 (scenario P, age 85+) to 1.14 (scenario O, age < 45) extra life years per patient. CONCLUSIONS: Our research demonstrated a novel way of using routinely collected registry data to estimate and evaluate the expected lifetime outcomes for ICU patients upon admission. The majority had high life expectancies. The youngest age groups seemed to benefit the most from ICU admission. The study raises the question whether availability and rationing of ICU services are too strict in Norway.

A non-handicapped cohort of low-birthweight children: growth and general health status at 11 years of age.

AIMS: To describe and compare physical growth, current health status, functional limitations and neurodevelopmental impairments (defined as low IQ, school problems or psychiatric disorder) at 11 y of age in a population of non-handicapped low-birthweight (LBW) children with that of normal-birthweight (NBW) children. METHODS: A population-based sample of 130 LBW children (weighing less than 2000 g at birth) without major handicaps, and a random sample of 131 NBW children born at term. Somatic and mental health and cognitive abilities were assessed through questionnaires to parents, a physical examination, standardized tests of cognitive function (WISC-R) and a semi-structured interview (Children Assessment Schedule). RESULTS: General somatic health status was similar for the LBW and NBW children. The LBW children were shorter (mean difference -2.5 cm; 95% CI -0.9 to -4.2) and had a smaller head circumference (mean difference -0.8 cm; 95% CI -0.4 to -1.1) but similar weights and body mass indices. Differences and similarities in anthropometric measures were the same at 5 and 11 y of age. The LBW children had higher systolic (mean difference 3.2 mmHg; 95% CI -0.6 to -0.3) but similar diastolic blood pressure. A higher proportion of LBW children had decreased visual acuity and hearing impairment. Forty per cent of LBW children had neurodevelopmental impairments, compared to 20% of NBW children (OR 2.6; 95% CI 1.5 to 4.5). CONCLUSION: At 11 y of age, survivors of moderately low birthweight without major handicaps may have generally good health, but are at risk of neurodevelopmental impairments.