Educational differences in years lived with disability due to mental and substance use disorders: a cohort study using nationwide Norwegian and Danish registries.

BACKGROUND: Findings from the Global Burden of Disease (GBD) study have shown that the burden of mental and substance use disorders is considerable, and unevenly distributed across demographic groups in the population. However, there is a lack of knowledge on how this burden differs by socioeconomic position. The aim of this study was to examine educational differences in years lived with disability (YLDs) from mental and substance use disorders among males and females in two high-income countries, taking comorbidity with other diseases into account. METHODS: The study included all registered residents in Denmark and Norway from 2011 to 2021. Diagnostic information was retrieved from records in the Norwegian National Patient Registry (NPR) and the Danish Psychiatric Central Research Register (PCRR) and used as proxy measures for disorder prevalence. Demographical and educational information was taken from administrative registries. The YLD is a measure of the non-fatal health loss in the population and was calculated by multiplying the duration of a disorder with a disability weight (DW), scaled between 0 and 1. Information on remission and DWs were retrieved from the GBD study and other sources, and disorder specific DWs were averaged by severity levels and adjusted for comorbidity. RESULTS: Educational gradients in YLD rates were found for mental and substance disorders overall, and for most of the specific disorders. The educational gradient was more pronounced for schizophrenia, intellectual disability and substance use disorders than for eating, anxiety, and affective disorders. Both higher YLD rates, and a larger attributed proportion of the total YLDs, were found for schizophrenia, intellectual disability, and substance use disorders in the groups with low versus high education. YLD rates for eating, anxiety, and affective disorders were more equal across educational levels, but constituted a smaller proportion of the total YLDs among the groups with low versus the groups with high educational level. CONCLUSION: Most of the disease burden related to mental and substance use disorders falls on those with the fewest years of education. This should be taken into consideration when public health targets aimed at improving mental health and reducing social inequalities in health are developed and implemented.

Third dose mRNA vaccination against SARS-CoV-2 reduces medical complaints seen in primary care: a matched cohort study.

BACKGROUND: SARS-CoV-2 mRNA vaccination has been associated with both side effects and a reduction in COVID-related complaints due to the decrease in COVID-19 incidence. We aimed to investigate if individuals who received three doses of SARS-CoV-2 mRNA vaccines had a lower incidence of (a) medical complaints and (b) COVID-19-related medical complaints, both as seen in primary care, when compared to individuals who received two doses. METHODS: We conducted a daily longitudinal exact one-to-one matching study based on a set of covariates. We obtained a matched sample of 315,650 individuals aged 18-70 years who received the 3rd dose at 20-30 weeks after the 2nd dose and an equally large control group who did not. Outcome variables were diagnostic codes as reported by general practitioners or emergency wards, both alone and in combination with diagnostic codes of confirmed COVID-19. For each outcome, we estimated cumulative incidence functions with hospitalization and death as competing events. RESULTS: We found that the number of medical complaints was lower in individuals aged 18-44 years who received three doses compared to those who received two doses. The differences in estimates per 100,000 vaccinated were as follows: fatigue 458 less (95% confidence interval: 355-539), musculoskeletal pain 171 less (48-292), cough 118 less (65-173), heart palpitations 57 less (22-98), shortness of breath 118 less (81-149), and brain fog 31 less (8-55). We also found a lower number of COVID-19-related medical complaints: per 100,000 individuals aged 18-44 years vaccinated with three doses, there were 102 (76-125) fewer individuals with fatigue, 32 (18-45) fewer with musculoskeletal pain, 30 (14-45) fewer with cough, and 36 (22-48) fewer with shortness of breath. There were no or fewer differences in heart palpitations (8 (1-16)) or brain fog (0 (- 1-8)). We observed similar results, though more uncertain, for individuals aged 45-70 years, both for medical complaints and for medical complaints that were COVID-19 related. CONCLUSIONS: Our findings suggest that a 3rd dose of SARS-CoV-2 mRNA vaccine administered 20-30 weeks after the 2nd dose may reduce the incidence of medical complaints. It may also reduce the COVID-19-related burden on primary healthcare services.

Disease-specific health spending by age, sex, and type of care in Norway: a national health registry study.

BACKGROUND: Norway is a high-income nation with universal tax-financed health care and among the highest per person health spending in the world. This study estimates Norwegian health expenditures by health condition, age, and sex, and compares it with disability-adjusted life-years (DALYs). METHODS: Government budgets, reimbursement databases, patient registries, and prescription databases were combined to estimate spending for 144 health conditions, 38 age and sex groups, and eight types of care (GPs; physiotherapists & chiropractors; specialized outpatient; day patient; inpatient; prescription drugs; home-based care; and nursing homes) totaling 174,157,766 encounters. Diagnoses were in accordance with the Global Burden of Disease study (GBD). The spending estimates were adjusted, by redistributing excess spending associated with each comorbidity. Disease-specific DALYs were gathered from GBD 2019. RESULTS: The top five aggregate causes of Norwegian health spending in 2019 were mental and substance use disorders (20.7%), neurological disorders (15.4%), cardiovascular diseases (10.1%), diabetes, kidney, and urinary diseases (9.0%), and neoplasms (7.2%). Spending increased sharply with age. Among 144 health conditions, dementias had the highest health spending, with 10.2% of total spending, and 78% of this spending was incurred at nursing homes. The second largest was falls estimated at 4.6% of total spending. Spending in those aged 15-49 was dominated by mental and substance use disorders, with 46.0% of total spending. Accounting for longevity, spending per female was greater than spending per male, particularly for musculoskeletal disorders, dementias, and falls. Spending correlated well with DALYs (Correlation r = 0.77, 95% CI 0.67-0.87), and the correlation of spending with non-fatal disease burden (r = 0.83, 0.76-0.90) was more pronounced than with mortality (r = 0.58, 0.43-0.72). CONCLUSIONS: Health spending was high for long-term disabilities in older age groups. Research and development into more effective interventions for the disabling high-cost diseases is urgently needed.

Production losses from morbidity and mortality by disease, age and sex in Norway.

AIM: The inclusion of production losses in health care priority setting is extensively debated. However, few studies allow for a comparison of these losses across relevant clinical and demographic categories. Our objective was to provide comprehensive estimates of Norwegian production losses from morbidity and mortality by age, sex and disease category. METHODS: National registries, tax records, labour force surveys, household and population statistics and data from the Global Burden of Disease were combined to estimate production losses for 12 disease categories, 38 age and sex groups and four causes of production loss. The production losses were estimated via lost wages in accordance with a human capital approach for 2019. RESULTS: The main causes of production losses in 2019 were mental and substance use disorders, totalling NOK121.6bn (32.7% of total production losses). This was followed by musculoskeletal disorders, neurological disorders, injuries, and neoplasms, which accounted for 25.2%, 7.4%, 7.4% and 6.5% of total production losses, respectively. Production losses due to sick leave, disability insurance and work assessment allowance were higher for females than for males, whereas production losses due to premature mortality were higher for males. The latter was related to neoplasms, cardiovascular disease and injuries. Across age categories, non-fatal conditions with a high prevalence among working populations caused the largest production losses. CONCLUSIONS: The inclusion of production losses in health care priority debates in Norway could result in an emphasis on chronic diseases that occur among younger populations at the expense of fatal diseases among older age groups.

Inequality in quality-adjusted life expectancy by educational attainment in Norway: an observational study.

BACKGROUND: Health inequalities are often assessed in terms of life expectancy or health-related quality of life (HRQoL). Few studies combine both aspects into quality-adjusted life expectancy (QALE) to derive comprehensive estimates of lifetime health inequality. Furthermore, little is known about the sensitivity of estimated inequalities in QALE to different sources of HRQoL information. This study assesses inequalities in QALE by educational attainment in Norway using two different measures of HRQoL. METHODS: We combine full population life tables from Statistics Norway with survey data from the Tromsø study, a representative sample of the Norwegian population aged ≥ 40. HRQoL is measured using the EQ-5D-5L and EQ-VAS instruments. Life expectancy and QALE at 40 years of age are calculated using the Sullivan-Chiang method and are stratified by educational attainment. Inequality is measured as the absolute and relative gap between individuals with lowest (i.e. primary school) and highest (university degree 4 + years) educational attainment. RESULTS: People with the highest educational attainment can expect to live longer lives (men: + 17.9% (95%CI: 16.4 to 19.5%), women: + 13.0% (95%CI: 10.6 to 15.5%)) and have higher QALE (men: + 22.4% (95%CI: 20.4 to 24.4%), women: + 18.3% (95%CI: 15.2 to 21.6%); measured using EQ-5D-5L) than individuals with primary school education. Relative inequality is larger when HRQoL is measured using EQ-VAS. CONCLUSION: Health inequalities by educational attainment become wider when measured in QALE rather than LE, and the degree of this widening is larger when measuring HRQoL by EQ-VAS than by EQ-5D-5L. We find a sizable educational gradient in lifetime health in Norway, one of the most developed and egalitarian societies in the world. Our estimates provide a benchmark against which other countries can be compared.

Preterm birth after the introduction of COVID-19 mitigation measures in Norway, Sweden, and Denmark: a registry-based difference-in-differences study.

BACKGROUND: Although some studies have reported a decrease in preterm birth following the start of the COVID-19 pandemic, the findings are inconsistent. OBJECTIVE: This study aimed to compare the incidences of preterm birth before and after the introduction of COVID-19 mitigation measures in Scandinavian countries using robust population-based registry data. STUDY DESIGN: This was a registry-based difference-in-differences study using births from January 2014 through December 2020 in Norway, Sweden, and Denmark. The changes in the preterm birth (<37 weeks) rates before and after the introduction of COVID-19 mitigation measures (set to March 12, 2020) were compared with the changes in preterm birth before and after March 12 from 2014 to 2019. The differences per 1000 births were calculated for 2-, 4-, 8-, 12-, and 16-week intervals before and after March 12. The secondary analyses included medically indicated preterm birth, spontaneous preterm birth, and very preterm (<32 weeks) birth. RESULTS: A total of 1,519,521 births were included in this study. During the study period, 5.6% of the births were preterm in Norway and Sweden, and 5.7% were preterm in Denmark. There was a seasonal variation in the incidence of preterm birth, with the highest incidence during winter. In all the 3 countries, there was a slight overall decline in preterm births from 2014 to 2020. There was no consistent evidence of a change in the preterm birth rates following the introduction of COVID-19 mitigation measures, with difference-in-differences estimates ranging from 3.7 per 1000 births (95% confidence interval, -3.8 to 11.1) for the first 2 weeks after March 12, 2020, to -1.8 per 1000 births (95% confidence interval, -4.6 to 1.1) in the 16 weeks after March 12, 2020. Similarly, there was no evidence of an impact on medically indicated preterm birth, spontaneous preterm birth, or very preterm birth. CONCLUSION: Using high-quality national data on births in 3 Scandinavian countries, each of which implemented different approaches to address the pandemic, there was no evidence of a decline in preterm births following the introduction of COVID-19 mitigation measures.

Body mass index and healthcare costs: using genetic variants from the HUNT study as instrumental variables.

BACKGROUND: Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and measurement error. METHODS: We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby exploiting the natural randomization of genetic variants that occurs at conception. We used data on measured height and weight, genetic information, and sociodemographic factors from the Nord-Trøndelag Health Studies (HUNT), and individual-level registry data on healthcare costs, educational level, registration status, and biological relatives. We studied associations between BMI and general practitioner (GP)-, specialist-, and total healthcare costs in the Norwegian setting using instrumental variable (IV) regressions, and compared our findings with effect estimates from ordinary least squares (OLS) regressions. The sensitivity of our findings to underlying IV-assumptions was explored using two-sample Mendelian randomization methods, non-linear analyses, sex-, healthcare provider-, and age-specific analyses, within-family analyses, and outlier removal. We also conducted power calculations to assess the likelihood of detecting an effect given our sample 60,786 individuals. RESULTS: We found that increased BMI resulted in significantly higher GP costs; however, the IV-based effect estimate was smaller than the OLS-based estimate. We found no evidence of an association between BMI and specialist or total healthcare costs. CONCLUSIONS: Elevated BMI leads to higher GP costs, and more studies are needed to understand the causal mechanisms between BMI and specialist costs.

Parental income gradients in adult health: a national cohort study.

BACKGROUND: Disparities in health by adult income are well documented, but we know less about the childhood origins of health inequalities, and it remains unclear how the shape of the gradient varies across health conditions. This study examined the association between parental income in childhood and several measures of morbidity in adulthood. METHODS: We used administrative data on seven complete Norwegian birth cohorts born in 1967-1973 (N = 429,886) to estimate the association between parental income from birth to age 18, obtained from tax records available from 1967, linked with administrative registries on health. Health measures, observed between ages 39 and 43, were taken from registry data on consultations at primary health care services based on diagnostic codes from the International Classification of Primary Care (ICPC-2) and hospitalizations and outpatient specialist consultations registered in the National Patient Registry (ICD-10). RESULTS: Low parental income during childhood was associated with a higher risk of being diagnosed with several chronic and pain-related disorders, as well as hospitalization, but not overall primary health care use. Absolute differences were largest for disorders related to musculoskeletal pain, injuries, and depression (7-9 percentage point difference). There were also differences for chronic disorders such as hypertension (8%, CI 7.9-8.5 versus 4%, CI 4.1-4.7) and diabetes (3.2%, CI 3.0-3.4 versus 1.4%, CI 1.2-1.6). There was no difference in consultations related to respiratory disorders (20.9%, CI 20.4-21.5 versus 19.7%, CI 19.2-20.3). Childhood characteristics (parental education, low birth weight, and parental marital status) and own adult characteristics (education and income) explained a large share of the association. CONCLUSIONS: Children growing up at the bottom of the parental income distribution, compared to children in the top of the income distribution, had a two- to threefold increase in somatic and psychological disorders measured in adulthood. This shows that health inequalities by socioeconomic family background persist in a Scandinavian welfare-state context with universal access to health care.

The impact of primary care physician density on perinatal health: Evidence from a natural experiment.

We examined the impact of primary care physician density on perinatal health outcomes in Norway. From 1992 and onwards, primary care physicians who chose to work in selected remote municipalities were given an annual reduction in their student loan. This reduction, combined with increased supply of physicians, led to an increase in the density of primary care physicians in these selected municipalities. Our register-based population study showed that this increase in physician density significantly improved perinatal health in terms of fewer fetal deaths and increased birth weight. The richness of the data allowed us to perform several robustness tests.

The relationship between body mass index and income: Using genetic variants from HUNT as instrumental variables.

Several studies have estimated effects of body mass index (BMI) on labour market outcomes, and these studies have mixed findings. A significant challenge has been to adequately control for omitted variables, selection, reverse causality, and measurement error. We examine the impact of BMI on income using genetic variants as instrumental variables for BMI. Individual-level pre-tax income from tax records was merged with health survey data containing measured height and weight, and data on genetic variants. The analyses were stratified by sex and a variety of methods were used to explore the sensitivity and validity of the instrumental variable (IV) strategy. For females we found that BMI had a negative effect on the logarithm of income. The effect estimated from the IV models (-0.02) was larger than the effect estimated from naïve ordinary least squares regressions (-0.01). For males, the coefficients for the effect of BMI on income were imprecise, and both positive and negative coefficients were estimated depending on the estimation method. Our results suggest that females are susceptible to reduced income levels following increased BMI.

Body mass index and lifetime healthcare utilization.

BACKGROUND: Overweight and obesity is a major global public health challenge, and understanding the implications for healthcare systems is essential for policy planning. Past studies have typically found positive associations between obesity and healthcare utilization, but these studies have not taken into consideration that obesity is also associated with early mortality. We examined associations between body mass index (BMI, reported as kg/m2) and healthcare utilization with and without taking BMI-specific survival into consideration. METHODS: We used nationally representative data on 33 882 adults collected between 2002 and 2015. We computed BMI- and age-specific primary and secondary care utilization and multiplied the estimated values with gender-, age-, and BMI-specific probabilities of surviving to each age. Then, we summed the average BMI-specific utilization between 18 and 85 years. RESULTS: During a survival-adjusted lifetime, males with normal weight (BMI: 18.5-24.9) had, on average, 167 primary care, and 77 secondary care contacts. In comparison, males with overweight (BMI: 25.0-29.9), category I obesity (BMI: 30.0-34.9), and category II/III obesity (BMI ≥35.0) had 11%, 41%, and 102% more primary care, and 14%, 29%, and 78% more secondary care contacts, respectively. Females with normal weight had, on average, 210 primary care contacts and 91 secondary care contacts. Females with overweight, category I obesity, and category II/III obesity had 20%, 34%, and 81% more primary care contacts, and 26%, 16%, and 16% more secondary care contacts, respectively. CONCLUSION: The positive association between BMI and healthcare utilization was reduced, but not offset, when BMI-specific survival was taken into consideration. Our findings underpin previous research and suggest that interventions to offset the increasing prevalence of overweight, and especially obesity, are warranted.

Association of Household Income With Life Expectancy and Cause-Specific Mortality in Norway, 2005-2015.

Importance: Examining causes of death and making comparisons across countries may increase understanding of the income-related differences in life expectancy. Objectives: To describe income-related differences in life expectancy and causes of death in Norway and to compare those differences with US estimates. Design and Setting: A registry-based study including all Norwegian residents aged at least 40 years from 2005 to 2015. Exposures: Household income adjusted for household size. Main Outcomes and Measures: Life expectancy at 40 years of age and cause-specific mortality. Results: In total, 3 041 828 persons contributed 25 805 277 person-years and 441 768 deaths during the study period (mean [SD] age, 59.3 years [13.6]; mean [SD] number of household members per person, 2.5 [1.3]). Life expectancy was highest for women with income in the top 1% (86.4 years [95% CI, 85.7-87.1]) which was 8.4 years (95% CI, 7.2-9.6) longer than women with income in the lowest 1%. Men with the lowest 1% income had the lowest life expectancy (70.6 years [95% CI, 69.6-71.6]), which was 13.8 years (95% CI, 12.3-15.2) less than men with the top 1% income. From 2005 to 2015, the differences in life expectancy by income increased, largely attributable to deaths from cardiovascular disease, cancers, chronic obstructive pulmonary disease, and dementia in older age groups and substance use deaths and suicides in younger age groups. Over the same period, life expectancy for women in the highest income quartile increased 3.2 years (95% CI, 2.7-3.7), while life expectancy for women in the lowest income quartile decreased 0.4 years (95% CI, -1.0 to 0.2). For men, life expectancy increased 3.1 years (95% CI, 2.5-3.7) in the highest income quartile and 0.9 years (95% CI, 0.2-1.6) in the lowest income quartile. Differences in life expectancy by income levels in Norway were similar to differences observed in the United States, except that life expectancy was higher in Norway in the lower to middle part of the income distribution in both men and women. Conclusions and Relevance: In Norway, there were substantial and increasing gaps in life expectancy by income level from 2005 to 2015. The largest differences in life expectancy between Norway and United States were for individuals in the lower to middle part of the income distribution.

Disease burden in Norway in 2016. / Sykdomsbyrden i Norge i 2016.

BAKGRUNN: For å kunne møte helseutfordringer i befolkningen trenger vi oversikt over befolkningens helsetilstand. I Norge har vi tradisjonelt hatt god oversikt over dødsårsaker, men vi vet mindre om byrden fra tilstander som medfører sykelighet, såkalt ikke-dødelig helsetap. Vårt mål var å beskrive den totale sykdomsbyrden i Norge i 2016, utviklingen de siste ti årene samt kjønnsforskjeller i sykdomsbyrde. MATERIALE OG METODE: Vi brukte resultater fra det globale sykdomsbyrdeprosjektet Global Burden of Diseases, Injuries and Risk Factors Study (GBD), som kvantifiserer ikke-dødelig helsetap slik at det kan måles på samme skala som dødelighet i form av tapte leveår. Summen av tapte leveår og ikke-dødelig helsetap gir sykdomsbyrdemålet helsetapsjusterte leveår (DALY). RESULTATER: Ikke-smittsomme sykdommer som hjerte- og karsykdom, kreft, kronisk obstruktiv lungesykdom og demens var viktige årsaker til tapte leveår hos begge kjønn i Norge i 2016. Ikke-dødelig helsetap utgjorde 52 % av sykdomsbyrden målt i helsetapsjusterte leveår. Spesielt muskel- og skjelettsykdommer, psykiske lidelser og ruslidelser var viktige. De siste ti årene har sykdomsbyrden (i aldersjusterte rater) sunket for mange tilstander som medfører tapte leveår, men ikke for tilstander som gir ikke-dødelig helsetap. FORTOLKNING: Ikke-dødelig helsetap utgjør en stor og økende andel av sykdomsbyrden i den norske befolkningen, noe som vil gi nye utfordringer for helsevesenet.

Educational differences in life expectancy over five decades among the oldest old in Norway.

BACKGROUND: Socioeconomic inequalities in life expectancy have been shown among the middle aged and the youngest of the old individuals, but the situation in the oldest old is less clear. The aim of this study was to investigate trends in life expectancy at ages 85, 90 and 95 years by education in Norway in the period 1961-2009. METHODS: This was a register-based population study including all residents in Norway aged 85 and over. Individual-level data were provided by the Central Population Register and the National Education Database. For each decade during 1961-2009, death rates by 1-year age groups were calculated separately for each sex and three educational categories. Annual life tables were used to calculate life expectancy at ages 85 (e85), 90 (e90) and 95 (e95). RESULTS: Educational differentials in life expectancy at each age were non-significant in the early decades, but became significant over time. For example, for the decade 2000-9, a man aged 90 years with primary education had a life expectancy of 3.4 years, while a man with tertiary education could expect to live for 3.8 years. Similar numbers in women were 4.1 and 4.5 years, respectively. Even among 95-year-old men, statistically significant differences in life expectancy were found by education in the two last decades. CONCLUSION: Education matters regarding remaining life expectancy also for the oldest old in Norway. Life expectancy at these ages is low, so a growth of 0.5 years in the life expectancy differential is sizeable.

Musculoskeletal disorders in Norway: prevalence of chronicity and use of primary and specialist health care services.

BACKGROUND: Uncertainty exists with regards to the extent of prevalence and health care use for musculoskeletal disorders in Norway. The aim of this study was to estimate the prevalence of chronic musculoskeletal disorders and to estimate the prevalence of persons receiving primary and specialist health services for these disorders. METHODS: We used three data-sources. First, four discrete years of the nationally representative cross-sectional Survey of Health and Living Conditions (SHLC) conducted in 2002, 2005, 2008 and 2012 by Statistics Norway. Second, we used the Norwegian Patient Registry (NPR) to estimate the proportion of the population who used specialist health services in 2012. Third, we used the national register dataset for reimbursement of primary care physicians, chiropractors and physiotherapists (KUHR) to estimate the proportion of the population attending primary care physicians, chiropractors or physiotherapists in 2012. Age- and sex-specific prevalence/utilization estimates for musculoskeletal disorders were calculated. RESULTS: In 2012, 18% of men and 27% of women reported musculoskeletal disorders lasting for six months or more in the SHLC. Primary health care services reimbursed for musculoskeletal disorders were used by 37% of women and 30% of men. Of these 32% (women) and 26% (men) were physician contacts and between 5 and 9% physiotherapist or chiropractor or combined contact types. Corresponding numbers for specialist services were 5% in men and 7% in women, where the majority was out-patient consultations. Low back and neck pain were the most common diagnoses both in the general population and as reason for health care utilization. We found that musculoskeletal disorders increased with age, however our results showed no variation in prevalence of chronic disorders between 2002 and 2012. CONCLUSION: Chronic musculoskeletal disorders were common in the general population, with higher prevalence among women compared to men, and increasing prevalence with age. Musculoskeletal disorders had considerable impact on the use of primary and specialist health services in Norway. The use of register data on health service utilization may be a useful source for monitoring population trends, and for estimating the burden in terms of health and health service use.

Comparison of data from the Cause of Death Registry and the Norwegian Patient Register.

BACKGROUND: The quality of the data in the Cause of Death Registry is crucial to produce reliable statistics on causes of death. The Cancer Registry of Norway uses data from the Norwegian Patient Register to request information from hospitals regarding patients registered with cancer in the patient registry, but not in the cancer registry. We wanted to investigate whether data from the Norwegian Patient Register can also be used to advantage in the Cause of Death Registry. MATERIAL AND METHOD: Data from the Cause of Death Registry on deaths that occurred during the period 2009 ­ 2011 (N = 124,098) were collated with data on contact with somatic hospitals and psychiatric institutions during the last year of life, retrieved from the Norwegian Patient Register. Causes of death were grouped in the same way as in standard statistics on causes of death. RESULTS: Out of 124,098 deaths, altogether 34.9% occurred in somatic hospitals. A total of 80.9% of all deceased had been admitted to a somatic hospital and/or had attended an outpatient consultation during their last year of life. The proportion with hospital contact was highest for those whose cause of death was cancer. In cases of unknown/unspecified cause of death, more than half also had contact with hospitals, but the majority of these were registered with only outpatient consultations. Altogether 5.4% of all deceased had been admitted to and/or had an outpatient consultation in a psychiatric institution during their last year of life. For those whose cause of death was suicide, this proportion amounted to 41.8%. INTERPRETATION: In case of incomplete information on the cause of death, data from the Norwegian Patient Register can supply valuable information on where the patient has been treated, thus enabling the Cause of Death Registry to contact the hospitals in question. However, any potential benefit is restricted by the fact that deceased persons with unknown/unspecified causes of death had less frequently been admitted to hospital during their last year of life.

Are the Norwegian health research investments in line with the disease burden?

BACKGROUND: The relationship between research funding across therapeutic areas and the burden of disease in Norway has not been investigated. Further, few studies have looked at the association between national research investments and the global disease burden. The aim of the present study was to analyze the correlation between a significant part of Norwegian investment in health research and the burden of disease across therapeutic areas, using both Norwegian and global burden of disease estimates. METHODS: We used research investment records for 2012 from the Research Council of Norway, and the investment records distributed through liaison committees between regional health authorities and universities. Both were classified by the Health Research Classification System (HRCS). Furthermore, we used the years of life lost and Disability Adjusted Life Years (DALYs) for Norway and globally from the Global Burden of Disease 2010 project. We created a matrix to match the expenditures by HRCS with the values from the Global Burden of Disease project. RESULTS: Disease-specific research funding increased with the Norwegian burden of disease measured as years of life lost (correlation coefficient = 0.73). Similar findings were done when the Norwegian disease burden was measured as DALYs (correlation coefficient = 0.62). The correlation between research funding and the global disease burden was low both when years of life lost (correlation coefficient = 0.11) and DALYs (correlation coefficient = 0.12) were used. Generally, when the disease burden was relatively high in Norway compared with the rest of the world, research investments were also high. CONCLUSIONS: Across therapeutic areas, the Norwegian research investments appeared aligned with the Norwegian disease burden. The correlation between the Norwegian research investments and the global disease burden was much lower.

Does subsidized orthodontic treatment reduce inequalities in access? Evidence from Norway based on population register data.

OBJECTIVE: An important part of Norwegian welfare policy is to provide subsidized orthodontic treatment for children and adolescents. The objective of this policy is that dental services should be allocated according to children's need for treatment, and not according to parents' ability to pay. The probability of receiving orthodontic treatment independent of parent's household income was examined. METHODS: The study population encompassed children and adolescents aged 10-18 years in 2019 (n = 354 439). Information about whether they had started orthodontic treatment was obtained from the Norwegian Health Economics Administration. The key independent variable was net equalized household income. Inequalities were measured using concentration indices, which were estimated according to the severity of the malocclusion (very great need, great need, obvious need and no need). Two indices were used to measure relative inequality: the unstandardized concentration index and the partial concentration index. Absolute inequality was measured using the corrected concentration index. Relevant control variables were included in some of the analyses. RESULTS: The unstandardized indices were in the range 0.04 (very great need) to 0.05 (obvious need). For all three groups of severity, the 95% confidence intervals overlapped. The values of the partial indices were significantly lower than the values of the unstandardized indices. The partial indices were in the range 0.008 (very great need) to 0.03 (obvious need). The 95% confidence intervals for the partial indices did not overlap with the 95% confidence intervals of the unstandardized indices. For all three groups of severity, the indices that measured absolute inequality were close to zero. CONCLUSIONS: It is possible to achieve the egalitarian aim of equality in service provision by subsidizing orthodontic treatment. This is possible within a system where the cost of orthodontic treatment is reimbursed according to the criteria of need. These criteria function in such a way that patients with the greatest need for orthodontic treatment are given the highest priority.

The healthcare costs of increased body mass index-evidence from The Trøndelag Health Study.

BACKGROUND: Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses. METHODS: In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations. RESULTS: There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations. CONCLUSION: The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.

Inequality in access to dental services in a market-based dental care system: A population study from Norway 1975-2018.

OBJECTIVE: To examine income-related inequalities in access to dental services from 1975 to 2018. In Norway, dental care services for adults are privately financed. This may lead to income-related inequalities in access. In the early 1970s, that is, at the beginning of the study period, there were marked inequalities in access to dental services according to personal income. However, from the beginning of the 1970s, there has been a large increase in gross national income per capita in Norway as a result of the growth of the oil and gas industry. This increase in income also meant that people with a low income in 1975 had a rise in their level of income. According to the law of diminishing utility, an increase in income leads to higher consumption of dental services for people with a low level of income compared to people with a high level of income. The study hypothesis is that the inequalities in access to dental services that existed in 1975 became less over time. METHODS: Statistics Norway collected samples of cross-sectional health survey data for the following years: 1975, 1985, 1995, 2002, 2008, 2012 and 2018. For each sample, individuals 21 years and older were drawn randomly from the non-institutionalized adult population using a two-stage stratified cluster sample technique. Inequalities were measured using the concentration index. The dependent variable was the use of dental services during the last year, and the key independent variable was equivalized household income. RESULTS: The concentration index for inequalities in use of dental services according to income decreased from 0.10 (95% CI = 0.09, 0.11) in 1975 to 0.04 (95% CI = 0.03, 0.05) in 2018. The decrease was particularly large from 2002 to 2012. This was a period with a large growth in gross national income. CONCLUSION: People with a low income had a marked increase in their purchasing power from 1975 to 2018. This coincided with an increase in demand for dental care for this low-income group.