Nationella kvalitetsregister har sedan lång tid redovisat skillnader mellan vårdgivare vad gäller insatser och resultat, såsom överlevnad. Dessa fynd har varit vägledande för viktiga förbättringsarbeten i vården. Emellertid visar nationella data liknande skillnader också inom vårdgivare, mellan patienter med olika ålder eller kön samt mellan grupper med olika socioekonomisk situation (SES), särskilt utbildning och inkomst. Det är sannolikt att liknande SES skillnader i vårdens insatser och resultat, som observerats i nationella data, också kan identifieras i Östergötland. I Östergötland finns omfattande data om vårdens insatser och resultat. Dock kräver koppling av sjukvårdsdata till SCB-data avseende SES mått, såsom utbildning och yrke, idag forskningsstudier. Därför kan vi idag inte ge underlag om vårdens jämlikhet såsom skillnader i vårdens kvalitet och resultat utifrån SES, som stöd till politiska beslut om jämlik vård eller till löpnade förbättringsarbete. Denna rapport beskriver ett pilotprojekt som har analyserat en forskningsdatabas där data, efter godkännande av forskningsetisk kommitté, har kopplats till data på patienternas SES. Syfte: Att analysera skillnader i vårdens insatser och resultat baserat på SES, för patienter som vårdats för akut hjärtinfarkt i Östergötland.

Metod:

Data för patienter vårdade vid sjukhus i Östergötland för akut hjärtinfarkt mellan 2006 och 2016 inhämtandes från kvalitetsregistret SWEDEHEART; delregistren RIKS-HIA (N= 8498) och SEPHIA (N = 4094), SCB databas samt Socialstyrelsens administrativa register. SES mått: Utbildning (högsta uppnådda nivå) och inkomst (disponibel inkomst i tertiler). Bestämningsfaktorer: ålder, kön, civilstånd, tidigare sjukdom, medicinering, riskfaktorer (rökning, BMI, systoliskt blodtryck, lipider), rutin labdata (Hb, njurfunktion, blodsocker), medicinska undersökningar/ingrepp (Ekokardiografisk undersökning, ballongvidgning (PCI) och by-passoperation (CABG), läkemedel vid utskrivning, deltagande i SEPHIA. Utfall: MACE (Major Adverse Cardiovascular Event; hjärtinfarkt, stroke eller död), samt enskilda komponenter av MACE inom 5 år. Skillnader i risk (hazard ratio, HR) mellan inkomstgrupper analyserades via Cox regressionsmodeller med stegvis kontroll för bestämningsfaktorer och möjliga förväxlingsfaktorer.

Resultat:

Risk för MACE efter 5 år var ökad hos patienter med lägst jämfört med högst inkomst, HR 2.88 (CI 2.59-3.20). En stor del av skillnaden i prognos förklarades av skillnad i ålder mellan grupperna. Övriga bestämningsfaktorer förklarade tillsammans en ytterligare del av skillnaden. Efter justering för dessa kvarstod en överrisk på ca 30% för de med lägst inkomst, HR 1.34 (CI 1.12-1.60). Vid analys av patienterna <75 år var risk för MACE på samma sätt förhöjd hos de med lägst inkomst; HR 2.09, (CI 1.77-2.46). Denna påverkades marginellt av kontroll för ålder, kön och civilstånd, men sjönk substantiellt efter kontroll för patienternas riskfaktorer och tidigare sjukdom vid ankomst, HR 1.33 (1.09-1.62). Kontroll, för insatser under vårdtiden påverkade risken marginellt, men efter kontroll också för deltagande i SEPHIA var risken för MACE efter 5 år inte längre statistiskt signifikant HR 1,27 (CI; 0.99–1.63).

Konklusion:

Vi finner i analyser av kvalitetsregisterdata över patienter som vårdats för akut hjärtinfarkt betydligt sämre prognos för patientgruppen med lägst jämfört med högst SES, mätt som disponibel inkomst. Vår analys kan inte peka på orsakssamband och inte avgöra vilka faktorer som är viktigast, utöver ålder. Skillnaden i prognos påverkades dock ganska lite av skillnader i behandling under det akuta vårdtillfället. Däremot påverkades prognosen, för de yngre patienterna (under 75 år), av skillnader i riskfaktorer och tidigare sjukdomar. Efter justering även för deltagande i sekundärpreventivt program var skillnaden i prognos inte längre signifikant. Det är angeläget att identifiera och tillämpa sätt att stödja och hjälpa patienter med lägre SES för att förbättra prognosen vid insjuknande i hjärtinfarkt. Våra data belyser betydelsen av preventiva insatser, både sekundärpreventiva, för att minska återinsjuknande, och primärpreventiva för att minska riskfaktorer i hela befolkningen och särskilt i grupper med lägre socioekonomi. En med individen samproducerad hälso- och sjukvård, kommunikation i en hälsolitterat organisation och vårdmöten som stärker individens resurser, är möjliga vägar till förbättring.

Läkaren har en viktig roll att uppmärksamma ohälsosamma levnadsvanor, ge personcentrerat rådgivande samtal och hänvisa vidare vid behov. 

Kunskap och färdigheter kring samtal om levnadsvanor som ingår i ST-målbeskrivningen för kliniska specialiteter bör säkerställas i lämpligt kursformat.

Kursen bör utgå från nationella riktlinjer för levnadsvanor och nationellt vårdprogram för levnadsvanor.

Etiska principer lägger grunden för levnadsvane­arbetet.

Kursen bör innehålla kunskap om hur levnadsvanor påverkar symtom, riskfaktorer och sjukdomar. 

Att träna på personcentrerat samtal samt att diskutera lokala rutiner och läkarens roll i arbetet med levnadsvanor är värdefulla inslag i kursen.

The physician has an important role in identifying unhealthy lifestyles, offering counselling and, if necessary, referring the patient to another profession or care unit. Therefore, knowledge and skills related to lifestyle habits are included in the goal description for residents in all clinical specialities. 

SK courses in lifestyle habits should be based on National Guidelines for Prevention and Treatment – Support for Governance and Management (2018), issued by the Swedish National Board of Health and Welfare. Preferably, the course should include training sessions where the participants can practice the counselling techniques. The ethical principles provide the foundation for work related to lifestyle habits. The course should include evidence-based knowledge on how lifestyle habits affect symptoms, risk factors, and diseases, as well as the effect of changing lifestyle habits. Also, discussions about local routines and the physician’s role in addressing lifestyle habits are valuable components of the course.

OBJECTIVES: To investigate the association between type I collagen α1 chain (COL1α1) levels and coronary artery disease (CAD) by using absolute quantification in plasma. Also, to investigate the correlates of COL1α1 to clinical characteristics and circulating markers of collagen metabolism.

DESIGN: Life conditions, Stress and Health (LSH) study: prospective cohort study, here with a nested case-control design.Assessing Platelet Activity in Coronary Heart Disease (APACHE) study: prospective cohort study.

SETTING: LSH: primary care setting, southeast Sweden.APACHE: cardiology department, university hospital, southeast Sweden.

PARTICIPANTS: LSH: 1007 randomly recruited individuals aged 45-69 (50% women). Exclusion criteria was serious disease. After 13 years of follow-up, 86 cases with primary endpoint were identified and sex-matched/age-matched to 184 controls.

APACHE: 125 patients with myocardial infarction (MI), 73 with ST-elevation MI and 52 with non-ST-elevation MI.

EXCLUSION CRITERIA: Intervention study participation, warfarin treatment and short life expectancy.

PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome was the association between baseline COL1α1 and first-time major event of CAD, defined as fatal/non-fatal MI or coronary revascularisation after 13 years. Secondary outcomes were the association between the collagen biomarkers PRO-C1 (N-terminal pro-peptide of type I collagen)/C1M (matrix metalloproteinase-mediated degradation of type I collagen) and CAD; temporal change of COL1α1 after acute MI up to 6 months and lastly, correlates between COL1α1 and patient characteristics along with circulating markers of collagen metabolism.

RESULTS: COL1α1 levels were associated with CAD, both unadjusted (HR=0.69, 95% CI=0.56 to 0.87) and adjusted (HR=0.55, 95% CI=0.41 to 0.75). PRO-C1 was associated with CAD, unadjusted (HR=0.62, 95% CI=0.47 to 0.82) and adjusted (HR=0.61, 95% CI=0.43 to 0.86), while C1M was not. In patients with MI, COL1α1 remained unchanged up to 6 months. COL1α1 was correlated to PRO-C1, but not to C1M.

CONCLUSIONS: Plasma COL1α1 was independently and inversely associated with CAD. Furthermore, COL1α1 appeared to reflect collagen synthesis but not degradation. Future studies are needed to confirm whether COL1α1 is a clinically useful biomarker of CAD.

The need to visualise the complexity of the determinants of population health and their interactions inspired the development of the rainbow model. In this commentary we chronicle how variations of this model have emerged, including the initial models of Haglund and Svanstrom (1982), Dahlgren and Whitehead (1991), and the ostgota model (2014), and we illustrate how these models have been influential in both public health and beyond. All these models have strong Nordic connections and are thus an important Nordic contribution to public health. Further, these models have underpinned and facilitated other examples of Nordic leadership in public health, including practical efforts to address health inequalities and design new health policy approaches. Apart from documenting the emergence of rainbow models and their wide range of contemporary uses, we examine a range of criticisms levelled at these models - including limitations in methodological development and in scope. We propose the time is ripe for an updated generic determinants of health model, one that elucidates and preserves the core value in older models, while recognising the developments that have occurred over the past decades in our understanding of the determinants of health. We conclude with an example of a generic model that fulfills the general purposes of a determinants of health model while maintaining the necessary scope for further adjustments to be made in the future, as well as adjustments to location or context-specific purposes, in education, research, health promotion and beyond.

Objectives

The aim of this study was to distinguish independent and shared effects of material/structural factors and psychosocial resources in explaining educational inequalities in self-rated health (SRH) by using structural equation modelling.

Study design

Cross-sectional survey.

Methods

Data were derived from a questionnaire sent to a random sample of the population in five counties in Sweden in 2008. The study population (aged 25–75 years) included 15,099 men and 17,883 women. Exploratory structural equation modelling was used to analyse the pathways from educational level to SRH.

Results

The pathway including both material/structural factors (e.g. financial buffer and unemployment) and psychosocial resources (e.g. sense of coherence and social participation) explained about 40% of educational differences in SRH for both men and women. The pathways including only the independent effects of psychosocial resources (14% in men and 20% in women) or material/structural factors (9% and 18%, respectively) explained substantial but smaller proportions of the differences.

Conclusions

The major pathway explaining educational inequalities in SRH included both material/structural factors and psychosocial resources. Therefore, to reduce educational inequalities in SRH, interventions need to address both material/structural conditions and psychosocial resources across educational groups.

Background: The health behaviors smoking, risky alcohol consumption, insufficient physical activity, and poor diet constitute the main contributors to non-communicable diseases. Clustering of risk behaviors is common and increases the risk of these diseases. Despite health benefits, it is difficult to change health behaviors. Psychosocial factors could play a role in health behavior change, with research showing relationships between unfavorable psychosocial factors and health risk behaviors. However, many studies only investigated one or two health behaviors at a time. The present study, therefore, aimed to investigate associations between a broad range of psychosocial factors and multiple health risk behaviors in a general middle-aged population in Sweden. Methods: A cross-sectional design was used to investigate a random sample from the general population in Sweden (n = 1007, 45-69 years, 50% women). Questionnaire data on health behaviors (smoking, alcohol consumption, physical activity, and fruit/vegetable intake) and psychosocial factors, with both psychological and social resources (social integration, emotional support, perceived control, self-esteem, sense of coherence and trust) and psychological risk factors (cynicism, vital exhaustion, hopelessness and depressiveness), were analyzed. Logistic and ordinal logistic regression were used to analyze associations between psychosocial factors and multiple (0-1, 2 or 3-4) health risk behaviors. Results: A total of 50% of the sample had two health risk behaviors and 18% had three health risk behaviors. After adjusting for age, sex, education, employment status, and immigrant status, eight out of 10 psychosocial factors (exceptions: social integration and self-esteem) showed significant odds ratios (ORs) in the expected directions; low levels of psychosocial resources and high levels of psychosocial risk factors were associated with multiple risk behaviors. The strongest associations with multiple risk behaviors were seen for vital exhaustion (adjusted (adj.) OR 1.28; confidence interval (CI) 1.11-1.46), depressiveness (adj. OR 1.32, CI 1.14-1.52), and trust (adj. OR 0.80, CI 0.70-0.91). When controlling for all psychosocial factors in the same model, only the association with trust remained statistically significant (adj. OR 0.89, CI 0.73-1.00, p = 0.050). Associations with individual health behaviors were fewer and scattered, with no psychosocial factor being related to all four behaviors. Conclusions: Examining associations between a broad range of psychosocial factors and multiple health risk behaviors revealed consistent and significant associations for almost all psychosocial factors. These associations were stronger compared to associations to single health risk behaviors. Our findings support the relevance of considering psychosocial aspects in interventions aimed at health behavior change, especially for people with multiple health risk behaviors.

The aim of this study was to evaluate the usefulness of EQ-5D as a patient-reported outcome measure using different analytical methods. Especially we used the Paretian Classification of Health Change, to see if this gave better information compared to measures that are more traditional. For the evaluation we used data from patients with chronic heart failure (HF).

Purpose To study the predictive ability of each of the eight scales of SF-36 on 13-year all-cause mortality and incident coronary heart disease (CHD) in a general middle-aged population. Methods The population-based, longitudinal "Life-conditions, Stress and Health" study, in 2003-2004 enrolled 1007 persons aged 45-69 years (50% female), randomly sampled from the general population in ostergotland, Sweden. Variables at baseline included the SF-36 (health-related quality of life, HRQoL) and self-reported disease. Incident CHD (morbidity and mortality) and all-cause mortality data for the study population during the first 13 years from baseline were obtained from national Swedish registries. Results Seven of the eight SF-36 scales predicted CHD (sex- and age-adjusted Hazard Ratios up to 2.15; p amp;lt;= 0.05), while only the Physical Functioning scale significantly predicted all-cause mortality. Further adjustments for presence of (self-reported) disease did not, in most cases, alter these significant predictions. Conclusion Low SF-36 scores predict risk of CHD, also after adjustment for present disease, supporting the biopsychosocial model of health and disease. Measures of HRQoL yield important information and can add to the cardiopreventive toolbox, including primary prevention efforts, as it is such a simple and relatively inexpensive tool.

Identification of biomarkers can help monitor and prevent cardiovascular disease (CVD) risk. We performed an exploratory analysis to identify potential biomarkers for coronary heart disease (CHD) in participants from the Life Conditions, Stress, and Health study. A total of 1,007 participants (50% women), randomly selected from the general population, were followed for incident CHD at 8 and 13 years of follow-up. Plasma levels of 184 CVD-related biomarkers were measured in samples collected at baseline in 86 cases with CHD and 184 age- and sex-matched controls by proximity extension assay. Biomarker levels were presented as normalized protein expression values (log 2 scale). After adjusting for confounding factors, 6 biomarkers showed significant association with incident CHD at 13 years. In a sensitivity analysis, this association remained significant at 8 years for 3 biomarkers; collagen alpha-1(I) chain (COL1A1), bone morphogenetic protein-6 (BMP-6), and interleukin-6 receptor alpha chain (IL-6R alpha). When entering these biomarkers in the full adjustment model simultaneously, their association with incident CHD at 13 years remained significant, hazards ratio being 0.671, 0.335, and 2.854, respectively per unit increase in normalized protein expression values. Subjects with low COL1A1, low BMP-6, and high IL-6R alpha levels had a hazards ratio of 5.097 for incident CHD risk (p = 0.019), compared with those without. In conclusion, we identified COL1A1, BMP-6 and IL-6Ra as biomarkers for incident CHD over a long-term follow-up in this exploratory analysis. For COL1A1 and BMP-6 this has not been previously reported. Further studies are needed to confirm our findings and establish their clinical relevance. (C) 2019 Elsevier Inc. All rights reserved.