Hospital variations in failure to rescue after abdominal surgery: a nationwide, retrospective observational study.

OBJECTIVES: This study aims to determine hospital variation and intensive care unit characteristics associated with failure to rescue after abdominal surgery in Norway. DESIGN: A nationwide retrospective observational study. SETTING: All 52 hospitals in Norway performing elective and acute abdominal surgery. PARTICIPANTS: All 598 736 patients undergoing emergency and elective abdominal surgery from 2011 to 2021. PRIMARY OUTCOME MEASURE: Primary outcome was failure to rescue within 30 days (FTR30), defined as in-hospital or out-of-hospital death within 30 days of a surgical patient who developed at least one complication within 30 days of the surgery (FTR30). Other outcome variables were surgical complications and hospital FTR30 variation. Statistical analysis was conducted separately for general surgery and abdominal surgery. RESULTS: The 30-day postoperative complication rate was 30.7 (183 560 of 598 736 surgeries). Of general surgical complications (n=25 775), circulatory collapse (n=6127, 23%), cardiac arrhythmia (n=5646, 21%) and surgical infections (n=4334, 16 %) were most common and 1507 (5.8 %) patients were reoperated within 30 days. One thousand seven hundred and forty patients had FTR30 (6.7 %). The severity of complications was strongly associated with FTR30. In multivariate analysis of general surgery, adjusted for patient characteristics, only the year of surgery was associated with FTR30, with an estimated linear trend of -0.31 percentage units per year (95% CI (-0.48 to -0.15)). The driving distance from local hospitals to the nearest referral intensive care unit was not associated with FTR30. Over the last decade, FTR30 rates have varied significantly among similar hospitals. CONCLUSIONS: Hospital factors cannot explain Norwegian hospitals' significant FTR variance when adjusting for patient characteristics. The national FTR30 measure has dropped around 30% without a corresponding fall in surgical complications. No association was seen between rural hospital location and FTR30. Policy-makers must address microsystem issues causing high FTR30 in hospitals.

Physiotherapy use is increased for up to nine months after receiving respiratory support for COVID-19.

AIM: To explore whether physiotherapy use is increased after hospitalization with COVID-19 with or without respiratory support vs. other respiratory tract infections (RTI). METHODS: In all Norwegian residents aged 18-80 years who were hospitalized with COVID-19 (N = 5,344) or other RTI (N = 82,235) between July 1st 2017 and August 1st 2021, we used a pre-post study design to explore the weekly individual average physiotherapy use in community care from 12 weeks prior to hospital admission, to 36 weeks (9 months) after hospital discharge for individuals who received and who did not receive respiratory support. RESULTS: Prior to the hospital stay, COVID-19 patients and patients with other RTI had ~ 40-60 physiotherapist consultations per 1000 inpatients per week. COVID-19 patients on respiratory support had a higher increase in physiotherapy use after discharge than persons with other RTI on respiratory support (an additional 27.3 (95% confidence interval = 10.2 to 44.4) consultations per 1000 for men, and 41.8 (13.7 to 69.9) per 1000 for women)). The increase in physiotherapy use lasted for 6 months for men, and 9 months for women. COVID-19 inpatients without respiratory support had a similar up-to-9-months-change post-discharge physiotherapy use as inpatients with other RTI without respiratory support (-0.2 (-0.7 to 0.2) for men, and 0.09 (-6.4 to 6.6) for women). CONCLUSION: The need for physiotherapy was increased for up to 9 months after having COVID-19 requiring respiratory support vs. other RTI requiring respiratory support. No difference between diseases was seen for individuals who were hospitalized but not on respiratory support.

Does a Code for Acute Myocardial Infarction Mean the Same in All Norwegian Hospitals? A Likelihood Approach to a Medical Record Review.

Objective: Health registries are important data sources for epidemiology, quality monitoring, and improvement. Acute myocardial infarction (AMI) is a common, serious condition. Little is known about variation in the positive predictive value (PPV) of a coded AMI diagnosis and its association with hospital quality indicators. The present study aimed to investigate the relationship between PPV and registry-based 30-day mortality after AMI admission and between-hospital variation in PPV. Study Design and Setting: An electronic record review was performed in a nationwide sample of Norwegian hospitals. Clinical signs and cardiac troponin measurements were abstracted and analyzed using a mixture model for likelihood ratios and parametric bootstrapping. Results: The overall PPV was estimated to be 97%. We found no statistically significant association between hospital PPV and the classification of hospitals into low, intermediate, and high registry-based 30-day mortality. There was significant variation between hospitals, with a PPV range of 91-100%. Conclusion: We found no evidence that variation in PPV of AMI diagnosis can explain variation between hospitals in registry-based 30-day mortality after admission. However, PPV varied significantly between hospitals. We were able to use a very efficient statistical approach to the analysis and handling of various sources of uncertainty.

The impact of daylight on suicide rates.

Since Durkheim and Morselli found a spring peak in suicides in the late 19th century, researchers have presented possible explanations, including daylight variation, for this seasonal pattern. Our identification strategy exploits the idiosyncratic variation in daylight within Norwegian regions, arising from the country's substantial latitudinal range. We use full population data for a period of 45 years in a pre-registered research design. We find a small and non-significant relationship: One extra hour of daylight increases the suicide rate by merely 0.75 % (95 % CI: -0.4 % to 1.9 %).

Pandemic trends in health care use: From the hospital bed to self-care with COVID-19.

AIM: To explore whether the acute 30-day burden of COVID-19 on health care use has changed from February 2020 to February 2022. METHODS: In all Norwegians (N = 493 520) who tested positive for SARS-CoV-2 in four pandemic waves (February 26th, 2020 -February 16th, 2021 (1st wave dominated by the Wuhan strain), February 17th-July 10th, 2021 (2nd wave dominated by the Alpha variant), July 11th-December 27th, 2021 (3rd wave dominated by the Delta variant), and December 28th, 2021 -January 14th, 2022 (4th wave dominated by the Omicron variant)), we studied the age- and sex-specific share of patients (by age groups 1-19, 20-67, and 68 or more) who had: 1) Relied on self-care, 2) used outpatient care (visiting general practitioners or emergency ward for COVID-19), and 3) used inpatient care (hospitalized ≥24 hours with COVID-19). RESULTS: We find a remarkable decline in the use of health care services among COVID-19 patients for all age/sex groups throughout the pandemic. From 83% [95%CI = 83%-84%] visiting outpatient care in the first wave, to 80% [81%-81%], 69% [69%-69%], and 59% [59%-59%] in the second, third, and fourth wave. Similarly, from 4.9% [95%CI = 4.7%-5.0%] visiting inpatient care in the first wave, to 3.6% [3.4%-3.7%], 1.4% [1.3%-1.4%], and 0.5% [0.4%-0.5%]. Of persons testing positive for SARS-CoV-2, 41% [41%-41%] relied on self-care in the 30 days after testing positive in the fourth wave, compared to 16% [15%-16%] in the first wave. CONCLUSION: From 2020 to 2022, the use of COVID-19 related outpatient care services decreased with 29%, whereas the use of COVID-19 related inpatient care services decreased with 80%.

Healthcare use in 700 000 children and adolescents for six months after covid-19: before and after register based cohort study.

OBJECTIVES: To explore whether and for how long use of healthcare services is increased among children and adolescents after covid-19. DESIGN: Before and after register based study. SETTING: General population of Norway. PARTICIPANTS: Norwegians aged 1-19 years (n=706 885) who were tested for SARS-CoV-2 from 1 August 2020 to 1 February 2021 (n=10 279 positive, n=275 859 negative) or not tested (n=420 747) and were not admitted to hospital, by age groups 1-5, 6-15, and 16-19 years. MAIN OUTCOME MEASURES: Monthly percentages of all cause and cause specific healthcare use in primary care (general practitioner, emergency ward) and specialist care (outpatient, inpatient) from six months before to about six months after the week of being tested for SARS-CoV-2, using a difference-in-differences approach. RESULTS: A substantial short term relative increase in primary care use was observed for participants during the first month after a positive SARS-CoV-2 test result compared with those who tested negative (age 1-5 years: 339%, 95% confidence interval 308% to 369%; 6-15 years: 471%, 450% to 491%; 16-19 years: 401%, 380% to 422%). Use of primary care for the younger age groups was still increased at two months (1-5 years: 22%, 4% to 40%; 6-15 years: 14%, 2% to 26%) and three months (1-5 years: 26%, 7% to 46%, 6-15 years: 15%, 3% to 28%), but not for the oldest group (16-19 years: 11%, -2% to 24% and 6%, -7% to 19%, respectively). Children aged 1-5 years who tested positive also showed a minor long term (≤6 months) relative increase in primary care use (13%, -0% to 26%) that was not observed for the older age groups, compared with same aged children who tested negative. Results were similar yet the age differences less pronounced compared with untested controls. For all age groups, the increase in primary care visits was due to respiratory and general or unspecified conditions. No increased use of specialist care was observed. CONCLUSION: Covid-19 among children and adolescents was found to have limited impact on healthcare services in Norway. Preschool aged children might take longer to recover (3-6 months) than primary or secondary school students (1-3 months), usually because of respiratory conditions.

Impacts of mild COVID-19 on elevated use of primary and specialist health care services: A nationwide register study from Norway.

AIM: To explore the temporal impact of mild COVID-19 on need for primary and specialist health care services. METHODS: In all adults (≥20 years) tested for SARS-CoV-2 in Norway March 1st 2020 to February 1st 2021 (N = 1 401 922), we contrasted the monthly all-cause health care use before and up to 6 months after the test (% relative difference), for patients with a positive test for SARS-CoV-2 (non-hospitalization, i.e. mild COVID-19) and patients with a negative test (no COVID-19). RESULTS: We found a substantial short-term elevation in primary care use in all age groups, with men generally having a higher relative increase (men 20-44 years: 522%, 95%CI = 509-535, 45-69 years: 439%, 95%CI = 426-452, ≥70 years: 199%, 95%CI = 180-218) than women (20-44 years: 342, 95%CI = 334-350, 45-69 years = 375, 95%CI = 365-385, ≥70 years: 156%, 95%CI = 141-171) at 1 month following positive test. At 2 months, this sex difference was less pronounced, with a (20-44 years: 21%, 95%CI = 13-29, 45-69 years = 38%, 95%CI = 30-46, ≥70 years: 15%, 95%CI = 3-28) increase in primary care use for men, and a (20-44 years: 30%, 95%CI = 24-36, 45-69 years = 57%, 95%CI = 50-64, ≥70 years: 14%, 95%CI = 4-24) increase for women. At 3 months after test, only women aged 45-70 years still had an increased primary care use (14%, 95%CI = 7-20). The increase was due to respiratory- and general/unspecified conditions. We observed no long-term (4-6 months) elevation in primary care use, and no elevation in specialist care use. CONCLUSION: Mild COVID-19 gives an elevated need for primary care that vanishes 2-3 months after positive test. Middle-aged women had the most prolonged increased primary care use.

Correlation between compensated patient claims and 30-day mortality.

OBJECTIVE: To test if compensation claims from patients (reported to the Norwegian System of Patient Injury Compensation) are correlated with the existing quality indicator of 30-day mortality (based on data from Norwegian Patient Registry). This correlation has not been previously evaluated. DESIGN: The association between patient claims and 30-day mortality at hospital trust level was assessed by the Pearson correlation coefficient. SETTING: The Norwegian System of Patient Injury Compensation is a governmental agency under the Ministry of Health and Care Services and deals with patient-reported complaints about incorrect treatment in the public and private healthcare services. Patient-reported claims may be an indicator of healthcare quality, as 30-day mortality. PARTICIPANTS: All 19 Norwegian hospital trusts. INTERVENTIONS: : None. MAIN OUTCOME MEASURE: Patient claims rates, 30-day mortality and Pearson correlation coefficient. RESULTS: Both number of deaths within 30 days and number of claims have declined over time. High correlation (0.77, P < 0.001) was found between number of deaths within 30 days and the total number of claims. In addition, an even stronger association was found with approved claims, with a correlation coefficient of 0.83 (P < 0.001). Moreover, adjusted 30-day mortality was significantly correlated with the patient-claim rate using number of bed-days as denominator, but not when using number of discharges. CONCLUSIONS: The results from the present study indicate an association between compensation claims from patients and 30-day mortality, suggesting that both parameters reflect the latent quality of care for the hospital trusts, but they may capture different aspects of care.

The value of Facebook in nation-wide hospital quality assessment: a national mixed-methods study in Norway.

OBJECTIVES: The objective was to assess the possibility of using a combination of official and unofficial Facebook ratings and comments as a basis for nation-wide hospital quality assessments in Norway. METHODS: All hospitals from a national cross-sectional patient experience survey in 2015 were matched with corresponding Facebook ratings. Facebook ratings were correlated with both case-mix adjusted and unadjusted patient-reported experience scores, with separate analysis for hospitals with official site ratings and hospitals with unofficial site ratings. Facebook ratings were also correlated with patient-reported incident scores, hospital size, 30-day mortality and 30-day readmission. Facebook comments from 20 randomly selected hospitals were analysed, contrasting the content and sentiments of official versus unofficial Facebook pages. RESULTS: Facebook ratings were significantly correlated with most patient-reported indicators, with the highest correlations relating to unadjusted scores for organisation (0.60, p<0.000) and nursing services (0.57, p<0.000). Facebook ratings were significantly correlated with hospital size (-0.40, p=0.003) and 30-day mortality (0.31, p=0.040). Sentiment analysis showed that 84.7% of the comments from unofficial Facebook sites included neutral comments that did not give any specific description of experiences of the quality of care at the hospital. Content analysis identified common themes on official and unofficial Facebook pages. CONCLUSIONS: Facebook ratings were associated with patient-reported indicators, hospital size, and 30-day mortality. Qualitative comments from official Facebook are more relevant for hospital evaluation than unofficial sites. More research is needed on using Facebook ratings as a standalone indicator of patient experiences in national quality measurement, and such ratings should be reported together with research-based patient experience indicators and with explicit criteria for the inclusion of unofficial sites.

Hospital Variation in 30-Day Mortality for Patients With Stroke; The Impact of Individual and Municipal Socio-Demographic Status.

Background Thirty-day mortality after hospitalization for stroke is commonly reported as a quality indicator. However, the impact of adjustment for individual and/or neighborhood sociodemographic status ( SDS ) has not been well documented. This study aims to evaluate the role of individual and contextual sociodemographic determinants in explaining the variation across hospitals in Norway and determine the impact when testing for hospitals with low or high mortality. Methods and Results Patient Administrative System data on all 45 448 patients admitted to hospitals in Norway with an incident stroke diagnosis from 2005 to 2009 were included. The data were merged with data from several databases to obtain information on vital status (dead/alive) and individual SDS variables. Logistic regression models were compared to estimate the predictive effect of individual and neighborhood SDS on 30-day mortality and to determine outlier hospitals. All individual SDS factors, except travel time, were statistically significant predictors of 30-day mortality. Of the municipal variables, only the municipal variable proportion of low income was statistically significant as a predictor of 30-day mortality. Including sociodemographic characteristics of the individual and other characteristics of the municipality improved the model fit. However, performance classification was only changed for 1 (out of 56) hospital, from "significantly high mortality" to "nonoutlier." Conclusions Our study showed that those stroke patients with a lower SDS have higher odds of dying after 30 days compared with those with a higher SDS , although this did not have a substantial impact when classifying providers as performing as expected, better than expected, or worse than expected.

How Many Deaths from Colorectal Cancer Can Be Prevented by 2030? A Scenario-Based Quantification of Risk Factor Modification, Screening, and Treatment in Norway.

Background: Colorectal cancer mortality can be reduced through risk factor modification (adherence to lifestyle recommendations), screening, and improved treatment. This study estimated the potential of these three strategies to modify colorectal cancer mortality rates in Norway.Methods: The potential reduction in colorectal cancer mortality due to risk factor modification was estimated using the software Prevent, assuming that 50% of the population in Norway-who do not adhere to the various recommendations concerning prevention of smoking, physical activity, body weight, and intake of alcohol, red/processed meat, and fiber-started to follow the recommendations. The impact of screening was quantified assuming implementation of national flexible sigmoidoscopy screening with 50% attendance. The reduction in colorectal cancer mortality due to improved treatment was calculated assuming that 50% of the linear (positive) trend in colorectal cancer survival would continue to persist in future years.Results: Risk factor modification would decrease colorectal cancer mortality by 11% (corresponding to 227 prevented deaths: 142 men, 85 women) by 2030. Screening and improved treatment in Norway would reduce colorectal cancer mortality by 7% (149 prevented deaths) and 12% (268 prevented deaths), respectively, by 2030. Overall, the combined effect of all three strategies would reduce colorectal cancer mortality by 27% (604 prevented deaths) by 2030.Conclusions: Risk factor modification, screening, and treatment all have considerable potential to reduce colorectal cancer mortality by 2030, with the largest potential reduction observed for improved treatment and risk factor modification.Impact: The estimation of these health impact measures provides useful information that can be applied in public health decision-making. Cancer Epidemiol Biomarkers Prev; 26(9); 1420-6. ©2017 AACR.

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture.

BACKGROUND: The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. METHODS: We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed. RESULTS: The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes. CONCLUSIONS: This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.

Regional variations in cancer survival: Impact of tumour stage, socioeconomic status, comorbidity and type of treatment in Norway.

Cancer survival varies by place of residence, but it remains uncertain whether this reflects differences in tumour, patient and treatment characteristics (including tumour stage, indicators of socioeconomic status (SES), comorbidity and information on received surgery and radiotherapy) or possibly regional differences in the quality of delivered health care. National population-based data from the Cancer Registry of Norway were used to identify cancer patients diagnosed in 2002-2011 (n = 258,675). We investigated survival from any type of cancer (all cancer sites combined), as well as for the six most common cancers. The effect of adjusting for prognostic factors on regional variations in cancer survival was examined by calculating the mean deviation, defined by the mean absolute deviation of the relative excess risks across health services regions. For prostate cancer, the mean deviation across regions was 1.78 when adjusting for age and sex only, but decreased to 1.27 after further adjustment for tumour stage. For breast cancer, the corresponding mean deviations were 1.34 and 1.27. Additional adjustment for other prognostic factors did not materially change the regional variation in any of the other sites. Adjustment for tumour stage explained most of the regional variations in prostate cancer survival, but had little impact for other sites. Unexplained regional variations after adjusting for tumour stage, SES indicators, comorbidity and type of treatment in Norway may be related to regional inequalities in the quality of cancer care.

A comparison of relative and cause-specific survival by cancer site, age and time since diagnosis.

Relative survival (RS) estimates are widely used by cancer registries, mainly because they do not rely on the well-documented deficiencies of cause of death information. The aim of our study was to compare 5-year cause-specific survival (CSS) estimates and 5-year RS estimates for different cancer sites by age and time since diagnosis, and discuss possible reasons for observed differences. Using data from the Cancer Registry of Norway, we identified 200,008 patients diagnosed with cancer at one of the 48 sites included in this analysis during the period 1996-2005, and followed them up until the end of 2010. CSS estimates were calculated (i) considering cause of death to be the cancer that was originally diagnosed and (ii) considering the cause of death to be a cancer within the same organ system. For most cancer sites the difference between CSS and RS estimates was small (<5%). The greatest differences were seen for rarer cancers such as mediastinum and Kaposi sarcoma. Including deaths from the same organ system in the calculation of CSS further reduced the differences for many sites. For younger age groups and shorter time since diagnosis, RS and CSS estimates tended to be similar, whereas CSS estimates tended to be lower than RS estimates with longer time since diagnosis in the oldest age groups. When compared to RS estimates CSS estimates were reliable for most of the cancer sites included in our analysis. There are, however, some exceptions where CSS estimates may not be recommended, including for rarer cancers and for patients aged 85 and above.