Comparative health services research across populations: the unused opportunities in big data

Health claims databases used for kidney research around the world

Health claims databases offer opportunities for studies on large populations of patients with kidney disease and health outcomes in a non-experimental setting. Among others, their unique features enable studies on healthcare costs or on longitudinal, epidemiological data with nationwide coverage. However, health claims databases also have several limitations. Because clinical data and information on renal function are often lacking, the identification of patients with kidney disease depends on the actual presence of diagnosis codes only. Investigating the validity of these data is therefore crucial to assess whether outcomes derived from health claims data are truly meaningful. Also, one should take into account the coverage and content of a health claims database, especially when making international comparisons. In this article, an overview is provided of international health claims databases and their main publications in the area of nephrology. The structure and contents of the Dutch health claims database will be described, as well as an initiative to use the outcomes for research and the development of the Dutch Kidney Atlas. Finally, we will discuss to what extent one might be able to identify patients with kidney disease using health claims databases, as well as their strengths and limitations.

Risk of Recurrence After Surviving Severe Sepsis: A Matched Cohort Study

OBJECTIVES

To examine the risk of recurrence in adults who survived first-episode severe sepsis for at least 3 months.

DESIGN

A matched cohort study.

SETTING

Inpatient claims data from Taiwan's National Health Insurance Research Database.

SUBJECTS

We analyzed 10,818 adults who survived first-episode severe sepsis without recurrence for at least 3 months in 2000 (SS group; mean age, 62.7 yr; men, 54.7%) and a group of age/sex-matched (1:1) population controls who had no prior history of severe sepsis. All subjects were followed from the study entry to the occurrence of end-point, death, or until December 31, 2008, whichever date came first.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Primary end-point was severe sepsis that occurred after January 1, 2001 (the study entry). Relative risk of the end-point was assessed using competing risk regression model. During the follow-up period, severe sepsis and death occurred in 35.0% and 26.5% of SS group and in 4.3% and 18.6% of controls, respectively, representing a covariate-adjusted sub-hazard ratio of 8.89 (95% CI, 8.04-9.83) for the risk of recurrence. In stratified analysis by patient characteristics, the sub-hazard ratios ranged from 7.74 in rural area residents to 23.17 in young adults. In subgroup analysis by first-episode infection sites in SS group, the sub-hazard ratios ranged from 4.82 in intra-abdominal infection to 9.99 in urinary tract infection.

CONCLUSIONS

Risk of recurrence after surviving severe sepsis is substantial regardless of patient characteristics or infection sites. Further research is necessary to find underlying mechanisms for the high risk of recurrence in these patients.

Big data for bipolar disorder

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

The epidemiology of hospitalised acute kidney injury not requiring dialysis in England from 1998 to 2013: retrospective analysis of hospital episode statistics

AIMS

Epidemiology studies of acute kidney injury (AKI) have focused on cases requiring dialysis but those not requiring dialysis represent the majority. To address this gap, we interrogated hospital episode statistics (HES) to investigate population trends in temporal epidemiology of AKI not requiring dialysis between 1998 and 2013.

METHODOLOGY

In this retrospective observational study of HES data covering the entire English National Health Service, we identified 1,136,167 AKI events, not requiring dialysis, diagnosed between 1998 and 2013. We explored the effect of age, gender, ethnicity, Charlson's comorbidity score (CCS), method of admission, diagnosis period and AKI in diagnosis codes on temporal changes in the incidence and case-fatality of AKI with specific examination of its predictors.

RESULT

The incidence of AKI increased from 15,463 cases (317 pmp) in 1998-1999 to 213,700 cases (3995 pmp) in 2012-2013. There was increase in proportion of people over 75 years from 51.1% in 1998-1999 to 63.4% in 2012-2013. Overall unadjusted case-fatality decreased from 42.3% in 1998-2003 to 27.1% in 2008-2013, p < 0.001. Compared with 1998-2003, the multivariable adjusted odds ratio for death was 0.64 in 2003-2008 (95% CI 0.63-0.65) and 0.35 in 2008-2013 (95% CI 0.34-0.35). Odds for death were higher for patients over 85 years (2.93; 95% CI 2.89-2.97), CCS of more than five (2.75; 95% CI 2.71-2.79), emergency admissions (2.14; 95% CI 2.09-2.18) and AKI in the secondary diagnosis code (1.35; 95% CI 1.33-1.36) and AKI in other diagnoses codes (2.17; 95% CI 2.15-2.20).

CONCLUSIONS

In England, the incidence of AKI not requiring dialysis has increased and case-fatality has decreased over last 15 years. Efforts to reduce the incidence of AKI and improve survival should focus on elderly people, emergency admissions and those with multi-morbidity.