OLAP for health statistics: how to turn a simple spreadsheet into a powerful analytical tool

Over the last ten years, Online Analytical Processing (OLAP) has become a very popular tool for interactive analysis of multidimensional information. Providing online operation and flexible summarising, tabulating and charting options, it has become an essential part of the decision support process in corporate setting. Our aim is to demonstrate how easily applicable and useful OLAP can be in the public sector. To achieve that, we used data compiled from different sources for the purpose of exploring the relations between causes of death (according to ICD-10) and socio-economic characteristics (educational level, marital status, profession, etc.) for selected years 1992, 1995 and 1998 in Slovenia. Using a standard personal computer and the Windows® platform, the application was implemented in Microsoft® Excel 2000, without any programming. After data cleansing (elimination of incorrect entries, duplicates and inconsistencies based on exploratory statistical methods), the case-based spreadsheet data was instantly converted into an OLAP application with the user-friendly pivot table technology. A bonus of this approach is that the results can be made directly accessible over the WWW by publishing the workbook to a web server. Provided that the user has Microsoft® Internet Explorer and Microsoft® Office 2000 installed, all the drill-in, drill-out and dimension-swapping capabilities are accessible within the browser, while the data source remains fully protected. Privacy constraints are respected since all the information is only provided at the aggregate level. Even though our dataset provides exhaustive coverage of mortality at the national level, storage and processing capabilities did not prove to be an issue. Hence, we argue that OLAP methodology should find a place in health statistics. With proper data collection and/or transformations, informative comparisons within the study population and with international databases become readily accessible. The key advantage of OLAP over relational database management systems and ordinary tables is interactive browsing of multidimensional and hierarchical data, while OLAP can also aid data integrity checking and reporting

Socioeconomic inequalities in cancer mortality between and within countries in Europe: a population-based study

Background: Reducing socioeconomic inequalities in cancer is a priority for the public health agenda. A systematic assessment and benchmarking of socioeconomic inequalities in cancer across many countries and over time in Europe is not yet available. Methods: Census-linked, whole-of-population cancer-specific mortality data by socioeconomic position, as measured by education level, and sex were collected, harmonized, analysed, and compared across 18 countries during 1990–2015, in adults aged 40–79. We computed absolute and relative educational inequalities; temporal trends using estimated-annual-percentage-changes; the share of cancer mortality linked to educational inequalities. Findings: Everywhere in Europe, lower-educated individuals have higher mortality rates for nearly all cancer-types relative to their more highly-educated counterparts, particularly for tobacco/infection-related cancers [relative risk of lung cancer mortality for lower- versus higher-educated = 2.4 (95% confidence intervals: 2.1–2.8) among men; = 1.8 (95% confidence intervals: 1.5–2.1) among women]. However, the magnitude of inequalities varies greatly by country and over time, predominantly due to differences in cancer mortality among lower-educated groups, as for many cancer-types higher-educated have more similar (and lower) rates, irrespective of the country. Inequalities were generally greater in Baltic/Central/East-Europe and smaller in South-Europe, although among women large and rising inequalities were found in North–Europe (relative risk of all cancer mortality for lower- versus higher-educated ≥1.4 in Denmark, Norway, Sweden, Finland and the England/Wales). Among men, rate differences (per 100,000 person-years) in total-cancer mortality for lower-vs-higher-educated groups ranged from 110 (Sweden) to 559 (Czech Republic); among women from approximately null (Slovenia, Italy, Spain) to 176 (Denmark). Lung cancer was the largest contributor to inequalities in total-cancer mortality (between-country range: men, 29–61%; women, 10–56%). 32% of cancer deaths in men and 16% in women (but up to 46% and 24%, respectively in Baltic/Central/East-Europe) were associated with educational inequalities. Interpretation: Cancer mortality in Europe is largely driven by levels and trends of cancer mortality rates in lower-education groups. Even Nordic-countries, with a long-established tradition of equitable welfare and social justice policies, witness increases in cancer inequalities among women. These results call for a systematic measurement, monitoring and action upon the remarkable socioeconomic inequalities in cancer existing in Europe. Funding: This study was done as part of the LIFEPATH project, which has received financial support from the European Commission (Horizon 2020 grant number 633666), and the DEMETRIQ project, which received support from the European Commission (grant numbers FP7-CP-FP and 278511). SV and WN were supported by the French Institut National du Cancer (INCa) (Grant number 2018-116). PM was supported by the Academy of Finland (#308247, # 345219) and the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101019329). The work by Mall Leinsalu was supported by the Estonian Research Council (grant PRG722)

Educational inequalities in avoidable mortality in Europe

BACKGROUND: We compared the magnitude of educational inequalities in mortality avoidable by medical care in 16 European populations and determined the contribution of inequalities in avoidable mortality to educational inequalities in life expectancy in Europe. METHODS: We obtained mortality data for people aged 30-64 years. For each country, the association between level of education and avoidable mortality was measured with the use of regression-based inequality indexes. Life table analysis was used to calculate the contribution of avoidable causes of death to inequalities in life expectancy between lower and higher educated groups. RESULTS: Educational inequalities in avoidable mortality were present in all countries of Europe and in all types of avoidable causes of death. Especially large educational inequalities were found for infectious diseases and conditions that require acute care in all countries of Europe. Inequalities were larger in Central Eastern European (CEE) and Baltic countries, followed by Northern and Western European countries, and smallest in the Southern European regions. This geographic pattern was present in almost all types of avoidable causes of death. Avoidable mortality contributed between 11 and 24% to the inequalities in Partial Life Expectancy between higher and lower educated groups. Infectious diseases and cardio-respiratory conditions were main contributors to this difference. CONCLUSION: Inequalities in avoidable mortality were present in all European countries, but were especially pronounced in CEE and Baltic countries. These educational inequalities point to an important role of healthcare services in reducing inequalities in health

More variation in lifespan in lower educated groups: evidence from 10 European countries

Background Whereas it is well established that people with a lower socio-economic position have a shorter average lifespan, it is less clear what the variability surrounding these averages is. We set out to examine whether lower educated groups face greater variation in lifespans in addition to having a shorter life expectancy, in order to identify entry points for policies to reduce the impact of socio-economic position on mortality. Methods We used harmonized, census-based mortality data from 10 European countries to construct life tables by sex and educational level (low, medium, high). Variation in lifespan was measured by the standard deviation conditional upon survival to age 35 years. We also decomposed differences between educational groups in lifespan variation by age and cause of death. Results Lifespan variation was higher among the lower educated in every country, but more so among men and in Eastern Europe. Although there was an inverse relationship between average life expectancy and its standard deviation, the first did not completely predict the latter. Greater lifespan variation in lower educated groups was largely driven by conditions causing death at younger ages, such as injuries and neoplasms. Conclusions Lower educated individuals not only have shorter life expectancies, but also face greater uncertainty about the age at which they will die. More priority should be given to efforts to reduce the risk of an early death among the lower educated, e.g. by strengthening protective policies within and outside the health-care system

Educational inequalities in tuberculosis mortality in sixteen European populations

OBJECTIVE: We aim to describe the magnitude of socioeconomic inequalities in tuberculosis (TB) mortality by level of education in male, female, urban, and rural populations in several European countries. DESIGN: Data were obtained from the Eurothine project covering 16 populations between 1990 and 2003. Age- and sex-standardized mortality rates, the Relative Index of Inequality, and the slope index of inequality were used to assess educational inequalities. RESULTS: The number of TB deaths reported was 8530, with a death rate of 3 per 100 000 per year, of which 73% were males. Educational inequalities in TB mortality were present in all European populations. Inequalities in TB mortality were larger than in total mortality. Relative and absolute inequalities were large in Eastern Europe, and Baltic countries but relatively small in Southern countries and in Norway, Finland, and Sweden. Mortality inequalities were observed among both men and women, and in both rural and urban populations. CONCLUSIONS: Socioeconomic inequalities in TB mortality exist in all European countries. Firm political commitment is required to reduce inequalities in the social determinants of TB incidence. Targeted public health measures are called for to improve vulnerable groups’ access to treatment and thereby reduce TB mortality