Record Linkage Procedures Research Articles

A Bayesian Multi-Layered Record Linkage Procedure to Analyze Functional Status of Medicare Patients with Traumatic Brain Injury

Understanding associations between injury severity and post-acute care recovery for older adults with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients’ injury, demographics, and long-term health outcomes. This data is despersed across two different datasets: Medicare claims data and the National Trauma Data Bank (NTDB). Because of privacy regulations, unique identifiers are not available to link records across these two data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We implement the proposed method to examine the associations between functional status assessments and TBI patients' ability to be independent following their injury by linking records from Medicare enrollment records and the NTDB.

Delays in treatment initiation and conclusion in women with stage IA to IIIB cervical cancer: A survival study in a hospital-based cohort from a developing country

AimTo evaluate the effect of delays in stage IA to IIIB cervical cancer treatment initiation and conclusion on hospital-based survival among Brazilian women. MethodsA retrospective follow-up study was conducted in a stage IA to IIIB cervical cancer cohort treated from 2012 and 2014 and followed until December 31, 2017 in Rio de Janeiro. Delay in treatment initiation definition was defined based on the Brazilian law of 60 days for treatment initiation after diagnosis. Delay in treatment conclusion was defined based on the literature and sample distributions: < 120/121–200/> 200 days. The endpoint was death(from all causes or cervical cancer). Death causes and dates were obtained by a record linkage procedure between the hospital cancer registry and the Mortality Information System. Global 36-month survival and HRs were estimated by the KaplanMeier method and proportional Cox regression models, respectively. ResultsFrom 865 patients, 269(31.1%) died over the median follow-up time of 27 months. Delay on treatment initiation(>60-days) was 92.8%, while the delay in treatment conclusion(>120 days) was 87.5%. Overall survival was 61.3% (<60-days:62.6%; 61–90 days:63.5%). Among stage IIB-IIIB, women treated < 60-days presented 40.1% survival, while 61–90-days had 52.5%, and > 90-days had 53.3%. Delays in treatment conclusion significantly reduced survival[72.2%(<120-days) to 60.7%(>200-days)]. Multivariate analysis showed that delays in treatment initiation did not affect 36-month death risk. Compared to women concluding treatment in < 120-days, those taking 121–200-days or > 200-days showed increases in death risk of 89%(95%CI:1.10–3.24) and 111%(95%CI:1.31–3.39), respectively, regardless of age, stage, treatment protocol, and time to treatment initiation. ConclusionDelays in cervical cancer treatment conclusion (but not treatment initiation) affected 36-month survival and death risk among Brazilians.

Cancer and Pregnancy: Estimates in Italy from Record-Linkage Procedures between Cancer Registries and the Hospital Discharge Database.

The aim of this study is to describe the frequency and trend of pregnancy-associated cancer (PAC) in Italy, an increasingly relevant phenomenon due to postponing age at childbirth. To this purpose, a population-based retrospective longitudinal study design based on cohorts of women aged 15-49 diagnosed with cancer and concomitant pregnancy is proposed. The study uses 19 population-based Cancer Registries, covering about 22% of Italy, and linked at an individual level with Hospital Discharge Records. A total of 2,861,437 pregnancies and 3559 PAC are identified from 74,165 women of the cohort with a rate of 1.24 PAC per 1000 pregnancies. The most frequent cancer site is breast (24.3%), followed by thyroid (23.9%) and melanoma (14.3%). The most frequent outcome is delivery (53.1%), followed by voluntary termination of pregnancy and spontaneous abortion (both 12.0%). The trend of PAC increased from 2003 to 2015, especially when the outcome is delivery, thus confirming a new attitude of clinicians to manage cancer throughout pregnancy. This represents the first attempt in Italy to describe PAC from Cancer Registries data; the methodology is applicable to other areas with the same data availability. Evidence from this study is addressed to clinicians for improving clinical management of women with PAC.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study.

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

LINKS. A System for Historical Family Reconstruction in the Netherlands

LINKS stands for 'LINKing System for historical family reconstruction' and is a software system to link nominal data from the Dutch archives and ultimately reconstruct historical individuals and families. We present the background and philosophy of this matching system and explain its data structure and functioning. Currently the core data of the LINKS system consists of indexed civil certificates. These certificates are available from 1812 — the start of the Dutch Vital Registration — until the year they are confidential based on privacy laws. For more than 20 years, thousands of volunteers have been working to build this index, which contains not only the names of newborn, married and deceased persons, but also the names of their parents, places of birth, ages and sometimes their occupational titles. The software system LINKS includes the standardization of all input before linking, nominal record linkage procedures and identification of all unique persons involved in the system. All processes are repeatable and a strict distinction is maintained between source data, standardized, linked and enriched data and released data. Moreover, LINKS also informs archives about all kinds of errors and inconsistencies found during the cleaning and matching process. We will discuss two matching systems, the first is the original querying system that runs within a MySQL database environment and the second is a newly developed system, called burgerLinker, which is based on knowledge graphs and which is designed as a system that can be used independently from LINKS and is made available as open source software. Finally, we present the most important releases of LINKS data so far: two national releases that link birth and parental marriage certificates, creating families and pedigrees and an integrated dataset of persons, families and family trees in four provinces.

Completeness and Factors Affecting Community Workers' Reporting of Births and Deaths in the Countrywide Mortality Surveillance for Action in Mozambique.

Since March 2018, the Countrywide Mortality Surveillance for Action project, implemented as a national sample registration system by the Mozambique Instituto Nacional de Saude and the Instituto Nacional de Estatistica in 700 geographic clusters randomly distributed across the 11 provinces, has trained and deployed community surveillance agents (CSAs) to report births and deaths in each cluster prospectively. An independent, retrospective data collection was conducted to assess the completeness of surveillance data. Record linkage procedures were used to match households and vital events reported in the two data sources. We calculated birth and death reporting rates and used a regression model to determine factors associated with the likelihood of vital events being reported by the CSAs. Between March 2018 and December 2019, CSAs reported 54% of births (8,787/16,421) and 45% of deaths (1,726/3,867). Births of smaller cluster sizes (< 1,000 people) were more likely to be reported (adjusted odds ratio [aOR] = 1.45; 95% CI = 1.15-1.83) compared with those of larger cluster sizes (> 1,500 people). Deaths of rural clusters were more likely to be reported (aOR = 1.41; 95% CI = 1.07-1.85) than those of urban clusters. Adult deaths were more likely to be reported (aOR = 1.49; 95% CI = 1.10-2.02) than child deaths. Our findings suggest that a fully functioning sample vital registration system must adopt a dual system with high-quality surveys or other ways to estimate underregistration periodically, consider a smaller cluster size manageable by a community worker, and pay special attention to urban clusters as underreporting is larger.

Microsimulation of an educational attainment register to predict future record linkage quality.

Population wide educational attainment registers are necessary for educational planning and research. Regular linking of databases is needed to build and update such a register. Without availability of unique national identification numbers, record linkage must be based on quasi-identifiers such as name, date of birth and sex. However, the data protection principle of data minimization aims to minimize the set of identifiers in databases. Therefore, the German Federal Ministry of Research and Education commissioned a study to inform legislation on the minimum set of identifiers required for a national educational register. To justify our recommendations empirically, we implemented a microsimulation of about 20 million people. The simulated register accumulates changes and errors in identifiers due to migration, regional mobility, marriage, school career and mortality, thereby allowing the study of errors on longitudinal datasets. Updated records were linked yearly to the simulated register using several linkage methods. Clear-text methods as well as privacy-preserving (PPRL) methods were compared. The results indicate linkage bias if only the primary identifiers are available in the register. More detailed identifiers, including place of birth, are required to minimize linkage bias. The amount of information available to identify a person for matching is more critical for linkage quality than the record linkage method applied. Differences in linkage quality between the best procedures (probabilistic linkage and multiple matchkeys) are minor. Microsimulation is a valuable tool for designing record linkage procedures. By modelling the processes resulting in changes or errors in quasi-identifiers, predicting data quality to be expected after the implementation of a register seems possible.

An Italian multicentre distributed data research network to study the use, effectiveness, and safety of immunosuppressive drugs in transplant patients: Framework and perspectives of the CESIT project.

The goal of post-transplant immunosuppressive drug therapy is to prevent organ rejection while minimizing drug toxicities. In clinical practice, a multidrug approach is commonly used and involves drugs with different mechanisms of action, including calcineurin inhibitors (CNI) (tacrolimus or cyclosporine), antimetabolite (antimet) (mycophenolate or azathioprine), inhibitors of mechanistic target of rapamycin (mTOR) (sirolimus or everolimus), and/or steroids. Although evidence based on several randomized clinical trials is available, the optimal immunosuppressive therapy has not been established and may vary among organ transplant settings. To improve the knowledge on this topic, a multiregional research network to Compare the Effectiveness and Safety of Immunosuppressive drugs in Transplant patients (CESIT) has been created with the financial support of the Italian Medicines Agency. In this article, we describe the development of this network, the framework that was designed to perform observational studies, and we also give an overview of the preliminary results that we have obtained. A multi-database transplant cohort was enrolled using a common data model based on healthcare claims data of four Italian regions (Lombardy, Veneto, Lazio, and Sardinia). Analytical datasets were created using an open-source tool for distributed analysis. To link the National Transplant Information System to the regional transplant cohorts, a semi-deterministic record linkage procedure was performed. Overall, 6,914 transplant patients from 2009–19 were identified: 4,029 (58.3%) for kidney, 2,219 (32.1%) for liver, 434 (6.3%) for heart, and 215 (3.1%) for lung. As expected, demographic and clinical characteristics showed considerable variability among organ settings. Although the triple therapy in terms of CNI + antimet/mTOR + steroids was widely dispensed for all settings (63.7% for kidney, 33.5% for liver, 53.3% for heart, and 63.7% for lung), differences in the active agents involved were detected. The CESIT network represents a great opportunity to study several aspects related to the use, safety, and effectiveness of post-transplant maintenance immunosuppressive therapy in real practice.

Private record linkage with linkage maps

AbstractPrivate record linkage is an actively pursued research area to facilitate the linkage of database records under the constraints of regulations that do not allow linkage agents to learn sensitive identities of record owners. Recent works have shown that linkage using commutative ciphers, which were discarded earlier for efficiency concerns, can be made feasible by leveraging precomputations, data parallelism, and probabilistic key reuse approaches. In this work, we propose further optimizations that can be performed to improve the runtime efficiency of such an approach. We transition from modular exponentiation ciphers to elliptic curve operations to improve precomputation time, eliminate memory intensive comparisons of encrypted values, and introduce data structures to detect negative comparisons. We benchmark the proposed approach using real world demographics data, and provide an extensive study of the parametric aspects of the approach. We also supplement our execution time results with an assessment of the residual privacy risk left by the approach. The approach can perform a linkage of two datasets with 105 records each in 20 minutes in a commodity laptop. This is achieved by eliminating the need to compare more than 70% of the record pairs. By design, the linkage accuracy is also retained at the same level as a nonprivate record linkage procedure.

Microsimulation of an educational attainment register to study record linkage quality.

Population covering educational attainment registers have been proven helpful for planning and research concerning educational efforts. Regular linking of different databases is needed to build and update such a register. Without unique national identification numbers, record linkage must be based on quasi-identifiers such as names, date of birth and sex. High-quality record linkage require the unique identification of persons. Therefore, available identifiers should be sufficient for unique identification despite missing identifiers for some cases. Redundant identifiers can achieve this goal. However, the data protection principle of data minimization, as recommended in the European General Data Protection Regulation, aims to avoid additional data if possible for the given purpose. Therefore, a ministry commissioned a simulation study to inform legislators on the minimum set of identifiers needed for a national register. A microsimulation of the population consisting of nearly 20 million people was implemented to generate data on accumulating changes and errors in identifiers over ten simulated years. The simulation covered, for example, international migration, regional mobility, marriages, school careers and mortality. Each event triggered changes of identifiers according to specified error probability models. The resulting data were linked by different record-linkage procedures. Linkage quality and linkage bias dependent on the available identifiers were assessed. We report on the design of the simulation study, the linkage results and recommendations for the minimum set of identifiers. The results may be helpful for the design of other population covering registers.

A Bayesian MultiLayer Record Linkage Procedure to Analyze Post-Acute Care Recovery of Patients with Traumatic Brain Injury.

Understanding associations between injury severity and postacute care recovery for patients with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients' injury, demographics, and healthcare utilization, which are dispersed across multiple data sets. Because of privacy regulations, unique identifiers are not available to link records across these data sets. Record linkage methods identify records that represent the same patient across data sets in the absence of unique identifiers. With a large number of records, these methods may result in many false links. Health providers are a natural grouping scheme for patients, because only records that receive care from the same provider can represent the same patient. In some cases, providers are defined within each data set, but they are not uniquely identified across data sets. We propose a Bayesian record linkage procedure that simultaneously links providers and patients. The procedure improves the accuracy of the estimated links compared to current methods. We use this procedure to merge a trauma registry with Medicare claims to estimate the association between TBI patients' injury severity and postacute care recovery.

Drug Prescriptions among Italian and Immigrant Pregnant Women Resident in Italy: A Cross-Sectional Population-Based Study.

Ensuring drug safety for pregnant women through prescription drug monitoring is essential. The aim of this study was to describe the prescription pattern of medicines among pregnant immigrant women from countries with high migratory pressure (HMPCs) compared to pregnant Italian women. The prevalence of drug prescriptions among the two study populations was analysed through record linkage procedures applied to the administrative databases of eight Italian regions, from 2016 to 2018. The overall prevalence of drug prescription was calculated considering all women who received at least one prescription during the study period. Immigrants had a lower prevalence of drug prescriptions before (51.0% vs. 58.6%) and after pregnancy (55.1% vs. 60. 3%). Conversely, during pregnancy, they obtained a slightly higher number of prescriptions (74.9% vs. 72.8%). The most prescribed class of drugs was the blood and haematopoietic organs category (category ATC B) (56.4% vs. 45.9%, immigrants compared to Italians), followed by antimicrobials (31.3% vs. 33.7%). Most prescriptions were appropriate, while folic acid administration 3 months before conception was low for both study groups (3.9% immigrants and 6.2% Italians). Progesterone seemingly was prescribed against early pregnancy loss, more frequently among Italians (16.5% vs. 8.1% immigrants). Few inappropriate medications were prescribed among antihypertensives, statins and anti-inflammatory drugs in both study groups.

Education and Employment Trajectories in NEPS-ADIAB: The Survey Data of the National Educational Panel Study Linked to Administrative Data of the Institute for Employment Research

Abstract NEPS-ADIAB is a large-scale data product from Germany that provides a unique source for the empirical analysis of life course research questions. It is jointly administered by the Institute for Employment Research (IAB) and the Leibniz Institute for Educational Trajectories. NEPS-ADIAB includes survey data from the National Educational Panel Study (NEPS) and administrative data from the IAB, combined at the individual level using a record linkage procedure. While the NEPS data cover issues such as educational trajectories and decisions, competencies and skills, learning environments, attitudes, etc., the administrative data consist of detailed employment histories dating back to 1975, plus extensive information on establishments. The linkage of both data collections results in a considerable research potential. NEPS-ADIAB is particularly suitable for analyses of the nexus between educational and employment careers, short- and long-term returns to education, transitions from school to vocational training and working life, adult education and lifelong learning, as well as discontinuities in professional careers due to parenthood or unemployment. Contextual characteristics about establishments as well as geographic indicators can additionally be taken into account. The regularly updated and expanded NEPS-ADIAB data are accessible to the entire scientific community free of charge via on-site and remote access.

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case.

BackgroundLinking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).ObjectiveThis study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.MethodsA Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.ResultsThe whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.ConclusionsThis study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

Record Linkage in Clinical Cancer Registration: Experiences and Findings from Lower Saxony.

In cancer registries, record linkage procedures are used to link records of the same patient from different health care providers. In the Clinical Cancer Registry of Lower Saxony, a multi-level combination of exact assignment using the statutory health insurance number and a probabilistic procedure with control numbers and address data is applied. The procedure implemented in the register application assigns the incoming messages in this way as far as possible automatically. The aim of the observation carried out was to check the efficiency of the match variables and threshold values used, above which manual assignment is required. Weak points were identified and approaches to solutions were developed.

Mortality inequalities in Rome: the role of individual education and real estate market

Abstract Background Several indicators at individual and area level have been used to stratify the population. The aim of this work was to investigate the association between real estate prices, education and health status in Rome. Methods We used the administrative cohort of residents in Rome at the 2011 census. We followed subjects aged 18+ from 2011 to 2016 using anonymous record linkage procedures with administrative databases. The census includes several individual information, comprising sex, age, education, and residential urbanistic zone. From mortality register we collected date and cause of death. Real estate prices (€/m2) were available for each urbanistic zone. We classified the population in five categories according to the quintile of the distribution (1=highest prices, 5=lowest prices). We used adjusted Cox regression models to estimate the hazard ratios (HRs) and 95%CIs. Results We selected 2,053,668 subjects, not living in institutions at the time of the census survey. Women were 54% of the population; the mean age was 52 years (sd 18), 22.5% had a high education (university degree) and 37% a low education (≤ junior high school). During the follow-up, 126,548 subjects died. As expected educational level was strongly associated with all-cause mortality (39% higher risk of low vs. high educated). The mortality risk increased with decreasing real estate prices, taking account of age and sex. Compared to subjects living in highest prices areas, those living in level 2 areas had a HR = 1.08 (95%CI: 1.06-1.10), those in level 3 had a HR = 1.14 (95%CI: 1.12-1.16), those in level 4 a HR = 1.16 (95%CI 1.14-1.18), and finally those living in the cheapest areas a HR = 1.22 (95%CI: 1.20-1.25). When we considered both education and real estate prizes, the HRs marginally decreased. Conclusions There is an independent association of both individual education and socioeconomic characteristics of the area of residence and all-cause mortality. Key messages There are strong socioeconomic inequalities in Rome. A simple indicator such as real estate prices can be used to tackle inequalities.

COVID-19 infections in the Friuli Venezia Giulia Region (Northern Italy): a population-based retrospective analysis.

to study the cumulative incidence, the demographics and health conditions of the population tested for COVID-19, and to map the evolving distribution of individual cases in the population of the Friuli Venezia Giulia Region (North-Eastern Italy). population-based observational study based on a record linkage procedure of databases included in the electronic health information system of the Friuli Venezia Giulia Region. the study group consisted of individuals who resided in the Friuli Venezia Giulia Region and who underwent COVID-19 testing from 01.03 to 24.04.2020. The study group was identified from the laboratory database, which contains all the microbiological testing performed in regional facilities. Tested people were categorized into positive or negative cases, based on test results. probability of being tested for and cumulative incidence of COVID-19. the cumulative probability of being tested for COVID-19 was 278/10,000 inhabitants, while the cumulative incidence was 22 cases/10,000. Out of 33,853 tested people, 2,744 (8.1%) turned out to be positive for COVID-19. Women were tested more often than men (337 vs 216/10,000), and they showed a higher incidence of infection than men (25 and 19 infected cases/10,000 residents, respectively). Both cumulative incidence and cumulative probability of being tested were higher in the elderly population. About 25% of infected people was hosted in retirement homes and 9% was represented by healthcare workers. Thirty seven percent of positive cases had hypertension, 15% cardiologic diseases, while diabetes and cancer characterized 11.7% and 10% of the infected population, respectively. The geographic distribution of positive cases showed a faster spread of the infection in the city of Trieste, an urban area with the highest regional population density. the COVID-19 pandemic did not hit the Friuli Venezia Giulia Region as hard as other Northern Italian Regions. In the early phase, as documented in this study, the COVID-19 pandemic particularly affected women and elderly people, especially those living in retirement homes in Trieste.

Clinical, demographical characteristics and hospitalisation of 3,010 patients with Covid-19 in Friuli Venezia Giulia Region (Northern Italy). A multivariate, population-based, statistical analysis.

to describe the clinical and demographical characteristics of COVID-19 infected people in the Friuli Venezia Giulia Region (FVG, Northern Italy). retrospective cohort study with an individual level record linkage procedure of different administrative databases. the cohort included 3,010 patients residing in FVG who tested positive for COVID-19 between 1 March and 15 May 2020, 2020. Regional hospital admissions and deaths without hospital admissions up to June 1st, 2020 were analysed. Determinants of the probability of a highly severe illness were investigated in terms of hospitalisations or death without hospital admission. COVID-19 patients were identified from regional epidemiological data warehouse. Demographical and clinical variables such as gender, age, patient's comorbidities, vaccinations, ARBs/sartans prescriptions, and geographical residence variables were collected by linking different databases. Descriptive analyses were performed. Logistic multivariate regressions were used to estimate the probability of hospitalisation or death, whichever came first. Model coefficients and odds ratios (OR) were reported. COVID-19 population in FVG had a mean age of 60 years and 59% were females. The study found that 37% had hypertension while patients with cardiologic diseases, diabetes, and cancer were around 15%; 22% of the cases were residing in retirement homes. Approximately 30% received flu or pneumococcal vaccination and a similar proportion of patients had at least one prescription of ARBs /sartans in the previous 6 months. Statistical models showed a higher probability of a worst course of disease for males, elderly, highly complicated (in terms of resource use) subjects, in the presence of cardiologic diseases, diabetes, and pneumococcal vaccination. People living in retirement homes had a lower probability of hospitalisation/death without hospital admission. The cohort was divided into two groups: COVID-19 patients infected in the territory and infected in retirement homes. Among COVID-19 patients infected in the territory, the probability of hospitalisation/death was higher for males, for older individuals, and for those with comorbidities. Diabetes resulted to be a risk factor (OR 1.79; 95%CI 1.23-2.62), as well as pneumococcal vaccination (OR 1.64; 95%CI: 1.18-2.29), which is a likely proxy of fragile patients with pulmonary disease. The flu vaccination showed a potential protective effect with a 40% lower probability of hospitalisation or death (OR 0.62; 95%CI 0.44-0.85). Among the retirement homes cohort group, a higher probability of a bad course of disease emerged for males and for more complex patients. the greatest risk of hospitalisation/death as a measure of more severe illness was confirmed for males, elderly, and for individuals with comorbidities. Flu vaccination seemed to have had a protective effect while pneumococcal vaccination likely identified a group of high-risk patients to be actively monitored. For patients infected in the territory, different hospitalisation strategies were implemented by the regional health districts.

Mortality inequalities in Rome: the role of individual education and neighbourhood real estate market

to investigate the association between real estate prices, education, and mortality. cohort study. residents in Rome at the 2011 Italian Census, not living in institutions, and living in the address reported in the Census survey. People aged 18-99 years were followed from 2011 to 2016 using anonymous record linkage procedures with administrative databases. The Census includes several individual information, such as gender, age, education, residential neighbourhood. Data and cause of death were collected from mortality register. Real estate prices (euros/m2) were available for each neighbourhood. adjusted Cox regression models (hazard ratios - HRs and 95%CIs) were used to estimate the association among individual education, real estate price in the neighbourhood, and mortality. the subjects selected were 2,051,376 (54% women, 22.5% with high education level). During the follow-up, 127,352 subjects died. Taking into account gender, age, marital status, and real estate prices, education level was strongly associated with all-cause mortality; compared to highly educated the higher mortality, risk was 35% (95%CI 32%-37%) for low education level and 16% (95%CI 14%-19%) for medium education level. Taking into account the same factors and education level, each increase of 1,000 euros in price/m2 was inversely associated with mortality (HR 0.96, 95%CI 0.96-0.97). there is an independent association between the two indicators and mortality in Rome. A simple indicator such as real estate prices can be used to tackle inequalities.

Dementia among migrants and ethnic minorities in Italy: rationale and study protocol of the ImmiDem project

IntroductionDue to the ongoing demographic and epidemiological transitions, estimating the phenomenon of dementia in migrants and minority groups, exploring its characteristics and challenges and implementing dedicated healthcare policies, constitute emerging...