A crucial step for planning effective public health policies for migrants with dementia is the collection of data on the local dimensions of the phenomenon and patients\' characteristics.
This study aimed to identify and characterize migrants with dementia in the Lazio region using health administrative databases.
Residents with dementia aged 50 years or older, living in the Lazio region as of December 31, 2018, were identified using a validated algorithm based on hospital discharge(s), claims for antidementia drugs, and co-payment exemption for dementia. Migrants were defined as people born abroad and grouped in migrants from High Migratory Pressure Countries (HMPCs) and Highly Developed Countries (HDCs). Overall and age-specific prevalence rates were estimated in native- and foreign-born patients.
Dementia was ascertained in 38,460 residents. Among them, 37,280 (96.9%) were born in Italy, 337 (0.9%) were migrants from HDCs, and 843 (2.2%) from HMPCs. Dementia prevalence was higher among natives (1.15%, 95% CI 1.14-1.16) relative to migrants from HDCs (0.60%, 95% CI 0.54-0.67) and HMPCs (0.29%, 95% CI 0.27-0.31). The prevalence of comorbidities did not differ between groups. Migrants with dementia had a lower likelihood of receiving antidementia treatments compared with natives (51.6% in migrants from HDCs, 49.3% in migrants from HMPCs, and 53.5% among Italians).
Routinely collected data in healthcare administrative databases can support the identification of migrants with dementia. Migrants exhibited a lower age-standardized prevalence of registered dementia and lower access to dedicated treatments than Italians. These findings are suggestive of underdiagnosis and undertreatment of dementia in migrants.

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population.

Known risk factors for multiple sclerosis (MS) include smoking, a low vitamin D status, obesity, and EBV, while the inflammatory feature of the disease strongly suggests the presence of additional infectious agents. The association between use of antibiotics and MS risk that could shed light on these factors is still undetermined. We aimed to evaluate the association between antibiotics and MS risk, in the Emilia-Romagna region (RER), Italy.
All adult patients with MS seen at any RER MS center (2015-2017) were eligible. For each of the 877 patients included, clinical information was collected and matched to 5 controls (RER residents) (n = 4,205) based on age, sex, place of residence, and index year. Information on antibiotic prescription was obtained through the linkage with the RER drug prescription database.
Exposure to any antibiotic 3 years prior to the index year was associated with an increased MS risk (OR = 1.52; 95% CI = 1.29-1.79). Similar results were found for different classes. No dose-response effect was found.
Our results suggest an association between the use of antibiotics and MS risk in RER population. However, further epidemiological studies should be done with information on early life and lifestyle factors.

OBJECTIVE: Micro-elimination of hepatitis C virus (HCV) in people living with HIV (PLHIV) and co-infected with HCV has been proposed as a key contribution to the overall goal of HCV elimination. While other studies have examined micro-elimination in HIV-treated cohorts, few have considered HCV micro-elimination among those not treated for HIV or at a national level.
METHODS: Through data linkage of national and sentinel surveillance data, we examined the extent of HCV testing, diagnosis and treatment among a cohort of PLHIV in Scotland identified through the national database of HIV-diagnosed individuals, up to the end of 2017.
RESULTS: Of 5018 PLHIV, an estimated 797 (15%) had never been tested for HCV and 70 (9%) of these had undiagnosed chronic HCV. The odds of never having been tested for HCV were the highest in those not on HIV treatment [adjusted odds ratio (aOR) = 7.21, 95% confidence interval (CI): 5.15-10.10). Overall HCV antibody positivity was 11%, and it was at its highest among people who inject drugs (49%). Most of those with chronic HCV (91%) had attended an HCV treatment clinic but only half had been successfully treated (54% for those on HIV treatment, 12% for those not) by the end of 2017. The odds of never having been treated for HCV were the highest in those not on HIV treatment (aOR = 3.60, 95% CI: 1.59-8.15).
CONCLUSIONS: Our data demonstrate that micro-elimination of HCV in PLHIV is achievable but progress will require increased effort to engage and treat those co-infected, including those not being treated for their HIV.

Nonparticipation limits the power of epidemiological studies, and can cause bias. In a case-control study on pleural malignant mesothelioma (MM), we found low participation in interviews (63%) among controls. Our goal was to characterize nonresponder controls and assess nonresponse bias in our study. We selected all nonresponder controls (204) and a random sample of responder controls (174). Data were obtained linking hospital admissions and town registrars, and concordance between sources was assessed. Nonresponse bias was evaluated using a logistic regression model applying the inverse probability weighting approach. The odds ratio (OR) for the status of the respondents was 0.61 (95% confidence interval (CI): 0.33-1.16) for controls aged 61-70, 0.37 (CI: 0.20-0.66) for those aged 71-80, and 0.40 (CI: 0.20-0.80) for those aged above 80 (reference group: ≤60 years). Controls with low education level had lower OR (0.47; CI: 0.26-0.84). After adjustment, the ORs for MM by categories of cumulative exposure to asbestos were similar to the unadjusted results, ranging from 4.6 (CI: 1.8-11.7) for cumulative exposures between 0.1 and 1 f/mL-y to 57.5 (CI: 20.2-163.9) above 10 f/mL-y. Responder controls were younger and had higher education level. Nevertheless, there was little evidence of bias from nonresponse in the risk estimates of MM.

Although barriers exist to secondary use of primary care electronic medical record (EMR) data, the Alliance for Healthier Communities (the Alliance) in Ontario, Canada has successfully created one of the largest structured primary care EMR datasets in Canada. In 2018, the Alliance and the Canadian Institute for Health Information (CIHI), an organization that provides comparable and actionable data to accelerate improvements in health across Canada, entered into a partnership to share EMR data. In this paper, we describe (i) the processes that enabled the collection of structured EMR data by the Alliance; (ii) how CIHI connected with the Alliance to share data and assess its quality; and, (iii) demonstrate the value of linking structured EMR data to administrative acute care data in illustrating the patient journey through the care continuum, using COPD as a case study.
CIHI and the Alliance entered into a formal data sharing agreement that enabled the sharing of linkable structured EMR data by the Alliance\'s 75 community health centres (CHCs) with CIHI. By leveraging the Alliance\'s Business Intelligence Reporting Tool, 3 years of EMR data containing patient-level clinical data were shared with CIHI. CIHI assessed the EMR data quality using its Data Source Assessment Tool to ensure fitness for analytical use. By linking the patient level EMR records with hospital records (CIHI\'s discharge abstract database (DAD) and the national ambulatory care reporting system (NACRS)), we examined aspects of COPD patient management in primary care and followed their journey through the health care continuum, including follow-up in primary care after hospital discharge.
Alliance EMR data representing approximately 570,000 patients and 8.5 million primary care encounters between April 1, 2015 and March 31, 2018 were shared with CIHI. A data quality assessment, centered on completeness and concordance, confirmed that the data was fit for analytical purposes. Overall, 13,023 enrolled primary care patients were identified as having COPD, representing an overall crude prevalence of 8.7%. The average age of COPD patients was 64 years and equally affected males and females. Patients were most likely to have completed high school education or equivalent, speak English, live alone, and have a household income less than $15,000. They most commonly had between 10-19 primary care encounters a year with a range of providers where they most commonly sought services for health advice/ instructions, to discuss their treatment plans and for medication renewals. By linking the EMR data to CIHI\'s NACRS and DAD, we found that 74.1% of COPD patients had at least one ED visit and that 34.4% of COPD patients had at least one acute care hospitalization during the study period. Further, 16.2% of ED visits resulted in an acute care hospital admission. Of those hospitalized, the majority of COPD patients were discharged home (81.6%) and received timely follow-up in primary care (81.0% within 30 days).
Structured and linkable EMR data provides opportunities to examine the patient journey through the care continuum in an innovative way. Using structured EMR data from the Alliance, linked with CIHI\'s NACRS and DAD databases, we were able to generate a cohort of patients with COPD, explore the complexities of their primary care encounters and follow them through the continuum of care, namely emergency department visits and hospitalizations. It is hoped that the partnership between the Alliance and CIHI will help drive future efforts on addressing the gap in comparable EMR data in Canada, and internationally.

The role of early-life environmental exposures on Inflammatory Bowel Disease (IBD) onset remains unclear. We aimed to quantify the impact of perinatal conditions and antibiotic use in the first 6 and 12 months of life, on the risk of childhood-onset IBD, in a birth cohort of the region Friuli-Venezia Giulia (Italy). A nested case-control design on a longitudinal cohort of 213,515 newborns was adopted. Conditional binomial regression models were used to estimate Odds Ratios (OR) with 95% confidence intervals (CI) for all analyzed risk factors. We identified 164 individuals with IBD onset before the age of 18 years and 1640 controls. None of the considered perinatal conditions were associated with IBD. Analyses on antibiotic exposure were based on 70 cases and 700 controls. Risks were significantly higher for children with ≥4 antibiotic prescriptions in the first 6 and 12 months of life (OR = 6.34; 95%CI 1.68-24.02 and OR = 2.91; 95%CI 1.31-6.45, respectively). This association was present only among patients with Crohn\'s disease and those with earlier IBD onset. We found that perinatal characteristics were not associated to IBD, while the frequent use of antibiotics during the first year of life was associated to an increased risk of developing subsequent childhood-onset IBD.

Objectives: We set forth to estimate the number of those with Parkinson\'s disease (PD) in Hungary, a country with a single-payer health insurance system covering 10 million inhabitants. Methods: We analyzed all hospital and outpatient reports from neurological services and pharmacy reports of prescription refills. We cross-checked clinically administered diagnosis of PD with prescription refills of antiparkinsonian medications using record linkage. We used the ICD-10 code of G20 in any diagnostic category to find all cases with possible PD. For case certification those patients were considered to have PD who were recorded with G20 code in at least 2 calendar years. For a more conservative estimation we determined the number of those who also refilled antiparkinsonian medication. Results: Between 2010 and 2012 there were 46,383 subjects with certified PD by clinical criteria. Crude and age-standardized incidence were 49/100,000/year (95% CI: 45-53), and 56/100,000/year (95% CI: 51-60). Crude and age standardized prevalence rates were 404/100,000 (95% CI: 392-416) and 471/100,000 (95% CI: 456-485). Of all clinically certified PD patients 72% refilled antiparkinsonian medications. Discussion: The incidence and prevalence of PD in Hungary is higher than earlier estimates, which should be considered in organizing healthcare services for this patient group.

Record linkage is a valuable and efficient tool for connecting information from different data sources. The National Center for Health Statistics (NCHS) has linked its population-based health surveys with administrative data, including Medicare enrollment and claims records. However, the linked NCHS-Medicare files are subject to missing data; first, not all survey participants agree to record linkage, and second, Medicare claims data are only consistently available for beneficiaries enrolled in the Fee-for-Service (FFS) program, not in Medicare Advantage (MA) plans. In this research, we examine the usefulness of multiple imputation for handling missing data in linked National Health Interview Survey (NHIS)-Medicare files. The motivating example is a study of mammography status from 1999 to 2004 among women aged 65 years and older enrolled in the FFS program. In our example, mammography screening status and FFS/MA plan type are missing for NHIS survey participants who were not linkage eligible. Mammography status is also missing for linked participants in an MA plan. We explore three imputation approaches: (i) imputing screening status first, (ii) imputing FFS/MA plan type first, (iii) and imputing the two longitudinal processes simultaneously. We conduct simulation studies to evaluate these methods and compare them using the linked NHIS-Medicare files. The imputation procedures described in our paper would also be applicable to other public health-related research using linked data files with missing data issues arising from program characteristics (e.g., intermittent enrollment or data collection) reflected in administrative data and linkage eligibility by survey participants.

Unconnected data silos make it difficult to view a patient\'s accident and emergency case in a comprehensive way, i.e. across all sectors. This includes the emergency medical services (EMS), medical data in a hospital\'s electronic health record (EHR) as well as event data recorders (EDRs) collecting information about the circumstances of the accident and emergency event. In this paper, we propose a conceptual architecture which introduces a novel case-based record linkage approach and the international standard accident number (ISAN) as a master case index for linking data from EDR, EMS, and EHR.