Childhood vaccination uptake among children born in Aotearoa New Zealand based on parental nationality.

Migrants and refugees generally experience immunization inequities compared to their host populations. Childhood vaccination coverage rates are influenced by a complex set of interrelated factors, including child and parental nativity. Coverage rates for MMR, pertussis, and HPV vaccines were compared among children born in Aotearoa New Zealand (NZ) of overseas-born parents or NZ-born parents. A nationwide retrospective cohort study was conducted using linked, de-identified data. Logistic regression models examined the most influential factors contributing to differences in timely vaccine uptake. Of the total study population who had received all scheduled vaccines (N = 760,269), 32.9% were children of migrant parents. Children of migrant parents had higher rates of complete and timely uptake for MMR, pertussis, and HPV vaccinations compared to non-migrant children. NZ-born children of migrant parents were significantly more likely to receive MMR and pertussis-containing vaccines on-time compared to those of non-migrants. All included factors, except for the child's gender and parents' English ability, significantly influenced vaccine uptake. Among NZ-born children of migrant parents, additional logistic modeling found significant differences based on parental duration of residence, visa group, and region of nationality. Findings point to the importance of differentiating between parent versus child nativity when examining immunization coverage. While vaccination rates were higher for NZ-born children of migrant parents, compared to non-migrant parents, timely coverage rates across both groups were below national targets. Continued efforts are needed to improve timely immunization service delivery to address suboptimal and inequitable coverage.

FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis.

The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.

Comparing primary caregivers' reported injury data with routinely recorded injury data to assess predictors of childhood injury.

BACKGROUND: Linking self-reported data collected from longitudinal studies with administrative health records is timely and cost-effective, provides the opportunity to augment information contained in each and can offset some of the limitations of both data sources. The aim of this study was to compare maternal-reported child injury data with administrative injury records and assess the level of agreement. METHODS: A deterministic linkage was undertaken to link injury-related data from the Growing up in New Zealand (GUiNZ) study to routinely collected injury records from New Zealand's Accident Compensation Corporation (ACC) for preschool children. The analyses compared: (i) the characteristics of mothers with linked data vs. those without, (ii) injury incidences from maternal recall with those recorded in ACC injury claims, and (iii) the demographic characteristics of concordant and discordant injury reports, including the validity and reliability of injury records from both data sources. RESULTS: Of all mothers who responded to the injury questions in the GUiNZ study (n = 5836), more than 95% (n = 5637) agreed to have their child's record linked to routine administrative health records. The overall discordance in injury reports showed an increasing trend as children grew older (9% at 9 M to 29% at 54 M). The mothers of children with discordance between maternal injury reports and ACC records were more likely to be younger, of Pacific ethnicity, with lower educational attainment, and live in areas of high deprivation (p < 0.001). The level of agreement between maternal injury recall and ACC injury record decreased (κ = 0.83 to κ = 0.42) as the cohort moved through their preschool years. CONCLUSIONS: In general, the findings of this study identified that there was underreporting and discordance of the maternal injury recall, which varied by the demographic characteristics of mothers and their child's age. Therefore, linking the routinely gathered injury data with maternal self-report child injury data has the potential to augment longitudinal birth cohort study data to investigate risk or protective factors associated with childhood injury.

MMR vaccine coverage and associated factors among overseas-born refugee children resettled in Aotearoa New Zealand: a national retrospective cohort study.

Background: Refugee children may be under-immunised against common vaccine-preventable diseases due to a myriad of factors related to their migration journey. Methods: This retrospective cohort study explored the rates and determinants of enrolment on the National Immunisation Register (NIR) and measles, mumps and rubella (MMR) coverage among refugee children up to 18 years old who resettled in Aotearoa New Zealand (NZ) from 2006 to 2013. Univariate and multivariable logistic regression were conducted to determine associations. Findings: Of the cohort (N = 2796), two thirds of the children (69%) were enrolled on the NIR. Among this sub-cohort (n = 1926), less than one third (30%) were age-appropriately vaccinated with MMR. MMR coverage was highest among younger children and improved over time. Logistic modelling revealed that visa category, year of arrival, and age group were significant factors that influenced NIR enrolment and MMR vaccine uptake. Those arriving via asylum seeking, family reunification and humanitarian pathways were less likely to be enrolled and vaccinated compared to refugees who entered under the national quota programme. More recent arrivals and younger children were more likely to be enrolled and vaccinated compared to children who arrived in NZ longer ago and were older. Interpretation: Resettled refugee children have suboptimal NIR enrolment and MMR coverage rates which varied significantly by visa category, highlighting the need for immunisation services to better engage with all refugee families. These findings suggest that broad structural factors related to policy and immunisation service delivery may influence the differentials seen. Funding: Health Research Council of New Zealand (18/586).

Identifying vulnerable populations at a university during the COVID-19 pandemic.

Objective: Persons with high-risk for severe COVID-19 illness require special attention when considering university operations during the novel coronavirus pandemic. The objective of this study was to determine the number of students who fall within a high-risk category according to Centers for Disease Control and Prevention (CDC) guidelines using linked databases. Participants: Students enrolled at a large public University and who accessed the student health center between 2016 and 2020. Methods: Clinical data were linked with both university student enrollment and disability services databases to provide a comprehensive, de-identified dataset of students at higher medical risk of severe COVID-19 illness. Results: 1902 unique students (14% of the student health center population) were identified as having one or more high-risk condition. Conclusions: Utilizing a large and longitudinally linked student database provides universities with valuable information to make critical administrative decisions about how best to accommodate high-risk students to reduce their medical risk when returning to in-person instruction.

Careful: An administrative child welfare and electronic health records linked dataset.

Between 2012 and 2017, N = 2814 youth between the ages of 4 and 20 were in child protective services (CPS) custody in Hamilton County, Ohio, and placed in out-of-home care. Child welfare administrative records were extracted and linked to electronic health records for all encounters at Cincinnati Children's Hospital Medical Center, with n = 2787 (99.1%) of records successfully linked prior to de-identifying the data for research purposes. Child welfare administrative data fields in the dataset include demographics, dates of entry into and exit from protective custody and out-of-home care, reasons for entry into custody, dates of placement changes, reasons for placement changes, and types of placement (e.g., foster home, kinship home, group home, residential treatment, independent living). Electronic health records (EHR) data fields include demographics, all inpatient and outpatient encounters with medications, diagnoses, screening results, laboratory test results, flowsheet data, and problem list entries. Data have been coded to capture broader categories of health needs and encounter details, medications, and other health concerns. Due to the high representation of children in CPS custody and out-of-home care who are also represented in the EHR data, this dataset provides a comprehensive view of the medical needs and health concerns for school-aged children in CPS custody in an entire county. As a result, these data can be useful for understanding the emergence of global and specific health concerns, frequency of healthcare use, and placement stability for all youth in CPS custody in this community, accounting for variation due to other health and child welfare factors. These data are likely generalizable to other mid-sized urban communities where academic medical centers provide healthcare for children in CPS custody. De-identified data may be made available to other researchers with approved data transfer agreements between academic institutions in place.

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

BACKGROUND: Linking 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]). OBJECTIVE: This 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: This 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.

Day-to-day discovery of preprint-publication links.

Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint-publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint-publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative (covid-nma.com). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint-publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers' workflows and enhance a daily preprint-publication chase that is useful to all readers, including systematic reviewers. This preprint-publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

Vaccine-Preventable Disease-Associated Hospitalisations Among Migrant and Non-migrant Children in New Zealand.

Migrants may experience a higher burden of vaccine-preventable disease (VPD)-associated hospitalisations compared to the host population. A retrospective cohort study from 2006 to 2015 was conducted that linked de-identified data from government sources using Statistic NZ's Integrated Data Infrastructure. VPD-related hospitalisations were compared between three cohorts of children from birth to 5 years old: foreign-born children who migrated to NZ, children born in NZ of recent migrant mothers, and a comparator group of children born in NZ without a recent migration background. VPD-related hospitalisation rates were higher among NZ-born non-migrant children compared to NZ-born migrant and foreign-born children for all of the diseases of interest. For instance, 5.21% of NZ-born non-migrant children were hospitalised at least once due to all-cause gastroenteritis compared to 4.47% of NZ-born migrant children and only 1.13% of foreign-born migrant children. The overall hospitalisation rate for NZ-born non-migrant children was 3495 hospitalisations per 100,000 person years. Among children with migrant backgrounds, higher hospitalisation rates were noted among those of Pacific ethnicity and those with refugee backgrounds. Those arriving on Pacific visa schemes were hospitalised at rates ranging from 2644/100,000 person years among foreign-born migrant children and 4839/100,000 person years among NZ-born migrant children. Foreign-born quota refugee children and NZ-born children of quota refugee mothers were hospitalised at a rate of 4000-5000/100,000 person years. It is important to disaggregate migrant data to improve our understanding of migrant health. Children need to be age-appropriately vaccinated, and other individual and environmental factors addressed, to reduce the risk of infectious diseases.

Trust, security and public interest: striking the balance A narrative review of previous literature on public attitudes towards the sharing, linking and use of administrative data for research.

This narrative literature review explores previous findings in relation to the UK public's attitudes towards the sharing, linking and use of public sector administrative data for research. A total of 16 papers are included in the review, for which data was collected between the years 2006-2018. The review finds, on the basis of previous literature on the topic, that the public is broadly supportive of administrative data research if three core conditions are met: public interest, privacy and security, and trust and transparency. None of these conditions is sufficient in isolation; the literature shows public support is underpinned by fulfillment of all three. However, it also shows that in certain cases where the standard of one condition is very high - particularly public interest - this could mean the standard of another may, if necessary, be lower. An appropriate balance must be struck, and the proposed benefits of sharing and using data for research must outweigh the potential risks. Broad, conditional support for the use of administrative data in research has not only been found consistently, but has also been held over time. Most studies identified by this review have focused on exploring the views of the general public towards the acceptability of administrative data use in broad terms. However, with the exception of that related to healthcare data, the review identified little work focused on gaining input from relevant demographics and communities in relation to specific data types or areas of research. In addition to fulfilling the core conditions of public support identified by broader work, initiatives making use of administrative data should aim to seek the views of relevant sub-sectors of the public in the development of research in relation to specific issues.

Why Do Data Users Say Health Care Data Are Difficult to Use? A Cross-Sectional Survey Study.

BACKGROUND: There has been significant effort in attempting to use health care data. However, laws that protect patients' privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users' needs for data linking from practical perspectives. OBJECTIVE: The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users' data linking needs. METHODS: We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. RESULTS: In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants' responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. CONCLUSIONS: This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.

Applying an Electronic Health Records Data Quality Framework Across Service Sectors: A Case Study of Juvenile Justice System Data.

CONTEXT: Integrating electronic health records (EHR) with other sources of administrative data is key to identifying factors affecting the long-term health of traditionally underserved populations, such as individuals involved in the justice system. Linking existing administrative data from multiple sources overcomes many of the limitations of traditional prospective studies of population health, but the linking process assumes high levels of data quality and consistency within administrative data. Studies of EHR, unlike other types of administrative data, have provided guidance to evaluate the utility of big data for population health research. CASE DESCRIPTION: Here, an established EHR data quality framework was applied to identify and describe the potential shortcomings of administrative juvenile justice system data collected by one of four case management systems (CMSs) across 12 counties in a Midwest state. The CMS data were reviewed for logical inconsistencies and compared along the data quality dimensions of plausibility and completeness. MAJOR THEMES: After applying the data quality framework, several patterns of logical inconsistencies within the data were identified. To resolve these inconsistencies, recommendations regarding data entry, review, and extraction are offered. CONCLUSION: The recommendations related to achieving quality justice system data can be applied to future efforts to link administrative databases from multiple sources. Increasing trust in administrative data quality related to vulnerable populations ultimately improves knowledge of pressing public health concerns.

Exploring immunisation inequities among migrant and refugee children in New Zealand.

Migrants may experience immunisation inequities compared with the host population related to barriers with accessing immunisations in their home countries, while migrating and/or post-arrival. This retrospective cohort study explored vaccination rates among migrant and non-migrant children in New Zealand (NZ). Linked de-identified data from various government sources from 1 January 2006 to 31 December 2015 were analysed using Statistic NZ's Integrated Data Infrastructure. Vaccination rates were compared between three cohorts of children aged up to 5 years: foreign-born children who migrated to NZ; children born in NZ of migrant mothers; and a comparator group of children born in NZ to non-migrant mothers. Less than half of foreign-born children (46%) had a record in the NZ National Immunisation Register compared with 95% and 96% among migrant and non-migrant NZ-born children, respectively. Foreign-born migrant children had lower age-appropriate reported vaccination rates by vaccine of interest, ethnicity and visa category compared with NZ-born children. Migrant children from Pacific ethnicities had lower reported coverage than other ethnicities. High rates of not age-appropriately vaccinated were noted among foreign-born children on refugee, Pacific and humanitarian visa schemes. This study highlights possible shortfalls around immunisation data, particularly about recording vaccinations given overseas for foreign-born children, and potential challenges around engagement with immunisation services for migrant children. However, results highlight the successful engagement of quota refugee children as part of NZ's refugee orientation programme. It is important to monitor vaccination coverage by migrant and refugee background to inform improvements to policy and practice for wider population health benefits.

He Tamariki Kokoti Tau-Tackling Preterm: a data-linkage methodology to explore the clinical care pathway in preterm deliveries.

BACKGROUND: Significant health inequities exist around maternal and infant health for Maori, the indigenous people of Aotearoa New Zealand - and in particular around a premature (preterm) delivery. Maori babies are more likely to be born preterm (8.1%, compared to an overall rate of 7.4%) and they are more likely to have a preterm death. An essential part of redressing these disparities is to examine the clinical care pathway and outcomes associated with preterm deliveries. This paper describes a protocol utilising national and local health collections to enable such a study. DESIGN: This is a retrospective cohort study comprising 5 years data pertaining to preterm deliveries from 2010 to 2014. These data are generated from linked national administrative and local health information collections to explore a range of neonatal outcomes and infant mortality in relation to the antenatal care pathway and known risk factors for preterm delivery. This study is being conducted within a Kaupapa Maori paradigm that dismisses victim blaming and seeks to intervene at structural levels to improve the health and wellbeing of Maori whanau (family). SIGNIFICANCE OF THE STUDY: Our data-linkage methodology optimises the utility of New Zealand health collections to address a significant health issue. Our findings will fill the information gaps around the burden of preterm delivery by quantifying the incidence of preterm delivery and adverse neonatal and infant outcomes in Aotearoa New Zealand. It will explore access to evidenced based care including use of steroids before birth, and appropriate place of delivery. The results from this study will inform maternity care services to improve management of preterm deliveries - both locally and internationally. This in turn will improve the preterm sequela by reducing the long-term health burden and health inequities.

Exploring Concordance of Patient-Reported Information on PatientsLikeMe and Medical Claims Data at the Patient Level.

BACKGROUND: With the emergence of data generated by patient-powered research networks, it is informative to characterize their correspondence with health care system-generated data. OBJECTIVES: This study explored the linking of 2 disparate sources of real-world data: patient-reported data from a patient-powered research network (PatientsLikeMe) and insurance claims. METHODS: Active patients within the PatientsLikeMe community, residing in the United States, aged 18 years or older, with a self-reported diagnosis of multiple sclerosis or Parkinson's disease (PD) were invited to participate during a 2-week period in December 2014. Patient-reported data were anonymously matched and compared to IMS Health medical and pharmacy claims data with dates of service between December 2009 and December 2014. Patient-level match (identity), diagnosis, and usage of disease-modifying therapies (DMTs) were compared between data sources. RESULTS: Among 603 consenting patients, 94% had at least 1 record in the IMS Health dataset; of these, there was 93% agreement rate for multiple sclerosis diagnosis. Concordance on the use of any treatment was 59%, and agreement on reports of specific treatment usage (within an imputed 5-year period) ranged from 73.5% to 100%. CONCLUSIONS: It is possible to match patient identities between the 2 data sources, and the high concordance at multiple levels suggests that the matching process was accurate. Likewise, the high degree of concordance suggests that these patients were able to accurately self-report their diagnosis and, to a lesser degree, their treatment usage. Further studies of linked data types are warranted to evaluate the use of enriched datasets to generate novel insights.

Linking mortuary data improves vital statistics on cause of death of children under five years in the Western Cape Province of South Africa.

OBJECTIVES: Reducing child mortality requires good information on its causes. Whilst South African vital registration data have improved, the quality of cause-of-death data remains inadequate. To improve this, data from death certificates were linked with information from forensic mortuaries in Western Cape Province. METHODS: A local mortality surveillance system was established in 2007 by the Western Cape Health Department to improve data quality. Cause-of-death data were captured from copies of death notification forms collected at Department of Home Affairs Offices. Using unique identifiers, additional forensic mortuary data were linked with mortality surveillance system records. Causes of death were coded to the ICD-10 classification. Causes of death in children under five were compared with those from vital registration data for 2011. RESULTS: Cause-of-death data were markedly improved with additional data from forensic mortuaries. The proportion of ill-defined causes was halved (25-12%), and leading cause rankings changed. Lower respiratory tract infections moved above prematurity to rank first, accounting for 20.8% of deaths and peaking in infants aged 1-3 months. Only 11% of deaths from lower respiratory tract infections occurred in hospital, resulting in 86% being certified in forensic mortuaries. Road traffic deaths increased from 1.1-3.1% (27-75) and homicides from 3 to 28. CONCLUSIONS: The quality and usefulness of cause-of-death information for children in the WC was enhanced by linking mortuary and vital registration data. Given the death profile, interventions are required to prevent and manage LRTI, diarrhoea and injuries and to reduce neonatal deaths.

Neurodegeneration and mirror image agnosia.

BACKGROUND: Normal Percept with abnormal meaning (Agnosias) has been described from nineteenth century onwards. Later literature became abundant with information on the spectrum of Prosopagnosias. However, selective difficulty in identifying reflected self images with relatively better cognitive functions leads to problems in differentiating it from non-organic psychosis. AIM: In the present study, we investigated patients with dementia who showed difficulty in identifying reflected self images while they were being tested for problems in gnosis with reference to identification of reflected objects, animals, relatives, and themselves and correlate with neuropsychological and radiological parameters. PATIENTS AND METHODS: Five such patients were identified and tested with a 45 cm × 45 cm mirror kept at 30-cm distance straight ahead of them. RESULTS: Mirror image agnosia is seen in patients with moderate stage posterior dementias who showed neuropsychological and radiological evidence of right parietal dysfunction. CONCLUSION: Interpretation of reflected self images perception in real time probably involves distinct data-linking circuits in the right parietal lobe, which may get disrupted early in the course of the disease.