Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system.

BACKGROUND: Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes. METHODS: Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014-2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions. RESULTS: Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs. CONCLUSIONS: The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Validation of US CDC National Death Index mortality data, focusing on differences in race and ethnicity.

OBJECTIVES: The US Center for Disease Control and Prevention's National Death Index (NDI) is a gold standard for mortality data, yet matching patients to the database depends on accurate and available key identifiers. Our objective was to evaluate NDI data for future healthcare research studies with mortality outcomes. METHODS: We used a Kaiser Permanente Mid-Atlantic States' Virtual Data Warehouse (KPMAS-VDW) sourced from the Social Security Administration and electronic health records on members enrolled between 1 January 2005 to 31 December 2017. We submitted data to NDI on 1 036 449 members. We compared results from the NDI best match algorithm to the KPMAS-VDW for vital status and death date. We compared probabilistic scores by sex and race and ethnicity. RESULTS: NDI returned 372 865 (36%) unique possible matches, 663 061 (64%) records not matched to the NDI database and 522 (<1%) rejected records. The NDI algorithm resulted in 38 862 records, presumed dead, with a lower percentage of women, and Asian/Pacific Islander and Hispanic people than presumed alive. There were 27 306 presumed dead members whose death dates matched exactly between the NDI results and VDW, but 1539 did not have an exact match. There were 10 017 additional deaths from NDI results that were not present in the VDW death data. CONCLUSIONS: NDI data can substantially improve the overall capture of deaths. However, further quality control measures were needed to ensure the accuracy of the NDI best match algorithm.

National data linkage assessment of live births and deaths in Mexico: Estimating under-five mortality rate ratios for vulnerable newborns and trends from 2008 to 2019.

BACKGROUND: Linked datasets that enable longitudinal assessments are scarce in low and middle-income countries. OBJECTIVES: We aimed to assess the linkage of administrative databases of live births and under-five child deaths to explore mortality and trends for preterm, small (SGA) and large for gestational age (LGA) in Mexico. METHODS: We linked individual-level datasets collected by National statistics from 2008 to 2019. Linkage was performed based on agreement on birthday, sex, residential address. We used the Centre for Data and Knowledge Integration for Health software to identify the best candidate pairs based on similarity. Accuracy was assessed by calculating the area under the receiver operating characteristic curve. We evaluated completeness by comparing the number of linked records with reported deaths. We described the percentage of linked records by baseline characteristics to identify potential bias. Using the linked dataset, we calculated mortality rate ratios (RR) in neonatal, infants, and children under-five according to gestational age, birthweight, and size. RESULTS: For the period 2008-2019, a total of 24,955,172 live births and 321,165 under-five deaths were available for linkage. We excluded 1,539,046 records (6.2%) with missing or implausible values. We succesfully linked 231,765 deaths (72.2%: range 57.1% in 2009 and 84.3% in 2011). The rate of neonatal mortality was higher for preterm compared with term (RR 3.83, 95% confidence interval, [CI] 3.78, 3.88) and for SGA compared with appropriate for gestational age (AGA) (RR 1.22 95% CI, 1.19, 1.24). Births at <28 weeks had the highest mortality (RR 35.92, 95% CI, 34.97, 36.88). LGA had no additional risk vs AGA among children under five (RR 0.92, 95% CI, 0.90, 0.93). CONCLUSIONS: We demonstrated the utility of linked data to understand neonatal vulnerability and child mortality. We created a linked dataset that would be a valuable resource for future population-based research.

Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis.

BACKGROUND AND AIMS: Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known. METHODS: We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively. RESULTS: The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient. CONCLUSIONS: Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes.

Why and how we can use data linkage in oral health research: a narrative review.

OBJECTIVES: Poor oral health, impacting health and wellbeing across the life-course, is a costly and wicked problem. Data (or record) linkage is the linking of different sets of data (often administrative data gathered for non-research purposes) that are matched to an individual and may include records such as medical data, housing information and sociodemographic information. It often uses population-level data or 'big data'. Data linkage provides the opportunity to analyse complex associations from different sources for total populations. The aim of the paper is to explore data linkage, how it is important for oral health research and what promise it holds for the future. METHODS: This is a narrative review of an approach (data linkage) in oral health research. RESULTS: Data linkage may be a powerful method for bringing together various population datasets. It has been used to explore a wide variety of topics with many varied datasets. It has substantial current and potential application in oral health research. CONCLUSIONS: Use of population data linkage is increasing in oral health research where the approach has been very useful in exploring the complexity of oral health. It offers promise for exploring many new areas in the field.

Multi-state survival models with treatment effects and biomarkers: Simulations for study design assessment.

BACKGROUND: Comparative effectiveness studies of cancer therapeutics in observational data face confounding by patterns of clinical treatment over time. The validity of survival analysis in longitudinal health records depends on study design choices including index date definition and model specification for covariate adjustment. METHODS: Overall survival in cancer is a multi-state transition process with mortality and treatment switching as competing risks. Parametric Weibull regression quantifies proportionality of hazards across lines of therapy in real-world cohorts of 12 solid tumor types. Study design assessments compare alternative analytic models in simulations with realistic disproportionality. The multi-state simulation framework is adaptable to alternative treatment effect profiles and exposure patterns. RESULTS: Event-specific hazards of treatment-switching and death are not proportional across lines of therapy in 12 solid tumor types. Study designs that include all eligible lines of therapy per subject showed lower bias and variance than designs that select one line per subject. Confounding by line number was effectively mitigated across a range of simulation scenarios by Cox proportional hazards models with stratified baseline hazards and inverse probability of treatment weighting. CONCLUSION: Quantitative study design assessment can inform the planning of observational research in clinical oncology by demonstrating the potential impact of model misspecification. Use of empirical parameter estimates in simulation designs adapts analytic recommendations to the clinical population of interest.

Geriatric assessment domains to predict overall survival in older cancer patients: An analysis of functional status, comorbidities, and nutritional status as prognostic factors.

Cancer treatments for older patients must account for heterogeneity in health and functional status. Guidelines advocate the use of geriatric assessments (GAs), but comprehensive assessments are laborious and the utility of specific GA domains remains unclear. The identification of specific domains as prognostic factors may support survival predictions and treatment decisions. We aimed to evaluate the associations between several GA domains and overall survival in older cancer patients. We linked cancer registry data and administrative claims data from cancer patients residing in Osaka Prefecture, Japan. The subjects were patients aged ≥70 years who received a diagnosis of gastric, colorectal, or lung cancer between 2010 and 2014 at 36 designated cancer care hospitals. The following three GA domains were assessed at cancer diagnosis: functional status through activities of daily living (ADL), comorbidities, and nutritional status through body mass index. Cox proportional hazards models were constructed for the three cancer types to estimate each domain's prognostic effect while adjusting for gender, age, and cancer stage. Adjusted hazard ratios (HRs) for all-cause mortality were calculated. We identified 5,559, 4,746, and 4,837 patients with gastric, colorectal, and lung cancer respectively. ADL impairment (HRs: 1.39-3.34, 1.64-2.86, and 1.24-3.21 for gastric, colorectal, and lung cancer, respectively), comorbidities (1.32-1.58, 1.33-1.97, and 1.19-1.29 for gastric, colorectal, and lung cancer, respectively), and underweight (1.36, 1.51, and 1.54 for gastric, colorectal, and lung cancer, respectively) were significantly associated with poorer overall survival. In contrast, overweight was significantly associated with improved overall survival (HRs: 0.82 and 0.89 for gastric and lung cancer respectively). The addition of the three domains increased the models' C-statistics (0.816 to 0.836, 0.764 to 0.787, and 0.759 to 0.783 for gastric, colorectal, and lung cancer respectively). Incorporating these factors into initial patient evaluations during diagnosis may aid prognostic predictions and treatment strategies in geriatric oncology.

Patient Record Linkage for Data Quality Assessment Based on Time Series Matching.

BACKGROUND: Huge amounts of data are collected by healthcare providers and other institutions. However, there are data protection regulations, which limit their utilisation for secondary use, e.g. RESEARCH: In scenarios, where several data sources are obtained without universal identifiers, record linkage methods need to be applied to obtain a comprehensive dataset. OBJECTIVES: In this study, we had the objective to link two datasets comprising data from ergometric performance tests in order to have reference values to free text annotations for assessing their data quality. METHODS: We applied an iterative, distance-based time series record linkage algorithm to find corresponding entries in the two given datasets. Subsequently, we assessed the resulting matching rate. The implementation was done in Matlab. RESULTS: The matching rate of our record linkage algorithm was 74.5% for matching patients' records with their ergometry records. The highest rate of appropriate free text annotations was 87.9%. CONCLUSION: For the given scenario, our algorithm matched 74.5% of the patients. However, we had no gold standard for validating our results. Most of the free text annotations contained the expected values.

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

Record linkage study of the pathogen-specific burden of respiratory viruses in children.

BACKGROUND: Reliance on hospital discharge diagnosis codes alone will likely underestimate the burden of respiratory viruses. OBJECTIVES: To describe the epidemiology of respiratory viruses more accurately, we used record linkage to examine data relating to all children hospitalized in Western Australia between 2000 and 2012. PATIENTS/METHODS: We extracted hospital, infectious disease notification and laboratory data of a cohort of children born in Western Australia between 1996 and 2012. Laboratory records of respiratory specimens collected within 48 hours of admission were linked to hospitalization records. We calculated the frequency and rates of virus detection. To identify groups where under-ascertainment for respiratory viruses was greatest, we used logistic regression to determine factors associated with failure to test. RESULTS AND CONCLUSIONS: Nine percentage of 484 992 admissions linked to a laboratory record for respiratory virus testing. While 62% (n = 26 893) of laboratory-confirmed admissions received respiratory infection diagnosis codes, 38% (n = 16 734) had other diagnoses, notably viral infection of unspecified sites. Of those tested, incidence rates were highest for respiratory syncytial virus (247 per 100 000 child-years) followed by parainfluenza (63 per 100 000 child-years). Admissions among older children and those without a respiratory diagnosis were associated with failure to test for respiratory viruses. Linked data can significantly enhance diagnostic codes when estimating the true burden of disease. In contrast to current emphasis on influenza, respiratory syncytial virus and parainfluenza were the most common viral pathogens among hospitalized children. By characterizing those failing to be tested, we can begin to quantify the under-ascertainment of respiratory viruses.

Linking routinely collected social work, education and health data to enable monitoring of the health and health care of school-aged children in state care ('looked after children') in Scotland: a national demonstration project.

BACKGROUND AND OBJECTIVES: Children in state care ('looked after children') have poorer health than children who are not looked after. Recent developments in Scotland and elsewhere have aimed to improve services and outcomes for looked after children. Routine monitoring of the health outcomes of looked after children compared to those of their non-looked after peers is currently lacking. Developing capacity for comparative monitoring of population-based outcomes based on linkage of routinely collected administrative data has been identified as a priority. To our knowledge there are no existing population-based data linkage studies providing data on the health of looked after and non-looked after children at national level. Smaller scale studies that are available generally provide very limited information on linkage methods and hence do not allow scrutiny of bias that may be introduced through the linkage process. STUDY DESIGN AND METHODS: National demonstration project testing the feasibility of linking routinely collected looked after children, education and health data. PARTICIPANTS: All children in publicly funded school in Scotland in 2011/12. RESULTS: Linkage between looked after children data and the national pupil census classified 10,009 (1.5%) and 1757 (0.3%) of 670,952 children as, respectively, currently and previously looked after. Recording of the unique pupil identifier (Scottish Candidate Number, SCN) on looked after children returns is incomplete, with 66% of looked after records for 2011/12 for children of possible school age containing a valid SCN. This will have resulted in some under-ascertainment of currently and, particularly, previously looked after children within the general pupil population. Further linkage of the pupil census to the National Health Service Scotland master patient index demonstrated that a safe link to the child's unique health service (Community Health Index) number could be obtained for a very high proportion of children in each group (94%, 95% and 95% of children classified as currently, previously, and non-looked after, respectively). In general, linkage rates were higher for older children and those living in more affluent areas. Within the looked after group, linkage rates were highest for children with the fewest placements and for those in permanent fostering. CONCLUSIONS: This novel data linkage demonstrates the feasibility of monitoring population-based health outcomes of school-aged looked after and non-looked after children using linked routine administrative data. Improved recording of the unique pupil identifier number on looked after data returns would be beneficial. Extending the range of personal identifiers on looked after children returns would enable linkage to health data for looked after children who are not in publicly funded schooling (i.e. those who are preschool or postschool, home schooled or in independent schooling).

Acute care hospitalization by Aboriginal identity, Canada, 2006 through 2008.

BACKGROUND: National data about acute care hospitalization of Aboriginal people are scarce. This study addresses that information gap by describing patterns of hospitalization by Aboriginal identity for leading diagnoses for all provinces and territories except Quebec. DATA AND METHODS: The 2006 Census was linked to the 2006/2007-to-2008/2009 Discharge Abstract Database, which contains hospital records from all acute care facilities in Canada (excluding Quebec). With these linked data, hospital records could be examined by Aboriginal identity, as reported to the census. Hospitalizations were grouped by International Classification of Diseases (ICD-10) chapters based on "the most responsible diagnosis." Age-standardized hospitalization rates were calculated per 100,000 population, and rate ratios (RR) were calculated for Aboriginal groups relative to non-Aboriginal people. RESULTS: Hospitalization rates were almost invariably higher for First Nations living on and off reserve, Métis, and Inuit living in Inuit Nunangat than for the non-Aboriginal population, regardless of ICD diagnostic chapter. The ranking of age-standardized hospitalization rates by frequency of diagnoses varied slightly by Aboriginal identity. RRs were highest among First Nations living on reserve, especially for endocrine, nutritional and metabolic diseases (RR = 4.9), mental and behavioural disorders (RR = 3.6), diseases of the respiratory system (RR = 3.3), and injuries (RR = 3.2). As well, the rate for endocrine, nutritional and metabolic diseases was high among First Nations living off reserve (RR = 2.7). RRs were also high among Inuit for mental and behavioural disorders (RR = 3.3) and for diseases of the respiratory system (RR = 2.7). INTERPRETATION: Hospitalization rates varied by Aboriginal identity, and were consistent with recognized health disparities between Aboriginal and non-Aboriginal people. Because many factors besides health affect hospital use, further research is required to understand differences in hospital use by Aboriginal identity. These national data are relevant to health policy formulation and service delivery planning.

Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching.

BACKGROUND: Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient's records may be improperly split across multiple profiles in the MPI. OBJECTIVES: We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE. METHODS: We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE. RESULTS: We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value < 0.001). CONCLUSIONS: This method may assist HIEs in detecting split patient records.

Effect of socioeconomic status on hepatocellular carcinoma incidence and stage at diagnosis, a population-based cohort study.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) incidence is increasing worldwide and cirrhosis is the most important risk factor predominantly caused by chronic viral hepatitis infection. We studied the impact of socioeconomic status (SES) on HCC incidence and stage at diagnosis among viral hepatitis cases. METHODS: A population-based retrospective cohort study was conducted through the Ontario Cancer Registry linked data. Incidence rates were calculated using person-time methodology. Association between SES (income quintile) and HCC incidence was assessed using proportional-hazards regression. The impact of SES on HCC stage was investigated using logistic regression. RESULTS: Among 11 350 individuals diagnosed with viral hepatitis between 1991 and 2010, a crude HCC incidence rate of 21.4 cases per 1000 person-years was observed. Adjusting for age, gender, urban/rural residence and year of viral hepatitis diagnosis, a significant association was found between SES and HCC incidence, with an increased risk among individuals in the lowest three income quintiles (incidence rate ratio, IRR = 1.235; 95% CI: 1.074-1.420; IRR = 1.183; 95% CI: 1.026-1.364; IRR = 1.158; 95% CI: 1.000-1.340 respectively). No significant association between SES and HCC incidence was found after additionally adjusting for risk factors associated with HCC. However, HCC risk factors such as cirrhosis and HIV are associated with SES. Furthermore, no association was found between SES and HCC stage. CONCLUSIONS: The association between SES and HCC incidence is likely because of differences in risk factors across income quintiles. Investigating how SES affects HCC incidence facilitates an understanding of which populations are at elevated risk for HCC.

Birth of identity: understanding changes to birth certificates and their value for identity resolution.

INTRODUCTION: Identity information is often used to link records within or among information systems in public health and clinical settings. The quality and stability of birth certificate identifiers impacts both the success of linkage efforts and the value of birth certificate registries for identity resolution. OBJECTIVE: Our objectives were to describe: (1) the frequency and cause of changes to birth certificate identifiers as children age, and (2) the frequency of events (ie, adoptions, paternities, amendments) that may trigger changes and their impact on names. METHODS: We obtained two de-identified datasets from the Utah birth certificate registry: (1) change history from 2000 to 2012, and (2) occurrences for adoptions, paternities, and amendments among births in 1987 and 2000. We conducted cohort analyses for births in 1987 and 2000, examining the number, reason, and extent of changes over time. We conducted cross-sectional analyses to assess the patterns of changes between 2000 and 2012. RESULTS: In a cohort of 48 350 individuals born in 2000 in Utah, 3164 (6.5%) experienced a change in identifiers prior to their 13th birthday, with most changes occurring before 2 years of age. Cross-sectional analysis showed that identifiers are stable for individuals over 5 years of age, but patterns of changes fluctuate considerably over time, potentially due to policy and social factors. CONCLUSIONS: Identities represented in birth certificates change over time. Specific events that cause changes to birth certificates also fluctuate over time. Understanding these changes can help in the development of automated strategies to improve identity resolution.

Construction of a linked health and social care database resource--lessons on process, content and culture.

BACKGROUND: Combining routinely collected health and social care data on older people is essential to advance both service delivery and research for this client group. Little data is available on how to combine health and social care data; this article provides an overview of a successful data linkage process and discusses potential barriers to executing such projects. METHODS AND RESULTS: We successfully obtained and linked data on older people within Dundee from three sources: Dundee Social Work Department database (30,000 individuals aged 65 years and over), healthcare data held on NHS Tayside patients by the Health Informatics Centre (400,000 individuals), Dundee, and the Dundee of Medicine for the Elderly rehabilitation database (4300 individuals). Data were linked, anonymized and transferred to a Safe Haven environment to ensuring confidentiality and strict access control. Challenges were faced around workflows, culture and documentation. Exploiting the resultant data set raises further challenges centered on database documentation, understanding the way data were collected, dealing with missing data, data validity and collection at different time periods. CONCLUSION: Routinely collected health and social care data sets can be linked, but significant process barriers must be overcome to allow successful linkage and integration of data and its full exploitation.

Uncovering Hospitalists' Information Needs from Outside Healthcare Facilities in the Context of Health Information Exchange Using Association Rule Learning.

BACKGROUND: Important barriers to health information exchange (HIE) adoption are clinical workflow disruptions and troubles with the system interface. Prior research suggests that HIE interfaces providing faster access to useful information may stimulate use and reduce barriers for adoption; however, little is known about informational needs of hospitalists. OBJECTIVE: To study the association between patient health problems and the type of information requested from outside healthcare providers by hospitalists of a tertiary care hospital. METHODS: We searched operational data associated with fax-based exchange of patient information (previous HIE implementation) between hospitalists of an internal medicine department in a large urban tertiary care hospital in Florida, and any other affiliated and unaffiliated healthcare provider. All hospitalizations from October 2011 to March 2014 were included in the search. Strong association rules between health problems and types of information requested during each hospitalization were discovered using Apriori algorithm, which were then validated by a team of hospitalists of the same department. RESULTS: Only 13.7% (2 089 out of 15 230) of the hospitalizations generated at least one request of patient information to other providers. The transactional data showed 20 strong association rules between specific health problems and types of information exist. Among the 20 rules, for example, abdominal pain, chest pain, and anaemia patients are highly likely to have medical records and outside imaging results requested. Other health conditions, prone to have records requested, were lower urinary tract infection and back pain patients. CONCLUSIONS: The presented list of strong co-occurrence of health problems and types of information requested by hospitalists from outside healthcare providers not only informs the implementation and design of HIE, but also helps to target future research on the impact of having access to outside information for specific patient cohorts. Our data-driven approach helps to reduce the typical biases of qualitative research.

Use of acute care hospital services by immigrant seniors in Ontario: A linkage study.

BACKGROUND: Seniors constitute the largest group of hospital users. The increasing share of immigrants in Canada's senior population can affect the demand for hospital care. DATA AND METHODS: This study used the linked 2006 Census-Hospital Discharge Abstract Database to examine hospitalization during the 2004-to-2006 period, by immigrant status, of Ontario seniors living in the community. Hospitalization was assessed with logistic regressions; cumulative length of stay, with zero-truncated negative binomial regressions. All-cause hospitalization and hospitalizations specific to circulatory and digestive diseases were examined. RESULTS: Immigrant seniors had significantly low age-/sex-adjusted odds of hospitalization, compared with Canadian-born seniors (OR = 0.81). The odds varied from 0.4 among East Asians to 0.89 among Europeans, and rose with length of time since arrival from 0.54 for recent (1994 to 2003) to 0.86 for long-term (before 1984) immigrants. Adjustment for demographic and socio-economic characteristics did not change the overall patterns. Immigrants' cumulated length of hospital stay tended to be shorter than or similar to that of Canadian-born seniors. INTERPRETATION: Immigrant seniors, especially recent arrivals, had lower odds of hospitalization and similar time in hospital, compared with Canadian-born seniors. These patterns likely reflect differences in health status. Variations by world region and disease reflect the diverse health care needs of immigrant seniors.

Validity of deterministic record linkage using multiple indirect personal identifiers: linking a large registry to claims data.

BACKGROUND: Linking patient registries with administrative databases can enhance the utility of the databases for epidemiological and comparative effectiveness research. However, registries often lack direct personal identifiers, and the validity of record linkage using multiple indirect personal identifiers is not well understood. METHODS AND RESULTS: Using a large contemporary national cardiovascular device registry and 100% Medicare inpatient data, we linked hospitalization-level records. The main outcomes were the validity measures of several deterministic linkage rules using multiple indirect personal identifiers compared with rules using both direct and indirect personal identifiers. Linkage rules using 2 or 3 indirect, patient-level identifiers (ie, date of birth, sex, admission date) and hospital ID produced linkages with sensitivity of 95% and specificity of 98% compared with a gold standard linkage rule using a combination of both direct and indirect identifiers. CONCLUSIONS: Ours is the first large-scale study to validate the performance of deterministic linkage rules without direct personal identifiers. When linking hospitalization-level records in the absence of direct personal identifiers, provider information is necessary for successful linkage.

"Whose data is it anyway?" The implications of putting small area-level health and social data online.

Data from electronic patient management systems, routine national health databases, and social administrative systems have increased significantly over the past decade. These data are increasingly used to create maps and analyses communicating the geography of health and illness. The results of these analyses can be easily disseminated on the web often without due consideration for the identification, access, ethics, or governance, of these potentially sensitive data. Lack of consideration is currently proving a deterrent to many organisations that might otherwise provide data to central repositories for invaluable social science and medical research. We believe that exploitation of such data is needed to further our understanding of the determinants of health and inequalities. Therefore, we propose a geographical privacy-access continuum framework, which could guide data custodians in the efficient dissemination of data while retaining the confidentiality of the patients/individuals concerned. We conclude that a balance of restriction and access is needed allowing linkage of multiple datasets without disclosure, enabling researchers to gather the necessary evidence supporting policy changes or complex environmental and behavioural health interventions.