Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study

Linked survey and statutory health insurance data evaluating healthcare utilization patterns and associated factors of persons with diabetes in Germany - latent class analysis

Persons with diabetes mellitus have complex healthcare needs. Existing disease management programmes (DMPs) are based on a one-size-fits-all approach. However, individuals might require more individualised care. This study aims to identify groups with different patterns of healthcare utilization among people with diabetes in Germany and factors associated with these different patterns. A cross-sectional survey was conducted among a random sample from a statutory health insurance (SHI) with diabetes (n = 1332) and linked to longitudinal SHI data. Latent class analysis was used to identify subgroups with similar patterns of healthcare utilization and factors associated with different patterns. Four patterns of healthcare utilization were identified among people with diabetes: 'low users' (20.8% of the total sample); 'low users with ophthalmologist visit' (45.2%); 'high users' (26.5%); and 'high users with mental health care' (7.5%). The classes differed significantly in age, sex, type, duration and severity of diabetes, DMP membership, diabetes training, health-related quality of life, and prevalence of depression. The 'high users with mental health care' class was for example younger, more female, had a lower quality of life and the highest prevalence of depression. This study may provide a first basis for thinking about targeted care in Germany beyond DMPs.

The ESCAPE Trial for Older People With Chronic Low Back Pain: A Feasibility Study of a Clinical Trial of Group-Based Exercise in Primary Health Care

Low back pain is a highly disabling health condition that generates high costs for patients and healthcare systems. For this reason, it is considered a serious public health problem worldwide. This pilot study aimed to assess the feasibility of a future randomized controlled trial (RCT) by evaluating adherence to treatment, contamination between groups, satisfaction with treatment, and understanding of the exercise instructions provided by the physiotherapist. Additionally, we sought to identify and implement necessary modifications to the exercise protocol for better suitability in older people. We conducted a prospective, registered pilot RCT comparing an 8-week group-based exercise program with a waiting list in older people (≥60 years old) with chronic low back pain. Sixty participants were recruited through social media, pamphlets, and invitations at community referral centers. The study demonstrated the feasibility of a full RCT. Participants reported high satisfaction with the treatment (i.e., 100% indicated willingness to return for future services) and a high understanding of the exercise instructions (i.e., 81.8% reported "very easy" comprehension). Adherence to the exercise program exceeded the average reported for group exercise interventions in older adults (i.e., 82.58%). Dropout was associated solely with preexisting physical activity levels. The exercise protocol was successfully adapted to better suit the needs of the older adult population. This pilot RCT demonstrates the feasibility of a full-scale RCT to evaluate the effectiveness of group exercise in improving pain intensity and disability in older adults with chronic low back pain. The implemented adjustments to the exercise protocol and overall study approach strengthen the methodological foundation and expected accuracy of the future RCT.

The Impact of Follow-Up on Mortality in Chronic Limb-Threatening Ischemia

BACKGROUND

Chronic limb-threatening ischemia (CLTI) is associated with high morbidity and mortality. As such, close follow-up is recommended to ensure patency of revascularization, limb viability, and optimization of cardiovascular risk factors. This study aimed to test the association between follow-up adherence and mortality, and to identify risk factors for nonadherence with recommended vascular follow-up.

METHODS

All patients hospitalized from 2019 to 2023 with infrainguinal CLTI and at least 30 days of posthospitalization survival were included. Patients were stratified based on adherence with any outpatient vascular follow-up within 1 year defined as any outpatient visit conducted after the index hospitalization in which CLTI of the index limb was addressed. The primary endpoint was 1-year mortality and risk factors for follow-up nonadherence were assessed to identify targets for improvement. Multivariable models adjusted for other relevant contributors to mortality including age, clinical comorbidities, medical therapies, and anatomic/clinical limb severity among others. Additional sensitivity analyses were conducted using various definitions of follow-up adherence to enhance reliability of the findings.

RESULTS

A total of 131 patients with a median age of 73 years were included. A majority had tissue loss (97, 74.1%), 118 (90.1%) underwent index revascularization and 13 (9.9%) received no intervention due to nonsalvageable disease or patient preference. The overall 1-year mortality rate was 19.8% and follow-up adherence was 83.2%. Nonadherence with vascular follow-up was associated with greater 1-year mortality (40.9% vs. 15.6%, odds ratio (OR) 6.67, P = 0.005), a finding which persisted when all definitions of follow-up were tested. Risk factors for follow-up nonadherence include transfer from another institution (30.2% vs. 10.2%, OR 3.704, P = 0.014) and lack of a primary care provider (66.7% vs. 11.8%, OR 14.603, P < 0.001).

CONCLUSIONS

Nonadherence with vascular follow-up is associated with higher 1-year mortality among patients with CLTI. Improved referral of CLTI patients to a vascular surgeon in the outpatient setting before the need for urgent interhospital transfer as well as care coordination through a primary care provider may help improve adherence with vascular follow-up.

The Association of Established Primary Care with Postoperative Outcomes Among Medicare Patients with Digestive Tract Cancer

BACKGROUND

Primary care (PC) is essential to overall wellness and management of comorbidities. In turn, patients without adequate access to PC may face healthcare disparities. We sought to characterize the impact of established PC on postoperative outcomes among patients undergoing a surgical procedure for a digestive tract cancer.

METHODS

Medicare beneficiaries with a diagnosis of hepatobiliary, pancreas, and colorectal cancer between 2005 and 2019 were identified within the Surveillance, Epidemiology, and End Results program and Medicare-linked database. Individuals who did versus did not have PC encounters within 1-year before surgery were identified. A postoperative textbook outcome (TO) was defined as the absence of complications, no prolonged hospital stay, no readmission within 90 days, and no mortality.

RESULTS

Among 63,177 patients, 50,974 (80.7%) had at least one established PC visit before surgery. Patients with established PC were more likely to achieve TO (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.09-1.19) with lower odds for complications (OR, 0.85; 95% CI, 0.72-0.89), extended hospital stay (OR, 0.86; 95% CI, 0.81-0.94), 90-day readmission (OR, 0.94; 95% CI, 0.90-0.99), and 90-day mortality (OR, 0.87; 95% CI, 0.79-0.96). In addition, patients with established PC had a 4.1% decrease in index costs and a 5.2% decrease in 1-year costs. Notably, patients who had one to five visits with their PC in the year before surgery had improved odds of TO (OR, 1.21; 95% CI, 1.16-1.27), whereas individuals with more than 10 visits had lower odds of a postoperative TO (OR, 0.91; 95% CI, 0.84-0.98).

CONCLUSION

Most Medicare beneficiaries with digestive tract cancer had established PC within the year before their surgery. Established PC was associated with a higher probability of achieving ideal outcomes and lower costs. In contrast, patients with more than 10 PC appointments, which was likely a surrogate of overall comorbidity burden, experienced no improvement in postoperative outcomes.

Improving uptake of population health management through scalable analysis of linked electronic health data

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.

Association of Established Primary Care Use With Postoperative Mortality Following Emergency General Surgery Procedures

Importance

Sixty-five million individuals in the US live in primary care shortage areas with nearly one-third of Medicare patients in need of a primary care health care professional. Periodic health examinations and preventive care visits have demonstrated a benefit for surgical patients; however, the impact of primary care health care professional shortages on adverse outcomes from surgery is largely unknown.

Objective

To determine if preoperative primary care utilization is associated with postoperative mortality following an emergency general surgery (EGS) operation among Black and White older adults.

Design, Setting, and Participants

This was a retrospective cohort study that took place at US hospitals with an emergency department. Participants were Medicare patients aged 66 years or older who were admitted from the emergency department for an EGS condition between July 1, 2015, and June 30, 2018, and underwent an operation on hospital day 0, 1, or 2. The analysis was performed during December 2022. Patients were classified into 1 of 5 EGS condition categories based on principal diagnosis codes; colorectal, general abdominal, hepatopancreatobiliary, intestinal obstruction, or upper gastrointestinal. Mixed-effects multivariable logistic regression was used in the risk-adjusted models. An interaction term model was used to measure effect modification by race.

Exposure

Primary care utilization in the year prior to presentation for an EGS operation.

Main Outcome and Measures

In-hospital, 30-day, 60-day, 90-day, and 180-day mortality.

Results

A total of 102 384 patients (mean age, 73.8 [SD, 11.5] years) were included in the study. Of those, 8559 were Black (8.4%) and 93 825 were White (91.6%). A total of 88 340 patients (86.3%) had seen a primary care physician in the year prior to their index hospitalization. After risk adjustment, patients with primary care exposure had 19% lower odds of in-hospital mortality than patients without primary care exposure (odds ratio [OR], 0.81; 95% CI, 0.72-0.92). At 30 days patients with primary care exposure had 27% lower odds of mortality (OR, 0.73; 95% CI, 0.67-0.80). This remained relatively stable at 60 days (OR, 0.75; 95% CI, 0.69-0.81), 90 days (OR, 0.74; 95% CI, 0.69-0.81), and 180 days (OR, 0.75; 95% CI, 0.70-0.81). None of the interactions between race and primary care physician exposure for mortality at any time interval were significantly different.

Conclusions and Relevance

In this observational study of Black and White Medicare patients, primary care utilization had no impact on in-hospital mortality for Black patients, but was associated with decreased mortality for White patients. Primary care utilization was associated with decreased mortality for both Black and White patients at 30, 60, 90 and 180 days.

No Healthcare Utilization and Death

BACKGROUND

An inappropriately low frequency of healthcare utilization has been reported to be associated with poor control of chronic diseases, accelerating healthcare disparities. However, the evidence is limited regarding the association between no healthcare utilization and mortality.

OBJECTIVES

To examine whether individuals without healthcare utilization have the increased risks of mortality among the US general population.

DESIGN

Prospective cohort study PARTICIPANTS: Adults aged ≥ 20 years (n = 39,067) in the National Health and Nutrition Examination Survey (NHANES)1999-2014 linked to national mortality data through December 2015.

MAIN MEASURES

The exposure was the number of visits to healthcare providers during the past year (healthcare utilization): none, 1-3 times (referent), 4-9 times, or ≥ 10 times. Cox hazard regression models were employed to estimate the adjusted hazard ratios (aHR) of all-cause, cardiovascular, and cancer mortality adjusting for socio-demographic characteristics and comorbidities.

KEY RESULTS

During a median follow-up of 7.4 years, participants without visit over the past year showed higher risks of all-cause mortality (aHR [95% CI] = 1.16 [1.04-1.30]) and cardiovascular mortality (aHR [95% CI] = 1.62 [1.28-2.05]) than those who visited the office 1-3 times. We found no evidence of the association between no visit and cancer mortality. The association between no providers' office visit and all-cause mortality was stronger among males (aHR [95% CI] = 1.22 [1.06-1.40]) than females (aHR [95% CI] = 0.97 [0.79-1.19]; p-for-interaction = 0.01) and among uninsured individuals (aHR [95% CI] = 1.22 [0.98-1.51]) than insured individuals (aHR [95% CI] = 1.09 [0.95-1.25]; p-for-interaction = 0.04).

CONCLUSION

No providers' office visit over a year was associated with increased risks of all-cause and cardiovascular mortality. Further investigations are warranted to identify the underlying reasons for the elevated mortality risks due to no healthcare utilization.

Willingness to use community health centres for initial diagnosis: the role of policy incentives among Chinese patients

The aim of the study is to investigate the effect of policy incentives on residents' willingness to use community health centres for initial diagnosis. A cross-sectional survey with specific multiple price-list experiments was conducted in Guizhou, China. We were able to use in-depth individual interviews with a total of 422 participants. Our results showed that both financial and non-financial policy incentives can facilitate the utilisation of the community health centres. Approximately 60% of the respondents reported an increase in their willingness to use community health centres after the presentation of the financial policy, whereas 50% of respondents expressed an increase in their willingness to use community health centres with the non-financial policy. However, to some specific subgroups, such as residents with low trust, residents without chronic disease, residents with less healthcare visits and risk-averters, the impact of policy incentives were limited. The policy incentives are useful tools to attract more visitors to community health centres for initial diagnosis; however, their incentive effects vary in different subgroups. Thus, to change patients' perceptions regarding healthcare provider choice for initial diagnosis, policymakers should consider the heterogeneous responses of patients to policy incentives and focus their efforts on key cohorts.

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

BACKGROUND

A high proportion of health care services are persistently utilized by a small subpopulation of patients. To improve clinical outcomes while reducing costs and utilization, population health management programs often provide targeted interventions to patients who may become persistent high users/utilizers (PHUs). Enhanced prediction and management of PHUs can improve health care system efficiencies and improve the overall quality of patient care.

OBJECTIVE

The aim of this study was to detect key classes of diseases and medications among the study population and to assess the predictive value of these classes in identifying PHUs.

METHODS

This study was a retrospective analysis of insurance claims data of patients from the Johns Hopkins Health Care system. We defined a PHU as a patient incurring health care costs in the top 20% of all patients' costs for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in 2014-2015. We applied latent class analysis (LCA), an unsupervised clustering approach, to identify patient subgroups with similar diagnostic and medication patterns to differentiate variations in health care utilization across PHUs. Logistic regression models were then built to predict PHUs in the full population and in select subpopulations. Predictors included LCA membership probabilities, demographic covariates, and health utilization covariates. Predictive powers of the regression models were assessed and compared using standard metrics.

RESULTS

We identified 164,221 patients with continuous enrollment between 2013 and 2015. The mean study population age was 19.7 years, 55.9% were women, 3.3% had ≥1 hospitalization, and 19.1% had 10+ outpatient visits in 2013. A total of 8359 (5.09%) patients were identified as PHUs in both 2014 and 2015. The LCA performed optimally when assigning patients to four probability disease/medication classes. Given the feedback provided by clinical experts, we further divided the population into four diagnostic groups for sensitivity analysis: acute upper respiratory infection (URI) (n=53,232; 4.6% PHUs), mental health (n=34,456; 12.8% PHUs), otitis media (n=24,992; 4.5% PHUs), and musculoskeletal (n=24,799; 15.5% PHUs). For the regression models predicting PHUs in the full population, the F1-score classification metric was lower using a parsimonious model that included LCA categories (F1=38.62%) compared to that of a complex risk stratification model with a full set of predictors (F1=48.20%). However, the LCA-enabled simple models were comparable to the complex model when predicting PHUs in the mental health and musculoskeletal subpopulations (F1-scores of 48.69% and 48.15%, respectively). F1-scores were lower than that of the complex model when the LCA-enabled models were limited to the otitis media and acute URI subpopulations (45.77% and 43.05%, respectively).

CONCLUSIONS

Our study illustrates the value of LCA in identifying subgroups of patients with similar patterns of diagnoses and medications. Our results show that LCA-derived classes can simplify predictive models of PHUs without compromising predictive accuracy. Future studies should investigate the value of LCA-derived classes for predicting PHUs in other health care settings.

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

OBJECTIVE

To use latent class analysis to identify unobservable subpopulations amongst the heterogeneous population and explore the relationship between subpopulations and incident diabetes among Chinese adults.

METHODS

The retrospective study included 32,312 Chinese adults without diabetes at baseline. Latent class indicators included demographic and clinical variables. The outcome was incident diabetes. The relationship between latent class and outcome was evaluated with Cox proportional hazard regression analysis.

RESULTS

After screening, the two-class latent class model best fits the population. Participants in class 2 are characterized by higher age, body mass index, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, serum creatinine, serum urea nitrogen, alanine aminotransferase, and a higher proportion of males, ever/current smokers and drinkers, but lower high-density lipoprotein cholesterol and a lower proportion of family history of diabetes. The risk of diabetes in class 2 was 5.451 times (HR: 6.451, 95%CI: 4.179-9.960, P < 0.00001) and 5.264 times (HR: 6.264, 95%CI: 4.680-8.385, P < 0.00001) higher than that in class 1 during 3-year and 5-year follow-up, respectively.

CONCLUSIONS

We used latent class analysis to identify two distinct subpopulations with differential risk of diabetes during 3-year and 5-year follow-up.

Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

BACKGROUND

Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients.

METHODS

The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed.

RESULTS

Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients' race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies.

CONCLUSIONS

Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.

Differences in Clinical Outcomes of Adults Referred to a Homeless Transitional Care Program Based on Multimorbid Health Profiles: A Latent Class Analysis

Background: People experiencing homelessness face significant medical and psychiatric illness, yet few studies have characterized the effects of multimorbidity within this population. This study aimed to (a) delineate unique groups of individuals based on medical, psychiatric, and substance use disorder profiles, and (b) compare clinical outcomes across groups. Methods: We extracted administrative data from a health system electronic health record for adults referred to the Durham Homeless Care Transitions program from July 2016 to June 2020. We used latent class analysis to estimate classes in this cohort based on clinically important medical, psychiatric and substance use disorder diagnoses and compared health care utilization, overdose, and mortality at 12 months after referral. Results: We included 497 patients in the study and found 5 distinct groups: "low morbidity" (referent), "high comorbidity," "high tri-morbidity," "high alcohol use," and "high medical illness." All groups had greater number of admissions, longer mean duration of admissions, and more ED visits in the 12 months after referral compared to the "low morbidity" group. The "high medical illness" group had greater mortality 12 months after referral compared to the "low morbidity" group (OR, 2.53, 1.03-6.16; 95% CI, 1.03-6.16; p = 0.04). The "high comorbidity" group (OR, 5.23; 95% CI, 1.57-17.39; p < 0.007) and "high tri-morbidity" group (OR, 4.20; 95% CI, 1.26-14.01; p < 0.02) had greater 12-month drug overdose risk after referral compared to the referent group. Conclusions: These data suggest that distinct groups of people experiencing homelessness are affected differently by comorbidities, thus health care programs for this population should address their risk factors accordingly.

Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles

IMPORTANCE

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.

OBJECTIVE

To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.

MAIN OUTCOMES AND MEASURES

Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile.

RESULTS

The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management.

CONCLUSIONS AND RELEVANCE

The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes

BACKGROUND

An aging population and increasing multimorbidity challenge health care systems worldwide. Patient segmentation aims to recognize groups of patients with similar needs, offer targeted services to these groups, and reduce the burden of health care. In this study, the unique Finnish innovation Navigator, a web-based service for patient segmentation, is presented. Both patients and health care professionals complete the electronic questionnaire concerning patients' coping in everyday life and health state. Thus, it considers the patient perspective on self-care. One of four customership-strategy (CS) groups (self-acting, community, cooperating, and network) is then proposed in response to the answers given. This resulting strategy helps both professionals to coordinate patient health care and patients to utilize appropriate health services.

OBJECTIVE

This study aims to determine the feasibility, validity, and reliability of the Navigator service in the segmentation of patients with diabetes into four CS groups in a primary care setting. Patient characteristics concerning demographic status, chronic conditions, disabilities, health-related quality of life, and well-being in different CS groups will be described. We hypothesize that patients in the network group will be older, have more illnesses, chronic conditions or disabilities, and require more health care services than patients in the self-acting group.

METHODS

In this mixed methods study, data collection was based on questionnaires (user experience of Navigator, demographic and health status, World Health Organization Disability Assessment Schedule 2.0, EuroQol 5D, Wellbeing Questionnaire 12, and the Diabetes Treatment Satisfaction Questionnaire) issued to 300 patients with diabetes and on user-experience questionnaires for and semistructured focus-group interviews with 12 nurses. Navigator-database reports and diabetes-care values (blood pressure, BMI, HbA1c, low-density lipoprotein, albumin-creatinine, smoking status) were collected. Qualitative and descriptive analyses were used to study the feasibility, content, concurrent, and face validity of Navigator. While criterion and concurrent validity were examined with correlations, reliability was examined by calculating Cohen kappa and Cronbach alpha. Construct validity is studied by performing exploratory-factor analysis on Navigator data reports and by hypothesis testing. The values, demographics, and health status of patients in different groups were described, and differences between groups were studied by comparing means. Linear regression analysis was performed to assess which variables affect CS group variation.

RESULTS

Data collection was completed in September 2019, and the first feasibility results are expected by the end of 2020. Further results and publications are expected in 2021 and 2022.

CONCLUSIONS

This is the first scientific study concerning Navigator's psychometric properties. The study will examine the segregation of patients with diabetes into four CS groups in a primary care setting and the differences between patients in groups. This study will assist in Navigator's further development as a patient segmentation method considering patients' perspectives on self-care. This study will not prove the effectiveness or efficacy of Navigator; therefore, it is essential to study these outcomes of separate care pathways.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/20570.

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study

OBJECTIVES

To use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm.

DESIGN

Individuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis.

SETTING

A major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK.

PARTICIPANTS

1 013 940 individuals from 78 contributing general practices.

RESULTS

Compared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions.

CONCLUSIONS

PHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients

OBJECTIVE

With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns.

RESEARCH DESIGN AND METHODS

Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient's age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models.

RESULTS

Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82-13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75-30.63), stroke (HR 19.37, 95% CI 16.92-22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90-15.36), and mortality (HR 3.47, 95% CI 3.17-3.80).

CONCLUSIONS

T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Identifying optimal indicators and purposes of population segmentation through engagement of key stakeholders: a qualitative study

BACKGROUND

Various population segmentation tools have been developed to inform the design of interventions that improve population health. However, there has been little consensus on the core indicators and purposes of population segmentation. The existing frameworks were further limited by their applicability in different practice settings involving stakeholders at all levels. The aim of this study was to generate a comprehensive set of indicators and purposes of population segmentation based on the experience and perspectives of key stakeholders involved in population health.

METHODS

We conducted in-depth semi-structured interviews using purposive sampling with key stakeholders (e.g. government officials, healthcare professionals, social service providers, researchers) involved in population health at three distinct levels (micro, meso, macro) in Singapore. The interviews were audio-recorded and transcribed verbatim. Thematic content analysis was undertaken using NVivo 12.

RESULTS

A total of 25 interviews were conducted. Eight core indicators (demographic characteristics, economic characteristics, behavioural characteristics, disease state, functional status, organisation of care, psychosocial factors and service needs of patients) and 21 sub-indicators were identified. Age and financial status were commonly stated as important indicators that could potentially be used for population segmentation across three levels of participants. Six intended purposes for population segmentation included improving health outcomes, planning for resource allocation, optimising healthcare utilisation, enhancing psychosocial and behavioural outcomes, strengthening preventive efforts and driving policy changes. There was consensus that planning for resource allocation and improving health outcomes were considered two of the most important purposes for population segmentation.

CONCLUSIONS

Our findings shed light on the need for a more person-centric population segmentation framework that incorporates upstream and holistic indicators to be able to measure population health outcomes and to plan for appropriate resource allocation. Core elements of the framework may apply to other healthcare settings and systems responsible for improving population health.

TRIAL REGISTRATION

The study was approved by the SingHealth Institutional Review Board (CIRB Reference number: 2017/2597).

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

BACKGROUND

High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality.

METHODS

We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality.

RESULTS

Among 752 patients, a 3-class best fit model was selected: Class 1 "Frail, cognitively impaired and physically dependent", Class 2 "Pre-frail, but largely physically independent" and Class 3 "Physically independent". The 3 classes have distinct demographics, medical and socioeconomic characteristics (p <  0.05), 30- and 90-day readmission (p <  0.05) and mortality (p <  0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21-3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76-27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57-29.02, p <  0.001).

CONCLUSIONS

We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

Characteristics and Health Care Utilization of Different Segments of a Multiethnic Asian Population in Singapore

IMPORTANCE

Descriptive population-level health data are critical components of the evidence base for population health policy. Human populations often display marked heterogeneity in their health status among subgroups of the population. The recent widespread adoption of electronic health records provides opportunities to use routine real-world health care data to examine population health.

OBJECTIVE

To report population sociodemographic characteristics, health conditions, health care utilization, and health care costs for different population segments of a multiethnic Asian population divided according to a modified British Columbia Population Segmentation Framework.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cross-sectional study used 2016 data from the Singapore Eastern Regional Health System, the largest Regional Health System in Singapore. Data were obtained from deidentified national-level electronic health records at the Ministry of Health Singapore. Participants included all residents in the Singapore Eastern Regional Health System catchment area in 2016. The descriptive analysis was conducted in August 2018.

MAIN OUTCOMES AND MEASURES

Socioeconomic profiles, disease prevalence, health care utilization, and cost patterns of population segments.

RESULTS

The total size of the study population in 2016 was 1 181 024 residents (576 663 [48.83%] male; median [interquartile range] age, 40 [22-57] years). The population was divided into 8 segments: healthy with no outpatient utilization (493 483 residents), healthy with outpatient utilization (259 909 residents), healthy with inpatient admissions (49 588 residents), low complex (215 134 residents), medium complex (79 350 residents), high complex (44 445 residents), cancer (34 217 residents), and end of life (4898 residents). Overall, the 3 most prevalent conditions were chronic kidney disease (31.89%), hypertension (18.52%), and lipid disorders (18.33%). Distributions of chronic conditions differed across the segments. Different segments had varying health care utilization patterns: the high-complex segment had the highest number of primary care clinic visits (mean [SD], 4.25 [5.46] visits), the cancer segment had the highest number of specialist outpatient clinic visits (mean [SD], 5.55 [8.49] visits), and the end-of-life segment had the highest numbers of accident and emergency department visits (mean [SD], 1.80 [1.88] visits) and inpatient admissions (mean [SD], 1.99 [1.89] admissions) during 2016. For health care costs, specialist outpatient clinic and inpatient care together made up more than 85% of the total cost of nearly 2 billion Singapore dollars. The end-of-life segment contributed approximately 50% of the health care cost per capita of 60 000 Singapore dollars.

CONCLUSIONS AND RELEVANCE

Different population segments displayed heterogeneity in sociodemographic characteristics, health conditions, health care utilization, and health care cost patterns. This critical health information can be used as baseline data to inform regional and national health priorities for health services research and policy.