Validation and application of a needs‐based segmentation tool for cross‐country comparisons

1 INTRODUCTION

Chronic conditions have been called “the healthcare challenge of this century” by the World Health Organization.1 The prevalence of patients with numerous and complex health care needs related to one or more chronic conditions is expected to increase as life expectancy continues to rise.2, 3 An increasing life expectancy also exposes individuals to a greater number of changes over their life course, particularly to older-age life transitions not captured in their medical diagnoses. Changes over the life course, such as changes in social networks, relationships, living arrangements, or employment, influence the complexity of individuals' health care, including their ability to meet their own basic needs and rely on support from others.4 Therefore, the health system is facing not only an increase in the number of individuals with specific clinical diagnoses3 but also a growing number of individuals with complex needs related to a combination of multiple medical conditions and health-related social needs.

Lynn et al.5 developed a needs-based population segmentation approach for improving a health system's effectiveness and efficiency in meeting the needs of the population it serves. Within a population, this approach identifies segments or clusters of individuals with similar care goals and similar types and intensity of needs. Such an ontology can, for example, measure the extent to which a particular population segment has less than desirable outcomes relative to a given benchmark. Policy makers can use these estimates to assess whether that segment's health and health-related social service needs are met and, if not, to develop targeted interventions.5

While no clear definition of a “need” exists, a service is defined as “needed” when a typical individual with a set of characteristics that define a segment will likely benefit from receiving that service (in terms of reducing the likelihood of experiencing a more adverse health state).6 To date, the operationalization of population needs-based segmentation has been restricted to specific subgroups (e.g., the frail elderly or individuals with possible palliative care needs)7-10 or to data-driven approaches relying on electronic records to cluster individuals according to the risk of both poor outcomes and high cost.8

In this paper, we introduce an adapted version of the Simple Segmentation Tool (SST)11—developed by Duke-NUS Medical School—that we call the “Cross-Country Simple Segmentation Tool” (CCSST). The CCSST segments the population into clinically significant global impressions (GI) segments (i.e., categories in terms of health status and medical complexity) and complicating factors (CFs) (e.g., experiencing fragmented care) are indicative of health and health-related social services needs, respectively. Operationally, for the needs-based population segmentation approach, a consensus panel of medical and social service experts defined “need” as any actionable service with a high probability of notable benefit.7

In our framework, health needs are principally services that would need to be provided by a physician or other health provider trained and licensed to diagnose, prescribe, and perform procedures; and health-related social services needs are those that a physician, nurse, social worker, family member, volunteer, or even a specific technology can provide. While the SST was originally developed for clinical settings, it was modified for administration through special-purpose community surveys,11, 12 from which the CCSST is adapted. The CCSST uses data from the Survey of Health, Aging and Retirement in Europe (SHARE) and related international surveys available from the Gateway to Global Aging Data.13 Using data that are readily available in many countries allows us to compare health care needs across countries.

The objective of this article is to present the CCSST and demonstrate a cross-country benchmarking application, comparing countries according to (1) the fraction of individuals with high medical needs and (2) the relative hazards of transitioning between lower and higher needs segments, and between those segments and death.

2 METHODS

The methods section is structured as follows. First, we briefly introduce the survey data. Second, we present and describe the CCSST, a segmentation tool comprising five ordinal GI segments and four binary CF indicators. The GI segment categorizes individuals into one of five ordinal medical complexity categories. The four CFs identify patient characteristics that, if present, would increase the complexity of care for the conditions in the GI designation. Third, using Cox proportional hazard models, we assess the CCSST for predictive validity by modeling the association of the CCSST segments with mortality. Fourth, we describe how we applied the CCSST to cross-country comparisons by (a) comparing the proportion of individuals in different states of need and (b) comparing proportional hazards of transitioning between those states using a continuous-time Markov process.

2.1 Description of survey data

The SHARE is a multidisciplinary, cross-national panel database consisting of seven biennial survey waves. It contains microdata on health, socioeconomic status, and the social networks of individuals older than 50 years.10 The survey was conducted biannually in 29 countries, starting in 2004. Interviews were conducted in person with the target individuals and their partners. To ensure reliable convergence of the transition probability estimation, we include those 14 countries with at least 8 years of survey participation. The 14 countries are Austria, Belgium, Czech Republic, Denmark, France, Germany, Greece, Israel, Italy, the Netherlands, Poland, Spain, Sweden, and Switzerland.

2.2 Development of the CCSST: Mapping survey data to GI and CFs

The CCSST is based on both clinical and survey versions of the SST, which has been validated for inter-rater reliability in a clinical setting,7 and for predictive validity in both clinical and survey settings11, 12 as well as having face validity.7 In both of these applications, questions were added to the data collection instruments, with the specific goal of assessing a specific set of GI categories and CFs. However, given that the survey data were not created for this purpose, we designed and constructed a mapping algorithm by using the foundational principles of the SST (see Supporting Information Data S1 for the original SST). We found the data sufficient for the five ordinal GI segments: healthy, chronic asymptomatic, chronic symptomatic, long course of decline, and limited reserve with serious exacerbation—in line with the previous survey version of the SST.12 Although the original SST included a sixth state (short period of decline before dying), predicting rapid decline with survey data is not feasible. We, therefore, collapsed the sixth state, short period of decline before dying, into the fifth state, limited reserve, and serious exacerbation.12 The qualifying criteria for each GI segment are defined through clinical indicators, self-reported physician diagnosis of chronic disease, and performance-based tests conducted during the survey interview.

A trade-off to limiting ourselves to data in aging surveys,13, 14 thereby making the tool widely accessible, was that the CCSST was able to capture only four of the eight CFs described in the original SST: functional assessment (dependence on caregiver assistance), social support in case of need, frequent transitions between inpatient and outpatient care, and needing to take five or more prescription medications daily. The other CFs are need for nursing or rehabilitation services, activation in care, disruptive behavioral features, and having multiple health care providers in multiple locations. Even though we were able to capture only four CFs, preliminary results on the effect of the four we identified had been found earlier to have a significant association with adverse health outcomes.11 We confirmed the consistency of all variables used for both GI segments and CFs across all waves for all countries. The descriptive mapping file for the GI segments and CFs is given in Table 1.

TABLE 1. Descriptive mapping table for GI segments and CFs GI segmentsa CCSST definition Example Segmentation condition Healthy No more than minimal symptomatic conditions and no asymptomatic conditions that increase risk Acute URTI No Chronic disease, no difficulty with any activity of daily living, not cognitively impaired and not classified as frail Chronic asymptomatic Chronic conditions (not curable once acquired or has persisted >3 months despite treatment) that are asymptomatic but notable for increasing preventable risk Asymptomatic diabetes EITHER Non-life-threatening chronic disease AND Global Activity Limitation Index indicated “not limited” OR Classified as depressed according to the EURO-D scale AND Global Activity Limitation Index indicated “not limited” Chronic stable Chronic conditions that are relatively stable but associated with symptoms that interfere with or restrict usual function or would generally be sufficient to trigger care-seeking. Include conditions that are silent (relatively asymptomatic) but severe Symptomatic Parkinson's disease EITHER Life-threatening chronic condition AND Global activity limitation index indicated either “not limited” or “limited, but not severely” OR Non-life-threatening chronic disease AND Global Activity Limitation Index indicated either “limited but not severely” or “severely limited” OR Classified as depressed according to the EURO-D scale AND Global activity limitation index indicated either “limited but not severely” or “severely limited” Long course of decline Long (months to years) dwindling course of loss of reserve in multiple organ systems; typically elderly. Decline may be characterized by geriatric syndromes or recurrent exacerbations of multiple co-dominant medical (nonsocial) conditions Frail elderly with dementia Classified as frail OR Classified as cognitively impaired Limited reserve and serious exacerbation Single dominant medical (nonsocial) condition associated with recurrent exacerbations Frequent flares of COPD Life-threatening chronic condition AND Global Activity Limitation Index indicated “severely limited” CFs CCSST definition Example Segmentation condition Functional assessment Deficit implies dependence on caregiver assistance to perform basic or instrumental activities of daily living: The individual is otherwise unable to perform tasks independently

0 = no deficit

1 = any ADL or IADL deficit

Basic activities of daily living: Dressing (including putting on shoes and socks), walking across a room, bathing or showering, eating (including cutting up food), getting in or out of bed and using the toilet (including getting up or down)

Instrumental activities of daily living: Using a map, preparing a hot meal, shopping for groceries, making a telephone call, taking medications, doing work around the house or garden, and managing money (paying bills, keeping track of expenses)

Social support in case of need Family or friends who provide support through companionship and basic health care services in case of need. Companionship support includes assistance with major medical decision making. Basic health care services include support with activities of daily living/instrumental activities of daily living, as well as skilled nursing tasks (i.e., health care tasks requiring specific skills training. Can often be performed by a caregiver or domestic worker if properly trained).

0 = has support for basic health care services or companionship

1 = no support

Support for basic health care services or companionship: Someone is in the household helping regularly with personal care. Any friend, neighbor, or family member from outside the household has provided any kind of help in the past 12 months A family respondent is helping with the completion of the survey Frequent transitions between inpatient and outpatient care Number of overnight inpatient admissions during the past 12 months. Individuals with a high number of transitions between care venues are more likely to experience fragmented care.

0 = less than three times

1 = three or more times

Individuals most frequently transitioning between venues of care: Using information regarding the number of times stayed overnight in hospital during last 12 months. Create a threshold identifying the individuals who are in the top 95% of respondents, based on the sample in wave 1: three times Polypharmacy (five or more medications prescribed per day) The burden of having five or more prescription medications to take daily. Prescription medication categories are counted.

0 = fewer than five prescription medication categories

1 = five or more prescription categories

Prescription medication:

Count the number of categories of medications reported taking.

Categories include drugs for: high blood cholesterol, high blood pressure, coronary or cerebrovascular diseases, other heart diseases, diabetes, joint pain or for joint inflammation, other pain (e.g., headache, back pain, etc.), sleep problems, anxiety or depression, osteoporosis, stomach burns, bronchitis, and suppressing inflammation (only glucocorticoids or steroids)

Abbreviations: CCSST, cross-country simple segmentation tool; CF, complicating factors; GI, global impressions. a GI segments become consecutively more severe. If an individual qualifies for more than one health state, that individual should be assigned to the most severe of the health states.

The clinical indicators we used for segmentation include frailty, the global activity limitation index, activities of daily living, the EURO-D depression score, and cognitive impairment. Frailty was determined through the SHARE operationalization15 of Fried's “Frailty as a phenotype.”16 The global activity limitation index is a global single-item instrument that measures long-standing activity limitations (6 months or more) that general health problems cause and that inhibit activities commonly undertaken by survey participants.17, 18 The EURO-D depression symptom scale is a 12-point index identifying an individual's validated depression state for comparing the prevalence and risk of depression across countries.19-21 Finally, we measure cognitive impairment by individuals' scoring below a cutoff value in a performance-based cognitive function score consisting of the mean normalized score of five cognitive function tests: verbal fluency, immediate recall, delayed recall, orientation, and numeracy.22 The cutoff value was defined as 1.5 standard deviations below the mean score obtained by the total survey Wave 1 sample.

Chronic disease diagnoses—classified as either potentially life-threatening or non-life-threatening—were determined through discussions with physicians on the project team and individuals involved in designing previous versions of the SST.7, 11, 12 As chronic conditions are by definition persistent over the life span, all participants, from the time of their diagnosis, are assigned to all future waves. Life-threatening chronic diseases were defined as conditions likely to dominate an individual's health care management and planning—including exacerbations not dealt with by a primary physician, and in which the likely effects of failure to monitor physiological parameters with prompt follow-up would be serious (e.g., avoidable hospitalization or death). The self-reported physician-diagnosed conditions identified as life-threatening chronic diseases are myocardial infarction, congestive heart failure, stroke, chronic obstructive lung disease, hip fractures, and cancer (including leukemia or lymphoma, but excluding minor skin cancers). Hip fractures are considered life-threatening across all health systems because the morbidity of health conditions is assessed independently of available health system resources.23

Non-life-threatening chronic diseases were defined as all other conditions requiring continuous medical surveillance after symptoms have persisted for more than 3 months. Medical surveillance of non-life-threatening chronic diseases can usually be conducted by a combination of the individual, their primary care provider, and social care actors within the community. The following self-reported diagnoses were identified as non-life-threatening chronic diseases: high blood pressure or hypertension, high blood cholesterol, diabetes or high blood sugar, stomach or duodenal ulcer, peptic ulcer, Parkinson's disease, cataracts, and arthritis-related conditions. For arthritis-related conditions, a composite indicator combines the diagnoses osteoarthritis, rheumatism, and osteoporosis available only in the first four waves of the survey, with rheumatoid arthritis and osteoarthritis or other rheumatism available from the fifth wave onward. All self-reported diagnoses or composite indicators used are present in each wave and show consistency across waves.

The CFs were selected by a consensus panel of medical and social service experts from the evidence that such factors were predictive of adverse outcomes and that receiving the corresponding non-physician and social services has the potential for alleviating the CF effects.7 The list did not include social determinants of health, such as housing or food insecurity, that are outside the control of a conventional health system.

2.3 Assessing the predictive validity of the CCSST

To assess the predictive validity of the CCSST, we assigned individuals to segments by GI and the presence of at least one CF. In the absence of data on the utilization of acute services (e.g., emergency department visits, hospitalization, or nursing home placement), we assessed predictive validity by using the degree to which baseline categorization was associated with mortality. Using Cox proportional hazard modeling, we calculated hazard ratios for mortality between least and most severe GI categories (healthy and limited reserve and serious exacerbation), and between individuals with a CF and individuals without a CF, for each of the four variables measured, while controlling for age, age-squared, and gender.

2.4 Cross-country ranking of per capita high medical needs

For our results to match the national populations of individuals who are 50 years or older, we applied calibrated cross-sectional weights computed separately by country16, 17 to individuals segmented into GI segments, with and without CFs to each wave. The weighted segments were grouped into those indicative of high medical needs GI segments (long course of decline or limited reserve and serious exacerbation) and those who were not (healthy, chronic asymptomatic, or chronic symptomatic). The results are ranked according to the proportion of each population with high medical needs.

2.5 Cross-country comparison of hazard of transition

Once again, individuals were grouped into those who had high medical needs (i.e., into one of the two most severe GI segments) and those who did not. Transition probabilities were estimated on the assumption that the trajectories of individuals in panel data reflect a continuous-time Markov process.24, 25 This approach incorporates the available, incomplete data on transitions from one state to another, with the instantaneous state determined during the non-informative biannual panel interview wave across interview waves.

This approach allows for multiple observation types: two health states, a censored state determined at arbitrary times, and death (which is determined either exactly or arbitrarily). Censored observations are observations in which individuals had some form of personal interaction with representatives of the survey but did not provide sufficient information for us to segment them by using the CCSST. As these individuals were confirmed to be alive, they were assumed to either have high medical needs or not. Instantaneous transitions,26 governed by a set of transition intensities, were permitted in both directions—between not having high medical needs and having high medical needs, with death defined as an absorbing state, that is, allowing transitions only into the death state. We modeled the instantaneous risk of moving from one state to another by using log-linear models that control for country, gender, age classes (50–59 years, 60–69 years, 70–79 years, 80+ years), and the presence of any CF as covariates.27 Estimates from these log-linear models are exponentiated and interpreted as hazard ratios.

We ensured a reliable model convergence by (a) minimizing the number of groups (high medical needs, no high medical needs, and death) by including CFs as a time-inhomogenous covariate, rather than as part of the state stratification, and (b) restricting our analyses to countries with at least 8 years of observations to ensure that enough state transitions occurred. We evaluated different combinations of GI segments per health state to make certain that the rankings were robust and not the result of modeling Simpson's paradox, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined.9 We performed the analyses in the R statistical computing environment10 by using the msm software package.11 Although we estimate transition probabilities using a continuous-time Markov process, a Markov simulation model is beyond the scope of this paper.

3 RESULTS 3.1 Descriptive statistics of CCSST

For our 14 countries in the survey dataset between 2004 and 2017, 96,396 individuals contributed 270,208 observations. These observations included all regular panel interviews (232,386 observations), life-course interviews (27,617 observations), and end-of-life interviews (10,205 observations) conducted with the next-of-kin in the case of death. Of the 232,386 regular panel interview observations, the CCSST algorithm was able to assign 229,722 (98.9%) into a GI category (ranging from 97.6% for Austria to 99.5% for Denmark). A similarly high proportion of observations in the population were assigned to the presence or absence of a CF (231,953 observations, 99.8%), ranging from 99.5% for Israel and 99.9% for Switzerland. Among the survey population included, 10,205 individuals were confirmed deceased during the follow-up. See Supporting Information Data S2 for the segmentation results, shown per country, per interview wave.

3.2 Predictive validity of the CCSST

In the Cox regression adjusted for age, age-squared, and gender, hazard ratios for mortality associated with high medical needs were significantly greater than 1 for all countries (see results on the predictive validity of GI segments in Table 2). Similarly, having a CF was associated with a higher likelihood of mortality, with 12 of 14 countries having hazard ratios statistically significant at the 1% level (see results on the predictive validity of CF segments in Table 2). The Kaplan–Meier survival curves associated with mortality by GI segments and the presence of any CFs appear in Supporting Information Data S3 (Figures 1A–D and 2A–D).

TABLE 2. Predictive validity of the GI segments and presence of CFs CCSST aspect Country Observations Deaths Cox regression of time to death controlled for gender age, age-squared Hazard ratio p value CI – LB CI – UB GI segments Austria 5162 565 4.39 <0.001 3.27 5.88 Germany 5864 392 6.27 <0.001 4.34 9.06 Sweden 5289 727 5.29 <0.001 4.03 6.94 Netherlands 3255 265 4.86 <0.001 3.28 7.19 Spain 7055 1299 3.76 <0.001 2.89 4.88 Italy 5304 714 3.68 <0.001 2.72 4.98 France 6092 634 4.61 <0.001 3.31 6.41 Denmark 4651 629 4.83 <0.001 3.63 6.42 Greece 3662 582 1.92 <0.001 1.37 2.69 Switzerland 3630 286 3.41 <0.001 2.25 5.18 Belgium 6772 701 5.24 <0.001 3.89 7.07 Israel 3035 561 4.00 <0.001 2.83 5.66 Czech Republic 6391 952 4.08 <0.001 3.15 5.28 Presence of CFs Poland 2016 504 2.23 <0.001 1.58 3.15 Austria 5305 581 2.14 <0.001 1.80 2.55 Germany 5929 398 2.12 <0.001 1.71 2.62 Sweden 5346 739 1.74 <0.001 1.49 2.02 Netherlands 3557 275 1.90 <0.001 1.48 2.43 Spain 7144 1312 1.50 <0.001 1.33 1.69 Italy 5396 720 1.49 <0.001 1.28 1.75 France 6227 649 1.82 <0.001 1.54 2.16 Denmark 4674 635 1.76 <0.001 1.49 2.06 Greece 3737 597 1.18 0.057 1.00 1.41 Switzerland 3672 291 1.71 <0.001 1.35 2.16 Belgium 6814 706 1.70 <0.001 1.45 2.00 Israel 3115 572 1.76 <0.001 1.46 2.12 Czech Republic 6457 962 1.76 <0.001 1.54 2.01 Poland 2026 506 1.18 0.094 0.97 1.43 Abbreviations: CCSST, cross-country simple segmentation tool; CF, complicating factors; GI, global impressions; LB, lower bound; UB, upper bound. image Fractional CCSST-assigned global impressions (GI) segments with and without complicating factors (CFs) per country, ranked by the population fraction with high medical needs (i.e., the two most severe GI segments). Data were collected in 2015 for all countries except the Netherlands, for which we use data from 2013 [Color figure can be viewed at wileyonlinelibrary.com] image Cross-country comparison of (1) prevalence of having high medical needs (i.e., classified in either of the two most severe global impressions (GI) segments) or not, and (2) hazard rates between no high medical needs and high medical needs segments, and between high medical needs and death [Color figure can be viewed at wileyonlinelibrary.com] 3.3 Cross-country ranking of per capita high medical needs

The most recent country-specific per capita proportions of GI segments with and without CFs are show

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