Rescue and recovery workers involved in the emergency response and cleanup efforts following the 2001 World Trade Center (WTC) attacks were exposed to high levels of toxicants and intense psychological trauma. In the years following the attacks, studies have documented a high level of disease burden among the WTC General Responder Cohort (GRC) (Crane et al., 2013). About one-half of these responders have developed abnormal spirometry and respiratory diseases (Dasaro et al., 2017), and approximately one-third have developed mental health conditions such as depression and post-traumatic stress disorder (Singh et al., 2020). Additionally, studies have observed elevated risk of various cancers among this cohort (Shapiro et al., 2020; Solan et al., 2013). As the cohort ages, it is expected that responders, similar to older adults in the community, will accumulate multiple comorbidities and develop age-related syndromes such as osteoarthritis and hypertension (Fried et al., 2004); in fact, cognitive impacts are now also documented as WTC-related “emerging conditions” (Clouston et al., 2020). Thus, there is a pressing need to characterize, assess, and monitor the development of age-related syndromes among this cohort, as the WTC exposures may alter the trajectory of aging among these responders.

Frailty is characterized by physiological declines and vulnerability to adverse health outcomes; as a validated conceptual framework for understanding age-related health problems and variability, it may be applied to understand the trajectory of age-related changes associated with WTC exposures. Frailty is a recognized syndrome common among older adults resulting from cumulative declines across multiple physiological symptoms (Chen et al., 2014; Ko and Walston, 2012). It is characterized by an individual’s increased vulnerability to adverse events (including mortality, morbidity, disability, and hospitalizations) occurring after exposure to stressors (Clegg et al., 2013; Morley et al., 2013). Because it can have a serious impact on quality of life, frailty assessment is now recommended in routine clinical examinations among older adults (Morley et al., 2013).

A validated and commonly used approach to measure frailty is the Frailty Index model (Mitnitski et al., 2001), which conceptualizes frailty as the accumulation of functional and health deficits resulting from (and indicative of) a diminishing ability to maintain normal function/homeostasis. With this “deficit accumulation” model, frailty is measured by computing the proportion of deficits in multiple domains of health and well-being: disability, functional impairment, health conditions, and comorbidities. The frailty index has been developed and validated on various secondary datasets and found to be associated with adverse outcomes, such as mortality and hospitalizations (Kojima et al., 2018; Theou et al., 2018; Woo et al., 2006). These results are consistent across various studies, despite the fact that the deficits used to construct the frailty index vary from study to study (Bergman et al., 2007; Mitnitski et al., 2002). As a construct, the frailty index is fairly robust to the deficits used in its construction, as long as they meet certain criteria; for example, deficits should be related to health status, deficit prevalence should increase with age (without plateauing/saturating too early), and the deficits as a group should be sufficiently large and span multiple domains of health (Searle et al., 2008). This gives the frailty index the flexibility to be adapted for use with different secondary data.

An alternative approach for measuring frailty is the Frailty Phenotype, which conceptualizes frailty as a distinct syndrome, assessed via a fixed set of five criteria representing the presence of specific clinical signs, symptoms, and physiology (Fried etal., 2001). However, because of its greater flexibility and consistency, in this study we focus on the deficit accumulation approach to frailty, the Frailty Index model.

Conventionally, the frailty index is constructed largely using clinically observable health deficits, that is, impairments in activities of daily living, disease, weakness, limited mobility, cognitive changes, sensory loss, etc. Recent years have seen the introduction of a new frailty index instrument constructed exclusively using laboratory test results, that is, serum biomarkers of organ and overall physiological function (Howlett et al., 2014). Referred to as FI-Lab, it is thought to assess physiological dysregulation, that is, systemic, organ-level dysregulation that occurs as a direct result of molecular- or cellular-level damage (e.g., oxidative stress and inflammation). This dysregulation may be a precursor to the clinically observable (“macroscopic”) functional deficits assessed in the conventional frailty index (sometimes referred to as FI-Clinical, to distinguish it from the FI-Lab instrument) (Zaslavsky et al., 2013).

Because it offers a large degree of latitude in the variables used in its construction, the frailty index approach is especially suitable for the WTC cohort, who have had repeated health-related assessments with information managed by the World Trade Center Health Program (WTCHP) General Responder Data Center. In previous work, using this cohort we developed and validated the WTC FI-Lab, a laboratory test-based frailty index for assessing age-related deterioration in physiological function (Bello et al., 2018). This frailty index was constructed using standard laboratory test biomarkers and spirometric indices (see Bello et al. (2018) for full details).

In the present study, we directly assess clinically evident deficits using data collected on the WTC GRC. As part of the WTCHP, a battery of clinical data has routinely been collected. We will use these data to develop and validate a clinical frailty index (henceforth referred to as WTC FI-Clinical) to characterize the burden of age-related clinical deficits in this population, as well as to identify factors associated/correlated with frailty. Relative to laboratory-based frailty indices, clinical frailty indices are considered the more standard tool for frailty assessment. The validity of the WTC FI-Clinical will be assessed by evaluating its relationship with all-cause mortality. With this newly created index, we will assess and characterize clinical frailty among WTC cohort members aged 40 years and older, starting from the year 2012 (approximately a decade after 9/11) through 2017. We will then examine the relationship of the WTC FI-Clinical with various cohort characteristics, as well as with the physiological frailty index, WTC FI-Lab.

Summarized in Table 1 are the baseline characteristics of the WTC GRC sample used in this study.

In total, 30 items were selected for the WTC FI-Clinical. Table 2 lists the items/deficits, deficit coding protocol, and data sources.

In general, the items selected for WTC FI-Clinical spanned multiple domains of health and well-being: disability, functional impairment, and health conditions. These domains overlap significantly with those from other frailty indices (e.g., the 70-item Frailty Index developed in Rockwood et al. (2005)). Examples of items that were not selected for our WTC FI-Clinical include presence/absence of physician-observed abnormalities in neck physical examination and questions about presence of acid reflux/regurgitation into mouth or throat.

For each subject, a frailty index score was computed, as described in the Methods section. Figure 1 shows the distribution of the WTC FI-Clinical in the study sample. The distribution shows extreme right-skewness, indicating a preponderance of near-zero frailty scores in our study sample (13.4% had scores of zero). The mean score was 0.18 (SD: 0.17), and the median score was 0.13 (IQR: 0.267). The 99th percentile was 0.63 (below the 0.7 threshold considered to be the theoretical limit of deficit accumulation (Rockwood and Mitnitski, 2006)).

Age showed a strong correlation with mean WTC FI-Clinical, with Pearson correlation coefficient ρ = 0.9 (p <.0001). Figure 2 shows the relationship in the study sample between age and average WTC FI-Clinical.

We also assessed the association of WTC FI-Clinical (expressed as deficit counts) with various cohort characteristics. Figure 3 summarizes the incidence rate ratios (and 95% CIs) for the continuous variables (age and year of index visit) and for non-baseline categories of the categorical variables (race, education, WTC exposure severity, sex, smoking status, pre-9/11 occupation, and enrollment year).

Baseline (referent) categories for nominal variables were as follows: race: white; education: >high school; WTC exposure severity: low exposure; sex: female; smoking status: current smoker: pre-9/11 occupation: other occupation; enrollment year: >2008.

These results indicate that multiple demographic and risk factors are associated with clinical frailty. In particular, we found that those with high/very high WTC exposure severity had higher frailty, relative to those with low exposure severity (p = 0.005). Those in the protective occupation category (e.g., law enforcement and emergency medical services) up to and at the time of 9/11 had significantly lower frailty (p < .0001) compared to those in the other category (i.e., all other occupations outside of construction, protective, cleaning/maintenance and installation/repair services). And relative to this other occupations category, those in the construction-related occupations had higher frailty (p < .0001). Males in this cohort had higher frailty relative to females (p < .0001), as did Hispanics relative to whites (p < .0001). Relative to (self-reported) current smokers, former smokers, and those who reported never having smoked had significantly lower frailty (p = 0.002 and p < .0001, respectively). Those less educated (high school education or less) had significantly higher frailty (p < .0001) relative to those with greater than high school education.

In the study sample, the median survival time (measured from index visit date) was 4.2 years, and the censoring rate was 98.8%. In a Cox Proportional Hazards model, WTC FI-Clinical showed a strong association with all-cause mortality (p < .0001), after adjustment for the following potential confounders: age, sex, race/ethnicity, pre-9/11 occupation, education, smoking status, and WTCHP enrollment year. The Kaplan-Meier plot in Figure 4 shows survival curves for subjects scoring below and above the median value of WTC FI-Clinical (logrank test: p < .0001):

WTC FI-Clinical showed reasonable predictive accuracy for mortality, with a Harrell’s concordance index of 0.69 (95% CI: 0.631–0.749).

Both WTC FI-Clinical and WTC FI-Lab were independently associated with all-cause mortality (p = .0012 and p < .0001, respectively). Pearson correlation of WTC FI-Clinical with WTC FI-Lab was 0.2 (p < .0001), in line with prior estimates of the concordance between these frailty instruments (Rockwood et al., 2015). Figure 5 shows the relationship between WTC FI-Clinical and WTC FI-Lab.

In this study, we constructed a clinical frailty index using data routinely collected to monitor WTC general responders. This index was constructed using data elements spanning multiple domains: clinical conditions, health symptoms, and ability to perform activities of daily living. The newly developed WTC FI-Clinical showed a strong association with all-cause mortality, even after adjustment for potentially confounding risk factors. It also showed decent discriminative ability in the prediction of mortality. Multiple cohort characteristics were associated with WTC FI-Clinical. Some of the associations we observed for socio-demographic factors and smoking status have been previously reported in other studies (de Carvalho Mello et al., 2014). For pre-9/11 occupation, we found that construction workers had higher frailty while protective services workers had lower frailty, relative to all other occupations outside of construction, protective, cleaning/maintenance and installation/repair services. This aligns with observations from a prior WTC cohort study which showed that all-cause mortality among construction workers was elevated relative to protective services workers (Stein et al., 2015). Construction workers are exposed to a variety of occupational risks and hazards, for example, chemical toxicants, physical agents (such as the use of vibrating tools), ergonomic hazards, noise, and outdoor/weather conditions (Aarhus et al., 2018; Hajaghazadeh et al., 2019; House et al., 2010). Given this constellation of occupational risks, we believe the increased relative frailty in this occupation group is expected, although this is definitely an understudied area. On the other hand, the protective services occupation group (comprised primarily of New York Police Department personnel) tends to be fitter than the general population since qualification for these occupations often requires passing fitness criteria (and periodic physical and mental tests); thus, the healthy worker effect (Choi, 1992) is particularly pronounced for this group (Solan et al., 2013). Of note, severity of WTC exposure (on 9/11 and over the subsequent cleanup effort) showed a significant association with WTC FI-Clinical over a decade later.

While a mild correlation was observed between WTC FI-Clinical and the previously constructed WTC FI-Lab (Bello et al., 2018), we found both frailty measures to be independently associated with mortality, suggesting they provide somewhat independent information about mortality risk. This trend has been observed in other studies comparing clinical and laboratory test (biomarker)–based frailty indices (Howlett et al., 2014; Mitnitski et al., 2015; Rockwood et al., 2015). It has been suggested that laboratory test–based and clinical frailty influence mortality via different pathways (Blodgett et al., 2016). Further, these frailty measures have also been found to be independently associated with hospital/healthcare utilization and medication use (Blodgett et al., 2016). These findings suggest these two types of frailty indices may provide potentially complementary information about frailty. The key distinction between laboratory-based and clinical frailty indices is that the former, based entirely on biochemical and physiological parameters, is believed to represent the burden of pre-clinical or sub-clinical deficits (deficits at the molecular and cellular level [microscopic]), while the latter is based on clinically evident (macroscopic) deficits (Blodgett et al., 2016). One salient piece of evidence supporting this distinction is the fact that telomere attrition (a correlate of cellular-level aging) has been reported to be associated with laboratory-based frailty indices but not clinical frailty indices (Dent et al., 2018; Bello et al., 2019).

We found a significant association between WTC FI-Clinical and 9/11 exposure severity. This is a relevant relationship that was not observed with WTC FI-Lab. FI-Clinical is constructed with clinical deficits and, therefore, likely provides a more detailed and holistic picture of clinical health than the biomarker-focused FI-Lab. The observed relationship between FI-Clinical and 9/11 exposure severity could be driven by aspects of health that are related to WTC exposure but are not captured by the FI-Lab. Future studies will be dedicated to more comprehensive analyses and comparisons of these frailty indices, including contrasting their ability to predict various WTC-related outcomes and geriatric syndromes.

The findings we report herein need to be considered in light of certain limitations of our study. This study was carried out only on the subset of the WTC GRC, whose monitoring visits were conducted at the largest WTCHP clinic, Mount Sinai Selikoff Centers for Occupational Health in New York City. However, we chose to restrict our analysis to those visiting this clinic because, relative to monitoring visits at other WTCHP clinics, data elements required for computation of WTC FI-Lab and FI-Clinical were more readily available, and the Mount Sinai Selikoff cohort is representative of the larger WTC GRC (Dasaro et al., 2017). Furthermore, WTC exposure characterization suffers from potential misclassification because of recall bias. Responders enrolling in the WTCHP several years after 9/11 may have had inaccurate recollections of their experiences during that period, which impacts exposure assessment accuracy (Shapiro et al., 2020). Due to the uniqueness of our cohort, the generalizability of our findings is likely limited to other cohorts of WTC rescue and recovery workers. Another potential limitation concerns the relatively young age of our study sample (median age in the 50s). With only 2% over the age of 70, this is a largely middle-aged sample. However, multiple studies have focused on frailty in early and late middle-age cohorts and have highlighted its significance in these age groups (Griffin et al., 2018; Gordon et al., 2020; Prince et al., 2019; Thorpe et al., 2016), as well as the need for early identification (Hanlon et al., 2018). This is particularly relevant for our cohort since the long-term influence of exposure to multi-dimensional disasters such as 9/11 on aging is relatively unexplored, and identification of modifiable risk factors for premature aging has a relevant impact for the WTC Health Program. Given the high documented disease burden among this cohort, this study is timed at a critical inflection point at which members are beginning to age into the risk of morbidities of older age. This puts us in a good position to begin tracking signs of early aging, and the progression as the cohort ages. Monitoring frailty at this stage will ultimately enable us to identify those at greatest risk for negative consequences as they age and develop/test appropriate interventions. Cognitive impairment is one of the most salient features of frailty, and many frailty index formulations include items assessing cognition (Rockwood et al., 2005; Clegg et al., 2016; Canevelli et al., 2017; Shahrokni et al., 2019; Won et al., 2020). Cognitive testing is not a routine part of WTCHP monitoring assessments; therefore, our frailty index includes only two brief items related to cognitive function. However, efforts are currently underway to incorporate formal cognitive assessments into routine WTCHP monitoring, and future studies will utilize more comprehensive cognitive data.

Despite these limitations, we have successfully developed a clinical frailty assessment tool using available data collected for nearly two decades on the WTC cohort: more than 30,000 individuals. Calculation of the WTC FI-Clinical does not require collection of any additional data elements, thus paving the way for large-scale retrospective analyses of aging within this cohort. Also, this measure will be useful for current and future WTC research projects focused on the long-term health and well-being of responders across a number of areas, for example, cognitive health, which is already an ongoing area of focus (Clouston et al., 2019, 2020).

The WTC cohort may be especially at risk for negative effects of aging because of previous exposure to environmental toxicants and intense psychological trauma; as such, monitoring of frailty will be critical in the next decade. Frailty can be reduced or delayed through various interventions such as exercise, nutritional supplementation, vitamin D, cognitive training, behavioral therapy, reduction of polypharmacy, and also interventions that could be delivered through clinical care such as comprehensive geriatric assessment and tailored management (Macdonald et al., 2020; Marucci et al., 2019; Morley et al., 2013; Ng et al., 2015; Tarazona-Santabalbina et al., 2016; Walston et al., 2018). For example, the Frailty Intervention Trial (Fairhall et al., 2008), a randomized controlled trial of an intervention comprising an individualized exercise program and management of medical, psychological, and social problems, demonstrated significant improvement in risk factors for falls (Fairhall et al., 2014). For the purposes of optimally managing individuals with a higher degree of vulnerability, frailty can and should be identified at an early stage. Within our cohort, the newly developed WTC FI-Clinical can be deployed in routine clinical follow-up, which will not only enable frailty monitoring within this cohort, but also identify individuals at risk for further decline based on both accumulated deficits and physiological change. Having a reliable WTC cohort–specific frailty instrument will also enable development and testing of potential interventions and early implementation of emerging therapies from other frailty intervention trials. Potential future approaches to target factors impacting both frailty and its manifestations may include solutions that leverage the increasing use of technology for monitoring and support, especially those tested during the course of the current pandemic.

In summary, we have developed and validated a frailty index for quantifying clinically evident deficits, one that serves as a key frailty assessment tool for use in this aging and vulnerable cohort. Ongoing and future research on the WTC general responders should include frailty assessments in order to monitor and improve their long-term health.