Abstract
Background
despite the well-known adverse health effects of smoking, evidence of these effects on frail individuals is still scarce.
Aims
to assess whether frailty influences the association between smoking and mortality.
Methods
individuals ≥50 years from the Mexican Health and Aging Study were analysed. Mortality rates from a 17-year follow-up were compared between smoking status groups (never, previous and current) and other smoking behaviour-related characteristics (pack-years, age commenced and cessation). Baseline variables were included to adjust the Cox regression models. First, models were adjusted for the whole sample, including an interaction term between the frailty index (FI) and smoking variables. A second set of models were stratified by FI levels: 0.00–0.10, 0.11–0.20, 0.21–0.30 and ≥ 0.31.
Results
from a total 14,025 individuals, mean age was 62.4 (95% confidence interval [95% CI]: 62.1–62.8) and 53.9% were women (95% CI: 52.4–55.6). Main results from the survival analyses showed that when including FI interaction term with smoking status, comparing current to never smoking, the hazard ratio (HR) was 2.03 (95% CI: 1.07–3.85, P = 0.029), and comparing current to previous smoking, the HR was 2.13 (95% CI: 1.06–4.26, P = 0.032). Models stratified by FI levels showed a significant HR only for the two highest level groups. Similar results were found for the smoking behaviour-related characteristics.
Discussion
our results suggest that frailty could modify smoking mortality risk. Other smoking characteristics were impacted by frailty, in particular, cessation. It was noteworthy that having ≥10 years of tobacco cessation was beneficial for frail individuals.
Conclusions
smoking has a higher toll on frail individuals, but ceasing is still beneficial for this group.
Keywords: frailty, tobacco use, lifestyle behaviours, smoking cessation, older people
Key Points
Smoking-related mortality is impacted by frailty status.
Frailer individuals are more susceptible to smoking-related mortality.
Frailer individuals who ceased smoking more than 10 years equal their mortality risk to never smokers.
Introduction
Frailty is a condition that increases the risk of adverse health outcomes, when exposed to stressors. It has been described more frequently in older individuals, and usually co-exists with a decline in physical and cognitive function [1, 2]. The mechanisms by which stressors affect a frail individual are still a matter of investigation [3], but it is thought that almost any negative stimulus could make frail individuals frailer, leading to poor health and higher mortality rates as compared to non-frail individuals or with lower frailty levels [4]. Moreover, frailty is a consequence of the convergence of chronic diseases, life-course conditions (e.g. socioeconomic determinants) and lifestyle behaviours (e.g. smoking, lack of physical activity, risky alcohol drinking, etc.) [5–7].
Evidence shows that positive lifestyle behaviours may curb the noxious effects of frailty, prevent its appearance or, at the very least, delay it [8, 9]. For instance, physical activity has multiple benefits on health, with similar effects to that of senolytic drugs [10]. On the other hand, habits such as tobacco smoking are poorly studied in frail subjects [11]. However, available data have shown an association between frailty and smoking status [7, 12–14]. Smoking is a global epidemic, which is singlehandedly responsible for about 6 million deceases per year around the world, most of which are premature [15, 16]. In particular, a secondary analysis of the Finnish National Causes of Death registry found that current smoking was an independent mortality risk factor for older adults [17]. Moreover, Nash et al. reported similar findings, with an inverse association between age smoking commenced and the number of cigarettes smoked per day [18]. Noteworthy, a meta-analysis conducted in 2012 found that both current and previous smoking increased mortality risk when [19].
Apart from increased mortality rates, other adverse outcomes resulting from tobacco use have been established. For instance, it has been linked to different types of cancer [20], pulmonary and cardiovascular diseases [21–24], and more recently to severe adverse outcomes in patients infected with COVID-19 [25]. Adverse effects of smoking continue to play an important role in older adults’ health. Some examples of these effects include a higher frequency of periodontal disease—leading to edentulism and worsening nutritional status [26, 27], altered bone metabolism [28], higher prevalence of osteoarthritis [29], worse mental health [30], lower vitamin D levels [31] and higher frequency of sarcopenia [32], to name a few.
On the other hand, a study conducted by Kojima et al. found that smoking cessation may potentially prevent or even delay frailty onset [12]. Even so, older adults are less aware of the dangers of tobacco use, less prone to cessation and are not referred as frequently as younger adults to specialised clinics on smoking cessation [33–36], the focus of public health initiatives has led to encouraging quitting among adults below the age of 40 years, and clinicians are also less inclined to refer older patients for cessation support programmes [37].
Frailty has been linked to smoking [12–14], and has also shown to modify the effects of various interventions [38, 39]. Therefore, thinking of smoking as a stressor, we hypothesise that frail individuals should have a higher rate of adverse outcomes—such as mortality—when compared to non-frail subjects. Therefore, the aim of our study was to assess whether frailty modifies the effect of smoking on mortality.
Methods
Study design
The Mexican Health and Aging Study (MHAS) is a cohort with a representative sample (country level) of individuals aged 50 years or older. In brief, this study aims at determining factors that impact ageing in Mexican individuals. The MHAS includes a baseline assessment in 2001, with follow-up data available for years 2003, 2012, 2015 and 2018, collecting information from a wide array of topics obtained from face-to-face interviews conducted by trained personnel. Further details about MHAS can be found elsewhere [40, 41].
Data sources
For purpose of this work, data from MHAS baseline assessment in 2001 and follow-up information on survival status through 2018 were used. The main questionnaires were used for the baseline variables, and the next-of-kin (NOK) questionnaires were employed for information regarding mortality.
Study cohort
All individuals ≥50 years old with complete data at baseline and at 2018 or NOK-reported death at any of the timepoints were included. A comparison between individuals with and without complete baseline data is available (see Supplementary Table S1).
Exposures: smoking status
Smoking status was assessed through the questions: ‘Have you smoked more than 100 cigarettes or 5 packs in your lifetime; not including pipes or cigars?’ and ‘Do you smoke cigarettes now?’ Resulting in three categories: never, previous and current smoking. Smoking behaviour-related characteristics were also analysed. Pack-years were calculated from the following question: ‘About how many cigarettes or packs do you usually smoke or smoked in a day?’ The answer was multiplied by the number of years the subject had smoked (result of subtracting current age from smoking age commenced), subtracting the years of smoking cessation for previous smoking. Further categorisation included: <10 pack-years, 10–19 pack-years and ≥ 20 pack-years. Smoking age commenced was also explored: ‘At what age did you started smoking?’, transforming results into: <10 years, 10–19 years and ≥ 20 years. Finally, the number of years since smoking cessation was collected from those who reported having ceased; dividing it into the following groups: <10 years and ≥ 10 years since smoking cessation.
Outcome of interest
All-cause mortality was our outcome of interest, including time to event for survival analysis. For those who survived, follow-up days were calculated as the difference between the interview date in 2001 and that from 2018; in a similar fashion, time to event was calculated as the difference in days from the baseline interview to the reported date of death.
Baseline characteristics
To adjust the multivariate models, previously reported variables from multiple dominions were included [18, 19, 42]. Sociodemographic characteristics included: age in years and sex, along with marital status (married/civil union versus without a couple), number of completed years of formal education and self-rated financial status (dichotomous poor/fair versus good/very good/excellent). Lifestyle behaviours such as physical activity and risky alcohol intake were included. Risky alcohol use was defined as ≥2 drinks per day for women, and ≥ 3 drinks per day for men [43], while physical activity was defined as exercising ≥3 times a week in the last year. Chronic diseases were asked by ‘Does a health professional has ever told you that you have. . .' including the following conditions: chronic obstructive pulmonary disease (COPD), myocardial infarction, diabetes mellitus, hypertension, stroke and cancer. The frailty index (FI) was used to assess frailty, with a total of 48 health-related variables according to a standard procedure [44]. Due to the nature of the FI, it was further divided into four levels: 0.00 to 0.10, 0.11 to 0.20, 0.21 to 0.30 and ≥ 0.31 (i.e. non-frail, vulnerable, frail and very frail), as suggested by other authors [45, 46]. The complete description of the variables used for calculating the FI is available in Supplementary Table S2.
Statistical analysis
We conducted a descriptive analysis of all the variables, and bivariate analyses by survival status for smoking-related characteristics (i.e. number of pack-years, age started smoking and years since smoking cessation), using chi-square tests for all the variables (except for education and FI were t-tests were used).
Descriptive statistics for mortality rates and causes of death were described for smoking status categories. In addition, Kaplan–Meier curves were plotted to assess for the differences between smoking status groups and the other smoking behaviour-related characteristics; log-rank tests were used for statistical significance.
Cox regression models were fitted to test the association with mortality with hazard ratios (HRs). Two set of models were included: in the first set, interaction terms between frailty and smoking variables were included for the whole sample, unadjusted and adjusted for baseline characteristics. Smoking behaviour-related variables were included in the regressions as a continuous variable (Appendix) and categorised as previously described. The second set of regressions was stratified by frailty levels and presented unadjusted and adjusted for baseline variables too. To compare head-to-head different categories, the reference value was alternated. For example, in smoking status model 2 the reference category is ‘never smoked’ and model 3 has as reference ‘previous smoking’.
Expansion weights provided by the MHAS team were used for statistical analysis, reporting 95% confidence intervals (95% CI) for means or proportions. These expansion weights and the rest of the datasets can be found in the MHAS website (mhasweb.org/Data), and its rationale is available elsewhere [41]. All statistical analyses were performed using STATA 17.0 (Stata Corp 4905 Lakeway Dr; College Station, TX, USA).
Sensitivity analysis
Since there were losses to follow-up, three different imputations were used to assess changes on HR estimates according to these scenarios: best possible outcome (all subjects lost to follow-up considered to be alive), worst possible outcome (all subjects lost to follow-up were imputed as deceased) and random outcome assignation (each subject lost to follow-up was randomly assigned alive or deceased).
Results
General description
From a total of 15,402 individuals, 14,025 (90.8%) had complete data at baseline, representing 13,233,869 subjects. Frailty and smoking status were not significantly different between individuals with complete baseline data and those without complete data. The complete comparison is available in Supplementary Table S1.
The mean age was 62.4 (95% CI: 62.1–62.8) years, most were women (53.9%, 95% CI: 52.2–55.6), with a mean number of education years of 3.9 (95% CI: 3.8–4.1) and 67.4% were married (95% CI: 65.8–69.1). Those who never smoked composed of 57.4% (95% CI: 55.7–59.0) of the sample, subjects who used to smoke made up 25.3% (95% CI: 23.9–26.9) of the sample, and 17.2% (95% CI: 16.0–18.4) of the subjects currently smoked. FI mean was 0.24 (95% CI: 0.24–0.25), with the lowest mean for those who currently smoked (0.21, 95% CI: 0.20–0.22). Years in school, self-reported financial status, stroke and cancer were not significantly different between smoking categories (Table 1).
Table 1.
Main baseline characteristics according to smoking status
| Variables | Total (N = 14,025, representing 13,233,869) | Smoking status | P-value | ||
|---|---|---|---|---|---|
| Never smoked 57.4% (95% CI: 55.7–59.0) | Used to smoke 25.3% (95% CI: 23.9–26.9) | Currently smokes 17.2% (95% CI: 16.0–18.4) | |||
| Age, mean (95% CI) | 62.4 (62.1–62.8) | 62.2 (61.7–62.7) | 64.1 (63.4–64.7) | 60.6 (60.0–61.3) | 0.003 |
| Women, % (95% CI) | 53.9 (52.2–55.6) | 72.0 (69.8–74.0) | 30.8 (27.7–34.0) | 27.5 (24.4–30.8) | <0.001 |
| Education, mean (95% CI) | 3.9 (3.8–4.1) | 3.7 (3.5–3.9) | 4.0 (3.7–4.3) | 4.8 (4.5–5.1) | 0.102 |
| Married, % (95% CI) | 67.4 (65.8–69.1) | 65.5 (63.3–67.7) | 69.1 (65.5–72.5) | 71.3 (67.9–74.5) | 0.021 |
| Fair/poor self-rated financial status, % (95% CI) | 80.7 (79.3–82.0) | 80.6 (78.7–82.4) | 81.8 (79.0–84.2) | 79.3 (76.1–82.2) | 0.494 |
| Physical activity, % (95% CI) | 32.3 (30.7–33.9) | 28 (26.1–30) | 37 (33.8–40.4) | 39.6 (36.1–43.1) | <0.001 |
| Risky alcohol drinking, % (95% CI) | 8.4 (7.5–9.3) | 5.5 (4.4–6.8) | 10 (8.4–11.9) | 15.7 (13.3–18.4) | <0.001 |
| COPD, % (95% CI) | 5.6 (4.9–6.3) | 5.2 (4.3–6.3) | 7.5 (6.1–9.3) | 3.8 (2.6–5.5) | 0.002 |
| Diabetes mellitus, % (95% CI) | 15.0 (13.8–16.2) | 15.0 (13.5–16.7) | 18.1 (15.5–21.1) | 10.2 (8.4–12.4) | <0.001 |
| Hypertension, % (95% CI) | 35.8 (34.2–37.4) | 38.7 (36.5–40.9) | 38.3 (35.1–41.7) | 22.2 (19.5–25.2) | <0.001 |
| Heart attack, % (95% CI) | 2.6 (2.2–3.2) | 2.1 (1.6–2.9) | 4.5 (3.3–5.9) | 1.7 (1.0–2.8) | <0.001 |
| Stroke, % (95% CI) | 2.2 (1.8–2.7) | 2 0.0 (1.4–2.8) | 3.0 (2.1–4.2) | 1.6 (1.1–2.4) | 0.067 |
| Cancer, % (95% CI) | 1.8 (1.4–2.3) | 2.2 (1.6–3.0) | 1.5 (1.0–2.3) | 1.2 (0.7–2.0) | 0.088 |
| FI, mean (95% CI) | 0.24 (0.24–0.25) | 0.25 (0.24–0.26) | 0.25 (0.24–0.26) | 0.21 (0.20–0.22) | <0.001 |
Survival analysis
The median follow-up period was 6,538 days and 12,131 individuals had complete follow-up information (86.4%). Mortality for the entire sample was 41.7% (95% CI: 39.9–43.4), and all the variables were significantly different for survival status in the bivariate analysis. Infectious diseases were the main cause of mortality for the whole sample (15.2%; 95% CI: 13.5–17.2). Noteworthy, cancer was the main cause of death for those who currently smoked (15.2%, 95% CI: 10.7–21.1) (Supplementary Table S3).
Those who never smoked displayed significantly better survival (61.8%, 95% CI: 59.4–64.1) than those who used to smoke (50.5%, 95% CI: 46.7–54.1) or individuals who currently smoked (58.5%, 95% CI: 54.6–62.3), with a particularly evident effect when compared to their counterparts after 50 months of smoking (see Table 2 and Figure 1A). Regarding pack-years, we found that 22.0% had <10 pack-years (95% CI: 20.5–23.5), 6.4% had 10–19 pack-years (95% CI: 5.6–7.2) and 13.1% had 20 pack-years or more (95% CI: 12.1–14.1). The highest mortality was found in those with 20 pack-years or more (54.1% 95% CI: 49.9–58.2). Regarding aging at which the individual started smoking, our results displayed that 1.2% started smoking before the age of 10 years (95% CI: 0.9–1.6), 23.4% started between the ages of 10 and 19 years (95% CI: 22.1–24.8) and 17.3% started smoking after age of 17.2% (95% CI: 15.9–18.6). The highest mortality was found in those who started smoking before the age of 10 years (65.3%; 95% CI: 52.4–76.3). As for those who used to smoke, 8.0% reported smoking cessation less than 10 years ago (95% CI: 7.2–8.8), and 17.3% had stopped smoking 10 years ago or more (95% CI: 16.0–18.7). The highest survival was found in those who stopped smoking less than 10 years ago (52.9% 95% CI: 47.5–58.1) (see Table 2 and Figure 1D).
Table 2.
Mortality according to smoking status, number of pack-years categories, age when starting smoking and number of years since stopped smoking
| Death 41.7% (95% CI: 39.9–43.4) | Alive 58.3% (95% CI: 56.5–60.0) | P-value* | ||
|---|---|---|---|---|
| Smoking status | Never | 38.2 (35.9–40.5) | 61.8 (59.4–64.1) | <0.001 |
| Former | 49.5 (45.9–53.2) | 50.5 (46.7–54.1) | ||
| Current | 41.4 (37.7–45.3) | 58.5 (54.6–62.3) | ||
| Pack-years | <10 | 40.1 (36.1–44.2) | 59.9 (55.7–63.8) | <0.001 |
| 10–19 | 48.2 (41.7–54.8) | 51.8 (45.1–58.2) | ||
| ≥20 | 54.1 (49.9–58.2) | 45.9 (41.8–50.0) | ||
| Age started smoking | <10 | 65.3 (52.4–76.3) | 34.7 (23.6–47.6) | <0.001 |
| 10–19 | 46.1 (42.8–49.5) | 53.9 (50.5–57.1) | ||
| ≥20 | 44.6 (39.9–49.3) | 55.4 (50.6–60.0) | ||
| Number of years since stopped smoking | <10 | 47.1 (41.8–52.4) | 52.9 (47.5–58.1) | <0.001 |
| ≥10 | 50.6 (45.9–55.3) | 49.4 (44.6–54.0) | ||
*Log-rank test P-values.
Figure 1.
Kaplan–Meier curves −6,641 days of total follow-up. The category ‘never smoked’ is included in all graphs for reference. 95% CIs are shown in the shaded regions of each curve. (A) Smoking status. (B) Pack-years. (C) Age started smoking. (D) Smoking cessation.
Figure 1.
Continued.
There was a significant association between the interaction term of frailty and smoking status for the comparison of subjects who currently smoked with those who never smoked (HR 2.03, 95% CI: 1.07–3.85) and those who currently smoked with individuals who used to smoke (HR 2.13; 95% CI: 1.06–4.26) (see Table 3). Adjusted stratified Cox regression models showed that when comparing subjects who currently smoked with those who never smoked, significant HR were found only in the groups with higher frailty levels: FI 0.21–0.30 HR 1.44 (95% CI: 1.27–1.63) and FI ≥0.31 HR 1.54 (95% CI: 1.33–1.78). Similarly, when current smokers were compared to those who used to smoke, results were significant only for the higher frailty levels (see Supplementary Table S4).
Table 3.
Adjusted Cox regression models including the interaction term between FI and smoking status
| HR (95% CI, P-value) | |||
|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |
| Smoking status Never smoked Used to smoke Currently smoke |
Reference 1.39 (1.21–1.61, P < 0.001) 1.40 (1.19–1.65, P < 0.001) |
Reference 1.08 (0.92–1.26, 0.332) 1.13 (0.94–1.36, 0.183) |
– Reference 1.04 (0.85–1.27, 0.646) |
| FI*smoking status Never smoked Used to smoke Currently smoke |
Reference 1.73 (1.66–1.81, P < 0.001) 1.67 (1.58–1.75, P < 0.001) |
Reference 0.95 (0.57–1.58, 0.857) 2.03 (1.07–3.85, 0.029) |
– Reference 2.13 (1.06–4.26, 0.032) |
| FI | 1.03 (1.02–1.03, P < 0.001) | 4.63 (3.31–6.47, <0.001) | |
| Age | – | 1.07 (1.07–1.08, <0.001) | |
| Women | – | 1.55 (1.45–1.67, <0.001) | |
| Married | – | 0.82 (0.76–0.87, <0.001) | |
| Years in school | – | 0.99 (0.98–0.99, 0.048) | |
| Fair/poor self-rated financial status | – | 1.05 (0.96–1.13, 0.233) | |
| Risky alcohol use | – | 0.91 (0.81–1.02, 0.117) | |
| Physical activity | – | 0.77 (0.72–0.83, <0.001) | |
| COPD | – | 1.03 (0.92–1.15, 0.551) | |
| Diabetes mellitus | – | 2.31 (2.15–2.47, <0.001) | |
| Hypertension | – | 1.07 (1.01–1.14, 0.027) | |
| Heart attack | – | 1.26 (1.10–1.44, <0.001) | |
| Stroke | – | 1.25 (1.07–1.46, 0.003) | |
| Cancer | – | 1.31 (1.07–1.61, 0.008) | |
Model 1: unadjusted; Model 2: adjusted to: age, sex, marital status, years in school, self-rated financial status, risky alcohol drinking, physical activity, COPD, heart attack, diabetes mellitus, hypertension, stroke, cancer, and FI, with ‘Never smoked’ as the reference category for ‘smoking status’; Model 3: same as Model 2 but with ‘Used to smoke’ as the reference category for ‘smoking status’.
Regressions including an interaction term between frailty and smoking characteristics (pack-years, smoking start age and time since smoking cessation) were only significant for time since smoking cessation (see Supplementary Table S5). Similarly, when including the variables as continuous in the model, only time since smoking cessation was significant for the whole sample and for the FI 0.21–0.30 group (HR 0.98; 95% CI: 0.97–0.99, P = 0.001) (Supplementary Table S6). Having a pack-year ≥20 was consistently associated with mortality, across frailty levels. Mortality was significantly associated with a smoking start age before 20 years in the two groups with the highest FI score. Finally, smoking cessation (regardless of the time since cessation) also had a lower mortality risk for those within the two highest FI score groups (see Table 4).
Table 4.
Cox regression models according to number of pack-years categories, age when starting smoking and number of years since stopped smoking, stratified by frailty levelsa
| FI 0–0.1, HR (95% CI) | FI 0.11–0.2, HR (95% CI) | FI 0.11–0.21-0.3, HR (95% CI) | FI ≥0.3 (95% CI) | |||||
|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |
| Pack-yearsb | ||||||||
| <10 10–19 ≥ 20 |
0.95 (0.70–1.28) 1.17 (0.76–1.81) 2.07 (1.56–2.76)c |
0.96 (0.66–1.26) 1.16 (0.74–1.81) 1.45 (1.06–1.99) d |
1.11 (0.97–1.28) 1.29 (1.04–1.61)d 1.75 (1.51–2.04)c |
1.01 (0.87–1.17) 0.98 (0.78–1.24) 1.12 (0.95–1.33) |
1.10 (0.95–1.27) 1.42 (1.15–1.75)d 2.12 (1.84–2.44)c |
1.09 (0.93–1.26) 1.06 (0.85–1.32) 1.42 (1.21–1.66)c |
1.17 (1.04–1.33)d 1.65 (1.36–1.99)c 1.67 (1.47–1.90)c |
1.19 (1.04–1.35)d 1.30 (1.06–1.58)d 1.22 (1.06–1.41)d |
| Age started smoking (years)b | ||||||||
| <10 10–19 ≥20 |
1.33 (0.33–5.40) 1.45 (1.13–1.86)d 1.09 (0.79–1.52) |
0.58 (0.14–2.40) 1.23 (0.93–1.62) 1.03 (0.73–1.46) |
2.35 (1.56–3.53)c 1.35 (1.19–1.54)c 1.26 (1.08–1.46)d |
1.33 (0.88–2.02) 1.00 (0.87–1.16) 1.08 (0.92–1.26) |
2.29 (1.56–3.36)c 1.61 (1.42–1.82)c 1.18 (1.01–1.37)d |
1.57 (1.06–2.31)d 1.22 (1.06–1.41)c 1.10 (0.94–1.29) |
1.95 (1.36–2.79)c 1.49 (1.33–1.67)c 1.28 (1.13–1.46)c |
1.45 (1.01–2.09)d 1.25 (1.10–1.42)d 1.15 (1.01–1.31)d |
| Number of years since stopped smokinge | ||||||||
| <10 ≥10 |
0.81 (0.60–1.08) 1.28 (0.92–1.79) |
0.96 (0.58–1.59) 0.99 (0.70–1.40) |
0.97 (0.77–1.21) 1.24 (1.05–1.48)d |
0.86 (0.69–1.08) 0.86 (0.72–1.03) |
0.82 (0.66–1.02) 1.08 (0.91–1.28) |
0.68 (0.55–0.85)d 0.74 (0.62–0.89)d |
1.10 (0.91–1.32) 1.15 (0.98–1.36) |
0.80 (0.66–0.97)d 0.68 (0.57–0.80)c |
aModel 1: unadjusted; Model 2: adjusted to: age, sex, marital status, years in school, self-rated financial status, risky alcohol drinking, physical activity, COPD, heart attack, diabetes mellitus, hypertension, stroke, cancer and FI.
b‘Never smoked’ as the reference category.
c P < 0.001.
d P = 0.05 to 0.001.
e‘Currently smoke’ as the reference group.
Sensitivity analyses
As shown in the supplementary tables, after imputation, the estimates were not different as compared to those of the original regression (see Supplementary Table S7), for the smoking categories comparison, age started smoking and years from cessation. When randomly imputing the outcome, for smoking status and time since stopped smoking, mortality was significantly associated for the three highest FI score groups.
Discussion
Our results suggest that frailty could modify the effect of smoking on mortality and improve this risk when frail older adults cease to continue smoking. To the best of our knowledge, our results are novel and confirm the notion that frail individuals have worse outcomes when facing stressors (like tobacco smoking). Furthermore, we found higher mortality rates for those with higher total pack-years and those who started smoking early in life, with a similar modifying effect from frailty.
The association of mortality and tobacco use has been consistently described [42], along with other somatic and psychological symptoms [47]. Nonetheless, a gap in knowledge persists when it comes to frailty. However, it should be noted that there is extensive data on how smoking—or even secondhand smoking—eventually leads to frailty [9, 12–14, 48, 49]. Moreover, the path leading an individual into frailty has been related to lung health; for example, COPD has shown to be closely related to frailty, and increases 2-fold the odds of being frail [50]. In this regard, further research should clarify the underpinnings of the interplay between smoking and frailty trajectories in the older individual. For example, individuals in our study that currently smoked had the lowest FI mean, possibly reflecting a higher rate of smoking cessation on the frailest individuals; or a resistant feature of a group that continues smoking.
Our results are like what has been previously reported on total pack-years smoked and smoking age commenced [18, 19, 42]. Having smoked ≥20 total pack-years is consistently associated with mortality, as it is having started early in life this habit. This is of particular importance, since many older adults were born in a world without smoking restrictions and were vulnerable to its exposure since very early in life [36, 37]. A generational perspective is also necessary when approaching this problem.
It is no surprise that smoking cessation policies are focused mainly on younger individuals rather than on older adults, as happens with other preventive health policies. However, our results show how smoking cessation advice should be emphasised in later life too, emphasising the specific benefits of quitting in these age group. On the other hand, smoking cessation initiatives should address myths that misrepresent tobacco as not harmful into old age [11, 35]. Hence, a life-course approach that alerts about these effects into old age might prompt stakeholders to create policies aiming towards strengthen restrictions on tobacco consumption for all groups of age. In addition, smoking cessation should be considered as a part of interventions aiming towards decreasing the effect of frailty [8, 9]. Some programmes focusing on smoking cessation in older adults have been implemented around the world. For instance, Age UK, a non-for-profit organisation based in London, offers information to patients seeking to learn about the effects of tobacco consumptions, as well as links to the National Health Service Stop Smoking Service. Moreover, the Center for Social Gerontology, based in Ann Arbor, has a website with brochures targeted for the older adult population, smoking related issues. Unfortunately, low- and middle-income countries are still not aware of the benefits of implementing these policies and health inequalities for older adults prevail [16, 37]. Therefore, it is important to generate evidence on this matter and inform through convenient channels.
Our study has some limitations. For instance, the exposure to smoking was only assessed at baseline, and there should be some impact of those who continued smoking and those who stopped smoking during the follow-up period that would not be apparent from our analysis (i.e. smoking trajectories). Our report did not include second-hand smoking; thus, our results could underestimate this exposure that has been associated by itself with mortality. In addition, there is always residual confounding; therefore, other variables should be considered in future studies that could bias this association. Finally, other tools for frailty classification could render different results and some of them could be further divided into the well-known categories: robust, pre-frail and frail; research questions worth to be answered.
Conclusions
As research on frailty grows, it is clearer that this is a condition that could be considered as a late-life complication of different problems throughout life. Smoking tobacco seems to impact the older adult by increasing frailty but also by increasing mortality for those who smoked or currently smoke. However, smoking cessation seems to decrease mortality event for those in the higher range of frailty. Therefore, efforts should not stop even for those into older age and considered to be very frail, since according to our results, they still benefit from stopping this habit.
Supplementary Material
Acknowledgements
The Institutional Review Boards and Ethics Committees of the University of Texas Medical Branch in the USA, the Instituto Nacional de Estadística y Geografía, the Instituto Nacional de Salud Pública and Instituto Nacional de Geriatría in Mexico approved the study. All study subjects signed an informed consent form.
Contributor Information
Daniela Patiño-Hernández, Semillero de Neurociencias y Envejecimiento, Ageing Institute, Medical School, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; Internal Medicine Department, Hospital San Ignacio, Bogotá D.C., Colombia.
Óliver Guillermo Pérez-Bautista, Smoking Clinic, Instituto Nacional de Enfermedades Respiratorias, Ciudad de México 14080, México.
Mario Ulises Pérez-Zepeda, Semillero de Neurociencias y Envejecimiento, Ageing Institute, Medical School, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; Departamento de Investigación, Research Headquarters, Instituto Nacional de Geriatría, Ciudad de México 10200, México; Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México, Huixquilucan, Estado de México 52786, México.
Carlos Cano-Gutiérrez, Semillero de Neurociencias y Envejecimiento, Ageing Institute, Medical School, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; Internal Medicine Department, Hospital San Ignacio, Bogotá D.C., Colombia.
Declaration of Conflicts of Interest
None.
Declaration of Sources of Funding
The Mexican Health and Aging Study is partly sponsored by the National Institutes of Health/National Institute on Aging in the USA (Grant Number NIH R01AG018016) and the Instituto Nacional de Estadística y Geografía in Mexico.
Statement of Human and Animal Rights
All procedures performed in this study were in accordance with the 1964 Helsinki Declaration and amendments.
Data Availability
Data for this study is openly available after simple registration at http://mhasweb.org/Data.aspx.
References
- 1. Howlett SE, Rutenberg A, Rockwood K. The degree of frailty as a translational measure of health in aging. Nature Aging 2021; 1: 651–65. [DOI] [PubMed] [Google Scholar]
- 2. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc 2012; 60: 1487–92. [DOI] [PubMed] [Google Scholar]
- 3. Junius-Walker U, Onder G, Soleymani Det al. The essence of frailty: a systematic review and qualitative synthesis on frailty concepts and definitions. Eur J Intern Med 2018; 56: 3–10. [DOI] [PubMed] [Google Scholar]
- 4. Rutenberg AD, Mitnitski AB, Farrell SG, Rockwood K. Unifying aging and frailty through complex dynamical networks. Exp Gerontol 2018; 1: 126–9. [DOI] [PubMed] [Google Scholar]
- 5. Mitnitski AB, Rutenberg AD, Farrell S, Rockwood K. Aging, frailty and complex networks. Biogerontology 2017; 18: 433–46. [DOI] [PubMed] [Google Scholar]
- 6. Mitnitski A, Rockwood K. Aging as a process of deficit accumulation: its utility and origin. Interdiscip Top Gerontol 2015; 40: 85–98. [DOI] [PubMed] [Google Scholar]
- 7. Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health 2018; 3: e323–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Borda MG, Perez-Zepeda MU, Samper-Ternent R, Gomez RC, Avila-Funes JA, Cano-Gutierrez CA. The influence of lifestyle behaviors on the incidence of frailty. J Frailty Aging 2020; 9: 144–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Abe T, Nofuji Y, Seino Set al. Healthy lifestyle behaviors and transitions in frailty status among independent community-dwelling older adults: The Yabu cohort study. Maturitas 2020; 136: 54–9. [DOI] [PubMed] [Google Scholar]
- 10. Chen XK, Yi ZN, Wong GTet al. Is exercise a senolytic medicine? A systematic review. Aging Cell 2021; 20: e13294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Nguyen LU, Chen TC. Why older adults should quit tobacco and how they can. Generations J 2021; 44: 1–7. [Google Scholar]
- 12. Kojima G, Iliffe S, Jivraj S, Liljas A, Walters K. Does current smoking predict future frailty? The English longitudinal study of ageing. Age Ageing 2018; 47: 126–31. [DOI] [PubMed] [Google Scholar]
- 13. Kojima G, Iliffe S, Walters K. Smoking as a predictor of frailty: a systematic review. BMC Geriatr 2015; 22: 131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Amiri S, Behnezhad S. Systematic review and meta-analysis of the association between smoking and the incidence of frailty. Neuropsychiatr 2019; 33: 198–206. [DOI] [PubMed] [Google Scholar]
- 15. Bilano V, Gilmour S, Moffiet Tet al. Global trends and projections for tobacco use, 1990-2025: an analysis of smoking indicators from the WHO Comprehensive Information Systems for Tobacco Control. Lancet 2015; 385: 966–76. [DOI] [PubMed] [Google Scholar]
- 16. Theilmann M, Lemp JM, Winkler Vet al. Patterns of tobacco use in low and middle income countries by tobacco product and sociodemographic characteristics: nationally representative survey data from 82 countries. BMJ 2022; 30: e067582. 10.1136/bmj-2021-067582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Barengo NC, Antikainen R, Harald K, Jousilahti P. Smoking and cancer, cardiovascular and total mortality among older adults: The Finrisk Study. Prev Med Rep 2019; 14: 100875. 10.1016/j.pmedr.2019.100875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Nash SH, Liao LM, Harris TB, Freedman ND. Cigarette smoking and mortality in adults aged 70 years and older: results from the NIH-AARP cohort. Am J Prev Med 2017; 52: 276–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Gellert C, Schottker B, Brenner H. Smoking and all-cause mortality in older people: systematic review and meta-analysis. Arch Intern Med 2012; 172: 837–44. [DOI] [PubMed] [Google Scholar]
- 20. Hecht SS. Tobacco carcinogens, their biomarkers and tobacco-induced cancer. Nat Rev Cancer 2003; 3: 733–44. [DOI] [PubMed] [Google Scholar]
- 21. Champagne BM, Sebrié EM, Schargrodsky H, Pramparo P, Boissonnet C, Wilson E. Tobacco smoking in seven Latin American cities: the CARMELA study. Tob Control 2010; 19: 457–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Mons U, Muezzinler A, Gellert Cet al. Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: meta-analysis of individual participant data from prospective cohort studies of the CHANCES consortium. BMJ 2015; 350: h1551. 10.1136/bmj.h1551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Feller D, Kun J, Ruzsics Iet al. Cigarette smoke-induced pulmonary inflammation becomes systemic by circulating extracellular vesicles containing wnt5a and inflammatory cytokines. Front Immunol 2018; 9: 1724. 10.3389/fimmu.2018.01724. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kim EJ, Yoon SJ, Kim YE, Go DS, Jung Y. Effects of aging and smoking duration on cigarette smoke-induced COPD severity. J Korean Med Sci 2019; 34: e90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Ahluwalia IB, Myers M, Cohen JE. COVID-19 pandemic: an opportunity for tobacco use cessation. Lancet Public Health 2020; 5: e577. 10.1016/S2468-2667(20)30236-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Islas-Granillo H, Borges-Yanez A, Medina-Solis CEet al. Tooth-loss experience and associated variables among adult mexicans 60 years and older. P R Health Sci J 2016; 35: 88–92. [PubMed] [Google Scholar]
- 27. Calzada MT, Posada-Lopez A, Gutierrez-Quiceno B, Botero JE. Association between tobacco smoking, dental status and self-perceived oral health in elderly adults in Colombia. J Cross Cult Gerontol 2021; 36: 187–200. [DOI] [PubMed] [Google Scholar]
- 28. Trevisan C, Alessi A, Girotti Get al. The impact of smoking on bone metabolism, bone mineral density and vertebral fractures in postmenopausal women. J Clin Densitom 2020; 23: 381–9. [DOI] [PubMed] [Google Scholar]
- 29. Cho BW, Kim DS, Kwon HM, Yang IH, Lee WS, Park KK. Cross-sectional association between hypercholesterolemia and knee pain in the elderly with radiographic knee osteoarthritis: data from the Korean National Health and Nutritional Examination Survey. J Clin Med 2021; 10: 933. 10.3390/jcm10050933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Monroe DC, McDowell CP, Kenny RA, Herring MP. Dynamic associations between anxiety, depression, and tobacco use in older adults: results from The Irish Longitudinal Study on Ageing. J Psychiatr Res 2021; 139: 99–105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Carrillo-Vega MF, Garcia-Pena C, Gutierrez-Robledo LM, Perez-Zepeda MU. Vitamin D deficiency in older adults and its associated factors: a cross-sectional analysis of the Mexican Health and Aging Study. Arch Osteoporos 2017; 12: 8. 10.1007/s11657-016-0297-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Jo Y, Linton JA, Choi Jet al. Association between cigarette smoking and sarcopenia according to obesity in the middle-aged and elderly korean population: the Korea National Health and Nutrition Examination Survey (2008–2011). Korean J Fam Med 2019; 40: 87–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Marinho V, Laks J, Coutinho ES, Blay SL. Tobacco use among the elderly: a systematic review and meta-analysis. Cad Saude Publica 2010; 26: 2213–33. [DOI] [PubMed] [Google Scholar]
- 34. Marinho V, Blay SL, Andreoli SB, Gastal F. A prevalence study of current tobacco smoking in later life community and its association with sociodemographic factors, physical health and mental health status. Soc Psychiatry Psychiatr Epidemiol 2008; 43: 490–7. [DOI] [PubMed] [Google Scholar]
- 35. Brown LM. Tobacco myths: the older adult perspective. J Gerontol Nurs 2015; 41: 9–13. [DOI] [PubMed] [Google Scholar]
- 36. Jordan H, Hidajat M, Payne N, Adams J, White M, Ben-Shlomo Y. What are older smokers' attitudes to quitting and how are they managed in primary care? An analysis of the cross-sectional English Smoking Toolkit Study. BMJ Open 2017; 7: e018150. 10.1136/bmjopen-2017-018150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. McAfee T, Malone RE, Cataldo J. Ignoring our elders: tobacco control's forgotten health equity issue. Tob Control 2021; 30: 479–80. [DOI] [PubMed] [Google Scholar]
- 38. Simpson FR, Pajewski NM, Beavers KMet al. Does the impact of intensive lifestyle intervention on cardiovascular disease risk vary according to frailty as measured via deficit accumulation? J Gerontol A Biol Sci Med Sci 2021; 76: 339–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Pérez-Zepeda MU, Martínez-Velilla N, Kehler DS, Izquierdo M, Rockwood K, Theou O. The impact of an exercise intervention on frailty levels in hospitalised older adults: secondary analysis of a randomised controlled trial. Age Ageing 2022; 51: afac028. 10.1093/ageing/afac028. [DOI] [PubMed] [Google Scholar]
- 40. Wong R, Espinoza M, Palloni A. Mexican older adults with a wide socioeconomic perspective: health and aging. Salud Publica Mex 2007; 49: S436–47. [DOI] [PubMed] [Google Scholar]
- 41. Wong R, Michaels-Obregon A, Palloni A. Cohort profile: the Mexican Health and Aging Study (MHAS). Int J Epidemiol 2017; 46: e2. 10.1093/ije/dyu263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Inoue-Choi M, Shiels MS, McNeel TS, Graubard BI, Hatsukami D, Freedman ND. Contemporary associations of exclusive cigarette, cigar, pipe, and smokeless tobacco use with overall and cause-specific mortality in the United States. JNCI Cancer Spectr 2019; 3: pkz036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. CDC . Alcohol Use and Your Health. https://www.cdc.gov/alcohol/fact-sheets/alcohol-use.htm2022 (20 August 2021, date last accessed).
- 44. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr 2008; 8: 24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Hoover M, Rotermann M, Sanmartin C, Bernier J. Validation of an index to estimate the prevalence of frailty among community-dwelling seniors. Health Rep 2013; 24: 10–7. [PubMed] [Google Scholar]
- 46. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: comparing the frailty index and phenotype. Arch Gerontol Geriatr 2015; 60: 464–70. [DOI] [PubMed] [Google Scholar]
- 47. Crothers K, Griffith TA, McGinnis KAet al. The impact of cigarette smoking on mortality, quality of life, and comorbid illness among HIV-positive veterans. J Gen Intern Med 2005; 20: 1142–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. DeClercq V, Duhamel TA, Theou O, Kehler S. Association between lifestyle behaviors and frailty in Atlantic Canadian males and females. Arch Gerontol Geriatr 2020; 91: 104207. 10.1016/j.archger.2020.104207. [DOI] [PubMed] [Google Scholar]
- 49. Garcia-Esquinas E, Navas-Acien A, Rodriguez-Artalejo F. Exposure to secondhand tobacco smoke and the frailty syndrome in US older adults. Age 2015; 37: 26. 10.1007/s11357-015-9762-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Marengoni A, Vetrano DL, Manes-Gravina E, Bernabei R, Onder G, Palmer K. The relationship between COPD and frailty: a systematic review and meta-analysis of observational studies. Chest 2018; 154: 21–40. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data for this study is openly available after simple registration at http://mhasweb.org/Data.aspx.