Prevalence of frailty in older people in Veneto (Italy)*

Figures & data

Table 1. Characteristics of the analyzed sample.

	All	Females	Males
N	2407 (100.00%	1393 (57.87%)	1014 (42.13%)
Age (sd)	75.97 (7.65)	76.7 (8.00)	74.94 (7.02)
Frailty (Rockwood score > 4) (%)	23.18%	28.06%	16.46%
Multimorbidity (Chalson Score ≥4) (%)	21.10%	21.80%	20.11%
The proportions of patients affected by the clinical conditions illustrated below were calculated on a subsample of 2280 patients evaluated for the CSHA Clinical Frailty Scale score and characterized by an optimal quality of the historical data collected in their medical records (F = 1318 M = 962)*
Myocardial infarct	2.72%	1.21%	4.78%
Congestive heart failure	4.87%	4.17%	5.82%
Peripheral vascular disease	5.09%	2.28%	8.94%
Cerebrovascular disease	21.93%	21.02%	23.18%
Dementia	2.06%	2.66%	1.25%
Chronic pulmonary disease	21.32%	20.26%	22.77%
Connective tissue disease	4.82%	5.92%	3.33%
Ulcer disease	5.53%	4.48%	6.96%
Mild liver disease	0.88%	0.61%	1.25%
Diabetes	18.90%	15.55%	23.49%
Diabetes with end organ damage	0.13%	0.08%	0.21%
Hemiplegia	0.44%	0.38%	0.52%
Moderate or severe renal disease	7.98%	6.75%	9.67%
Any tumor	7.85%	12.22%	1.87%
Lymphoma	1.05%	1.06%	1.04%
Leukemia	0.44%	0.68%	0.10%
Moderate or severe liver disease	0.09%	nd	0.21%
Metastatic solid tumor	0.31%	0.30%	0.31%
AIDS	0.04%	0.08%	nd
Hypertension	70.31%	70.11%	70.58%
Depression	18.29%	21.78%	13.51%
Cellulitis – skin ulcers	5.04%	5.54%	4.37%
Dicumarol use	4.74%	3.79%	6.03%

(*see main text, see Appendix D).

Table 1 illustrates the characteristics of the analyzed sample. The chronic conditions (Diagnoses) represent the index diseases for the calculation of the Charlson Index; the percentages identify the prevalence of the diseases into the single strata (All, Females, Males); obviously a patient can have more than one disease.

Figure 1. Distribution of the Rockwoods scores in the 2407 65+ evaluated. For each category, the sum of the percentages gives 100.00%. The distribution of the values of the Rockwood’s frailty score shows to be approximately bimodal.

Table 2. Age and sex distribution of the prevalence of Frailty Status Status (so definied by a Rockwoods score >4) in the sample of 2407 elderly individuals evaluated with the Rockwoods Clincal Frailty Scale (% value and 95% confidence Intervals).

Age stratum		Female	Male	tot
> =65 < 75	n	621	513	1134
	Prevalence % (CI 95%)	7.24 (5.44–9.57)	5.65 (3.93–8.02)	6.52 (5.22–8.12)
> =75 < 85	n	503	392	895
	Prevalence % (CI 95%)	29.22 (25.41–33.34)	19.89 (16.23–24.14)	25.13 (22.40–28.08)
> =85	n	269	109	378
	Prevalence % (CI 95%)	73.97 (68.41–78.86)	55.04 (45.69–64.05)	68.51 (63.66–72.99)
tot	n	1393	1014	2407
	Prevalence % (CI 95%)	28.06 (25.77–30.48)	16.46 (14.31–18.88)	23.18 (21.53–24.91)

The prevalence of frailty shows to be higher in the female gender in all age-strata; in the whole sample it corresponds to 28.06% (25.77%–30.48%) for females and 16.46% (14.31%–18.88%) for males respectively (ratio F/M = 1.7).

Figure 2. Prevalences of mutually exclusive combinations of frailty and serious comorbidity. Standardization for sex of five classes of age (standard population: Veneto’s official data²⁵) – the combinations of frailty and multimorbidity illustrated by the graph are mutually exclusive: so, for each stratum of age the sum of the percentages gives 100.00%.

Table 3. Univariate and multivariate logistic regression models exploring the relationship between frailty (i.e.: Rockwood score >4) and comorbidity (i.e.: Charlson Score ≥4).

Logistic regression models – Outcome : to be frail (ie having CSHA Clinical Frailty Scale score >4) We analyzed 2280 elderly people assisted by doctors with good quality of data-input (see main text, see Appendix D)
	I.Monovariate logistic models OR (CI 95%) Z test P	II.Multivariate logistic model without interactions OR (CI 95%) Z test P	III.Multivariate logistic model with interactions OR (CI 95%) Z test P
To be female	OR = 1.83 (1.49–2.25) p < .0001	OR = 0.46 (0.22–0.69) p < .0001	OR = 1.58 (1.25–2.01) p < .0001
Having a Charlson Score ≥4	OR = 3.64 (2.93–4.51) p < .0001	OR = 1.18 (0.94–1.43) p < .0001	OR = 4.21 (3.20–5.53) p < .0001
To be aged ≥85	OR = 12.64 (9.77–16.34) p < .0001	OR = 2.42 (2.16–2.69) p < .0001	OR = 15.78 (11.44–21.76) p < .0001
Age ≥ 85 X Clarlson ≥4	–	–	ROR** = 0.33 (0.19–0.58)p < .0001
Model diagnostics
Pregibon test	–	Z -3.07 p = .002	Z = 0.32 p = .748
Hosmer-Lemeshow test	–	Chi2 = 18.18 df = 4 p = .0011	Chi2 = 3.05 df = 4 p = .54
AIC statistic	–	1952.257	1939.972

*Note: the exponentialized coefficient of the interaction variable is a ratio of odds ratios ROR.

We explored the details of the relationship between frailty and comorbidity through three models of logistic regression in which the condition of frailty was the outcome and age, sex and comorbidity were the predictors.

The multivariate model without interactions (II) does not fit well: this can be seen from the outpouts of the Pregibon test (p = .002) and the Hosmer-Lemeshow test (p = .0011); the AIC statistic (1952.27 versus 1939.972) shows also that the informative contribute is worst respect that of model III (i.e. that with the interaction).

The multivariate model with interactions (III) shows the better goodness of fit (Hosmer-Lemeshow test p = .54) and the better pattern of covariates (Pregibons test p = .748). To be female shows to be a significant predictor of frailty status and Age shows to be a significant confounder in the relations between comorbidity and frailty.

In detail, multimorbidity has been shown to be an independent predictor of frailty only for patients under the age of 85. The linear combination of the nonexponentialized coefficients of the variables involved in the interaction (third model of Table 4) allowed in fact to calculate for patients with multimorbidity (i.e. with Charlson score ≥4) compared to those without multimorbidity (i.e. with Charlson score <4) an Odds Ratio of frailty correspondent to OR = 4.21 (3.20–5.53) p < .0001 in subjects under 85 years and to OR = 1.42 (0.88–2.29) p = .149 in subjects 85+ years old, respectively. So, a serious comorbidity shows to be a prognostic factor for frailty only under 85 years of age.

Table 4. The table illustrates the prevalence of frailty in some clinical condition in 2280 elderly people evaluated for frailty status and with optimal data input (see main text, see Appendix D).

Conditions	All	Females	Males
Myocardial infarct	32.26%	37.50%	30.43%
Congestive heart failure	54.95%	70.91%	39.29%
Peripheral vascular disease	25.00%	30.00%	23.26%
Cerebrovascular disease	32.00%	37.55%	25.11%
Dementia	87.23%	94.29%	66.67%
Chronic pulmonary disease	24.49%	28.46%	19.63%
Connective tissue disease	44.55%	50.00%	31.25%
Ulcer disease	28.57%	37.29%	20.90%
Mild liver disease	40.00%	25.00%	50.00%
Diabetes	29.00%	36.10%	22.57%
Diabetes with end organ damage	33.33%		50.00%
Hemiplegia	60.00%	40.00%	80.00%
Moderate or severe renal disease	48.35%	52.81%	44.09%
Any tumor	26.82%	26.09%	33.33%
Lymphoma	45.83%	42.86%	50.00%
Leukemia	50.00%	55.56%
Moderate or severe liver disease	50.00%	50.00%	50.00%
Metastatic solid tumor	42.86%	50.00%	33.33%
AIDS
Hypertension	24.64%	29.98%	17.38%
Depression	32.37%	33.80%	29.23%
Cellulitis – skin ulcers	52.17%	53.42%	50.00%
Dicumarol use	54.63%	72.00%	39.66%

Table

Frailty Prevalence (CSHA Clinical Frailty Scale)	≥65 < 70	≥70 < 75	≥75 80	≥80 < 85	≥85
CI 95% lower limit	2.06%	7.13%	12.97%	32.49%	59.45%
Punctual extimation	3.56%	9.61%	16.08%	37.42%	64.74%
CI 95% upper limit	5.05%	12.09%	19.18%	42.35%	70.03%

Statistiche Demografiche ISTAT http://demo.istat.it/pop2017/index.html

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