A study of longitudinal data examining concomitance of pain and cognition in an elderly long-term care population

Figures & data

Figure 1 Sample method.

Table 1 Pain score items

Indicators
Variable	Description
Inferred/reported pain
(J2A) Pain frequency	Frequency resident complains or shows evidence of pain
(J2B) Pain intensity	Intensity of pain described or displayed by the resident
Pain sites score	Cumulative pain site index, items J2a-J3j, K1c; higher scores indicates more pain sites
Affect
(E1L) Sad facial expressions	Sad, pained, worried facial expressions, ie, furrowed brows
(E1M) Crying	Indicator of distress. Behavior is recorded by frequency in the last 30 days irrespective of the cause of the behavior (indicator)
(E3) Change in mood	Refers to status of any symptoms described in section E (mood); snapshot of current observation period, not just a point in time
Behavioral
(E1A) Negative statements	Resident made negative statements, eg, “Nothing matters, would rather be dead, what’s the use, regrets having lived so long”
(E1C) Repetitive verbalizations	Calling out for help, repeated statements
(E4DA) Inappropriate behavior frequency	Disruptive sounds, noisiness, screaming, self-abuse acts, sexual behavior or disrobing in public, smeared/threw feces, hoarding, rummaging through other’s belongings
(E1N) Repetitive physical movements	Pacing, hand wringing, restlessness, fidgeting, picking
(E4CA) Physically abusive frequency	Others are hit, shoved, scratched, sexually abused

Figure 2 Pain construct.

Table 2 Demographic of resident characteristics

(n = 56,494)	Mean ± SD/percent of total	Range
Age	83.3 ± 8.2	65–112
Sex
Male	20.4%
Female	79.6%
Marital status
Never married	14.7%
Married	14.9%
Widowed	60.2%
Separated	2.3%
Divorced	7.9%
Ethnicity
American Indian/Alaskan	0.3%
Native
Asian/Pacific Islander	1.2%
Black, not of Hispanic origin	11.7%
Hispanic	2.9%
White, not of Hispanic origin	83.9%
Language
English	94.6%
Spanish	2.4%
French	0.2%
Other	2.8%
Education level
No schooling	3.0%
8th grade/less	30.9%
9–11 grade	14.4%
High school	32.9%
Technical or trade school	4.1%
Some college	7.3%
Bachelor’s degree	4.2%
Graduate degree	1.7%
Not coded/missing	1.5%

Table 3 Diseases/events with potential pain symptoms

Disease	Number from total (n = 56,494)	Percent of total
Diabetes	11,885	21.0%
Peripheral vascular disease	6459	11.4%
Arthritis	19,013	33.7%
Complaint of joint pain	8018	14.2%
Hip fracture	2181	3.9%
Multiple sclerosis	447	0.8%
Emphysema/chronic obstructive pulmonary disease	7021	12.4%
Cancer	3031	5.4%
Renal failure	1382	2.4%
Pneumonia	498	0.9%
Respiratory infection	1277	2.3%
Septicemia	31	0.1%
Tuberculosis	20	0.0004%
Urinary tract infection	2865	5.1%
Wound infection	295	0.5%

Table 4 Longitudinal chart of the cognitive and pain scores

Cognitive status	2001	2002	2003
CPS mean score	2.9 ± 1.8	3.0 ± 1.9	3.2 ± 1.9
MMSE mean score	14.5 ± 7.8	13.7 ± 8.1	12.8 ± 8.3
Intact	13.6%	12.2%	10.4%
Mild	26.7%	24.4%	22.2%
Moderate	29.4%	29.0%	28.4%
Severe	30.3%	34.3%	39.0%
Average Pain Score	2.4 ± 2.9	2.34 ± 2.8	2.18 ± 2.8
Mode	0	0	0
Range (Possible range 0–34)	0–26	0–20	0–22
No pain symptoms reported	42.0%	43.0%	45.0%

Abbreviations: CPS, Cognitive Performance Scale; MMSE, Mini-Mental State Examination.

Figure 3 Covariance model 1 of 3-year concomitance of cognition and pain.

Figure 4 Covariance model 2 of 3-year concomitance of pain and cognition.

Table 5 Correlations

N = 56,494	Mean	SD	1	2	3	4	5	6
1. Pain score 2001	2.43	2.89	1.00
2. MMSE 2001	14.51	7.88	0.028**	1.00
3. Pain score 2002	2.34	2.85	0.635**	0.056**	1.00
4. MMSE 2002	13.59	8.20	0.022**	0.912**	0.041**	1.00
5. Pain 2003	2.1	2.77	0.492**	0.073**	0.606**	0.065**	1.00
6. MMSE 2003	12.63	8.36	0.019**	0.851**	0.036**	0.913**	0.052**	1.00

Note:

** Correlation is significant at the 0.01 level (one-tailed).

Abbreviation: MMSE, Mini-Mental State Examination.

Table 6 Goodness-of-fit statistics of the covariance model

Goodness-of-fit statistics	Model 1	Model 2
χ^2	2524.9	2828.6
Degrees of freedom (df)	4	4
P	0.000	0.000
Number of free parameters	17	17
χ^2/df	631.224	707.158
RMR	0.332	0.205
GFI	0.986	0.984
TLI	0.964	0.959
AGFI	0.924	0.915
RMSEA	0.106	0.112
Hoelter (0.01)	298	266

Abbreviations: AGFI, Adjusted Goodness-of-Fit Index; GFI, Goodness-of-Fit Index; RMR, root-mean-square residual; RMSEA, Root-Mean-Square Error of Approximation; TLI, Tucker–Lewis Index.

Table 7 Goodness-of-Fit statistical terms

Goodness-of-Fit statistics	Terms and understanding statistical output
χ^2 (chi-square)	Best for models with sample sizes between 75–100; for n > 100 chi-square is almost always significant since the magnitude is affected by the sample size; also affected by the size of correlations in the model, the larger the correlations the poorer the fit
Degrees of freedom (df)	The number of degrees of freedom and equals p–q (the # of sample moments subtract the # of parameters estimated)
P	The probability is ideally nonsignificant; however, significant models can still yield valuable theoretical construct information
Number of free parameters	Multiple times 5–10 to estimate required sample size for the study
χ^2/df	Use to compare models; this number should decrease from model to model; <5 is good, but must have P > 0.05; close to 1.0 means it is a correct model
RMR	Root-mean-square residual is the square root of the average amount that the sample variances and covariances differ from their estimates, smaller values are better
GFI (also GOF)	Slightly less than or equal (0–1) to 1 indicates a perfect fit; acceptable values are above 0.90; affected by sample size and can be large for poorly specified models
TLI	The Tucker–Lewis Index coefficient should be between 0–1, values close to 1 indicate a very good fit
AGFI (also AGOF)	Adjusted Goodness-of-Fit Index, takes into account the df available for testing the model; AGFI is bound by 1, which indicates a perfect fit; however it is not bound by 0
RMSEA	Should be less than 0.05; score of less than 0.05 indicates a close fit of the model in relation to the df. Not definitive but the rule of thumb is a RMSEA of 0.01 is an exact fit, a score of 0.08 or less indicates a reasonable error of approximation. A model with an RMSEA of greater than 0.1 should not be used – indicates a poor fit
Hoelter (0.05)	The largest sample size for which one would accept the hypothesis that the model is correct; the index should only be calculated if the chi-square is statistically significant. How small one’s sample size would have to be for chi-square to no longer be significant. Hoelter recommends values of at least 200, values ≤75 indicate a poor fit

Abbreviations: AGFI, Adjusted Goodness-of-Fit Index; GFI, Goodness-of-Fit Index; RMR, root-mean-square residual; RMSEA, Root-Mean-Square Error of Approximation; TLI, Tucker–Lewis Index.