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Table 1 Summary of published validity data on the PACIC

From: Psychometric properties of the patient assessment of chronic illness care measure: acceptability, reliability and validity in United Kingdom patients with long-term conditions

Author1 Country Mean scores (& Acceptability Structure Associations with: Associations with:
PACIC version N (Response rate) Cronbach’s alpha)    ·health conditions ·other measures
  Context a. PACIC; b. PA;    ·other measures of ·patient characteristics
   c. DS/PD; d. GS/T; e. PS/C; f. F/C (other reliability data)    chronic care ·interventions
Aragones [15] USA a.3.17 (0.87) Reports no ceiling or floor effects Type of analysis not clear No significant association with number of chronic conditions No significant association with age, sex, education, insurance, years in the US
Spanish language version Sample 1: 100/120 (83%) b. – f. 2.50 – 3.95 (all >0.6)   Factor loading analysis – most items correlated highly on proposed scales   
  Sample 2: 20 telephone interview follow ups       
  Spanish speaking Hispanics with diabetes in hospital ambulatory settings (Test Re-test 0.77)     
Carryer [16] New Zealand GP care/Nurse care     Professional self ratings much higher (on modified version of PACIC):
Modified PACIC for professionals Sample 1: 341 (85.3% - of those who expressed an interest in participating) a.2.7/3.3     a.4.0
b.2.9/3.5     b.4.3
c.3.1/3.7     c.3.8
d.2.3/3.2     d.3.8
Sample 2: 89 GPs & nurses e.2.8/3.5     e.4.1
Primary care patients and practitioners f.2.6/2.9     f.3.8
Gensichen [8] Germany a.3.25 (0.91) Ceiling effects: PA (12.9%) and PS/C (8.9%) EFA two factors (‘Patient activation Overall PACIC with number of conditions and PHQ9 both NS No significant associations with age, sex, education
German language version 442/485 (91.1%) b.3.65 (0.80) Floor effects: and problem solving’ and ‘ Goal setting and co-ordination’) 46.5% High correlations with all EUROPEP scales  
Patients with major depression in primary care c.3.47 (0.45) GS/T (4.6%)
d.2.97 (0.74) Missing data from 0.7% - 5.4% some items did not load as expected
e.3.69 (0.77)
f.2.83 (0.76)
Glasgow [7] USA a.2.60 (0.93) No items had ceiling effects CFA – moderate fit No variation in response across 6 most common long- term conditions (excluding diabetes patients who report better follow up); Higher PACIC scores associated with more conditions (r = 0.13, p<0.05) Correlations (PACIC and subscales) with patient characteristics all <=0.25; Higher overall PACIC related to age (higher) and gender (female); Gender significantly related to all subscales (0.14 to 0.25; P<0.05)
  Sample 1: 379/500 (76%) of which 283 had chronic condition (57%) b.2.99 (0.82) Floor effects identified, but not reported in detail
c.3.13 (0.77) 96% had no missing data
d.2.43 (0.84)
e.2.87 (0.90)
Sample 2: 82/100 sent follow up at +12 weeks (82%) of which 63 had chronic condition (63%) f.1.07 (0.86)
Primary care (3 month re-test 0.58) Overall PACIC and all subscales correlate significantly with Hibbard Activation and Safran Assessment of primary Care sub scales (with exception of PACIC F/C and Safran Integration sub scale)
Glasgow [12] USA a.3.2 (0.96) Adequate variability   No significant relationship to number of conditions Correlated with physical activity (r=0.17) but not fat consumption
Includes PACIC 5As 363 (63%) b.3.6 3-9% sub scale scores <1.5, (4% on summary scale)   Related to quality of care (composite lab assessment r=0.23) and composite self management support (r=0.25) No significant differences with sex, ethnicity or income
  Type 2 diabetes patients in primary care c.3.5 7-22% sub scale scores >4.5 (9% on summary scale)    
5As mean = 3.2
Goetz [17] Germany   Patients tended to gravitate to both end points (0% and 100%) FA indicated a 1 factor solution for the PACIC short form There was no correlation between the mean overall score of the PACIC short form and number of chronic conditions  
PACIC short form & revised scoring 264 (49%)   Non-response rates ranged from 4.2% - 12.5%    
Over 18 with at least one chronic condition in primary care
Gugiu [13] US (Short form PACIC – 11 items – Ordinal alpha = 0.955 (sample 1) and 0.963 (sample 2); Ordinal omega 0.956 (S1) & 0.963 (S2); Eight month Test re-test reliability (n=250) = 0.638)   EFA within a CFA No associations with HBa1c, LDL, microalbumin, BP  
Modified PACIC percentage scale Sample 1: 529/943 (55%) Unidimensional, 11 item variant
Sample 2: 361/943 (38%) (111 not in first sample)
Type 2 diabetics, large physician networks
Gugiu [9] USA (Short form PACIC – 11 items, Alpha 0.945, ordinal alpha 0.972, ordinal omega 0.973) Missing data 0.2% CFA Poor fit to 5 factors No associations with clinical indicators  
Modified PACIC percentage scale (linked to above) 539/943 (57%) to 2.8%, 8.9% failed to respond to at least 1 EFA 1–3 factors, 1 factor preferred   
  Type 2 diabetics, large physician networks   Kurtosis (trimodal, 43% 90-100%, 24% 0-10%)
Jackson [18] USA a.3.1     Non white patients more likely to report experience consistent with the CCM (OR 2.3) (PS and FU significant among subscales); Patients not completing high school more likely to report experience consistent with the CCM (OR 3.0) and subscales
204 (69%), but 189 (64%) complete information b.3.3
Patients with diabetes receiving VA primary care services c.3.6
Maindal [11] Denmark a.(0.94) Missing 0.5 – 2.9% CFA good fit for 2 indices, poor for 4 Patients with self-rated good health reported higher scores on ‘Patient Activation’, ‘Decision Support’ and ‘Goal Setting’; Patients with more than one additional disease rated lower on PA and DS No significant associations with sex, age
Danish Language version 1265/2476 but only 560 met criteria of diabetes > 2 years + medical treatment (22.6%) b. – f. (0.71 – 0.86) Floor effects: 2.7% - 69.2%, >15% for 17 items
Patients on national diabetes register   Ceiling effects: 4.0% – 4.04%, >15% for 12 items
Rosemann [19] Germany Male/Female Adequate variability Education and age predicted overall PACIC score in regression Significant relationships with disease duration, BMI, co-morbidities, PHQ sum, AIMS2F, Significant differences by gender and educational level (p<0.01), marital status and age (p<0.05),
German language version 1021/1250 (81.7%) a.2.79/2.67
PACIC 5As Patients with OA in primary care practices b.3.51/3.39
Rosemann [20] Germany a.2.44 (0.90) Adequate variability in the overall scale & all subscales   PACIC and GS/T and FU/C scores significantly higher for patients with co-morbid diabetes, but no significant associations with other co-morbidities (hypertension, depression, CHD, COPD) Age and gender showed weak correlations with overall PACIC and majority of subscales; no significant relationship with educational level.
German language version Sample 1: 236/300 – 78.6%. b.2.79 (0.85) Floor effects in 3 subscales (F/C - 4.6%; PA - 3.8%; and GS/T - 3.4%).   Strong correlations found between PACIC sub scales and EUROPEP as expected
PACIC 5As Sample 2: 71 of subset of 75 sent follow up questionnaire after 2 weeks c.2.56 (0.78) Ceiling effects below 1.3%
OA patients from 75 primary care practices d.2.31 (0.81)
e.2.48 (0.86)
f.2.01 (0.81)
(Test-retest - overall 0.81; PA 0.77; DSD/DS 0.78; GS/T 0.82; PS/C 0.79; FU/C 0.85.
Schmittdiel [10] USA Mean 2.7 71% completed all items, 90% completed 17+   Relationships similar for subgroup by disease Significant relationship with higher quality of life (OR 1.2); no relationship with adherence to medications (OR 1.06)
4108/6673 (61%)    Higher ratings of health care (OR 2.36), Significantly associated with greater engagement in self management behaviours (OR 1.21 to 1.41); use of self management services (OR 1.4)
Private health care members on one of six chronic disease registers
Szecenyi [21] Germany DMP/Non-DMP     Mean 3.2 DMP versus 2.68 non-DMP (significant p=0.001) and across all subscales except patient activation (p=0.05), greatest mean difference in F/C, least in PA
German language version 1532/3546 (42.2%) a.3.26/2.86
(1,399 valid responses = 39%) b.3.26/3.09
PACIC 5As Patients with type 2 diabetes in primary care, in or outside disease management programmes (DMPs) c.3.52/3.29
Taggart [14] Australia S 1 S 2 Sample 1: 73% completed all 20 items; 95% completed at least 17 items. EFA, both 2 factor solutions, 59% & 61% variance Higher PACIC scores associated with higher patient self-rated health Degree/diploma, retired, hypertension/IHD & greater duration of disease had negative associations with both factors and total PACIC scores; Employed and married/CH had negative associations with planned care factor and total PACIC score
Sample 1: 2552/2642 (96%) (2642 of 3349 asked & consented to take part) a. 3.01 a.3.07 Sample 2: 79% completed all 20 items; 95% completed at least 17 items. F1 SDM and SM (12 items across four scale) (alpha 0.939 & 0.943) SDM and AM positively associated with good health  
F2 Planned care (8 items across 3 scales) (Alphas 0.883 and 0.878)
Sample 2: 963/1000 (96%) (1000 out of 4167 consented to take part)     
Patients with CHD, hypertension and/or T2 diabetes in general practice
Wensing [22] Netherlands a. 2.9 (0.93) 22-35% missing data. Items 15, 17 & 20 had >30% non response PCA – five factors Association between PACIC and EUROPEP aggregated scores all positive as expected. Higher enablement in patients associated with lower PACIC scores – contrary to expectations
Dutch language version 165 (72%) b. 3.2 (0.85) Lowest response category used by >30% for 11 items. (7-76%) (70% variance explained; KMO 0.844; Bartlett’s p=0.000)
Randomly sampled patients with diabetes or COPD from four general practices (involved in a programme to enhance structured diabetes care) c. 3.5 (0.75) Highest response category used by >30% for 6 items (10 – 54%) Matched three pre- defined domains (but not delivery system/practice design nor follow up/co-ordination)
d. 2.5 (0.81)
e. 3.3 (0.87)
  1. 1PA = Patient activation ; DS/PD = Delivery system design; GS/T = Goal setting; PS/C = Problem solving; F/C = Follow up and Co-ordination; PHQ9 = Patient health Questionnaire; EUROPEP = European patient evaluation of general practice care; CFA = confirmatory factor analysis; EFA = exploratory factor analysis.