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Erschienen in: BMC Medical Research Methodology 1/2014

Open Access 01.12.2014 | Research article

Intracluster correlation coefficients for the Brazilian Multicenter Study on Preterm Birth (EMIP): methodological and practical implications

verfasst von: Giuliane J Lajos, Samira M Haddad, Ricardo P Tedesco, Renato Passini Jr, Tabata Z Dias, Marcelo L Nomura, Patrícia M Rheder, Maria H Sousa, Jose G Cecatti, for the Brazilian Multicenter Study on Preterm Birth study group

Erschienen in: BMC Medical Research Methodology | Ausgabe 1/2014

Abstract

Background

Cluster-based studies in health research are increasing. An important characteristic of such studies is the presence of intracluster correlation, typically quantified by the intracluster correlation coefficient (ICC), that indicate the proportion of data variability that is explained by the way of clustering. The purpose of this manuscript was to evaluate ICC of variables studied in the Brazilian Multicenter Study on Preterm Birth.

Methods

This was a multicenter cross-sectional study on preterm births involving 20 referral hospitals in different regions of Brazil plus a nested case–control study to assess associated factors with spontaneous preterm births. Estimated prevalence rates or means, ICC with 95% confidence intervals, design effects and mean cluster sizes were presented for more than 250 maternal and newborn variables.

Results

Overall, 5296 cases were included in the study (4,150 preterm births and 1,146 term births). ICC ranged from <0.001 to 0.965, with a median of 0.028. For descriptive characteristics (socio-demographic, obstetric history and perinatal outcomes) the median ICC was 0.014, for newborn outcomes the median ICC was 0.041 and for process variables (clinical management and delivery), it was 0.102. ICC was <0.1 in 78.4% of the variables and <0.3 for approximately 95% of them. Most of ICC >0.3 was found in some clinical management aspects well defined in literature such as use of corticosteroids, indicating there was homogeneity in clusters for these variables.

Conclusions

Clusters selected for Brazilian Multicenter Study on Preterm Birth had mainly heterogeneous findings and these results can help researchers estimate the required sample size for future studies on maternal and perinatal health.
Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

The idea for the study first arose in a discussion among RPJ, JGC and RPT and then was developed and implemented by the whole research team. The first version of the manuscript was drafted by GJL and SMH, and then complemented with the suggestions of the others. RPJ and JGC supervised the entire process. All authors contributed to the development of the study protocol and approved the final version of manuscript.
Abkürzungen
CRF
Clinical research form
Deff
Design effect
ICC
Intraclass correlation coefficient
SRS
Simple random sampling.

Background

Cluster-based studies involving aggregated units such as hospitals, health centers, schools or medical practices are increasingly being used in healthcare evaluation, especially in cluster randomized trials, which are perhaps the most high impact form of public health research/evaluation study design that can benefit from good extent estimates of ICC. In such situations, population groups (specific geographical areas), healthcare units (hospitals) or healthcare sectors are considered primary sampling units and generally all subjects belonging to each group are included to obtain data of interest [1, 2].
However, depending on the method of selection, data obtained from clusters may not be sufficiently representative to allow for generalization. Population observed in clusters can present a large degree of similarity in some characteristics (homogeneity), unlike when there is a simple random sampling (SRS), in which each individual has the same probability of being selected in the general population, with more heterogeneity [2].
Therefore, an important characteristic of cluster-based studies is to evaluate the proportion of data variability that is explained by means of clustering, and this reliability may be analyzed by measuring inter and intracluster variance [3].
Intracluster correlation coefficient (ICC), denoted by ρ, is defined as the ratio of the between-cluster variance to the total variance (both between and within clusters), and therefore has a value between 0 and 1 [4, 5]. Its value depends on the type of variable, cluster size and the prevalence of the condition [6]. Coefficients close to zero indicate that individuals within clusters are no more similar to each other than individuals from different clusters (the variable is randomly distributed among clusters); otherwise the values close to 1 reflect the homogeneity in a sample [7]. In other words, for cluster based population studies this heterogeneity (ICC close to zero) is desired as a proxy to the subjects being randomly selected.
The increase in variance due to clustering, compared to what would be obtained if sampling had been carried out by the SRS method, is calculated by design effect (Deff) [8]. It is given by 1 + (m-1) ICC, where m is the average cluster size [9]. Deff value is directly proportional to ICC and to the size of a cluster [10].
The ICC estimate in cluster studies is very useful for the development of new studies in the same field, because values obtained could be used as a correction factor for the calculation of sample size needed, thus avoiding underestimates, since in studies in which SRS is used, the sample size required to achieve sufficient statistical power is usually smaller [4].
The purpose of this manuscript is to evaluate the ICC of variables studied in the Brazilian Multicenter Study on Preterm Birth, a multicenter cross-sectional study on preterm births involving 20 referral hospitals in different regions of Brazil plus a nested case–control study. Estimated prevalence rates or means, ICC with 95% confidence intervals, design effects and average cluster sizes were also objectives for this study and they are presented for more than 250 maternal and neonatal variables.

Methods

The Brazilian Multicenter Study on Preterm Birth consisted of a multicenter cross-sectional study plus a nested case–control study to assess their associated factors implemented in referral obstetrical units (clusters) from several states of the country. The full research proposal has already been published elsewhere [11].
A single-stage cluster sampling was used. Clusters were selected by an invitation to 27 healthcare institutions that build a national network called Brazilian Network for Studies on Reproductive and Perinatal Health. They are located in the five geographical regions of the country, almost all of them are public institutions, and all of them receive both low and high risk pregnant women. Initially 26 centers accepted to participate, but 20 selected institutions were able to fully take part in the study.
The sample size was calculated using the official prevalence of preterm births in Brazil of around 6.5% [12]. Considering an acceptable absolute difference of about 0.25% between the sample and the population prevalence, and a type I error of 5%, initial surveillance of a sample size of 37,000 deliveries was necessary. For the case–control study component, the estimated sample size was 1,055 women in each group (cases and controls). The total number of preterm births estimated to be followed in both components of the study was around 3,600.
The participating centers performed a prospective surveillance of all patients admitted to give birth in order to identify preterm births. For this purpose and according to standard international definitions, preterm birth was considered that occurring before 37 completed weeks of gestational age evaluated by an ultrasound scan performed early in pregnancy, by a known date of the last menstrual period, or alternatively by the evaluation of the somatic age of the newborn. During the first months of the study, in order to complete the sample for the appropriate analysis of the factors associated with spontaneous preterm birth, a random sample of women who had full-term birth was also selected.
Data was collected during six to twelve months for each center, from April 2011 to March 2012, in a detailed form called “Questionnaire” including 306 variables from four sources: interview with women in the postpartum period, medical records and prenatal chart of the mother (before hospital discharge), and newborn medical records (within sixty days after birth, even if it remained in hospital for longer period). An electronic system of data entry called OpenClinica® was selected and a proper clinical research form (CRF) was designed for the input of data after the questionnaire of each case was completed and reviewed.
High quality data and reliable information was guaranteed by several steps: preparatory meetings, development of detailed manuals of operation, monitoring technical site visits to the centers, close monitoring of data collection and data entry, concurrent query management, checking for logical inconsistencies, and correction of database. The research proposal was firstly approved by the Institutional Review Board of the coordinating center and then confirmed by IRB of each other participating center.

Data analysis

In this study, each of the 20 participating centers (hospital) was considered a primary sampling unit (PSU) and there was no stratification of the PSU or weighting of the data. The subject (unit of analysis) was woman who delivered preterm (case) or at term (control).
Estimated prevalence (categorical variables) or means (continuous numeric variables), intracluster correlation coefficients (ICC), their respective 95% confidence intervals (CI), design effects (Deff) and mean cluster size of each variable were calculated. Software programs used for analysis were SPSS® version 20.0 [13] and Stata version 7.0 [14], taking into consideration the cluster sampling plan (centers) for data analysis.
According to Kish [2], ICC (Roh) is: ρ = (s 2 a  − s 2 b /b)/sˆ2, where s 2 a is the variance between clusters; s 2 b is the variance within clusters, b is the size of clusters and sˆ2 is the estimate of S 2 (variance in individual level). The estimate sˆ2 is obtained by: sˆ2 = s 2 a  + [(b − 1)/b]s 2 b . Stata’s equivalent computing formula for ICC [14] is: ICC = [(F − 1)a/n]/1 + (F − 1)a/n, where ‘F’ is the Snedecor’s F-value from the ANOVA table and ‘a’ is the number of groups. The variance estimate for ICC is obtained by an extensive asymptotic formula and because this it was not showed.
For this study, the Design effect - DEFF [2] is Deff = varactual(r)/varSRS(r) = s 2 a/a/s 2/n) where varactual(r) is the estimated variance according to the complex design being studied and varSRS(r) is the variance in the estimator considering the design as if it were calculated using a SRS of the same size, n.

Results

During fifteen months, 5,296 births were included in the study, 4,150 of them being preterm births (1,491 due to spontaneous preterm labor, 1,191 due to a prelabor premature rupture of membranes and 1,468 due to a therapeutic interruption of pregnancy either for a maternal or fetal condition) and a sample of 1,146 term births to be used as controls for the case–control component.
Clustering was not stratified by region. Proportionally more centers were located in the Southeast of the country and consequently over half of births were from this region (11/20 – 53.5%). The other centers were from Northeast region (7/20 – 35%), contributing with 34.8% of births studied, South region (2/20 – 10%), with 11.7% of births. The mean size of each cluster was 265 cases.

Estimated ICCs

Estimated ICCs are presented in Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 for each of 261 variables. Tables 2 and 8 show results for continuous numeric variables, while other tables present results for categorical variables or were categorized for analysis. In addition to ICC, the 95% confidence interval (CI), the design effect (Deff) and the mean cluster size (na), as well as the estimated prevalence (or mean) are presented. ICC ranged from <0.001 to 0.965, with a median of 0.028. ICC was < 0.1 in 78.5% of the variables and < 0.3 for 95% of them.
Table 1
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical maternal characteristics
Variables
P (%)
ICC
95% CI for ICC
Deff
na
Skin color (white)
43.1
0.145
0.058-0.233
42.2
265
Marital status (with a partner)
77.7
0.008
<0.001-0.016
3.2
265
Schooling (>8 years)
60.5
0.030
0.008-0.053
10.6
261
Children under 5 years (≥1)
27.1
0.005
<0.001-0.011
2.4
265
Time since last delivery (until 12 months)
8.4
0.011
<0.001-0.022
2.8
155
Previous cerclage
1.1
0.001
<0.001-0.004
1.2
264
Previous preterm birth
17.3
0.007
<0.001-0.013
3.2
264
Previous preterm birth of multiples
1.0
<0.001
<0.001-0.003
1.2
264
Previous preterm labor
7.4
0.011
0.001-0.021
4.3
264
Previous prelabor PROM
7.2
0.002
<0.001-0.006
1.8
264
Previous indicated preterm birth
7.7
0.004
<0.001-0.009
2.0
263
Previous newborn weight < 2500 g
14.8
0.010
<0.001-0.019
4.1
262
Previous chronic diseases:
   ● Chronic hypertension
8.2
0.004
<0.001-0.009
2.4
265
   ● Diabetes mellitus
2.1
0.010
0.001-0.019
3.6
265
   ● Thyroid disease
1.8
0.012
0.002-0.023
4.4
265
   ● Cardiac disease
1.3
0.002
<0.001-0.005
1.4
265
   ● Lung disease
2.9
0.006
<0.001-0.012
2.8
265
   ● Renal disease
1.8
0.013
0.002-0.024
4.7
265
   ● Digestive disease
1.3
0.009
0.001-0.018
3.3
265
   ● Hematological disease
1.4
0.012
0.002-0.023
4.7
265
   ● Neurological disease
1.2
0.008
<0.001-0.016
3.7
265
   ● Psychiatric disease
1.4
0.022
0.005-0.038
7.0
265
   ● HIV
1.3
0.006
<0.001-0.012
2.6
265
   ● Other
6.5
0.033
0.009-0.057
11.8
265
Table 2
Estimates of mean, intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for numeric maternal characteristics
Variable
Mean
ICC
95% CI for ICC
Deff
na
Age (years)
26.1
0.018
0.004-0.033
5.3
265
Month stopped working
6.9
0.015
<0.001-0.032
2.6
99
Workload (hours daily)
8.0
0.040
0.007-0.072
6.9
98
Pre-pregnancy weight (Kg)
62.1
0.021
0.005-0.038
6.6
250
Height (m)
1.6
0.041
0.011-0.071
9.8
238
Final weight (Kg)
73.2
0.022
0.005-0.040
6.4
237
Weight gain in pregnancy (Kg)
10.9
0.012
0.001-0.023
4.5
229
Initial Body Mass Index (Kg/m2)
24.4
0.012
0.001-0.024
4.5
230
Final Body Mass Index (Kg/m2)
28.7
0.016
0.002-0.030
5.5
220
Number of pregnancies
2.4
0.006
<0.001-0.013
2.8
265
Number of vaginal deliveries
0.8
0.005
<0.001-0.011
2.5
265
Number of cesarean sections
0.3
0.014
0.002-0.025
4.7
265
Number of abortions
0.3
0.006
<0.001-0.013
2.3
265
Number of uterine curettage
0.2
0.008
<0.001-0.015
2.9
264
Table 3
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for maternal socio-demographic characteristics
Variable
P (%)
ICC
95% CI for ICC
Deff
na
Household (rural)
9.8
0.097
0.034-0.159
32.9
264
Homeownership
57.5
0.041
0.012-0.070
15.2
265
Paved street
78.7
0.181
0.077-0.286
60.0
262
Piped water
94.2
0.090
0.031-0.149
30.0
263
Sewer
86.8
0.191
0.083-0.300
53.8
261
Family income (>US$ 400.00)
38.8
0.103
0.037-0.168
28.8
244
Paid work
42.6
0.036
0.010-0.063
10.8
263
Paid work in pregnancy
88.8
0.041
0.008-0.073
7.4
112
Strenuous work
43.4
0.037
0.006-0.068
5.5
99
Standing work
61.4
0.017
<0.001-0.034
2.8
99
Night work
19.5
0.033
0.004-0.061
4.3
98
Housework (alone)
50.7
0.019
0.004-0.034
7.3
265
Table 4
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical variables of process during pregnancy
Variable
P (%)
ICC
95% CI for ICC
Deff
na
Healthcare facility used for prenatal care:
    
   ● Primary health care unit
71.3
0.117
0.044-0.191
31.1
256
   ● Hospital
34.3
0.185
0.079-0.291
46.2
256
   ● Private clinic
9.3
0.051
0.015-0.086
15.9
256
   ● Other
0.3
0.005
<0.001-0.011
2.4
256
   ● Without prenatal care
3.2
0.003
<0.001-0.008
2.2
256
Prenatal care by physician
89.7
0.195
0.085-0.305
64.5
256
Start of prenatal care (1st trimester)
64.8
0.034
0.009-0.059
8.9
219
Number of prenatal care visits (≥6)
58.8
0.054
0.016-0.092
13.7
231
Ultrasound during prenatal care
98.4
0.001
<0.001-0.004
1.4
254
Physical effort
42.0
0.053
0.016-0.089
14.8
263
Depression
32.5
0.073
0.024-0.122
26.2
263
Anxiety
65.5
0.099
0.035-0.163
38.0
263
Smoking
13.5
0.020
0.004-0.036
7.7
265
Use of alcohol
15.9
0.031
0.008-0.054
10.2
263
Illicit drugs use (during or before)
4.9
0.015
0.002-0.027
5.8
265
Vaginal discharge treatment (self-reported)
36.6
0.010
0.001-0.020
4.1
264
Vulvovaginitis:
     
   ● Bacterial vaginosis
12.9
0.039
0.008-0.069
11.1
160
   ● Candidiasis
13.5
0.061
0.016-0.106
11.2
160
   ● Trychomoniasis
1.4
0.011
<0.001-0.023
4.9
160
   ● Other vulvovaginitis
0.9
0.030
0.005-0.054
6.2
160
Vulvovaginitis treatment (registered)
24.1
0.073
0.020-0.126
15.1
164
Urinary infection treatment (self-reported)
36.3
0.018
0.004-0.032
6.7
261
Urinary infection (registered)
32.9
0.032
0.008-0.057
10.0
209
   ● Asymptomatic bacteriuria
15.7
0.084
0.027-0.140
23.2
184
   ● Cystitis
7.1
0.028
0.006-0.050
7.9
184
   ● Pyelonephritis
2.0
0.003
<0.001-0.008
2.0
184
Urinary treatment (registered)
2.1
0.075
0.023-0.126
18.2
184
Periodontal infection
17.0
0.036
0.010-0.063
14.5
262
Other infection
9.1
0.019
0.004-0.035
7.5
263
   ● Unknown fever
1.8
0.024
0.006-0.043
11.1
265
   ● Diarrhea fever
0.9
0.006
<0.001-0.012
3.2
265
   ● HIV - diagnosis in pregnancy
0.6
0.002
<0.001-0.006
2.1
265
   ● Pneumonia
0.5
<0.001
<0.001-0.003
1.2
265
   ● Tuberculosis
<0.1
<0.001
<0.001-0.003
0.8
265
   ● Sinusitis/tonsillitis
3.4
0.015
0.003-0.028
6.7
265
   ● Hepatitis
0.2
0.007
<0.001-0.014
4.2
265
   ● Genital herpes
<0.1
0.001
<0.001-0.004
1.4
265
   ● Toxoplasmosis
0.5
0.009
<0 001-0.018
3.4
265
Anemia
29.2
0.046
0.013-0.078
13.4
259
Iron replacement
84.9
0.037
0.001-0.063
12.1
264
Bleeding
23.9
0.012
0.001-0.022
4.6
264
   ● Bleeding in first trimester
12.2
0.006
<0.001-0.013
2.6
264
   ● Bleeding in second trimester
6.7
0.002
<0.001-0.006
1.6
264
   ● Bleeding in third trimester
6.3
0.013
0.002-0.024
6.1
264
Table 5
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical variables of process during pregnancy
Variable
P(%)
ICC
95% CI for ICC
Deff
na
Hospitalization
22.3
0.030
0.008-0.052
10.0
265
Reasons for hospitalization:
     
   ● Emesis
0.6
0.006
0.001-0.013
2.3
264
   ● Uterine contraction
5.7
0.014
0.002-0.026
5.3
264
   ● Amniorrhexis
2.2
0.009
0.001-0.017
4.0
264
   ● Bleeding
2.6
0.008
<0.001-0.016
3.0
264
   ● Maternal disease
8.9
0.029
0.007-0.050
10.1
264
   ● Fetal disease
0.8
0.028
0.007-0.049
6.0
264
Syphilis
1.6
0.004
<0.001-0.009
1.7
265
Anemia (registered)
32.0
0.070
0.023-0.118
24.1
238
Treatment for anemia
52.6
0.283
0.138-0.428
74.8
213
Short cervix (US)
1.4
0.011
<0.001-0.022
4.0
209
Cervical insufficiency
2.1
0.005
<0.001-0.012
2.6
230
Cerclage
1.4
0.019
0.003-0.034
5.6
238
Uterine anomalies
0.6
<0.001
<0.001-0.003
0.6
237
Fibroid
1.9
0.002
<0.001-0.006
1.5
233
Maternal diseases:
     
   ● Diabetes
5.7
0.027
0.006-0.047
7.8
254
   ● Gestational hypertension
7.7
0.025
0.006-0.045
9.4
254
   ● Preeclampsia/eclampsia/HELLP
16.2
0.062
0.019-0.104
22.5
254
   ● Chronic hypertension
5.7
0.007
<0.001-0.014
2.8
254
   ● Other chronic infection
0.7
0.010
0.001-0.020
4.5
254
   ● Thyroid diseases
1.6
0.027
0.006-0.047
8.2
254
   ● Renal disease
1.2
0.008
<0.001-0.015
3.1
254
   ● Sickle cell anemia
0.3
0.002
<0.001-0.006
1.5
254
   ● Other chronic anemia
0.5
<0.001
<0.001-0.003
0.7
254
   ● Cardiac disease
1.1
0.003
<0.001-0.008
1.9
254
   ● Lung disease
1.5
0.009
<0.001-0.017
3.8
254
   ● Epilepsy
0.6
0.001
<0.001-0.004
1.5
254
   ● Systemic lupus erythematous
0.5
0.020
0.004-0.036
4.6
254
   ● Other collagenoses
0.2
0.001
<0.001-0.004
1.4
254
   ● Digestive disease
0.6
0.006
<0.001-0.013
3.1
254
   ● Bariatric surgery
<0.1
<0.001
<0.001-0.003
0.8
254
   ● Psychiatric disease
1.0
0.015
0.003-0.028
5.4
254
   ● Orthopedic disease
0.2
<0.001
<0.001-0.003
0.9
254
   ● Neoplasms
0.2
0.001
<0.001-0.004
1.4
254
   ● Thrombosis or thrombophilia
0.4
0.006
<0.001-0.013
2.4
254
Fetal malformation
5.5
0.146
0.057-0.236
35.9
246
Fetal growth restriction
9.3
0.019
0.004-0.035
6.9
246
Other fetal morbidity
7.4
0.386
0.219-0.554
101.5
246
Triplets
2.0
<0.001
<0.001-0.030
1.0
22
Infertility treatment
4.4
<0.001
<0.001-0.031
0.9
22
Multiple monochorionic pregnancy
35.8
0.046
<0.001-0.111
2.0
18
Multiple monoamniotic pregnancy
5.8
0.038
<0.001-0.098
1.9
18
Twin-to-twin transfusion syndrome
5.4
<0.001
<0.001-0.036
0.9
18
Table 6
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical variables of process during labor
Variable
P(%)
ICC
95% CI for ICC
Deff
na
Mode of onset of labor (spontaneous)
55.3
0.018
0.004-0.032
6.5
265
Intrapartum antibiotic (ATB)
51.8
0.194
0.084-0.304
71.8
260
   ● ATB for fever
0.5
0.003
<0.001-0.008
1.8
252
   ● ATB for GBS colonization
1.9
0.019
0.004-0.034
5.6
252
   ● ATB for risk factor to GBS
20.0
0.148
0.058-0.238
48.2
252
   ● ATB for other reasons
29.1
0.384
0.217-0.550
148.0
252
Analgesics during labor:
     
   ● Epidural
4.2
0.200
0.087-0.313
43.3
259
   ● Epidural plus spinal anesthesia
3.7
0.201
0.088-0.314
74.3
259
   ● Spinal anesthesia
20.1
0.338
0.181-0.495
112.8
259
   ● Meperidine
0.8
0.018
0.004-0.033
6.6
259
   ● Tramadol
0.2
0.002
<0.001-0.006
1.4
259
   ● Benzodiazepines
0.1
0.008
<0.001-0.017
3.6
259
   ● Antispasmodics
2.2
0.071
0.023-0.119
21.2
259
   ● Oral analgesics
2.0
0.091
0.031-0.150
23.0
259
   ● Other analgesics
2.4
0.102
0.036-0.168
46.6
259
Mode of delivery (vaginal)
48.8
0.024
0.006-0.043
7.7
265
Episiotomy
38.7
0.176
0.068-0.283
31.5
126
Forceps
3.9
0.056
0.014-0.099
12.9
116
Cesarean indication:
     
   ● Fetal distress
25.7
0.016
0.001-0.031
3.8
133
   ● Cephalic-pelvic disproportion
2.8
0.016
0.001-0.032
3.2
133
   ● Two or more cesarean scars
9.8
0.006
<0.001-0.014
2.0
133
   ● Pelvic or other abnormal fetal presentation
15.6
0.012
<0.001-0.025
2.9
133
   ● Functional dystocia
2.2
0.022
0.003-0.041
3.8
133
   ● Diabetes
1.8
0.013
<0.001-0.027
3.3
133
   ● Arterial hypertension
22.7
0.043
0.011-0.075
7.4
133
   ● Cardiac disease
0.6
0.009
<0.001-0.020
1.6
133
   ● HIV
1.6
0.005
<0.001-0.012
1.7
133
   ● Placenta previa
2.0
0.006
<0.001-0.014
1.6
133
   ● Abruptio placentae
4.8
0.005
<0.001-0.013
1.9
133
   ● Uterine rupture
0.1
0.006
<0.001-0.015
1.1
133
   ● Fetal malformation
3.2
0.133
0.051-0.215
18.9
133
   ● Fetal macrosomia
1.7
0.002
<0.001-0.008
1.4
133
   ● Maternal choice
1.0
0.037
0.008-0.065
7.2
133
   ● Other
17.1
0.082
0.027-0.137
14.9
133
Type of incision (segmental transverse)
96.3
0.193
0.081-0.304
13.5
126
Table 7
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical newborn outcome variables
Variable
P(%)
ICC
95% CI for ICC
Deff
na
Diagnosis of gestational age (US)
45.4
0.264
0.128-0.399
84.8
265
Stillborn
3.1
0.026
0.006-0.046
7.5
265
Intubation at delivery
13.4
0.013
0.002-0.024
4.1
248
Use of surfactant
12.6
0.015
0.002-0.027
4.4
245
Fetal malformation
9.5
0.078
0.026-0.130
19.6
246
Ventilatory support
42.6
0.041
0.011-0.070
15.1
249
Neonatal morbidity
60.3
0.126
0.047-0.205
33.4
248
   ● Sepsis
27.7
0.051
0.011-0.091
8.3
144
   ● Respiratory distress
73.4
0.061
0.014-0.107
9.9
148
   ● Pneumothorax
3.6
0.041
0.007-0.075
8.2
141
   ● Cerebral hemorrhage (1–4)
8.7
0.052
0.007-0.097
5.8
114
   ● Lung hemorrhage
3.7
0.028
0.004-0.053
5.7
143
   ● Hematologic dysfunction
51.0
0.267
0.116-0.417
71.7
146
   ● Endocrine dysfunction
22.0
0.119
0.036-0.201
30.3
145
   ● Renal dysfunction
6.4
0.013
<0.001-0.027
3.5
145
   ● Immune dysfunction
6.5
0.092
0.025-0.158
22.1
145
   ● Musculoskeletal morbidity
8.6
0.190
0.071-0.310
38.4
146
   ● Gastrointestinal dysfunction
43.2
0.340
0.168-0.512
70.6
146
   ● Hypovolemia
10.4
0.026
0.003-0.049
6.0
146
   ● Necrotizing enterocolitis
2.4
0.020
0.001-0.038
3.2
145
   ● Convulsion/anticonvulsants
4.8
0.039
0.007-0.071
6.7
146
   ● Vasoactive amines
12.2
0.019
0.001-0.037
3.5
146
   ● Pneumonia
5.6
0.118
0.036-0.200
15.6
145
   ● Oxygen therapy with 28 days
8.0
0.021
0.002-0.041
3.8
145
   ● Oxygen therapy with 56 days
2.9
0.012
<0.001-0.025
2.8
143
   ● Degree of retinopathy (1–3)
4.8
0.028
<0.001-0.056
4.2
99
Condition at discharge (live)
91.8
0.014
0.002-0.026
4.1
252
Table 8
Estimates of mean, intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for numeric newborn outcome variables
Variable
Mean
ICC
95% CI for ICC
Deff
na
Gestational age (weeks)
34.5
0.031
0.008-0.055
10.4
265
Birth weight (g)
2321.1
0.033
0.009-0.058
11.6
264
Birth weight 2° twin (g)
1905.2
0.007
<0.001-0.043
1.4
21
APGAR 1st minute
7.3
0.032
0.008-0.056
8.6
261
APGAR 1st minute 2° twin
6.7
0.042
<0.001-0.098
2.2
21
APGAR 5th minute
8.6
0.041
0.012-0.070
11.5
261
APGAR 5th minute 2° twin
8.3
0.002
<0.001-0.034
1.1
21
Head circumference (cm)
31.7
0.031
0.008-0.055
10.1
236
Head circumference 2° twin (cm)
30.8
0.018
<0.001-0.067
1.1
18
Stature (cm)
44.3
0.031
0.007-0.054
10.3
237
Stature 2° twin (cm)
42.3
0.025
<0.001-0.077
1.4
18
Length of ICU stay (days)
8.4
0.088
0.028-0.148
21.4
220
Length of hospital stay (days)
13.3
0.037
0.009-0.065
8.5
235
Age of newborn at sepsis (days)
4.6
0.173
0.054-0.292
7.2
39
Age of newborn at death (days)
8.9
0.088
<0.001-0.179
2.7
17
Table 9
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for categorical management variables in spontaneous labor conditions or preterm due to pPROM
Variable
P(%)
ICC
95% CI for ICC
Deff
na
Preterm birth due to spontaneous labor:
     
Use of corticosteroids
28.5
0.032
0.002-0.062
5.2
73
Corticosteroids (betamethasone)
86.4
0.851
0.754-0.948
18.8
21
Use of tocolytic agents
23.6
0.068
0.015-0.121
8.7
72
Association of tocolytic agents
9.9
0.368
0.167-0.570
8.4
17
Therapeutic failure of tocolysis
11.4
0.165
0.029-0.301
4.3
17
Use of magnesium sulphate (neuroprotection)
3.9
0.070
0.016-0.125
9.3
70
Use of antibiotics
42.8
0.262
0.117-0.407
28.8
72
Intravenous antibiotic
93.3
0.321
0.127-0.515
10.1
31
Association of antibiotic
15.3
0.144
0.025-0.263
12.9
30
Group B streptococcus screening
24.3
0.286
0.131-0.442
26.2
65
Preterm birth due to pPROM:
     
Use of corticosteroids
40.5
0.042
0.002-0.083
3.7
53
Corticosteroids (betamethasone)
85.0
0.965
0.941-0.990
23.5
21
Use of tocolytic agents
17.7
0.547
0.364-0.729
38.3
56
Use of antibiotics
78.2
0.233
0.095-0.371
18.3
54
Intravenous antibiotic
91.0
0.366
0.180-0.552
14.4
41
Association of antibiotic
20.9
0.245
0.093-0.397
20.1
41
Group B streptococcus screening
36.3
0.441
0.260-0.622
27.9
50
Hydration solution (saline)
11.0
0.419
0.235-0.602
20.1
52
Table 10
Estimates of prevalence (P), intracluster correlation coefficients (ICC), their respective 95% CI, design effect (Deff), and mean cluster size (n a ) for diagnosis and management among categorical variables related to therapeutic preterm delivery
Variable
P(%)
ICC
95% CI for ICC
Deff
na
Therapeutic delivery for maternal disease
74.6
0.102
0.032-0.172
9.6
73
Therapeutic delivery for fetal disease
54.1
0.065
0.016-0.115
7.0
73
Maternal disease responsible for interruption of pregnancy:
   ● Diabetes
7.3
0.063
0.011-0.115
5.5
54
   ● Gestational hypertension
12.9
0.144
0.048-0.240
9.4
54
   ● Chronic hypertension
15.3
0.009
<0.001-0.027
1.8
54
   ● Preeclampsia
58.2
0.079
0.017-0.140
5.4
54
   ● Eclampsia
3.2
0.017
<0.001-0.041
1.8
54
   ● HELLP syndrome
9.4
0.012
<0.001-0.031
1.2
54
   ● Abruptio placentae
7.7
0.009
<0.001-0.026
1.6
54
   ● Previous placentae
3.3
0.001
<0.001-0.013
0.8
54
Fetal disease responsible for interruption of pregnancy:
   
   ● Fetal distress
32.6
0.052
0.010-0.095
6.1
71
   ● Fetal growth restriction
19.8
0.037
0.004-0.069
4.4
71
   ● Malformation
5.2
0.144
0.052-0.236
13.5
71
   ● Other fetal condition
15.1
0.161
0.061-0.262
15.8
71
Exams to evaluate fetal condition:
     
   ● Cardiotocography
61.0
0.299
0.148-0.451
23.7
67
   ● Dopplerfluxometry
61.1
0.159
0.059-0.260
14.4
67
   ● Fetal biophysical profile
32.2
0.508
0.331-0.686
43.5
67
   ● Fetal movements control
4.3
0.101
0.030-0.172
12.3
67
   ● Other exam
12.9
0.041
0.005-0.077
4.1
67
Determinant exams for diagnosis:
     
   ● Cardiotocography
23.2
0.104
0.032-0.176
10.1
70
   ● Dopplerfluxometry
29.8
0.064
0.014-0.113
6.4
70
   ● Fetal biophysical profile
14.6
0.290
0.142-0.438
24.0
70
   ● Fetal echocardiography
1.2
0.030
0.001-0.058
4.6
70
   ● Maternal hepatic dysfunction
15.9
0.194
0.079-0.308
19.9
70
   ● Maternal hematologic dysfunction
21.0
0.278
0.134-0.423
29.9
70
   ● Other
41.2
0.102
0.031-0.173
10.0
70
Maternal or fetal attempted treatment
57.9
0.056
0.012-0.101
5.7
71
Use of corticosteroids
42.6
0.059
0.012-0.105
6.2
70
Maternal condition at hospital discharge (cured)
24.8
0.153
0.057-0.249
15.7
73
Tables 1 and 2 presents some variables related to maternal characteristics, including clinical and obstetrical history. ICCs ranged from <0.001 to 0.145 (median 0.011). Table 3 shows the socio-demographic variables studied, and ICC ranged from 0.017 to 0.191 (median 0.041). Tables 4 and 5 presents variables related to pregnancy characteristics with ICC ranging from 0.001 to 0.386 (median 0.015). The variables related to labor conditions were presented in Table 6. It can be observed that ICC ranged from 0.002 to 0.384, with a median of 0.022. Tables 7 and 8 shows variables related to perinatal outcomes and ICC were < 0.1 in 81% of them. The most important outcome variable, newborn morbidities, is presented in Table 7. Tables 9 and 10 present some variables analyzed specifically for preterm births and are related to management. Most variables in Table 9 showed ICC greater than 0.3 and the greatest ICC of this study (0.965) was relative to the variable “corticosteroids use”, a management aspect well defined and well-established in all obstetric protocols, so there were high degree of homogeneity in clusters in these variables. The median of ICCs was 0.274. The median ICC in Table 10 was 0.079.

Estimated deffs

Estimated Deffs are presented in Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 for each of 261 variables. Deff ranged from 0.6 to 148.0, with a median of 6.1.
Deff were under 5.0 in 74% of variables in Tables 1 and 2, ranging from 1.2 to 42.2 (median 3.65). Table 3 presents Deff values ranging from 2.8 to 60 (median 13.0). In variables related to gestational process (Tables 4 and 5), Deff values ranged from 0.6 to 101.5 (median 4.9). The variables related to labor conditions (Table 6) showed Deff ranging from 1.1 to 148 (median 6.6), with 60% of them under 8.0. In Tables 7 and 8, related to perinatal outcomes, Deff values ranged from 1.1 to 84.8 (median 7.8). Tables 9 and 10 presented Deff median of 16.35 and 7.0, respectively.
We can observe that greater Deff median is present in process variables (Table 9), and greater ICCs.

Discussion

This study presents a large number of intracluster correlation coefficients whose values can be considered low (close to zero) in most variables, showing intracluster heterogeneity.
The greater ICC values were found in process variables, especially management in spontaneous preterm labor conditions, as corticosteroids use, Group B streptococcus screening, use of tocolytic agents and use of antibiotic. Indeed, the mean ICC value for these variables was 10 times higher than the mean ICC of the study. The variable with the highest ICC was “corticosteroids – betamethasone”, with a value of 0.965. The prevalence of this variable was 85%, showing a high degree of homogeneity in this management for preterm labor. These findings are in accordance with the literature that describes ICC values generally higher for variables related to process compared to those variables related to outcome [15, 16].
In the field of maternal and perinatal healthcare, Taljaard et al. calculated ICC values based on data obtained from secondary/tertiary services [16]. Comparing with our study, they found an overall median ICC of 0.067 versus 0.028. For maternal and newborn outcome variables, their median ICCs was 0.011 (versus 0.014), and 0.054 (versus 0.041), respectively. The findings of those investigators showed that, for variables associated to process, ICC values tend to be > 0.07. The present findings are in agreement with this observation.
Pagel et al.[17] estimated ICC for a range of outcomes using data from five community-based clusters randomized controlled trials in three low-income countries. Estimated ICC values for mortality outcomes were lower than those for process outcomes, with narrower confidence intervals throughout for trials with larger number of clusters.
All comparisons show that the smaller the cluster size, the higher the ICC and the opposite occurs regarding the prevalence of the condition. Estimates of intracluster correlation are much less reliable for rare outcomes and the size of the cluster had a greater impact than the number of clusters on the reliability of estimates for rare outcomes [17].
Furthermore, higher healthcare levels tend to increase the degree of homogeneity [18, 19]. The size of ICC increases if the ICC represents data from secondary rather than primary care. This may be a reflection of the underlying heterogeneity of the datasets under consideration as the conditions represented across the different datasets were diverse. Although numerically small (average 0.01), such differences can have a substantial effect on sample size, even when the average of cluster is small [15]. The clusters in this study are secondary and tertiary hospitals, most of them are teaching hospitals, with the majority of procedures performed in conformity with evidence-based healthcare protocols.
Stratified randomization had the effect of reducing estimates of cluster correlation [15]. However, in the same way that in Brazilian Network for Surveillance of Severe Maternal Morbidity Study [20], which found ICC values close to zero, the selection of clusters did not performed stratification by region. The distribution of centers in this study, with almost half located in southeast region, is in accordance with the actual distribution of healthcare institutions and the proportionality of births per region in the country [21, 22].
The large number of intracluster correlation coefficients presented in this study, considered low (close to zero) in most of variables, can probably be seen as a good parameter of variance for calculating sample size in new studies in the field of perinatal and maternal health [15].
We can, however, to identify some possible limitations of the study, including the fact that we used a non-probabilistic sample from the centers (hospitals). Therefore, strictly speaking, the findings cannot be generalized to other populations. However, the majority of hospitals included in the study was third level referral hospitals taking care of high risk pregnancies and preterm babies. Probably the results would be applicable to other centers with such characteristics, irrespective of being private or public, especially in middle income countries like Brazil.

Conclusions

The Brazilian Multicenter Study on Preterm Birth, developed as part of the Brazilian Network for Studies on Reproductive and Perinatal Health, to the best of our knowledge is the first cross sectional multicenter study on this topic in the country. It represents a planned comprehensive assessment of preterm birth in Brazil and ICC values calculation and analysis of more than 250 maternal and newborn variables, showed heterogeneity of data in selected clusters. These findings increase reliability of study estimates and allow the use of these results to calculate the required sample size for future research studies in maternal and perinatal health.

Acknowledgements

The authors thank the CNPq (Brazilian National Research Council) and Fapesp (Foundation for Support to Research of the State of Sao Paulo) for the financial sponsorship of this study, Process Fapesp 2009/53245-5 (Call AP.PPSUS-1). This publication was also sponsored by Fapesp (Processo 2014/08183-0)
The Brazilian Multicenter Study on Preterm Birth study group: Sergio T Marba, Ruth Guinsburg, Francisco E Martinez, Vilma Zotarelli, Lucio T Gurgel, Francisco E Feitosa, George N Chaves, Ana M Porto, Isabela C Coutinho, Antonio C Barbosa Lima, Elias F Melo Jr, Débora F Leite, Melania M Amorim, Adriana SO Melo, Fabiana O Melo, Marília G Martins, Marynea V Nunes, Cláudio S Paiva, Moises D Lima, Djacyr M Freire, Edson G Tristão, Denis J Nascimento, Renato T Souza, Carlos A Menezes, Marcelo Aquino, Janete Vettorazzi, Cintia E Senger, Augusta MB Assumpção, Marcela AF Guedes, Maria EL Moreira, Vera T Borges, Nelson L Maia Filho, Jacinta P Mathias, Eduardo Souza, Ana CP Zamarian, Silvana M Quintana, Patrícia PS Melli, Fátima A Lotufo, Kaliane Uzilin, Elvira A Zanette, Carla B Andreucci, Tenilson A Oliveira, Laércio R Oliveira, Nelson Sass, Mirian RF Silveira, Pedro R Coutinho, Luciana Siqueira.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

The idea for the study first arose in a discussion among RPJ, JGC and RPT and then was developed and implemented by the whole research team. The first version of the manuscript was drafted by GJL and SMH, and then complemented with the suggestions of the others. RPJ and JGC supervised the entire process. All authors contributed to the development of the study protocol and approved the final version of manuscript.
Literatur
1.
Zurück zum Zitat Ukoumunne OC, Gulliford MC, Chinn S, Sterne JA, Burney PG: Methods for evaluating area-wide and organisation-based interventions in health and health care: a systematic review. Health Technol Assess. 1999, 3 (5): iii-92-PubMed Ukoumunne OC, Gulliford MC, Chinn S, Sterne JA, Burney PG: Methods for evaluating area-wide and organisation-based interventions in health and health care: a systematic review. Health Technol Assess. 1999, 3 (5): iii-92-PubMed
2.
Zurück zum Zitat Kish L: Survey sampling. 1965, New York: John Wiley & Sons Kish L: Survey sampling. 1965, New York: John Wiley & Sons
3.
Zurück zum Zitat Donner A, Koval JJ: Design considerations in the estimation of the intraclass correlation. Ann Hum Genet. 1982, 46: 271-277. 10.1111/j.1469-1809.1982.tb00718.x.CrossRefPubMed Donner A, Koval JJ: Design considerations in the estimation of the intraclass correlation. Ann Hum Genet. 1982, 46: 271-277. 10.1111/j.1469-1809.1982.tb00718.x.CrossRefPubMed
4.
Zurück zum Zitat Campbell MK, Grimshaw JM, Elbourne DR: Intracluster correlation coefficients in cluster randomised trials: empirical insights into how should they be reported. BMC Med Res Methodol. 2009, 4: 9-CrossRef Campbell MK, Grimshaw JM, Elbourne DR: Intracluster correlation coefficients in cluster randomised trials: empirical insights into how should they be reported. BMC Med Res Methodol. 2009, 4: 9-CrossRef
5.
Zurück zum Zitat Killip S, Mahfoud Z, Pearce K: What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Ann Fam Med. 2004, 2: 204-208. 10.1370/afm.141.CrossRefPubMedPubMedCentral Killip S, Mahfoud Z, Pearce K: What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Ann Fam Med. 2004, 2: 204-208. 10.1370/afm.141.CrossRefPubMedPubMedCentral
6.
Zurück zum Zitat Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ: Patterns of intra-cluster correlation from primary care research to inform study design and analysis. J Clin Epidemiol. 2004, 57: 785-794. 10.1016/j.jclinepi.2003.12.013.CrossRefPubMed Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ: Patterns of intra-cluster correlation from primary care research to inform study design and analysis. J Clin Epidemiol. 2004, 57: 785-794. 10.1016/j.jclinepi.2003.12.013.CrossRefPubMed
7.
Zurück zum Zitat Hayes R, Moulton LH: Cluster Randomised Controlled Trials. 2009, New York: Chapman &Hall/CRC PressCrossRef Hayes R, Moulton LH: Cluster Randomised Controlled Trials. 2009, New York: Chapman &Hall/CRC PressCrossRef
9.
Zurück zum Zitat Hayes RJ, Benett S: Simple size calculation for cluster-randomised trials. Int J Epidemiol. 1999, 28 (2): 319-326. 10.1093/ije/28.2.319.CrossRefPubMed Hayes RJ, Benett S: Simple size calculation for cluster-randomised trials. Int J Epidemiol. 1999, 28 (2): 319-326. 10.1093/ije/28.2.319.CrossRefPubMed
10.
Zurück zum Zitat Eldridge SM, Ukoumunne OC, Carlin JB: The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions. Int Stat Rev. 2009, 77 (3): 378-394. 10.1111/j.1751-5823.2009.00092.x.CrossRef Eldridge SM, Ukoumunne OC, Carlin JB: The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions. Int Stat Rev. 2009, 77 (3): 378-394. 10.1111/j.1751-5823.2009.00092.x.CrossRef
11.
Zurück zum Zitat Passini R, Tedesco RP, Marba ST, Cecatti JG, Guinsburg R, Martinez FE, Nomura ML, Brazilian Network of Studies on Reproductive and Perinatal Health: Brazilian multicenter study on prevalence of preterm birth and associated factors. BMC Pregnancy Childbirth. 2010, 10: 22-10.1186/1471-2393-10-22.CrossRefPubMedPubMedCentral Passini R, Tedesco RP, Marba ST, Cecatti JG, Guinsburg R, Martinez FE, Nomura ML, Brazilian Network of Studies on Reproductive and Perinatal Health: Brazilian multicenter study on prevalence of preterm birth and associated factors. BMC Pregnancy Childbirth. 2010, 10: 22-10.1186/1471-2393-10-22.CrossRefPubMedPubMedCentral
12.
Zurück zum Zitat Tedesco RP, Passini R, Cecatti JG, Camargo RS, Pacagnella RC, Sousa MH: Estimation of preterm birth rate, associated factors and maternal morbidity from a Demographic and Health Survey in Brazil. Matern Child Health J. 2013, 17 (9): 1638-1647. 10.1007/s10995-012-1177-6.CrossRefPubMed Tedesco RP, Passini R, Cecatti JG, Camargo RS, Pacagnella RC, Sousa MH: Estimation of preterm birth rate, associated factors and maternal morbidity from a Demographic and Health Survey in Brazil. Matern Child Health J. 2013, 17 (9): 1638-1647. 10.1007/s10995-012-1177-6.CrossRefPubMed
13.
Zurück zum Zitat SPSS for Windows: Release 20.0. 2011, New York: SPSS Inc SPSS for Windows: Release 20.0. 2011, New York: SPSS Inc
14.
Zurück zum Zitat StataCorp: College Station. Stata Statistical Software: Release 7. 2001, College Station, TX: StataCorp LP StataCorp: College Station. Stata Statistical Software: Release 7. 2001, College Station, TX: StataCorp LP
15.
Zurück zum Zitat Campbell MK, Fayers PM, Grimshaw JM: Determinants of the intracluster correlation coefficient in cluster randomised trials: the case of implementation research. Clin Trials. 2005, 2: 99-107. 10.1191/1740774505cn071oa.CrossRefPubMed Campbell MK, Fayers PM, Grimshaw JM: Determinants of the intracluster correlation coefficient in cluster randomised trials: the case of implementation research. Clin Trials. 2005, 2: 99-107. 10.1191/1740774505cn071oa.CrossRefPubMed
16.
Zurück zum Zitat Taljaard M, Donner A, Villar J, Wojdyla D, Velazco A, Bataglia V, Bataglia V, Faundes A, Langer A, Narváez A, Valladares E, Carroli G, Zavaleta N, Shah A, Campodónico L, Romero M, Reynoso S, de Pádua KS, Giordano D, Kublickas M, Acosta A, World Health Organization 2005 Global Survey on Maternal and Perinatal Health Research Group: Intracluster correlation coefficients from the WHO Global Survey on Maternal and Perinatal Health: implications for implementation research. Paediat Perinatal Epidemiol. 2005, 2008 (22): 117-125. Taljaard M, Donner A, Villar J, Wojdyla D, Velazco A, Bataglia V, Bataglia V, Faundes A, Langer A, Narváez A, Valladares E, Carroli G, Zavaleta N, Shah A, Campodónico L, Romero M, Reynoso S, de Pádua KS, Giordano D, Kublickas M, Acosta A, World Health Organization 2005 Global Survey on Maternal and Perinatal Health Research Group: Intracluster correlation coefficients from the WHO Global Survey on Maternal and Perinatal Health: implications for implementation research. Paediat Perinatal Epidemiol. 2005, 2008 (22): 117-125.
17.
Zurück zum Zitat Pagel C, Prost A, Lewycka S, Das S, Colbourn T, Mahapatra R, Azad K, Costello A, Osrin D: Intracluster correlation coefficients and coefficients of variation for perinatal outcomes from five cluster-randomised controlled trials in low and middle-income countries: results and methodological implications. Trials. 2011, 12: 151-10.1186/1745-6215-12-151.CrossRefPubMedPubMedCentral Pagel C, Prost A, Lewycka S, Das S, Colbourn T, Mahapatra R, Azad K, Costello A, Osrin D: Intracluster correlation coefficients and coefficients of variation for perinatal outcomes from five cluster-randomised controlled trials in low and middle-income countries: results and methodological implications. Trials. 2011, 12: 151-10.1186/1745-6215-12-151.CrossRefPubMedPubMedCentral
18.
Zurück zum Zitat Piaggio G, Carroli G, Villar J, Pinol A, Bakketeig L, Lumbiganon P, Bergsjø P, Al-Mazrou Y, Ba'aqeel H, Belizán JM, Farnot U, Berendes H, WHO Antenatal Care Trial Research Group: Methodological considerations on the design and analysis of an equivalence stratified cluster randomization trial. Stat Med. 2001, 20: 401-416. 10.1002/1097-0258(20010215)20:3<401::AID-SIM801>3.0.CO;2-1.CrossRefPubMed Piaggio G, Carroli G, Villar J, Pinol A, Bakketeig L, Lumbiganon P, Bergsjø P, Al-Mazrou Y, Ba'aqeel H, Belizán JM, Farnot U, Berendes H, WHO Antenatal Care Trial Research Group: Methodological considerations on the design and analysis of an equivalence stratified cluster randomization trial. Stat Med. 2001, 20: 401-416. 10.1002/1097-0258(20010215)20:3<401::AID-SIM801>3.0.CO;2-1.CrossRefPubMed
19.
Zurück zum Zitat Campbell SM, Braspenning J, Hutchinson A, Marshall M: Research methods used in developing and applying quality indicators in primary care. Qual Safe Health Care. 2002, 11: 358-364. 10.1136/qhc.11.4.358.CrossRef Campbell SM, Braspenning J, Hutchinson A, Marshall M: Research methods used in developing and applying quality indicators in primary care. Qual Safe Health Care. 2002, 11: 358-364. 10.1136/qhc.11.4.358.CrossRef
20.
Zurück zum Zitat Haddad SM, Sousa MH, Cecatti JG, Parpinelli MA, Costa ML, Souza JP, and for the Brazilian Network for Surveillance of Severe Maternal Morbidity Group: Intraclass correlation coefficients in the Brazilian network for surveillance of severe maternal morbidity study. BMC Pregnancy Childbirth. 2012, 12: 101-10.1186/1471-2393-12-101.CrossRefPubMedPubMedCentral Haddad SM, Sousa MH, Cecatti JG, Parpinelli MA, Costa ML, Souza JP, and for the Brazilian Network for Surveillance of Severe Maternal Morbidity Group: Intraclass correlation coefficients in the Brazilian network for surveillance of severe maternal morbidity study. BMC Pregnancy Childbirth. 2012, 12: 101-10.1186/1471-2393-12-101.CrossRefPubMedPubMedCentral
21.
Zurück zum Zitat Brazilian Ministry of Health: National Registry of Health Facilities. 2013, Available at http://cnes.datasus.gov.br/Lista_Tot_Es_Estado.asp. Accessed on November 27th, 2013 Brazilian Ministry of Health: National Registry of Health Facilities. 2013, Available at http://​cnes.​datasus.​gov.​br/​Lista_​Tot_​Es_​Estado.​asp.​ Accessed on November 27th, 2013
22.
Zurück zum Zitat Brazilian Ministry of Health: SINASC – National Information System on Live Births. 2011, Available at http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sinasc/cnv/nvuf.def. Accessed on November 27th, 2013 Brazilian Ministry of Health: SINASC – National Information System on Live Births. 2011, Available at http://​tabnet.​datasus.​gov.​br/​cgi/​tabcgi.​exe?​sinasc/​cnv/​nvuf.​def.​ Accessed on November 27th, 2013
Metadaten
Titel
Intracluster correlation coefficients for the Brazilian Multicenter Study on Preterm Birth (EMIP): methodological and practical implications
verfasst von
Giuliane J Lajos
Samira M Haddad
Ricardo P Tedesco
Renato Passini Jr
Tabata Z Dias
Marcelo L Nomura
Patrícia M Rheder
Maria H Sousa
Jose G Cecatti
for the Brazilian Multicenter Study on Preterm Birth study group
Publikationsdatum
01.12.2014
Verlag
BioMed Central
Erschienen in
BMC Medical Research Methodology / Ausgabe 1/2014
Elektronische ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-14-54

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