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

  • Giuliane J Lajos1Email author,
  • Samira M Haddad1,
  • Ricardo P Tedesco1,
  • Renato Passini Jr1,
  • Tabata Z Dias1,
  • Marcelo L Nomura1,
  • Patrícia M Rheder1,
  • Maria H Sousa2,
  • Jose G Cecatti1, 2 and
  • for the Brazilian Multicenter Study on Preterm Birth study group
BMC Medical Research MethodologyBMC series ¿ open, inclusive and trusted201414:54

DOI: 10.1186/1471-2288-14-54

Received: 27 November 2013

Accepted: 14 April 2014

Published: 22 April 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.

Keywords

Intracluster correlation coefficient Preterm birth Spontaneous preterm labor Premature rupture of membranes Indicated preterm delivery Neonatal morbidity

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 = (s2 a  − s2 b /b)/sˆ2, where s2 a is the variance between clusters; s2 b is the variance within clusters, b is the size of clusters and sˆ2 is the estimate of S2 (variance in individual level). The estimate sˆ2 is obtained by: sˆ2 = s2 a  + [(b − 1)/b]s2 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) = s2a/a/s2/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.

Abbreviations

CRF: 

Clinical research form

Deff: 

Design effect

ICC: 

Intraclass correlation coefficient

SRS: 

Simple random sampling.

Declarations

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.

Authors’ Affiliations

(1)
Department of Obstetrics and Gynecology, School of Medical Sciences, University of Campinas
(2)
Center for Studies in Reproductive Health of Campinas (Cemicamp)

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© Lajos et al.; licensee BioMed Central Ltd. 2014

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.

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