Intracluster correlation coefficients from the 2005 WHO Global Survey on Maternal and Perinatal Health: implications for implementation research
Why this work is in the frame
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Bibliographic record
Abstract
Cluster-based studies involving aggregate units such as hospitals or medical practices are increasingly being used in healthcare evaluation. An important characteristic of such studies is the presence of intracluster correlation, typically quantified by the intracluster correlation coefficient (ICC). Sample size calculations for cluster-based studies need to account for the ICC, or risk underestimating the sample size required to yield the desired levels of power and significance. In this article, we present values for ICCs that were obtained from data on 97,095 pregnancies and 98,072 births taking place in a representative sample of 120 hospitals in eight Latin American countries. We present ICCs for 86 variables measured on mothers and newborns from pregnancy to the time of hospital discharge, including 'process variables' representing actual medical care received for each mother and newborn. Process variables are of primary interest in the field of implementation research. We found that overall, ICCs ranged from a minimum of 0.0003 to a maximum of 0.563 (median 0.067). For maternal and newborn outcome variables, the median ICCs were 0.011 (interquartile range 0.007-0.037) and 0.054 (interquartile range 0.013-0.075) respectively; however, for process variables, the median was 0.161 (interquartile range 0.072-0.328). Thus, we confirm previous findings that process variables tend to have higher ICCs than outcome variables. We demonstrate that ICCs generally tend to increase with higher prevalences (close to 0.5). These results can help researchers calculate the required sample size for future research studies in maternal and perinatal health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it