Many observational studies of causal effects occur in settings with clustered treatment assignment. In studies of this type, treatment is applied to entire clusters of units. For example, an educational intervention might be administered to all the students in a school. We develop a matching algorithm for multilevel data based on a network flow algorithm. Earlier work on multilevel matching relied on integer programming, which allows for balance targeting on specific covariates but can be slow with larger data sets. Although we cannot directly specify minimal levels of balance for individual covariates, our algorithm is fast and scales easily to larger data sets. We apply this algorithm to assess a school-based intervention through which students in treated schools were exposed to a new reading program during summer school. In one variant of the algorithm, where we match both schools and students, we change the causal estimand through optimal subset matching to better maintain common support. In a second variant, we relax the common support assumption to preserve the causal estimand by only matching on schools. We find that the summer intervention does not appear to increase reading test scores. In a sensitivity analysis, however, we determine that an unobserved confounder could easily mask a larger treatment effect.

Instrumental variable methods, subject to appropriate identification assumptions, enable consistent estimation of causal effects in the presence of unobserved confounding. Near–far matching has been proposed as one analytic method to improve inference by strengthening the effect of the instrument on the exposure and balancing observable characteristics between groups of subjects with low and high values of the instrument. However, in settings with hierarchical data (e.g. patients nested within hospitals), or where several covariate interactions must be balanced, conventional near–far matching algorithms may fail to achieve the requisite covariate balance. We develop a new matching algorithm, that combines near–far matching with refined covariate balance, to balance large numbers of nominal covariates while also strengthening the instrumental variable. This extension of near–far matching is motivated by a case-study that aims to identify the causal effect of prompt admission to an intensive care unit on 7-day and 28-day mortality.

Objective

Delayed antimicrobial therapy in sepsis is associated with increased hospital mortality, but the impact of antimicrobial timing on long-term outcomes is unknown. We tested the hypothesis that hourly delays to antimicrobial therapy are associated with 1-year mortality in pediatric severe sepsis.

Design

Retrospective observational study.

Setting

Quaternary academic pediatric intensive care unit (PICU) from February 1, 2012 to June 30, 2013.

Patients

One hundred sixty patients aged ≤21 years treated for severe sepsis.

Interventions

None.

Measurements and main results

We tested the association of hourly delays from sepsis recognition to antimicrobial administration with 1-year mortality using multivariable Cox and logistic regression. Overall 1-year mortality was 24% (39 patients), of whom 46% died after index PICU discharge. Median time from sepsis recognition to antimicrobial therapy was 137 min (IQR 65-287). After adjusting for severity of illness and comorbid conditions, hourly delays up to 3 h were not associated with 1-year mortality. However, increased 1-year mortality was evident in patients who received antimicrobials ≤1 h (aOR 3.8, 95% CI 1.2, 11.7) or >3 h (aOR 3.5, 95% CI 1.3, 9.8) compared with patients who received antimicrobials within 1 to 3 h from sepsis recognition. For the subset of patients who survived index PICU admission, antimicrobial therapy ≤1 h was also associated with increased 1-year mortality (aOR 5.5, 95% CI 1.1, 27.4), while antimicrobial therapy >3 h was not associated with 1-year mortality (aOR 2.2, 95% CI 0.5, 11.0).

Conclusions

Hourly delays to antimicrobial therapy, up to 3 h, were not associated with 1-year mortality in pediatric severe sepsis in this study. The finding that antimicrobial therapy ≤1 h from sepsis recognition was associated with increased 1-year mortality should be regarded as hypothesis-generating for future studies.