Data Improvement Through Simplification: Implications for Low-Resource Settings

Background

The focus of many data collection efforts centers on creation of more granular data. The assumption is that more complex data are better able to predict outcomes. We hypothesized that data are often needlessly complex. We sought to demonstrate this concept by examination of the American Society of Anesthesiologists (ASA) scoring system.

Methods

First, we created every possible consecutive two, three and four category combinations of the current five category ASA score. This resulted in 14 combinations of simplified ASA. We compared the predictive ability of these simplified scores for postoperative outcomes for 2.3 million patients in the NSQIP database. Individual model performance was assessed by comparing receiver operator characteristic (ROC) curves for each model with the standard ASA.

Results

Two of our 4-category models and one of our 3-category models had ability to predict all outcomes equivalent to standard ASA. These results held for all outcomes and on all subgroups tested. The performance of the three best performing simplified ASA scores were also equivalent to the standard ASA score in the univariate analysis and when included in a multivariate model.

Conclusions

It is assumed that the most granular data and use of the largest number of variables for risk-adjusted predictions will increase accuracy. This complexity is often at the expense of utility. Using the single best predictor in surgical outcomes research, we have shown this is not the case. In this example, we demonstrate that one can simplify ASA into a 3-category variable without losing any ability to predict outcomes.

     

Validation of an Electronic Surgical Outcomes Database at Mbarara Regional Referral Hospital,Uganda

Background

Accurate, complete and sustainable methods of tracking patients and outcomes in low-resource settings are imperative as we launch efforts to improve surgical care globally. The Surgical services QUality Assessment Database (SQUAD) at the Mbarara Regional Referral Hospital in Uganda is one of very few electronic surgical databases in a low-resource setting. We evaluated the completeness and accuracy of SQUAD.

Methods

Data were prospectively collected on 20 of the most clinically relevant variables captured by SQUAD for all general surgery patients admitted to MRRH over a two-week period. Patients were followed until discharge, death or hospital day 30; whichever occurred first. These data were compared to that in SQUAD for the same time period for completeness and accuracy.

Results

Of 186 unique patients seen over the two-week period, 172 (92.5%) were captured by SQUAD. The capture rate was greater than 86% for each of the 20 variables evaluated, except American Society of Anesthesiologists score, which had a 69% capture rate. Regarding accuracy, there was almost perfect agreement for 16/20 variables (all k > 0.81), substantial agreement for 2/20 variables (k 0.63, 0.73) and moderate agreement for the remaining 2/20 variables (k 0.43, 0.48) between SQUAD and the prospectively collected data.

Conclusion

SQUAD is an electronic surgical database that has been implemented and sustained in a low-resource setting. For the 20 variables evaluated, the data within SQUAD are highly complete and accurate. This database may serve as a model for the development of additional surgical databases in low-resource environments.

     

Infant Mortality in Albania

During the post-war period in Albania, the infant mortalityrate (IMR) was reduced substantially from 121 in 1950 to about30 in the mid-1980s. The IMR is now increasing. In 1991, theestimated IMR was 34. Several factors account for the increase:a worsening economic situation; a high prevalence of malnutrition;the former pronatalist policies prohibiting family planning;and the breakdown of the system of maternal and child care.These problems are manifest in the leading causes of infantdeath in Albania: respiratory infections; congenital anomalies;and diarrhoeal diseases. There is an urgent need for short-term,emergency programmes aimed at (1) improving nutritional status;and (2) preventing and treating respiratory infections and diarrhoea1disease in infants. Medium and long-range programmes are alsoneeded to strengthen the already existing infrastructure forprimary maternal and child health care and family planning.