Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool.

BACKGROUND: Perioperative mortality rate is regarded as a credible quality and safety indicator of perioperative care, but its documentation in low- and middle-income countries is poor. We developed and tested an electronic, provider report-driven method in an East African country. METHODS: We deployed a data collection tool in a Kenyan tertiary referral hospital that collects case-specific perioperative data, with asynchronous automatic transmission to central servers. Cases not captured by the tool (nonobserved) were collected manually for the last two quarters of the data collection period. We created logistic regression models to analyze the impact of procedure type on mortality. RESULTS: Between January 2014 and September 2015, 8,419 cases out of 11,875 were captured. Quarterly data capture rates ranged from 423 (26%) to 1,663 (93%) in the last quarter. There were 93 deaths (1.53%) reported at 7 days. Compared with four deaths (0.53%) in cesarean delivery, general surgery (n = 42 [3.65%]; odds ratio = 15.80 [95% CI, 5.20 to 48.10]; P < 0.001), neurosurgery (n = 19 [2.41%]; odds ratio = 14.08 [95% CI, 4.12 to 48.10]; P < 0.001), and emergency surgery (n = 25 [3.63%]; odds ratio = 4.40 [95% CI, 2.46 to 7.86]; P < 0.001) carried higher risks of mortality. The nonobserved group did not differ from electronically captured cases in 7-day mortality (n = 1 [0.23%] vs. n = 16 [0.58%]; odds ratio =3.95 [95% CI, 0.41 to 38.20]; P = 0.24). CONCLUSIONS: We created a simple solution for high-volume, prospective electronic collection of perioperative data in a lower- to middle-income setting. We successfully used the tool to collect a large repository of cases from a single center in Kenya and observed mortality rate differences between surgery types.

Recruitment methods for survey research: Findings from the Mid-South Clinical Data Research Network.

PURPOSE: The objective of this study was to report survey response rates and demographic characteristics of eight recruitment approaches to determine acceptability and effectiveness of large-scale patient recruitment among various populations. METHODS: We conducted a cross sectional analysis of survey data from two large cohorts. Patients were recruited from the Mid-South Clinical Data Research Network using clinic-based recruitment, research registries, and mail, phone, and email approaches. Response rates are reported as patients who consented for the survey divided by the number of eligible patients approached. RESULTS: We contacted more than 90,000 patients and 13,197 patients completed surveys. Median age was 56.3years (IQR 40.9, 67.4). Racial/ethnic distribution was 84.1% White, non-Hispanic; 9.9% Black, non-Hispanic; 1.8% Hispanic; and 4.0% other, non-Hispanic. Face-to-face recruitment had the highest response rate of 94.3%, followed by participants who "opted-in" to a registry (76%). The lowest response rate was for unsolicited emails from the clinic (6.1%). Face-to-face recruitment enrolled a higher percentage of participants who self-identified as Black, non-Hispanic compared to other approaches (18.6% face-to-face vs. 8.4% for email). CONCLUSIONS: Technology-enabled recruitment approaches such as registries and emails are effective for recruiting but may yield less racial/ethnic diversity compared to traditional, more time-intensive approaches.