Busseola fusca (Fuller) (Lepidoptera: Noctuidae) is a major pest of maize and sorghum in many countries of tropical Africa. Hitherto, research on this important pest has been hampered by the occurrence of a six‐month long diapause in the last larval stage and the lack of an artificial diet for rearing the insect in the laboratory. Incorporating 4 to 8‐week‐old sorghum powder in a nutritionally adequate diet and rearing larvae individually in vials at ambient laboratory conditions (25–30 °C, 50–80% r.h., and L12: D12) have made it possible to rear 15 successive non‐diapausing generations of B. fusca capable of producing between 35 to 40 healthy pupae/litre of diet and upto 70% pupation without loss of vigour or reproductive capacity. Five to six generations were completed per year and the overall mean developmental period (egg‐egg) was 68 days (egg 6, larval 45, pre‐pupal 1, pupal 14 and pre‐oviposition 2 days). Larval period lasted 70 days in the first generation compared to 32.3 days in the fifteenth generation. Average fecundity increased from 158.0 to 394.6 eggs per female with a concomitant increase of egg hatch from 44.8 to 79.6% in the first and fifteenth generation, respectively.

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BackgroundRoutine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.MethodsRDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.Results27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).ConclusionsThis manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.

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