ArticleCureus2020
Influenza: National Trends Using the National Inpatient Sample Database from 1993 to 2015.
Article in Cureus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background There is a significant impact of influenza on individuals, families, and societies both economically and clinically. This significant impact is a result of hospital admissions, medication expenses, side effects, secondary bacterial infections, and more days off from work or other forms of reduced productivity for the patients or their caretakers. Our objective is to present the trends in the rate of hospital discharges per 100,000 population from the years 1993 through 2015, the mean age, and the inpatient mortality rate. Methods This is a retrospective study utilizing the National Inpatient Sample (NIS) from 1993 through 2015. Discharges due to influenza from 1993 to 2015 were identified, and the rate of discharges per 100,000 population, inpatient mortality, and mean age of discharged patients were trended. Linear regression was used to assess if the deviation from horizontal was statistically significant for the trends of the rate of discharges per 100,000 population, mean age, and percentage of the inpatient mortality. Result The mean age and inpatient mortality vary from year to year. The linear regression analysis for the trends was not statistically significant, and for the percentage of the inpatient mortality, the deviation from horizontal was not significant, P-value 0.75 and F-value: 0.09. Similarly, for the mean age, the deviation from horizontal was not significant with a P-value of 0.97 and an F-value of 0.001. However, the linear regression analysis for the rate of discharges per 100,000 population was remarkable for a statistically significant deviation from the horizontal with a P-value of 0.0002 and an F value of 19.5. Conclusion Recent advancements in influenza detection have made the detection feasible, quick, and cost-effective. However, the role of these advanced modalities on the outcome is still controversial. Our analysis revealed a significant increase in the rate of discharges due to influenza, but there was no significant change in the parentage of the inpatient mortality over the years between 1993 - 2015. Advanced influenza virus detection tests are now recommended in both outpatient (including emergency department) and inpatient admissions. The recent increase in inpatient admissions could be due to better detection modalities. However, no change in the percentage of inpatient mortality makes the impact of these detection tests on the outcome questionable. A further prospective study is warranted to assess the impact of these tests on the outcome.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.