Potential Impact of Co-Infections and Co-Morbidities Prevalent in Africa on Influenza Severity and Frequency: A Systematic Review

<div><p>Infectious diseases and underlying medical conditions common to Africa may affect influenza frequency and severity. We conducted a systematic review of published studies on influenza and the following co-infections or co-morbidities that are prevalent in Africa: dengue, malaria, measles, meningococcus, <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies, and malnutrition. Articles were identified except for influenza and PCP. Very few studies were from Africa. Sickle cell disease, dengue, and measles co-infection were found to increase the severity of influenza disease, though this is based on few studies of dengue and measles and the measles study was of low quality. The frequency of influenza was increased among patients with sickle cell disease. Influenza infection increased the frequency of meningococcal disease. Studies on malaria and malnutrition found mixed results. Age-adjusted morbidity and mortality from influenza may be more common in Africa because infections and diseases common in the region lead to more severe outcomes and increase the influenza burden. However, gaps exist in our knowledge about these interactions.</p></div

Table_1_Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach.DOCX

IntroductionMeasurement error in gestational age (GA) may bias the association of GA with a health outcome. Ultrasound-based GA is considered the gold standard and is not readily available in low-resource settings. We corrected for measurement error in GA based on fundal height (FH) and date of last menstrual period (LMP) using ultrasound from the sub-cohort and adjusted for the bias in associating GA with neonatal mortality and low birth weight (MethodsWe used data collected from 01/2015 to 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (N = 2,750). We used regression calibration to correct for measurement error in FH- and LMP-based GA accounting for maternal and child characteristics. We applied logistic regression to associate GA with neonatal mortality and low birth weight, with and without calibrating FH- and LMP-based GA.ResultsCalibration improved the precision of LMP (correlation coefficient, ρ from 0.48 to 0.57) and FH-based GA (ρ from 0.82 to 0.83). Calibrating FH/LMP-based GA eliminated the bias in the mean GA estimates. The log odds ratio that quantifies the association of GA with neonatal mortality increased by 29% (from −0.159 to −0.205) by calibrating FH-based GA and by more than twofold (from −0.158 to −0.471) by calibrating LMP-based GA.ConclusionCalibrating FH/LMP-based GA improved the accuracy and precision of GA estimates and strengthened the association of GA with neonatal mortality/LBW. When assessing GA, neonatal public health and clinical interventions may benefit from calibration modeling in settings where ultrasound may not be fully available.</p

Summary of studies evaluating influenza vaccine among individuals with hemoglobinopathies.

<p>HI = hemagglutinin inhibition.</p><p>Seroprotection was defined as >1:40 HI titers.</p

PRISMA flow diagram for systematic reviews of influenza and dengue (D), malaria (MR), measles (MS), meningococcus (MN), <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies (H), and malnutrition (MT).

<p>PRISMA flow diagram for systematic reviews of influenza and dengue (D), malaria (MR), measles (MS), meningococcus (MN), <i>Pneumocystis jirovecii</i> pneumonia (PCP), hemoglobinopathies (H), and malnutrition (MT).</p

Associations of co-infections and co-morbidities prevalent in Africa with increased severity or incidence of influenza.

<p>Applicable references from the systematic review and the literature are included.</p><p>Associations of co-infections and co-morbidities prevalent in Africa with increased severity or incidence of influenza.</p

Influenza Illness among Case-Patients Hospitalized for Suspected Dengue, El Salvador, 2012

<div><p>We estimate the proportion of patients hospitalized for suspected dengue that tested positive for influenza virus in El Salvador during the 2012 influenza season. We tested specimens from 321 hospitalized patients: 198 patients with SARI and 123 patients with suspected dengue. Among 121 hospitalized suspected dengue (two co-infected excluded) patients, 28% tested positive for dengue and 19% positive for influenza; among 35 with suspected dengue and respiratory symptoms, 14% were positive for dengue and 39% positive for influenza. One percent presented co-infection between influenza and dengue. Clinicians should consider the diagnosis of influenza among patients with suspected dengue during the influenza season.</p></div

Respiratory symptoms among hospitalized patients with suspected dengue, 4 hospitals in El Salvador, during July 1 through September 30, 2012.

<p>Respiratory symptoms among hospitalized patients with suspected dengue, 4 hospitals in El Salvador, during July 1 through September 30, 2012.</p

Laboratory test results for influenza and dengue viruses in patients with suspected dengue or severe acute respiratory illness (SARI) at hospital admission, 4 hospitals in El Salvador, during July 1 through September 30, 2012.

<p>Laboratory test results for influenza and dengue viruses in patients with suspected dengue or severe acute respiratory illness (SARI) at hospital admission, 4 hospitals in El Salvador, during July 1 through September 30, 2012.</p

Characteristics of Hospitalized Cases with Influenza A (H1N1)pdm09 Infection during First Winter Season of Post-Pandemic in China

<div><h3>Background</h3><p>Influenza A (H1N1)pdm09 (2009 H1N1) re-circulated as the predominant virus from January through February 2011 in China. National surveillance of 2009 H1N1 as a notifiable disease was maintained to monitor potential changes in disease severity from the previous season.</p> <h3>Methodology/Principal Findings</h3><p>To describe the characteristics of hospitalized cases with 2009 H1N1 infection and analyze risk factors for severe illness during the 2010–2011winter season in China, we obtained surveillance data from hospitalized cases with 2009 H1N1 infection from November 2010 through May 2011, and reviewed medical records from 701 hospitalized cases. Age-standardized risk ratios were used to compare the age distribution of patients that were hospitalized and died due to 2009 H1N1 between the 2010–2011winter season to those during the 2009–2010 pandemic period. During the 2010–2011 winter season, children less than 5 years of age had the highest relative risk of hospitalization and death, followed by adults aged 65 years or older. Additionally, the relative risk of hospitalized cases aged 5–14 and 15–24 years was lower compared to children less than 5 years of age. During the winter season of 2010–2011, the proportions of adults aged 25 years or older for hospitalization and death were significantly higher than those during the 2009–2010 pandemic period. Being male, having a chronic medical condition, delayed hospital admission (≥3 days from onset) or delayed initiation of antiviral treatment (≥5 days from onset) were associated with severe illness among non-pregnant patients ≥2 years of age.</p> <h3>Conclusions/Significance</h3><p>We observed a change in high risk groups for hospitalization for 2009 H1N1 during the winter months immediately following the pandemic period compared to the high risk groups identified during the pandemic period. Our nationally notifiable disease surveillance system enabled us to understand the evolving epidemiology of 2009 H1N1 infection after the pandemic period.</p> </div

Associations between Meteorological Parameters and Influenza Activity in Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and Israeli Districts

<div><p>Background</p><p>Studies in the literature have indicated that the timing of seasonal influenza epidemic varies across latitude, suggesting the involvement of meteorological and environmental conditions in the transmission of influenza. In this study, we investigated the link between meteorological parameters and influenza activity in 9 sub-national areas with temperate and subtropical climates: Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and all 6 districts in Israel.</p><p>Methods</p><p>We estimated weekly influenza-associated influenza-like-illness (ILI) or Acute Respiratory Infection (ARI) incidence to represent influenza activity using data from each country’s sentinel surveillance during 2000–2011 (Spain) and 2006–2011 (all others). Meteorological data was obtained from ground stations, satellite and assimilated data. Two generalized additive models (GAM) were developed, with one using specific humidity as a covariate and another using minimum temperature. Precipitation and solar radiation were included as additional covariates in both models. The models were adjusted for previous weeks’ influenza activity, and were trained separately for each study location.</p><p>Results</p><p>Influenza activity was inversely associated (p<0.05) with specific humidity in all locations. Minimum temperature was inversely associated with influenza in all 3 temperate locations, but not in all subtropical locations. Inverse associations between influenza and solar radiation were found in most locations. Associations with precipitation were location-dependent and inconclusive. We used the models to estimate influenza activity a week ahead for the 2010/2011 period which was not used in training the models. With exception of Ljubljana and Israel’s Haifa District, the models could closely follow the observed data especially during the start and the end of epidemic period. In these locations, correlation coefficients between the observed and estimated ranged between 0.55 to 0.91and the model-estimated influenza peaks were within 3 weeks from the observations.</p><p>Conclusion</p><p>Our study demonstrated the significant link between specific humidity and influenza activity across temperate and subtropical climates, and that inclusion of meteorological parameters in the surveillance system may further our understanding of influenza transmission patterns.</p></div