The age profile of respiratory syncytial virus burden in preschool children of low- and middle-income countries: A semi-parametric, meta-regression approach

Abstract

Background: Respiratory syncytial virus (RSV) infections are among the primary causes of death for children under 5 years of age worldwide. A notable challenge with many of the upcoming prophylactic interventions against RSV is their short duration of protection, making the age profile of key interest to the design of prevention strategies.

Methods and findings: We leverage the RSV data collected on cases, hospitalizations, and deaths in a systematic review in combination with flexible generalized additive mixed models (GAMMs) to characterize the age burden of RSV incidence, hospitalization, and hospital-based case fatality rate (hCFR). Due to the flexible nature of GAMMs, we estimate the peak, median, and mean incidence of infection to inform discussions on the ideal "window of protection" of prophylactic interventions. In a secondary analysis, we reestimate the burden of RSV in all low- and middle-income countries. The peak age of community-based incidence is 4.8 months, and the mean and median age of infection is 18.9 and 14.7 months, respectively. Estimating the age profile using the incidence coming from hospital-based studies yields a slightly younger age profile, in which the peak age of infection is 2.6 months and the mean and median age of infection are 15.8 and 11.6 months, respectively. More severe outcomes, such as hospitalization and in-hospital death have a younger age profile. Children under 6 months of age constitute 10% of the population under 5 years of age but bear 20% to 29% of cases, 28% to 39% of hospitalizations, and 38% to 50% of deaths. On an average year, we estimate 28.23 to 31.34 million cases of RSV, between 2.95 to 3.35 million hospitalizations, and 16,835 to 19,909 in-hospital deaths in low, lower- and upper middle-income countries. In addition, we estimate 17,254 to 23,875 deaths in the community, for a total of 34,114 to 46,485 deaths. Globally, evidence shows that community-based incidence may differ by World Bank Income Group, but not hospital-based incidence, probability of hospitalization, or the probability of in-hospital death (p ≤ 0.01, p = 1, p = 0.86, 0.63, respectively). Our study is limited mainly due to the sparsity of the data, especially for low-income countries (LICs). The lack of information for some populations makes detecting heterogeneity between income groups difficult, and differences in access to care may impact the reported burden.

Conclusions: We have demonstrated an approach to synthesize information on RSV outcomes in a statistically principled manner, and we estimate that the age profile of RSV burden depends on whether information on incidence is collected in hospitals or in the community. Our results suggest that the ideal prophylactic strategy may require multiple products to avert the risk among preschool children.

Fig 1. Relationship between cases, hospitalizations, and deaths in children from birth to 59 months of age.

(A) OMs (age-specific). There are 2 ways to construct outcomes splines of the number of cases, hospitalizations, and deaths from the data in the literature, corresponding to OM I and II. The boxes show incidence and the ovals show the probability of progressing from a case in the community to a case in the hospital or from a case in the hospital to a fatal case. It should be noted that the product of incidence and probability yields an incidence. The colored boxes show the splines (splines I–IV) that we estimated from data in the literature, and the black boxes show incidence derived as products of our splines. In OM I, we use the incidence of community-acquired RSV cases (spline I) and the probability of hospitalization given infection (spline III) and deaths among inpatients (spline IV) and we derive the incidence of hospitalizations and the incidence of death due to RSV. In OM II, we use the incidence of RSV cases from hospital-based studies (spline II) and the probabilities of hospitalization and inpatient death and we back-calculate the community-based incidence and the incidence of death due to RSV. From these spline models, we also estimated the peak, median, and mean age of infection. (B) BMs of disease in population. There are 2 ways to calculate the burden of disease in one country: for BM I, we apply the country population size to the incidences derived in (A) and aggregate cases into subgroups according to age; for BM II, we take the number of cases in the country as calculated by Li and colleagues’ risk-factor model, apply the proportion of cases that occur in each month of age according to our splines, and aggregate cases into subgroups according to age. Because there are 2 ways to calculate age-specific incidence in part (A) and 2 ways to calculate burden in part (B), 4 sets of burden estimates result. A full mathematical derivation of this is found in Section S1.4 in S1 text. BM, burden model; OM, outcomes model; RSV, respiratory syncytial virus.

Fig 2

Splines of (A) community-based incidence, (B) hospital-based incidence, (C) probability of hospitalization, and (D) probability of death among hospitalized cases by income group designation. The bands correspond to the 95% confidence intervals of each parameter at each age. The pink bands present the “global” splines—derived from GAMMs with no income group predictor—and the blue bands present the “income group” splines—derived from GAMMs that include a predictor for the World Bank’s country income group designation. For the community-based incidence spline and the probability of hospitalization spline, the LIC estimates are identical to the LMIC estimates because no data exists from LIC settings. GAMM, generalized additive mixed model; hCFR, hospital-based case fatality rate; LIC, low-income countries; LMIC, lower-middle-income countries; UMIC, upper middle-income countries.

Fig 3. RSV cases, hospitalizations, and deaths per 1,000 person-years according to OM I and II.

95% confidence intervals of cases, hospitalizations, and deaths per 1,000 person-years according to OM I and II, detailed in Fig 1 and Section S1.4 in S1 Text. The orange bands arise from OM I, calculated by taking the splines of community-based incidence (Spline I), probability of hospitalization (Spline III), and probability of death among hospitalized cases (Spline IV). The purple bands arise from OM II, calculated by taking the splines of hospital-based incidence (Spline II), probability of hospitalization (Spline III) to back-calculate cases in the community, and probability of death among hospitalized cases (Spline IV). LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; RSV, respiratory syncytial virus; UMIC, upper middle-income countries.

Fig 4. Mean age, median age, and peak age of infection, hospitalization, and deaths due to RSV.

The segments correspond to 95% confidence intervals of each of the summaries and the dots correspond to the median estimate of each of the summaries; these are shown in orange when calculated using OM I and in purple when calculated using OM II. LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; RSV, respiratory syncytial virus; UMIC, upper-middle-income countries.

Fig 5. Proportions of each outcome that fall under key age brackets.

Uncertainty intervals are presented in Fig A in S3 Text. LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; UMIC, upper middle-income countries.

Fig 6. Burden of RSV cases, hospitalizations, and deaths in low- and middle-income countries, by World Bank income group classification.

Number and 95% confidence intervals of cases, hospitalizations, and deaths per 1,000 person-years according to OMs I and II and BMs I and II, as detailed in Fig 1 and Sections S1.4 and S1.6 in S1 Text. Because we used Li’s country-specific model as the basis for the number of cases in BM II, the cases do not differ between OM I and OM II; hospitalization and in-hospital death outcomes differ due to the different ways in which conditional probability splines were applied in OM I vs. OM II. Burden stratified by age group can also be found in the supplement (see S3 Text, Figs F–H). BM, burden model; LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; RSV, respiratory syncytial virus; UMIC, upper-middle-income countries.

Fig 7. Burden of RSV cases, hospitalizations, and deaths in low- and middle-income countries, by age group.

Number and 95% confidence intervals of cases, hospitalizations, and deaths per 1,000 person-years according to SMs I and II and BMs I and II, as detailed in Fig 1 as well as Sections S1.4 and S1.6 in S1 Text. Burden stratified by age group can also be found in the supplement (see Figs F–H in S3 Text). BM, burden model; RSV, respiratory syncytial virus; SM, spline model.