Summary
Background
Community-level estimates of influenza burden among older adults are important to inform prevention strategies, but limited data exist in India. This study determines the incidence and associated risk factors of influenza-associated acute respiratory infection (ARI) among older adults (age ≥ 60 years) using surveillance data across four community-based sites in India during 2018–2023.
Methods
Acute upper respiratory (AURI) and lower-respiratory infections (ALRI) were identified through weekly domiciliary surveillance by trained healthcare workers among older adults residing in four community-based surveillance sites in India. Nasal and throat swabs were collected from all individuals with ALRI episodes and from a subset of individuals with AURI episodes for influenza RT-PCR testing. Predominant clades and subclades were detected by whole genome sequencing. Incidence rates per 1000 person-years of influenza-associated AURI and ALRI were calculated for pre-COVID-19 (July 2018–March 2020) and during COVID-19 pandemic (September 2020–March 2023) periods. Risk factors for influenza-associated ALRI were identified using Poisson regression.
Findings
During the 19,914.2 person-years of follow-up, 23,576 AURI and 1952 ALRI episodes were identified. Influenza A accounted for 190 (73.1%) AURI and 140 (82.8%) ALRI episodes. Influenza A(H3N2), particularly subclade 3C.2a1b.2a predominated overall, though circulation patterns varied across sites and time periods. Incidence rates of influenza-associated AURI and ALRI were 39.2 and 8.5/1000 person-years respectively. AURI and ALRI incidence rates were higher in pre-COVID-19 (60.6 and 12.4/1000 person-years) than during the COVID-19 (25.1 and 5.9/1000 person-years) period. Factors associated with influenza ALRI included pre-COVID-19 and latter part of COVID-19 pandemic (2022–2023) periods, Chennai and Ballabgarh surveillance sites, age group 65–69 and greater than equal to 75 years, female sex, chronic respiratory disease, and presence of any disability.
Interpretation
These findings highlight the burden of influenza-associated respiratory illness among older adults and has elucidated individual and household-level factors associated with severe disease. The results provide potential evidence to inform future prevention strategies including vaccination programmes prioritising older adults.
Funding
This study was supported by National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC) through the cooperative agreement (U01IP001074) with AIIMS New Delhi.
Keywords: Influenza, Respiratory tract infections, Seasons, Risk factors, Aged, India
Research in context.
Evidence before this study
Influenza is a leading cause of respiratory and circulatory mortality among the older adults globally. Influenza vaccination guidelines in India do not classify older adults as a priority group for vaccination. Evidence on the incidence of influenza and its associated risk factors among older adults in India is limited, although such data are crucial to inform national immunization policy. We searched the PubMed database for articles published in English using the terms ‘Influenza’, ‘Older adult’, ‘Adult’, ‘Aged’ ‘Frail elderly’, ‘India’ from January 1, 2015, to July 16, 2025. Of the 92 articles that met the search criteria only one study from India reported the community-based incidence rate of influenza among the older adults with acute lower respiratory infection (ALRI).
Added value of this study
This study from India documented trends of influenza-associated acute upper respiratory infection (AURI) and ALRI through multi-centric community-based cohorts from four distinct geographic regions during both the pre-COVID-19 and during COVID-19 periods. We observed that periods of high influenza activity coincided with the monsoon and winter seasons at each study site. Influenza incidence decreased during 2021–2022 reflecting COVID-19 pandemic related disruptions and surged back to pre-pandemic levels in 2022–2023. We also identified key risk factors for influenza-associated ALRI at the community level. Female sex, the presence of disabilities (including visual, auditory or locomotor impairments), chronic respiratory conditions (self-reported chronic obstructive or restrictive lung diseases, lung malignancy, and current or past tuberculosis) and belonging to Ballabgarh or Chennai sites and pre- and end-of-COVID-19 periods were associated with a higher risk of developing influenza-associated ALRI at the community-level.
Implications of all the available evidence
Combined insights from existing evidence and the current community-based study underscore the importance of strengthening influenza surveillance systems to appropriately inform prevention strategies for older adults in India. While prior studies have primarily relied on facility-based data and have not adequately examined the impact of the COVID-19 pandemic on influenza transmission patterns, emerging community-level evidence from this multi-centric study fills critical knowledge gaps on incidence and risk factors of influenza among older adults. Collectively, the data contribute to the evidence base required to evaluate prevention strategies, aid the development of a targeted influenza vaccination strategy. Future research could be undertaken on cost-effectiveness evaluation of vaccination strategies for this population.
Introduction
Adults older than 60 years of age, especially individuals suffering from chronic medical conditions are at high risk of developing severe consequences from influenza, including hospitalisation and death.1,2 Influenza-associated respiratory mortality among older adults in India was 51.1 (95% confidence interval [CI] 9.2–93.0) per 100,000 population between 2010 and 2013.1 With the proportion of older adults in India projected to increase from 10% in 2022 to nearly 20% by 2050,3 the burden of influenza in this age group is expected to rise substantially. Globally, older adults are considered a priority group for seasonal influenza vaccination because of their disproportionately high risk of severe disease, hospitalization and death.4,5 The national influenza vaccination guidelines of India (2017) classify older adults as a desired rather than a recommended group for influenza vaccination.6 This could be partially due to a lack of adequate evidence on influenza burden among this population. Additionally, concerns regarding vaccine strain mismatch and moderate vaccine effectiveness, as noted in national guidelines, may also influence vaccination policy and uptake.6 Currently available data, both globally and in India, largely come from facility-based surveillance systems, which tend to underestimate the community-level burden as the major proportion of influenza infections are asymptomatic and most symptomatic cases are often self-managed without medical consultation.7, 8, 9 Robust community-level data on incidence and risk factors in this population are crucial to inform strategies such as vaccination, behavioural interventions and also for future cost-effectiveness assessments.7
Influenza surveillance in India is being conducted through the Virus Research and Diagnostic Laboratory Network, a facility-based platform operating in select government hospitals.10 This approach underestimates the true burden and incidence in the community, limiting its utility for informing influenza vaccination strategies. To address these gaps, the Indian Network of Population-Based Surveillance Platforms for Influenza and other Respiratory Viruses among the Elderly (INSPIRE), a multi-site prospective cohort study, was established.11 INSPIRE aimed to generate comprehensive evidence on the burden and risk factors for influenza-associated respiratory illnesses to inform national and sub-national decisions on influenza management and vaccination strategies. This study reports the incidence of influenza-associated respiratory illnesses among the older adults (age ≥ 60 years) from four INSPIRE network sites in India during 2018–2023. We also assessed association between potential risk factors and influenza-associated acute lower respiratory infections (ALRI) in this population.
Methods
INSPIRE, was a multi-site, community-based surveillance, among a dynamic prospective cohort of individuals older than or equal to 60 years, established at health and demographic surveillance systems sites managed by ICMR National Institute of Epidemiology, Chennai (South), All India Institute of Medical Sciences (AIIMS), New Delhi (North), ICMR National Institute for Research in Bacterial Infections, Kolkata (East) and ICMR National Institute of Virology, Pune (West), representing four geographic regions of India. All sites were urban, except the Ballabgarh site of AIIMS. AIIMS, New Delhi coordinated the study, while the Centers for Disease Control and Prevention (CDC), Atlanta, USA, provided technical support. The detailed methodology for the site selection, enrolment, and establishment of the cohort has been published previously.11 The surveillance was conducted from 2 July 2018 to 31 March 2023, with an interruption from 21 March to 6 September 2020 due to COVID-19-related restrictions.
Trained medical social workers collected data using handheld tablets on the Open Data Kit (ODK) platform. At enrolment, they collected socio-demographic details and individual-level factors such as smoking status, pre-morbid conditions, disability (vision problems, hearing problems, and physical movement disorders) and contact with school-going children from all included participants. Premorbid conditions and disability were based on participants self-report and were not independently verified through medical records or clinical examination. Household-level information, such as socio-economic conditions, use of solid fuel, and ventilation (availability of windows), was also recorded. Additionally, household asset details were documented to estimate wealth quintiles. Height and weight were measured using the SECA 213 stadiometer and SECA 803 digital weighing scale.
At each site, five trained staff nurses conducted weekly domiciliary surveillance (Monday to Friday) and administered a structured questionnaire to identify acute respiratory infections (ARI). On each surveillance day, nurses collected nasal and throat samples using flocked swabs from all ALRI cases. However, sample collection was performed only in a subset of acute upper respiratory infection (AURI) episodes, which varied by surveillance period. During July 2018–March 2020, one nurse was randomly assigned on each surveillance day to collect respiratory specimens from all AURI cases identified by him/her.11 Surveillance was interrupted during 21 March to 6 September 2020 due to COVID-19-related restrictions. Subsequently from September 2020 to March 2023, AURI sampling was resumed based on operational feasibility, and the proportion sampled varied because specimens were also evaluated for SARS-CoV-2. The nurses measured the respiratory rate by counting chest movement over 60 s12 and measured axillary temperature using an Omron MC 246 thermometer. ARI patients were classified into ALRI or AURI (see below for definitions). The specimens were placed in viral transport medium and transported under cold chain to the virology laboratory of the respective study sites within 24 h. The central coordinating team and site investigators provided induction and refresher trainings to the study personnel. We pretested the questionnaires for operational feasibility before initiating the study and used custom data management software for data validation.
ARI was defined as new onset or worsening of cough or breathing difficulty in the last seven days. Worsening of cough was defined as the appearance of sputum, or change in colour of sputum, or increase in the amount of sputum, or appearance of blood with sputum in the last 7 day as compared to the previous week. ALRI was defined as symptom of acute lower respiratory tract illness (cough and at least one among breathlessness, wheeze or chest pain), tachypnoea (respiratory rate >20/min) and at least one systemic feature (measured temperature of ≥38 °C or reported fever with sweating, shivers, aches and pain). AURI was defined as individuals with ARI who do not meet the case definition for ALRI.11 New episode of AURI/ALRI was defined as presence of new symptoms of AURI/ALRI after a symptom-free interval of 14 days. Influenza-associated ARI was defined as all AURI/ALRI episodes where the influenza virus was detected by real-time reverse transcription polymerase chain reaction (RT-PCR) in throat and nasal samples. Any disability included self-reported vision problems, hearing problems, and physical movement disorders. Chronic respiratory diseases included self-reported chronic obstructive or restrictive lung diseases, lung malignancy, and current or past tuberculosis.
Procedures
Respiratory specimens underwent viral RNA extraction and were tested using RT-PCR for detection and typing of influenza viruses.11 In addition, respiratory syncytial virus (RSV) and SARS-CoV2 were identified by RT-PCR during the surveillance. Quality assurance of the laboratory component involved annual proficiency testing by participating laboratories and quarterly concordance testing of study specimens (20% of positives and 5% of negatives) at the coordinating laboratory at AIIMS, New Delhi.
Whole genome sequencing (WGS) was performed on influenza A and B-positive samples with cycle threshold (Ct) values ≤27.13 Viral RNA was extracted using the MagMAX™ Viral RNA Isolation kit (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. Multi segment RT-PCR (MRT-PCR) was conducted following CDC protocol14 to amplify all 8 fragments of Influenza A and B viruses. Amplicons were visualized on 2% precast E-Gel agarose gels, purified with PCR purification Kit (Qiagen, Germany) and quantified with Qubit dsDNA HS Assay kit (Thermo Fisher Scientific, USA). Libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina Inc, USA) and sequenced with MiSeq reagent 150 cycle kit V3 (Illumina Inc, USA) on the MiSeq platform (Illumina Inc, USA). Generated fastq files were then quality checked, curated and assembled with CDC MIRA pipeline.15 Phylogenetic analysis was conducted in MEGA version 6 using the Neighbor-Joining method (tie-ne option), using WHO-recommended Southern Hemisphere (SH) and Northern Hemisphere (NH) vaccine viruses (2013–2018) as reference strains.
Statistical analysis
We summarized demographic, clinical characteristics, behavioural habits, economic status and household level factors as frequencies with percentages. Household wealth was assessed using a composite index based on household asset ownership and housing characteristics. Principal component analysis was performed on pooled household data from all sites to generate a standardized wealth score for each household. The cohort-wide distribution of scores was divided into quintiles (poorest, poor, middle, rich, and richest), and participants were assigned to quintiles according to their household score. We calculated annual incidence for a 12-month period from 1 July to 30 June. Pre-pandemic period was defined as from July 2018 to March 2020 and the COVID-19 pandemic period from September 2020 to March 2023.16,17 We calculated influenza positivity using the number of AURI or ALRI episodes in which samples were collected as the denominator. As only a subset of symptomatic AURI episodes was sampled due to operational constraints, we applied stratified influenza positivity (by calendar quarter, age group, sex, and study site) to all surveillance-detected symptomatic AURI episodes to estimate influenza-associated AURI burden assuming AURI episodes sampled are representative of all AURI episodes. We calculated the incidence rate of influenza-associated AURI and ALRI using person-years as the denominator with 95% confidence intervals (CI), accounting for uncertainty in extrapolated count of AURI episodes using the normal approximation method. We described the types of influenza infections by year and site. We estimated crude rate ratios (RR) with 95% CI for ALRI using a generalized linear model with Poisson regression, log link and robust standard errors to identify factors influencing influenza-associated ALRI. Directed acyclic graphs (DAGs) were constructed a priori using DAGitty18 to represent the assumed causal relationships between exposures, outcome (influenza-associated ALRI), and potential confounders. As the causal structure differed across exposures, minimally sufficient adjustment sets were identified separately for each exposure. Distinct multivariable models were then fitted to estimate adjusted rate ratios (aRR) with 95% CI. Rate ratios compared the incidence of influenza-associated ALRI with individuals without influenza-associated ALRI in the cohort. We performed all analyses using STATA release 17 Stata Corp (2021).
Ethics statement
The study protocol was approved by Institutional Ethics Committees of ICMR-NIE, Chennai (ID # NIE/IHEC/201701-03), AIIMS, New Delhi (IEC-283/02.06.2017) ICMR-NIRBI, Kolkata (NICED-A-1/2017-IEC), ICMR-NIV, Pune (NIV-IEC/2018/D5) and received clearance from the Health Ministry Screening Committee on 8 November 2017 (Ref.: 2017-4262). CDC relied on the determination from AIIMS, New Delhi ethics committee (Protocol No: 7145). Written informed consent was obtained from all participants before enrolment. The participants with any health issue reported during the surveillance period were referred to the nearest public health facility for management.
Role of the funding source
This study was a cooperative agreement and personnel from the funding agency were involved in all aspects of the study.
Results
Between July 2018 and March 2023, 7240 participants older than or equal to 60 years were enrolled across four sites (Fig. 1). Approximately half (48.4%) of the participants were in the 60–64 years age group and more than half (59.5%) were women. Less than one-fifth (17.0%) of the participants were current smokers. Obesity was present in 9.6% of the participants. Hypertension (38.7%) and diabetes mellitus (21.3%) were the most common self-reported co-morbidities, More than half (56.1%) of the households in Ballabgarh site were in the wealthiest quintile, whereas in Kolkata, a similar (55.9%) proportion of households were in the lowest quintile. School-going children lived in 46.6% of participants’ households. Solid fuel usage was reported in 35.3% of households, with the lowest use in Pune (12.5%) and the highest in Ballabgarh (91.9%) sites. The majority (90.7%) of the households had provision for ventilation. Influenza vaccination among the participants was low across the sites (Ballabgarh: 1, Chennai: 12, Kolkata: 3, Pune: 4) (Table 1).
Fig. 1.
Participant recruitment and follow-up process.
Table 1.
Characteristics of study participants.
| Ballabgarh 1673 (%) | Chennai 1989 (%) | Kolkata 2096 (%) | Pune 1482 (%) | Total 7240 (%) | |
|---|---|---|---|---|---|
| Individual level characteristics | |||||
| Age group (years) | |||||
| 60–64 | 576 (34.4) | 925 (46.5) | 1167 (55.7) | 839 (56.6) | 3507 (48.4) |
| 65–69 | 524 (31.3) | 506 (25.5) | 542 (25.9) | 373 (25.2) | 1945 (26.9) |
| 70–74 | 279 (16.7) | 295 (14.8) | 243 (11.6) | 160 (10.8) | 977 (13.5) |
| ≥75 | 294 (17.6) | 263 (13.2) | 144 (6.8) | 110 (7.4) | 811 (11.2) |
| Sex | |||||
| Male | 701 (41.9) | 820 (41.2) | 817 (39.0) | 595 (40.1) | 2933 (40.5) |
| Female | 972 (58.1) | 1169 (58.8) | 1279 (61.0) | 887 (59.9) | 4307 (59.5) |
| Currently smoking | 711 (42.5) | 153 (7.7) | 291 (13.9) | 76 (5.1) | 1231 (17.0) |
| BMI (N = 6509)a | |||||
| Underweight (≤18.5) | 336 (20.2) | 152 (7.9) | 252 (15.0) | 148 (11.8) | 888 (13.6) |
| Normal weight (18.5–24.9) | 872 (52.6) | 883 (46.2) | 842 (50.2) | 685 (54.4) | 3282 (50.4) |
| Overweight (25.0–29.9) | 340 (20.5) | 633 (33.1) | 417 (24.8) | 322 (25.6) | 1712 (26.4) |
| Obese (≥30.0) | 111 (6.7) | 245 (12.8) | 168 (10.0) | 103 (8.2) | 627 (9.6) |
| Self-reported comorbidities | |||||
| Diabetes mellitus | 164 (9.8) | 738 (37.1) | 394 (18.8) | 249 (16.8) | 1545 (21.3) |
| Hypertension | 557 (33.3) | 918 (46.1) | 893 (42.6) | 437 (29.5) | 2805 (38.7) |
| Cardiovascular diseasesb | 175 (10.5) | 236 (11.9) | 302 (14.4) | 89 (6.0) | 802 (11.1) |
| Chronic respiratory diseasesc | 232 (13.9) | 257 (12.9) | 233 (11.1) | 60 (4.1) | 782 (10.8) |
| Other co-morbiditiesd | 188 (11.2) | 1369 (68.8) | 782 (37.3) | 503 (33.9) | 2842 (39.2) |
| Any disabilitye | 1312 (78.4) | 1626 (81.7) | 1483 (70.7) | 626 (42.2) | 5047 (69.7) |
| Ever taken influenza vaccine | 1 (0.1) | 12 (0.6) | 3 (0.1) | 4 (0.3) | 20 (0.3) |
| Household level characteristics (N = 5461) | |||||
| School going children in household | 804 (65.9) | 518 (36.9) | 735 (43.4) | 488 (42.7) | 2545 (46.6) |
| Use of solid fuelf | 1121 (91.9) | 431 (30.9) | 235 (13.9) | 143 (12.5) | 1930 (35.3) |
| Ventilation provisions | 1178 (96.6) | 1340 (95.4) | 1451 (85.7) | 985 (86.2) | 4954 (90.7) |
| Wealth quintile | |||||
| First | 70 (5.7) | 93 (6.6) | 983 (58.0) | 57 (5.0) | 1203 (22.0) |
| Second | 102 (8.4) | 198 (14.1) | 489 (28.9) | 368 (32.2) | 1157 (21.2) |
| Third | 108 (8.9) | 272 (19.4) | 159 (9.4) | 530 (46.4) | 1069 (19.6) |
| Fourth | 255 (20.9) | 531 (37.8) | 54 (3.2) | 183 (16.0) | 1023 (18.7) |
| Fifth | 685 (56.1) | 311 (22.1) | 8 (0.5) | 5 (0.4) | 1009 (18.5) |
BMI: Body mass index data not available for 731 participants (Ballabgarh: 14, Chennai: 76, Kolkata: 417, Pune: 224).
Includes self–reported stroke and heart disease.
Includes self–reported chronic respiratory illness and current/past tuberculosis.
Includes self–reported malignancy, liver disease, kidney disease, arthritis, anemia and depression.
Includes self–reported vision problem, hearing problem, physical movement disorders.
Includes charcoal, straw and shrubs, coal, cow dung cake, wood and kerosene use in household cooking/heating.
The analysis included 1,038,385 weekly visits, contributing 19,914.2 person-years of follow-up (pre-COVID-19 7919.9; COVID-19 pandemic 11,994.3) (Fig. 1). We excluded the remaining 53,253 visits, primarily due to non-availability of participants (47,280).
Overall, 8346 (35.4%) and 1921 (98.4%) swab samples were collected from AURI and ALRI cases respectively. Influenza positivity among the sampled cases was higher among the ALRI [8.8% (95% CI 7.5–10.1)] than AURI cases [3.1% (95% CI: 2.7–3.5)]. Dual infections (influenza A with B, RSV and SARS-CoV2) were also seen in six AURI and four ALRI episodes (Table 2).
Table 2.
Positivity and profile of influenza among acute upper respiratory infection (AURI) and acute lower respiratory infection (ALRI) episodes.
| AURI |
ALRI |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| July’18–June’19 | July’19–March’20c | September’20–June’21 | July’21–June’22 | July’22–March’23 | Overall | July’18–June’19 | July’19–March’20c | September’20–June’21 | July’21–June’22 | July’22–March’23 | Overall | |
| Positivity % (na/Nb) | ||||||||||||
| Overall | 4.7% (61/1300) | 3.8% (52/1366) | 1.6% (14/874) | 2.4% (66/2754) | 3.3% (67/2052) | 3.1% (260/8346) | 10.3% (60/584) | 8.8% (38/431) | 6.3% (12/191) | 5.8% (24/414) | 11.6% (35/301) | 8.8% (169/1921) |
| Site | ||||||||||||
| Ballabgarh | 2.1% (10/483) | 2.8% (16/571) | 0.9% (4/442) | 2.2% (20/914) | 1.6% (9/571) | 2.0% (59/2981) | 10.7% (25/233) | 8.3% (22/265) | 5.1% (4/78) | 5.0% (4/80) | 14.8% (13/88) | 9.1% (68/744) |
| Chennai | 3.6% (8/225) | 5.0% (9/179) | 1.1% (2/174) | 1.5% (13/870) | 4.1% (30/728) | 2.8% (62/2176) | 7.6% (13/171) | 13.2% (9/68) | 3.0% (2/66) | 8.0% (20/251) | 9.3% (18/194) | 8.3% (62/750) |
| Kolkata | 7.8% (25/321) | 4.3% (12/279) | 4.5% (8/176) | 4.2% (27/643) | 1.1% (3/280) | 4.4% (75/1699) | 13.9% (14/101) | 8.6% (3/35) | 14.3% (6/42) | 0.0% (0/57) | 0.0% (0/9) | 9.4% (23/244) |
| Pune | 6.6% (18/271) | 4.5% (15/337) | 0.0% (0/82) | 1.8% (6/327) | 5.3% (25/473) | 4.3% (64/1490) | 10.1% (8/79) | 6.3% (4/63) | 0.0% (0/5) | 0.0% (0/26) | 40.0% (4/10) | 8.7% (16/183) |
| Age (years) | ||||||||||||
| 60–64 | 3.7% (18/487) | 4.2% (22/523) | 2.4% (8/333) | 2.4% (28/1168) | 3.9% (37/949) | 3.3% (113/3460) | 11.5% (22/192) | 11.3% (16/142) | 7.7% (5/65) | 6.2% (10/161) | 14.3% (19/133) | 10.4% (72/693) |
| 65–69 | 4.9% (20/405) | 2.9% (13/452) | 0.7% (2/272) | 2.7% (23/861) | 2.6% (16/622) | 2.8% (74/2612) | 12.1% (24/198) | 9.6% (13/136) | 9.4% (5/53) | 7.2% (8/111) | 9.2% (8/87) | 9.9% (58/585) |
| 70–74 | 5.8% (13/225) | 5.1% (11/216) | 2.3% (3/129) | 2.3% (9/395) | 3.4% (10/293) | 3.7% (46/1258) | 4.1% (4/97) | 5.3% (4/76) | 3.1% (1/32) | 4.1% (3/73) | 6.1% (3/49) | 4.6% (15/327) |
| ≥75 | 5.5% (10/183) | 3.4% (6/175) | 0.7% (1/140) | 1.8% (6/330) | 2.1% (4/188) | 2.7% (27/1016) | 10.3% (10/97) | 6.5% (5/77) | 2.4% (1/41) | 4.3% (3/69) | 15.6% (5/32) | 7.6% (24/316) |
| Sex | ||||||||||||
| Male | 5.4% (28/520) | 4.1% (22/537) | 1.6% (5/317) | 2.3% (23/998) | 2.8% (20/724) | 3.2% (98/3096) | 9.8% (21/215) | 7.5% (12/160) | 2.7% (2/74) | 6.8% (10/146) | 6.7% (7/104) | 7.4% (52/699) |
| Female | 4.2% (33/780) | 3.6% (30/829) | 1.6% (9/557) | 2.4% (43/1756) | 3.5% (47/1328) | 3.1% (162/5250) | 10.6% (39/369) | 9.6% (26/271) | 8.5% (10/117) | 5.2% (14/268) | 14.2% (28/197) | 9.6% (117/1222) |
| Etiological Profile % (n) | ||||||||||||
| Overall | 61 | 52 | 14 | 66 | 67 | 260 | 60 | 38 | 12 | 24 | 35 | 169 |
| Influenza A | 65.6% (40) | 51.9% (27) | 100.0% (14) | 69.7% (46) | 89.6% (60) | 71.9% (187) | 93.3% (56) | 65.8% (25) | 100.0% (12) | 62.5% (15) | 85.7% (30) | 81.7% (138) |
| (H1N1)pdm09 | 60.0% (24) | 37.0% (10) | 21.4% (3) | 23.9% (11) | 63.3% (38) | 46.0% (86) | 60.7% (34) | 20.0% (5) | 25.0% (3) | 13.3% (2) | 63.3% (19) | 45.7% (63) |
| (H3N2) | 40.0% (16) | 63.0% (17) | 78.6% (11) | 76.1% (35) | 36.7% (22) | 54.0% (101) | 39.3% (22) | 80.0% (20) | 75.0% (9) | 86.7% (13) | 36.7% (11) | 54.3% (75) |
| Influenza B | 34.4% (21) | 44.2% (23) | 0.0% (0) | 28.8% (19) | 6.0% (4) | 25.8% (67) | 6.7% (4) | 34.2% (13) | 0.0% (0) | 29.2% (7) | 8.6% (3) | 16.0% (27) |
| Victoria | 28.6% (6) | 100.0% (23) | 0.0% (0) | 100.0% (19) | 75.0% (3) | 76.1% (51) | 50.0% (2) | 100.0% (13) | 0.0% (0) | 100.0% (7) | 100.0% (3) | 92.6% (25) |
| Yamagata | 71.4% (15) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 22.4% (15) | 50.0% (2) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 7.4% (2) |
| Lineage not determined | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 25.0% (1) | 1.5% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) |
| Dual infection | 0.0% (0) | 3.8% (2) | 0.0% (0) | 1.5% (1) | 4.5% (3) | 2.3% (6) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 8.3% (2) | 5.7% (2) | 2.4% (4) |
| Influenza A/(H1N1)pdm09 + RSV | 0.0% (0) | 50.0% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 16.7% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) |
| Influenza A/(H3N2) + Influenza A/(H1N1)pdm09 | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 50.0% (1) | 0.0% (0) | 25.0% (1) |
| Influenza A/(H3N2) + Influenza B/Victoria | 0.0% (0) | 50.0% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 16.7% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) |
| Influenza B/Victoria + RSV | 0.0% (0) | 0.0% (0) | 0.0% (0) | 100.0% (1) | 0.0% (0) | 16.7% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 50.0% (1) | 0.0% (0) | 25.0% (1) |
| SARS-CoV-2 + Influenza A/(H1N1)pdm09 | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 66.7% (2) | 33.3% (2) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 100.0% (2) | 50.0% (2) |
| SARS-CoV-2 + Influenza A/(H3N2) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 33.3% (1) | 16.7% (1) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) | 0.0% (0) |
Influenza positive samples.
Number of samples collected during AURI and ALRI episodes.
Surveillance interrupted during 21 March–6 September 2020 due to COVID-19 pandemic associated lockdowns.
Among the influenza-associated ARI (429), we identified influenza A in 187 (71.9%) AURI and 138 (81.7%) ALRI cases across the study period. Among influenza A, A(H3N2) was predominant subtype associated AURI (54.1%) and ALRI (54.4%), however the pattern was not observed in 2018–2019 and 2022–2023, where A(H1N1)pdm09 was dominant. Among influenza B (94), Victoria was the major lineage (AURI: 76.2%; ALRI: 92.6%) and Yamagata was not observed after 2018–2019. No cases of influenza B-associated AURI and ALRI were seen in 2020–2021 (Table 2).
Among 215 influenza-positive samples with cycle threshold (Ct) ≤27 that underwent sequencing, 67 were A(H1N1)pdm09, 90 were A(H3N2), and 58 were B/Victoria. Phylogenetic analysis of influenza A subtypes and Influenza B/Victoria lineage showed year-wise and site-specific circulation patterns. The most prominent subclade of A(H1N1)pdm09 during 2018–2020 was 6 B.1 A.5a (60%) and during 2021–2023 was 6 B.1A.5a.2a (100%) (Fig. 2).
Fig. 2.
Phylogenetic analysis of influenza genes highlighting strains from four study sites with the WHO recommended (Northern and Southern Hemisphere) vaccine components during the surveillance period (July 2018–March 2023). a. A(H1N1)pdm09 HA gene; b. A(H3N2) HA gene; c. B(Victoria) HA gene. DEL: Ballabgarh; PUN: Pune; KOL: Kolkata; CHE: Chennai. 
2018, 
2019, 
2020, 
2021, 
2022, 
2023.
The incidence rate of influenza-associated AURI was 62.9/1000 person-years in 2018–2019 and 57.8/1000 person-years in 2019–2020. It decreased to 15.9/1000 person-years in 2020–2021, followed by an increase in 2021–2022 (26.9/1000 person-years) and 2022–2023 (31.7/1000 person-years) (Table 3). Incidence increased with age, peaking at 53.8/1000 person-years among individuals aged 70–74 years and was similar in males (40.6/1000 person-years) and females (40.5/1000 person-years) (Table 3).
Table 3.
Incidence rate (per 1000 person-years) for (A) influenza-associated acute upper respiratory infections (AURI) and (B) influenza-associated acute lower respiratory infections (ALRI) by period.
| Characteristics of participants (n) | Total person-years | Total influenza-associated AURIb/ALRI | Incidence rate (95% CI) |
|||||
|---|---|---|---|---|---|---|---|---|
| Pre COVID-19 period |
COVID-19 pandemic period |
Overall | ||||||
| July’18–June’19 | July’19–March’20a | September’20–June’21 | July’21–June’22 | July’22–March’23 | ||||
| (A) Influenza-associated AURI (extrapolated)b | ||||||||
| All Site (7240) | 19914.2 | 781 | 62.9 (55.4–70.3) | 57.8 (49.9–65.7) | 15.9 (11.7–20.1) | 26.9 (22.3–31.4) | 31.7 (25.8–37.5) | 39.2 (36.5–42.0) |
| Site | ||||||||
| Ballabgarh (1673) | 6033.6 | 200 | 35.4 (25.6–45.2) | 63.6 (48.5–78.7) | 12.2 (5.8–18.5) | 35.0 (25.2–44.8) | 19.3 (10.7–27.9) | 33.2 (28.6–37.8) |
| Chennai (1989) | 5049.9 | 135 | 34.6 (23.2–46.1) | 34.5 (22.3–46.6) | 8.9 (2.7–15.1) | 15.4 (8.7–22.0) | 44.8 (30.9–58.6) | 26.7 (22.2–31.2) |
| Kolkata (2096) | 5065.6 | 219 | 73.7 (57.8–89.6) | 51.1 (36.4–65.8) | 45.5 (30.0–61.0) | 36.0 (25.8–46.2) | 8.8 (2.9–14.7) | 43.2 (37.4–48.9) |
| Pune (1482) | 3765.1 | 228 | 130.2 (105.5–154.9) | 87.9 (65.8–110.0) | 0 | 17.6 (8.7–26.4) | 63.9 (45.3–82.6) | 60.5 (52.7–68.4) |
| Age (years) | ||||||||
| 60–64 (3507) | 9241.2 | 343 | 46.9 (36.9–57.0) | 61.9 (49.7–74.2) | 19.6 (12.8–26.4) | 23.7 (17.6–29.8) | 39.7 (30.6–48.9) | 37.2 (33.2–41.1) |
| 65–69 (1945) | 5763.1 | 222 | 69.4 (55.3–83.5) | 44.7 (31.9–57.6) | 7.6 (2.2–13.0) | 32.7 (23.2–42.2) | 29.3 (18.7–39.9) | 38.4 (33.4–43.5) |
| 70–74 (977) | 2758.7 | 148 | 86.1 (64.1–108.0) | 80.2 (55.8–104.7) | 26.3 (11.7–40.9) | 28.8 (15.6–42.0) | 38.3 (20.2–56.5) | 53.8 (45.1–62.4) |
| ≥75 (811) | 2151.2 | 99 | 88.7 (64.1–113.2) | 58.8 (35.7–81.9) | 9.1 (−0.4 to 18.6) | 25.9 (11.4–40.4) | 25.6 (7.6–43.6) | 45.8 (36.8–54.8) |
| Sex | ||||||||
| Male (2933) | 8053.2 | 327 | 70.7 (58.5–82.8) | 59.1 (46.7–71.5) | 15.2 (8.7–21.7) | 25.1 (18.1–32.2) | 28.5 (19.6–37.4) | 40.6 (36.2–45.0) |
| Female (4307) | 11861.0 | 480 | 61.3 (51.7–71.0) | 58.2 (47.9–68.5) | 15.7 (10.3–21.1) | 28.4 (22.3–34.4) | 39.4 (31.0–47.7) | 40.5 (36.8–44.1) |
| (B) Influenza-associated ALRI | ||||||||
| All Site (7240) | 19914.2 | 169 | 13.8 (10.7–17.7) | 10.7 (7.8–14.6) | 3.5 (2.0–6.1) | 4.9 (3.3–7.2) | 9.8 (7.0–13.6) | 8.5 (7.3–9.9) |
| Site | ||||||||
| Ballabgarh (1673) | 6033.6 | 68 | 17.8 (12.0–26.4) | 20.6 (13.6–31.3) | 3.4 (1.3–9.2) | 2.8 (1.1–7.6) | 13.0 (7.6–22.4) | 11.3 (8.9–14.3) |
| Chennai (1989) | 5049.9 | 62 | 12.8 (7.4–22.1) | 10.1 (5.2–19.4) | 2.2 (0.6–8.9) | 14.9 (9.6–23.1) | 20.0 (12.6–31.7) | 12.3 (9.6–15.7) |
| Kolkata (2096) | 5065.6 | 23 | 12.5 (7.4–21.1) | 3.3 (1.1–10.2) | 8.3 (3.7–18.3) | 0 | 0 | 4.5 (3.0–6.8) |
| Pune (1482) | 3765.1 | 16 | 9.8 (4.9–19.5) | 5.8 (2.2–15.4) | 0 | 0 | 5.6 (2.1–15.0) | 4.2 (2.6–6.9) |
| Age (Years) | ||||||||
| 60–64 (3507) | 9241.2 | 72 | 12.4 (8.2–18.9) | 10.1 (6.2–16.5) | 3.1 (1.3–7.4) | 4.1 (2.2–7.6) | 10.4 (6.6–16.2) | 7.8 (6.2–9.8) |
| 65–69 (1945) | 5763.1 | 58 | 18.0 (12.1–26.8) | 12.4 (7.2–21.4) | 5.0 (2.1–12.1) | 5.7 (2.9–11.5) | 8.0 (4.0–16.0) | 10.1 (7.8–13.0) |
| 70–74 (977) | 2758.7 | 15 | 5.8 (2.2–15.6) | 7.7 (2.9–20.6) | 2.1 (0.3–15.0) | 4.7 (1.5–14.6) | 6.7 (2.2–20.8) | 5.4 (3.3–9.0) |
| ≥75 (811) | 2151.2 | 24 | 17.7 (9.5–32.8) | 11.8 (4.9–28.4) | 2.6 (0.4–18.3) | 6.4 (2.0–19.7) | 16.5 (6.9–39.7) | 11.2 (7.5–16.6) |
| Sex | ||||||||
| Male (2933) | 8053.2 | 52 | 11.4 (7.4–17.5) | 8.2 (4.6–14.4) | 4.8 (2.6–9.0) | 5.1 (2.8–9.5) | 5.0 (2.4–10.5) | 6.5 (4.9–8.5) |
| Female (4307) | 11861.0 | 117 | 15.5 (11.3–21.2) | 12.4 (8.4–18.2) | 4.8 (2.6–9.0) | 4.7 (2.8–7.9) | 12.8 (8.8–18.5) | 9.9 (8.2–11.8) |
Surveillance interrupted during 21 March–6 September 2020 due to COVID-19 pandemic associated lockdowns.
AURI testing is done for a subset of cases and is extrapolated for analysis (95% CI reflect uncertainty in the extrapolation for the total count of AURI).
The incidence rate of influenza-associated ALRI was 13.8/1000 person-years in 2018–2019 and 10.7/1000 person-years in 2019–2020. It decreased to 3.5/1000 person-years in 2020–2021, and then increased to 4.9/1000 person-years in 2021–2022 and 9.8/1000 person-years in 2022–2023 (Table 3). The peak incidence occurred among individuals aged ≥75 years at 11.2/1000 person-years and was higher in females (9.9/1000 person-years) (Table 3).
Overall, individuals between 65 and 69 [RR 1.8 (95% CI: 1.04–3.3)] and greater than or equal to 75 years [RR 2.1 (95% CI: 1.07–3.9)], females [RR 1.5 (95% CI: 1.1–2.1)], individuals with chronic respiratory disease [aRR 2.0 (95% CI: 1.3–3.3)] and those with any disability [aRR 3.7 (95% CI: 1.8–7.7)] had a higher risk of influenza-associated ALRI. Higher risk was observed during the pre-pandemic years [2018–2019: RR 4.0 (95% CI: 2.1–7.4), 2019–2020: RR 3.1 (95% CI: 1.6–5.9)] and in the later pandemic period [2022–2023: RR 2.8 (95% CI: 1.5–5.4)] and among individuals from Chennai [RR 2.7 (95% CI: 1.5–4.6)] and Ballabgarh sites [RR 2.9 (95% CI: 1.7–5.0)] (Table 4).
Table 4.
Factors associated with influenza-associated acute lower respiratory infection (ALRI) among older adults (>60 years) in India.
| N (7240) | Influenza associated ALRI cases (n = 169) | Total person-years (19,914.2) | Crude incidence rate ratioa | Adjusted incidence rate ratio |
|---|---|---|---|---|
| Age group (years)b | ||||
| 60–64 | 72 | 9241.2 | 1.4 (0.8–2.5) | – |
| 65–69 | 58 | 5763.1 | 1.8 (1.04–3.3) | – |
| 70–74 | 15 | 2758.7 | Reference | – |
| ≥75 | 24 | 2151.2 | 2.1 (1.07–3.9) | – |
| Sexb | ||||
| Male | 52 | 8053.2 | Reference | – |
| Female | 117 | 11,861.0 | 1.5 (1.10–2.1) | – |
| Year of surveillance (July–June)b | ||||
| 2018–2019 | 60 | 4353.6 | 4.0 (2.1–7.4) | – |
| 2019–2020 | 38 | 3566.3 | 3.1 (1.6–5.9) | – |
| 2020–2021 | 12 | 3470.6 | Reference | – |
| 2021–2022 | 24 | 4939.9 | 1.4 (0.7–2.8) | – |
| 2022–2023 | 35 | 3583.8 | 2.8 (1.5–5.4) | – |
| Siteb | ||||
| Chennai | 68 | 6033.6 | 2.7 (1.5–4.6) | – |
| Ballabgarh | 62 | 5049.9 | 2.9 (1.7–5.0) | – |
| Kolkata | 23 | 5065.6 | 1.1 (0.6–2.0) | – |
| Pune | 16 | 3765.1 | Reference | – |
| Currently smokingh | ||||
| No | 131 | 16,172.0 | Reference | Reference |
| Yes | 38 | 3742.2 | 1.3 (0.87–1.8) | 1.4 (0.88–2.1) |
| Body mass indexi | ||||
| Underweight (≤18.5) | 19 | 1223.9 | 1.6 (0.95–2.8) | 1.5 (0.85–2.6) |
| Normal weight (18.5–24.9) | 43 | 4533.4 | Reference | Reference |
| Overweight (25.0–29.9) | 26 | 2346.4 | 1.2 (0.7–1.9) | 1.1 (0.7–1.9) |
| Obese (≥30.0) | 12 | 853.1 | 1.5 (0.8–2.8) | 1.4 (0.7–2.7) |
| Diabetes mellitusj | ||||
| No | 121 | 15,941.0 | Reference | Reference |
| Yes | 48 | 3973.2 | 1.6 (1.1–2.2) | 1.5 (0.95–2.45) |
| Hypertensionk | ||||
| No | 91 | 12,321.6 | Reference | Reference |
| Yes | 78 | 7592.6 | 1.4 (1.02–1.9) | 1.3 (0.85–2.0) |
| Cardiovascular diseasesl,c | ||||
| No | 151 | 17,865.0 | Reference | Reference |
| Yes | 18 | 2049.2 | 1.04 (0.6–1.7) | 0.7 (0.4–1.5) |
| Chronic respiratory diseasem,d | ||||
| No | 135 | 18,188.6 | Reference | Reference |
| Yes | 34 | 1725.6 | 2.7 (1.8–3.9) | 2.0 (1.3–3.3) |
| Other co-morbiditiesn,e | ||||
| No | 96 | 13,451.0 | Reference | Reference |
| Yes | 73 | 6463.2 | 1.6 (1.2–2.1) | 1.5 (0.92–2.3) |
| Any disabilityo,f | ||||
| No | 26 | 6387.5 | Reference | Reference |
| Yes | 143 | 13,526.7 | 2.6 (1.7–3.9) | 3.7 (1.8–7.7) |
| Wealth quintilep | ||||
| First | 22 | 3545.4 | 0.90 (0.5–1.6) | 1.1 (0.5–2.1) |
| Second | 18 | 3903.1 | 0.7 (0.4–1.2) | 0.7 (0.4–1.3) |
| Third | 25 | 3634.1 | Reference | Reference |
| Fourth | 47 | 4005.9 | 1.7 (1.04–2.8) | 1.3 (0.8–2.2) |
| Fifth | 57 | 4825.7 | 1.7 (1.1–2.7) | 1.2 (0.7–2.1) |
| School going children in householdq | ||||
| No | 84 | 10,397.4 | Reference | Reference |
| Yes | 85 | 9516.8 | 1.10 (0.8–1.5) | 1.0 (0.7–1.4) |
| Use of solid fuel in householdr,g | ||||
| No | 86 | 11,873.7 | Reference | Reference |
| Yes | 83 | 8040.5 | 1.4 (1.05–1.92) | 0.91 (0.6–1.4) |
| Ventilation provision in households | ||||
| No | 11 | 1471.7 | Reference | Reference |
| Yes | 158 | 18,442.5 | 1.14 (0.6–2.1) | 0.8 (0.4–1.4) |
Rate ratios compare the incidence of influenza-associated ALRI with individuals without influenza-associated ALRI in the cohort.
No confounders identified.
Includes self-reported stroke and heart disease.
Includes self-reported chronic respiratory illness and current/past tuberculosis.
Includes self-reported malignancy, liver, kidney, arthritis, anemia, depression.
Includes any vision problem, any hearing problem, physical movement disorders.
Includes charcoal, straw and shrubs, coal, cow dung cake, wood and kerosene use in household cooking/heating.
Adjusted for Age, Sex, Site, Wealth quintiles.
Adjusted for Age, Any physical disability, Current smoking, Sex, Site, Use of solid fuel in household, Wealth quintiles.
Adjusted for Age, Any physical disability, BMI, Chronic respiratory diseases, Current smoking, School going children in household, Sex, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Age, Any physical disability, BMI, Current smoking, Diabetes, Other co morbidities, Sex, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Age, BMI, Current smoking, Diabetes, Hypertension, Sex, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Age, Any physical disability, BMI, Current smoking, Diabetes, School going children in household, Sex, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Age, BMI, Current smoking, Sex, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Age, BMI, Current smoking, Sex, Site, Use of solid fuel in household, Wealth quintiles.
Adjusted for Age, Sex, Site, Year of surveillance.
Adjusted for Current smoking, Site, Use of solid fuel in household, Ventilation provision in household, Wealth quintiles.
Adjusted for Current smoking, Sex, Site, Ventilation provision in household, Wealth quintiles.
Adjusted for Current smoking, Sex, Site, Wealth quintiles.
Discussion
This community-based study documented the incidence of influenza-associated AURI and ALRI among older adults across four sites in India. Influenza activity varied by site, season, and COVID-19 pandemic period with influenza A(H3N2) consistently the dominant subtype. Female sex, chronic respiratory disease, any physical disability and residence in Ballabgarh or Chennai site were significant risk factors for influenza-associated ALRI. Highest risk of influenza was also seen during the monsoon seasons of the respective sites.
Published literature show wide variation in influenza positivity, which was influenced by study type, population, case definitions, proportion tested, period and geographic location.8,19, 20, 21, 22 No previous study from India has reported influenza positivity among AURI cases in the community setting. AURI influenza positivity in the current study was comparable to community clinic-based ILI surveillance in Ballabgarh (4.5%–6.3%) and pre-COVID-19 ALRI positivity (11.3–12.5%) was comparable with hospital-based severe acute respiratory illness surveillance (15.4%) and higher than a report among individuals admitted with acute medical conditions (11%) in India.22, 23, 24 The observed lower proportion of both AURI and ALRI positivity in period between July 2020–June 2022 is consistent with Indian and global reports of reduced influenza activity during the COVID-19 pandemic, reflecting reduced influenza activity during the pandemic followed by resurgence after relaxation of restrictions.25, 26, 27, 28
Peak influenza activity in this study varied across sites and coincided with local monsoon and winter months, which is consistent with patterns documented in India and globally.20,29 Temperate regions typically see winter peaks, while tropical areas show less seasonality.29 Predominant circulating subtype, Influenza A(H3N2) observed in this study is consistent with reports from India and globally during 2021–2023.25,30,31 Before 2019, A(H1N1)pdm09 and A(H3N2) often altered and a similar trend continued into 2019–2020 as seen in other studies from India.20,24,31 The observed low activity of B/Yamagata aligns with data from India and other global settings.24,30 These findings support WHO’s recommendation for trivalent influenza vaccine.32
Phylogenetic analysis showed that the circulating strains in this study were similar to global circulation patterns and aligned with the vaccine recommended strains. Co-circulation of multiple clades including drifted strains was observed across different years and sites, which aligned with the global evolutionary pattern of influenza viruses, particularly Influenza A(H3N2).25,30,31 These findings underscore the importance of continuous genomic surveillance to elucidate the inter-epidemic evolution of influenza viruses within India and to assess their role in regional or global dissemination.
Community-based studies estimating incidence rates across the full spectrum of influenza severity are limited in India and globally.8,9,19,21,22,26 Hospital-based studies may underestimate the true burden of influenza as a large proportion of infections (81–90%) in the general population are mild,9 and do not require hospitalization.7 This underestimation is further compounded by age-related differences in healthcare utilization, with healthcare-seeking decreasing from 58% among individuals between 60 and 69 years to 11% among those older than equal to 80 years.33 Previous reports show that, although the incidence of influenza was higher among people belonging to younger age groups, more than two-thirds of deaths occur in individuals older than or equal to 60 years.9 Community-based prospective studies provide data on influenza-associated ALRI, that are crucial for determining the impact of intervention strategies, such as targeted vaccination.34 A study from a rural population in northern India reported a similar incidence rate of ALRI (7.9/1000 person-years) during 2015–2017.8 Existing evidence shows that in some age groups, males are at higher risk for more severe influenza disease with seasonal strains while females have been more severely affected by pandemic strains during the 1957 and 2009 influenza pandemics.35
We observed a temporary decline in incidence beginning in 2020, consistent with Indian and global reports.36 The Global Burden of Disease Study 2021 documented a 60.3% overall decline in influenza episodes during the COVID-19 pandemic, with a sharper reduction in high income countries (91.5%) compared to South Asian countries (44%).37 This decline was largely attributed to non-pharmaceutical interventions (NPI) such as face mask use and mobility restrictions, which reduced influenza virus transmission.37,38 The resurgence of influenza activity from 2021, following relaxation of pandemic-related NPI,38 was also evident from our study with influenza-associated ALRI rates in 2022–2023 approaching pre-COVID-19 pandemic levels. Countries with stricter and longer COVID-19 restrictions experienced a more severe resurgence of influenza.26 However, India and other LMIC countries could not enforce such stricter NPI due to pandemic fatigue and prevailing socio-economic conditions.39 Additionally, varying levels of lockdown adherence across different periods in India40 likely contributed to differences in influenza activity observed across study sites during the COVID-19 surveillance period.
Our study identified chronic respiratory disease and disabilities as risk factors for influenza-associated ALRI in older adults consistent with previous studies that have reported multiple determinants of severe influenza-associated respiratory illnesses including male sex, tobacco smoke, alcohol abuse, malnutrition, regular contact with children, poor personal hygiene, and comorbid conditions such as cardiovascular diseases, chronic respiratory diseases, neurological disorders, diabetes, malignancies, chronic renal or liver disease and chronic infections.8,9,37 Several of these factors also contribute to frailty, which may further increase susceptibility to ALRI.41
This study has some limitations. Three out of four study sites were urban and conducted within established health and demographic surveillance systems; therefore, the findings may not be fully representative of older adults residing in rural or non-surveillance settings across India. However, inclusion of geographically distinct regions enhances the diversity of the cohort. Operational reasons prevented sampling of all AURI cases, which may have affected the influenza positivity and incidence estimates. Although we used a standardized clinical definition for diagnosing ALRI at the community level, cases were not radiologically confirmed due to logistical constraints inherent to community-based studies. Clinical and radiographic diagnoses of pneumonia do not always overlap, and reliance on clinical criteria may have led to some misclassification. In addition, premorbid conditions and disability were based on participant self-report and were not clinically verified. This could have resulted in inaccurate classification of exposure status and may have influenced the observed association between disability and influenza-associated ALRI. Risk factors for ALRI were assessed using information collected at enrolment, these characteristics may have changed over time, potentially influencing the estimated rate ratios.
This study presents incidence of influenza associated AURI and ALRI among older adults across four geographically distinct sites in India. The increasing proportion of older adults in India and the incidence of influenza among them supports consideration of influenza prevention and control strategies in this population group. The data on influenza associated AURI and ALRI burden, associated risk factors and seasonality from the community settings contribute to the evidence base required for the development of targeted influenza vaccination strategy. This data could be used to inform cost-effectiveness analyses for vaccination in this population and future research is essential to improve understanding of the role of non-pharmaceutical interventions in influenza transmission. The resurgence of influenza after the end of the COVID-19 pandemic and its return to pre-pandemic levels further reiterates the importance of sustained influenza surveillance to monitor annual epidemics, inform policy and develop prevention strategies.
Contributors
Conceptualization: AK, SS, LD, RA, AOP, RP, CPGK, RK, SK, SDB, VP, AKC, KL, EAB, KR.
Data curation: KB, PH, AOP, PS, VP.
Formal analysis: RP, CPGK, AOP, PS, VP, KB, PH.
Funding acquisition: AK.
Investigation: RP, CPGK, RA, RK, SK, SDB, VP, AKC, AC, MV, GGP, UB, RG, MM, SD, AsC, PM, PS, KS, NKB, PC, SBP, AH, LD, AK.
Supervision: RP, CPGK, RA, AOP, RK, SK, SDB, MV, GGP, UB, RG, MM, PM, KS, NKB, PC, SBP, AH, SS, LD, AK.
Writing—original draft: RP, CPGK, AOP, AK.
Writing—review & editing: RP, CPGK, RA, AOP, RK, SK, SDB, VP, AKC, KB, PH, KL, EAB, KRT, SS, LD, AK. All authors have read and approved the final version of the manuscript.
Data sharing statement
De-identified participant data can be made available upon reasonable request and in accordance with the General Data Protection Regulation (GDPR) two years after the publication of the primary results. Proposals should be directed toward anand.drk@gmail.com.
Declaration of interests
AK was supported by funding from Centers for Disease Control and Prevention (CDC). The funding was received through the affiliated institution. All authors have no conflicting interest.
Acknowledgements
This study was supported by National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC) through the cooperative agreement (U01IP001074). The funding is intended for strengthening pandemic preparedness in the host country to protect the populations in the US and globally from the spread of infectious diseases. The findings and conclusions in this report are those of authors and do not necessarily represent the views of the US Centers for Disease Control and Prevention (CDC). We thank the study participants and the project staff for their contributions to this study.
Footnotes
Supplementary data related to this article can be found at https://doi.org/10.1016/j.lansea.2026.100767.
Appendix A. Supplementary data
References
- 1.Narayan V.V., Iuliano A.D., Roguski K., et al. Burden of influenza-associated respiratory and circulatory mortality in India, 2010-2013. J Glob Health. 2020;10(1) doi: 10.7189/jogh.10.010402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hagiwara Y., Harada K., Nealon J., Okumura Y., Kimura T., Chaves S.S. Seasonal influenza, its complications and related healthcare resource utilization among people 60 years and older: a descriptive retrospective study in Japan. PLoS One. 2022;17(10) doi: 10.1371/journal.pone.0272795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.International Institute for Population Sciences & United Nations Population Fund 2023 . 2023. India ageing report 2023, caring for our elders: institutional responses.https://india.unfpa.org/sites/default/files/pub-pdf/20230926_india_ageing_report_2023_web_version_.pdf [Google Scholar]
- 4.WHO . 2020. Seasonal influenza vaccines: an overview for decision-makers.https://iris.who.int/bitstream/handle/10665/336951/9789240010154-eng.pdf?ua=1 [Google Scholar]
- 5.Goldin S., Brooks D., Jorgensen P., et al. Seasonal influenza vaccination: a global review of national policies in 194 WHO member states in 2022. Vaccine. 2024;42(26) doi: 10.1016/j.vaccine.2024.126274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Seasonal influenza: guidelines for vaccination with influenza vaccine. 2017. https://mohfw.gov.in/sites/default/files/Seasonal%20Influenza%20-%20Guidelines%20for%20vaccination%20with%20Influenza%20Vaccine%20%28updated%29.pdf [Google Scholar]
- 7.Hayward A.C., Fragaszy E.B., Bermingham A., et al. Comparative community burden and severity of seasonal and pandemic influenza: results of the flu watch cohort study. Lancet Respir Med. 2014;2(6):445–454. doi: 10.1016/S2213-2600(14)70034-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kumar R., Dar L., Amarchand R., et al. Incidence, risk factors, and viral etiology of community-acquired acute lower respiratory tract infection among older adults in rural North India. J Glob Health. 2021;11 doi: 10.7189/jogh.11.04027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hwang S.H., Lee H., Jung M., et al. Incidence, severity, and mortality of influenza during 2010-2020 in Korea: a nationwide study based on the population-based National Health Insurance Service database. J Korean Med Sci. 2023;38(8) doi: 10.3346/jkms.2023.38.e58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Department of Health Research. Ministry of Health and Family Welfare. Government of India. Virus research and diagnostic laboratory network quarterly bulletin April - June 2025. https://www.icmr.gov.in/icmrobject/uploads/Static/1760511586_vrdln_apr_june_2025.pdf. Accessed November 10, 2025.
- 11.Krishnan A., Dar L., Amarchand R., et al. Cohort profile: indian network of population-based surveillance platforms for influenza and other respiratory viruses among the elderly (INSPIRE) BMJ Open. 2021;11(10) doi: 10.1136/bmjopen-2021-052473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bickley L.S., Szilagyi P.G., Hoffman R.M. Lippincott Williams & Wilkins, a Wolters Kluwer Business; 2021. Bates' Guide to Physical Examination and History Taking, 13e. [Google Scholar]
- 13.Global Influenza Hospital Surveillance Network Core Protocol. 2025. https://gihsn.org/wp-content/uploads/2025/05/GIHSN-Core-Protocol-2024-2025.pdf? [Google Scholar]
- 14.Influenza Division, Virology, Surveillance and Diagnosis Branch. Genomics and Diagnostic Team. Centers for Disease Control and Prevention . 2023. Multi-segment reverse Transcription-PCR (MRT-PCR) of influenza A and B viruses.https://cdcgov.github.io/MIRA/articles/influenza-a-and-b-mrt-pcr.html [Google Scholar]
- 15.Rambo-Martin B., Lacek K., Chau R. 2023. MIRA: Portable, Interactive Application for High-quality Influenza, SARS-CoV-2 and RSV Genome Assembly, Annotation, and Curation.https://cdcgov.github.io/MIRA/authors.html#citation [Google Scholar]
- 16.Burki T. WHO ends the COVID-19 public health emergency. Lancet Respir Med. 2023;11(7):588. doi: 10.1016/S2213-2600(23)00217-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Press Information Bureau . 2020. Ministry of Health & Family Welfare 2020 Achievements.https://pib.gov.in/PressReleasePage.aspx?PRID=1684546 [Google Scholar]
- 18.Textor J., van der Zander B., Gilthorpe M.S., Liskiewicz M., Ellison G.T. Robust causal inference using directed acyclic graphs: the R package ‘dagitty'. Int J Epidemiol. 2016;45(6):1887–1894. doi: 10.1093/ije/dyw341. [DOI] [PubMed] [Google Scholar]
- 19.Adler A.J., Eames K.T., Funk S., Edmunds W.J. Incidence and risk factors for influenza-like-illness in the UK: online surveillance using flusurvey. BMC Infect Dis. 2014;14:232. doi: 10.1186/1471-2334-14-232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Chadha M.S., Potdar V.A., Saha S., et al. Dynamics of influenza seasonality at sub-regional levels in India and implications for vaccination timing. PLoS One. 2015;10(5) doi: 10.1371/journal.pone.0124122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Fowler K.B., Gupta V., Sullender W., et al. Incidence of symptomatic A(H1N1)pdm09 influenza during the pandemic and post-pandemic periods in a rural Indian community. Int J Infect Dis. 2013;17(12):e1182–e1185. doi: 10.1016/j.ijid.2013.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hirve S., Krishnan A., Dawood F.S., et al. Incidence of influenza-associated hospitalization in rural communities in Western and Northern India, 2010-2012: a multi-site population-based study. J Infect. 2015;70(2):160–170. doi: 10.1016/j.jinf.2014.08.015. [DOI] [PubMed] [Google Scholar]
- 23.Saha S., Gupta V., Dawood F.S., et al. Estimation of community-level influenza-associated illness in a low resource rural setting in India. PLoS One. 2018;13(4) doi: 10.1371/journal.pone.0196495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chadha M., Prabhakaran A.O., Choudhary M.L., et al. Multisite surveillance for influenza and other respiratory viruses in India: 2016-2018. PLoS Glob Public Health. 2022;2(11) doi: 10.1371/journal.pgph.0001001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.WHO . WHO; 2025. Influenza Laboratory Surveillance Information. [Google Scholar]
- 26.Zhang L., Duan W., Ma C., et al. An intense out-of-season rebound of influenza activity after the relaxation of coronavirus disease 2019 restrictions in Beijing, China. Open Forum Infect Dis. 2024;11(4) doi: 10.1093/ofid/ofae163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Jain A., Mahesh S., Prakash O., Khan D.N., Verma A.K., Rastogi Y. Effect of COVID-19 pandemic on influenza; observation of a tertiary level virology laboratory. Virusdisease. 2024;35(1):27–33. doi: 10.1007/s13337-024-00860-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sharmila F., Muthamizhkumaran S., Ratchagadasse V.R., Ramamurthy N., Sistla S., Dhodapkar R. Influence of COVID-19 over seasonal influenza activity in southern India. Indian J Med Microbiol. 2024;47 doi: 10.1016/j.ijmmb.2024.100531. [DOI] [PubMed] [Google Scholar]
- 29.Li Y., Reeves R.M., Wang X., et al. Global patterns in monthly activity of influenza virus, respiratory syncytial virus, parainfluenza virus, and metapneumovirus: a systematic analysis. Lancet Glob Health. 2019;7(8):e1031–e1045. doi: 10.1016/S2214-109X(19)30264-5. [DOI] [PubMed] [Google Scholar]
- 30.Zheng L., Lin Y., Yang J., Fang K., Wu J., Zheng M. Global variability of influenza activity and virus subtype circulation from 2011 to 2023. BMJ Open Respir Res. 2023;10(1) doi: 10.1136/bmjresp-2023-001638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Potdar V., Vijay N., Mukhopadhyay L., et al. Pan-India influenza-like illness (ILI) and severe acute respiratory infection (SARI) surveillance: epidemiological, clinical and genomic analysis. Front Public Health. 2023;11 doi: 10.3389/fpubh.2023.1218292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.World Health Organization . 2025. Frequently asked questions: transitioning to trivalent seasonal influenza vaccines.https://cdn.who.int/media/docs/default-source/documents/epp/pip-dev/transitioning-to-trivalent-seasonal-influenza-vaccines_may-2025-1.pdf?sfvrsn=5e7bd792_3#:∼:text=Page%201-,Frequently%20Asked%20Questions:%20Transitioning%20to%20Trivalent%20Seasonal%20Influenza%20Vaccines,between%20trivalent%20and%20quadrivalent%20vaccines? [Google Scholar]
- 33.Roopani, Dumka N., Ahmad T., Hannah E., Kotwal A. Health facility utilization and Healthcare-seeking behaviour of the elderly population in India. J Family Med Prim Care. 2023;12(5):902–916. doi: 10.4103/jfmpc.jfmpc_553_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Malosh R.E., Martin E.T., Ortiz J.R., Monto A.S. The risk of lower respiratory tract infection following influenza virus infection: a systematic and narrative review. Vaccine. 2018;36(1):141–147. doi: 10.1016/j.vaccine.2017.11.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ursin R.L., Klein S.L. Sex differences in respiratory viral pathogenesis and treatments. Annu Rev Virol. 2021;8(1):393–414. doi: 10.1146/annurev-virology-091919-092720. [DOI] [PubMed] [Google Scholar]
- 36.Feinmann J. Analysis reveals global post-covid surge in infectious diseases. BMJ. 2024;385 doi: 10.1136/bmj.q1348. [DOI] [PubMed] [Google Scholar]
- 37.Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the global Burden of Disease Study 2021. Lancet Infect Dis. 2024 doi: 10.1016/S1473-3099(24)00176-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Lee S.S., Viboud C., Petersen E. Understanding the rebound of influenza in the post COVID-19 pandemic period holds important clues for epidemiology and control. Int J Infect Dis. 2022;122:1002–1004. doi: 10.1016/j.ijid.2022.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Schaner S., Theys N., Angrisani M., et al. Adherence to COVID-19 protective behaviours in India from May to December 2020: evidence from a nationally representative longitudinal survey. BMJ Open. 2022;12(2) doi: 10.1136/bmjopen-2021-058065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Dhanasekaran V., Sullivan S., Edwards K.M., et al. Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination. Nat Commun. 2022;13(1):1721. doi: 10.1038/s41467-022-29402-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Saha S., Amarchand R., Kumar R., et al. A vicious cycle of frailty and acute lower respiratory infection among community-dwelling adults (≥ 60 years): findings from a multi-site INSPIRE cohort study, India. PLoS Glob Public Health. 2024;4(12) doi: 10.1371/journal.pgph.0003903. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.