Abstract
Introduction
Community-acquired pneumonia (CAP) is an important cause of hospitalisation among older adults. Assessing costs of CAP hospitalisation aids in economic evaluation of preventive interventions and guides policy decisions.
Methods
We estimated resource utilisation rates and costs from a societal perspective among adults aged >60 years admitted with CAP in eight public and eight private hospitals in four Indian cities (ie, National Capital Region—Delhi, Kolkata, Pune and Chennai) from December 2018 to March 2020. We interviewed patients, reviewed medical records and bills to estimate resources used, direct medical cost of diagnosis and treatment; direct non-medical cost of travel, lodging and food; and indirect cost of patients and caregivers’ lost income from admission to discharge. Mean costs with SD by hospital type, age group, chronic condition, critical care (intensive care unit, ICU) and virus detection are presented in US dollars (US$). Linear regression after log transformation was conducted to identify determinants of total cost.
Results
We analysed data from 1009 CAP patients in private (63%) and public (37%) hospitals with a median age of 68 (IQR: 63–75) years. Influenza was detected in 121 (12%) and respiratory syncytial virus (RSV) in 21 (2%). Mean length of stay was 6.2 (SD 4.8) days; 37% required ICU admission. Antibiotics and antivirals were used in 96% and 23% of admissions, respectively. Mean (SD) CAP hospitalisation cost was US$305 (244) in public and US$1210 (1019) in private hospitals; US$1024 (1095) in influenza and US$943 (778) in RSV-associated CAP. Regression analysis showed that cost was higher in hospitalisation in private hospitals, those requiring ICU care and among persons with comorbid conditions.
Conclusions
Substantial resources were used, and costs were incurred during CAP hospitalisation among older adults. The findings could aid in cost–benefit analyses of interventions to reduce pneumonia burden, including influenza, RSV or pneumococcal vaccination in older adults.
Keywords: economics, Primary Prevention, Communicable Disease Control, Public Health Practice
WHAT IS ALREADY KNOWN ON THIS TOPIC
Community-acquired pneumonia (CAP) is an important cause of hospitalisation among older adults in India. However, information on the cost of CAP hospitalisations among older adults, especially for influenza-associated CAP, is limited from lower-middle-income countries like India.
WHAT THIS STUDY ADDS
Substantial resources are used during CAP hospitalisation leading to considerable costs equivalent to 16%–65% of per capita net national income depending on public sector versus private sector hospitalisations.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Information on the cost of CAP hospitalisation would aid economic evaluation of preventive interventions and guide policy decision.
Introduction
Acute lower respiratory infections (ALRIs), including community-acquired pneumonia (CAP), are a major cause of morbidity and mortality among older adults. As per the Global Burden of Disease study 2016, more than 60 million ALRI episodes and 1 million deaths due to ALRI were estimated to occur annually among adults aged >70 years. Nearly 50% of these ALRI deaths were attributed to Streptococcus pneumoniae and influenza, which are both vaccine preventable.1 Although effective vaccines are available against influenza and are recommended for older adults, adoption of influenza vaccination policy is limited in lower-middle and low-income countries.2 3 Lack of precise data about the economic burden for determining the cost-effectiveness of influenza vaccine is considered an important reason for delays in establishing national influenza immunisation policies.4 Most (86%) of the publications about the cost of hospitalisation for CAP have been from high-income countries in Europe, North America and Western Pacific. Also, hospitalisation cost varies widely across geographical regions. For example, mean costs per inpatient episode varied from US dollars (US$) 166 (range: US$154–US$368) in the Southeast Asian region to US$17 833 (range: US$1022–US$30 069) in the Americas.5 Recent evaluations from lower-middle-income countries (LMICs) suggest that influenza vaccine is cost-effective or cost-saving among older adults and highlighted the need for additional studies from LMIC in this target group.6 7
India has more than 138 million adults aged ≥60 years, and in this age group, respiratory infections are among the top-10 causes of mortality.8 9 The estimated annual respiratory deaths attributed to influenza infection alone among older adults in India were nearly 35 000 during 2010–2013.10 Nevertheless, population level coverage of vaccines to prevent CAP among older adults in India continues to be low (influenza vaccines 1.5% and pneumococcal vaccine 0.6%).11 At present, the National Program for Health Care of Elderly does not offer pneumococcal and influenza vaccines.12 As per the National Vaccine Policy, cost-effectiveness of vaccination is an important criterion to be assessed before the introduction of new vaccines.13 Studies reporting the cost-effectiveness of these vaccines among older adults are not available from India, and even studies reporting the cost of CAP hospitalisation in older adults are limited in India.6 14 Local estimates would be useful to inform economic evaluation of CAP prevention strategies in India.
To address this need, we conducted this study as a part of the Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses (INSPIRE).15 In this paper, we present the resource used and cost incurred during CAP hospitalisation among older adults aged ≥60 years in India, with a focus on influenza-associated CAP, and identified the factors associated with these costs.
Methods
Study setting
We conducted this study in 16 hospitals that were a part of the INSPIRE located in four sites in India. The sites were national capital region-Delhi (North), Kolkata (East), Pune (West) and Chennai (South).15 We included four hospitals from each site which were selected on the basis of operational convenience. Of the four hospitals on a site, one was public secondary, one was public tertiary and two were private sector hospitals: one with less than 100 beds and one with more than 100 beds.
Study population and sample size estimation
Between December 2018 and March 2020, we screened all older adult patients admitted to the medicine, pulmonology and geriatric wards in 16 selected hospitals for CAP. We used an operational case definition adapted from the British Thoracic Society definition of CAP.16 It was defined as new onset cough within the last 7 days along with one lower respiratory tract symptom (dyspnoea, chest pain) and at least one systemic feature (sweating, reported fever, shivers, body ache or temperature ≥38°C) and tachypnoea (respiratory rate >20 per min).16 In addition, all patients diagnosed with pneumonia by the treating physician were also eligible for the study. Patients who had been hospitalised for >48 hours in the hospital or any other facility were excluded to rule out hospital-acquired pneumonia.
We estimated that a sample size of 760 CAP patients would be required to estimate the cost of influenza-associated CAP based on average cost of severe acute respiratory infection (SARI) reported by Peasah et al (US$376) with 5% precision, assuming an SD of 25% of mean and an influenza positivity of 12.7% among older adults with SARI.14 17 All persons who met the case definition and consented to participate were included in the study, and each was followed on alternate days till discharge or death. Costs incurred postdischarge were collected through telephonic interview at day 7 of discharge.
Data collection
We collected information about age, sex, health insurance coverage, comorbid conditions, date of symptom onset, date of hospitalisation and clinical features through trained nurses by interview and review of records at the time of enrollment and during follow-up visits. Nasal and throat swabs collected at the time of enrolment were tested for influenza and respiratory syncytial virus (RSV) by RT-PCR using a standardised lab testing protocol. Information about resource use, including medication, respiratory/non-respiratory procedures, radiology and laboratory investigations like haematological, biochemical, serological and microbiological tests, was collected from medical records at the time of enrollment and during follow-up. Information about length of stay (LOS) in general ward, admission in intensive care unit (ICU), high dependency unit (HDU) and use of mechanical ventilators was also recorded.
We used a societal approach in estimating the cost. Open Data Kit-based semistructured questionnaires were used to record direct medical and non-medical costs by interviewing the patient/caregivers during visits and by reviewing hospital bills after discharge. The direct medical cost included costs incurred for consultation, treatment, diagnosis and bed charges. We used multiple approaches to estimate these costs (online supplemental table 1). We carried out a cost description study in four public tertiary hospitals and three public secondary hospitals to estimate the cost per day for consultation and bed charges for ICU and non-ICU care using a standard method.18 To estimate the cost of treatment and diagnosis, we conducted a medical record audit and collected details about the cost of all medications and investigations ordered in a purposive sample of patients (ie, 95 from public hospital and 118 from private hospital). We used rates listed under rate contracts available in public sector hospital, central government health scheme and affordable medicine scheme (Pradhan Mantri Bhartiya Janaushadhi Pariyojana) available for the period to cost investigation, procedures and medicine in public sector hospitals.19 20 For the private sector hospitals, the rate list provided by hospitals was used to cost medication and investigation. Direct non-medical costs, including the cost of transportation, food and lodging incurred by the patient, were recorded during the interview. We also captured the number of caregivers and caregiving days associated with hospitalisation for each patient and used this to estimate indirect costs.
Data analysis
All data were cleaned and analysed using Stata V.16. CAP patients who were referred to another facility or left against medical advice were excluded from the analysis. We compared the proportion of CAP patients who used different resources by hospital type (ie, public secondary, public tertiary and private) and influenza detection. χ2 test was used to test the significance of such associations. Among those who had resource utilisation, we compared the mean resource utilisation by hospital type and influenza detection. Statistical significance of difference in mean was assessed using analysis of variance (ANOVA) and t-test. We considered a p<0.05 as significant.
For all cost components for which multiple data sources were available (eg, interview and hospital bill), the higher recorded cost was used. For primary analysis, the cost of services (consultation and bed) in public sector hospitals was computed by multiplying the LOS with standard estimates for hospitalisation cost used in cost-effectiveness analysis indexed to 2019 using Consumer Price Index.21 Estimates of cost per bed day and cost per consultation in public sector hospitals derived from cost description study and available from previous studies were used to estimate the cost of services in sensitivity analysis (online supplemental table 2).22 23
Medical record audit data were used to compute average cost per diagnostic category (haematology, microbiology, serology, biochemistry and radiology) and average cost of treatment per day by site and type of hospital. Estimates from medical record audit were used to compute the cost of medication, investigation and procedures among public sector hospital admissions and in private sector hospital admissions among whom these data were not available through interview or from hospital bills (online supplemental table 1). Estimated cost of investigations was computed for each patient by multiplying number of laboratory tests, radiological investigations and procedures with average cost in different diagnostic test categories. Estimated cost of medicine was computed by multiplying days of medicine use with average cost of medicines per day which was obtained by medical record audit analysis.
Direct medical cost was calculated by adding the cost of consultation, bed/room charges, medication and investigation. Direct non-medical cost was calculated by adding the cost of transportation, food and lodging expenses of the caretaker. Indirect costs incurred by the affected older adult patient and adult caregivers were estimated using a human capital approach.24 The daily income of the patient (US$5.1) was computed by dividing per capita net national annual income (NNI-US$1859) by 365 days. For adult caregivers, the reported income was used if available, and for those without income, per capita NNI was used. Total cost of hospitalisation was obtained by adding direct medical, direct non-medical and indirect cost. All costs were estimated in Indian rupees (INR) and converted to US Dollars (US$) at the exchange rate for 2019 (US$1=INR70.4).25
We computed the mean and SD for direct medical cost, direct non-medical cost, indirect cost and total cost. Mean total cost was compared across age groups, sex, hospital type, influenza detection, comorbidity status, ICU admission status and health insurance status, and statistical significance was tested using the Student’s t-test and ANOVA where required. Linear regression analyses after log transformation were performed using factors with p values less than 0.1 in bivariate analysis.26 Per cent change in cost with change in associated factors was computed by formula (exp(βi)−1)×100.27
We conducted sensitivity analyses for public sector hospitals by estimating total cost using estimates from the cost descriptive study and estimates from previously published studies instead of standard estimates for costing consultation and bed charges (online supplemental table 2).23 A sensitivity analysis was also conducted to estimate the cost in private sector hospitals without using the imputed cost derived from medical record audit.
Patient and public involvement
Public and patients were not involved in the development of the study protocol. However, the ethical committees of participating institutes had representatives from the public, and recommendations of the ethics committees were incorporated in the protocol. The study findings are planned to be communicated through public lectures and pamphlets.
Results
Characteristics of CAP patients
We screened 18 769 patients aged ≥60 years admitted in 16 selected hospitals with operational case definition of CAP and identified 1090 (5.8%) eligible patients who gave consent (online supplemental figure 1). Of these, data were available from 1009 patients of which 635 (63%) were from private hospitals and 374 (37%) were from public sector hospitals. The median age of enrolled patients was 68 (IQR: 63–75) and 39% were females. Health insurance coverage was reported by 59% of patients admitted in private hospital and 10% of patient admitted in public hospital. Nearly 80% of patients reported at least one comorbidity condition. Chronic respiratory disease was reported in 49%, hypertension in 41%, diabetes in 28% and cardiovascular disease in 17% of CAP cases. Influenza virus was detected in 121 (12%) and RSV in 21 (2%) of patients (table 1).
Table 1. Characteristics of older adults with community-acquired pneumonia (CAP) in selected hospitals in INSPIRE network, India 2018–2020 (N=1009).
| Characteristics | All n (%) | Public secondary n (%) | Public tertiary n (%) | Private n (%) | P value |
|---|---|---|---|---|---|
| All CAP | 1009 | 62 | 312 | 635 | |
| Age | |||||
| 60–74 | 737 (73) | 46 (74.2) | 229 (73.4) | 462 (72.8) | 0.90 |
| ≥75 | 272 (27) | 16 (25.8) | 83 (26.6) | 173 (27.2) | |
| Female | 395 (39.1) | 25 (40.3) | 123 (39.4) | 247 (38.9) | 0.90 |
| Site | |||||
| Chennai | 240 (23.8) | 10 (16.1) | 130 (41.7) | 100 (15.7) | 0.001 |
| Delhi | 394 (39) | 22 (35.5) | 87 (27.9) | 285 (44.9) | |
| Kolkata | 230 (22.8) | 16 (25.8) | 50 (16) | 164 (25.8) | |
| Pune | 145 (14.4) | 14 (22.6) | 45 (14.4) | 86 (13.5) | |
| Health insurance coverage | 410 (40.6) | 2 (3.2) | 36 (11.5) | 372 (58.6) | 0.001 |
| Comorbidity | |||||
| None | 210 (20.8) | 23 (37.1) | 61 (19.6) | 126 (19.8) | 0.01 |
| One comorbid condition | 327 (32.4) | 19 (30.6) | 94 (30.1) | 214 (33.7) | |
| More than one | 472 (46.8) | 20 (32.3) | 157 (50.3) | 295 (46.5) | |
| In-hospital mortality | 108 (10.7) | 1 (1.6) | 27 (8.7) | 80 (12.6) | 0.01 |
| Influenza | 121 (12) | 6 (9.7) | 29 (9.3) | 86 (13.5) | 0.14 |
| RSV | 21 (2.1) | 0 (0) | 6 (1.9) | 15 (2.4) | 0.44 |
INSPIRE, Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses; RSV, respiratory syncytial virus.
Resource utilisation
Mean (SD) duration from symptom onset to hospital admission was 7 (8.7) days. Mean (SD) LOS for CAP hospitalisation was 5.5 (4) days in private hospitals, 7.7 (6) days in public tertiary and 5.9 (3.9) days in public secondary hospitals (p<0.001) (table 2). The proportion of CAP patient admitted in ICU or HDU was 37%. This was higher in private sector hospitalisation (52%) as compared with public tertiary hospitalisation (14%) (p<0.001). Mean (SD) days of ICU admission was 4.2 (3) days in private hospitals and 5.9 (6.5) days in public tertiary hospitals. Use of mechanical ventilation was observed in 11% of private hospital CAP patients and in 6% of public tertiary CAP patients (p=0.002). Haematological tests were conducted among 96%, biochemical tests in 91%, respiratory radiology among 88%, microbiological tests among 71% and serological tests among 36% of CAP patients. Radiological and laboratory investigations were significantly (p<0.05) more common among patients admitted in private hospitals as compared with public hospitals (table 2 and online supplemental table 3).
Table 2. Resource utilisation among older adults admitted with community-acquired pneumonia in selected hospitals under INSPIRE network by type of hospital, India 2018–2020.
| Resources* | Number (%) of patients using resources | ||||
|---|---|---|---|---|---|
| AllN=1009 | Public SecondaryN=62 | Public tertiaryN=312 | PrivateN=635 | P value | |
| ICU/HDU admissions | 378 (37.5) | 0 (0) | 45 (14.4) | 333 (52.4) | 0.001 |
| Ventilator use | 89 (8.8) | 0 (0) | 19 (6.1) | 70 (11) | 0.002 |
| Respiratory procedures | 163 (16.2) | 1 (1.6) | 35 (11.2) | 127 (20) | 0.001 |
| Non-respiratory procedures | 517 (51.2) | 21 (33.9) | 105 (33.7) | 391 (61.6) | 0.001 |
| Respiratory radiology | 883 (87.5) | 41 (66.1) | 251 (80.4) | 591 (93.1) | 0.001 |
| Non-respiratory radiology | 35 (3.5) | 0 (0) | 6 (1.9) | 29 (4.6) | 0.034 |
| Serological tests | 366 (36.3) | 19 (30.6) | 65 (20.8) | 282 (44.4) | 0.001 |
| Biochemical tests | 919 (91.1) | 39 (62.9) | 283 (90.7) | 597 (94) | 0.001 |
| Haematology tests | 964 (95.5) | 56 (90.3) | 291 (93.3) | 617 (97.2) | 0.003 |
| Microbiology tests | 714 (70.8) | 27 (43.5) | 187 (59.9) | 500 (78.7) | 0.001 |
| Use of antibiotics | 972 (96.3) | 61 (98.4) | 287 (92) | 624 (98.3) | 0.001 |
| Use of antivirals | 230 (22.8) | 11 (17.7) | 35 (11.2) | 184 (29) | 0.001 |
| Caregiver days utilised | 769 (76.2) | 52 (83.8) | 274 (87.8) | 443 (69.7) | 0.001 |
| Mean length of stay in day (SD) | 6.2 (4.8) | 5.9 (3.9) | 7.7 (6) | 5.5 (4) | 0.001 |
| Mean No. consultations (SD) | 12.6 (10.7) | 8.4 (5.4) | 11.1 (9.7) | 13.9 (11.4) | 0.001 |
Mean (SD) of resource use is given in online supplemental table 3.
HDU, high dependency unit; ICU, intensive care unit; INSPIRE, Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses.
Mean (SD) LOS did not significantly differ between influenza-associated CAP (5.9 (4.5) days) and non-influenza-associated CAP (6.3 (4.9) days) (p=0.36) (table 3). While admission in ICU/HDU was observed in 45% of influenza-associated CAP, it was 36% of non-influenza-associated CAP (p=0.08). The proportion of patients who had respiratory radiology, respiratory procedures, serology test, haematology test and microbiology test was significantly (p<0.05) higher among influenza-associated CAP as compared with non-influenza-associated CAP (table 3).
Table 3. Resource utilisation among older adults admitted with community-acquired pneumonia in selected hospitals under INSPIRE network by influenza detection, India 2018–2020.
| Number (%) of patients using resources | Mean (SD) of resource used | |||||
|---|---|---|---|---|---|---|
| Influenza positive(N=121) | Influenza negative(N=888) | P value | Influenza positive | Influenza negative | P value | |
| Length of stay | 5.9 (4.5) | 6.3 (4.9) | 0.36 | |||
| Consultation | 121 (100) | 888 (100) | 12.9 (8.8) | 12.6 (11) | 0.7 | |
| ICU admissions/days of admission | 54 (44.6) | 324 (36.5) | 0.08 | 4.5 (2.7) | 4.4 (3.8) | 0.77 |
| Ventilator use/days of use | 15 (12.4) | 74 (8.3) | 0.13 | 2.9 (3.2) | 3.7 (3.9) | 0.45 |
| Respiratory procedure | 28 (23.1) | 135 (15.2) | 0.03 | 1.3 (0.6) | 1.8 (1.4) | 0.08 |
| Non-respiratory procedure | 55 (45.5) | 462 (52) | 0.18 | 2.1 (1.2) | 2.1 (1.4) | 0.96 |
| Respiratory radiology | 113 (93.4) | 770 (86.7) | 0.04 | 2.7 (2.2) | 2.4 (2.1) | 0.25 |
| Non-respiratory radiology | 2 (1.7) | 33 (3.7) | 0.25 | 1 (0) | 1.1 (0.2) | 0.72 |
| Serological test | 54 (44.6) | 312 (35.1) | 0.04 | 1.4 (0.7) | 2 (1.9) | 0.03 |
| Biochemical test | 110 (90.9) | 809 (91.1) | 0.9 | 3.7 (3.3) | 4.3 (4.8) | 0.17 |
| Haematology test | 120 (99.2) | 844 (95) | 0.04 | 2.7 (2.1) | 2.9 (2.5) | 0.28 |
| Microbiology test | 95 (78.5) | 619 (69.7) | 0.04 | 1.8 (1.2) | 2.2 (1.8) | 0.04 |
| Use of antibiotics | 120 (99.2) | 852 (95.9) | 0.08 | |||
| Use of antiviral | 48 (39.7) | 182 (20.5) | 0.001 | |||
| Caregiver days utilised | 78 (64.5) | 691 (77.8) | 0.01 | 6.0 (4.0) | 6.5 (4.8) | 0.35 |
ICU, intensive care unit; INSPIRE, Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses.
Use of oral or intravenous antibiotics was observed in 972 (96%) of patients regardless of aetiology. Antivirals were received by 23% of patients: 29% in those admitted in private hospitals and 12.9% in public sector admission (p=0.001). Antivirals were used in 30% of influenza-associated CAP patients admitted with 48 hours of symptom onset as compared with 43% of those admitted after 48 hours(p=0.02). Among influenza-associated CAP, antibiotic use was observed in 99% and antiviral use in 40%.
Cost of hospitalisation
The mean total cost incurred in CAP hospitalisation was US$875 (47% of per capita NNI) out of which mean direct medical cost was US$798 (43% of per capita NNI) (table 4). The mean total cost of CAP hospitalisation was lesser at secondary public hospital (US$166, 9% of per capita NNI) and tertiary public hospital (US$333, 18% of per capita NNI), compared with private sector hospital (US$1210, 65% of per capita NNI). Similarly, for influenza-associated CAP hospitalisations, the mean total cost was US$1320 in private hospitals, US$325 in public tertiary and US$163 in public secondary hospitals.
Table 4. Mean cost associated with community-acquired pneumonia (CAP) hospitalisation among older adults across type of hospitals (in US$) in INSPIRE, 2018–2020.
| All CAPmean (SD) in US$ | Influenza associated CAPmean (SD) inUS$ | Non-Influenza associated CAPmean (SD) in US$ | |
|---|---|---|---|
| Public secondary | |||
| Number | 62 | 6 | 56 |
| Total cost | 166 (98) | 163 (80) | 166 (100) |
| Direct medical cost | 108 (70) | 108 (76) | 108 (70) |
| Diagnostic cost | 14 (12) | 10 (10) | 15 (12) |
| Treatment cost | 19 (15) | 18 (17) | 19 (15) |
| Consultation and bed cost | 74 (49) | 80 (51) | 74 (50) |
| Direct non-medical | 7 (7) | 6 (5) | 7 (8) |
| Indirect cost | 51 (30) | 49 (10) | 51 (31) |
| Public tertiary | |||
| Number | 312 | 29 | 283 |
| Total cost | 333 (254) | 325 (343) | 334 (244) |
| Direct medical cost | 222 (186) | 232 (326) | 221 (166) |
| Diagnostic cost | 39 (45) | 49 (79) | 38 (40) |
| Treatment cost | 53 (57) | 57 (103) | 52 (51) |
| Consultation and bed cost | 126 (98) | 106 (74) | 128 (100) |
| Direct non-medical | 34 (44) | 31 (41) | 34 (45) |
| Indirect cost | 77 (59) | 61 (43) | 79 (60) |
| Private | |||
| Number | 635 | 86 | 549 |
| Total cost | 1210 (1019) | 1320 (1160) | 1193 (995) |
| Direct medical cost | 1148 (1008) | 1260 (1157) | 1130 (982) |
| Diagnostic cost | 238 (283) | 277 (263) | 232 (286) |
| Treatment cost | 319 (398) | 378 (630) | 310 (348) |
| Consultation and bed cost | 479 (438) | 511 (365) | 474 (449) |
| Direct non-medical | 15 (30) | 15 (33) | 15 (29) |
| Indirect cost | 47 (37) | 46 (41) | 48 (36) |
| All | |||
| Number | 1009 | 121 | 888 |
| Total cost | 875 (930) | 1024 (1095) | 854 (904) |
| Direct medical cost | 798 (927) | 956 (1097) | 776 (900) |
| Diagnostic cost | 163 (246) | 209 (248) | 157 (245) |
| Treatment cost | 219 (343) | 283 (553) | 210 (303) |
| Consultation and bed cost | 344 (393) | 392 (362) | 338 (397) |
| Direct non-medical | 20 (35) | 18 (35) | 21 (35) |
| Indirect cost | 57 (47) | 50 (41) | 58 (47) |
INSPIRE, Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses.
Determinants of cost
In bivariate analysis (table 5), the mean total cost was higher among those with one or more comorbidity (p<0.001), private hospital admission (p<0.001), those with health insurance (p<0.001), those admitted to the ICU (p<0.001) and those who died in hospital (p<0.001). Statistically significant differences in total cost were not found across age categories, sex, influenza and RSV detection. Regression analysis suggests that cost incurred was higher in private hospital admission by 146% (95% CI 121.5% to 172.8%), in ICU admission by 141% (95% CI 119.1% to 165.7%) and with health insurance coverage by 15% (95% CI 4.7% to 25.5%) after adjusting for hospital type, comorbidity, influenza positivity, ICU care and in-hospital mortality.
Table 5. Determinants of total cost of community-acquired pneumonia hospitalisation among older adults in selected hospitals under INSPIRE network, India 2018–2020.
| Number | Total cost mean(SD) in US$ | P value* | Exponential of βcoefficient (95% CI)† | Per cent change in cost with unit change in factor (95% CI) | |
|---|---|---|---|---|---|
| Age group | |||||
| 60–74 | 737 | 851 (871) | 0.18 | – | |
| ≥75 | 272 | 939 (1073) | – | ||
| Sex | |||||
| Female | 395 | 862 (878) | 0.73 | ||
| Male | 614 | 883 (963) | – | ||
| RSV | |||||
| Negative | 988 | 873 (933) | 0.73 | ||
| Positive | 21 | 943 (778) | – | ||
| Influenza | |||||
| Negative | 888 | 854 (905) | 0.06 | ||
| Positive | 121 | 1024 (1095) | 1.02 (0.91 to 1.14) | 2 (−8.57 to 13.79) | |
| Comorbidity | |||||
| None | 210 | 690 (689) | 0.001 | ||
| One | 327 | 812 (840) | 1.05 (0.93 to 1.17) | 4.65 (−6.59 to 17.24) | |
| More than 1 | 472 | 1000 (1059) | 1.1 (0.99 to 1.22) | 9.57 (−1.39 to 21.75) | |
| Hospital type | |||||
| Public | 374 | 305 (244) | 0.001 | ||
| Private | 635 | 1210 (1019) | 2.46 (2.22 to 2.73) | 145.83 (121.54 to 172.79) | |
| Health insurance | |||||
| No | 599 | 630 (718) | 0.001 | ||
| Yes | 410 | 1232 (1079) | 1.15 (1.05 to 1.26) | 14.62 (4.65 to 25.53) | |
| ICU | |||||
| No | 627 | 475 (498) | 0.001 | ||
| Yes | 382 | 1531 (1089) | 2.41 (2.19 to 2.66) | 141.24 (119.06 to 165.66) | |
| In-hospital mortality | |||||
| No | 901 | 834 (918) | 0.001 | ||
| Yes | 108 | 1212 (964) | 0.18 | 0.79 (0.69 to 0.91) | −20.81 (−31.11 to −8.97) |
The predicted cost was computed by formula exp(model)×exp(RMSE2/2). The mean cost of hospitalisation was US$875 and predicted cost using this model was US$876.
Student’s t-test.
Linear regression model after log transformation. Factors with p value less than 0.1 in bivariate analysis were included in regression model. The intercept was 206 and RMSE was 0.632. Per cent change in cost with unit change in factor was computed by formula (exp(βi)−1)×100.
ICU, intensive care unit; INSPIRE, Indian Network of Population-based Surveillance Platforms for Influenza and other Respiratory viruses; RMSE, residual mean square error; RSV, respiratory syncytial virus.
Sensitivity analysis
Sensitivity analysis using estimates from the cost descriptive study showed that mean (SD) of total cost of CAP was US$213 (US$127) in public secondary and US$650 (US$801) in public tertiary which were higher than compared with estimates from primary analysis (online supplemental table 4). Using estimates for consultation and bed cost from published literature, the mean total cost for public secondary hospital was US$147 (US$86) and public tertiary hospital estimates was US$328 (US$251). Sensitivity analysis by excluding the private hospital admissions with incomplete medical records suggests that mean (SD) total cost among private sector hospitalisation (N=403) was US$1260 (US$1130).
Discussion
We found that the cost of CAP hospitalisation was substantial. For example, the mean cost of CAP hospitalisation was equivalent to 16% of per capita NNI in public sector hospitalisations and 65% of per capita NNI in private sector hospitalisations. Resource utilisation was higher in private hospitals CAP admission as compared with public sector admissions. Among the public sector hospitalisation, mean total cost of CAP in tertiary care hospitalisation was twice the cost incurred in secondary care hospitalisation. Resource utilisation and cost were similar in influenza-associated CAP and non-influenza-associated CAP.
While antibiotics were administered in almost all (99%) of influenza-associated CAP patients, antiviral use was observed in only 30% of influenza CAP when patients were admitted within 2 days of symptom onset. Public sector clinicians were less likely to use antivirals compared with private sector. Even though current Ministry of Health and Family Welfare (MoHFW) guidelines recommend empirical use of antivirals in all older adults with suspected influenza-associated hospitalisation, the practice of these guidelines, especially in public sector institutions, may be improved through sensitisation and periodic reminders during influenza seasons.28 More judicious antimicrobial use could also reduce costs and resource utilisation, as well as prevent antimicrobial resistance.
In our study, the mean (SD) total cost of CAP hospitalisation among older adults was US$1210 (1019) in private sector hospitals and US$305 (244) in public sector hospitals. Peasah et al estimated the cost of hospitalisation for the management of acute respiratory illnesses in two cities in India.14 While the public sector hospitalisation cost in our study was similar to that reported by Peasah et al (US$174) for this age group after adjusting for inflation, the private sector cost in our study was more than double of previously reported (US$414). The higher cost in private sector hospitalisation in our study may be attributed to differences in management protocol as indicated by higher rates of radiological, laboratory investigation and ICU admissions among private hospital CAP patients in our study. The private sector hospitalisation cost in our study was higher than that reported by studies from Vietnam (US$643), but lower than that reported from China (US$2729), Japan (US$4851) and South Korea (US$1782).29,31 The mean total cost in our study was almost one-twentieth the cost of CAP hospitalisation in high-income western countries (US$17 805).5 In our study, no statistically significant difference was observed between the total cost of influenza-associated hospitalisation (US$1024) and non-influenza-associated hospitalisation (US$854). The cost of influenza-associated CAP hospitalisation in the private sector was comparable to the cost reported from South Africa (US$1059), but lower than reported from China (US$2729) and western countries (US$1776–US$9830).32,34
Our study showed that private hospital admission and ICU care caused a substantial increase in cost, and having health insurance led to only a marginal increase in cost. Nearly 40% of patient admitted to private hospitals did not have health insurance coverage in our study. As per the National Sample Survey Organization 75th survey in India, private hospital admission contributed to 58% of all hospitalisation. The same survey also showed that only 19.9% of the urban population in India had health insurance coverage and only 17% of total medical expenditure was reimbursed.35 Hirve et al reported a high respiratory hospitalisation rate among older adults in India ranged from 6 to 18.7 per 1000 person-years, and influenza-associated hospitalisation rates ranged from 0.6 to 3 per 1000 person-years during 2010–2012.36 All these indicate that CAP hospitalisation among older adults leads to considerable economic burden, and a large portion would be out-of-pocket health expenditure as the majority seek treatment from private hospital admission and insurance coverage is low. Prevention of CAP associated hospitalisations and increasing insurance coverage, for example, as envisaged by the 2018 launch of the national health insurance scheme—Ayushman Bharat Pradhan Mantri Jan Arogya Yojana, could help reduce the overall disease and economic burden due to CAP in India.37 Influenza vaccination is one such intervention, and it could reduce the risk of influenza-associated hospitalisation by half.38 In the past 10 years, several countries in the region started influenza vaccination programme for older adults to reduce the SARI burden, and this intervention was found to be cost effective.7 39 The cost estimates from our study should add to the evidence for introducing influenza vaccination among older adults in India.
The strength of our study is its multicentric approach encompassing 16 health facilities across 4 distinct regions in India with representation from both public and private sectors, as well as secondary and tertiary facilities. The study tries to capture both direct and indirect costs of hospitalisations among older adults aged >60 years in influenza-associated and non-influenza-associated CAP with sufficient sample size. The study also had limitations. The study hospitals were selected conveniently, and all were in urban areas and hence may not be generalisable to all CAP admissions. While only 34% of the population live in urban areas in India, most secondary and tertiary care facilities are in urban areas of India, as indicated by the National Health Profile, which shows 63% hospital beds to be in urban areas.9 40 We did not capture the actual reimbursement of direct medical costs incurred to those who had health insurance coverage, nor did we study the impact of hospitalisation on household income. But our study aimed to estimate the societal cost of CAP, which would be more useful for informing policy. We also did not evaluate the outpatient costs of CAP incurred before hospitalisation. However, we anticipate this would not have significantly impacted the study results because outpatient costs may contribute to <6% of total cost of severe illnesses.29 Our study concluded in March 2020 and, therefore, did not include any cases of COVID-19. While severe COVID-19 cases requiring critical care can incur higher costs than those with other CAP, mild or moderate COVID-19 cases were found to have lower costs.41 Given that hospitalisation due to COVID-19 has significantly declined since 2020–2021, findings from our study remain relevant for influenza-associated CAP.42
Our study suggests that hospitalisation for CAP in older adults is associated with a substantial economic burden. Although MoHFW and medical professional associations endorse preventive measures such as influenza and pneumococcal vaccination to prevent CAP for many years, coverage of these vaccines remains low.43 Nevertheless, the COVID-19 pandemic has shown the feasibility of administering vaccines as a public health intervention for preventing pneumonia in this age group, resulting in high coverage.44 Cost estimates from this study could be used to inform policy regarding the introduction of these vaccines in this high-risk group for prevention of pneumonia among older adults.
Supplementary material
Acknowledgements
We acknowledge the support of all the investigators and coinvestigators, hospital management, our research staff, study participants and other members of the community who helped us in conducting this study. We also acknowledge International Reagent Resource (IRR) support in supplying reagents for testing samples. Comments received from the CDC e-clearance system to improve the manuscript are acknowledged.
Footnotes
Funding: The study was funded by Centers for Disease Prevention and Control, Atlanta, USA under Co-operative Agreement U01IP001074. The funder participated in the design and conduct of the study; analysis and interpretation of the data; preparation, review or approval of the manuscript.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: The study was approved by the institutional ethical committee of All India Institute of Medical Sciences, New Delhi (AIIMS-IEC-283/02.06.17), where the principal investigator is based. Ethical approval was also obtained from local ethics committees of participating institutions including National Institute of Epidemiology (NIE-IHEC/2017-03), National Institute of Virology (NIV-IEC/2018/D5), National Institute of Research in Bacterial Infections (NICED-A-1/2017-IEC). Written informed consent of the patients or family caregivers was obtained before enrolment into this study.
Data availability free text: Data are available on reasonable request to AK.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available on reasonable request.
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Associated Data
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Supplementary Materials
Data Availability Statement
Data are available on reasonable request.