Modelling the economic burden of SARS-CoV-2 infection in health care workers in four countries

The study investigates the society-wide economic burden attributable specifically to SARS-CoV-2 infections among health care workers (HCWs) during the first pandemic year in selected low- and middle-income settings. The research question centers on quantifying direct and indirect costs from HCW infections, including onward community transmission and disruptions to essential health services, and expressing these costs relative to national health expenditures and per-infection metrics. The context is that HCWs faced disproportionately higher infection risk, with ramifications beyond individual illness: increased secondary transmission to households and patients, and service delivery disruptions that could raise maternal and child mortality. The purpose is to inform policy by revealing the magnitude and pathways of costs, thereby motivating investment in infection prevention and control (IPC), workforce protection, and resilient service delivery. The importance lies in capturing costs often overlooked in aggregate pandemic economic estimates and in highlighting vulnerabilities in LMIC health systems with scarce human resources.

Prior economic analyses largely quantified overall COVID-19 costs, including productivity losses from premature deaths, without isolating the burden due to HCW infections. Existing HCW-focused studies emphasized absenteeism costs: an Iranian study estimated US$1.3 million in absenteeism among 1,958 HCWs, and a Greek COI study included absenteeism/presenteeism and direct medical costs. However, comprehensive estimates in LMICs capturing both direct and indirect costs, secondary transmission from HCWs, and economic impacts of disrupted essential services have been lacking. The literature also documents higher SARS-CoV-2 infection risks among HCWs versus the general population across settings. Broader research links HCW shortages to substantial societal costs, including elevated maternal and under-five mortality when HCW density falls, underscoring the potential scale of service disruption impacts during pandemics.

Design: Cost-of-illness (COI) modeling from a societal perspective for the first pandemic year (March 1, 2020–February 28, 2021) across five sites: Kenya, Eswatini, Colombia, Western Cape (South Africa), and KwaZulu-Natal (South Africa). Framework: Three pathways from HCW infection to economic costs. - Pathway 1: Primary SARS-CoV-2 infections and deaths among HCWs. Costs include direct medical, direct non-medical (meals, transportation), and indirect productivity losses due to illness and premature death. - Pathway 2: Secondary infections and deaths among close contacts attributable to exposure to infected HCWs (household members and inpatients). Same cost components as Pathway 1. - Pathway 3: Excess non-COVID mortality (maternal and under-five) due to health workforce disruptions (absenteeism, reduced productivity). Economic valuation uses human capital approach for lost lifetime productivity due to premature deaths. Data sources: (a) Primary collection from authorities on HCW infections/deaths and HCW counts; (b) World Development Indicators for demographics, GDP per capita, health expenditure; (c) Johns Hopkins COVID-19 dataset; (d) peer-reviewed/grey literature for treatment costs, severity composition, length of stay, absence duration; (e) assumptions and extrapolations (e.g., cost ratios from Kenya for home vs facility care applied to other sites). Epidemiologic estimation: - Pathway 1: Observed HCW infections and deaths from site authorities. - Pathway 2: Secondary infections = total population infections × Population Attributable Risk (PAR). Close contacts defined as HCW household members and 20% of inpatients in main analysis. PAR_i = E_i(OR_i−1) / [E_i(OR_i−1)+1], where E_i is share of population who are close contacts and OR_i is odds ratio of infection due to exposure to HCWs (derived from literature and adjusted via log-linear regression for site-specific HCW risks). Secondary deaths = secondary infections × site-specific case fatality rate. - Pathway 3: Convert HCW absence duration and reduced productivity among remaining HCWs (assumed 10% in main analysis) into an effective reduction in HCW density; apply elasticities of under-five mortality rate (U5MR) and maternal mortality ratio (MMR) with respect to HCW density to estimate excess deaths in children under five and mothers. Costing approach: - Direct medical costs: treatment cost per case by severity (81% mild-moderate, 14% severe, 5% critical); 80% of mild-moderate managed at home, 20% facility-based. - Direct non-medical costs: travel and meals when seeking or receiving facility-based care. - Indirect costs (survivors): lost productivity during illness = average daily wage × absence duration (16.44 days). - Indirect costs (deaths): lost future productivity valued using GDP per capita as annual productivity proxy, discounted at 3%, with productive years based on life expectancy at age of death. All costs expressed in 2020 US$. Scenario and sensitivity analyses: - Three scenarios varying four key parameters: share of inpatients as close contacts (10%, 20%, 30%), reduction in HCW productivity (5%, 10%, 15%), and elasticities of MMR and U5MR (mean±1.96 SD). Main analysis uses moderate scenario. One-way sensitivity analyses vary parameters individually from low to high. Stochastic sensitivity (10,000 iterations) with beta distributions for contact share, productivity reduction, and elasticities; gamma distributions for treatment costs; SDs generally assumed at 20% of means; produce 95% CIs from percentiles. Site selection rationale: data availability and representativeness; the two South African provinces accounted for ~40% of national burden by Feb 2021.

Incidence and infection burden: - HCW COVID-19 incidence exceeded that of the general population in all sites: nearly 10× higher in Kenya; 7–8× higher in Western Cape (WC) and KwaZulu-Natal (KZN), South Africa; only slightly higher in Colombia (50.2 vs. 44.7 per 1,000). - Table 1 (selected numbers): HCW infections: Kenya 3,400; Eswatini 464; Colombia 42,142; SA-WC 10,111; SA-KZN 16,299. HCW deaths: Kenya 33; Eswatini 10; Colombia 196; SA-WC 108; SA-KZN 386. Secondary transmission (Pathway 2): - Secondary infections attributable to HCW exposure: Kenya 9,939; Eswatini 2,607; Colombia 43,786; SA-WC 41,162; SA-KZN 69,331. - Secondary deaths exceeded HCW deaths in every site: Kenya 175; Eswatini 95; Colombia 1,177; SA-WC 1,648; SA-KZN 2,141. Service disruption impacts (Pathway 3): - Excess maternal deaths: Kenya 243; Eswatini 6; Colombia 29; SA-WC 4; SA-KZN 8. - Excess under-five deaths: Kenya 1,499; Eswatini 34; Colombia 235; SA-WC 70; SA-KZN 206. Economic costs by pathway (Table 2, 2020 US$): - Pathway 1 subtotal (primary HCW infections): Kenya $5.22M; Eswatini $2.00M; Colombia $128.89M; SA-WC $88.82M; SA-KZN $163.47M. Indirect costs dominate in Eswatini, WC, KZN. - Pathway 2 subtotal (secondary): Kenya $14.95M; Eswatini $9.94M; Colombia $243.13M; SA-WC $236.50M; SA-KZN $344.79M. Indirect costs account for ~45–71% of pathway 2 totals across sites. - Pathway 3 subtotal (excess maternal/child deaths): Kenya $93.03M; Eswatini $4.25M; Colombia $51.84M; SA-WC $12.60M; SA-KZN $36.38M. Total economic burden (Table 3): - Kenya $113.20M (95% CI $62.68–$190.34); Eswatini $16.19M ($13.69–$19.81); Colombia $423.86M ($390.25–$470.31); SA-WC $337.91M ($302.48–$377.02); SA-KZN $544.64M ($504.99–$590.72). - Share of total health expenditure: Colombia 1.51% (1.39–1.68%); SA-WC 8.38% (7.50–9.35%); SA-KZN 8.21% (7.61–8.90%); Kenya 2.03% (1.12–3.41%); Eswatini 5.01% (4.24–6.13%). - Cost per HCW infection: Kenya $33,619; Eswatini $35,659; Colombia $10,105; SA-WC $33,781; SA-KZN $34,226. Ratios to GDP per capita range from 1.54 (Colombia) to 17.98 (Kenya). Pathway contributions (Fig. 2): - Kenya: Pathway 3 dominates (82.2% of total), Pathway 2 ~13.2%. - Eswatini, Colombia, SA-WC, SA-KZN: Pathway 2 accounts for majority (57.4–70.0%); Pathway 1 near one-third in Colombia and South African provinces; Pathway 3 relatively smaller. Scenario analysis (Table 4): - Low-impact total losses ranged from $11.58M (Eswatini) to $471.96M (SA-KZN); high-impact from $23.21M (Eswatini) to $633.22M (SA-KZN). - As % of total health expenditure: low-impact up to 7.11% (SA-WC/KZN); high-impact up to 9.89% (SA-WC). Sensitivity: Kenya most sensitive to Pathway 3 parameters; South African provinces sensitive to the share of inpatients considered close contacts; productivity impact notably influenced Eswatini and Kenya estimates.

The analysis demonstrates that SARS-CoV-2 infections among HCWs imposed substantial societal economic costs in the first pandemic year, especially where HCW infection rates far exceeded those in the general population. While primary HCW infections are costly, the larger burden arises from onward transmission to close contacts and from disruptions in essential services, particularly maternal and child health. These findings align with literature linking HCW shortages to excess mortality and underscore that protecting HCWs yields outsized societal benefits. The costs are largely preventable through robust IPC and WASH implementation, adequate PPE, training, monitoring, and broader workforce support (including psychological and family support) to maintain service delivery during crises. Sites with lower HCW density bore higher costs relative to health expenditure, highlighting vulnerability where workforce buffers are thin. Policies to attract, retain, and effectively deploy HCWs, coupled with targeted safeguards for maternal and child health (e.g., task-shifting, interventions less dependent on HCW density), are critical to health system resilience. The modeled burden attributable to HCW infections is consistent with prior estimates from other outbreaks (e.g., Ebola) where HCW losses and service disruptions amplified societal costs. Communicating these economic consequences can support sustained investment beyond episodic pandemic responses.

This study isolates and quantifies the societal economic burden attributable to HCW SARS-CoV-2 infections via three pathways—primary HCW morbidity/mortality, secondary transmission to close contacts, and service disruption leading to excess maternal and child deaths—across five LMIC sites in the first pandemic year. Economic losses were sizable in absolute terms and as shares of health expenditure, with costs per HCW infection often multiple times GDP per capita. The results highlight that preventing HCW infections through comprehensive IPC, adequate WASH, PPE, and workforce support is both a moral imperative and an economic necessity. Protecting essential services, especially maternal and child health, should be prioritized in preparedness plans. Future research should refine transmission attribution with context-specific epidemiologic models, capture longer-term workforce pipeline effects, include community health workers, and broaden cost categories (e.g., mental health, long COVID, training/replacement costs) to provide a more complete estimate of societal burden.

- Secondary transmission was not modeled with an infectious disease transmission model; odds ratios for close contacts were drawn from a high-income setting and adjusted via regression, introducing uncertainty. - Underreporting of infections and deaths likely leads to conservative estimates; data limitations necessitated assumptions and proxy values (e.g., non-medical costs in Eswatini from South Africa). - South African site data excluded private sector HCWs, potentially underestimating Pathway 1 costs; public–private comparability assumed for Pathways 2 and 3. - Community health workers were excluded due to data gaps; including them would likely increase estimated losses, especially in Kenya, Eswatini, and South Africa. - Some cost components were not fully captured: long-term absenteeism, replacement/training costs, presenteeism beyond assumed 10% productivity reduction, burnout, mental health impacts, and long COVID. - Estimates of productivity changes among remaining HCWs are assumed (10% in main analysis) and influential in sensitivity analyses (notably in Kenya). - The analysis focuses on the first pandemic year with low vaccination, PPE shortages, and constrained system capacity; subsequent years likely have lower burdens due to improved conditions. - Site-level averages may mask within-site heterogeneity; parameters may not generalize nationally beyond the study sites.