Economic Burden and distribution of Household Expenditures on Snakebites: Assessing Catastrophic Health Expenditures and Financial Hardship in Rural Uganda

Economic Burden and distribution of Household Expenditures on Snakebites: Assessing Catastrophic Health Expenditures and Financial Hardship in Rural Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic Burden and distribution of Household Expenditures on Snakebites: Assessing Catastrophic Health Expenditures and Financial Hardship in Rural Uganda Michael Muhoozi, Simon Kasasa, Joan Tusabe, Gati Wambura, Paul Mukama Ategyeka, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7653113/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction : Snakebites remain a major yet long-neglected public health burden, disproportionately affecting the poor communities in rural Uganda. Households continue to unfairly spend out of pocket on snakebites and are further pushed into poverty. The study estimated household economic burdens, Catastrophic Health Expenditure (CHE), and associated socioeconomic inequalities from snakebites in Eastern Uganda. Methods This was a cross-sectional study that used an ingredient-based approach to estimate the household costs of management and productivity loss of a snakebites from the household perspective. Two districts from Eastern Uganda with the highest incidence cases were visited for household survey to elicit costs borne by the snake bite victims at community level. CHE was evaluated using expenditure thresholds of 10%, 25%, and 40%. Inequalities were assessed using concentration indices, slope index of inequality (SII), relative index of inequality (RII), Oaxaca–Blinder decomposition, and quantile regression. Results CHE related to snakebite was experienced by 31.4% of households at the 40% threshold, with the poorest quintile most affected (χ²=23.97, p < 0.001; SII = − 0.173, p = 0.011). Significant disparities in economic burden were found by occupation (hospital costs p = 0.002; productivity loss p = 0.020), socioeconomic status (hospital costs p = 0.003), and hospital visit type (p < 0.001). Outpatient care was strongly protective (p < 0.001), while hospital admissions drove up costs (p < 0.001). Quantile regression showed outpatient care significantly reduced financial burdens (p < 0.001), whereas hospitalization increased costs substantially (p < 0.001). Decomposition analysis showed structural inequities: middle-income patients faced 55.2% higher unexplained costs than the poorest. Conclusion Snakebites led to substantial financial hardship and entrenched inequities for households in Eastern Uganda. Policy responses should prioritize affordable treatment access and financial risk protection for the poorest and most vulnerable populations. Snakebites Economic Burden Financial hardship Rural health Healthcare Costs Background Snakebites pose great economic burden affecting many of the poorest rural low middle-income countries. Globally, 2.7 million people are bitten by snakes; and up to 580,000 and 20,000 snake bites require treatment and lead to fatality annually respectively in Sub-Saharan Africa ( 1 , 2 ). In Uganda, a recent household based study on indicated the incidence of snakebites at 31.6%; majority of which were from the Eastern region ( 3 ). While some hospital, community-based surveys have established statistics on the incidence of snake bites( 3 – 5 ), no studies have been done to estimate the economic burden of snakebites in Uganda. Evidence estimates the economic burden to vary between US $ 126,319 in Burkina Faso to US $ 13,802,550 in Sri Lanka ( 6 ). More so, these studies under-report, do not all cost components, use only the providers’ perspective and capture only short-term costs. Use of such estimates can misrepresent the actual economic burden in Uganda where only 1.4% of snake bite victims go to hospitals ( 3 ). This is because of the costs incurred by the household respondents in using local remedies and accessing hospital are most likely to be missed. More so, fatalities, disabilities and costs during referrals from lower-level facilities are inaccurately estimated because of missing records. The communities in Uganda continue experience unknown, unfairly and unaccounted for Catastrophic Health Expenditures (CHE) due snakebites. The World Health Organization (WHO) aims to reduce deaths and disabilities caused by snakebites by a half by the year 2030, alleviating its burden on disadvantaged communities ( 7 ). Global recognition of this burden has seen Uganda integrate snakebites into the country’s Neglected Tropical Diseases program. Consequently, Uganda recently drafted snakebite prevention and management strategy ( 8 ). Despite these policy advances, critical gaps remain. There is an absence of distribution and cost estimates of management snakebite-associated morbidities, mortalities and disabilities. Therefore, the health system and the community continue to struggle with severe cases, high costs of treatment and difficulty in making case to the government and funders for interventions. More so, cost effective strategies and commodities require cost estimates to justify investments. This study therefore estimated the economic burden including: distribution, CHE, direct and indirect costs for snakebites envenoming in Kamuli and Iganga districts of Eastern Uganda grounded on an adapted Donabedian model (See supplemental material Table S1 ). Methods Study design and setting This cross-sectional study was based on snakebite events that had already occurred. We retrospectively collected additional data on activities, resource use, and associated costs related to those events. Two districts of Kamuli and Iganga from Eastern Uganda with the highest incidence cases were visited for household survey (See supplemental material Fig. 1). These were selected based on Ministry of Health surveillance data and prior reports of elevated case numbers. Both districts are predominantly agrarian, with most residents engaged in subsistence farming and limited access to tertiary health facilities. Study population and sampling Households were eligible if they reported at least one snakebite incident in the preceding 12 months, confirmed by either (i) a clinical diagnosis from a health facility record, or (ii) a detailed, consistent narrative of the event during the household interview. All snake bite victims identified from reports from the community were considered for interview and verified using probes. The diagnosis of venomous snakebites was difficult at health facility and mostly depended on the right description by victims about the snake ( 9 ). We identified households using a two-stage approach: ( 1 ) review of health facility outpatient and inpatient registers in selected districts, and ( 2 ) referral lists from community health workers and village leaders, to capture cases treated outside formal healthcare. Eligibility criteria Eligible individuals were all adults aged 18 years and above, in good health ,able to express themselves verbally and had personally incurred costs due to snakebite. Adults aged 18 years and above were selected because they bear majority of the costs incurred in a typical household ( 10 ).Additionally, majority of the incident cases of snakebites in these communities occur within this age group ( 3 ). Cases where the primary injury was not a snakebite, or where costs were primarily due to other conditions, were excluded. Data collection Data collection tools were designed to capture CHE of patients and caregivers assessing from the start of the event up to when the event completely resolved. We followed up individuals through home visits or using a telephone interview to record out-of-pocket payments incurred. Telephone interviews were conducted when three consecutive attempts to household made were futile. Household heads were interviewed to gather household costs and victims’ costs (in case they incurred the costs). Costs borne directly by the victims were solicited from the victims themselves and (or) from those who incurred them other than themselves (victims). Victims of snakebites were accessed through household heads after confirmation of a snakebite (using screening questions) for interviews. The study tools were pretested to ensure their validity, reliability, clarity, suitability, and logical flow; based on the results, necessary revisions were made prior to their deployment in the field.. The tools were pre-tested at a non-participating community in Mayuge District with similar snake bite incidence and community characteristics as the study districts. Research assistants with experience in conducting costing studies and interviews were trained for five days in preparation for data collection. Training included; general understanding of costing approaches, costing perspectives, estimation skills and probing skills. The research assistants were also trained in practical interviewing (including role plays), and note taking during the interview processes. Data management and analysis Costing approaches An ingredient-based approach was used to estimate the cost of management and productivity loss of a snakebite from the household perspective. Human capital approach was used to estimate indirect costs. This was estimated by a product of time loss getting care and average income of household head. Differences within direct and indirect costs for a snakebite event and socio-economic status, service provider status, age, gender and residence were tested using relevant statistical tests. These included tests for normality or equal variance and when the hypotheses were rejected the Wilcoxon and Kruskal-Wallis rank tests were used when applicable. In this study, principle component analysis (PCA) approach was used to score asset; which are ranked to determine asset quintiles ( 11 , 12 ). Catastrophic health expenditures CHE were calculated using direct costs over the annually income and expenditures for households visited. Data collected on expenditures comprised of healthcare expenses like cost of drugs, bed costs, travel costs, meals and other expenses including going to traditional herbalists and healers. A household experienced catastrophic health expenditures if it spent more than the following thresholds on an event of a snake bite: 10% of its income, 25% of its monthly expenditures or 40% of its monthly expenditures without food ( 13 ). These categories were analysed by performing a chi-square test of independence to assess the unadjusted relationship between wealth quintiles and CHE. Wealth related Equity and Distribution Analysis To assess socioeconomic inequalities in treatment costs and CHE, the study used the Lorenz curves and calculated both Wagstaff and Erreygers concentration indices. The slope index of inequality (SII) and relative index of inequality (RII) quantified absolute and relative disparities, respectively. Multivariate and quantile regression models were used to adjust for demographic and socio-economic confounders, estimating the effect of wealth and other predictors on the distribution of financial burden. Blinder–Oaxaca decomposition was used to partition cost differences across wealth groups into explained (endowment) and unexplained (structural) components. Results Social demographic characteristics of study participants A total of 150 households reporting a snakebite incident within the past 12 months were interviewed. Participants had an average age of 40.4 years (SD: 12.9), with the 30–39-year age group most represented at 34.0%. The victims' mean age was 38.2 years (SD: 14.4). More than half (53.3%) of the households were headed by males and had 3–5 members (52.0%). Similarly, over a half (54.7%) of the victims were males. Majority of participants were from rural areas (92.0%) while 42.7% were involved in farming. Nearly one-third (35.3%) of participants spent between 150,000/= (39.6 USD) to 300,000/= (79.2 USD)monthly. See Table 1. Other characteristics of repondents were collected detailed in Supplementary Table S2-3 and Supplementary Figs. 2 and 3. Catastrophic health expenditures related to the incident of a snake bite by asset quintile. Categories of CHE were analysed to assess the disparity among wealth quintiles. The poorest and poor groups were disproportionately affected with majority spending following into 40% threshold at statistically significant level (χ² = 23.9653, p < 0.001). See Table 2. We detailed (in supplementary files) the other categories of the CHE and non-CHE of a snake bite across the three thresholds 40% (supplementary Fig. 4), 25% (supplementary Fig. 5) and 10% (supplementary Fig. 6). Table 2 CHE thresholds categorised by wealth quintiles Wealth Quintiles Experienced Catastrophic Health Expenditure thresholds CHE at 10% CHE at 25% CHE at 40% Poorest 11 6 47 Poor 15 10 41 Middle 5 5 12 Wealthy 10 8 33 Wealthiest 4 3 10 Total 45 32 143 CHE 10%: χ² = 2.6964, p = 0.610 CHE 25%, χ²= 5.2573, p = 0.262 CHE 40%, χ² = 23.9653, p < 0.001 Counts shown are cases (households for only those the experienced CHE) for each threshold, by wealth quintile and is not equal to the overall sample size (N = 150). Slope and Relative Indices of Inequality in Catastrophic Health Expenditure Our findings indicate that at CHE at the 10% threshold, the SII was 0.218 (95% CI: − 0.039–0.475), indicating a trend toward higher CHE among wealthier individuals, though this was not statistically significant (p = 0.094). At the 25% threshold (CHE2), the SII was smaller and also non-significant (SII = 0.109; p = 0.336). At the 40% threshold, we observed a significant pro-poor inequality, with an SII of − 0.173 (95% CI: − 0.305 – − 0.041; p = 0.011), indicating that poorer individuals are more likely to experience CHE at this higher threshold. The poorest experienced CHE at 83% the rate of the average. See Table 3. Table 3 Slope and Relative Indices of Inequality in Catastrophic Health Expenditure (CHE) at 10%, 25%, and 40% Thresholds by Socioeconomic Rank Variable SII (95% CI) RII Mean Outcome p-value CHE at 10% 0.218 (–0.039–0.475) 1.14 0.191 0.094 CHE at 25% 0.109 (–0.114–0.331) 1.07 0.159 0.336 CHE at 40% –0.173 (–0.305 – − 0.041) 0.83 1.04 0.011 Note: SII = Slope Index of Inequality; RII = Relative Index of Inequality. Positive SII indicates higher values among wealthier individuals; negative SII indicates higher values among poorer individuals. Quantile Regression of Financial Burden In adjusted quantile regression models, household wealth was inversely associated with financial burden across the distribution, though not significantly, with the largest reduction observed at the 75th percentile (–3.9 pp; p = 0.306). Adults aged 30–49 and 50 + experienced significantly lower burdens at the lower tail (–8.7 pp, p = 0.018; − 9.6 pp, p = 0.022, respectively), but not at higher quantiles. Gender and residence showed no significant effects, though urban and male respondents tended to bear slightly higher burdens. Occupational categories were not significantly associated with burden, but manual labor showed a positive association at the upper tail in prior models. Outpatient care was strongly protective, reducing burden by 24.1–85.8 pp across quantiles (all p < 0.01), while hospital-based care significantly increased burden by 30.1–63.4 pp (all p < 0.001). iTraditional healer uses and household size had no measurable impact. Severity was positively associated with burden, particularly for severe cases, but estimates were not statistically significant. See Table 4. Table 4 Quantile regression coefficients of Financial Burden (% of monthly income) Predictor 25th percentile 50th percentile 75th percentile Wealth score (per quintile) –0.813 (0.993) –2.609 (1.596) –3.919 (3.817) Age < 30 years 1 1 1 30–49 years –8.729* (3.635) –4.953 (5.843) –16.070 (13.975) 50 + years –9.553* (4.116) –9.516 (6.617) –24.571 (15.826) Gender Males 1 1 1 Female –4.088 (2.669) –1.977 (4.291) 2.775 (10.263) Residence Rural 1 1 1 Urban 4.191 (4.514) –0.000 (7.256) 7.259 (17.353) Occupation Manual Labour 1 1 1 Professional –5.477 (3.397) –1.623 (5.460) –6.321 (13.059) Sales & services –0.966 (3.250) –1.039 (5.225) –4.858 (12.495) Visit type Inpatient 1 1 1 Outpatient –24.074*** (4.713) –22.226** (7.576) –85.834*** (18.120) Went to hospital No 1 1 1 Yes 30.126*** (3.451) 46.543*** (5.547) 63.358*** (13.267) Used traditional remedies No 1 1 1 Yes 0.538 (3.847) 2.547 (6.185) 15.026 (14.792) Severity IPD moderately severe 1 1 1 OPD non-severe 22.768 (12.117) 35.501 (19.478) 56.066 (46.584) Severe 15.586 (11.319) 28.528 (18.195) 53.899 (43.516) Family Size ≤ 2 members 1 1 1 3–5 members 1.612 (6.933) 2.810 (11.145) 5.884 (26.654) 6–10 members 3.264 (7.184) 3.596 (11.549) 2.692 (27.620) > 10 members 1.414 (7.581) 4.826 (12.186) 0.728 (29.146) Note. Estimates are coefficient (SE) with 95% confidence interval in brackets. * p < 0.05; ** p < 0.01; *** p < 0.001. Models estimated at τ = 0.25, 0.50, 0.75 with 200 bootstrap replications; N = 145; Pseudo R² = 0.332, 0.388, 0.420. Costs incurred by the snakebite victims Using Blinder–Oaxaca decomposition, we examined the mean cost differentials between the middle-wealth quintile (Q3) and other wealth groups, partitioning the observed gaps into explained (endowment) and unexplained (coefficient and interaction) components. Wealthier patients incurred higher treatment costs compared to poorer patients. Decomposition analysis showed that 44.8% of the cost gap between the middle quintile and the poorest quintile was explained by observable characteristics (e.g., type of facility, age, occupation), while 55.2% was unexplained, suggesting structural inequities in access, quality, or intensity of care. A similar pattern was observed between the richest and middle quintiles, with 43% of the cost gap unexplained. Table 5. Table 5 Oaxaca–Blinder decomposition of mean OverallCosts (high quintile 3 vs lower quintiles) Comparison H L R = H–L E (endowments) C (coefficients) CE (interaction) % explained % unexplained vs Q1 260.0 K 76.0 K 184.0 K 81.0 K 88.0 K 12.0 K 44.80% 55.20% vs Q2 260.0 K 200.0 K 60.0 K 130.0 K 44.0 K –110.0 K 211.70% –111.7% vs Q4 260.0 K 140.0 K 120.0 K 14.0 K 150.0 K –49.0 K 12.10% 87.90% vs Q5 260.0 K 120.0 K 140.0 K 77.0 K 110.0 K –50.0 K 56.60% 43.40% Notes: H = mean prediction for quintile 3; L = for comparison quintile; R = raw gap. E, C, CE sum to R (E + C + CE = R). % explained = (E + D·CE)/R, % unexplained = (C + (1–D) ·CE)/R; D ≈ relative size of Q3 in pooled sample (20–46%). K-1000 Reference: pooled model coefficients. Discussion This study provides the first community-level evidence from Uganda that snakebites is not only an emergency but also a significant and inequitable economic shock for affected households. The findings show that costs of management of a snakebite and productivity losses can consume a substantial share of household resources, with the poorest bearing a disproportionately high risk of CHE( 7 , 13 ). By linking financial burden to socioeconomic position, care pathway, and structural inequities in cost allocation, our results add critical detail to the understanding of snakebite’s broader public health and economic impact in rural sub-Saharan Africa. Our findings are consistent with reports from other low- and middle-income countries showing that even modest absolute treatment costs can be catastrophic relative to income in rural settings ( 6 , 14 ). In Sri Lanka, where public healthcare is free, median household costs were lower than those observed here, yet still imposed significant hardship( 15 ). The high CHE prevalence in our cohort mirrors WHO’s observation that nearly any hospital-treated snakebite in poor communities can meet CHE thresholds CHE( 7 ). Similar torural India, where households often resort to debt or asset sales to cover treatment ( 16 ), our data indicated that many Ugandan families face acute financial distress after snakebite. This aligns with the conceptualization of snakebite as a “disease of poverty”—a term applied due to its disproportionate burden on agricultural workers in low-resource settings and its capacity to entrench poverty through direct and indirect costs( 17 , 18 ). The magnitude of burden varied sharply with care pathway. Hospital admissions were associated with substantially greater costs and income loss than outpatient treatment, consistent with the greater severity and higher resource intensity of inpatient cases ( 19 ). Outpatient care was strongly protective in our quantile regression models, supporting the hypothesis that early, less severe cases avoid costly escalation( 20 ). Traditional healer use had no measurable net impact, suggesting that patients may pay for both traditional and biomedical care when symptoms progress, a pattern documented in Myanmar and parts of sub-Saharan Africa ( 21 ). Our Blinder–Oaxaca decomposition revealed that more than half of the cost gap between the poorest and middle-income patients was unexplained by observable characteristics such as age or facility type, pointing to structural inequities in care pricing and provision. Similar unexplained gaps were observed at higher income levels, suggesting that ability to pay may influence treatment intensity or pricing, creating a two-tiered care environment ( 22 ). This form of horizontal inequity where patients with similar clinical needs face different costs has been documented in other out-of-pocket financing contexts and undermines equity in access to effective care( 23 ). Occupation and age shaped the nature of losses. Professionals tended to incur higher productivity losses than farmers or manual laborers, likely reflecting higher daily wages and less substitutable work ( 24 ). Peak burden was observed among adults aged 30–39 years, a prime working age with heavy family responsibilities, amplifying the household impact( 25 ). Older adults bore slightly less measurable financial loss, possibly due to reduced earning capacity or support from adult children, though other studies associate older age with worse clinical outcomes and potentially higher treatment needs( 26 , 27 ). The high CHE rates and wealth-linked disparities highlight the urgency of integrating financial protection into snakebite management in Uganda. Policy options include subsidising and waiving in and out of hospital fees for the poorest as well as decentralising antivenom availability to lower-level facilities( 7 , 28 ). Such measures could prevent progression, reduce the need for costly inpatient stays, and avoid treatment delays driven by cost concerns [20]. Strengthening antivenom supply chains and regulating prices is essential; current African market prices can represent several months’ income for rural households ( 29 ). Culturally sensitive strategies—such as engaging traditional healers in early referral pathways—may improve timely access to biomedical care( 30 ). Beyond the health sector, social protection measures, including emergency cash transfers or agricultural insurance, could help households recover financially after severe snakebite envenoming, aligning with WHO’s global target to halve the snakebite burden by 2030( 31 ). Limitations This cross-sectional, retrospective design relied on self-reported data, which may be subject to recall bias, especially for indirect costs. Seasonal income variation was not captured, and intangible costs—such as psychological distress, disability, or social disruption were excluded, likely underestimating total burden( 32 ). The study could possibly have selection bias if the most financially devastated households were unavailable for interview. Finally, the study was conducted in two high-incidence districts, which may limit generalisability to areas with different incidence rates, health service availability, or snake species. Prospective cohort studies could better quantify long-term costs, productivity losses, and coping strategies, including debt and asset liquidation ( 33 ). Given the strong protective effect of outpatient care, cost-effectiveness analyses of early-intervention models are needed. Mixed-methods research could illuminate cultural and behavioural determinants of care-seeking, informing locally tailored strategies. Trials of financial protection mechanisms, including insurance schemes and cash transfers, could test scalable solutions to prevent impoverishment after snakebite. Conclusion Snakebites in rural Uganda imposes a substantial and inequitable economic burden, particularly on the poorest households, and structural inequities exacerbate these disparities. Addressing this dual clinical and economic challenge requires integrated strategies spanning prevention, timely treatment, and financial risk protection. Such measures are essential to break the cycle in which a single envenoming can plunge a vulnerable household deeper into poverty, and to realise both the health and equity goals of the global snakebite agenda. Declarations Ethics approval and consent to participate Approval to conduct the study was obtained from the Makerere university school of Biomedical Sciences Research and Ethics committee (IRB Number: SBS-2022-272). Permission was also sought at districts level (from the office of the district health offices Kamuli and Iganga), local council chairpersons and the health facility in-charges. Written informed consent were signed and kept secured in a lockable cabinet at the study site. During the study, questionnaires had serial numbers instead of individuals’ names for confidentiality purposes. Individuals were informed of their right to agree to participate or withdraw from the study at any time without fear of any negative repercussions. After informing them of the purpose and procedure related to the study, a written informed consent was sought from the study participants. All principles of research involving human subjects outlined in the Declaration of Helsinki were adhered to. Consent for publication Not applicable Availability of data and materials Data will be provided on request from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the Early Career Grants program of the Royal Society of Tropical Medicine and Hygiene (RSTMH). The views expressed herein are solely those of the authors and do not necessarily reflect the official positions of the RSTMH. Authors' contributions MM contributed to the conception and design of the study, data analysis, drafting and final review of the manuscript. SK provided oversight for the study design, guided data interpretation, and critically reviewed and revised the manuscript preparation and write-up. JT assisted in data acquisition and interpretation, and contributed to the drafting and revision of the manuscript. GW and PMA participated in data analysis, interpretation and critical revision of the manuscript for intellectual content. PA, BDN and VAJ contributed to the analysis and interpretation of data and substantially revised the manuscript. DK supervised the research, provided critical insights into the interpretation of findings, and reviewed the manuscript substantively. All authors reviewed and approved the final manuscript. Acknowledgements The authors would like to thank Makerere University Center for Health and Population Research, the Iganga-Mayuge Health Demography Site staff, district officials and all the village scouts that supported in the data collection. We are grateful to students and supervisors from Makerere University School of public health for their part in data collection. References WHO. Snakebite envenoming fact sheet 2019 [cited 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/snakebite-envenoming. WHO. Prevalence of snakebite envenoming 2021 [updated 26/04/2021; cited 2021 26/04/2021]. Available from: https://www.who.int/snakebites/epidemiology/en/. John B Ddamulira SK, Alfred Mubangizi, Julius Kyaligonza , Susan Kizito THE BURDEN OF SNAKEBITE AND SNAKEBITE ENVENOMING IN UGANDA: A COMMUNITY SURVEY AND FACILITY AUDIT. Health SoP; 2021. HEPS HAIHa. Fact sheet snakebite incidents, response & antivenom supply (Uganda). 2018. Health Action International(HAI) H. 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Snakebite envenoming: A systematic review and meta-analysis of global morbidity and mortality. PLoS neglected tropical diseases. 2024;18(4):e0012080. Sharma SK, Chappuis F, Jha N, Bovier PA, Loutan L, Koirala S. Impact of snake bites and determinants of fatal outcomes in southeastern Nepal. The American journal of tropical medicine and hygiene. 2004;71(2):234-8. Warrell DA. Guidelines for the management of snake-bites. 2010. Chippaux J-P. Guidelines for the production, control and regulation of snake antivenom immunoglobulins. Biologie aujourd'hui. 2010;204(1):87-91. Harrison RA, Oluoch GO, Ainsworth S, Alsolaiss J, Bolton F, Arias A-S, et al. Preclinical antivenom-efficacy testing reveals potentially disturbing deficiencies of snakebite treatment capability in East Africa. PLoS neglected tropical diseases. 2017;11(10):e0005969. Tusabe J, Muhoozi M, Kajungu D, Mukose A, Kasasa S, Sebina Kibira SP. Knowledge, perceptions and healthcare practices of communities for management of snakebites in Kamuli District, Eastern Uganda. Transactions of The Royal Society of Tropical Medicine and Hygiene. 2025;119(4):418-31. Marzo F, Mori H. Crisis response in social protection: World Bank, Washington, DC; 2012. Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes: Oxford university press; 2015. Jayawardana S, Gnanathasan A, Arambepola C, Chang T. Chronic musculoskeletal disabilities following snake envenoming in Sri Lanka: a population-based study. PLoS neglected tropical diseases. 2016;10(11):e0005103. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 30 Sep, 2025 Editor invited by journal 23 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 18 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7653113","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527759737,"identity":"a4df4e29-83f8-4a54-95d4-e205d3095796","order_by":0,"name":"Michael 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Globally, 2.7\u0026nbsp;million people are bitten by snakes; and up to 580,000 and 20,000 snake bites require treatment and lead to fatality annually respectively in Sub-Saharan Africa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Uganda, a recent household based study on indicated the incidence of snakebites at 31.6%; majority of which were from the Eastern region (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile some hospital, community-based surveys have established statistics on the incidence of snake bites(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), no studies have been done to estimate the economic burden of snakebites in Uganda. Evidence estimates the economic burden to vary between US\u003cspan\u003e$\u003c/span\u003e126,319 in Burkina Faso to US\u003cspan\u003e$\u003c/span\u003e13,802,550 in Sri Lanka (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). More so, these studies under-report, do not all cost components, use only the providers\u0026rsquo; perspective and capture only short-term costs. Use of such estimates can misrepresent the actual economic burden in Uganda where only 1.4% of snake bite victims go to hospitals (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This is because of the costs incurred by the household respondents in using local remedies and accessing hospital are most likely to be missed. More so, fatalities, disabilities and costs during referrals from lower-level facilities are inaccurately estimated because of missing records. The communities in Uganda continue experience unknown, unfairly and unaccounted for Catastrophic Health Expenditures (CHE) due snakebites.\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) aims to reduce deaths and disabilities caused by snakebites by a half by the year 2030, alleviating its burden on disadvantaged communities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Global recognition of this burden has seen Uganda integrate snakebites into the country\u0026rsquo;s Neglected Tropical Diseases program. Consequently, Uganda recently drafted snakebite prevention and management strategy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these policy advances, critical gaps remain. There is an absence of distribution and cost estimates of management snakebite-associated morbidities, mortalities and disabilities. Therefore, the health system and the community continue to struggle with severe cases, high costs of treatment and difficulty in making case to the government and funders for interventions. More so, cost effective strategies and commodities require cost estimates to justify investments. This study therefore estimated the economic burden including: distribution, CHE, direct and indirect costs for snakebites envenoming in Kamuli and Iganga districts of Eastern Uganda grounded on an adapted Donabedian model (See supplemental material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was based on snakebite events that had already occurred. We retrospectively collected additional data on activities, resource use, and associated costs related to those events.\u003c/p\u003e\u003cp\u003eTwo districts of Kamuli and Iganga from Eastern Uganda with the highest incidence cases were visited for household survey (See supplemental material Fig.\u0026nbsp;1). These were selected based on Ministry of Health surveillance data and prior reports of elevated case numbers. Both districts are predominantly agrarian, with most residents engaged in subsistence farming and limited access to tertiary health facilities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003eHouseholds were eligible if they reported at least one snakebite incident in the preceding 12 months, confirmed by either (i) a clinical diagnosis from a health facility record, or (ii) a detailed, consistent narrative of the event during the household interview. All snake bite victims identified from reports from the community were considered for interview and verified using probes. The diagnosis of venomous snakebites was difficult at health facility and mostly depended on the right description by victims about the snake (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We identified households using a two-stage approach: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) review of health facility outpatient and inpatient registers in selected districts, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) referral lists from community health workers and village leaders, to capture cases treated outside formal healthcare.\u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eEligible individuals were all adults aged 18 years and above, in good health ,able to express themselves verbally and had personally incurred costs due to snakebite. Adults aged 18 years and above were selected because they bear majority of the costs incurred in a typical household (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).Additionally, majority of the incident cases of snakebites in these communities occur within this age group (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Cases where the primary injury was not a snakebite, or where costs were primarily due to other conditions, were excluded.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData collection tools were designed to capture CHE of patients and caregivers assessing from the start of the event up to when the event completely resolved. We followed up individuals through home visits or using a telephone interview to record out-of-pocket payments incurred. Telephone interviews were conducted when three consecutive attempts to household made were futile. Household heads were interviewed to gather household costs and victims\u0026rsquo; costs (in case they incurred the costs). Costs borne directly by the victims were solicited from the victims themselves and (or) from those who incurred them other than themselves (victims). Victims of snakebites were accessed through household heads after confirmation of a snakebite (using screening questions) for interviews. The study tools were pretested to ensure their validity, reliability, clarity, suitability, and logical flow; based on the results, necessary revisions were made prior to their deployment in the field.. The tools were pre-tested at a non-participating community in Mayuge District with similar snake bite incidence and community characteristics as the study districts. Research assistants with experience in conducting costing studies and interviews were trained for five days in preparation for data collection. Training included; general understanding of costing approaches, costing perspectives, estimation skills and probing skills. The research assistants were also trained in practical interviewing (including role plays), and note taking during the interview processes.\u003c/p\u003e\n\u003ch3\u003eData management and analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCosting approaches\u003c/h2\u003e\u003cp\u003eAn ingredient-based approach was used to estimate the cost of management and productivity loss of a snakebite from the household perspective. Human capital approach was used to estimate indirect costs. This was estimated by a product of time loss getting care and average income of household head.\u003c/p\u003e\u003cp\u003eDifferences within direct and indirect costs for a snakebite event and socio-economic status, service provider status, age, gender and residence were tested using relevant statistical tests. These included tests for normality or equal variance and when the hypotheses were rejected the Wilcoxon and Kruskal-Wallis rank tests were used when applicable. In this study, principle component analysis (PCA) approach was used to score asset; which are ranked to determine asset quintiles (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCatastrophic health expenditures\u003c/h3\u003e\n\u003cp\u003eCHE were calculated using direct costs over the annually income and expenditures for households visited. Data collected on expenditures comprised of healthcare expenses like cost of drugs, bed costs, travel costs, meals and other expenses including going to traditional herbalists and healers. A household experienced catastrophic health expenditures if it spent more than the following thresholds on an event of a snake bite: 10% of its income, 25% of its monthly expenditures or 40% of its monthly expenditures without food (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These categories were analysed by performing a chi-square test of independence to assess the unadjusted relationship between wealth quintiles and CHE.\u003c/p\u003e\n\u003ch3\u003eWealth related Equity and Distribution Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess socioeconomic inequalities in treatment costs and CHE, the study used the Lorenz curves and calculated both Wagstaff and Erreygers concentration indices. The slope index of inequality (SII) and relative index of inequality (RII) quantified absolute and relative disparities, respectively. Multivariate and quantile regression models were used to adjust for demographic and socio-economic confounders, estimating the effect of wealth and other predictors on the distribution of financial burden. Blinder\u0026ndash;Oaxaca decomposition was used to partition cost differences across wealth groups into explained (endowment) and unexplained (structural) components.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eSocial demographic characteristics of study participants\u003c/h2\u003e\n \u003cp\u003eA total of 150 households reporting a snakebite incident within the past 12 months were interviewed. Participants had an average age of 40.4 years (SD: 12.9), with the 30\u0026ndash;39-year age group most represented at 34.0%. The victims\u0026apos; mean age was 38.2 years (SD: 14.4). More than half (53.3%) of the households were headed by males and had 3\u0026ndash;5 members (52.0%). Similarly, over a half (54.7%) of the victims were males. Majority of participants were from rural areas (92.0%) while 42.7% were involved in farming. Nearly one-third (35.3%) of participants spent between 150,000/= (39.6 USD) to 300,000/= (79.2 USD)monthly. See Table 1. Other characteristics of repondents were collected detailed in Supplementary Table S2-3 and Supplementary Figs. 2 and 3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatastrophic health expenditures related to the incident of a snake bite by asset quintile.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCategories of CHE were analysed to assess the disparity among wealth quintiles. The poorest and poor groups were disproportionately affected with majority spending following into 40% threshold at statistically significant level (\u0026chi;\u0026sup2; = 23.9653, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). See Table 2. We detailed (in supplementary files) the other categories of the CHE and non-CHE of a snake bite across the three thresholds 40% (supplementary Fig.\u0026nbsp;4), 25% (supplementary Fig.\u0026nbsp;5) and 10% (supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCHE thresholds categorised by wealth quintiles\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWealth Quintiles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eExperienced Catastrophic Health Expenditure thresholds\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHE at 10%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHE at 25%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHE at 40%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWealthiest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e143\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCHE 10%: \u0026chi;\u0026sup2; = 2.6964, p\u0026thinsp;=\u0026thinsp;0.610 CHE 25%, \u0026chi;\u0026sup2;= 5.2573, p\u0026thinsp;=\u0026thinsp;0.262 CHE 40%, \u0026chi;\u0026sup2; = 23.9653, \u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e Counts shown are cases (households for only those the experienced CHE) for each threshold, by wealth quintile and is not equal to the overall sample size (N\u0026thinsp;=\u0026thinsp;150).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eSlope and Relative Indices of Inequality in Catastrophic Health Expenditure\u003c/h2\u003e\n \u003cp\u003eOur findings indicate that at CHE at the 10% threshold, the SII was 0.218 (95% CI: \u0026minus;\u0026thinsp;0.039\u0026ndash;0.475), indicating a trend toward higher CHE among wealthier individuals, though this was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.094). At the 25% threshold (CHE2), the SII was smaller and also non-significant (SII\u0026thinsp;=\u0026thinsp;0.109; p\u0026thinsp;=\u0026thinsp;0.336). At the 40% threshold, we observed a significant pro-poor inequality, with an SII of \u0026minus;\u0026thinsp;0.173 (95% CI: \u0026minus;\u0026thinsp;0.305 \u0026ndash; \u0026minus;\u0026thinsp;0.041; p\u0026thinsp;=\u0026thinsp;0.011), indicating that poorer individuals are more likely to experience CHE at this higher threshold. The poorest experienced CHE at 83% the rate of the average. See Table 3.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSlope and Relative Indices of Inequality in Catastrophic Health Expenditure (CHE) at 10%, 25%, and 40% Thresholds by Socioeconomic Rank\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSII (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHE at 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.218 (\u0026ndash;0.039\u0026ndash;0.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHE at 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109 (\u0026ndash;0.114\u0026ndash;0.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHE at 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.173 (\u0026ndash;0.305 \u0026ndash; \u0026minus;\u0026thinsp;0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: SII\u0026thinsp;=\u0026thinsp;Slope Index of Inequality; RII\u0026thinsp;=\u0026thinsp;Relative Index of Inequality. Positive SII indicates higher values among wealthier individuals; negative SII indicates higher values among poorer individuals.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eQuantile Regression of Financial Burden\u003c/h2\u003e\n \u003cp\u003eIn adjusted quantile regression models, household wealth was inversely associated with financial burden across the distribution, though not significantly, with the largest reduction observed at the 75th percentile (\u0026ndash;3.9 pp; p\u0026thinsp;=\u0026thinsp;0.306). Adults aged 30\u0026ndash;49 and 50\u0026thinsp;+\u0026thinsp;experienced significantly lower burdens at the lower tail (\u0026ndash;8.7 pp, p\u0026thinsp;=\u0026thinsp;0.018; \u0026minus;\u0026thinsp;9.6 pp, p\u0026thinsp;=\u0026thinsp;0.022, respectively), but not at higher quantiles. Gender and residence showed no significant effects, though urban and male respondents tended to bear slightly higher burdens. Occupational categories were not significantly associated with burden, but manual labor showed a positive association at the upper tail in prior models. Outpatient care was strongly protective, reducing burden by 24.1\u0026ndash;85.8 pp across quantiles (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while hospital-based care significantly increased burden by 30.1\u0026ndash;63.4 pp (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). iTraditional healer uses and household size had no measurable impact. Severity was positively associated with burden, particularly for severe cases, but estimates were not statistically significant. See Table 4.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eQuantile regression coefficients of Financial Burden (% of monthly income)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25th percentile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50th percentile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75th percentile\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWealth score (per quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.813 (0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;2.609 (1.596)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;3.919 (3.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;8.729* (3.635)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;4.953 (5.843)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;16.070 (13.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;9.553* (4.116)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;9.516 (6.617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;24.571 (15.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;4.088 (2.669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;1.977 (4.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.775 (10.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.191 (4.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.000 (7.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.259 (17.353)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManual Labour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;5.477 (3.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;1.623 (5.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;6.321 (13.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSales \u0026amp; services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.966 (3.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;1.039 (5.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;4.858 (12.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVisit type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;24.074*** (4.713)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;22.226** (7.576)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;85.834*** (18.120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWent to hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.126*** (3.451)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e46.543*** (5.547)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.358*** (13.267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eUsed traditional remedies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538 (3.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.547 (6.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.026 (14.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSeverity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPD moderately severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOPD non-severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.768 (12.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.501 (19.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.066 (46.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.586 (11.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.528 (18.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.899 (43.516)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFamily Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2 members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;5 members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.612 (6.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.810 (11.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.884 (26.654)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;10 members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.264 (7.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.596 (11.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.692 (27.620)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10 members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.414 (7.581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.826 (12.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728 (29.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote. Estimates are coefficient (SE) with 95% confidence interval in brackets. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Models estimated at \u0026tau;\u0026thinsp;=\u0026thinsp;0.25, 0.50, 0.75 with 200 bootstrap replications; N\u0026thinsp;=\u0026thinsp;145; Pseudo R\u0026sup2; = 0.332, 0.388, 0.420.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eCosts incurred by the snakebite victims\u003c/h2\u003e\n \u003cp\u003eUsing Blinder\u0026ndash;Oaxaca decomposition, we examined the mean cost differentials between the middle-wealth quintile (Q3) and other wealth groups, partitioning the observed gaps into explained (endowment) and unexplained (coefficient and interaction) components. Wealthier patients incurred higher treatment costs compared to poorer patients. Decomposition analysis showed that 44.8% of the cost gap between the middle quintile and the poorest quintile was explained by observable characteristics (e.g., type of facility, age, occupation), while 55.2% was unexplained, suggesting structural inequities in access, quality, or intensity of care. A similar pattern was observed between the richest and middle quintiles, with 43% of the cost gap unexplained. Table\u0026nbsp;5.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOaxaca\u0026ndash;Blinder decomposition of mean OverallCosts (high quintile 3 vs lower quintiles)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026thinsp;=\u0026thinsp;H\u0026ndash;L\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE (endowments)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC (coefficients)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCE (interaction)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% explained\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% unexplained\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evs Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evs Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;110.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;111.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evs Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;49.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evs Q5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;50.0 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes: H\u0026thinsp;=\u0026thinsp;mean prediction for quintile 3; L\u0026thinsp;=\u0026thinsp;for comparison quintile; R\u0026thinsp;=\u0026thinsp;raw gap. E, C, CE sum to R (E\u0026thinsp;+\u0026thinsp;C\u0026thinsp;+\u0026thinsp;CE\u0026thinsp;=\u0026thinsp;R). % explained = (E\u0026thinsp;+\u0026thinsp;D\u0026middot;CE)/R, % unexplained = (C + (1\u0026ndash;D) \u0026middot;CE)/R; D\u0026thinsp;\u0026asymp;\u0026thinsp;relative size of Q3 in pooled sample (20\u0026ndash;46%). K-1000 Reference: pooled model coefficients.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first community-level evidence from Uganda that snakebites is not only an emergency but also a significant and inequitable economic shock for affected households. The findings show that costs of management of a snakebite and productivity losses can consume a substantial share of household resources, with the poorest bearing a disproportionately high risk of CHE(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). By linking financial burden to socioeconomic position, care pathway, and structural inequities in cost allocation, our results add critical detail to the understanding of snakebite\u0026rsquo;s broader public health and economic impact in rural sub-Saharan Africa.\u003c/p\u003e\u003cp\u003eOur findings are consistent with reports from other low- and middle-income countries showing that even modest absolute treatment costs can be catastrophic relative to income in rural settings (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In Sri Lanka, where public healthcare is free, median household costs were lower than those observed here, yet still imposed significant hardship(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The high CHE prevalence in our cohort mirrors WHO\u0026rsquo;s observation that nearly any hospital-treated snakebite in poor communities can meet CHE thresholds CHE(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Similar torural India, where households often resort to debt or asset sales to cover treatment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), our data indicated that many Ugandan families face acute financial distress after snakebite. This aligns with the conceptualization of snakebite as a \u0026ldquo;disease of poverty\u0026rdquo;\u0026mdash;a term applied due to its disproportionate burden on agricultural workers in low-resource settings and its capacity to entrench poverty through direct and indirect costs(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe magnitude of burden varied sharply with care pathway. Hospital admissions were associated with substantially greater costs and income loss than outpatient treatment, consistent with the greater severity and higher resource intensity of inpatient cases (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Outpatient care was strongly protective in our quantile regression models, supporting the hypothesis that early, less severe cases avoid costly escalation(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Traditional healer use had no measurable net impact, suggesting that patients may pay for both traditional and biomedical care when symptoms progress, a pattern documented in Myanmar and parts of sub-Saharan Africa (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Our Blinder\u0026ndash;Oaxaca decomposition revealed that more than half of the cost gap between the poorest and middle-income patients was unexplained by observable characteristics such as age or facility type, pointing to structural inequities in care pricing and provision. Similar unexplained gaps were observed at higher income levels, suggesting that ability to pay may influence treatment intensity or pricing, creating a two-tiered care environment (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This form of horizontal inequity where patients with similar clinical needs face different costs has been documented in other out-of-pocket financing contexts and undermines equity in access to effective care(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOccupation and age shaped the nature of losses. Professionals tended to incur higher productivity losses than farmers or manual laborers, likely reflecting higher daily wages and less substitutable work (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Peak burden was observed among adults aged 30\u0026ndash;39 years, a prime working age with heavy family responsibilities, amplifying the household impact(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Older adults bore slightly less measurable financial loss, possibly due to reduced earning capacity or support from adult children, though other studies associate older age with worse clinical outcomes and potentially higher treatment needs(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe high CHE rates and wealth-linked disparities highlight the urgency of integrating financial protection into snakebite management in Uganda. Policy options include subsidising and waiving in and out of hospital fees for the poorest as well as decentralising antivenom availability to lower-level facilities(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Such measures could prevent progression, reduce the need for costly inpatient stays, and avoid treatment delays driven by cost concerns [20].\u003c/p\u003e\u003cp\u003eStrengthening antivenom supply chains and regulating prices is essential; current African market prices can represent several months\u0026rsquo; income for rural households (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Culturally sensitive strategies\u0026mdash;such as engaging traditional healers in early referral pathways\u0026mdash;may improve timely access to biomedical care(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Beyond the health sector, social protection measures, including emergency cash transfers or agricultural insurance, could help households recover financially after severe snakebite envenoming, aligning with WHO\u0026rsquo;s global target to halve the snakebite burden by 2030(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis cross-sectional, retrospective design relied on self-reported data, which may be subject to recall bias, especially for indirect costs. Seasonal income variation was not captured, and intangible costs\u0026mdash;such as psychological distress, disability, or social disruption were excluded, likely underestimating total burden(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The study could possibly have selection bias if the most financially devastated households were unavailable for interview. Finally, the study was conducted in two high-incidence districts, which may limit generalisability to areas with different incidence rates, health service availability, or snake species.\u003c/p\u003e\u003cp\u003eProspective cohort studies could better quantify long-term costs, productivity losses, and coping strategies, including debt and asset liquidation (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Given the strong protective effect of outpatient care, cost-effectiveness analyses of early-intervention models are needed. Mixed-methods research could illuminate cultural and behavioural determinants of care-seeking, informing locally tailored strategies. Trials of financial protection mechanisms, including insurance schemes and cash transfers, could test scalable solutions to prevent impoverishment after snakebite.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSnakebites in rural Uganda imposes a substantial and inequitable economic burden, particularly on the poorest households, and structural inequities exacerbate these disparities. Addressing this dual clinical and economic challenge requires integrated strategies spanning prevention, timely treatment, and financial risk protection. Such measures are essential to break the cycle in which a single envenoming can plunge a vulnerable household deeper into poverty, and to realise both the health and equity goals of the global snakebite agenda.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval to conduct the study was obtained from the Makerere university school of Biomedical Sciences Research and Ethics committee (IRB Number: SBS-2022-272). Permission was also sought at districts level (from the office of the district health offices Kamuli and Iganga), local council chairpersons and the health facility in-charges. Written informed consent were signed and kept secured in a lockable cabinet at the study site. During the study, questionnaires had serial numbers instead of individuals\u0026rsquo; names for confidentiality purposes. Individuals were informed of their right to agree to participate or withdraw from the study at any time without fear of any negative repercussions. After informing them of the purpose and procedure related to the study, a written informed consent was sought from the study participants. All principles of research involving human subjects outlined in the Declaration of Helsinki were adhered to.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be provided on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing\u0026nbsp;interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Early Career Grants program of the Royal Society of Tropical Medicine and Hygiene (RSTMH). The views expressed herein are solely those of the authors and do not necessarily reflect the official positions of the RSTMH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMM contributed to the conception and design of the study, data analysis, drafting and final review of the manuscript. SK provided oversight for the study design, guided data interpretation, and critically reviewed and revised the manuscript preparation and write-up. JT assisted in data acquisition and interpretation, and contributed to the drafting and revision of the manuscript. GW and PMA participated in data analysis, interpretation and critical revision of the manuscript for intellectual content. PA, BDN and VAJ contributed to the analysis and interpretation of data and substantially revised the manuscript. DK supervised the research, provided critical insights into the interpretation of findings, and reviewed the manuscript substantively. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Makerere University Center for Health and Population Research, the Iganga-Mayuge Health Demography Site staff, district officials and all the village scouts that supported in the data collection. We are grateful to students and supervisors from Makerere University School of public health for their part in data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Snakebite envenoming fact sheet 2019 [cited 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/snakebite-envenoming.\u003c/li\u003e\n\u003cli\u003eWHO. Prevalence of snakebite envenoming 2021 [updated 26/04/2021; cited 2021 26/04/2021]. 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Snakebite mortality in India: a nationally representative mortality survey. PLoS neglected tropical diseases. 2011;5(4):e1018.\u003c/li\u003e\n\u003cli\u003eHarrison RA, Hargreaves A, Wagstaff SC, Faragher B, Lalloo DG. Snake envenoming: a disease of poverty. PLoS neglected tropical diseases. 2009;3(12):e569.\u003c/li\u003e\n\u003cli\u003eLongbottom J, Shearer FM, Devine M, Alcoba G, Chappuis F, Weiss DJ, et al. Vulnerability to snakebite envenoming: a global mapping of hotspots. The Lancet. 2018;392(10148):673-84.\u003c/li\u003e\n\u003cli\u003eNanyonga SM, Matafwali SK, Kibira D, Kitutu FE. Treatment and treatment outcomes of snakebite envenoming in Uganda: a retrospective analysis. Transactions of The Royal Society of Tropical Medicine and Hygiene. 2025;119(7):796-803.\u003c/li\u003e\n\u003cli\u003eAlcoba G, Chabloz M, Eyong J, Wanda F, Ochoa C, Comte E, et al. Snakebite epidemiology and health-seeking behavior in Akonolinga health district, Cameroon: Cross-sectional study. 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PLoS neglected tropical diseases. 2016;10(11):e0005103.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Snakebites, Economic Burden, Financial hardship, Rural health, Healthcare Costs","lastPublishedDoi":"10.21203/rs.3.rs-7653113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7653113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003e: Snakebites remain a major yet long-neglected public health burden, disproportionately affecting the poor communities in rural Uganda. Households continue to unfairly spend out of pocket on snakebites and are further pushed into poverty. The study estimated household economic burdens, Catastrophic Health Expenditure (CHE), and associated socioeconomic inequalities from snakebites in Eastern Uganda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional study that used an ingredient-based approach to estimate the household costs of management and productivity loss of a snakebites from the household perspective. Two districts from Eastern Uganda with the highest incidence cases were visited for household survey to elicit costs borne by the snake bite victims at community level. CHE was evaluated using expenditure thresholds of 10%, 25%, and 40%. Inequalities were assessed using concentration indices, slope index of inequality (SII), relative index of inequality (RII), Oaxaca\u0026ndash;Blinder decomposition, and quantile regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCHE related to snakebite was experienced by 31.4% of households at the 40% threshold, with the poorest quintile most affected (χ\u0026sup2;=23.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SII = \u0026minus;\u0026thinsp;0.173, p\u0026thinsp;=\u0026thinsp;0.011). Significant disparities in economic burden were found by occupation (hospital costs p\u0026thinsp;=\u0026thinsp;0.002; productivity loss p\u0026thinsp;=\u0026thinsp;0.020), socioeconomic status (hospital costs p\u0026thinsp;=\u0026thinsp;0.003), and hospital visit type (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Outpatient care was strongly protective (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while hospital admissions drove up costs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Quantile regression showed outpatient care significantly reduced financial burdens (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas hospitalization increased costs substantially (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Decomposition analysis showed structural inequities: middle-income patients faced 55.2% higher unexplained costs than the poorest.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSnakebites led to substantial financial hardship and entrenched inequities for households in Eastern Uganda. Policy responses should prioritize affordable treatment access and financial risk protection for the poorest and most vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Economic Burden and distribution of Household Expenditures on Snakebites: Assessing Catastrophic Health Expenditures and Financial Hardship in Rural Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 23:14:50","doi":"10.21203/rs.3.rs-7653113/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-11T20:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47907801128777606948937423772777157036","date":"2025-12-02T09:42:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T14:55:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T12:03:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T08:41:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T08:40:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-18T23:00:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"313dd591-1a1b-4abd-811c-1f90d989712f","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T23:14:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 23:14:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7653113","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7653113","identity":"rs-7653113","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}