key: cord-0302949-tmoomzp3 authors: Klarman, M.; Schon, J.; Cajusma, Y.; Maples, S.; Beau de Rochars, V. M.; Baril, C.; Nelson, E. J. title: Opportunities to catalyze improved healthcare access in pluralistic systems: a cross-sectional study in Haiti date: 2020-12-04 journal: nan DOI: 10.1101/2020.12.03.20243394 sha: 216d5c63f221f093627a2742361cf48f1c294eb3 doc_id: 302949 cord_uid: tmoomzp3 Introduction. Gains to ensure global healthcare access are at risk of stalling because some old resilient challenges require new solutions. Our objective was to study a pluralistic healthcare system that is reliant on both conventional and non-conventional providers to discover opportunities to catalyze renewed progress. Methods. A cross-sectional study was conducted among households with children less than 5 years of age in Haiti. Households were randomly sampled geographically with stratifications for population density. Household questionnaires with standardized cases (intentions) were compared to self-recall of health events (behaviors). The connectedness of households and their providers was determined by network analysis. Results. A total of 568 households (incorporating 2900 members) and 65 providers were enrolled. Households reported 636 health events in the prior month. Households sought care for 35% (n=220) and treated with home remedies for 44% (n=277). The odds of seeking care increased 217% for severe events (aOR=3.17; 95%CI 1.99-5.05; p< 0.001). The odds of seeking care from a conventional provider increased by 37% with increasing distance (aOR=1.37; 95%CI 1.06-1.79; p=0.016). Despite stating an intention to seek care from conventional providers, there was a lack of congruence in practice that favored non-conventional providers (McNemar's Chi-squared Test p<0.001). Care was sought from primary providers for 68% (n=150) of cases within a three-tiered network; 25% (n=38/150) were non-conventional. Conclusion. Addressing geographic barriers, possibly with technology solutions, should be prioritized to meet healthcare seeking intentions while developing approaches to connect non-conventional providers into healthcare networks when geographic barriers cannot be overcome. 2 Improving access to healthcare is one of the highest global health priorities set by the 3 Sustainable Development Goals (SDG) (2015) 1 . SDG 3.8 seeks to "achieve universal 4 health coverage (UHC)", however the current rate of progress is insufficient to reach this 5 target by 2030. Low and middle income countries (LMIC) are the furthest off track 2 3 , 6 and UHC tracking indicators show no significant gains for children between 2010 and 7 2020 4 . The COVID-19 pandemic will exacerbate the limited progress 5 . Innovative 8 approaches are needed to overcome resilient barriers to catalyze progress to achieve 9 universal healthcare access. governmental organization (NGO) facilities. Non-conventional providers can be defined 26 as traditional healers, medication vendors, unlicensed practitioners and pharmacists. 27 Provider selection exposes conflicts between a patient's intention ('would do') and 28 behavior ('did do') 23 24 . These conflicts represent an opportunity to reveal unanticipated 29 solutions to improve access to care. 30 The associated networks within pluralistic healthcare systems rely on the relationships 31 among and between conventional and non-conventional providers 25 26 . Conventional 32 primary care providers, including community health workers, are assumed to be the first 33 access point into healthcare systems 27 , however non-conventional providers have an 34 important and underappreciated role 28 . Non-conventional providers often practice in 35 parallel without disclosure to conventional providers which creates alternative pathways 36 for seeking care 29 . These complex relationships are not adequately understood, yet are 37 essential to improving healthcare access [30] [31] [32] . 38 Haiti was chosen as a generalizable setting to address these knowledge gaps. Access 39 to healthcare in Haiti is low at 23% nationwide and 5% among the rural population 33 . targeted for enrollment; low =18, medium =24, and high =53 per grid cell. The first 62 house encountered in a polygon was recruited. Grid-cells were surveyed sequentially 63 (e.g. low, medium, high) and randomly ordered within each category across a 12-month 64 study period (Aug 2018-July 2019). Although ten grid-cells of each density were healthcare seeking behavior for standardized cases and health events (appendix). A source, primary source of income, primary transportation method, sanitation type, 90 household floor, wall and roof type, and grid cell density. Provider characteristics were 91 described similarly. The two primary analyses compared (i) households that did and did 92 not seek care for health events and (ii) households that did and did not seek care from a 93 conventional provider. For care-seeking, we used bivariate and multivariate logistic 94 regression models with seeking care as the dependent variable and expressed the 95 results as odds ratios (ORs) and adjusted odds ratios (aORs) for care sought. Similar 96 methods were used to compare the selection of seeking care from a conventional 97 versus non-conventional provider. Health events were analyzed as discrete variables. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint Two-mode whole networks 44 45 of households and providers compared provider types 99 identified/sought in standardized cases and health events; sub-analyses were 100 performed for respiratory infection ('cough' or 'cold' with fever), diarrhoeal illness 101 ('diarrhoea' with/without blood), and "other" cases (all other illness types not categorized 102 as respiratory or diarrhoea). Ego networks 44 46 were generated for commonly identified 103 providers in the whole-network analysis. One-mode provider referral networks 44 were 104 generated. Statistical significance was defined at α=0.05 and 95% confidence intervals 105 are provided. Missingness that was not excluded was for provider types identified by 106 households but not located and enrolled; for regression models, case-wise deletion was 107 used. Analyses were completed in Stata (v11) and the igraph, and in R (R Foundation 108 for Statistical Computing; packages included 'sf' by E. Pebesma and R. Bivand 2018) 47 common perceived cause of health events was humoral pathology 49 (41%), considered 123 an imbalance of 'hot' and 'cold' within the body caused by environmental exposures. Forty percent of health events were considered severe. Half of all health events started 125 at nighttime (55%), of these, 36% were severe ( (table S3) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint Among surveyed providers, 68% (n=44) had a business license. Variation was observed 148 by grid cell density; only 4 providers were identified in the low-density grid-cells and 149 none were available after 8 pm (table S3) . Congruence between care seeking intentions and behaviors by provider type. 170 Comparisons of intentions versus behaviors for the type of provider sought was 171 conducted using data from the household standardized cases ('where would you go?') 172 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint and the household health events ('where did you go?'). Among all health event types, 173 there was a lack of congruence between intentions and behaviors (OR =0.15; 95%CI 174 0.04-0.43; p<0.001); 27 of the 31 non-congruent events were attributed to households 175 that intended to seek care from a conventional provider but switched to non-176 conventional (table S5) . In bivariate analysis, ARI with diarrhoea and informal 177 transportation were associated with increased non-congruence. In contrast, HoH 178 education level of secondary and above and high-density grid-cells were associated 179 with congruence. Multivariate analysis found no significant factors (table S6) . Multivariate analysis identified increased distance, education, and higher population 219 density as correlates of seeking care from a conventional provider. These findings 220 highlight that once a household decides to seek care, poverty and geographic isolation 221 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint again influence the decision to seek care from a conventional versus non-conventional is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint households by mobile healthcare services either physically or electronically through 247 telemedicine. These findings should be viewed within the context of the study limitations. First, the 249 standardized case questionnaires were designed a priori to investigate the decision-250 making process to seek care for children with respiratory or diarrhoeal disease at night. The analytic strategy to compare standardized cases as intentions and health events as CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint To best describe what is known about the subject in this manuscript, we performed two is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted December 4, 2020. ; https://doi.org/10.1101/2020.12.03.20243394 doi: medRxiv preprint 368 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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