Important Pediatric Conditions in Low- and Middle-Income Countries: A Clinician and Data-Driven Approach

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Vissoci, João

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Kozhumam, Arthi Shankar

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2022-06-15T20:01:55Z

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2022

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Global Health

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Background: Emergency care sensitive conditions are defined as those for which rapid diagnosis and early intervention improve patient outcomes. This thesis aimed to develop a list of important pediatric conditions in low- and middle-income countries (LMIC) to be used for further studies on pediatric epidemiology and resource utilization. Methods: A survey of 79 conditions was sent to LMIC physicians, who rated each condition on three categories (time sensitivity, preventability, and commonality) on a scale of 1-9. Responses were matched to Brazil pediatric hospitalization, ambulatory, and mortality data from 2015-2020. Results: 17 physicians completed the first Round of the survey, and 3 of these (17.65%) completed the second Round. Overall, 67 of the 79 (84.21%) were rated as highly time-sensitive and 26 (32.91%) highly preventable. Survey conditions with the highest ratings overall or country overlap (n=11), that were country-specific but highly rated in all three categories (n=8), or that comprised ~1%+ of hospitalizations (n=9), ~0.5%+ of ambulatory visits (n=6), and ~0.5%+ of mortality cases (n=8) were combined with the most common acute non-elective causes of hospitalizations (n=7) and mortality (n=9) into a list of 29 consolidated conditions overall (excluding overlap). These 29 accounted for 37.83% of hospitalizations, 8.97% of ambulatory visits, and 29.17% of mortality cases. 31 of the 79 survey conditions were age-specific and 32 context-specific. Conclusions: These 29 should be targeted in future health system utilization and burden studies. The modified Delphi approach is important in reaching provider consensus.

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https://hdl.handle.net/10161/25344

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Public health

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Health sciences

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Public health education

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emergency medicine

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Health systems

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LMIC

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Pediatrics

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Important Pediatric Conditions in Low- and Middle-Income Countries: A Clinician and Data-Driven Approach

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Master's thesis

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23.375342465753423

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2024-05-26T00:00:00Z

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