Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Abstract

A B S T R A C TOBJECTIVES: Children with multiple complex chronic conditions (MCCs) represent a small fraction of our communities but a disproportionate amount of health care cost and mortality. Because the temporal trends of children with MCCs within a geographically well-defined US pediatric population has not been previously assessed, health care planning and policy for this vulnerable population is limited. METHODS:In this population-based, repeated cross-sectional study, we identified and enrolled all eligible children residing in Olmsted County, Minnesota, through the Rochester Epidemiology Project, a medical record linkage system of Olmsted County residents. The pediatric complex chronic conditions classification system version 2 was used to identify children with MCCs. Five-year period prevalence and incidence rates were calculated during the study period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and characterized by age, sex, ethnicity, and socioeconomic status (SES) by using the housing-based index of socioeconomic status, a validated individual housing-based SES index. Age-, sex-, and ethnicity-adjusted prevalence and incidence rates were calculated, adjusting to the 2010 US total pediatric population. RESULTS:Five-year prevalence and incidence rates of children with MCCs in Olmsted County increased from 1200 to 1938 per 100 000 persons and from 256 to 335 per 100 000 person-years, respectively, during the study period. MCCs tend to be slightly more prevalent among children with a lower SES and with a racial minority background. CONCLUSIONS:Both 5-year prevalence and incidence rates of children with MCCs have significantly increased over time, and health disparities are present among these children. The clinical and financial outcomes of children with MCCs need to be assessed for formulating suitable health care planning given limited resources.

Fifteen-year trajectories of multimorbidity and polypharmacy in Dutch primary care—A longitudinal analysis of age and sex patterns

“…For the youngest age group, the available literature is very limited. Bjur et al (2019) compared the incidence and prevalence of multimorbidity within the 0-17 age group over three consecutive 5-year periods (with the index years 1999, 2004 and 2009 for incidence and 2004, 2009 and 2014 for prevalence) [25]. The incidence rates were 2.5, 2.7 and 3.3 per 1000 personyears respectively, compared with the higher rate of 7.2 per 1000 person-years that we found.…”

“…Age is an important factor driving the development of polypharmacy and the accumulation of chronic health conditions accelerates the use of multiple drugs. Particular attention should be paid to polypharmacy because it is associated with many drug-related problems such as adverse drug events, drug-drug-interactions, medication non-adherence, functional decline, and cognitive impairment [10,24,25,33]. However, the clinical trajectory of a substantial portion of our patients accelerated and led to a further increase in polypharmacy across all age and sex groups.…”

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

“…Most studies did not stratify IRs by sex, with only one reporting IRs among male and female participants separately 130 ; this study reported a higher IR among women than in men. Of the 11 studies reporting race and/or ethnicity, five reported incidence rate(s) stratified by race and/or ethnicity, 109 , 112 , 123 , 129 , 132 one adjusted incidence rates for race and/or ethnicity, 136 and two presented both adjusted overall and stratified incidence rate(s) by race and/or ethnicity. 129 , 130 Two studies consisting solely of South and East Asian ethnicities (Malay, Chinese, and Indian) 110 , 112 reported higher IRs than the remaining studies reporting IRs in White, Non-White, Black/African-American, Hispanic, and Māori populations.…”

An individual-level socioeconomic measure for assessing algorithmic bias in health care settings: A case for HOUSES index

“…Other pertinent variables: While our focus is to quantify algorithmic bias by SES, we also considered other readily available demographic characteristics (age, sex, and race/ethnicity), and pediatric chronic conditions defined by Feudtner et al (an accepted measure of pediatric chronic conditions in literature). 49 These variables are extracted from patient's EHR. For chronic conditions, ICD-9 diagnostic and procedure codes were used.…”

“…9) overweight,26,47 8) asthma outcome48 , 10) multiple complex chronic conditions,49 11) serum 25 (OH)D concentration 50 B. Acute conditions Adults: 12) Risk of accidental falls 51 , 13) osteoporotic fracture incidence 52 Children: 14) Risk of invasive pneumococcal disease,53 15) risk of bronchiolitis, pneumonia, urinary tract infection, adverse childhood experiences 46 C. Other health outcomes Adults: 16) All-cause hospitalization,41 17) smoking status,54 18) detecting survey biases,55 19) end of life care access (advance directives, social work consultation)56 Children: 20) Low birth weight, household smoking status, 26,47 21) pertussis vaccine up-to-date status,57 22) self-rated health58 , 23) HPV vaccine (initiation and completion) up-to-date…”