Community- versus Individual-Level Indicators to Identify Pediatric Health Care Need

Abstract

Increasingly, geographic information systems employing spatial data are being used to identify communities with poorer health care status. Since health care indicators are strongly linked to income, could these data, usually based on adult indicators, be used for pediatric health care need? We hypothesized that individuallevel indicators such as quality of life scales (QOL) would be better than communitylevel indicators at identifying families with poorer health care practices. Surveys and medical record reviews were used for a sample of 174 caregivers of young children. Lower level of income was associated with poorer scores on several QOL domains, and on the primary health practices (i.e., non-urgent emergency room use and lack of age-appropriate immunization status). One community-level indicator, the medically underserved area (MUA), was almost as good as the best individual-level indicators at predicting primary health care practices. The community-level indicator of MUA appears to meet its initial intent, providing information on the location of very lowincome individuals with high health care need even among a sample of Medicaidinsured children with an identified health care provider.

Geographic Variance in Maryland’s Potentially Preventable Emergency Visits: Comparison of Explanatory Models

“…Both per capita income and percent of female-headed households explained significant variance in PPV rates in all four models. The finding of higher PPV rates in lower income neighborhoods is consistent with previous small area spatial or partial spatial analyses incorporating area characteristics (Davies et al, 2017; Johnson et al, 2012; Ludden et al, 2018; Murday & Corley, 2008; Zlotnick, 2006). Higher rates of female-headed households have been associated with poverty (Gould, 2012), but have not been widely studied in small area analyses of PPV rates.…”

“…Spatial analyses excel at measuring community-level constructs, such as adequacy of primary care in a given area, and capturing neighborhood-level effects. Zlotnick (2006) found that the community level indicator, living in a Medically Underserved Area, was better at predicting non-urgent ED use in young children than individual-level indicators, such as family income. Place of residence is also important in understanding health inequities that are unexplained by individual socioeconomic status and health behaviors alone (Finch, Do, Basurto-Davila, Bird & Escarce, 2008).…”

Community Socioeconomic Disadvantage and the Survival of Infants With Congenital Heart Defects

“…Although we did not have information on the medical insurance status of infants, previous studies found that medical insurance explained some of the observed racial/ethnic disparities. 16,57 Therefore, it is plausible that referral patterns and access to and use of advances in pediatric cardiac care may be important predictors of disparity in survival among infants with a CHD, and that addressing these issues might be an important step in reducing these disparities. Because survival estimates vary across CHD subtypes, there might be a concern that unequal distribution of subtypes by race/ethnicity could explain differences in survival by race and ethnicity; however, a previous study showed modest variation in birth prevalence by race/ethnicity.…”

Timely and Appropriate Healthcare Access for Newborns: A Neighborhood‐Based, Improvement Science Approach

“…For example, lower rates of pediatric immunization have been shown for low-income neighborhoods compared with higher income neighborhoods (Chi, Momany, & Jones, 2013;Van Berckelaer, Mitra, & Pati, 2011). Similarly, studies have found relationships between neighborhood poverty and ED use for nonurgent conditions such as otitis media, upper respiratory infections, and gastroenteritis (Suruda, Burns, Knight, & Dean, 2005;Zlotnick, 2007). There is evidence to suggest that interdisciplinary, neighborhood-based approaches may be effective in improving pediatric health outcomes (Nicholas et al, 2005;Spielman et al, 2006).…”