The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics.

OBJECTIVE: Independent living is desirable for many older adults. Although several factors such as physical and cognitive functions are important predictors for nursing home placement (NHP), it is also reported that socioeconomic status (SES) affects the risk of NHP. In this study, we aimed to examine whether an individual-level measure of SES is associated with the risk of NHP after accounting for neighborhood characteristics. DESIGN: A population-based study (Olmsted County, Minnesota, USA). SETTING AND PARTICIPANTS: Older adults (age 65+ years) with no prior history of NHP. METHODS: Electronic health records (EHR) were used to identify individuals with any NHP between April 1, 2012 (baseline date) and April 30, 2019. Association between the (HOUsing-based index of SocioEconomic Status (HOUSES) index, an individual-level SES measure based on housing characteristics of current residence, and risk of NHP was tested using random effects Cox proportional hazard model adjusting for area deprivation index (ADI), an aggregated SES measure that captures neighborhood characteristics, and other pertinent confounders such as age and chronic disease burden. RESULTS: Among 15,031 older adults, 3341 (22.2%) experienced NHP during follow-up period (median: 7.1 years). At baseline date, median age was 73 years old with 55% female persons, 91% non-Hispanic Whites, and median number of chronic conditions of 4. Accounting for pertinent confounders, the HOUSES index was strongly associated with risk of NHP (hazard ratio 1.89; 95% confidence interval 1.66‒2.15 for comparing the lowest vs highest quartiles), which was not influenced by further accounting for ADI. CONCLUSIONS AND IMPLICATIONS: This study demonstrates that an individual-level SES measure capturing current individual-specific socioeconomic circumstances plays a significant role for predicting NHP independent of neighborhood characteristics where they reside. This study suggests that older adults who are at higher risk of NHP can be identified by utilizing the HOUSES index and potential individual-level intervention strategies can be applied to reduce the risk for those with higher risk.

Paired Indoor and Outdoor Nitrogen Dioxide Associated With Childhood Asthma Outcomes in a Mixed Rural-Urban Setting: A Feasibility Study.

INTRODUCTION: Nitrogen dioxide (NO2) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO2 levels in childhood asthma in a mixed rural-urban setting of North America. METHODS: This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO2 levels in urban and rural residential areas and the association with asthma control status collected from participants' asthma diaries during the study period. RESULTS: Outdoor NO2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO2 was significantly associated with urban residential living area (P = .014) and lower socioeconomic status (SES) (P = .027). For both seasons, indoor NO2 was significantly higher (P < .05) in rural versus urban areas and in homes with gas versus electric stoves (P < .05). Asthma control status was not associated with level of indoor or outdoor NO2 in this cohort. CONCLUSIONS: NO2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO2 concentration with health effect on asthma in mixed rural-urban settings.

Role of geographic risk factors and social determinants of health in COVID-19 epidemiology: Longitudinal geospatial analysis in a midwest rural region.

Background: Studies examining the role of geographic factors in coronavirus disease-2019 (COVID-19) epidemiology among rural populations are lacking. Methods: Our study is a population-based longitudinal study based on rural residents in four southeast Minnesota counties from March through October 2020. We used a kernel density estimation approach to identify hotspots for COVID-19 cases. Temporal trends of cases and testing were examined by generating a series of hotspot maps during the study period. Household/individual-level socioeconomic status (SES) was measured using the HOUSES index and examined for association between identified hotspots and SES. Results: During the study period, 24,243 of 90,975 residents (26.6%) were tested for COVID-19 at least once; 1498 (6.2%) of these tested positive. Compared to other rural residents, hotspot residents were overall younger (median age: 40.5 vs 43.2), more likely to be minorities (10.7% vs 9.7%), and of higher SES (lowest HOUSES [SES] quadrant: 14.6% vs 18.7%). Hotspots accounted for 30.1% of cases (14.5% of population) for rural cities and 60.8% of cases (27.1% of population) for townships. Lower SES and minority households were primarily affected early in the pandemic and higher SES and non-minority households affected later. Conclusion: In rural areas of these four counties in Minnesota, geographic factors (hotspots) play a significant role in the overall burden of COVID-19 with associated racial/ethnic and SES disparities, of which pattern differed by the timing of the pandemic (earlier in pandemic vs later). The study results could more precisely guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) to those residing in hotspots.

Development and initial testing of a new socioeconomic status measure based on housing data.

Socioeconomic status (SES) has been associated with many health outcomes. Commonly used datasets such as medical records often lack data on SES but do include address information. The authors sought to determine whether an SES measure derived from housing characteristics is associated with other SES measures and outcomes known to be associated with SES. The data come from a telephone survey of parents/guardians of children aged 1-17 years who resided in Olmsted County, Minnesota, and Jackson County, Missouri. Seven variables related to housing and six neighborhood characteristics obtained from local government assessor's offices in Olmsted County, Minnesota, were appended to survey responses. An SES index derived from housing characteristics (hereafter, HOUSES) was constructed using principal components factor analysis. For criterion validity, we assessed Pearson's correlation coefficients between HOUSES and other SES measures, including self-reported parents' educational levels, income, Hollingshead Index, and Nakao-Treas Index. For construct validity, we determined the association between HOUSES and outcomes, risks of low birth weight, overweight, and smoking exposure at home. We applied HOUSES to subjects in another community by formulating HOUSES from housing data of subjects in Jackson County, Missouri, using the same statistical algorithm as HOUSES for subjects in Olmsted County, Minnesota. We found that HOUSES had modest to good correlation with other SES measures. Overall, as hypothesized, HOUSES was inversely associated with outcome measures assessed among subjects from both counties. HOUSES may be a useful surrogate measure of individual SES in epidemiologic research, especially when SES measures for individuals are not available.

Mobile home residence as a risk factor for adverse events among children in a mixed rural-urban community: A case for geospatial analysis.

BACKGROUND: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural-urban community. METHODS: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics (; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES). RESULTS: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22-2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes. CONCLUSIONS: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural-urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research.

Spatio-temporal comparison of pertussis outbreaks in Olmsted County, Minnesota, 2004-2005 and 2012: a population-based study.

OBJECTIVE: Two pertussis outbreaks occurred in Olmsted County, Minnesota, during 2004-2005 and 2012 (5-10 times higher than other years), with significantly higher incidence than for the State. We aimed to assess whether there were similar spatio-temporal patterns between the two outbreaks. SETTING: Olmsted County, Minnesota, USA PARTICIPANTS: We conducted a population-based retrospective cohort study of all Olmsted County residents during the 2004-2005 and 2012 outbreaks, including laboratory-positive pertussis cases. PRIMARY OUTCOME MEASURE: For each outbreak, we estimated (1) age-specific incidence rate using laboratory-positive pertussis cases (numerator) and the Rochester Epidemiology Project Census (denominator), a medical record-linkage system for virtually all Olmsted County residents, and (2) pertussis case density using kernel density estimation to identify areas with high case density. To account for population size, we calculated relative difference of observed density and expected density based on age-specific incidence. RESULTS: We identified 157 and 195 geocoded cases in 2004-2005 and 2012, respectively. Incidence was the highest among adolescents (ages 11 to <14 years) for both outbreaks (9.6 and 7.9 per 1000). The 2004-2005 pertussis outbreak had higher incidence in winter (52% of cases) versus summer in 2012 (53%). We identified a consistent area with higher incidence at the beginning (ie, first quartile) of two outbreaks, but it was inconsistent for later quartiles. The relative difference maps for the two outbreaks suggest a greater role of neighbourhood population size in 2012 compared with 2004-2005. CONCLUSIONS: Comparing spatio-temporal patterns between two pertussis outbreaks identified a consistent geographical area with higher incidence of pertussis at the beginning of outbreaks in this community. This finding can be tested in future outbreaks, and, if confirmed, can be used for identifying epidemiological risk factors clustered in such areas for geographically targeted intervention.

Individual housing-based socioeconomic status predicts risk of accidental falls among adults.

PURPOSE: Accidental falls are a major public health concern among people of all ages. Little is known about whether an individual-level housing-based socioeconomic status measure is associated with the risk of accidental falls. METHODS: Among 12,286 Mayo Clinic Biobank participants residing in Olmsted County, Minnesota, subjects who experienced accidental falls between the biobank enrollment and September 2014 were identified using ICD-9 codes evaluated at emergency departments. HOUSES (HOUsing-based Index of SocioEconomic Status), a socioeconomic status measure based on individual housing features, was also calculated. Cox regression models were utilized to assess the association of the HOUSES (in quartiles) with accidental fall risk. RESULTS: Seven hundred eleven (5.8%) participants had at least one emergency room visit due to an accidental fall during the study period. Subjects with higher HOUSES were less likely to experience falls in a dose-response manner (hazard ratio: 0.58; 95% confidence interval: 0.44-0.76 for comparing the highest to the lowest quartile). In addition, the HOUSES was positively associated with better health behaviors, social support, and functional status. CONCLUSIONS: The HOUSES is inversely associated with accidental fall risk requiring emergency care in a dose-response manner. The HOUSES may capture falls-related risk factors through housing features and socioeconomic status-related psychosocial factors.

Assessing health disparities in children using a modified housing-related socioeconomic status measure: a cross-sectional study.

OBJECTIVES: Socioeconomic status (SES) is a well-established risk factor for many health outcomes. Recently, we developed an SES measure based on 4 housing-related characteristics (termed HOUSES) and demonstrated its ability to assess health disparities. In this study, we aimed to evaluate whether fewer housing-related characteristics could be used to provide a similar representation of SES. STUDY SETTING AND PARTICIPANTS: We performed a cross-sectional study using parents/guardians of children aged 1-17 years from 2 US Midwestern counties (n=728 in Olmsted County, Minnesota, and n=701 in Jackson County, Missouri). PRIMARY AND SECONDARY OUTCOME MEASURES: For each participant, housing-related characteristics used in the formulation of HOUSES (assessed housing value, square footage, number of bedrooms and number of bathrooms) were obtained from the local government assessor's offices, and additional SES measures and health outcomes with known associations to SES (obesity, low birth weight and smoking exposure) were collected from a telephone survey. Housing characteristics with the greatest contribution for predicting the health outcomes were added to formulate a modified HOUSES index. RESULTS: Among the 4 housing characteristics used in the original HOUSES, the strongest contributions for predicting health outcomes were observed from assessed housing value and square footage (combined contribution ranged between 89% and 96%). Based on this observation, these 2 were used to calculate a modified HOUSES index. Correlation between modified HOUSES and other SES measures was comparable to the original HOUSES for both locations. Consistent with the original HOUSES formula, the strongest association with modified HOUSES was observed with smoking exposure (OR=0.24 with 95% CI 0.11 to 0.49 for comparing participants in highest HOUSES vs lowest group; overall p<0.001). CONCLUSIONS: The modified HOUSES requires only 2 readily available housing characteristics thereby improving the feasibility of using this index as a proxy for SES in multiple communities, especially in the US Midwestern region.

Comparison of individual-level versus area-level socioeconomic measures in assessing health outcomes of children in Olmsted County, Minnesota.

BACKGROUND: Socioeconomic status (SES) is an important determinant of health, but SES measures are frequently unavailable in commonly used datasets. Area-level SES measures are used as proxy measures of individual SES when the individual measures are lacking. Little is known about the agreement between individual-level versus area-level SES measures in mixed urban-rural settings. METHODS: We identified SES agreement by comparing information from telephone self-reported SES levels and SES calculated from area-level SES measures. We assessed the impact of this agreement on reported associations between SES and rates of childhood obesity, low birth weight <2500 g and smoking within the household in a mixed urban-rural setting. RESULTS: 750 households were surveyed with a response rate of 62%: 51% male, 89% Caucasian; mean child age 9.5 years. Individual-level self-reported income was more strongly associated with all three childhood health outcomes compared to area-level SES. We found significant disagreement rates of 22-31%. The weighted Cohen's κ indices ranged from 0.15 to 0.22, suggesting poor agreement between individual-level and area-level measures. CONCLUSION: In a mixed urban-rural setting comprised of both rural and urbanised areas, area-level SES proxy measures significantly disagree with individual SES measures, and have different patterns of association with health outcomes from individual-level SES measures. Area-level SES may be an unsuitable proxy for SES when individual rather than community characteristics are of primary concern.

A two-county comparison of the HOUSES index on predicting self-rated health.

BACKGROUND: Mortality, incidence of most diseases, and prevalence of adverse health behaviours follow an inverse gradient with social class. Many proxies for socioeconomic status (SES) exist; however, each bears a different relation to health outcomes, probably following a different aetiological pathway. Additionally, data on SES can be quite difficult to gather. Five measures of SES were compared, including a novel measure, the HOUSES index, in the prediction of self-rated health (SRH) in two Midwestern settings, Olmsted County, Minnesota, and Jackson County, Missouri. METHODS: Using a probability sampling design, a cross-sectional telephone survey was administered to a randomised sample of households. The questionnaire collected a variety of sociodemographic and personal health information. The dependent variable, SRH, was dichotomised into excellent/very good/good versus fair/poor health. Information for the HOUSES index was collected through public property records and corroborated through the telephone questionnaire. Participants were parents/guardians of children aged 1-17 residing in Olmsted County (n = 746) and Jackson County (n = 704). RESULTS: The HOUSES index was associated with adverse SRH in Jackson County adults. All five SES measures were significant predictors in this group. Composite SES indices showed significant associations with SRH in Olmsted County adults. CONCLUSIONS: The HOUSES index makes a unique contribution to the measurement of SES and prediction of health outcomes. Its utility is qualified by specific social contexts, and it should be used in concert with other SES indices.