American Indians travel great distances for obstetrical care: Examining rural and racial disparities.

Rural, American Indian/Alaska Native (AI/AN) people, a population at elevated risk for complex pregnancies, have limited access to risk-appropriate obstetric care. Obstetrical bypassing, seeking care at a non-local obstetric unit, is an important feature of perinatal regionalization that can alleviate some challenges faced by this rural population, at the cost of increased travel to give birth. Data from five years (2014-2018) of birth certificates from Montana, along with the 2018 annual survey of the American Hospital Association (AHA) were used in logistic regression models to identify predictors of bypassing, with ordinary least squares regression models used to predict factors associated with the distance (in miles) birthing people drove beyond their local obstetric unit to give birth. Logit analyses focused on hospital-based births to Montana residents delivered during this time period (n = 54,146 births). Distance analyses focused on births to individuals who bypassed their local obstetric unit to deliver (n = 5,991 births). Individual-level predictors included maternal sociodemographic characteristics, location, perinatal health characteristics, and health care utilization. Facility-related measures included level of obstetric care of the closest and delivery hospitals, and distance to the closest hospital-based obstetric unit. Findings suggest that birthing people living in rural areas and on American Indian reservations were more likely to bypass to give birth, with bypassing likelihood depending on health risk, insurance, and rurality. AI/AN and reservation-dwelling birthing people traveled significantly farther when bypassing. Findings highlight that distance traveled was even farther for AI/AN people facing pregnancy health risks (23.8 miles farther than White people with pregnancy risks) or when delivering at facilities offering complex care (14-44 miles farther than White people). While bypassing may connect rural birthing people to more risk-appropriate care, rural and racial inequities in access persist, with rural, reservation-dwelling AI/AN birthing people experiencing greater likelihood of bypassing and traveling greater distances when bypassing.

Impacts of the US southeast wood pellet industry on local forest carbon stocks.

We assessed the net impacts of a wood-dependent pellet industry of global importance on contemporaneous local forest carbon component pools (live trees, standing-dead trees, soils) and total stocks. We conducted post-matched difference-in-differences analyses of forest inventory data between 2000 and 2019 to infer industrial concurrent and lagged effects in the US coastal southeast. Results point to contemporaneous carbon neutrality. We found net incremental effects on carbon pools within live trees, and no net effects on standing-dead tree nor soil pools. However, we found concurrent lower carbon levels in soils, mixed effects associated with increased procurement pressures and large mill pelletization capacity, and possible spillover effects on standing-dead tree carbon pools beyond commercial procurement distances. There is robust evidence that although some trade-offs between carbon pools exist, the wood pellet industry in this particular context and period has met the overall condition of forest carbon neutrality.

Nested-Solution Facility Location Models.

Classical facility location models can generate solutions that do not maintain consistency in the set of utilized facilities as the number of utilized facilities is varied. We introduce the concept of nested facility locations, in which the solution utilizing p facilities is a subset of the solution utilizing q facilities, for all i ≤ p < q ≤ j, given some lower limit i and upper limit j on r, the number of facilities that will be utilized in the future. This approach is demonstrated with application to the p-median model, with computational testing showing these new models achieve reductions in both average regret and worst-case regret when r ≠ p facilities are actually utilized.

Evaluating disparities in access to obstetric services for American Indian women across Montana.

PURPOSE: Pregnant women across the rural United States have increasingly limited access to obstetric care, especially specialty care for high-risk women and infants. Limited research focuses on access for rural American Indian/Alaskan Native (AIAN) women, a population warranting attention given persistent inequalities in birth outcomes. METHODS: Using Montana birth certificate data (2014-2018), we examined variation in travel time to give birth and access to different levels of obstetric care (i.e., the proportion of individuals living within 1- and 2-h drives to facilities), by rurality (Rural-Urban Continuum Code) and race (White and AIAN people). FINDINGS: Results point to limited obstetric care access in remote rural areas in Montana, especially higher-level specialty care, compared to urban or urban-adjacent rural areas. AIAN women traveled significantly farther than White women to access care (24.2 min farther on average), even compared to White women from similarly rural areas (5-13 min farther, after controlling for sociodemographic characteristics, risk factors, and health care utilization). AIAN women were 20 times more likely to give birth at a hospital without obstetric services and had less access to complex obstetric care. Poor access was particularly pronounced among reservation-dwelling AIAN women. CONCLUSIONS: It is imperative to consider racial disparities and health inequities underlying poor access to obstetric services across rural America. Current federal policies aim to reduce maternity care professional shortages. Our findings suggest that racial disparities in access to complex obstetric care will persist in Montana unless facility-level infrastructure is also expanded to reach areas serving AIAN women.

Assessment of the Effect of Urban Agriculture on Achieving a Localized Food System Centered on Chicago, IL Using Robust Optimization.

In this work, nonrobust (average yield) and robust (varying yield) optimization techniques were applied to find the minimum radius required from the center of Chicago, Illinois, United States (U.S.) and land area by type to meet the population's nutritional needs given yield data for conventional and urban agricultural products. Twenty-eight nutrients were considered, and land type availability was defined using satellite data. No mix of food items were able to satisfy the vitamin D, vitamin B12, and calcium needs within a radius up to 650 km. With vitamin D fortification, radii between 175 and 185 km (nonrobust) and 205 and 220 km (robust) were found across scenarios. The inclusion of urban agriculture reduced the radius by 10-15 km and increased the diversity of foods in the solution. When vitamin B12 was supplemented, the radii could be reduced to 105-120 km (nonrobust) and 115-130 km (robust). This work demonstrates the need to include a full list of nutrients when evaluating the feasibility of localizing food systems. Key nutrient fortification or supplementation may significantly reduce the land area required to meet the nutritional needs of a population.

Expansion of US wood pellet industry points to positive trends but the need for continued monitoring.

Implementation of the European Union Renewable Energy Directive has triggered exponential growth in trading of pelletized wood fibers. Over 18 million tons of wood pellets were traded by EU member countries in 2018 of which a third were imported from the US. Concerns exist about negative impacts on US forests but systematic assessments are currently lacking. We assessed variability in fundamental attributes for timberland structure and carbon stocks within 123 procurement landscapes of wood pellet mills derived from over 38 thousand forest inventory plots in the eastern US from 2005 to 2017. We found more carbon stocks in live trees, but a fewer number of standing-dead trees, associated with the annual operation of large-scale wood pellet mills. In the US coastal southeast-where US pellet exports to the EU originate-there were fewer live and growing-stock trees and less carbon in soils with every year of milling operation than in the rest of the eastern US-which supplies the domestic market. Greater overlap of mills' procurement areas exhibited discernible increments across selected carbon stocks. These trends likely reflect more intensive land management practices. Localized forest impacts associated with the wood pellet industry should continue to be monitored.

Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns.

A major source of primary health care for millions of Americans, community health centers (CHCs) act as a key point of access for diabetes care. The ability of a CHC to deliver high quality care, that supports patients' management of their diabetes, may be impacted by the unique set of resources and constraints it faces, both in terms of characteristics of its patient population and aspects of operations. This study examines how patient and regional characteristics, staffing patterns, and efficiency were associated with diabetes management at CHCs (percentage of patients with uncontrolled diabetes, HbA1C > 9%). Data on a sample of 1229 CHCs came from multiple sources. CHC-level information was obtained from the Uniform Data System and regional-level information from the Behavioral Risk Factor Surveillance System and the US Census American Community Survey. A clustering methodology, latent class analysis, identified seven underlying staffing patterns at CHCs. Data envelopment analysis was performed to evaluate the efficiency of CHCs, relative to centers with similar staffing patterns. Finally, generalized linear models were used to examine the association between staffing patterns, efficiency, and patient and regional-level characteristics. Findings from this study have sociological, practical, and methodological implications. Findings highlight that the intersection of patient racial composition with regional racial composition is significant; diabetes control appears to be worse at CHCs serving racial minorities living in predominantly White areas. Findings suggest that CHCs that incorporate more behavioral health care into their staffing mix have lower rates of uncontrolled diabetes among their patients. Finally, greater efficiency in CHC operations is associated with better diabetes control among patients. By identifying sociodemographic and operational characteristics associated with better hemoglobin control among diabetes patients, the current study contributes to our understanding of both health care operations and health inequalities.

Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight.

Community health centers (CHCs) provide comprehensive medical services to medically under-served Americans, helping to reduce health disparities. This study aimed to identify the unique compositions and contexts of CHCs to better understand variation in access to early prenatal care and rates of low birth weights (LBW). Data include CHC-level data from the Uniform Data System, and regional-level data from the US Census American Community Survey and Behavioral Risk Factor Surveillance System. First, latent class analysis was conducted to identify unobserved subgroups of CHCs. Second, data envelopment analysis was performed to evaluate the operational efficiency of CHCs. Third, we used generalized linear models to examine the associations between the CHC subgroups, efficiency, and perinatal outcomes. Seven classes of CHCs were identified, including two rural classes, one suburban, one with large centers serving poor minorities in low poverty areas, and three urban classes. Many of these classes were characterized by the racial compositions of their patients. Findings indicate that CHCs serving white patients in rural areas have greater access to early prenatal care. Health centers with greater efficiency have lower rates of LBW, as do those who serve largely white patient populations in rural areas. CHCs serving poor racial minorities living in low-poverty areas had particularly low levels of access to early prenatal care and high rates of LBW. Findings highlight that significant diversity exists in the sociodemographic composition and regional context of US health centers, in ways that are associated with their operations, delivery of care, and health outcomes. Results from this study highlight that while the provision of early prenatal care and the efficiency with which a health center operates may improve the health of the women served by CHCs and their babies, the underlying social and economic conditions facing patients ultimately have a larger association with their health.

Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments.

Over 1300 federally-qualified health centers (FQHCs) in the US provide care to vulnerable populations in different contexts, addressing diverse patient health and socioeconomic characteristics. In this study, we use data envelopment analysis (DEA) to measure FQHC performance, applying several techniques to account for both quality of outputs and heterogeneity among FQHC operating environments. To address quality, we examine two formulations, the Two-Model DEA approach of Shimshak and Lenard (denoted S/L), and a variant of the Quality-Adjusted DEA approach of Sherman and Zhou (denoted S/Z). To mitigate the aforementioned heterogeneities, a data science approach utilizing latent class analysis (LCA) is conducted on a set of metrics not included in the DEA, to identify latent typologies of FQHCs. Each DEA quality approach is applied in both an aggregated (including all FQHCs in a single DEA model) and a partitioned case (solving a DEA model for each latent class, such that an FQHC is compared only to its peer group). We find that the efficient frontier for the aggregated S/L approach disproportionately included smaller FQHCs, whereas the aggregated S/Z approach's reference set included many larger FQHCs. The partitioned cases found that both the S/L and S/Z aggregated models disproportionately disfavored (different) members of certain classes with respect to efficiency scores. Based on these results, we provide general insights into the trade-offs of using these two models in conjunction with a clustering approach such as LCA.