Food Security, Access, and Challenges in New Mexico during COVID-19.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic disrupted food systems and economies across the United States. Public health measures, including stay-at-home orders, led to employment disruptions and food system shocks that increased barriers to food access. OBJECTIVES: We aimed to examine food insecurity and food access challenges in New Mexico (NM) during the COVID-19 pandemic. METHODS: A cross-sectional study using a validated survey was conducted in NM in May and June 2020. Adults 18 y and older were recruited through convenience sampling via email, websites, and targeted social media ads from major universities, nongovernmental organizations, state agencies, and media outlets. Survey questions assessed food insecurity and food-related challenges and worry. Bivariate and multivariate logistic regression examined relations between food insecurity and demographic characteristics. z Tests were used to compare the proportions of individuals who responded affirmatively to food challenge and worry questions between food-secure and food-insecure respondents. RESULTS: A total of 1487 residents participated in the study. Thirty percent of respondents reported experiencing food insecurity and 16% experienced very low food security since the pandemic started. Food insecurity was associated with each of 7 characteristics examined in bivariate logistic regression analyses. Multivariate logistic regression results showed that Hispanic (adjusted OR: 1.70; 95% CI: 1.18, 2.44) and female (adjusted OR: 1.78; 95% CI: 1.09, 2.90) respondents were more likely to experience food insecurity than non-Hispanic white and male respondents. Larger household sizes were associated with higher odds of food insecurity except for those in the lowest and highest income categories. z Tests showed that a higher proportion of food-insecure respondents experienced food-related challenges and worry than food-secure respondents. CONCLUSIONS: Disparities in food insecurity persisted during the COVID-19 pandemic and food-insecure individuals were more likely to report experiencing food-related challenges and worry. Researchers and policy makers in NM may consider continuing efforts to mitigate food access issues as the pandemic continues.

Intrafamilial Characteristics Are Important Predictors of Children's Diets in New Mexico's Border Colonias.

INTRODUCTION: We assessed selected nutritional indicators in Mexican-origin children in two low-income, rural colonias in New Mexico on the U.S.-Mexico border. These children are at higher risk for obesity and other chronic diseases linked to poor nutrition in childhood, but little is known about their diets. METHOD: We surveyed mothers of 202 children 6 to 10 years old about sociodemographic characteristics, family and child attitudes and behaviors, and the child's diet. We compared diet with dietary recommendations and used regression trees to identify significant predictors of recommended intake. RESULTS: Among families, 89.1% participated in Medicaid, and 52.5% participated in a Supplemental Nutrition Program. More children met recommendations for fruit (36.1%) than vegetables (1.5%). Greater vegetable intake was associated with a child's not thinking healthy food tasted bad, greater family activity, and younger maternal age. Only 5.0% of children met the recommendation for <10% of energy from added sugar, with the average child consuming 2.4 times that from sugar-sweetened beverages and snacks. Lower sugar intake was associated with less screen time, not having TV on during meals, and playing team sports. Family access to healthy food and child use of mobile food vendors, vending and convenience stores were not predictive of diet. CONCLUSION: Hispanic children in border colonias have poor diets that put them at risk for obesity and numerous chronic diseases. Addressing this problem will require changing family norms and attitudes toward healthy food, screen time behavior, and physical activity levels within families.

Early effects of COVID-19 on physical activity and screen time use among elementary school students in Columbus, New Mexico.

Adherence to national physical activity guidelines among youth ages 6-11 in the United States is low. The emergence of COVID-19 and the public health measures implemented in response may have decreased children's physical activity even further. We conducted an online survey among parents of students attending Columbus Elementary School in Columbus, New Mexico, a rural community on the US-Mexico border, to assess changes in children's physical activity and screen time use from summer 2019 to summer 2020. We also sought to identify important covariates. All parents (N = 55) and children (N = 87) identified as Hispanic; most parents were born in Mexico, while most children were born in the United States. Most parents (79.3%) reported a decrease in their children's physical activity from 2019 to 2020, and the vast majority of these parents reported that the changes were due to COVID-19 home confinement. The mean number of days children were physically active for >60 minutes significantly decreased, while daily screen time use increased. Having parents born in Mexico, infrequent family meals (<3/week), and not having community spaces for physical activity close by protected children from decreases in their level of physical activity from 2019 to 2020. Home-based exercise may serve as a suitable method of physical activity when public health responses to COVID-19 restrict community spaces. Future interventions should also be mindful of the role that parental nativity and related cultural factors may play in children's physical activity levels.

A Disparity beneath a Paradox: Cancer Mortality among Young Hispanic Americans in the US-Mexico Border Region.

The age-adjusted mortality rate for cancer in the US Hispanic population is two thirds that of the non-Hispanic white population, probably because of differences in smoking rates. We aimed to determine whether Hispanic white (HW) cancer mortality in the US-Mexico Border Region was also lower than that of the non-Hispanic white (NHW) border population, particularly in the younger population less likely to develop smoking-related cancer. We obtained age-adjusted cancer mortality rates from 1999 to 2017 for the 44 border counties, the four US-Mexico border states, and the rest of the US. We obtained cancer incidence rates for 1999-2016 from state registries. We stratified rates by age group, ethnicity, border state, urbanization, and cancer site. Age-adjusted border cancer mortality rates were 139.1/100,000 in the HW and 171.4 in the NHW populations, a ratio of 0.8. HW mortality rates were higher than NHW rates only for the 0-34 age group. State-specific HW cancer incidence rates for people 0-34 years old were 77%-80% of NHW rates. We also calculated mortality-incidence ratios (MIR) for the 0-34 population. Border mortality-incidence ratios were higher in the HW population. HW rates exceeded NHW rates for all cancer sites except skin cancer. The HW cancer disparity is due to poorer survival in the HW population, which might be due to limited access to prevention and treatment in a medically underserved area. Mortality among young border Hispanic residents might be reduced through efforts to improve insurance coverage and increase access to medical providers .

Odds ratios from logistic, geometric, Poisson, and negative binomial regression models.

BACKGROUND: The odds ratio (OR) is used as an important metric of comparison of two or more groups in many biomedical applications when the data measure the presence or absence of an event or represent the frequency of its occurrence. In the latter case, researchers often dichotomize the count data into binary form and apply the well-known logistic regression technique to estimate the OR. In the process of dichotomizing the data, however, information is lost about the underlying counts which can reduce the precision of inferences on the OR. METHODS: We propose analyzing the count data directly using regression models with the log odds link function. With this approach, the parameter estimates in the model have the exact same interpretation as in a logistic regression of the dichotomized data, yielding comparable estimates of the OR. We prove analytically, using the Fisher information matrix, that our approach produces more precise estimates of the OR than logistic regression of the dichotomized data. We also show the gains in precision using simulation studies and real-world datasets. We focus on three related distributions for count data: geometric, Poisson, and negative binomial. RESULTS: In simulation studies, confidence intervals for the OR were 56-65% as wide (geometric model), 75-79% as wide (Poisson model), and 61-69% as wide (negative binomial model) as the corresponding interval from a logistic regression produced by dichotomizing the data. When we analyzed existing datasets using our approach, we found that confidence intervals for the OR could be up to 64% shorter (36% as wide) compared to if the data had been dichotomized and analyzed using logistic regression. CONCLUSIONS: More precise estimates of the OR can be obtained directly from the count data by using the log odds link function. This analytic approach is easy to implement in software packages that are capable of fitting generalized linear models or of maximizing user-defined likelihood functions.

Patterns of Screen Time Among Rural Mexican-American Children on the New Mexico-Mexico Border.

INTRODUCTION: The prevalence of obesity is 26% among Hispanic children and teenagers and 47% among Hispanic adults. One contributor to obesity is sedentary behavior, such as using electronic screen devices (ie, screens). Low-income and Hispanic youths spend more time using such devices than other youths. METHODS: We interviewed 202 parents of Mexican-origin children aged 6 to 10 years in 2 rural communities near the US-Mexico border to determine screen use among children. We tested for associations between covariates and heavy screen use (≥4 hours/day) and calculated adjusted odds ratios (AORs) to identify independent, modifiable risk factors for such use. RESULTS: More than two-thirds (68.3%) of households had an annual income of less than $24,000, 89.1% spoke primarily Spanish, and 92.1% had internet access. The percentage of children with heavy screen use was 14.9% on weekdays and 25.2% on weekends. Smartphones were used by 62.4% of children, desktops or laptops by 60.9%; homework was the most common reason for use of these devices. One in 3 children used them for social media. Increased odds of heavy screen use were associated with having a television on while the child ate (weekday AOR = 3.02; 95% confidence interval [CI], 1.08-8.45 and weekend AOR = 2.38; 95% CI, 1.04-5.40) and using electronics to entertain (weekend AOR = 2.94; 95% CI, 1.15-7.51). More than 3 family meals per week (AOR = 0.40; 95% CI, 0.17-0.94 compared with ≤3 meals) and 2 or 3 family activities per week (AOR = 0.33; 95% CI, 0.12-0.87 compared with ≤1 activity) were associated with decreased odds of heavy weekend use. CONCLUSION: Even in low-income, Spanish-speaking communities, children have access to electronic devices, social media, and the internet, and a substantial fraction of them are heavy users. Efforts to reduce screen time might focus on understanding and changing the social norms that promote it.

Survival of Escherichia coli on Lettuce under Field Conditions Encountered in the Northeastern United States.

Although wildlife intrusion and untreated manure have been associated with microbial contamination of produce, relatively few studies have examined the survival of Escherichia coli on produce under field conditions following contamination (e.g., via splash from wildlife feces). This experimental study was performed to estimate the die-off rate of E. coli on preharvest lettuce following contamination with a fecal slurry. During August 2015, field-grown lettuce was inoculated via pipette with a fecal slurry that was spiked with a three-strain cocktail of rifampin-resistant nonpathogenic E. coli. Ten lettuce heads were harvested at each of 13 time points following inoculation (0, 2.5, 5, and 24 h after inoculation and every 24 h thereafter until day 10). The most probable number (MPN) of E. coli on each lettuce head was determined, and die-off rates were estimated. The relationship between sample time and the log MPN of E. coli per head was modeled using a segmented linear model. This model had a breakpoint at 106 h (95% confidence interval = 69, 142 h) after inoculation, with a daily decrease of 0.70 and 0.19 log MPN for 0 to 106 h and 106 to 240 h following inoculation, respectively. These findings are consistent with die-off rates obtained in similar studies that assessed E. coli survival on produce following irrigation. Overall, these findings provide die-off rates for E. coli on lettuce that can be used in future quantitative risk assessments.

Statistical Design Features of the Healthy Communities Study.

The Healthy Communities Study is designed to assess relationships between characteristics of community programs and policies targeting childhood obesity and children's BMI, diet, and physical activity. The study involved a complex data collection protocol implemented over a 2-year period (2013-2015) across a diverse sample of 130 communities, defined as public high school catchment areas. The protocol involved baseline assessment within each community that included in-person or telephone interviews regarding community programs and policies and in-home collection of BMI, nutritional, and physical activity outcomes from a sample of up to 81 children enrolled in kindergarten through eighth grade in public schools. The protocol also involved medical record reviews to establish a longitudinal trajectory of BMI for an estimated 70% of participating children. Staged sampling was used to collect less detailed measures of physical activity and nutrition across the entire sample of children, with a subset assessed using more costly, burdensome, and detailed measures. Data from the Healthy Community Study will be analyzed using both cross-sectional and longitudinal models that account for the complex design and correct for measurement error and bias using a likelihood-based Markov-chain Monte Carlo methodology. This methods paper provides insights into the complex design features of the Healthy Communities Study and may serve as an example for future large-scale studies that assess the relationship between community-based programs and policies and health outcomes of community residents.

Weight Status Measures Collected in the Healthy Communities Study: Protocols and Analyses.

The Healthy Communities Study is one of the largest studies to assess the relationship between characteristics of community programs and policies to prevent childhood obesity and obesity-related outcomes. The purpose of this paper is to describe the protocol that was developed for collecting the anthropometric data for the study and the procedures for analyzing the data. Data were collected from 2013 to 2015 and analyses will be completed by mid-2016. During in-home visits, Healthy Communities Study staff collected height, weight, and waist circumference measurements from child participants and height and weight measurements from adult participants. The protocol for obtaining these measurements was adapted from the protocol used by the National Health and Nutrition Examination Survey, with modifications to accommodate assessments conducted in homes rather than in a Mobile Examination Center. In addition to anthropometric data from in-home visits, the Healthy Communities Study collected retrospective height and weight measurements from the medical records of child participants. These data were used to calculate trajectories of BMI and BMI z-scores. The study implemented procedures for ensuring the accuracy of the in-home measurements and abstracted medical data. These procedures included automatically checking the ranges on entered data, reviewing data for end-digit patterns, and abstracting selected medical records using two independent abstractors to assess agreement. The collection of longitudinal height and weight measures will allow researchers to address several pressing questions related to how characteristics of community programs and policies are associated with obesity-related outcomes among children.