Increased COVID-19 mortality among immigrants compared with US-born individuals: a cross-sectional analysis of 2020 mortality data

OBJECTIVE

Multiple studies have shown that racially minoritized groups had disproportionate COVID-19 mortality relative to non-Hispanic White individuals. However, there is little known regarding mortality by immigrant status nationally in the United States, despite being another vulnerable population.

STUDY DESIGN

This was an observational cross-sectional study using mortality vital statistics system data to calculate proportionate mortality ratios (PMRs) and mortality rates due to COVID-19 as the underlying cause.

METHODS

Rates were compared by decedents' identified race, ethnicity (Hispanic vs non-Hispanic), and immigrant (immigrants vs US born) status. Asian race was further disaggregated into "Asian Indian," "Chinese," "Filipino," "Japanese," "Korean," and "Vietnamese."

RESULTS

Of the over 3.4 million people who died in 2020, 10.4% of all deaths were attributed to COVID-19 as the underlying cause (n = 351,530). More than double (18.9%, n = 81,815) the percentage of immigrants who died of COVID-19 compared with US-born decedents (9.1%, n = 269,715). PMRs due to COVID-19 were higher among immigrants compared with US-born individuals for non-Hispanic White, non-Hispanic Black, Hispanic, and most disaggregated Asian groups. Among disaggregated Asian immigrants, age- and sex-adjusted PMR due to COVID-19 ranged from 1.58 times greater mortality among Filipino immigrants (95% confidence interval [CI]: 1.53, 1.64) to 0.77 times greater mortality among Japanese immigrants (95% CI: 0.68, 0.86). Age-adjusted mortality rates were also higher among immigrant individuals compared with US-born people.

CONCLUSIONS

Immigrant individuals experienced greater mortality due to COVID-19 compared with their US-born counterparts. As COVID-19 becomes more endemic, greater clinical and public health efforts are needed to reduce disparities in mortality among immigrants compared with their US-born counterparts.

Improving diet, activity and wellness in adults at risk of diabetes: randomized controlled trial

OBJECTIVE

The purpose of this analysis is to examine the effect of an algorithm-driven online diabetes prevention program on changes in eating habits, physical activity and wellness/productivity factors.

METHODS

The intervention, Alive-PD, used small-step individually tailored goal setting and other features to promote changes in diet and physical activity. A 6-month randomized controlled trial was conducted among patients from a healthcare delivery system who had confirmed prediabetes (n =339). Change in weight and glycemic markers were measured in the clinic. Changes in physical activity, diet and wellness/productivity factors were self-reported. Mean age was 55 (s.d. 8.9) years, mean body mass index was 31 (s.d. 4.4) kg m(-2), 68% were white and 69% were male.

RESULTS

The intervention group increased fruit/vegetable consumption by 3.71 (95% confidence interval (CI) 2.73, 4.70) times per week (effect size 0.62), and decreased refined carbohydrates by 3.77 (95% CI 3.10, 4.44) times per week both significantly (P<0.001) greater changes than in the control group. The intervention group also reported a significantly greater increase in physical activity than in the control group, effect size 0.49, P<0.001. In addition, the intervention group reported a significant increase in self-rated health, in confidence in ability to make dietary changes and in ability to accomplish tasks, and a decrease in fatigue, compared with the control group. These changes paralleled the significant treatment effects on glycemic markers and weight.

CONCLUSIONS

In addition to promoting improvements in weight and glycemic markers, the Alive-PD program appears to improve eating habits and physical activity, behaviors important not just for diabetes prevention but for those with diagnosed diabetes or obesity. The improvements in wellness/productivity may derive from the diet and activity improvements, and from the satisfaction and self-efficacy of achieving goals.

Underdiagnosis of hypertension using electronic health records

BACKGROUND

Hypertension is highly prevalent and contributes to cardiovascular morbidity and mortality. Appropriate identification of hypertension is fundamental for its management. The rates of appropriate hypertension diagnosis in outpatient settings using an electronic health record (EHR) have not been well studied. We sought to identify prevalent and incident hypertension cases in a large outpatient healthcare system, examine the diagnosis rates of prevalent and incident hypertension, and identify clinical and demographic factors associated with appropriate hypertension diagnosis.

METHODS

We analyzed a 3-year, cross-sectional sample of 251,590 patients aged ≥18 years using patient EHRs. Underlying hypertension was defined as two or more abnormal blood pressure (ABP) readings ≥140/90 mm Hg and/or pharmaceutical treatment. Appropriate hypertension diagnosis was defined by the reporting of ICD-9 codes (401.0-401.9). Factors associated with hypertension diagnosis were assessed through multivariate analyses of patient clinical and demographic characteristics.

RESULTS

The prevalence of hypertension was 28.7%, and the diagnosis rate was 62.9%. The incidence of hypertension was 13.3%, with a diagnosis rate of 19.9%. Predictors of diagnosis for prevalent hypertension included older age, Asian, African American, higher body mass index (BMI), and increased number of ABP readings. Predictors for incident hypertension diagnosis were similar. In patients with two or more ABP readings, hypertension diagnosis was associated with significantly higher medication treatment rates (92.6% vs. 15.8%, P < 0.0001).

CONCLUSIONS

Outpatient EHR diagnosis rates are suboptimal, yet EHR diagnosis of hypertension is strongly associated with treatment. Targeted efforts to improve diagnosis should be a priority.

Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index

OBJECTIVE

To examine the relationship between body mass index (BMI) and metabolic syndrome for Asian Americans and non-Hispanic Whites (NHWs), given that existing evidence shows racial/ethnic heterogeneity exists in how BMI predicts metabolic syndrome.

RESEARCH DESIGN AND METHODS

Electronic health records of 43,507 primary care patients aged 35 years and older with self-identified race/ethnicity of interest (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese or NHW) were analyzed in a mixed-payer, outpatient-focused health-care organization in the San Francisco Bay Area.

RESULTS

Metabolic syndrome prevalence is significantly higher in Asians compared with NHWs for every BMI category. For women at the mean age of 55 and BMI of 25  kg  m(-2), the predicted prevalence of metabolic syndrome is 12% for NHW women compared with 30% for Asians; similarly for men, the predicted prevalence of metabolic syndrome is 22% for NHWs compared with 43% of Asians. Compared with NHW women and men with a BMI of 25  kg  m(-2), comparable prevalence of metabolic syndrome was observed at BMI of 19.6  kg  m(-2) for Asian women and 19.9  kg  m(-2) for Asian men. A similar pattern was observed in disaggregated Asian subgroups.

CONCLUSIONS

In spite of the lower BMI values and lower prevalence of overweight/obesity than NHWs, Asian Americans have higher rates of metabolic syndrome over the range of BMI. Our results indicate that BMI ranges for defining overweight/obesity in Asian populations should be lower than for NHWs.