Census Bureau's American Community Research Articles

Overdosing in a Motor Vehicle: Examination of Human, Geographic, and Environmental Factors.

Fentanyl, a type of opioid, in impaired driving cases increased across cities in the United States. No empirical studies have examined motor vehicle overdoses with fentanyl use. We investigated the magnitude of the motor vehicle overdose problem in Providence, RI, and the environmental, socioeconomic, and geographic conditions associated with motor vehicle overdose occurrence. This was a retrospective observational study of emergency medical services data on all suspected opioid overdoses between January 1, 2017, and October 31, 2020. The data contain forced-choice fields, such as age and biological sex, and an open-ended narrative in which the paramedic documented clinical and situational information. The overdoses were geocoded, allowing for the extraction of sociodemographic data from the U.S. Census Bureau's American Community Survey. Seven other data sources were included in a logistic regression to understand key risk factors and spatial patterns of motor vehicle overdoses. Of the 1,357 opioid overdose cases in this analysis, 15.2% were defined as motor vehicle overdoses. In adjusted models, we found a 61% increase in the odds of a motor vehicle overdose involvement for men versus women, a 16.8% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to the nearest gas station, and a 10.7% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to a buprenorphine clinic. There is a need to understand the interaction between drug use in vehicles to design interventions for decreasing driving after illicit drug use.

The associations of income and Black-White racial segregation with HIV outcomes among adults aged ≥18 years-United States and Puerto Rico, 2019.

To examine associations between Index of Concentration at the Extremes (ICE) measures for economic and racial segregation and HIV outcomes in the United States (U.S.) and Puerto Rico. County-level HIV testing data from CDC's National HIV Prevention Program Monitoring and Evaluation and census tract-level HIV diagnoses, linkage to HIV medical care, and viral suppression data from the National HIV Surveillance System were used. Three ICE measures of spatial polarization were obtained from the U.S. Census Bureau's American Community Survey: ICEincome (income segregation), ICErace (Black-White racial segregation), and ICEincome+race (Black-White racialized economic segregation). Rate ratios (RRs) for HIV diagnoses and prevalence ratios (PRs) for HIV testing, linkage to care within 1 month of diagnosis, and viral suppression within 6 months of diagnosis were estimated with 95% confidence intervals (CIs) to examine changes across ICE quintiles using the most privileged communities (Quintile 5, Q5) as the reference group. PRs and RRs showed a higher likelihood of testing and adverse HIV outcomes among persons residing in Q1 (least privileged) communities compared with Q5 (most privileged) across ICE measures. For HIV testing percentages and diagnosis rates, across quintiles, PRs and RRs were consistently greatest for ICErace. For linkage to care and viral suppression, PRs were consistently lower for ICEincome+race. We found that poor HIV outcomes and disparities were associated with income, racial, and economic segregation as measured by ICE. These ICE measures contribute to poor HIV outcomes and disparities by unfairly concentrating certain groups (i.e., Black persons) in highly segregated and deprived communities that experience a lack of access to quality, affordable health care. Expanded efforts are needed to address the social/economic barriers that impede access to HIV care among Black persons. Increased partnerships between government agencies and the private sector are needed to change policies that promote and sustain racial and income segregation.

The role of area-level socioeconomic disadvantage in racial disparities in cancer incidence in metropolitan Detroit.

Neighborhood deprivation is associated with both race and cancer incidence, but there is a need to better understand the effect of structural inequities on racial cancer disparities. The goal of this analysis was to evaluate the relationship between a comprehensive measure of neighborhood-level social disadvantage and cancer incidence within the racially diverse population of metropolitan Detroit. We estimated breast, colorectal, lung, and prostate cancer incidence rates using Metropolitan Detroit Cancer Surveillance System and US decennial census data. Neighborhood socioeconomic disadvantage was measured by the Area Deprivation Index (ADI) using Census Bureau's American Community Survey data at the Public Use Microdata Areas (PUMA) level. Associations between ADI at time of diagnosis and cancer incidence were estimated using Poisson mixed-effects models adjusting for age and sex. Attenuation of race-incidence associations by ADI was quantified using the "mediation" package in R. ADI was inversely associated with incidence of breast cancer for both non-Hispanic White (NHW) and non-Hispanic Black (NHB) women (NHW: per-quartile RR = 0.92, 95% CI 0.88-0.96; NHB: per-quartile RR = 0.94, 95% CI 0.91-0.98) and with prostate cancer incidence only for NHW men (per-quartile RR = 0.94, 95% CI 0.90-0.97). ADI was positively associated with incidence of lung cancer for NHWs and NHBs (NHW: per-quartile RR = 1.12, 95% CI 1.04-1.21; NHB: per-quartile RR = 1.37, 95% CI 1.25-1.51) and incidence of colorectal cancer (CRC) only among NHBs (per-quartile RR = 1.11, 95% CI 1.02-1.21). ADI significantly attenuated the relationship between race and hormone receptor positive, HER2-negative breast cancer (proportion attenuated = 8.5%, 95% CI 4.1-16.6%) and CRC cancer (proportion attenuated = 7.3%, 95% CI 3.7 to 12.8%), and there was a significant interaction between race and ADI for lung (interaction RR = 1.22, p < 0.0001) and prostate cancer (interaction RR = 1.09, p = 0.00092). Area-level socioeconomic disadvantage is associated with risk of common cancers in a racially diverse population and plays a role in racial differences in cancer incidence.

Association of Community-Level Socioeconomic Distress with Nutritional Status in People with Sickle Cell Anemia

Introduction: Sickle cell anemia (SCA) is characterized by a myriad of acute and chronic complications. These complications negatively impact the ability to achieve educational and occupational goals making this group a particularly vulnerable population to socioeconomic challenges (PMID 30540112). Furthermore, most patients with SCA in the U.S. are Black and are disproportionately affected by socioeconomic inequity due to the historical effects of systemic racism (PMCID: PMC4817358). In the general population, indices of socioeconomic distress are associated with poor nutrition (PMID 25573510). Patients with SCA also experience a high frequency of food insecurity (PMID 34811867). The association of community-level socioeconomic distress with nutritional status in people with SCA is unknown. Methods: We conducted a cross-sectional study to describe the burden of community-level socioeconomic distress and its impact on nutritional status in an SCA cohort (n=332) treated at an urban academic institution. The distressed community index (DCI), a composite of socioeconomic indicators (including no high school diploma, housing vacancy rate, adults not working, poverty rate, median income ratio, and changes in employment and establishments) that estimate the economic well-being of a community, was determined for each individual patient's residential address using 2014-2018 U.S. Census Bureau's American Community Survey and the Business Patterns database. DCI score ranges from 0-100 with a higher score indicating a more distressed community. The association of DCI with measures of nutritional status (serum albumin, body mass index [BMI], vitamin D 25-OH) and hemoglobin concentration were determined using linear regression. The analyses were adjusted for age, sex, and hydroxyurea use. The beta coefficients provided are based on increments of 10 for DCI. Median and interquartile ranges (IQR) are provided. Results: The median age of the cohort was 31 years (IQR, 24-41 years), 55% were female, 53% were on hydroxyurea therapy, and the median BMI was 23 kg/m2 (IQR, 21-26 kg/m2). The median DCI in this cohort was 87 (IQR, 61-96) with 61% of SCA patients living in a distressed tier (DCI≥ 80) and only 5% living in a prosperous tier (DCI< 20). A higher DCI, reflecting an increase in socioeconomic distress, was associated with a trend for a lower odds ratio (OR) of being on hydroxyurea therapy (OR 0.93; 95% CI 0.86-1.0). An increasing DCI was significantly associated with lower serum albumin (β= -0.02; P=0.016), BMI (β= -0.01; P=0.045), and vitamin D 25-OH levels (β= -0.7; P=0.001), adjusting for age, sex, and hydroxyurea use (Figure). Furthermore, SCA patients living in a more distressed community had a trend for lower hemoglobin concentrations (β= -0.05; P=0.077) with similar adjustments. Discussion: In conclusion, we demonstrate that a high proportion of patients with SCA treated at an urban academic institution live in distressed socioeconomic communities. Higher distressed community indices are associated with lower utilization of hydroxyurea and lower measures of nutritional status. Future studies investigating the effects of socioeconomic distress on access to outpatient care and healthy foods may guide public health strategies to improve health disparities in the SCA population. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Association between neighborhood socioeconomic status, built environment and SARS-CoV-2 infection among cancer patients treated at a Tertiary Cancer Center in New York City.

Racial and ethnic minority groups experience a disproportionate burden of SARS-CoV-2 illness and studies suggest that cancer patients are at a particular risk for severe SARS-CoV-2 infection. The objective of this study was examine the association between neighborhood characteristics and SARS-CoV-2 infection among patients with cancer. We performed a cross-sectional study of New York City residents receiving treatment for cancer at a tertiary cancer center. Patients were linked by their address to data from the US Census Bureau's American Community Survey and to real estate tax data from New York's Department of City Planning. Models were used to both to estimate odds ratios (ORs) per unit increase and to predict probabilities (and 95% CI) of SARS-CoV2 infection. We identified 2350 New York City residents with cancer receiving treatment. Overall, 214 (9.1%) were infected with SARS-CoV-2. In adjusted models, the percentage of Hispanic/Latino population (aOR=1.01; 95% CI, 1.005-1.02), unemployment rate (aOR=1.10; 95% CI, 1.05-1.16), poverty rates (aOR=1.02; 95% CI, 1.0002-1.03), rate of >1 person per room (aOR=1.04; 95% CI, 1.01-1.07), average household size (aOR=1.79; 95% CI, 1.23-2.59) and population density (aOR=1.86; 95% CI, 1.27-2.72) were associated with SARS-CoV-2 infection. Among cancer patients in New York City receiving anti-cancer therapy, SARS-CoV-2 infection was associated with neighborhood- and building-level markers of larger household membership, household crowding, and low socioeconomic status. We performed a cross-sectional analysis of residents of New York City receiving treatment for cancer in which we linked subjects to census and real estate date. This linkage is a novel way to examine the neighborhood characteristics that influence SARS-COV-2 infection. We found that among patients receiving anti-cancer therapy, SARS-CoV-2 infection was associated with building and neighborhood-level markers of household crowding, larger household membership, and low socioeconomic status. With ongoing surges of SARS-CoV-2 infections, these data may help in the development of interventions to decrease the morbidity and mortality associated with SARS-CoV-2 among cancer patients.

Nonadherence to treatment protocol for severe hypertension in pregnancy

Hypertensive disorders of pregnancy are one of the leading causes of maternal mortality and severe morbidity. The American College of Obstetricians and Gynecologists recommends treatment of persistent severe hypertension because this has been shown to improve overall outcomes. Treatment remains inconsistent and may be influenced by patient-level sociodemographic and clinical characteristics. This study aimed to identify which factors are associated with nonadherence to an institutional protocol for the treatment of severe hypertension in pregnancy. Retrospective cohort study of patients who had persistent severe hypertension (≥2 systolic blood pressures ≥160 mm Hg and/or diastolic blood pressures >110 mm Hg between 15 and 60 minutes apart) during their delivery hospitalization in 3 hospitals within an integrated health system from February 1, 2018 to March 1, 2020. Adherence to an institutional protocol was defined as receiving antihypertensive medication within 1 hour of a second severe blood pressure measurement. Demographic information, medical comorbidities, and delivery hospitalization characteristics were compared between women who received treatment based on institutional protocol and those who did not. Patient zone improvement plan codes were linked to neighborhood-level data from the US Census Bureau's American Community Survey to extract socioeconomic characteristics. A multivariable logistic regression was performed to evaluate factors associated with delayed treatment while adjusting for potential confounders. Of the 996 patients included, 449 (45%) received treatment within 60 minutes and 547 (55%) did not. Having an elevated, nonsevere range blood pressure (adjusted odds ratio, 0.55; 95% confidence interval, 0.38-0.79) or a severe range blood pressure (adjusted odds ratio, 0.25; 95% confidence interval, 0.16-0.38) on admission, persistent severe hypertension ≥1 hour before or after delivery (adjusted odds ratio, 0.27; 95% confidence interval, 0.27-0.45), and chronic hypertension (adjusted odds ratio, 0.58; 95% confidence interval, 0.37-0.93) were associated with timely treatment. Hospital site (adjusted odds ratio, 1.97; 95% confidence interval, 1.18-3.28) and increasing gestational age (adjusted odds ratio, 1.14; 95% confidence interval, 1.07-1.21) were associated with nonadherence to treatment protocol. A subanalysis evaluating treatment in 344 (35%) patients who had a nonelevated blood pressure on admission showed that White race, persistent severe hypertension within 1 hour of delivery, increasing gestational age, body mass index, twin gestation, preferred language other than English or Spanish, and a higher neighborhood unemployment rate were associated with nonadherence to treatment protocol. Several factors were associated with nonadherence to an institutional protocol for treatment of persistent severe hypertension. Provider bias may impact whether treatment is executed or not. Awareness of these risk factors may improve timely administration of antihypertensive medication in pregnant and postpartum patients.

Trends in demographic and employment characteristics of US emergency medical technicians and paramedics, 2011-2019.

BackgroundDescribing the US emergency medical services workforce is important to understand gaps in recruitment and retention and inform efforts to improve diversity. Our objective was to describe the characteristics and temporal trends of emergency medical technicians (EMTs) and paramedics in the United States.MethodsWe performed a repeated cross‐sectional evaluation of US Census Bureau's American Community Survey 1‐year Public Use Microdata Sample data sets from 2011–2019. We included respondents working as an EMT or paramedic. Survey‐weighted descriptive statistics of demographic and employment characteristics were calculated. Trend analysis was conducted using joinpoint regression to estimate slope and annual percent change (APC).ResultsThe total estimated number of EMTs and paramedics in the United States increased from 216,310 (95%CI 204,957–227,663) in 2011 to 289,830 (95%CI 276,918–302,743) in 2019 (APC 3.0%; 95%CI 1.4%, 4.7%). There was a slight increase in the proportion of females (2011, 31%; 2019, 35%). There was a significant decrease in proportion of non‐Hispanic whites (2011, 80%; 2019, 72%; APC −1.5%; 95%CI −2.0%, −0.9%) with concurrent increases in other racial/ethnic groups (e.g., Hispanics, 2011, 10%; 2019, 13%). About half worked >40 hours per week, with little change over time. Between 15% and 18% lived and worked in different states, and about 40% traveled ≥30 minutes to their workplace.ConclusionsThe number of EMTs and paramedics actively working in EMS as their primary paid occupation has increased over time. However, there have been only modest changes in their demographic diversity.

The Latino Resident Physician Shortage: A Challenge and Opportunity for Equity, Diversity, and Inclusion.

The purpose of this study is to examine the number of Latino physicians in residency training and Latino resident physician trends in the nation's 10 largest medical specialties in the United States and in the 4 states with the largest Latino populations: California, Florida, New York, and Texas. The authors used data from the United States Census Bureau's American Community Survey to determine Latino populations and a special report from the Association of American Medical Colleges to determine rates of Latino resident physicians in the United States and in California, Florida, New York, and Texas from 2001 to 2017. Rates of Latino residents in the nation's 10 specialties with the largest number of residents were also determined. From 2001 to 2017, the United States had an average of 37 resident physicians per 100,000 population. At the national level, however, Latino residents were underrepresented, with only 14 per 100,000 Latino population. At the state level, California and Texas, the 2 states with the largest Latino populations (39.4% and 39.7% of their population, respectively), had 5 and 9 Latino residents per 100,000 Latino population, respectively. Latino residents in California, Texas, Florida, and New York were also very underrepresented in the primary care specialties examined. The findings show a severe shortage of Latino resident physicians. While a similar shortage also exists in primary care specialties, the majority of Latinos in states with large Latino populations are consistently choosing to train in primary care. Investment and greater improvement in the representation of certain population groups in medicine and for combating the inequities existing in the current educational system is needed. The authors offer recommendations to increase the number of Latinos in residency programs and for increasing the number of Spanish-speaking physicians and Latino international medical graduates in residency programs.

Estimating uncertainty in a socioeconomic index derived from the American community survey

Socioeconomic indexes are widely used in public health to facilitate neighborhood-scale analyses. Although they are calculated with high levels of precision, they are rarely reported with accompanying measures of uncertainty (e.g., 90% confidence intervals). Here we use the variance replicate tables that accompany the United States Census Bureau's American Community Survey to report confidence intervals around the Yost Index, a socioeconomic index comprising seven variables that is frequently used in cancer surveillance. The Yost Index is reported as a percentile score from 1 (most affluent) to 100 (most deprived). We find that the average uncertainty for a census tract in the United States is plus or minus 8 percentiles, with the uncertainty a function of the value of the index itself. Scores at the extremes of the distribution are more precise and scores near the center are less precise. Less-affluent tracts have greater uncertainty than corresponding more-affluent tracts. Fewer than 50 census tracts of 72,793 nationally have unusual distributions of socioeconomic conditions that render the index uninformative. We demonstrate that the uncertainty in a census-based socioeconomic index is calculable and can be incorporated into any analysis using such an index.

The Use of Multimode Data Collection in Random Digit Dialing Cell Phone Surveys for Young Adults: Feasibility Study.

BackgroundYoung adults’ early adoption of new cell phone technologies have created challenges to survey recruitment but offer opportunities to combine random digit dialing (RDD) sampling with web mode data collection. The National Young Adult Health Survey was designed to test the feasibility of this methodology.ObjectiveIn this study, we compared response rates across the telephone mode and web mode, assessed sample representativeness, examined design effects (DEFFs), and compared cigarette smoking prevalence to a gold standard national survey.MethodsWe conducted a survey experiment where the sampling frame was randomized to single-mode telephone interviews, telephone-to-web sequential mixed mode, and single-mode web survey. A total of 831 respondents aged 18 to 34 years were recruited via RDD at baseline. A soft launch was conducted prior to main launch. We compared the web mode to the telephone modes (ie, single-mode and mixed mode) at wave 1 based on the American Association for Public Opinion Research response rate 3 for screening and extended surveys. Base-weighted demographic distributions were compared to the American Community Survey. The sample was calibrated to the US Census Bureau's American Community Survey to calculate DEFFs and to compare cigarette smoking prevalence to the National Health Interview Survey. Prevalence estimates are estimated with sampling weights and are presented with unweighted sample sizes. Consistency of estimates was judged by 95% CI.ResultsThe American Association for Public Opinion Research response rate 3 was higher in the telephone mode than in the web mode (24% and 30% vs 6.1% and 12.5%, for soft launch and main launch, respectively), which was reflected in response rate 3 for screening and extended surveys. During the soft launch, the extended survey and eligibility rate were low for respondents pushed to the web mode. To boost productivity and survey completes for the web condition, the main launch used cell phone numbers from the sampling frame where the sample vendor matched the number to auxiliary data, which suggested that the number likely belonged to an adult in the target age range. This increased the eligibility rate, but the screener response rate was lower. Compared to population distribution from the US Census Bureau, the telephone mode overrepresented men (57.1% [unweighted n=412] vs 50.9%) and those enrolled in college (40.3% [unweighted n=269] vs 23.8%); it also underrepresented those with a Bachelor of Arts or Science (34.4% [unweighted n=239] vs 55%). The web mode overrepresented White, non-Latinos (70.7% [unweighted n=90] vs 54.4%) and those with some college education (30.4% [unweighted n=40] vs 7.6%); it also underrepresented Latinos (13.6% [unweighted n=20] vs 20.7%) and those with a high school or General Education Development diploma (15.3% [unweighted n=20] vs 29.3%). The DEFF measure was 1.28 (subpopulation range 0.96-1.93). The National Young Adult Health Survey cigarette smoking prevalence was consistent with the National Health Interview Survey overall (15%, CI 12.4%-18% [unweighted 149/831] vs 13.5%, CI 12.3%-14.7% [unweighted 823/5552]), with notable deviation among 18- to 24-year-olds (15.6%, CI 11.3%-22.2% [unweighted 51/337] vs 8.7%, CI 7.1%-10.6% [unweighted 167/1647]), and those with education levels lower than Bachelor of Arts or Science (24%, CI 19.3%-29.4% [unweighted 123/524] vs 17.1%, CI 15.6%-18.7% [unweighted 690/3493]).ConclusionsRDD sampling for a web survey is not feasible for young adults due to its low response rate. However, combining this methodology with RDD telephone surveys may have a great potential for including media and collecting autophotographic data in population surveys.

The Impact of Gender and Sociodemographic Characteristics on Dentists' Practice Patterns, Employment Status, and Workforce Participation

Research ObjectiveGender diversification is rapidly occurring within the dental profession. Gender differences in practice choice (eg, employment vs practice ownership) and practice participation (eg, part‐time vs full‐time work hours) have been reported. It is important to understand how changes in the delivery system including workforce participation and preferences might impact the availability of services especially for underserved populations. The objectives of this study were to evaluate the variation in practice preferences among female and male dentists and to assess potential associations with socioeconomic and family factors.Study DesignThe current study used data from the Census Bureau's American Community Survey (ACS). The variables extracted describe dentists' sociodemographic characteristics including gender, age, race/ethnicity, presence of children or elders in the household, household income, and marital status, as well as dental practice characteristics such as employment status, practice setting, and work hours.Population StudiedThe 5‐year Public Use Microdata Sample data (2014–2018) from the ACS were utilized. Person‐level data collected through the questionnaire contain demographic, social, and economic information. The analytical sample consisted of 9993 dentists and 520,925 people living in the dentists' households. Survey data was weighted to generate unbiased estimates representative for the US population.Principal FindingsFemale dentists were younger (mean 43.3 years vs 53.6; P &lt; 0.001), more racially and ethnically diverse (58.1% vs 78.6% White, non‐Hispanic; P &lt; 0.001), and proportionally more foreign‐born (35.2% vs 17.7%; P &lt; 0.001) compared to male dentists. Female dentists were more likely to report being employed (55.1% vs 34.8%; P &lt; 0.001) or working part‐time (less than 30 hours per week) in dental practice (15.5% vs 11.0%; P = 0.001) than male dentists. Female dentists with children were more likely to report being an employee (64.3% vs 52.8%; P &lt; 0.001) and working part‐time (19.5% vs 14.3%; P &lt; 0.001) compared to female dentists without children in their care. Proportionally fewer female dentists left the workforce (ie, retired) in the past 12 months than male dentists (2.4% vs 3.6%; P = 0.002). Average income in the past 12 months was significantly lower (P &lt; 0.001) for female dentists (mean = $141,267; 95% Confidence Interval [CI] = $ 135,452 ‐$147,082) compared to male dentists (mean = $185,923; 95% CI = $135,452–$147,082).ConclusionsThe data indicate that the dental workforce is diversifying in gender and by race/ethnicity. The data about dental practice by gender is consistent with the current literature; female dentists were significantly more likely to be employed than to be self‐employed/a practice owner. This preference is consistent with evolving practice models including consolidations of smaller practices into group practices and changing business models for the profession. The wage gap by gender, which has been previously researched, is notable and difficult to explain.Implications for Policy or PracticeThe percentage of women entering and graduating from dental schools has increased over recent years achieving equity in numbers in dental schools in the US. Differences in employment status and workforce participation by gender are important preferences that should be monitored over time to ascertain if availability of services is affected by these trends.Primary Funding SourceHealth Resources and Services Administration.

Who is providing contraception care in the United States? An observational study of the contraception workforce

Contraception care is essential to providing comprehensive healthcare; however, little is known nationally about the contraception workforce. Previous research has examined the supply, distribution, and adequacy of the health workforce providing contraception services, but this research has faced a series of data limitations, relying on surveys or focusing on a subset of practitioners and resulting in an incomplete picture of contraception practitioners in the United States. This study aimed to construct a comprehensive database of the contraceptive workforce in the United States that provides the following 6 types of highly effective contraception: intrauterine device, implant, shot (depot medroxyprogesterone acetate), oral contraception, hormonal patch, and vaginal ring. In addition, we aimed to examine the difference in supply, distribution, the types of contraception services offered, and Medicaid participation. We constructed a national database of contraceptive service providers using multiple data sets: IQVIA prescription claims, preadjudicated medical claims, and the OneKey healthcare provider data set; the National Plan and Provider Enumeration System data set; and the Census Bureau's American Community Survey data on population demographics. All statistical analyses were descriptive, including chi-squared tests for groupwise differences and pairwise post hoc tests with Bonferroni corrections for multiple comparisons. Although 73.1% of obstetrician-gynecologists and 72.6% of nurse-midwives prescribed the pill, patch, or ring, only 51.4% of family medicine physicians, 32.4% of pediatricians, and 19.8% of internal medicine physicians do so. The ratio of all primary care providers prescribing contraception to the female population of reproductive age (ages, 15-44 years) varied substantially across states, with a range of 27.9 providers per 10,000 population in New Jersey to 74.2 providers per 10,000 population in Maine. In addition, there are substantial differences across states for Medicaid acceptance. Of the obstetrician-gynecologists providing contraception, the percentage of providers who prescribe contraception to Medicaid patients ranged from 83.9% (District of Columbia) to 100% (North Dakota); for family medicine physicians, it ranged from 49.7% (Florida) to 91.1% (Massachusetts); and for internal medicine physicians, it ranged from 25.0% (Texas) to 75.9% (Delaware). For in-person contraception, there were large differences in the proportion of providers offering the 3 different contraceptive method types (intrauterine device, implant, and shot) by provider specialty. This study found a significant difference in the distribution, types of contraception, and Medicaid participation of the contraception workforce. In addition to obstetrician-gynecologists and nurse-midwives, family medicine physicians, internal medicine physicians, pediatricians, advanced practice nurses, and physician assistants are important contraception providers. However, large gaps remain in the provision of highly effective services such as intrauterine devices and implants. Future research should examine provider characteristics, programs, and policies associated with the provision of different contraception services.

Disparities in cervical cancer incidence and neighborhood socioeconomic inequality in New York City

<h3>Objectives:</h3> In the United States, access to healthcare services is linked to socioeconomic status (SES). Cervical cancer is a preventable malignancy, and risk of developing the disease is linked to accessibility of preventive services such as screening and vaccination. The purpose of this study is to examine the association between neighborhood SES and the incidence of cervical cancer in women in New York City. <h3>Methods:</h3> We conducted a population-based, cross-sectional study of women residing in New York City from 2012-2016. We abstracted cases of cervical cancer and age-adjusted incidence rates by neighborhood (defined in terms of 55 Public Use Microdata Areas) from the New York State Cancer Registry and linked these data with the US Census Bureau's American Community Survey. We calculated an SES index for each neighborhood using a weighted combination of measures of crowding, real-estate values, poverty rates, incomes, educational attainment, and unemployment, which was developed and validated by the Agency for Healthcare Research and Quality. We fit a Poisson regression model with robust standard errors to estimate the association between cervical cancer incidence and neighborhood SES index, modeling SES as a continuous variable. We estimated the incidence rates, and rate ratio, associated with residence in lowest SES neighborhoods (10th percentile) compared with highest SES neighborhoods (90th percentile). <h3>Results:</h3> There were 932 cases of cervical cancer diagnosed between 2012-2016, corresponding to an age-adjusted incidence rate of 9.1 cases per 100,000 women years (95% CI 8.7-9.5). There was variation in age-adjusted cervical cancer incidence rates by neighborhoods, ranging from 4.4 cases (95% CI 2.9-7.4) to 14.7 cases (95% CI 10.8-19.5) per 100,000 women years. We identified substantial variation in socioeconomic and demographic factors among neighborhoods. Neighborhoods with the highest SES index values (top decile) were predominately populated by white residents (68.9%), with relatively few Hispanic (11.6%) and Black residents (4.5%). Conversely lowest SES index neighborhoods (bottom decile) were principally populated by Hispanic (60.1%) and Black residents (33.5%), and few White residents (3.0%). Neighborhood SES index was strongly associated with cervical cancer incidence. After adjustment for age, women residing in the lowest SES neighborhoods were 73% more likely to develop cervical cancer than those in the highest SES neighborhoods (inter-decile incidence rate ratio 1.73, 1.52-1.96, p<0.001) corresponding to incidence rates of 11.2 (95% CI, 10.3-12.2) and 6.5 (95% CI 5.9-7.1) cases per 100,000 women years respectively. <h3>Conclusions:</h3> In New York City, cervical cancer rates in neighborhoods with low SES are substantially higher than those in neighborhoods with high SES, despite their geographic proximity. Neighborhood-level preventive interventions may be an important component in eliminating inequities in cervical cancer incidence.

The rewards of municipal broadband: An econometric analysis of the labor market

With data from the U.S. Census Bureau's American Community Survey, we estimate the effect of a large-scale, government-owned broadband network in Chattanooga, Tennessee, on labor market outcomes. Difference-in-Differences, augmented with Coarsened Exact Matching, is used to estimate the causal effect of the network across nine labor market outcomes. We find no economically- nor statistically-significant effect on the labor market from the city's broadband investments.

Risk of Drug Overdose Mortality for Island-Born and US-Born Puerto Ricans, 2013-2019.

In the United States (US), individuals of Puerto Rican heritage die of drug overdoses at higher rates than other Hispanic groups or non-Hispanic Whites; yet, little is known about the extent to which drug overdose mortality affects island-born, versus US-born, Puerto Ricans. The distinction between Puerto Rican-born and US-born provides a starting point for culturally tailored services and interventions, as place of birth often informs language preferences and cultural identifications. Therefore, this study analyzed 2013-2019 death certificate data from the National Center for Health Statistics for 415,111 US deaths attributed to drug overdose. Drug overdose deaths were compared for island-born Puerto Ricans (N=3516), US-born Puerto Ricans (N=4949), and individuals not of Puerto Rican heritage (N=406,646). Drug overdose mortality rates, including age-specific and directly age-standardized rates, were calculated for each subgroup using population estimates from the US Census Bureau's American Community Survey. Results indicated that age-adjusted drug overdose mortality rates over the period of 2013-2019 were significantly higher for island-born than US-born Puerto Rican men (46.8 versus 34.6, per 100,000), with rates in both groups significantly higher than for men not of Puerto Rican heritage (24.0 per 100,000). For women, in contrast, drug overdose mortality rates were lower for island-born than US-born Puerto Ricans (8.6 versus 11.1, per 100,000). Within stateside Puerto Rican communities, island-born men experience a disproportionate burden of drug overdose mortality, necessitating targeted, culturally appropriate interventions built around the specific norms, circumstances, and lived experiences shared by Puerto Rican migrants who use drugs.

Social determinants of health and coronavirus disease 2019 in pregnancy

BackgroundThe social and physical environments in which people live affect the emergence, prevalence, and severity of both infectious and noninfectious diseases. There are limited data on how such social determinants of health, including neighborhood socioeconomic conditions, affect the risk of severe acute respiratory syndrome coronavirus 2 infection and severity of coronavirus disease 2019 during pregnancy.ObjectiveOur objective was to determine how social determinants of health are associated with severe acute respiratory syndrome coronavirus 2 infection and the severity of coronavirus disease 2019 illness in hospitalized pregnant patients in New York during the global coronavirus disease 2019 pandemic.Study DesignThis cross-sectional study evaluated all pregnant patients who delivered and had polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 between March 15, 2020, and June 15, 2020, at 7 hospitals within Northwell Health, the largest academic health system in New York. During the study period, universal severe acute respiratory syndrome coronavirus 2 testing protocols were implemented at all sites. Polymerase chain reaction testing was performed using nasopharyngeal swabs. Patients were excluded if the following variables were not available: polymerase chain reaction results, race, ethnicity, or zone improvement plan (ZIP) code of residence. Clinical data were obtained from the enterprise electronic health record system. For each patient, ZIP code was used as a proxy for neighborhood. Socioeconomic characteristics were determined by linking to ZIP code data from the United States Census Bureau's American Community Survey and the Internal Revenue Service's Statistics of Income Division. Specific variables of interest included mean persons per household, median household income, percent unemployment, and percent with less than high school education. Medical records were manually reviewed for all subjects with positive polymerase chain reaction test results to correctly identify symptomatic patients and then classify those subjects using the National Institutes of Health severity of illness categories. Classification was based on the highest severity of illness throughout gestation and not necessarily at the time of presentation for delivery.ResultsA total of 4873 patients were included in the study. The polymerase chain reaction test positivity rate was 11% (n=544). Among this group, 359 patients (66%) were asymptomatic or presymptomatic, 115 (21%) had mild or moderate coronavirus disease 2019, and 70 (13%) had severe or critical coronavirus disease 2019. On multiple logistic regression modeling, pregnant patients who had a positive test result for severe acute respiratory syndrome coronavirus 2 were more likely to be younger or of higher parity, belong to minoritized racial and ethnic groups, have public health insurance, have limited English proficiency, and reside in low-income neighborhoods with less educational attainment. On ordinal logit regression modeling, obesity, income and education were associated with coronavirus disease 2019 severity.ConclusionSocial and physical determinants of health play a role in determining the risk of infection. The severity of coronavirus disease 2019 illness was not associated with race or ethnicity but was associated with maternal obesity and neighborhood level characteristics such as educational attainment and household income.

The Impact of Socioeconomic Status on Time to Decannulation Among Children With Tracheostomies.

To determine if time to tracheostomy decannulation differs among children by socioeconomic status. Case series with chart review. Tertiary pediatric medical center. Patients (≤21 years old) who underwent tracheostomy from January 1, 2011, to December 31, 2016. Patients were divided into 2 groups based on their socioeconomic status (SES), low SES and high SES. Principal components analysis was used to create an index for SES using census data obtained by the US Census Bureau's American Community Survey 5 year data profile from 2013 to 2017. Statistical analysis was performed using a χ2 for categorical variables and Wilcoxon rank-sum test for continuous variables. A general linear model was constructed to control for clinical factors to understand the independent effect of SES on time to decannulation. In total, 215 patients were included; of these patients, 111 patients (52%) were included in the high-SES group and 104 patients (48%) were included in the low-SES group. There was a significant difference in the time to decannulation for children based on SES status, with those children in the low-SES group taking on average 10 months longer to decannulate (38.7 vs 28.0 months, P = .0007). Median follow-up was 44.1 months (interquartile range, 29.6-61.3 months). Health care disparities appear to exist among children undergoing decannulation of their tracheostomy tube. Patients with lower SES had a significantly longer time to decannulation than those with higher SES.

Virtualized Care Systems, Medical Artificial Intelligence, and Real-Time Clinical Monitoring in COVID-19 Diagnosis, Screening, Surveillance, and Prevention

(Alimadadi et al., 2020) In clinical settings, Internet of Medical Things optimizes patient-centric undertakings with remote patient monitoring, and, in clinical trials, accurately tracks vital signs, blood-sugar levels, and weight trends. (Usak et al., 2020) Internet of Things-assisted cloud-based health monitoring systems deploy heterogeneous physiological and environmental signals to supply contextual data through artificial intelligence-based diagnostic algorithms. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, AIR, Amwell, Ericsson ConsumerLab, Ginger, Kyruus, PwC, and Syneos Health, we performed analyses and made estimates regarding how connected wearable biomedical devices can assist in configuring precise diagnoses. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

Artificial Intelligence-Powered Diagnostic Tools, Networked Medical Devices, and Cyber-Physical Healthcare Systems in Assessing and Treating Patients with COVID-19 Symptoms

Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

Medical Big Data and Wearable Internet of Things Healthcare Systems in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients

Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. Results and Discussion Artificial intelligence-enabled wearable medical devices for preliminary disease detection and monitoring and physiochemical alterations assist in medical diagnosis, assessing infection levels and subsequent therapeutic decision through artificial intelligence-driven biosensors. (Jaleel et al., 2020) Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare by enabling data analytics-based smart medical services (Lazaroiu et al., 2021) in evidence-based decision making, remote monitoring, disease prevention and diagnoses, and risk factor identification.