Dataset Information

New Jersey COVID-19 municipal dataset.

ABSTRACT: Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8462753 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Secondary or high school (HS) educational professionals expressed concerns about dealing with environmental and occupational health and safety protocols due to COVID-19. Concerns related to fall 2020 school re-opening and getting back into in-person teaching-whether full-time, part-time or some other approved hybrid model-plus ongoing uncertainty with how the state and federal government will be handling matters about mandates for virtual learning, rapid testing, vaccine distribution, etc. These concerns were related to both their experience as educational professionals and genuine interest in personal and student well-being. This study was a cross-sectional online survey in early fall from mid-September-early October 2020. Of a possible maximum participation of 740 New Jersey (NJ) supervisory-level HS teachers and administrators (e.g., department chairs, district and school principals), 100 confirmed unique respondents (13.5%) consented and completed the survey. Of 100 experienced (mean 18 years teaching) participants, 70% responded to the gender identity question (overall, 61% female, 39% male; by NJ region, gender ratios were similar). There were statistically significant differences (using Fischer's exact test) between NJ regions regarding provision of online counseling and support services for teachers (p < 0.001); for resources and equipment for teachers to mediate online learning (p = 0.02); for assistive video technology tools (p = 0.03) and accessibility to structured online learning and professional development (p = 0.002); concerning learning aids to engage students in online instruction, online counseling, and support services for students and their families (p = 0.006); appropriate protocol is clean and disinfect areas used by a person with COVID-19 (p = 0.002); and, immediately separate staff and students who screen positive for COVID-19 (p = 0.03). There were few statistical differences by gender. This study reported what participants wanted regarding the development of future policies then implemented as reopening practices. Data can inform recommendations in NJ and elsewhere at federal, state, and local levels. Data provide new insights and valuable information to inform the consideration of acceptability of various policy measures among HS education professionals.

Project description:ImportanceSevere maternal morbidity (SMM) is a major risk factor for maternal mortality, yet little is known about geographic variation in SMM or factors associated with geographical variation at the local level. Municipal governments incur substantial expenditures providing services that are an essential part of residents' lives, but associations between municipal expenditures and SMM have not been previously examined.ObjectiveTo investigate variation in rates of SMM across municipalities in New Jersey, the contributions of individual-level characteristics and municipal expenditures to that variation, and associations between municipal expenditures and SMM.Design, setting, and participantsThis cross-sectional study analyzed 2008 to 2018 New Jersey birth files linked to maternal hospital discharge records and US Census municipal expenditures data. The birth files contain all birth records for New Jersey, and hospital discharge records contain information from all in-patient hospitalizations in New Jersey over the study period. Birth records were matched to maternal discharge records and expenditures data. Data were analyzed from August 2020 to August 2021.ExposuresIndividual-level characteristics and per capita municipal expenditures on education; public health; fire and ambulance; parks, recreation, and natural resources; housing and community development; public welfare; police; transportation; and libraries.Main outcomes and measuresSMM was identified using diagnosis and procedure codes developed by the Centers for Disease Control and Prevention to measure SMM.ResultsOf 1 001 410 individuals (mean [SD] age, 29.8 [5.9] years; 108 665 Asian individuals [10.9%]; 147 910 Black individuals [14.8%]; 280 697 Hispanic individuals [28.0%]; 447 442 White individuals [44.7%]) who gave birth in New Jersey hospitals from 2008 to 2018, 19 962 individuals (2.0%) had SMM. There was substantial municipality-level variation in SMM that was not fully explained by demographic characteristics. Municipal expenditures on fire and ambulance, transportation, health, housing, and libraries were negatively associated with SMM; $1000 higher annual expenditures per capita in these categories were associated with 35.4% to 67.3% lower odds of SMM (odds ratios, 0.33 [95% CI, 0.15-0.72] to 0.65 [95% CI, 0.46-0.91]). Expenditures on police were positively associated with SMM (odds ratio, 1.15 [95% CI, 1.04-1.28]).Conclusions and relevanceThe findings in this study regarding associations between spending on various types of services at the municipal level and SMM, holding constant overall spending, population size, and socioeconomic status at the municipal level, indicate that municipal budget allocation decisions were associated with SMM rates and point to the importance of future research investigating potential causal connections.

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Project description:The five boroughs of New York City (NYC) were early epicenters of the Covid-19 pandemic in the United States, with over 380,000 cases by May 31. High caseloads were also seen in nearby counties in New Jersey (NJ), Connecticut (CT) and New York (NY). The pandemic started in the area in March with an exponential rise in the number of daily cases, peaked in early April, briefly declined, and then, showed clear signs of a second peak in several counties. We will show that despite control measures such as lockdown and restriction of movement during the exponential rise in daily cases, there was a significant net migration of households from NYC boroughs to the neighboring counties in NJ, CT and NY State. We propose that the second peak in daily cases in these counties around NYC was due, in part, to the movement of people from NYC boroughs to these counties. We estimate the movement of people using "Change of Address" (CoA) data from the US Postal Service, provided under the "Freedom of Information Act" of 1967. To identify the timing of the second peak and the number of cases in it, we use a previously proposed SIR model, which accurately describes the early stages of the coronavirus pandemic in European countries. Subtracting the model fits from the data identified, we establish the timing and the number of cases, N CS , in the second peak. We then related the number of cases in the second peak to the county population density, P, and the excess Change of Address, E CoA, into each county using the simple model which fits the data very well with α = 0.68, β = 0.31 (R 2 = 0.74, p = 1.3e-8). We also find that the time between the first and second peaks was proportional to the distance of the county seat from NY Penn Station, suggesting that this migration of households and disease was a directed flow and not a diffusion process. Our analysis provides a simple method to use change of address data to track the spread of an infectious agent, such as SARS-Cov-2, due to migrations away from epicenters during the initial stages of a pandemic.

Dataset Information

New Jersey COVID-19 municipal dataset.

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