Christina I Nieves
Biographic Data
| ID | 1732491 |
|---|---|
| NAME | Christina I Nieves |
| GIVEN NAMES | Christina I |
| FAMILY NAME | Nieves |
| SIGNATURE | NIEVES C I |
| AFFILIATIONS | City University of New York |
| ORCID | 0000-0002-8248-025X |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 10 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Sliding down the socioeconomic health gradient of Covid-19 in New York City
Background: Distinct socioeconomic gradients in COVID-19 outcomes were observed across the United States, so an evaluation of individual resident characteristics related to economic deprivation (race or ethnicity, precarious employment, children in the household) was conducted to inform neighborhood reach strategies by the NYC Department of Health and Mental Hygiene. Methods: A cross-sectional survey was fielded to participants from a probability…
Examining variation within Hispanic ethnicity
Birthweight inequities in the United States have been persistent with variations observed across maternal age, race/ethnicity, education, and nativity status. However, the Hispanic/Latino population is often treated as a monolithic category, ignoring within-group diversity and heterogeneity of health outcomes. This study employed an intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) to examine birt…
Making sense of Maihda’s history and goals
“Be honest and gain trust”
Background: Distrust in government among people of color is a response to generations of systemic racism that have produced preventable health inequities. Higher levels of trust in government are associated with better adherence to government guidelines and policies during emergencies, but factors associated with trust and potential actions to increase trust in local government are not well understood. Methods: The COVID-19 Community Recovery stu…
The application of intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (Maihda) to examine birthweight inequities in New York City
Intersectional inequities in the birthweight gap between twin and singleton births
Adverse birth outcomes in New York City women
Adverse birth outcomes in New York City women
“Be honest and gain trust”
Background: Distrust in government among people of color is a response to generations of systemic racism that have produced preventable health inequities. Higher levels of trust in government are associated with better adherence to government guidelines and policies during emergencies, but factors associated with trust and potential actions to increase trust in local government are not well understood. Methods: The COVID-19 Community Recovery stu…
The application of intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (Maihda) to examine birthweight inequities in New York City
Intersectional inequities in the birthweight gap between twin and singleton births
Sliding down the socioeconomic health gradient of Covid-19 in New York City
Background: Distinct socioeconomic gradients in COVID-19 outcomes were observed across the United States, so an evaluation of individual resident characteristics related to economic deprivation (race or ethnicity, precarious employment, children in the household) was conducted to inform neighborhood reach strategies by the NYC Department of Health and Mental Hygiene. Methods: A cross-sectional survey was fielded to participants from a probability…
Examining variation within Hispanic ethnicity
Birthweight inequities in the United States have been persistent with variations observed across maternal age, race/ethnicity, education, and nativity status. However, the Hispanic/Latino population is often treated as a monolithic category, ignoring within-group diversity and heterogeneity of health outcomes. This study employed an intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) to examine birt…
Making sense of Maihda’s history and goals
Medicine (6 works) · Demography (5 works) · Health disparities and outcomes (5 works) · Sociology (5 works) · Demography (4 works) · Economics (4 works) · Ethnic group (4 works) · Food Security and Health in Diverse Populations (4 works) · Geography (4 works) · Gerontology (4 works)