Examining the Role of Quality of Institutionalized Healthcare on Maternal Mortality in the Dominican Republic.

The main study objective was to determine the extent to which the quality of institutionalized healthcare, sociodemographic factors of obstetric patients, and institutional factors affect maternal mortality in the Dominican Republic. COM-Poisson distribution and the Pearson correlation coefficient were used to determine the relationship of predictor factors (i.e., hospital bed rate, vaginal birth rate, teenage mother birth rate, single mother birth rate, unemployment rate, infant mortality rate, and sex of child rate) in influencing maternal mortality rate. The factors hospital bed rate, teenage mother birth rate, and unemployment rate were not correlated with maternal mortality. Maternal mortality increased as vaginal birth rates and infant death rates increased whereas it decreased as single mother birth rates increased. Further research to explore alternate response variables, such as maternal near-misses or severe maternal morbidity is warranted. Additionally, the link found between infant death and maternal mortality presents an opportunity for collaboration among medical specialists to develop multi-faceted solutions to combat adverse maternal and infant health outcomes in the DR.

The Environmental and Social Determinants of Health Matter in a Pandemic: Predictors of COVID-19 Case and Death Rates in New York City.

Our research objective was to determine which environmental and social factors were predictive of coronavirus disease 2019 (COVID-19) case and death rates in New York City (NYC), the original epicenter of the pandemic in the US, and any differential impacts among the boroughs. Data from various sources on the demographic, health, and environmental characteristics for NYC zip codes, neighborhoods, and boroughs were analyzed along with NYC government's reported case and death rates by zip code. At the time of analysis, the Bronx had the highest COVID-19 case and death rates, while Manhattan had the lowest rates. Significant predictors of a higher COVID-19 case rate were determined to be proportion of residents aged 65 years plus; proportion of residents under 65 years with a disability; proportion of White residents; proportion of residents without health insurance; number of grocery stores; and a higher ozone level. For COVID-19 death rates, predictors include proportion of residents aged 65 years plus; proportion of residents who are not US citizens; proportion on food stamps; proportion of White residents; proportion of residents under 65 years without health insurance; and a higher level of ozone. Results across boroughs were mixed, which highlights the unique demographic, socioeconomic, and community characteristics of each borough. To reduce COVID-19 inequities, it is vital that the NYC government center the environmental and social determinants of health in policies and community-engaged interventions adapted to each borough.

The Role of Knowledge on Genetic Counseling and Testing in Black Cancer Survivors at Increased Risk of Carrying a BRCA1/2 Mutation.

Genetic counseling and testing (GCT) for hereditary breast and ovarian cancers (HBOC) can inform treatment decisions in survivors. Black women at risk of HBOC have lower GCT engagement. There is limited data about Black survivors' experiences. The goals of this study were to: 1) examine the factors associated with HBOC knowledge and 2) assess the impact of knowledge on GCT engagement in a sample of Black survivors at risk of HBOC. Fifty Black at-risk breast/ovarian cancer survivors participated in a telephone-based survey. GCT use was measured across a continuum (awareness, referral, and use). The primary predictor variable was HBOC knowledge. Other clinical, socio-demographic, and psychosocial variables were included. Multiple linear and ordinal regression models (knowledge as the outcome and GCT as the outcome) assessed the predictors of knowledge and GCT engagement. Less than half (48 %) of survivors were referred to or used GCT services. Knowledge was moderate (M = 7.78, SD = 1.61). In the multivariable analysis, lower age (ß = -.34, p = .01) and lower stage (ß = -.318, p = .017) were associated with higher knowledge. Higher knowledge (ß = .567, p = .006) and higher self-efficacy (ß = .406, p = .001) were significantly associated with GCT engagement. Future interventions directed at increasing knowledge, self-efficacy, and improving the referral process are warranted.

Race Matters: Analyzing the Relationship between Colorectal Cancer Mortality Rates and Various Factors within Respective Racial Groups.

Colorectal cancer (CRC) is the third leading cause of mortality due to cancer (with over 50,000 deaths annually), representing 9% of all cancer deaths in the United States (1). In particular, the African-American CRC mortality rate is among the highest reported for any race/ethnic group. Meanwhile, the CRC mortality rate for Hispanics is 15-19% lower than that for non-Hispanic Caucasians (2). While factors such as obesity, age, and socio-economic status are known to associate with CRC mortality, do these and other potential factors correlate with CRC death in the same way across races? This research linked CRC mortality data obtained from the National Cancer Institute with data from the United States Census Bureau, the Centers for Disease Control and Prevention, and the National Solar Radiation Database to examine geographic and racial/ethnic differences, and develop a spatial regression model that adjusted for several factors that may attribute to health disparities among ethnic/racial groups. This analysis showed that sunlight, obesity, and socio-economic status were significant predictors of CRC mortality. The study is significant because it not only verifies known factors associated with the risk of CRC death but, more importantly, demonstrates how these factors vary within different racial groups. Accordingly, education on reducing risk factors for CRC should be directed at specific racial groups above and beyond creating a generalized education plan.

Modelling the effects of weather and climate on malaria distributions in West Africa.

BACKGROUND: Malaria is a leading cause of mortality worldwide. There is currently conflicting data and interpretation on how variability in climate factors affects the incidence of malaria. This study presents a hierarchical Bayesian modelling framework for the analysis of malaria versus climate factors in West Africa. METHODS: The hierarchical Bayesian framework takes into account spatiotemporal dependencies, and in this paper is applied to annual malaria and climate data from ten West African countries (Benin, Burkina Faso, Côte d'Ivoire, Gambia, Ghana, Liberia, Mali, Senegal, Sierra Leone, and Togo) during the period 1996-2006. RESULTS: Results show a statistically significant correspondence between malaria rates and the climate variables considered. The two most important climate factors are found to be average annual temperature and total annual precipitation, and they show negative association with malaria incidence. CONCLUSIONS: This modelling framework provides a useful approach for studying the impact of climate variability on the spread of malaria and may help to resolve some conflicting interpretations in the literature.

Racial/ethnic patterns of uptake of colorectal screening, National Health Interview Survey 2000-2008.

BACKGROUND: Lower access and/or utilization of colorectal screening are thought to be major contributors to the higher proportion of cancers among African Americans and Hispanics that are diagnosed at advanced stages of disease and the poorer outcomes observed among Hispanics and African Americans compared with non-Hispanic whites. We examine rates of initiation, utilization of specific screening modalities, adherence tocolorectal screening guidelines, and rate of uptake of colonoscopy among age-eligible African Americans, Hispanics and non-Hispanic whites. METHODS: Data on 46145 African American, Hispanic, and non-Hispanic white survey respondents to the 2000 and 2005 Cancer Control Modules and the 2003 and 2008 Sample Adult Cores of the National Health Interview Surveys are examined in these analyses. RESULTS: There was a modest increase in the initiation of colorectal screening among non-Hispanic whites, only and racial/ethnic disparities colorectal screening utilization persisted. The proportion of respondents for whom colonoscopy was the most complete guideline consistent exam received increased over time, while use of other modalities decreased among all racial/ethnic groups. CONCLUSION: More effort must be made to increase colorectal screening among the U.S. population in general but particularly among racial/ethnic minority populations. With the increased attention on prevention, there is also a need to increase knowledge of the strengths and limitations of specific screening modalities and the need to receive screening exams within recommended time intervals among both patients and providers making screening recommendations.

A modified version of Moran's I.

BACKGROUND: Investigation of global clustering patterns across regions is very important in spatial data analysis. Moran's I is a widely used spatial statistic for detecting global spatial patterns such as an east-west trend or an unusually large cluster. Here, we intend to improve Moran's I for evaluating global clustering patterns by including the weight function in the variance, introducing a population density (PD) weight function in the statistics, and conducting Monte Carlo simulation for testing. We compare our modified Moran's I with Oden's I*pop for simulated data with homogeneous populations. The proposed method is applied to a census tract data set. METHODS: We present a modified version of Moran's I which includes information about the strength of the neighboring association when estimating the variance for the statistic. We provide a power analysis on Moran's I, a modified version of Moran's I, and I*pop in a simulation study. Data were simulated under two common spatial correlation scenarios of local and global clustering. RESULTS: For simulated data with a large cluster pattern, the modified Moran's I has the highest power (43.4%) compared to Moran's I (39.9%) and I*pop (12.4%) when the adjacent weight function is used with 5%, 10%, 15%, 20%, or 30% of the total population as the geographic range for the cluster.For two global clustering patterns, the modified Moran's I (power > 25.3%) performed better than both Moran's I (> 24.6%) and I*pop (> 7.9%) with the adjacent weight function. With the population density weight function, all methods performed equally well.In the real data example, all statistics indicate the existence of a global clustering pattern in a leukemia data set. The modified Moran's I has the lowest p-value (.0014) followed by Moran's I (.0156) and I*pop (.011). CONCLUSIONS: Our power analysis and simulation study show that the modified Moran's I achieved higher power than Moran's I and I*pop for evaluating global and local clustering patterns on geographic data with homogeneous populations. The inclusion of the PD weight function which in turn redefines the neighbors seems to have a large impact on the power of detecting global clustering patterns. Our methods to improve the original version of Moran's I for homogeneous populations can also be extended to some alternative versions of Moran's I methods developed for heterogeneous populations.

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers.

BACKGROUND: The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated. METHODS: We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*(pop); and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. RESULTS: For simulated data with outlier patterns, Tango's MEET, Moran's I and I*(pop) had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*(pop) (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. CONCLUSION: SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*(pop) perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan.

Impact of geography on mammography use in California.

OBJECTIVE: Despite its benefit, about 30% of women report that they did not have a recent mammogram. We examine impact of distance, rural-urban residence, and other characteristics on mammography screening rates. METHODS: We linked data on 33,938 women aged 40-84 years from the 2003 and 2005 California Health Interview Survey with FDA data on the location of mammography facilities in California, and with socioeconomic and geographic variables from the 2000 Census. We use logistic regression models to estimate the impact of selected variables on a woman's probability of having had a recent mammogram and developed a new mapping scheme to help visualize differences in mammography use across California. RESULTS: Though distance to a facility did not impact a woman's probability of having had a recent mammogram, women who resided in urban areas had somewhat higher screening rates than those living in more rural areas, as displayed on our map. CONCLUSIONS: Our findings suggest that more research is needed on possible disparities in access to mammography between rural and non-rural areas in California. Therefore, data adequately powered to examine rural populations and to compare them with urban populations are needed.