Spatially varying predictors of teenage birth rates among counties in the United States

When everyone's doing it: The relative effects of geographical context and social determinants of health on teen birth rates

Geographic disparities in teen birth rates in the U.S. persist, despite overall reductions over the last two decades. Research suggests these disparities might be driven by spatial variations in social determinants of health (SDOH). An alternative view is that "place" or "geographical context" affects teen birth rates so that they would remain uneven across the U.S. even if all SDOH were constant. We use multiscale geographically weighted regression (MGWR) to quantify the relative effects of geographical context, independent of SDOH, on county-level teen birth rates across the U.S. Findings indicate that even if all counties had identical compositions with respect to SDOH, strong geographic disparities in teen birth rates would still persist. Additionally, local parameter estimates show the relationships between several components of SDOH and teen birth rates vary over space in both direction and magnitude, confirming that global regression techniques commonly employed to examine these relationships likely obscure meaningful contextual differences in these relationships. Findings from this analysis suggest that reducing geographic disparities in teen birth rates will require not only ameliorating differences in SDOH across counties but also combating community norms that contribute to high rates of teen birth, particularly in the southern U.S. Further, the results suggest that if geographical context is not incorporated into models of SDOH, the effects of such determinants may be interpreted incorrectly.

The prevalence of teenage pregnancy and early motherhood and its associated factors among late adolescent (15-19) years girls in the Gambia: based on 2019/20 Gambian demographic and health survey data

INTRODUCTION

Pregnancy and early motherhood among teenage girls is the current issue of public health burden in developing countries. Although the Gambia has one of the highest adolescent fertility rates in Africa, there is no data record about it in The Gambia. Therefore, this study aimed to assess the prevalence of pregnancy and early motherhood and its determinants among late adolescent girls in the Gambia.

METHODS

It is a secondary data analysis using the 2019-20 Gambian demographic and health survey data. A total of 2,633 weighted 15-19 years old girls were included in the study. Using Stata 14 version, a pseudo logistic regression analysis method was employed to declare factors significantly associated with pregnancy and early motherhood among 15-19 years old late-adolescent girls in the Gambia. Variables with a p-value of < 0.2 were entered into multivariable regression analysis, and after controlling other confounding factors adjusted odds ratio of 95% CI was applied to identify associated variables.

RESULTS

Pregnancy and early motherhood were found in 13.42% of late adolescent Gambian girls. Logistic regression analysis depicted that a unit increase in adolescent age was positively significantly associated with pregnancy and early motherhood (adjusted odds ratio [aOR] = 2.15; 95% confidence interval [CI] = 1.93,2.39), after period ended knowledge of ovulatory cycle (aOR = 1.99; 95% CI = 1.23,3.22), being from a family size of greater than ten (aOR = 1.25; 95 CI = 1.01,1.55) times more likely to become pregnant and early motherhood than their counterparts respectively. In contrast, rich in wealth (aOR = 0.35; 95% CI = 0.23,0.54), having primary education (aOR = 0.58; 95% CI = 0.43,0.79), secondary and above education (aOR = 0.12; 95% CI = 0.09,0.17).

CONCLUSION

Pregnancy and early motherhood remain significant public health challenges in the Gambia. Strengthening female education, empowerment, reproductive health life skill training and awareness, encouraging disadvantaged females, and designing timely policies and interventions are urgently needed.

Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

Background

In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia.

Methods

A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy.

Result

Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia.

Conclusion

geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

Adolescent Birth Rates and Rural-Urban Differences by Levels of Deprivation and Health Professional Shortage Areas in the United States, 2017-2018

Objectives. To examine the differences in adolescent birth rates by deprivation and Health Professional Shortage Areas (HPSAs) in rural and urban counties of the United States in 2017 and 2018.Methods. We analyzed available data on birth rates for females aged 15 to 19 years in the United States using the restricted-use natality files from the National Center for Health Statistics, American Community Survey 5-year population estimates, and the Area Health Resources Files.Results. Rural counties had an additional 7.8 births per 1000 females aged 15 to 19 years (b = 7.84; 95% confidence interval [CI] = 7.13, 8.55) compared with urban counties. Counties with the highest deprivation had an additional 23.1 births per 1000 females aged 15 to 19 years (b = 23.12; 95% CI = 22.30, 23.93), compared with less deprived counties. Rural counties with whole shortage designation had an additional 8.3 births per 1000 females aged 15 to 19 years (b = 8.27; 95% CI = 6.86, 9.67) compared with their urban counterparts.Conclusions. Rural communities across deprivation and HPSA categories showed disproportionately high adolescent birth rates. Future research should examine the extent to which contraceptive access differs among deprived and HPSA-designated rural communities and the impact of policies that may create barriers for rural communities.

Rural/Urban Differences in the Predictors of Opioid Prescribing Rates Among Medicare Part D Beneficiaries 65 Years of Age and Older

PURPOSE

While research has been done comparing rural/urban differences in opioid prescribing to the disabled Medicare Part D population, research on opioid prescribing among the aged Medicare Part D population is lacking. This study aims to fill this gap by exploring the predictors of opioid prescribing to aged Medicare Part D beneficiaries and investigating whether these predictors vary across rural and urban areas.

METHODS

This is an analysis of ZIP Codes in the continental United States (18,126 ZIP Codes) utilizing 2017 data from Centers for Medicare & Medicaid Services. The analytic approach includes aspatial descriptive analysis, exploratory spatial analysis with geographically weighted regression, and explanatory analysis with spatial error regime modeling.

FINDINGS

Both beneficiary and prescriber characteristics play an important role in determining opioid prescribing rates in urban ZIP Codes, but most of them fail to explain the opioid prescribing rates in rural ZIP Codes.

CONCLUSION

We identify potential spatial nonstationarity in opioid prescribing rates, indicating the complex nature of opioid-related issues. This means that the same stimulus may not lead to the same change in opioid prescribing rates, because the change may be place specific. By understanding the rural/urban differences in the predictors of opioid prescribing, place-specific policies can be developed that can guide more informed opioid prescribing practices and necessary interventions.

Contextual Variation in Early Adolescent Childbearing: A Multilevel Study From 33,822 Communities in 44 Low- and Middle-Income Countries

PURPOSE

Existing literature calls for a deeper examination into how local context influences adolescent sexual and reproductive health outcomes. We seek to describe individual and contextual variation in early adolescent childbearing (younger than 16 years) in 44 low- and middle-income countries by (1) examining the role of individual-level social disadvantage, (2) exploring the ecological influence of context at the country and community level, and (3) assessing whether ecological effects vary according to a woman's wealth.

METHODS

We used nationally representative data from 33,822 communities in 44 low- and middle-income countries. We employed multilevel modeling to examine the variation in early adolescent childbearing apportioned to the individual, community, and country levels.

RESULTS

Globally, poverty and low educational attainment are associated with early adolescent childbearing. After accounting for individual-level characteristics, significant residual variance remains at both the community and country levels. Routine, individual-level covariates explain 46.4% of the total variance at the community level and 21.3% of the total variance at the country level in relation to the baseline, age-adjusted model. The variance apportioned to the community level is estimated to equal 43.5% (95% confidence interval: .40, .49) of the total variance among the poorest women compared with 32.6% (95% confidence interval: .25, .39) among the richest women. Across countries, we find substantial heterogeneity in the variance observed at the community level.

CONCLUSIONS

Our results point to the need for a continued focus on multilevel interventions that include approaches to target both the individual and population levels. More research is needed to identify the mechanisms through which local context influences adolescent sexual and reproductive health outcomes.

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

Using geographically weighted regression for social inequality analysis: association between mentally unhealthy days (MUDs) and socioeconomic status (SES) in U.S. counties

This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.

Access to Abortion in Central Appalachian States: Examining County of Residence and County-Level Attributes

CONTEXT

Studies of how women's individual characteristics and place of residence are related to variability in gestational age at the time of abortion have not examined county of residence and county-level characteristics. The county level is potentially meaningful, given that county is the smallest geographic unit with policy implications.

METHODS

Data on 38,611 abortions that took place in North Carolina, Virginia and West Virginia in 2012 were used to study the relationship between gestational age and county-level attributes (e.g., metropolitan status and poverty). Three-level hierarchical linear models captured individuals nested in county of residence, clustered by state of residence, and adjusted for individual characteristics and distance traveled to care.

RESULTS

Eight percent of the variation in gestational age at abortion was attributable to county-level characteristics. Residents of counties characterized by persistent poverty obtained abortions 2.3 days later in gestation than those from counties not characterized by that level of economic hardship. Women living in nonmetropolitan counties obtained abortions 1.7 days later than those living in metropolitan counties, even after distance traveled and county-level poverty were controlled for.

CONCLUSION

County of residence is relevant to gestational age at the time of abortion for women in these three states. Evidence that county-level attributes are related to access adds insight to the consequences for women when the landscape of abortion service delivery shifts. Integrating county of residence into research on access to abortion services may be critical to capturing disparities in access.

Qualitative Assessment of Social Vulnerability to Flood Hazards in Romania

This paper investigates local-scale social vulnerability to flood hazards in Romania, aiming to identify the most vulnerable social and demographic groups across a wide range of geographical locations by considering three dimensions: demographic, socioeconomic, and the built environment. The purpose of the paper is threefold: first, it strives to improve the Social Vulnerability model (SoVI®) by applying a different weighting method adapted to the Romanian context, taking into consideration the municipalities exposed to flood movements. Second, it aims to develop an assessment model for the most vulnerable communities by measuring the heterogeneity according to local indicators related to disaster risks. Third, it aims to facilitate emergency managers to identify community sub-groups that are more susceptible to loss and to increase the resilience of local communities. To perform local-level vulnerability mapping, 28 variables were selected and three aggregated indexes were constructed with the help of the ArcGIS software. Moreover, a model of Geographically Weighted Regression (GWR) between communities directly affected by floods and localities with high- and very high values of the Local Social Vulnerability Index (LoSoVI) was used to explore the spatial relationship among them and to compare the appropriateness of Ordinary Least Square (OLS) and GWR for such modelling. The established GWR model has revealed that the negative effects of flood hazards are often associated with communities with a high degree of social vulnerability. Thus, the analysis is able to provide a more comprehensive picture on communities in desperate need of financial resources in order to have the ability to diminish the negative impacts of flood hazards and to provide a more sustainable society.

An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States

Suicide is a serious but preventable public health issue. Several previous studies have revealed a positive association between altitude and suicide rates at the county level in the contiguous United States. We assessed the association between suicide rates and altitude using a cross-county ecological study design. Data on suicide rates were obtained from a Web-based Injury Statistics Query and Reporting System (WISQARS), maintained by the U.S. National Center for Injury Prevention and Control (NCIPC). Altitude data were collected from the United States Geological Survey (USGS). We employed an ordinary least square (OLS) regression to model the association between altitude and suicide rates in 3064 counties in the contiguous U.S. We conducted a geographically weighted regression (GWR) to examine the spatially varying relationship between suicide rates and altitude after controlling for several well-established covariates. A significant positive association between altitude and suicide rates (average county rates between 2008 and 2014) was found in the dataset in the OLS model (R² = 0.483, p < 0.001). Our GWR model fitted the data better, as indicated by an improved R² (average: 0.62; range: 0.21–0.64) and a lower Akaike Information Criteria (AIC) value (13,593.68 vs. 14,432.14 in the OLS model). The GWR model also significantly reduced the spatial autocorrelation, as indicated by Moran’s I test statistic (Moran’s I = 0.171; z = 33.656; p < 0.001 vs. Moran’s I = 0.323; z = 63.526; p < 0.001 in the OLS model). In addition, a stronger positive relationship was detected in areas of the northern regions, northern plain regions, and southeastern regions in the U.S. Our study confirmed a varying overall positive relationship between altitude and suicide. Future research may consider controlling more predictor variables in regression models, such as firearm ownership, religion, and access to mental health services.

Teenage pregnancy and mental health beyond the postpartum period: a systematic review

Background

Teenage mothers are at increased risk for adverse social outcomes and short-term health problems, but long-term impacts on mental health are poorly understood. The aims of our systematic review were to determine the association between teenage pregnancy and mental health beyond the postpartum period, critically appraise the literature’s quality and guide future research.

Methods

We systematically searched MEDLINE, Embase, PsycINFO, CINAHL, Scopus and Web of Science from inception to June 2017 for peer-reviewed articles written in English or French. Data were collected using a modified Cochrane Data Extraction Form. Study quality was assessed using the Effective Public Health Practice Project critical appraisal tool. Heterogeneity of studies permitted only a qualitative synthesis.

Results

Nine quantitative studies comprising the results from analyses of 11 cohorts met our criteria and were rated as strong (n=5), moderate (n=2) or weak (n=2). Three cohorts found a statistically significant association between teenage pregnancy and poor long-term mental health after adjustment, three found a statistically significant association before but not after adjustment and five did not find a statistically significant association. Studies observed varying degrees of attenuation after considering social context. Studies with statistically significant findings tended to comprise earlier cohorts, with outcomes measured at older ages.

Conclusions

The association between teenage pregnancy and mental health beyond the postpartum period remains unclear. Future studies should employ age–period–cohort frameworks to disentangle effects of normative patterns and stress accumulation. Social factors are important in determining long-term mental health of teenage mothers and should be prioritised in prevention and intervention strategies.

Spatiotemporal trends in teen birth rates in the USA, 2003-2012

The objective of this analysis was to explore temporal and spatial variation in teen birth rates TBRs across counties in the USA, from 2003 to 2012, by using hierarchical Bayesian models. Prior examination of spatiotemporal variation in TBRs has been limited by the reliance on large-scale geographies such as states, because of the potential instability in TBRs at smaller geographical scales such as counties. We implemented hierarchical Bayesian models with space-time interaction terms and spatially structured and unstructured random effects to produce smoothed county level TBR estimates, allowing for examination of spatiotemporal patterns and trends in TBRs at a smaller geographic scale across the USA. The results may help to highlight US counties where TBRs are higher or lower and to inform efforts to reduce birth rates to adolescents in the USA further.

Age of First Arrest, Sex, and Drug Use as Correlates of Adult Risk Behaviors Among Rural Women in Jails

Incarcerated women frequently report initiation of substance use and sexual encounters at an early age, and often engage in high-risk drug use and sexual behaviors as adults. This study examined the timing of first sex, drug use, and arrest, as well as their unique influences on specific risky behaviors in adulthood, among a high-risk population of rural women recruited from jails. Ages of initiation were all positively and significantly correlated, and each independently increased the likelihood of several risky behaviors in adulthood. Implications are discussed for screening, intervention, and treatment targeting high-risk women and girls in rural areas, particularly within criminal justice settings.

Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling

BACKGROUND

Despite high vaccination coverage, pertussis incidence in the Netherlands is amongst the highest in Europe with a shifting tendency towards adults and elderly. Early detection of outbreaks and preventive actions are necessary to prevent severe complications in infants. Efficient pertussis control requires additional background knowledge about the determinants of testing and possible determinants of the current pertussis incidence. Therefore, the aim of our study is to examine the possibility of locating possible pertussis outbreaks using space-time cluster detection and to examine the determinants of pertussis testing and incidence using geographically weighted regression models.

METHODS

We analysed laboratory registry data including all geocoded pertussis tests in the southern area of the Netherlands between 2007 and 2013. Socio-demographic and infrastructure-related population data were matched to the geo-coded laboratory data. The spatial scan statistic was applied to detect spatial and space-time clusters of testing, incidence and test-positivity. Geographically weighted Poisson regression (GWPR) models were then constructed to model the associations between the age-specific rates of testing and incidence and possible population-based determinants.

RESULTS

Space-time clusters for pertussis incidence overlapped with space-time clusters for testing, reflecting a strong relationship between testing and incidence, irrespective of the examined age group. Testing for pertussis itself was overall associated with lower socio-economic status, multi-person-households, proximity to primary school and availability of healthcare. The current incidence in contradiction is mainly determined by testing and is not associated with a lower socioeconomic status.

DISCUSSION

Testing for pertussis follows to an extent the general healthcare seeking behaviour for common respiratory infections, whereas the current pertussis incidence is largely the result of testing. More testing would thus not necessarily improve pertussis control. Detecting outbreaks using space-time cluster detection is feasible but needs to adjust for the strong impact of testing on the detection of pertussis cases.

Diabetes prevalence is associated with different community factors in the diabetes belt versus the rest of the United States

OBJECTIVE

To investigate differences in community characteristics associated with diabetes prevalence between the Diabetes Belt and the rest of the contiguous United States (U.S.) METHODS: County-level adult diabetes prevalence estimates (i.e., percent of people [≥20 years] with diagnosed diabetes 2009) were used from the Centers for Disease Control and Prevention, in addition to data from the U.S. Census Bureau, U.S. Department of Agriculture, and U.S. Department of Health and Human Services, to carry out a spatial regime analysis to identify county-level factors correlated with diabetes prevalence in the Diabetes Belt versus the remainder of the U.S.

RESULTS

Counties outside of the Diabetes Belt demonstrated stronger positive associations between diabetes prevalence and persistent poverty and greater percentages of unemployed labor forces. For counties in the Diabetes Belt, diabetes prevalence showed a stronger positive association with natural amenities (e.g., temperate climate and topographic features) and a stronger negative association with fitness/recreation facility density.

CONCLUSIONS

Community-level correlates of diabetes prevalence differed between the Diabetes Belt and elsewhere in the U.S. Economic hardship was shown to be more relevant outside the Diabetes Belt, while recreational context effects were more pronounced among counties within the region. Prevention and treatment targets are geographically unique, and public health efforts should acknowledge these differences in crafting policy.

Mapping adolescent first births within three east African countries using data from Demographic and Health Surveys: exploring geospatial methods to inform policy

BACKGROUND

Early adolescent pregnancy presents a major barrier to the health and wellbeing of young women and their children. Previous studies suggest geographic heterogeneity in adolescent births, with clear "hot spots" experiencing very high prevalence of teenage pregnancy. As the reduction of adolescent pregnancy is a priority in many countries, further detailed information of the geographical areas where they most commonly occur is of value to national and district level policy makers. The aim of this study is to develop a comprehensive assessment of the geographical distribution of adolescent first births in Uganda, Kenya and Tanzania using Demographic and Household (DHS) data using descriptive, spatial analysis and spatial modelling methods.

METHODS

The most recent Demographic and Health Surveys (DHS) among women aged 20 to 29 in Tanzania, Kenya, and Uganda were utilised. Analyses were carried out on first births occurring before the age of 20 years, but were disaggregated in to three age groups: <16, 16/17 and 18/19 years. In addition to basic descriptive choropleths, prevalence maps were created from the GPS-located cluster data utilising adaptive bandwidth kernel density estimates. To map adolescent first birth at district level with estimates of uncertainty, a Bayesian hierarchical regression modelling approach was used, employing the Integrated Nested Laplace Approximation (INLA) technique.

RESULTS

The findings show marked geographic heterogeneity among adolescent first births, particularly among those under 16 years. Disparities are greater in Kenya and Uganda than Tanzania. The INLA analysis which produces estimates from smaller areas suggest "pockets" of high prevalence of first births, with marked differences between neighbouring districts. Many of these high prevalence areas can be linked with underlying poverty.

CONCLUSIONS

There is marked geographic heterogeneity in the prevalence of adolescent first births in East Africa, particularly in the youngest age groups. Geospatial techniques can identify these inequalities and provide policy-makers with the information needed to target areas of high prevalence and focus scarce resources where they are most needed.

The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants--An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots

Background

Hepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants.

Methods

Analysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002–2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants.

Results

HCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences.

Discussion

The detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic areas may not be appropriate. Future screening interventions need to consider the spatially varying association between HCV risk and associated demographic and socio-economic determinants.

Spatial Variation in the Relationship between Hispanic Concentration and County Poverty: A Migration Perspective

Racial/ethnic minority concentration is generally positively related to county poverty. Yet, spatial variation in this relationship may call into question the meaning attached to racial/ethnic concentration. We argue that racial/ethnic concentration reflects more than just the concentration of individuals from a disadvantaged group. In addition, we extend previous work by taking a migration perspective to explain spatial non-stationarity in racial/ethnic concentration’s relationship with county poverty. Factors related to the migration process, including migrant selectivity and spatial differentiation in place characteristics, could alter the relationship between a minority group’s concentration and poverty. We employ spatially informed methods and 2006–2010 American Community Survey data to examine the relationship between Hispanic concentration and county poverty. The GWR results indicate significant spatial variation in the percent Hispanic-county poverty relationship. Hispanic migration regimes capture some of the observed relationship non-stationarity, suggesting migration-related processes partially drive Hispanic-county poverty relationship non-stationarity. However, we discuss other explanations that should be considered in future research. This work advances research on spatial inequality by examining the social implications of migration and by investigating the role of place in shaping the meaning of minority concentration.

Regional disparities in obesity prevalence in the United States: A spatial regime analysis

OBJECTIVE

Significant clusters of high- and low-obesity counties have been demonstrated across the United States (US). This study examined regional disparities in obesity prevalence and differences in the related structural characteristics across regions of the US.

METHODS

Drawing on model-based estimates from the Centers for Disease Control and Prevention, regional differences in county-level adult obesity prevalence (percent of the adult population [≥ 20 years] that was obese [BMI ≥ 30 kg/m(2) ] within a county, 2009) were assessed with a LISA (Local Indicators of Spatial Association) analysis to identify geographic concentrations of high and low obesity levels. Regional regime analysis was utilized to identify factors that were differentially associated with obesity prevalence between regions of the US.

RESULTS

High- and low-obesity county clusters and the effect of a number of county-level characteristics on obesity prevalence differed significantly by region. These included the positive effect of African American populations in the South, the negative effect of Hispanic populations in the Northeast, and the positive effect of unemployed workers in the Midwest and West.

CONCLUSIONS

Our findings suggest the need for public health policies and interventions that account for different regional characteristics underlying obesity prevalence variation across the US.

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilisation in the United States of America (USA) and found that most of the relationships between late or no prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study was to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employed an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and that the results change after considering the SL effect of prenatal care utilisation. The GWR-SL approach allowed us to gain a placespecific understanding of prenatal care utilisation in USA counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., ordinary least squares and spatial lag models) and found that GWR-SL is the preferred modelling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilisation across space, and determine whether and how the level of prenatal care utilisation in neighbouring counties matters.

Health insurance coverage predicts lower childbearing among near-poor adolescents

PURPOSE

The impact of health insurance on adolescent childbearing takes on increased salience in the context of the ongoing United States health care debate. Health insurance coverage is important for accessing health care services, including reproductive health services, yet prior research has not examined the association between insurance coverage and childbearing. Consequently, the role of insurance in the prevention of adolescent childbearing has been unclear.

METHODS

Using three panels (2001, 2004, and 2008) of the nationally representative Survey of Income and Program Participation data, hierarchical multilevel logistic regression models test the association between pre-pregnancy health insurance coverage and childbearing for a sample of 7,263 unmarried adolescent women (aged 16-19 years), controlling for known correlates of adolescent childbearing. Analyses examine variations in the association based on family income.

RESULTS

The odds of reporting childbearing were almost twice as great for adolescents who were uninsured compared with those who were insured before a pregnancy occurred. Interaction models demonstrate this effect for near-poor adolescents (who are less likely to have health insurance coverage) compared with poor and more advantaged adolescents.

CONCLUSIONS

The findings of the current nationally representative study suggest that health insurance coverage is associated with a lower probability of childbearing for near-poor adolescents. Future research should examine potential mechanisms through which insurance coverage influences adolescent childbearing.

Spatializing health research: what we know and where we are heading

Beyond individual-level factors, researchers have adopted a spatial perspective to explore potentially modifiable environmental determinants of health. A spatial perspective can be integrated into health research by incorporating spatial data into studies or analysing georeferenced data. Given the rapid changes in data collection methods and the complex dynamics between individuals and environment, we argue that geographical information system (GIS) functions have shortcomings with respect to analytical capability and are limited when it comes to visualizing the temporal component in spatio-temporal data. In addition, we maintain that relatively little effort has been made to handle spatial heterogeneity. To that end, health researchers should be persuaded to better justify the theoretical meaning underlying the spatial matrix in analysis, while spatial data collectors, GIS specialists, spatial analysis methodologists and the different breeds of users should be encouraged to work together making health research move forward through addressing these issues.

Progress in Spatial Demography

BACKGROUND

Demography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis.

OBJECTIVES

We offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

CONCLUSION

Many important demographic questions can be studied and framed using spatial approaches. This will become even more evident as changes in the volume, source, and form of available demographic data-much of it geocoded-further alter the data landscape, and ultimately the conceptual models and analytical methods used by demographers. This overview provides a brief introduction to a rapidly changing field.