Sub-county Level Research Articles

Modelling geographic access and school catchment areas across public primary schools to support subnational planning in Kenya

ABSTRACT Understanding the location of schools relative to the population they serve is important to contextualise the time, students must travel and to define school catchment areas (SCAs) for planning. We assembled a spatio-temporal database of public primary schools (PPS), population density of school-going children (SGC), and factors affecting travel in 2009 and 2020 in Kenya. We combined the assembled datasets within cost distance and cost allocation algorithms to compute travel time to the nearest PPS and define SCAs. We elucidated travel time and marginalised SGC living outside 24-minutes, government's threshold at sub-county level (decision-making units). Weassembled 2170 PPS in 2009 and 4682 in 2020, an increase of 115.8%, while the average travel time reduced from 28 to 17 minutes between 2009 and 2020. Nationally, 65% of SGC were within 24-minutes’ catchment in 2009, which increased to 89% in 2020. Subnationally, 19 and 61 out of 62 sub-counties had over 75% of SGC within the same threshold, in 2009 and 2020, respectively. Findings can be used to target the marginalised SGC, and monitor progress towards attainment of national and Sustainable Development Goals. The framework can be applied in other contexts to assemble geocoded school lists, characterise travel time and model SCA.

Drivers and potential distribution of anthrax occurrence and incidence at national and sub-county levels across Kenya from 2006 to 2020 using INLA

Anthrax is caused by, Bacillus anthracis, a soil-borne bacterium that infects grazing animals. Kenya reported a sharp increase in livestock anthrax cases from 2005, with only 12% of the sub-counties (decentralised administrative units used by Kenyan county governments to facilitate service provision) accounting for almost a third of the livestock cases. Recent studies of the spatial extent of B. anthracis suitability across Kenya have used approaches that cannot capture the underlying spatial and temporal dependencies in the surveillance data. To address these limitations, we apply the first Bayesian approach using R-INLA to analyse a long-term dataset of livestock anthrax case data, collected from 2006 to 2020 in Kenya. We develop a spatial and a spatiotemporal model to investigate the distribution and socio-economic drivers of anthrax occurrence and incidence at the national and sub-county level. The spatial model was robust to geographically based cross validation and had a sensitivity of 75% (95% CI 65–75) against withheld data. Alarmingly, the spatial model predicted high intensity of anthrax across the Northern counties (Turkana, Samburu, and Marsabit) comprising pastoralists who are often economically and politically marginalized, and highly predisposed to a greater risk of anthrax. The spatiotemporal model showed a positive link between livestock anthrax risk and the total human population and the number of exotic dairy cattle, and a negative association with the human population density, livestock producing households, and agricultural land area. Public health programs aimed at reducing human-animal contact, improving access to healthcare, and increasing anthrax awareness, should prioritize these endemic regions.

Linkage between nutritional status of children 6-59 months and some socio-demographic and economic characteristics of mothers in Malava sub-county, Kakamega county

The world is currently battling the triple burden of malnutrition across all age groups including children. In Kenya, household socio-demographic and economic characteristics have been shown to contribute to malnutrition among children, but there is little known about the magnitude of their contribution at the sub-county level. This study aimed at determining the association between socio-demographic and economic characteristics of mothers and the nutritional status of their children 6-59 months old. This was a cross-sectional study conducted in November and December of 2021 in Malava Sub-county, Kakamega County, Kenya. Data were collected from households using semi-structured questionnaires administered to 344 randomly selected mothers who had children aged 6-59 months. Nutritional status was determined using anthropometric measurements translated to nutrition indicators using WHO Anthro software. Association between the study variables was determined using Chi-square test. About 53% of the mothers were aged < 25 years. Formal education of mothers was low with 14.5% having no education at all and 46.8% having completed primary level only. Poverty rate was high in the study area with 83.7% of the mothers being unemployed and 77.9% earning less than Ksh. 5,000 (50 USD) per month. Only 14.5% engaged in business as an economic activity. Malnutrition prevalence in Malava Sub-county was high with almost 21% of the children being stunted and 8.1% being severely stunted. The prevalence of wasting and underweight were at 12.5% and 7.5%, respectively. Significant associations were observed between the stunting and the age of the mother (p=0.036) and education level (p=0.023). Education was also significantly associated with wasting (p=0.008). Underweight was associated with education level (p=0.014), average household monthly income (p=0.019) and the household economic activity engagement (p=0.01). The study recommends a multi-sectoral collaboration in addressing the household demographic and economic characteristics that may help in reducing the prevalence of malnutrition in Malava Sub-county, Kakamega County. Key words: Children, Nutritional Status, Stunting, Wasting, Underweight, Malnutrition, Household, Malava, Kakamega

The census place project: A method for geolocating unstructured place names

Researchers use microdata to study the economic development of the United States and the causal effects of historical policies. Much of this research focuses on county- and state-level patterns and policies because comprehensive sub-county data is not consistently available. We describe a new method that geocodes and standardizes the towns and cities of residence for individuals and households in decennial census microdata from 1790–1940. We release public crosswalks linking individuals and households to consistently-defined place names, longitude-latitude pairs, counties, and states. Our method dramatically increases the number of individuals and households assigned to a sub-county location relative to standard publicly available data: we geocode an average of 83% of the individuals and households in 1790–1940 census microdata, compared to 23% in widely-used crosswalks. In years with individual-level microdata (1850–1940), our average match rate is 94% relative to 33% in widely-used crosswalks. To illustrate the value of our crosswalks, we measure place-level population growth across the United States between 1870 and 1940 at a sub-county level, confirming predictions of Zipf’s Law and Gibrat’s Law for large cities but rejecting similar predictions for small towns. We describe how our approach can be used to accurately geocode other historical datasets.

A Social Vulnerability Index for Air Pollution and Its Spatially Varying Relationship to PM2.5 in Uganda

Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5.

Prisoners of Scale: Downscaling Community Resilience Measurements for Enhanced Use

As improved data availability and disaster resilience knowledge help progress community resilience quantification schemes, spatial refinements of the associated empirical methods become increasingly crucial. Most existing empirically based indicators in the U.S. use county-level data, while qualitatively based schemes are more locally focused. The process of replicating resilience indices at a sub-county level includes a comprehensive study of existing databases, an evaluation of their conceptual relevance in the framework of resilience capitals, and finally, an analysis of the statistical significance and internal consistency of the developed metrics. Using the U.S. Gulf Coast region as a test case, this paper demonstrates the construction of a census tract-level resilience index based on BRIC (Baseline Resilience Indicators for Communities), called TBRIC. The final TBRIC construct gathers 65 variables into six resilience capitals: social, economic, community, institutional, infrastructural, and environmental. The statistical results of tract- and county-level BRIC comparisons highlight levels of divergence and convergence between the two measurement schemes and find higher reliability for the fine-scale results.

Annual burden of diseasein Nakaale, Karamoja: A descriptive, cross-sectional study.

Even with global Sustainable Development Goals aimed at reducing poverty by reaching those furthest behind first and reducing inequalities within countries, regions like Karamoja continue to score low on many health indices. To understand the Karamoja context, we aimed to systematically describe the burden of disease in Nakaale, which is a hard-to-reach parish in Nakapiripirit District, through disaggregated data. This descriptive, cross-sectional study was conducted between January and December 2019. We relied on secondary data collected from all clients seen at Akisyon a Yesu Presbyterian Clinic's outpatient department in Nakaale. Data was extracted from Uganda's Health Management Information System records using Excel and exported to Stata for analysis. We labelled, categorised, and estimated frequencies and proportions for the variables. We estimated the means and medians for normally distributed or skewed variables respectively. A total of 14,685 observations, different diagnoses (n = 163) and residential locations or villages (n = 189) were extracted and included in the analysis. Nearly half (48.9%) of the clients were under the age of five years. Infectious diseases (42%), respiratory diseases (19%), and gastrointestinal and hepatic diseases (17%) were most commonly reported. While many clients (42%) reside in the sub-county where the health facility is located, a larger proportion (58%) came from further away, including 15% from outside of the local district. In conclusion, Akiyson a Yesu Presbyterian Clinic serves a very young population in a catchment area well beyond what is expected of a Health Centre II, in breadth of diagnoses, geographically, and in sheer numbers. Data gathered in this study will inform policy at the clinic, subcounty, and district levels enabling accurate health service delivery for the local context.

Efficacy of an acceptance and commitment therapy-based smartphone application for helping rural populations quit smoking: Results from the iCanQuit randomized trial

Limited access to evidence-based smoking cessation interventions among rural populations contributes to high rates of cigarette smoking and poor cessation outcomes. Yet, accessible digital interventions for cessation focusing on rural populations are lacking. In a secondary analysis, we determined the acceptability and efficacy of an Acceptance and Commitment Therapy (ACT)-based smartphone application (iCanQuit) relative to a U.S. Clinical Practice Guidelines (USCPG)-based smartphone application (QuitGuide) for smoking cessation among rural participants enrolled in the two-arm randomized iCanQuit trial. Participants were enrolled between May 2017 and September 2018 and randomized to either receive iCanQuit or QuitGuide for 12-months. Rural residence was determined by sub-county level Rural-Urban Commuting Area codes. A total of 550 rural participants were recruited from 43 U.S. states. Self-reported complete-case 30-day point-prevalence abstinence was 15% (33/226) for iCanQuit vs. 9% (22/253) for QuitGuide at 3-months (OR = 1.83; 95% CI: 1.03, 3.25) and 29% (66/231) for iCanQuit vs. 25% (64/288) for QuitGuide at 12-months (OR = 1.19 95% CI: 0.80, 1.79). Retention rate was 89% at 12-months and did not differ by arm. iCanQuit vs. QuitGuide participants were significantly more engaged and satisfied with the iCanQuit application. Increased acceptance of internal cues to smoke mediated the effect of treatment on cessation. Findings suggest that iCanQuit had significantly higher short-term quit rates, descriptively higher long-term quit rates, and operated through its hypothesized mechanisms of action relative to QuitGuide. Future larger studies are needed to further evaluate the efficacy of and methods for disseminating the iCanQuit application for smoking cessation among U.S. rural adults nationwide.Trial registrationClinicalTrials.gov Identifier: NCT02724462

Effects of Iron and Vitamin A Levels on Pregnant Women and Birth Outcomes: Complex Relationships Untangled Using a Birth Cohort Study in Uganda

IntroductionWomen and infants are among the most vulnerable groups for micronutrient deficiencies. Pregnancy micronutrient status can affect birth outcomes and subsequent infants’ growth.MethodsWe determined the relationship between maternal iron and vitamin A status at delivery using several biomarkers (ferritin, soluble transferrin receptor [sTFR], body iron stores [BIS], hemoglobin and retinol binding protein [RBP]) and birth outcomes (body weight, Z-scores, head circumference, small-for-gestational-age and preterm birth) in rural Uganda. We investigated women who had serum results at the point of delivery and paired them to their infants at birth (n = 1244). We employed multivariable linear and logistic regression, adjusting for clustering at the subcounty level to determine the relationship between maternal micronutrients and birth outcomes.ResultsAfter adjusting for relevant factors, we found that maternal iron status (ferritin and BIS) and anemia (hemoglobin) were not significantly associated with the assessed birth outcomes. However, there was a significant association between serum sTFR and preterm births (AOR: 0.67; 95% CI 0.48–0.94). For Vitamin A, we observed a significant positive association between RBP and length-for-age (LAZ) at birth (β = 0.12, p < 0.030).DiscussionThese findings indicate that the relationship between maternal iron status and birth outcomes needs to be further investigated, because depending on the biomarker used the associations were either in favor of an adverse birth outcome or not significant. Additionally, they confirm that higher maternal RBP levels could be beneficial for birth outcomes.Clinicaltrials.gov as NCT04233944.

Reaching 95-95-95 targets: The role of private sector health facilities in closing the HIV detection gap-Kisumu Kenya, 2018.

HIV testing efficiency could be improved by focusing on high yield populations and identifying types of health facilities where people with undiagnosed HIV infection are more likely to attend. A retrospective cohort analysis of data collected during an integrated TB/HIV active case-finding intervention in Western Kenya. Data were analyzed from health facilities' registers on individuals who reported TB-suggestive symptoms between 1 July and 31 December 2018 and who had an HIV test result within one month following symptom screening. We used logistic regression with general estimating equations adjusting for sub-county level data to identify health facility-level predictors of new HIV diagnoses. Of 11,376 adults with presumptive TB identified in 143 health facilities, 1038 (9%) tested HIV positive. The median HIV positivity per health facility was 6% (IQR = 2-15%). Patients with TB symptoms were over three times as likely to have a new HIV diagnosis in private not-for-profit facilities compared to those in government facilities (adjusted odds ratio (aOR) 3.40; 95% CI = 1.96-5.90). Patients tested in hospitals were over two times as likely to have a new HIV diagnosis as those tested in smaller facilities (i.e., health centers and dispensaries) (aOR 2.26; 95% CI = 1.60-3.21). Individuals with presumptive TB who attended larger health facilities and private not-for-profit facilities had a higher likelihood of being newly diagnosed with HIV. Strengthening HIV services at these facilities and outreach to populations that use them could help to close the HIV diagnosis gap.

Drought Vulnerability Assessment and Cluster Analysis of Island Areas Taking Korean Island Areas at Eup (Town) and Myeon (Subcounty) Levels as Study Targets

The purpose of this study is to conduct drought vulnerability assessment and cluster analysis of Korean island areas at eup (town) myeon (subcounty) level. Drought vulnerability assessment was conducted using factor analysis and entropy method, and cluster analysis was analyzed using K-means, a nonhierarchical cluster analysis method. Vulnerability consisted of climate exposure, sensitivity, and adaptive capacity. Twenty-two indicators were used to evaluate and analyze vulnerability of drought in small island areas. The results of entropy method showed that winter rainfall, no rainfall days, agricultural population rate, cultivation area rate, water supply rate and groundwater capacity have a substantial impact on drought assessment. The overall assessment of vulnerability indicated that Seodo-myeon Ganghwa-gun, Seolcheon-myeon Namhae-gun, and Samsan-myeon Ganghwa-gun were most vulnerable to drought. The cluster analysis was evaluated by categorizing the regions into three clusters, and policy support and planning are needed to suit the characteristics of each cluster was observed.

Patterns of Water Use in California

Recent patterns of water use and supply in California are presented based on a new data set compiled from the California Department of Water Resources water balance data for 2002 through 2016. The water use and supply include surface water and groundwater, although groundwater reporting has been incomplete. These data are used to support the Water Plan released every 3 to 5 years and are the most comprehensive and finest spatial- and temporal-scale data set for California water resources. First, using the Bay–Delta watershed as a case example, we show that recent fluctuations in water use are highly correlated with variations in precipitation. Developed water supplies and use show these fluctuations, but they are modified by reservoir inflows and releases, groundwater supplies, and Delta outflows. Second, although the annually precipitated water supply in the Bay–Delta varies by about 30%, the developed water supply damps this considerably. The water management system maintained nearly constant agricultural water use even in periods of intense drought, with year-to-year variation of about 7%. Variability in urban water use is higher (∼20%), largely from conservation during periods of drought. Finally, this information can help improve water resource management because it connects regional-scale data to meaningful policy decision-making at county and sub-county levels. At a time when water policy and management are being re-evaluated across the American West in the light of changing climate, decision-making informed by science and data is urgently needed. The statewide water balance data provide the means to establish a consistent, quantitative framework for water resource analysis throughout the state.

Joint spatio-temporal modelling of adverse pregnancy outcomes sharing common risk factors at sub-county level in Kenya, 2016\u20132019

BackgroundAdverse pregnancy outcomes jointly account for a high proportion of mortality and morbidity among pregnant women and their infants. Furthermore, the burden attributed to adverse pregnancy outcomes remains high and inadequately characterised due to the intricate interplay of its etiology and shared set of important risk factors. This study sought to quantify and map the underlying risk of multiple adverse pregnancy outcomes in Kenya at sub-county level using a shared component space-time modelling framework.MethodsReported sub-county level adverse pregnancy outcomes count from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical spatio-temporal model was used to estimate the joint burden of adverse pregnancy outcomes in space (sub-county) and time (year). To improve the precision of our estimates over time and space, information across the outcomes were combined via the shared and the outcome-specific components using a shared component model with spatio-temporal interactions.ResultsOverall, the total number of adverse outcomes in pregnancy increased by 14.2% (95% UI: 14.0–14.5) from 88,816 cases in 2016 to 101,455 cases in 2019. Between 2016 and 2019, the estimated low birth weight rate and the pre-term birth rate were 4.5 (95% UI: 4.4–4.7) and 2.3 (95% UI: 2.2–2.5) per 100 live births. The stillbirth and neonatal death rates were estimated to be 18.7 (95% UI: 18.0–19.4) and 6.9 (95% UI: 6.4–7.4) per 1000 live births. The magnitude of the spatio-temporal variation attributed to shared risk was high for pre-term births, low birth weight, neonatal deaths, stillbirths and neonatal deaths, respectively. The shared risk patterns were dominant in sub-counties located along the Indian ocean coastline, central and western Kenya.ConclusionsThis study demonstrates the usefulness of a Bayesian joint spatio-temporal shared component model in exploiting specific and shared risk of adverse pregnancy outcomes sub-nationally. By identifying sub-counties with elevated risks and data gaps, our estimates not only assert the need for bolstering maternal health programs in the identified high-risk sub-counties but also provides a baseline against which to assess the progress towards the attainment of Sustainable Development Goals.

Evaluation of the Effect of the Development of Guidance and Counselling Programmes on Individual Self-Efficacy in the Administration Police Service of Kenya

The research evaluates the effect of the development of Guidance and Counselling programmes on individual self-efficacy in the Administration Police Service of Kenya. The study was guided by the Well-being theory by Martin Seligman. The study adopted the Ex-post-facto research design and stratified random sampling technique was used to select 367 participants from a population of 7,404 Administration Police (AP) officers while 20 key informants were purposively sampled. The study utilized both qualitative and quantitative methods in data collection. Self-administered questionnaires and an interview schedule were used to collect data. Pearson Product Moment Correlation Coefficient was used to establish the reliability of the instruments which yielded a reliability coefficient of 0.81.Statistical Package for Social Sciences (SPSS version 25.0) was used in analyzing the quantitative data. Qualitative data was analyzed inductively and deductively through thematic content analysis. The study revealed that though the Administration Police Service (APS) offered counselling services to its officers, not many officers had benefitted from the programmed services therefore these services were not effective in training self-efficacy among the officers.The study established that the low uptake of the psychological counselling programme can be attributed to the fact that the program has not been decentralized to the lowest level that is the sub-county which is the lowest centres of service delivery for the APS. Secondly there were few professional counsellors within the APS, the study also revealed that the officers were reluctant to take up the programme due to the fear of being stereotyped by their colleagues. The study recommends, the strengthening of the APS Psychological Counselling programme through the employment of professional counsellors to cater for the counselling needs of the APS; sanitization of the importance of mental health that is advocacy to the police officers and infrastructural development for spaces for carrying out the psychological services and the decentralization of the psychological counselling programme up to the lowest level of service delivery for the AP officers which is the Subcounty level.

Daily particulate matter and temperature from satellite-hybrid models and 1.5 million deaths: A time-stratified case-crossover analysis in Central Mexico

BACKGROUND AND AIM: Highly spatio-temporally resolved (daily 1x1-Km) fine particulate matter (PM₂.₅) and air temperature (Ta) models are being used to advance exposure science and refine health effect estimates in developed countries. Limited evidence exists about their use in densely populated areas from middle-income regions, and different exposure conditions. METHODS: We use a time-stratified case-crossover study design, matching cases to control days on day of week, month and year, for 1,516,442 non-external deaths (≥18 years-old) from 586 sub-county areas in Central Mexico (including Mexico City) from 2004-2019. Daily 1x1-Km PM₂.₅ (median=23.4μm³; IQR=17–30.6μm³) and Ta estimates from our new satellite-based models for the region were employed for exposure assessment at the sub-county level using population-weighted centroids. Acute associations of PM₂.₅ and Ta with mortality were estimated with distributed lag conditional logistic models. RESULTS:Mortality was significantly associated with 10μm³ PM₂.₅ increase with stronger effects for lag0 [1.44% (95% CI:1.37–1.50%)] and lag1 [0.67% (0.60–0.75%)], with a 1-week cumulative effect (lag06) of 3.52% (3.41–3.63%). The minimum mortality temperature (MMT) was estimated at 18.4°C (18.3–18.4°C), and used as reference to estimate effects from cold (p5=12°C) and heat (p95=20.6°C) from the daily Ta distribution. Heat effects peaked at lag0 [4.64% (4.40–4.89%)] and persisted for 2-days with a cumulative lag02 effect of 7.07% (6.83–7.32%). CONCLUSIONS:We performed the largest case-crossover study of PM₂.₅ and Ta with mortality in Mexico. Our linked datasets utilizing highly spatio-temporally resolved exposure models will enable inference for developing new concentration-response functions in the Central Mexico region. Additional analyses will investigate effect modification by individual characteristics (e.g., occupational classes, sex, and age) and heterogeneity of associations for specific causes of death. KEYWORDS: Mortality, Particulate matter, Temperature, Short-term exposure

The practical use of social vulnerability indicators in disaster management

As climate change focuses more frequent and intense disasters on vulnerable communities across the globe, mitigation and response resources need to be allocated more efficiently and equitably. Vulnerability assessments require time, skill, and cost money. In the United States (US), these assessments are mandated by the Federal Emergency Management Agency (FEMA) to qualify for federal funding. However, new trends in the literature clearly question their practical value. This study begins with a focused literature review which demonstrates that popular social vulnerability indices that create a single vulnerability score can diminish the significance of a lone variable, overlook the relevancy of all interconnected variables, and can result in contradictory policy recommendations. Next, existing case studies that used popular vulnerability assessment frameworks were compared to maps generated of the same study area that employed the single variable of poverty. These case study comparisons demonstrated how considering this greatest common variable among different vulnerable groups can often - quickly, efficiently, and inexpensively - reveal close to the same county and sub-county level community vulnerabilities detailed in costly assessments. Finally, a national survey of emergency managers was conducted to determine how much current social vulnerability indices were actually governing the ongoing distribution of resources to the communities being served. Results indicate that, while these indicator models may be underused nationally, those who do find them effective tend to be from higher income areas. This study questions the practical value of these indices for emergency management practice in the US and for meeting the goals of the Sendai Framework and other compacts.

A micro-based approach to evaluate the effect of water supply on health in Uganda

• We merge individual panel datasets with sub-county level administrative data on water supply projects. • We estimate fixed-effects panel regressions to account for spatial and temporal variation at the sub-county level. • Provision for improved water is found to have a significant effect on water usage and water collection times. • No statistically significant effects of improved water are found for reported diarrhoea incidence. • Our approach is a cost-effective means of evaluating development projects. This paper proposes a micro-based approach to investigate the impact of improved water provision on individual health outcomes in rural Uganda. We merge household and individual panel datasets with sub-county level administrative data on water supply projects. Our approach allows us to estimate fixed-effect panel data models which use temporal and spatial variation at the sub-county level as identifying variation. We find evidence that the installation of improved water sources leads to higher reported improved water usage, and shorter water collection times. However, increasing the sub-county rate of improved water sources per capita does not seem to be sufficient to lead to a statistically significant effect in the likelihood of individuals suffering from symptoms of illness associated with inadequate water supply. We argue that our micro-based approach provides a cost-effective means of evaluating development projects. The approach is scalable, i.e. it can be applied to other settings and countries.

A Vulnerability Assessment for a Future HIV Outbreak Associated With Injection Drug Use in Illinois, 2017-2018.

The current opioid crisis and the increase in injection drug use (IDU) have led to outbreaks of HIV in communities across the country. These outbreaks have prompted country and statewide examination into identifying factors to determine areas at risk of a future HIV outbreak. Based on methodology used in a prior nationwide county-level analysis by the US Centers for Disease Control and Prevention (CDC), we examined Illinois at the ZIP code level (n = 1,383). Combined acute and chronic hepatitis C virus (HCV) infection among persons <40 years of age was used as an outcome proxy measure for IDU. Local and statewide data sources were used to identify variables that are potentially predictive of high risk for HIV/HCV transmission that fell within three main groups: health outcomes, access/resources, and the social/economic/physical environment. A multivariable negative binomial regression was performed with population as an offset. The vulnerability score for each ZIP code was created using the final regression model that consisted of 11 factors, six risk factors, and five protective factors. ZIP codes identified with the highest vulnerability ranking (top 10%) were distributed across the state yet focused in the rural southern region. The most populous county, Cook County, had only one vulnerable ZIP code. This analysis reveals more areas vulnerable to future outbreaks compared to past national analyses and provides more precise indications of vulnerability at the ZIP code level. The ability to assess the risk at sub-county level allows local jurisdictions to more finely tune surveillance and preventive measures and target activities in these high-risk areas. The final model contained a mix of protective and risk factors revealing a heightened level of complexity underlying the relationship between characteristics that impact HCV risk. Following this analysis, Illinois prioritized recommendations to include increasing access to harm reduction services, specifically sterile syringe services, naloxone access, infectious disease screening and increased linkage to care for HCV and opioid use disorder.

Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California.

With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5percent White, 39.1percent Latino, 5.3percent Black, and 14.4percent Asian) experienced 59,258 COVID-19 deaths through April14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October2020, combining data from 15.4million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6percent versus 2.9percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.

ObjectivesAn Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.MethodsWe obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.ResultsA total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.ConclusionsDisparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.