Small Area Estimation Techniques Research Articles

Artificial intelligence to predict estimates of physical activity in small geographic areas

Abstract Background Artificial intelligence (AI) predictive models have been used to estimate the risk of a certain outcome occurring, using all available variables. Methods Using AI methods we have developed a small area estimation (SAE) technique to estimate the prevalence of leisure-time physical activity (LTPA), or who practices 150 minutes of LTPA per week, in small areas, in Belo Horizonte, Brazil, between 2009 and 2018, by the usage of k-means clustering. The VIGITEL, national telephone survey of Risk Factors for non-communicable diseases, was the main source. We applied available data to estimate prevalence of LTPA in 3300 census tracts, aggregated in 150 Primary Health Care (PHC) areas. The prevalence of PA was analyzed between two time periods, due to the low number of census tract interviews. With the available data, we estimate vulnerability clusters, derived from a Health Vulnerability Index (IVS), for the city of Belo Horizonte, according to socioeconomic, sanitation and health indicators, which measure the percentage of the population living in areas of vulnerability in the city. The index main purpose is to support public policy implementation. Results Nine clusters were identified according to the Silhouette coefficient analysis, i.e.: low (LO-0, LO-1), medium (ME-0, ME-1), high (HI-0, HI-1, HI-2) and very high) vulnerability (VH-0, VH-1. The prevalence of physical activity ranged from 23.7% (worst prevalence), in area of high vulnerability cluster (VH-1) to 45.5% in a low-risk cluster (LO-1) in period from 2009 to 2014. The prevalence of physical activity increased to 31.4% (high vulnerability cluster) to 52.81% (low-risk cluster) from 2014 to 2018. Conclusions The use of AI to estimate the prevalence of physical activity in small areas proved to be effective and highlighted important differences in the city. Thus, national surveys, with a small number of interviews per census sector, can be used to predict risks and health outcomes for small areas. Key messages • The study highlights the importance of using artificial intelligence to predict estimates of risk factors for chronic non-communicable diseases in small geographic areas. • The study showed less physical activity (PA) in areas of greater vulnerability. This can support health promotion policies and programs that encourage PA.

Exploring Veteran Mental Health Disparities: A Comparative Analysis of Rural and Urban Communities in the Midwest of the United States.

Veterans face unique mental health challenges influenced by their service experiences and post-military transitions into civilian life. Geographic location also plays an integral role in impacting veterans' outcomes and access to proper care. The purpose of this case study is to examine disparities between rural and urban veterans in the Midwest using data collected from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). Self-reported bad mental health days among veterans in rural and urban regions across twelve Midwestern states were analyzed through direct rate estimation and small area estimation techniques utilizing RStudio software. Higher rates of poor mental health days were ultimately observed among urban veterans in most states through both analyses. The results of the direct rate analysis revealed 13.5% of veterans reporting 14 + poor mental health days per month versus 9.5% in rural areas. Likewise, the results of the small area analysis demonstrated 12.2% of veterans reporting 14 + days of poor mental health days per month in urban areas versus 9.8% in rural areas. This highlights the significance of environmental stressors and social determinants of health in differentially impacting mental health outcomes. Thus, tailored interventions utilizing interdisciplinary teams are needed to meet the unique barriers for veterans in different geographic contexts. Despite the cross-sectional nature of the study and reliance on self-reported data, this case study provides valuable insights for mental health disparities among Midwest veterans. Creating a more equitable and accessible mental health landscape for veterans will require targeted and collaborative approaches.

Design based synthetic imputation methods for domain mean

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study.

Adverse Childhood Experiences Among U.S. Adults: National and State Estimates by Adversity Type, 2019–2020

Although adverse childhood experiences (ACEs) are associated with lifelong health harms, current surveillance data on exposures to childhood adversity among adults are either unavailable or incomplete for many states. In this study, recent data from a nationally representative survey were used to obtain the current and complete estimates of ACEs at the national and state levels. Current, complete, by-state estimates of adverse childhood experiences were obtained by applying small area estimation technique to individual-level data on adults aged ≥18 years from 2019-2020 Behavioral Risk Factor Surveillance System survey. The standardized questions about childhood adversity included in the 2019-2020 survey allowed for obtaining estimates of ACE consistent across states. All missing responses to childhood adversity questions (states did not offer such questions or offered them to only some respondents; respondents skipped questions) were predicted through multilevel mixed-effects logistic small area estimation regressions. The analyses were conducted between October 2022 and May 2023. An estimated 62.8% of U.S. adults had past exposure to ACEs (range: 54.9% in Connecticut; 72.5% in Maine). Emotional abuse (34.5%) was the most common; household member incarceration (10.6%) was the least common. Sexual abuse varied markedly between females (22.2%) and males (5.4%). Exposure to most types of adverse childhood experiences was lowest for adults who were non-Hispanic White, had the highest level of education (college degree) or income (annual income ≥$50,000), or had access to a personal healthcare provider. Current complete estimates of ACEs demonstrate high countrywide exposures and stark sociodemographic inequalities in the burden, highlighting opportunities to prevent adverse childhood experiences by focusing social, educational, medical, and public health interventions on populations disproportionately impacted.

Closing Reporting Gaps: A Comparison of Methods for Estimating Unreported Subgroup Achievement on NAEP

ABSTRACT The National Assessment of Educational Progress (NAEP) program only reports state-level subgroup results if it samples at least 62 students identifying with the subgroup. Since some subgroups constitute small proportions of many states’ general student populations, these minority subgroups are seldom sufficiently sampled to meet this sample size requirement. Consequently, education researchers and policymakers are regularly left without a comprehensive understanding of how states are supporting the learning and achievement of different subgroups of students, including underserved subgroups. Using grade 8 mathematics results in 2015, this study compares the performance of two separate techniques in predicting mean subgroup achievement on NAEP. Results demonstrate that a small area estimation technique, known as the Fay-Herriot (FH) model, represents a promising approach. Follow-up research should employ the FH technique with math achievement data from other testing years, as well as data from NAEP reading to evaluate the technique’s generalizability.

Child stunting prevalence determination at sector level in Rwanda using small area estimation

BackgroundStunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022. The recent 2019/2020 Rwanda Demographic Health Survey (RDHS) revealed that the prevalence of stunting in Rwanda among under five children was 33.5%. In Rwanda, there is no sufficient evidence on stunting status to guide prioritized interventions at the sector level, the lowest administrative unit for implementing development initiatives. This study aimed to provide reliable estimates of stunting prevalence in Rwanda at the sector level.MethodsIn this article, Small Area Estimation (SAE) techniques were used to provide sector level estimates of stunting prevalence in children under five in Rwanda. By plugging in relevant significant covariates in the generalized linear mixed model, model-based estimates are produced for all sectors with their corresponding Mean Square Error (MSE).ResultsThe findings showed that, overall, 40 out of 416 sectors had met the national target of having a stunting rate less than or equal to 19%, while 194 sectors were far from meeting this target, having a stunting rate higher than the national prevalence of 33.5% in the year 2020. The majority of the sectors with stunting prevalence that were higher than the national average of 33.5% were found in the Northern Province with 68 sectors out of 89 and in Western Province with 64 sectors out of 96. In contrast, the prevalence of stunting was lower in the City of Kigali where 14 out of 35 sectors had a stunting rate between 0 and 19%, and all sectors were below the national average. This study showed a substantial connection between stunting and factors such as household size, place of residence, the gender of the household head, and access to improved toilet facilities and clean water.ConclusionThe results of this study may guide and support informed policy decisions and promote localised and targeted interventions in Rwanda’s most severely affected sectors with a high stunting prevalence in Rwanda.

Subnational estimates of maternal mortality in Nigeria: Secondary Data Analysis of female siblings' survivorship histories.

High Maternal Mortality (MM) in Nigeria is complicated by the absence of reliable estimates at subnational levels. Obtaining accurate data at the state and geopolitical region levels is crucial for effective policy-making and targeted interventions. This study employs novel small area estimation techniques to derive plausible estimates of Maternal Mortality rates and ratios for Nigerian states and geopolitical regions. Data from 293,769 female siblings, provided by 114,154 women in the Nigeria Demographic and Health Surveys of 2008, 2013, and 2018, are used. Empirical Bayesian technique and the James-Stein estimator are applied to estimate MM Rates and Ratios, respectively. Maternal Mortality Ratio is highest in rural areas, Northern Nigeria states, and regions. While the South West exhibits lower MMRatio, the Northern regions, particularly the North-East, show consistently higher ratios. Mortality trends have decreased in the North West and South East regions but increased in the South West from 2008 to 2018. Addressing these disparities is essential for achieving sustainable development goals and improving maternal health in Nigeria.

Spatial heterogeneity in intimate partner violence across the 640 districts of India: a secondary analysis of a cross-sectional, population-based survey by use of model-based small-area estimation

Although intimate partner violence (IPV) against women is a substantial challenge in India, response is limited by little evidence on substate prevalence. District-level IPV estimates are essential in targeted response and prevention efforts, but cannot be directly calculated from the National Family Health Surveys (NFHS), which is the main source of nationally representative IPV estimates in India. We aimed to use small-area estimation techniques to derive reliable estimates of physical, emotional, and sexual IPV for the 640 districts of India. For this secondary analysis of a cross-sectional, population-based survey, we used model-based small-area estimation techniques linking data from the 2015-16 NFHS-4 and the 2011 Indian Population and Housing Census (2011 Indian Census) to derive district-level estimates of physical, emotional, and sexual IPV for the 640 districts of India in the previous 12 months. Only women who had ever been married aged 15-49 years, who were interviewed in NFHS-4, and who were included in the domestic violence module were eligible for inclusion in this analysis. Data collection occurred between Jan 20, 2015, and Dec 4, 2016. The 2011 Indian Census was conducted in all 640 districts from Feb 9 to Feb 28, 2011. It collected information on a range of data including sociodemographic data and housing characteristics. The primary outcomes of this analysis were the district-level mean proportions of women who experienced physical IPV, emotional IPV, and sexual IPV in the previous 12 months. This outcome was estimated for all women aged 15-49 years who had ever been married in the 640 districts of India that were included in the 2011 Indian Census. 699 686 women aged 15-49 years were interviewed in NFHS-4. One woman per household in a randomly selected 15% of households was chosen for participation in the domestic violence module, resulting in 83 397 (11·9%) of 699 686 women included. Of these 83 397 women, 14 377 (17·2%) were excluded as they had never been married and 3007 (3·6%) were excluded due to privacy limitations. The mean prevalence of physical IPV in the previous 12 months was 22·5% (95% CI 21·9-23·2), of emotional IPV in the previous 12 months was 11·4% (11·0-11·9), and of sexual IPV in the previous 12 months was 5·2% (4·9-5·5). Model-based estimates revealed intrastate and interstate IPV variations. In Bihar, which had the highest state-level physical IPV prevalence (35·1%, 33·3-37·0), district-level estimates varied from 23·5% (23·0-23·9) in Siwan to 42·7% (42·3-43·1) in Purbi Champaran. In Tamil Nadu, which had the highest state-level emotional IPV prevalence (19·0%, 17·4-20·8), district estimates ranged between 13·7% (13·2-14·1) in Kanniyakumari and 30·2% (29·5-30·8) in Sivaganga. Bihar also had the highest state-level sexual IPV prevalence (11·1%, 9·9-12·4), with estimates ranging between 6·3% (6·1-6·6) in Siwan and 18·1% (17·6-18·6) in Saharsa. Across districts, there was substantial spatial clustering of IPV prevalence. This reliable district-level estimation of IPV prevalence in the 640 districts of India has important policy implications. The ability to track substate levels of IPV over time enables the identification of progress in reducing IPV; recognises the heterogeneity of culture and context in India; and informs the targeting of resources, interventions, and prevention programmes to districts with the greatest need. Bill & Melinda Gates Foundation.

Measuring Vulnerability and Resilience in the Time of Change Using Small Area Estimation

The United States Census Bureau launched a new tool for national agencies and local communities, the Community Resilience Estimates (CRE). The CRE tracks social and economic vulnerability by measuring the capacity of individuals and households to cope with the external stresses of the impacts of a disaster.
 From the beginning of the pandemic, the negative effects of COVID-19 have been strongly related to certain individual and household characteristics. With access to granular microdata from the Census Bureau, the CRE maps the risk assessment of local populations down to the neighborhood level and allows national and community leaders to more efficiently respond to emergencies.
 Federal statistical agencies are uniquely positioned to provide the most accurate and timely measures for an individually focused community resilience indicator. We use detailed demographic and economic data about individuals to build these estimates. Having the richest data sources, the federal government can produce estimates with the most granularity, highest statistical quality, and broadest coverage, while still protecting privacy. We do this through modeling multiple sources of data using small area estimation techniques.
 This work will soon be built out to include exposure data (wind, flood, heat, fire, etc) so that federal agencies and researchers can perform experimental studies for better evidenced based policy making and evaluation.

Who is getting screened? Estimating uninsured and underinsured women utilizing NBCCEDP screening program in a high insurance state.

e18617 Background: The National Breast and Cervical Cancer Early Detection Program's (NBCCEDP) mission is to improve access to mammography and other health services for underserved women. Research has shown that this national program has improved access to mammography services. However, there is limited research describing the total number of individuals eligible to utilize these services. Howard et. al., described women eligible for NBCCEDP services at the state level. Hughes et. al. have improved upon this with the introduction of geographic small-area estimation techniques that allow us to estimate individuals below the census tract level. Our work builds upon Hughes et. al., by accounting for variations in uninsured and insurance status. Methods: We used a geospatial small area estimation technique, spatially adaptive filters, to calculate standardized incidence ratios describing the utilization rate of NBCCEDP services in Minnesota. The American Community Survey (2010-2014) data describing the insurance status was integrated into the spatially adaptive filters to account for the likelihood that an individual is uninsured. Results: We tested three models that integrate insurance status by age, sex, and race/ethnicity. Our composite model, which adjusts for all three insurance statuses, improves our estimates by reducing estimation error by 95%. We estimate that 49,914 uninsured and underinsured women are eligible to receive services within the state of Minnesota. In addition to an improved state estimate, we also provide estimates at county level to better understand the uptake of these services. Conclusions: This analysis addresses a significant gap in the literature by including secondary datasets that allow for a more accurate prediction of both the underlying population eligible to receive services and calculation of utilization rates to address programmatic goals. Spatially adaptive filters are not restricted to a specific administrative unit and insurance status provides a better screening utilization estimate.

Perception of insecurity in municipalities in Mexico: A small area estimation approach

In this paper, the percentage of the population aged 18 years and over with perception of insecurity during March and April 2021 is estimated for each municipality in Mexico using small area estimation techniques. Two methods are considered: the Empirical Best Linear Unbiased Predictor (EBLUP) and the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP), both based on the Fay-Herriot area-level model. The National Survey of Victimization and Perception of Public Safety 2021 (ENVIPE 2021, for its acronym in Spanish) is the base survey from which the variable object of estimation is obtained; the auxiliary variables that allow to establish the considered models are obtained from other information sources, such as the population and housing census and administrative records. The results are adjusted to satisfy the benchmarking property and are contrasted with direct estimates given by the same survey, ENVIPE 2021, to compare their reliability level.

Measuring Distribution of Wealth in Zambia Using Census Micro Data: An Application of Principal Component Analysis

Census data for Zambia was used to estimate the distribution of wealth in Zambia by constructing the Wealth Index as a measure of socioeconomic status using Principal Component Analysis. The reliability of the index is observed from three fronts; coherence, robustness and validity in representing household socioeconomic status. Classifying the households across all quartiles is highly consistent and robust. The index’s performance in predicting the welfare distribution is analogous to established and most widely used methods from Demographic Health Surveys, as evidenced by similarities in the statistical distributions. Unlike other survey estimates, the index has been produced at the subnational level, such as district, enabling the classification of Zambia’s districts according to their socioeconomic status. The index can be used to predict other socioeconomic outcomes, such as education and health, via Small Area Estimation techniques and determine district-level resource allocation by the central government.

GLAUCOMA, CATARACT, AND EMOTIONAL DISTRESS IN NEW ENGLAND: FINDINGS FROM THE HEALTHY AGING DATA REPORTS

Abstract Glaucoma and Cataract are two common leading causes of vision loss and major sources of disability and mental issues for older adults, but few studies investigated glaucoma and cataract among older adults from a community-level perspective. This study describes the rates of glaucoma and cataract among older adults 65+ in New England and examined the association of glaucoma and cataract and emotional distress (depression and anxiety) of older adults at the community level across 794 towns/cities (MA (351), NH (235), RI (39), and CT (169)) in New England. Data sources used to calculate rates of communities were: the American Community Survey (2014-2018 RI and CT, 2012-2016 MA and NH) and the Medicare Current Beneficiary Summary File (2016-2017 RI, 2015 MA and NH, 2016-2017 CT). Small area estimation techniques were used to calculate age-sex adjusted community rates for more than 150 health indicators (https://healthyagingdatareports.org/). Results showed CT had the highest rates of glaucoma (28.3%), RI had the highest rates of cataract (67.5%), and NH had the lowest rates of glaucoma and cataract (22.9% and 61.2%, respectively). After adjusting for age, gender, race, marital status, education, income, chronic diseases, communities with higher rates of glaucoma or cataract were significantly associated with higher rates of emotional distress compared to those with lower prevalence (glaucoma: β=0.06, p< 0.01, for depression, β=0.09, p< 0.001 for anxiety; cataract: β=0.08, p< 0.01 for depression, β=0.11, p< 0.01 for anxiety). Findings help practitioners and policymakers to allocate emotional support services to areas of high need due to eye diseases.

Integrating surveys with geospatial data through small area estimation to disaggregate SDG indicators: A practical application on SDG Indicator 2.3.1

With the adoption of the 2030 Agenda for Sustainable Development, the production of high quality disaggregated estimates of Sustainable Development Goal (SDG) indicators has taken greater significance. In this context, sample surveys are characterized by samples that are either not large enough to guarantee reliable direct estimates for all relevant sub-populations, or that do not cover all possible disaggregation domains. To address these issues, indirect estimation approaches such as small area estimation (SAE) techniques can be adopted. The literature on the use of SAE in official statistics is broad and in continuous progress, yet the number of case studies on SAE methods applied to SDG indicators can still be expanded. After a brief review of the main SAE approaches available along with their principal fields of application, the present paper aims contributing to fill this gap by presenting a case study on SAE to produce disaggregated estimates of SDG Indicator 2.3.1, measuring average labour productivity of small-scale food producers. The discussed empirical exercise is based on a Fay-Herriot area-level SAE model, integrating survey data with area-level auxiliary information retrieved from multiple trustworthy geospatial information systems. Area-level SAE models have the advantage of being easy to implement and do not require accessing survey microdata and unit-level auxiliary information. These characteristics, jointly with the great potentials offered by modern geospatial information systems, offer the possibility of producing good quality disaggregated estimates of SDG indicators at high frequency and granular disaggregation level.

Small Area Estimation in Diverse Timber Types Using Multiple Sources of Auxiliary Data

Abstract Informed forest management requires reliable information. As the demand for finer scale estimates has increased, so has the cost for obtaining them from design-based ground sampling. Small area estimation (SAE) is an estimation technique that leverages ancillary information to augment design-based samples with the goal of increasing estimate precision without increasing ground-based sample intensities. This work presents three case studies spanning an industrial timberland ownership in the United States making use of SAE techniques in operational forest inventories. Case studies include an inventory of pre-thin plantation loblolly pine (Pinus taeda L.) stands that had achieved crown closure in Alabama and Mississippi, a mixed pine–hardwood inventory in Alabama, and pre-thinning plantation Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in Washington State. Using area-level SAE techniques, vegetation indices derived from 10 m Sentinel imagery were shown to reduce estimate uncertainty for common stand parameters. Additionally, when available, lidar and age were shown to offer additional improvements in estimate precision. The results of this study indicate the operational potential for using commonly available auxiliary data for producing forest parameter estimates with enhanced precision. The implications of these findings span multiple inventory objectives including, for example, commercial forest management, carbon accounting, and wildfire fuel assessments. Study Implications: Forest management requires reliable quantitative information for informed decisions. Data from ground-based forest inventories are commonly used to construct design-unbiased direct estimates. Due to logistical and cost constraints, samples often do not provide estimates with sufficient precision for making confident decisions. The statistical estimation procedure, small area estimation, is able to leverage linearly related ancillary data across areas of interest to form composite estimates that have less uncertainty than direct estimates alone. This study shows how combining ground-based data with auxiliary data from remote sensing and stand records produced more precise estimates of forest stand parameters in three distinct timber types spanning a large ownership in the United States. Results indicate that significant inventory efficiency and confidence can be realized by incorporating commonly available auxiliary data into the estimation of forest characteristics.

Small Area Estimation of Zone-Level Malnutrition among Children under Five in Ethiopia

Child undernutrition is one of the 10 most significant public health problems worldwide. There is a rapidly growing demand to produce reliable estimates at the micro administrative level with small sample sizes. In this research, the authors employed small area estimation techniques to estimate the prevalence of malnutrition at the zonal level among children under five in Ethiopia. The small area estimation concept was sought for by linking the most recent possible survey data and census data in Ethiopia. The results show that there is spatial variation of stunting, wasting and being underweight across the zone level, showing different locations facing different challenges or different extents.

Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation

Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at finer geographic scales than most currently implemented in operational estimates using NFI sample data. In the past decade small area estimation techniques have been shown to increase precision in forest inventory estimates by combining field observations and remote-sensing. We sought to demonstrate the potential for improving the precision of forest inventory growing stock volume estimates for counties in United States of North Carolina, Tennessee, and Virginia, by pairing canopy height models from digital aerial photogrammetry (DAP) and field plot data from the United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30% for North Carolina county estimates were observed, with 19% average reductions in standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation among adjacent counties provided further gains in precision when the three states were treated as a single forest land population; however, analyses conducted one state at a time showed that good results could be achieved without accounting for spatial autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to 128% in North Carolina, compared to the current number of inventory plots. Results should allow for determining whether acquisition of statewide DAP would be cost-effective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI.

Simplifying Small Area Estimation With rFIA: A Demonstration of Tools and Techniques

The United States (US) Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) program operates the national forest inventory of the US. Traditionally, the FIA program has relied on sample-based approaches—permanent plot networks and associated design-based estimators—to estimate forest variables across large geographic areas and long periods of time. These approaches generally offer unbiased inference on large domains but fail to provide reliable estimates for small domains due to low sample sizes. Rising demand for small domain estimates will thus require the FIA program to adopt non-traditional estimation approaches that are capable of delivering defensible estimates of forest variables at increased spatial and temporal resolution, without the expense of collecting additional field data. In light of this challenge, the development of small area estimation (SAE) methods—estimation techniques that support inference on small domains—for FIA data has become an active and highly productive area of research. Yet, SAE methods remain difficult to apply to FIA data, due in part to the complex data structures and survey design used by the FIA program. Herein, we present the potential of rFIA, an open-source R package designed to increase the accessibility of FIA data, to simplify the application of a broad suite of SAE methods to FIA data. We demonstrate this potential via two case studies: (1) estimation of contemporary county-level forest carbon stocks across the conterminous US using a spatial Fay-Herriot model; and (2) temporally-explicit estimation of multi-decadal trends in merchantable wood volume in Washington County, Maine using a Bayesian multi-level model. In both cases, we show the application of SAE techniques offers considerable improvements in precision over FIA's traditional, post-stratified estimators. Finally, we offer a discussion of the potential role that rFIA and other open-source tools might play in accelerating the adoption of SAE techniques among users of FIA data.

Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)

BackgroundThe HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation.MethodsWe estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.ResultsThe results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts (n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates.ConclusionsHIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

Multimorbidity among Diverse Communities in New England: Findings from the Healthy Aging Data Reports

The risk for multimorbidity increases with age. Community burden of comorbidities in New England (NE) was assessed by comparing state and community rates of two measures (having no comorbidities and having 4 or more) among Medicare beneficiaries age 65+ in CT, MA, NH, and RI. Data sources were the Medicare Current Beneficiary Summary File (2014-2017) and the American Community Survey (2014-2018). Small area estimation techniques were used to calculate age-sex adjusted community rates. Multimorbidity was measured as people with zero or with 4 or more of the following chronic conditions: Alzheimer’s disease, asthma, atrial fibrillation, cancer (breast, colorectal, lung, and prostate), kidney disease, COPD, depression, diabetes, congestive heart failure, hypertension, hyperlipidemia, ischemic heart disease, osteoporosis, arthritis, and stroke. Rates for 4+ conditions: RI 63.8% (45.76-70.69%), CT 61.8% (47.82-70.05%), MA 60.7% (40-74.96%), NH 54.4% (36.67-62.99%). Results were mapped, showing the statewide and regional distribution of rates. Rates were much higher for having 4+ chronic conditions than not having any comorbidities. RI had the highest rates of 4+ and in MA the highest chronic disease rates were found in lower socioeconomic communities. CT has the highest number of diverse older residents and dual-eligible beneficiaries for Medicare and Medicaid in NE. The rates show late-life health disparities that have implications for independent living, quality of life, and mortality suggesting the need for policies to provide equitable access to care and resources to disadvantaged NE communities.