Small Area Estimation Methods Research Articles

Burden of liver cancer mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Understanding how specific populations are affected by liver cancer is important for identifying priorities, policies, and interventions to mitigate health risks and reduce disparities. This study aims to provide comprehensive analysis of rates and trends in liver cancer mortality for different racial and ethnic populations in the USA nationally and at the county level from 2000 to 2019. We applied small-area estimation methods to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate liver cancer mortality rates by county, racial and ethnic population, and year (2000-19) in the USA. Race and ethnicity were categorised as non-Latino and non-Hispanic American Indian or Alaska Native (AIAN), non-Latino and non-Hispanic Asian or Pacific Islander (Asian), non-Latino and non-Hispanic Black (Black), Latino or Hispanic (Latino), and non-Latino and non-Hispanic White (White). Estimates were adjusted using published misclassification ratios to correct for inaccuracies in race or ethnicity as recorded on death certificates, and then age-standardised. Mortality rate estimates are presented for all county and racial and ethnic population combinations with a mean annual population greater than 1000. Nationally, the age-standardised liver cancer mortality rate increased between the years 2000 (4·2 deaths per 100 000 population [95% uncertainty interval 4·1-4·3]) and 2016 (6·0 per 100 000 [5·9-6·1]), followed by a stabilisation in rates from 2016 to 2019 (6·1 per 100 000 [6·0-6·2]). Similar trends were observed across the AIAN, Black, Latino, and White populations, whereas the Asian population showed an overall decrease across the 20-year study period. Qualitatively similar trends were observed in most counties; however, the mortality rate and the rate of change varied substantially across counties, both within and across racial and ethnic populations. For the 2016-19 period, mortality continued to increase at a substantial rate in some counties even while it stabilised nationally. Nationally, the White population had the lowest mortality rate in all years, while the racial and ethnic population with the highest rate changed from the Asian population in 2000 to the AIAN population in 2019. Racial and ethnic disparities were substantial: in 2019, mortality was highest in the AIAN population (10·5 deaths per 100 000 [9·1-12·0]), notably lower for the Asian (7·5 per 100 000 [7·1-7·9]), Black (7·6 per 100 000 [7·3-7·8]), and Latino (7·7 per 100 000 [7·5-8·0]) populations, and lowest for the White population (5·5 [5·4-5·6]). These racial and ethnic disparities in mortality were prevalent throughout the country: in 2019, mortality was higher in minoritised racial and ethnic populations than in the White population living in the same county in 408 (87·7%) of 465 counties with unmasked estimates for the AIAN population, 604 (90·6%) of 667 counties for the Asian population, 1207 (81·2%) of 1486 counties for the Black population, and 1073 (73·0%) of 1469 counties for the Latino population. Although the plateau in liver cancer mortality rates in recent years is encouraging, mortality remains too high in many locations throughout the USA, particularly for minoritised racial and ethnic populations. Addressing population-specific risk factors and differences in access to quality health care is essential for decreasing the burden and disparities in liver cancer mortality across racial and ethnic populations and locations. US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

Small Area Estimation of Multidimensional Poverty in East Java Province Using Satellite Imagery

The government has so far focused on a monetary approach to overcoming poverty, while poverty is multidimensional. Holistic and accurate poverty indicators are needed as material for policy formulation, such as the Multidimensional Poverty Index (IKM), which is calculated from raw data from the National Socioeconomic Survey (SUSENAS). However, the direct estimation of the multidimensional poverty headcount (AKM) is only accurate at the provincial level, as seen from the relative standard error (RSE) of several districts and cities, which is still above 25 percent. Increasing the sample size requires time, effort, and cost, so the Small Area Estimation (SAE) method can be an alternative. Apart from using official statistics for accompanying variables, satellite imagery has the advantage of being up-to-date and available up to a granular level. This study aims to estimate the AKM at the district/city level in East Java Province by utilizing satellite imagery and official statistics in SAE. The results showed that SAE HB Beta-logistics, with the accompanying variables combined with satellite imagery and official statistics, has a higher accuracy than direct estimation.

Spatial Patterns of Piped Water and Sanitation in Rural Vietnam: An Application of Small Area Estimation

Diarrhea is one of the main causes of mortality for under five-year-old children, and this disease can be attributed to deficient hygiene, sanitation, and water supply. Detailed data on piped water and sanitation is very important for targeting, monitoring, and evaluating antipoverty programs. In this study, using a small area estimation method, we estimate per capita expenditure, the poverty rate, and the rates of households with piped water, improved latrines, and hand washing with soap at the provincial and district level in rural Vietnam. It shows that poorer provinces and districts tend to have remarkably lower access to piped water and improved latrine. Provinces in the Northern Mountain and Central Highlands have the lowest proportion of access to piped water and sanitation. There is a great spatial variation in poverty and access to water and sanitation. Within the same provinces, the level of poverty and access to water and sanitation varies largely across districts.

Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

A quasi-Bayesian Approach to Small Area Estimation Using Spatial Models

The empirical best linear unbiased prediction (EBLUP) method has been the dominant model-based approach in small area estimation. As an alternative to this frequentist method, the observed best prediction (OBP) method, also frequentist, was proposed by Jiang et al.[ 11 ] where the parameters of the model are estimated by minimizing an objective function which is implied by the total mean squared prediction error. In a recent article, Datta et al.[ 6 ] followed a general Bayesian approach, proposed recently by Bissiri et al.[ 2 ], to develop a quasi-Bayesian method by appropriately calibrating the objective function for the OBP method for the Fay-Herriot model. In a different article, Chung and Datta[ 4 ] demonstrated that in the absence of covariates with good predictive power the small area estimates from the standard Fay-Herriot model can be improved by using spatially dependent random effects. In this article, we develop a quasi-Bayesian small area estimation method using several spatial alternatives to the independent Fay-Herriot random effects model. Evaluation of the proposed method based on an application to estimation of four-person family median incomes for the U.S. states shows its usefulness. Limited but related simulation studies for the median incomes application reinforce our conclusion. AMS Subject Classification: 62F 15, 62D99

Validation of a small-area model for estimation of smoking prevalence at a subnational level.

Small-area estimation methods are an alternative to direct survey-based estimates in cases where a survey's sample size does not suffice to ensure representativeness. Nevertheless, the information yielded by small-area estimation methods must be validated. The objective of this study was thus to validate a small-area model. The prevalence of smokers, ex-smokers, and never smokers by sex and age group (15-34, 35-54, 55-64, 65-74, ≥75 years) was calculated in two Spanish Autonomous Regions (ARs) by applying a weighted ratio estimator (direct estimator) to data from representative surveys. These estimates were compared against those obtained with a small-area model applied to another survey, specifically the Spanish National Health Survey, which did not guarantee representativeness for these two ARs by sex and age. To evaluate the concordance of the estimates, we calculated the intraclass correlation coefficient (ICC) and the 95% confidence intervals of the differences between estimates. To assess the precision of the estimates, the coefficients of variation were obtained. In all cases, the ICC was ≥0.87, indicating good concordance between the direct and small-area model estimates. Slightly more than eight in ten 95% confidence intervals for the differences between estimates included zero. In all cases, the coefficient of variation of the small-area model was <30%, indicating a good degree of precision in the estimates. The small-area model applied to national survey data yields valid estimates of smoking prevalence by sex and age group at the AR level. These models could thus be applied to a single year's data from a national survey, which does not guarantee regional representativeness, to characterize various risk factors in a population at a subnational level.

Physical Inactivity, Inequalities, and Disparities Across Districts of Iran: A STEPs Survey-Based Analysis.

We aimed to estimate the prevalence of physical inactivity in all districts of Iran and the disparities between subgroups defined by various measures. Small area estimation method was employed to estimate the prevalence of physical inactivity in districts based on the remaining districts in which data on the level of physical inactivity were available. Various comparisons on the estimations were done based on socioeconomic, sex, and geographical stratifications to determine the disparities of physical inactivity among districts of Iran. All districts of Iran had a higher prevalence of physical inactivity compared with the world average. The estimated prevalence of physical inactivity among all men in all districts was 46.8% (95% uncertainty interval, 45.9%-47.7%). The highest and lowest estimated disparity ratio of physical inactivity were 1.95 and 1.14 in males, and 2.25 and 1.09 in females, respectively. Females significantly had a higher prevalence of 63.5% (62.7%-64.3%). Among both sexes, the poor population and urban residents significantly had higher prevalence of physical inactivity than rich population and rural residents, respectively. The high prevalence of physical inactivity among Iranian adult population suggests the urgent need to adopt population-wide action plans and policies to handle this major public health problem and avert the probable burden.

The burden of stomach cancer mortality by county, race, and ethnicity in the USA, 2000–2019: a systematic analysis of health disparities

There are persistent disparities in stomach cancer mortality among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically is not well understood. This analysis estimated age-standardised mortality for five racial-ethnic groups, in 3110 USA counties over 20 years, to describe spatial-temporal variations in stomach cancer mortality and disparities between racial-ethnic groups. Redistribution methods for insufficient cause of death codes and validated small area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual stomach cancer mortality rates. Estimates were stratified by county and racial-ethnic group (non-Latino and non-Hispanic [NL] American Indian or Alaska Native [AIAN], NL Asian or Pacific Islander [Asian], NL Black [Black], Latino or Hispanic [Latino], and NL White [White]) from 2000 to 2019. Estimates were corrected for misreporting of racial-ethnic group on death certificates using published misclassification ratios. We masked (ie, did not display) estimates for county and racial-ethnic group combinations with a mean annual population of less than 1000; thus, we report estimates for 3079 (of 3110) counties for the total population, and 474, 667, 1488, 1478, and 3051 counties for the AIAN, Asian, Black, Latino, and White populations, respectively. Between 2000 and 2019, national age-standardised stomach cancer mortality was lowest among the White population in every year. Nationally, stomach cancer mortality declined for all racial-ethnic groups across this time period, with the most rapid declines occurring among the Asian (percent decline 48.3% [45.1-51.1]) and Black populations (42.6% [40.2-44.6]). Mortality among the other racial-ethnic groups declined more moderately, decreasing by 36.7% (35.3-38.1), 35.1% (32.2-37.7), and 31.6% (23.9-38.0) among the White, Latino, and AIAN populations, respectively. Similar patterns were observed at the county level, although with wide geographic variation. In 2019, a majority of counties had higher mortality rates among minoritised racial-ethnic populations compared to the White population: 81.1% (377 of 465 counties with unmasked estimates for both racial-ethnic groups) among the AIAN population, 88.2% (1295 of 1469) among the Latino population, 99.4% (663 of 667) among the Asian population, and 99.9% (1484 of 1486) among the Black population. However, the size of these disparities ranged widely across counties, with the largest range from 0.3 to 17.1 among the AIAN population. Stomach cancer mortality has decreased substantially across populations and geographies in the USA. However, disparities in stomach cancer mortality among racial-ethnic groups are widespread and have persisted over the last two decades. Local-level data are crucial to understanding the scope of this unequal burden among minoritised groups in the USA. National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research, National Institutes of Health (contract #75N94019C00016).

Inequality in electricity consumption and economic growth: Evidence from a small area estimation study.

Our study uses a small area estimation method to estimate the average and inequality of per capita kWh consumption for small areas in Vietnam. It shows evidence of a large spatial heterogeneity in the electric power consumption between districts and provinces in Vietnam. Households in the mountains and highlands consumed remarkably less electricity than those in the delta and coastal areas. Notably, we find a U-shaped relationship between the inequality of electricity consumption and economic levels in Vietnam. In poor districts and provinces, there is very high inequality in electricity consumption. Inequality is lower in middle-income districts and provinces.

Analysis of unit level models for small area estimation in crop statistics assisted with satellite auxiliary information

Crop statistics for a small area, such as the community development block, are an increasingly important topic in agricultural statistics. Under normality assumptions, the classic Empirical Best Linear Unbiased Prediction (EBLUP) technique is effective for predicting small area means, however the Small Area Estimation (SAE) model can be heavily affected by the incidence of outliers or deviations from the expected distribution. The purpose of this study was to estimate variance, predict block-level wheat crop yield in the Hisar and Sirsa district of Haryana by classical SAE method and a robust random-effect predictor using a slight generalization of Huber’s Proposal 2. In the case of Sirsa district, the results of classical and robust unit level SAE were very close, but not in the case of Hisar district. This could be due to the influential observation found in the Hisar data set. More accurate EBLUP wheat yield estimates are obtained when the Huber-type M-estimation method is initialized by the least square regression estimator.

Spatial Prediction in Small Area Estimation

Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat p rior. F urther, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables.

Spatial Inequities in Life Expectancy in Small Areas of Buenos Aires, Argentina 2015–2017

Studies of life expectancy (LE) in small areas of cities are relatively common in high-income countries but rare in Latin American countries. Small-area estimation methods can help to describe and quantify inequities in LE between neighborhoods and their predictors. Our objective was to analyze the distribution and spatial patterning of LE across small areas of Ciudad Autónoma de Buenos Aires (CABA), Argentina, and its association with socioeconomic characteristics. As part of the SALURBAL project, we used georeferenced death certificates in 2015–2017 for CABA, Argentina. We used a spatial Bayesian Poisson model using the TOPALS method to estimate age- and sex-specific mortality rates. We used life tables to estimate LE at birth. We obtained data on neighborhood socioeconomic characteristics from the 2010 census and analyzed their associations. LE at birth was higher for women (median of across neighborhoods = 81.1 years) compared to men (76.7 years). We found a gap in LE of 9.3 (women) and 14.9 years (men) between areas with the highest and the lowest LE. Better socioeconomic characteristics were associated with higher LE. For example, mean differences in LE at birth in areas with highest versus lowest values of composite SES index were 2.79 years (95% CI: 2.30 to 3.28) in women and 5.61 years (95% CI: 4.98 to 6.24) in men. We found large spatial inequities in LE across neighborhoods of a large city in Latin America, highlighting the importance of place-based policies to address this gap.

Multivariate Small Area Estimation of Social Indicators: The Case of Continuous and Binary Variables

Large-scale sample surveys are not designed to produce reliable estimates for small areas. Here, small area estimation methods can be applied to estimate population parameters of target variables to detailed geographic scales. Small area estimation for noncontinuous variables is a topic of great interest in the social sciences where such variables can be found. Generalized linear mixed models are widely adopted in the literature. Interestingly, the small area estimation literature shows that multivariate small area estimators, where correlations among outcome variables are taken into account, produce more efficient estimates than do the traditional univariate techniques. In this article, the author evaluate a multivariate small area estimator on the basis of a joint mixed model in which a small area proportion and mean of a continuous variable are estimated simultaneously. Using this method, the author “borrows strength” across response variables. The author carried out a design-based simulation study to evaluate the approach where the indicators object of study are the income and a monetary poverty (binary) indicator. The author found that the multivariate approach produces more efficient small area estimates than does the univariate modeling approach. The method can be extended to a large variety of indicators on the basis of social surveys.

Regional Public Opinions on LGBTI People Equal Opportunities in Employment: Evidence from the Eurobarometer Programme using Small Area Estimation

In recent years, the attention to lesbian, gay, bisexual, transgender and intersex (LGBTI) people’ rights from institutions, society and scientific bodies has clearly progressed. Although equal opportunities in employment are promoted within European countries and by the EU legislation, there are still evident discriminations in Europe. Many LGBTI people still face bullying and anti-LGBTI discrimination in the workplace and job market. Considerably more progress must be made before every LGBTI person feels accepted and comfortable for who they are in the workplace. Importantly, views on equal opportunities in employment are characterised by spatial heterogeneity at a sub-national level. Therefore, it is necessary to disaggregate estimates of relevant indicators, at least, at a regional level. This is crucial to identify the regions requiring more attention by policy makers. However, large-scale sample surveys are not designed to produce precise and accurate sub-national estimates. Small area estimation methods offer powerful tools in this context. Here, we produce regional estimates of three indicators measuring views of discrimination in employment of people from LGBTI communities in Europe. The analyses are based on the Eurobarometer 91.4 2019. Our empirical evidence shows that the estimates produced by small area estimation are reliable, giving important information to policy makers.

Inequalities in the prevalence, diagnosis awareness, treatment coverage and effective control of diabetes: a small area estimation analysis in Iran

ObjectiveThis study aims to assess geographic inequalities in the prevalence, awareness of diagnosis, treatment coverage and effective control of diabetes in 429 districts of Iran.MethodsA modelling study by the small area estimation method, based on a nationwide cross-sectional survey, Iran STEPwise approach to surveillance (STEPS) 2016, was performed. The modelling estimated the prevalence, awareness of diagnosis, treatment coverage, and effective control of diabetes in all 429 districts of Iran based on data from available districts. The modelling results were provided in different geographical and socio-economic scales to make the comparison possible across the country.ResultsIn 2016, the prevalence of diabetes ranged from 3.2 to 19.8% for women and 2.4 to 19.1% for men. The awareness of diagnosis ranged from 51.9 to 95.7% for women and 35.7 to 100% for men. The rate of treatment coverage ranged from 37.2 to 85.6% for women and 24.4 to 80.5% for men. The rate of effective control ranged from 12.1 to 63.6% for women and 12 to 73% for men. The highest treatment coverage rates belonged to Ardebil for women and Shahr-e-kord for men. The highest effective control rates belonged to Sanandaj for women and Nehbandan for men. Across Iran districts, there were considerable differences between the highest and lowest rates of prevalence, diagnosis awareness, treatment coverage, and effective control of diabetes. The concentration indices of diabetes prevalence, awareness of diagnosis, and treatment coverage were positive and significant for both sexes.ConclusionFindings of this study highlight the existence of inequalities in diagnosis awareness, treatment coverage, and effective control of diabetes in all Iran regions. More suitable population-wide strategies and policies are warranted to handle these inequalities in Iran.

Design‐based composite estimation rediscovered

Small area estimation methods are used in surveys, where sample sizes are too small to get reliable direct estimates of parameters in some population domains. We consider design‐based linear combinations of direct and synthetic estimators and propose a two‐step procedure to approach the optimal combination. We construct the mean square error estimator suitable for this and any other linear composition that estimates the optimal one. We apply the theory to two design‐based compositions analogous to the empirical best linear unbiased predictors (EBLUPs) based on the basic area‐ and unit‐level models. The simulation study shows that the new methods are efficient compared to estimation using EBLUP.

Policy-oriented food insecurity estimation and mapping at district level in Pakistan

Purpose. Food insecurity maps reveal the spatial variability of relevant indicators in relevant units in geographically disaggregated levels. This study is based on a systematic analysis of the least studied areas related to food insecurity in Pakistan, such as district-level Small Area Estimation (SAE) analysis of food insecurity by integrating several well-established datasets, including PSLM 2014–2015 and HIES 2015–2016.&#x0D; Methodology / approach. We investigate the food insecurity situation at the district level in Pakistan by applying the household level technique of SAE method. The geographically disaggregated indicators of welfare are estimated by using SAE that integrates the census and survey datasets. This study estimates incidence and density indictors at the district level of food insecurity. The accessibility aspect of food security is taking into account by calculating monthly equivalent food expenditure per adult. In addition, the food insecurity headcount ratio is calculated to identify the food insecurity incidence at district level, and density are visualized using ‘spmap’ in STATA 14.&#x0D; Results. The results of this study indicate that the districts with low food insecurity incidence are dense in terms of food insecure people. The second least food insecure district, according to food insecurity incidence estimates, has become the most food insecure in terms of food insecurity density. However, the most food insecure district with respect to food insecurity incidence has been identified as one of the least food insecure districts in terms of food insecure people. For instance, Washuk district in Balochistan, has been identified as the most food insecure district with almost 93 % food insecurity incidence. However, Washuk has only 0.17 million food insecure people according to food insecurity density estimates.&#x0D; Originality / scientific novelty. The study highlighted the importance of food insecurity density estimates in addition to the food insecurity incidence for targeted policy interventions. In this study we have integrated a large and relatively smaller data set that covers most of the districts from all provinces of Pakistan for addressing the small sample issue which have been identified in previous studies. The variables that are common to both data sets are included after a screening process that include Variance Inflation Factor for multicollinearity, forward – backward selection criterion with model adjustment criterion either adjusted R2, Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC), least absolute shrinkage and selection operator (LASSO).&#x0D; Practical value / implications. The results of the study indicate that the policy makers should consider both the density and incidence of food insecurity for targeted policy interventions. This is because several districts with low food insecurity incidence are found to be dense with food insecure people. Moreover, the obtained results can be complemented by the results of the Integrated Food Security Phase Classification (IPC) which is based on relatively very small samples from few districts of three provinces. This can be useful in efficient implementation of food security policy and programs in targeted areas. Furthermore, the results highlight that the efforts reduce food insecurity should be targeted at district level in Pakistan.

Multilevel time-series models for small area estimation at different frequencies and domain levels

A small area estimation method is developed for repeatedly conducted multipurpose surveys. A multilevel time-series model is proposed that uses direct estimates for the most detailed domains observed at the highest frequency of the repeated survey. A consistent set of estimates at different aggregation levels is then derived by aggregation of the model-based predictions obtained for the most detailed domains observed at the highest frequency. The model borrows strength over time and space via smooth and local level trends at different aggregation levels. The model also borrows information from auxiliary series available from registers with coefficients that can vary over both domains and time. Regional domain random effects are allowed to vary smoothly over space according to a spatial autoregressive process. To account for the diversity of domains and for more volatile time-dependence, nonnormally distributed random effects and trend innovations are used via so-called global-local shrinkage priors. A Bayesian approach is taken, and the model is estimated by MCMC simulation. The method is illustrated with an application to the Dutch Labour Force Survey to produce monthly provincial and quarterly municipal unemployment figures.

Maternal hepatitis C prevalence and trends by county, US: 2016-2020.

Trends in the prevalence of hepatitis C virus (HCV) infection among women delivering live births may differ in rural vs. urban areas of the United States, but estimation of trends based on observed counts may lead to unstable estimates in rural counties due to small numbers. The objective of the study was to use small area estimation methods to provide updated county-level prevalence estimates and, for the first time, trends in maternal HCV infection among live births by county-level rurality. Cross-sectional natality data from 2016 to 2020 were used to estimate maternal hepatitis C prevalence using hierarchical Bayesian models with spatiotemporal random effects to produce annual county-level estimates of maternal HCV infection and trends over time. Models included a 6-level rural-urban county classification, year, maternal characteristics and county-specific covariates. Data were analysed in 2022. There were 90,764/18,905,314 live births (4.8 per 1000) with HCV infection reported on the birth certificate. Hepatitis C prevalence was higher among rural counties as compared to urban counties. Rural counties had the largest annual increases in maternal hepatitis C prevalence (per 1000 births) from 2016 to 2020 (micropolitan: 0.39; noncore: 0.40), with smaller increases among less densely populated urban counties (medium metro: 0.28; small metro: 0.28) and urban counties (large central metro:0.11; large fringe metro: 0.14). The prevalence of maternal HCV infection was the highest in rural counties, and rural counties saw the greatest average prevalence increase during 2016-2020. County-level data can help in monitoring rural-urban trends in maternal HCV infection to reduce geographic disparities.

Sistem Informasi Pendugaan Kekerasan Terhadap Perempuan dan Anak Menggunakan Metode Small Area Estimation

Based on data obtained from the SIMFONI-PPA Application, the Office of Women's Empowerment, Child Protection, Population Control and Family Planning (DP3AP2KB) Sambas Regency, in 2017 there were 95 cases of violence against women and children in Sambas Regency, then in 2018 there were 43 cases, followed by in 2019 as many as 49 cases, and in 2020 as many as 64 cases. This shows an increasing trend in the number of cases of violence that occur from year to year. The increasing trend is caused by the lack of access to information to make complaints, the processing of data on areas of violence that has not been integrated, so that the government does not have up-to-date data to anticipate. To support DP3AP2KB in handling and preventing violence, this research builds a system based on the estimation of violence against women and children by utilizing the Small Area Estimation Method, especially the IDW interpolation technique. This research was carried out by processing data through the ArcGIS application to determine the pattern of areas prone to violence, using interpolation techniques to predict unmeasured variables at various locations, with IDW assuming that the closer a point is to an unknown point, the greater the effect. . IDW uses the values measured at points around the location, to estimate the value of the variable at the location in question. Then the results of data processing is implemented in a web-based geographic information system. The system that has been built has been tested using the black box method and calculated using a Likert scale, obtaining a value of 86.20% which is included in the Very Good category.