Does health service funding go where the need is? A prototype spatial access analysis for new urban contracts data

Background Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government. Methods In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study. Results We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago’s high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout the city). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department. Conclusions The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results.

Use of Administrative Data for Waste Statistics in the Czech Republic

The Czech Statistical Office (CZSO) changed the calculation of the total volume of waste and also changed the definition of municipal waste compared to the previous methodology. This was made possible by the wider use of the existing administrative data source, the Integrated Environmental Reporting System (ISPOP). The change in the definition of municipal waste was a response to recent Eurostat activities, which led to a more precise definition. The original method no longer meets this definition.

Does Health Service Funding Go Where the Need Is? A Prototype Spatial Access Analysis for New Urban Contracts Data

Background | Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government.Methods | In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study.Results | We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago’s high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout thecity). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department.Conclusions | The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results.

Does Health Service Funding Go Where the Need Is? A Prototype Spatial Access Analysis for New Urban Contracts Data

Background Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government. Methods In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study. Results We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago’s high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout the city). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department. Conclusions The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results

Average Monthly Wage Means Difference Between Administrative Sources Vs. Business Survey, Case in Albania

This paper examines the differences resulting from calculating the means of Pay Roll records and personal revenues used as secondary data with results from business survey in an empirical study taking a panel. The use of “secondary data” as primary source for producing the official indicators is a challenge worldwide. In the past decades has also been considered as the way forward for raising productivity and reducing burden on businesses. If the Short Term Survey is sample survey the Pay Roll records are administrative data. The purpose for what they are gathered is different. But both could be used for providing statistical indicators. In this paper the panel not weighted data are taken into consideration where the same business is analyzed from two related sources. The paired t-test is used to compare the values of means from two related sources. In those conditions the difference between the means of the two sources is unlikely to be equal to zero. In this study the hypothesis test is designed to answer the question "Is the observed difference sufficiently large enough to indicate that the alternative hypothesis is true?" What does it mean in our case study the answer which comes in the form of a probability - the p-value? The paper shows some interesting findings about the means difference between the two sources within a year. The differences resulting from the conducted analysis come as a result of the definition used in both sources for the same indicator, errors in reporting and treatment of non-response in the survey and administrative data source, coding errors.

Outcomes in Hospitalized Ischemic Stroke Patients with Dementia on Admission: A Population-Based Cohort Study

BackgroundDementia prevalence is rising, and it will double in the next 20 years. This study sought to understand the prevalence of dementia in hospitalized patients with ischemic stroke, and its impact on outcomes.MethodsUsing the Canadian Institute of Health Information’s (CIHI) Discharge Abstract Database (DAD), all acute ischemic stroke admissions from April 2003 to March 2015 in Canada (excluding Quebec) were analyzed. Concurrent dementia at the time of admission was assessed based on hospital diagnostic codes. Characteristics and in-hospital outcomes were compared in patients with and without dementia using χ2 and negative binomial, as well as Poisson regression analysis.ResultsDuring the observed period, 313,138 people were admitted to a hospital in Canada for an ischemic stroke. Of those, 21,788 (7.0%) had a concurrent diagnosis of dementia. People with dementia had older median age (84 vs. 76 years; p<0.0001), were more often female (59.6% vs. 48.4%; p<0.0001) and more often had Charlson-Deyo Comorbidity Index ≥2 (64.5% vs. 43.5%; p<0.0001). Patients with dementia were less likely to be discharged to a rehabilitation facility (adjusted risk ratio [RR] 3.089, 95% confidence interval [CI] 2.992-3.188, p<0.0001) or home independently (adjusted RR 0.756, 95% CI 0.737-0.776, p<0.0001).InterpretationApproximately 1 in 13 hospitalized ischemic stroke patients has coded dementia. Patients with ischemic stroke and concurrent dementia have higher mortality, face significantly more dependence after stroke and utilize greater healthcare resources than stroke patients without dementia. Causative conclusions are limited by the administrative data source. Early care planning and coordination could potentially optimize outcomes.

Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

Administrative data sources are an important component of population data collection and they have been used in census data production in the Nordic countries since the 1960s. A large amount of information about the population is already collected in administrative data sources by governments. However, there are some challenges to using administrative data sources to estimate population counts by age, sex, and geographical area as well as population characteristics. The main limitation with the administrative data sources is that they only collect information from a subset of the population about specific events, and this may result in either undercoverage or overcoverage of the population. Another issue with the administrative data sources is that the information may not have the same quality for all population groups. This research aims to correct an inaccurate administrative data source by combining aggregate-level administrative data with more accurate marginal distributions or two-way marginal information from an auxiliary data source and produce accurate population estimates in the absence of a traditional census. The methodology developed is applied to estimate population counts by age, sex, and local authority area in England and Wales. The administrative data source used is the Patient Register which suffers from overcoverage, particularly for people between the ages of 20 and 50.