Challenges and lessons learned in promoting adoption of standardized local public health service delivery data through the application of the Public Health Activities and Services Tracking model

A Uniform Chart of Accounts: Strengthening Public Health Practice and Research Through Standardized Financial Data

CONTEXT

The COVID-19 pandemic made the long-standing need for a national uniform financial reporting standard for governmental public health agencies clear, as little information was available to quantify state and local public health agencies' financial needs during the pandemic response. Such a uniform system would also inform resource allocation to underresourced communities and for specific services, while filling other gaps in practice, research, and policy making. This article describes lessons learned and recommendations for ensuring broad adoption of a national Uniform Chart of Accounts (UCOA) for public health departments.

PROGRAM

Leveraging previous efforts, the UCOA for public health systems was developed through collaboration with public health leaders. The UCOA allows state and local public health agencies to report spending on activities and funding sources, along with practice-defined program areas and capabilities.

IMPLEMENTATION

To date, 78 jurisdictions have utilized the UCOA to crosswalk financial information at the program level, enabling comparisons with peers.

EVALUATION

Jurisdictions participating in the UCOA report perceptions of substantial up-front time investment to crosswalk their charts of accounts to the UCOA standard but derive a sense of valuable potential for benchmarking against peers, ability to engage in resource allocation, use of data for accountability, and general net positive value of engagement with the UCOA.

IMPLICATIONS FOR POLICY AND PRACTICE

The UCOA is considered a need among practice partners. Implementing the UCOA at scale will require government involvement, a reporting requirement and/or incentives, technical assistance, financial support for agencies to participate, and a means of visualizing the data.

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

The DASH model: Data for addressing social determinants of health in local health departments

Recent frameworks, models, and reports highlight the critical need to address social determinants of health for achieving health equity in the United States and around the globe. In the United States, data play an important role in better understanding community-level and population-level disparities particularly for local health departments. However, data-driven decision-making-the use of data for public health activities such as program implementation, policy development, and resource allocation-is often presented theoretically or through case studies in the literature. We sought to develop a preliminary model that identifies the factors that contribute to data-driven decision-making in US local health departments and describe relationships between them. Guided by implementation science literature, we examined organizational-level capacity and individual-level factors contributing to using data for decision-making related to social determinants of health and the reduction of county-level disparities. This model has the potential to improve implementation of public health interventions and programs aimed at upstream structural factors, by elucidating the factors critical to incorporating data in decision-making.

Identifying Spatial Matching between the Supply and Demand of Medical Resource and Accessing Carrying Capacity: A Case Study of Shenzhen, China

Previous Studies, such as the evaluation of the supply of and demand for regional medical resources and carrying capacity assessments, require further development. This paper aims to evaluate the carrying capacity and spatial distribution of medical resources in Shenzhen from the perspective of supply and demand, and to conduct a time-series variation of the coupling coordination degree from 1986 to 2019. The two-step floating catchment area method was employed to quantify the carrying capacity and coupling coordination degree method and spatial autocorrelation analysis were applied to analyze spatial distribution between supply and demand. The results were as follows. (1) The carrying capacity index in more than 50% of the districts was classified as low-grade. The percentage of regions with good grades was 8.27%. The regions with a high carrying capacity were distributed in the central and southeastern areas. (2) The coupling coordination continued to rise, increasing from 0.03397 in 1986 to 0.33627 in 2019. (3) The level of supply and demand for medical resources in Shenzhen increased from 1986 to 2019, and the highest degree of compatibility between the supply and the population size was largely concentrated in the western and eastern regions. This research can provide a theoretical reference for Shenzhen to rationally plan medical resources and improve the carrying capacity of medical resources.

Fairness of the Distribution of Public Medical and Health Resources

The fairness of health services is an important indicator of the World Health Organization's performance evaluation of health services, and the fairness of health resource allocation is the prerequisite for the fairness of health services. The research in this article aims to explore how to use health and medical resources fairly and effectively to allocate health resources in different fields, populations and projects, in order to achieve the maximization of social and economic benefits of health and medical resources. In the study of the distribution and equity of public health and medical resources, we comprehensively apply Gini coefficient, Theil index, Lorentz curve and difference index, based on the theory of health resource allocation and the theory of health equity, the province's health service resources have been researched and evaluated, combined with regional health planning theories and public health theories, a variety of scientific methods were used to analyze community health service resources at all levels across the country. At the same time, we reviewed the journal literature about the treatment of patients and children, and analyzed the patients admitted to medical institutions in various regions. The research in this paper found that from 2016 to 2020, the Gini coefficient of the province's health institutions according to population distribution has been fluctuating between 0.14 and 0.17. During this 5-year period, the Gini coefficient of the distribution of medical and health expenditures by population shows a downward trend year by year. From 2019, reach below 0.1, this shows that the fairness of the allocation of health resources according to population has a clear trend of improvement.

Analytic Approaches to Assess the Impact of Local Spending on Sexually Transmitted Diseases

OBJECTIVE

To compare the estimated associations between annual STD (sexually transmitted diseases) expenditures per capita and STD rates among Florida and Washington local health departments (LHDs) from 2001-2017, using two approaches--a longitudinal regression model with lagged STD spending, and a regression model with the Arellano-Bond panel estimator.

DATA SOURCES

Secondary data for LHDs were obtained from Florida and Washington state government offices and combined with county sociodemographic and health system data from the federal government.

STUDY DESIGN

We examined LHDs in Florida and Washington using a longitudinal panel study design to estimate ecological relationships between annual STD expenditures per capita and annual STD incidence rates from 2001 to 2017 with LHDs as the unit of analysis. We compared two regression models: generalized estimating equations (GEE) and the Arellano-Bond panel estimator (an instrumental variable approach).

DATA COLLECTION

The secondary data were combined to build a longitudinal panel database for LHDs in Florida and Washington from 2001 to 2017.

PRINCIPAL FINDINGS

In the GEE model with both states, greater STD spending in a prior year was associated unexpectedly with greater STD incidence rates in succeeding years. The Arellano-Bond models for both states had the expected inverse associations but were not significant. In the Arellano-Bond models for Florida, a $1 increase in STD spending in previous years was followed by decreases in STD incidence rates ranging between 29 and 59 points in succeeding years (0.09 ≥ p ≥ 0.04).

CONCLUSIONS

In longitudinal panel data for LHDs in two states, the Arellano-Bond estimator, or other instrumental variable approach, is preferred over conventional regression models to obtain unbiased estimates of the relationship between annual STD spending rates and annual STD rates. Future studies will require accurate, standardized, and detailed longitudinal data and rigorous analytic approaches, such as those illustrated in our study. This article is protected by copyright. All rights reserved.

Data, capacity-building, and training needs to address rural health inequities in the Northwest United States: a qualitative study

OBJECTIVE

Rural public health system leaders struggle to access and use data for understanding local health inequities and to effectively allocate scarce resources to populations in need. This study sought to determine these rural public health system leaders' data access, capacity, and training needs.

MATERIALS AND METHODS

We conducted qualitative interviews across Alaska, Idaho, Oregon, and Washington with individuals expected to use population data for analysis or decision-making in rural communities. We used content analysis to identify themes.

RESULTS

We identified 2 broad themes: (1) challenges in accessing or using data to monitor and address health disparities and (2) needs for training in data use to address health inequities. Participants faced challenges accessing or using data to address rural disparities due to (a) limited availability or access to data, (b) data quality issues, (c) limited staff with expertise and resources for analyzing data, and (d) the diversity within rural jurisdictions. Participants also expressed opportunities for filling capacity gaps through training-particularly for displaying and communicating data.

DISCUSSION

Rural public health system leaders expressed data challenges, many of which can be aided by informatics solutions. These include interoperable, accessible, and usable tools that help capture, access, analyze, and display data to support health equity efforts in rural communities.

CONCLUSION

Informatics has the potential to address some of the daunting data-related challenges faced by rural public health system leaders working to enhance health equity. Future research should focus on developing informatics solutions to support data access and use in rural communities.