Abstract
Policy Points.
While the coronavirus pandemic has underscored the important role of public health systems in protecting community health, it has also exposed weaknesses in the public health infrastructure that stem from chronic underfunding and fragmentation in delivery systems.
The results of our study suggest that the public health system structure can be strengthened through the targeted implementation of high‐value population health capabilities.
Prioritizing the delivery of value‐added population health capabilities can help communities efficiently use limited time and resources and identify the most effective pathways for building a stronger public health system and improving health outcomes over time.
Context
While the novel coronavirus pandemic has underscored the important role of public health systems in protecting community health, it has also exposed weaknesses in the public health infrastructure that stem from chronic underfunding and fragmentation in public health delivery systems. Information about the relative value in the implementation of recommended population health capabilities can help communities prioritize their use of limited time and resources and identify the most effective pathways for building a stronger public health system.
Methods
We used a longitudinal cohort design with data from the National Longitudinal Survey of Public Health Systems to examine longitudinal and geographic trends in the delivery of population health capabilities and their impact on system strength across communities in the United States. We used linear probability models to ascertain whether the delivery of certain capabilities added value to public health system strength.
Findings
Those communities with the strongest classification of public health system structure in both urban and rural areas implemented the largest set of population health capabilities. Results from the linear probability model indicate that a set of population health capabilities are associated with increased public health system strength. Key activities include allocating resources based on a community health plan, surveying the community for behavioral risk factors, analyzing the data on preventive services use, and engaging community stakeholders in health improvement planning (p < 0.01).
Conclusions
The results of this study suggest that public health systems can be strengthened through the targeted implementation of high‐value population health capabilities. Prioritizing the delivery of value‐added population health capabilities may help communities increase their public health system's capacity and improve health outcomes.
Keywords: public health, public health administration, longitudinal studies
Even though the novel coronavirus (covid‐19) pandemic has underscored the important role of public health systems in protecting community health, it has, at the same time, exposed weaknesses in public health system infrastructure that stem from chronic underfunding and fragmentation across delivery systems. 1 , 2 Strains on public health capacity limit the system's ability to deliver core population health capabilities and respond to emerging public health threats in a timely manner. 2 , 3 The spread of COVID‐19 has been compounded by additional population health challenges, including the growing chronic disease burden in the United States and persistent health inequities stemming from the social determinants of health, making critical the need for strategies that strengthen public health infrastructure and capabilities. 4 , 5 , 6 , 7
A large and growing body of research has identified population health capabilities that communities need to promote health, prevent disease and injury, and strengthen health equity for entire populations of residents. 8 , 9 , 10 These capabilities include regularly assessing the population's health needs and risks, engaging community stakeholders to establish goals and priorities for improving health, identifying evidence‐based solutions to address priorities, educating the public and policy officials about health priorities and solutions, finding and allocating community resources to support the implementation of solutions, and regularly evaluating progress in addressing health priorities. 8 These capabilities are now reflected in national guidelines and recommendations, including the Public Health Foundational Capabilities recommended by the National Academy of Medicine, the Public Health 3.0 Framework recommended by the US Department of Health and Human Services, and public health agency accreditation standards recommended by the Public Health Accreditation Board (PHAB). 11 , 12 , 13
Based on these recommended capabilities, our research team developed a method for classifying communities according to the strength of their public health systems, as indicated by the scope of population health capabilities implemented in each community and the network of community organizations that helped implement these capabilities. 14 Studies that applied this classification system to a national cohort of communities over time found that those communities achieving a broad scope of capabilities and dense networks of contributing organizations experience improved health outcomes over time compared with communities with more limited capabilities. 15 These findings add to the growing evidence base suggesting that meaningful improvements in population health can be achieved through cross‐sector partnerships that carry out effective public health strategies in their communities. 16 , 17 , 18 , 19
Although the importance of implementing the recommended population health capabilities to improve public health performance has been documented, little is known about which combinations of the capabilities are the most successful when seeking to strengthen a community's public health system. Information about the relative value of specific capabilities and organizational partners can help communities prioritize their use of limited time and resources and identify the most effective pathways for building a stronger public health system over time. We tried to fill that gap using a nationally representative cohort of communities to examine the impact of population health capabilities on public health system strength.
Data and Methods
Study Design and Sample
We used a longitudinal cohort design to examine patterns of public health capabilities delivered across communities in the United States and to ascertain whether the delivery of particular capabilities strengthened the public health system. The National Longitudinal Survey of Public Health Systems (NALSYS) contains a stratified random sample of the nation's 3,000 local public health officials (n = 397) who were surveyed in 1998, 2006, 2012, 2014, 2016, and 2018 to measure the availability of 20 core public health activities in their jurisdictions and the range of organizations that delivered each activity. 14 , 15 , 20 , 21 , 22 , 23 The survey sample initially included all US agencies meeting the national definition of a local health department serving a population of 100,000 or more residents but was expanded in 2014 to encompass communities serving smaller populations. For this analysis, we used observations from 2014, 2016, and 2018 to capitalize on the expanded sample to examine differences between urban and rural communities. The local public health jurisdiction was our unit of analysis.
Public Health System Strength
Our outcome variable of interest was a composite measure of public health system strength derived from NALSYS. A system's strength is measured using a classification scheme that determines whether a community has a limited, conventional, or comprehensive structure based on the results of a cluster analysis. The cluster analysis uses measures of the proportion of the 20 population health capabilities implemented in the community and the number of organizations contributing to the activities to derive system capital. 14 Comprehensive communities implement a larger range of capabilities with a broad set of community partners and achieve greater population health protections. Compared with their conventional and limited counterparts, individuals living in communities with comprehensive public health systems are less likely to die from preventable conditions like cardiovascular disease and diabetes. 15 We measured public health system strength using a dichotomous variable indicating whether or not a community's system had comprehensive capital.
Public Health Capabilities
NALSYS includes a set of capabilities considered essential to protect a community's population health. Activities range from the implementation of community health needs assessments with a diverse set of community partners to the investigation of adverse health events, and represent the three core functions of public health: assurance, assessment, and policy development (see Table 1 for the full list of activities). The capabilities included in NALSYS reflect activities identified and recommended by the federal consensus panels tasked with the development of methods to assess the performance of local public health systems. 24 , 25 , 26 , 27 , 28 Twenty dichotomous variables indicating whether a community implemented the population health capability measured in NALSYS served as our primary explanatory variables.
Table 1.
Average Portion of Communities Delivering Core Population Health Capabilities in Comprehensive and Noncomprehensive Communities by Geographic Location (%)
| Urban | Rural | |||||
|---|---|---|---|---|---|---|
| Comp. | Not Comp. | % Difference | Comp. | Not Comp. | % Difference | |
| Assessment | ||||||
| Conduct periodic assessment of community health status and needs | 97.6 | 72.5 | −25.7 | 98.3 | 65.4 | −33.5 |
| Survey community for behavioral risk factors | 89.3 | 49.2 | −44.9 | 90.3 | 38.8 | −57.0 |
| Investigate adverse health events, outbreaks, and hazards | 100.0 | 97.9 | −2.1 | 100.0 | 96.0 | −4.0 |
| Conduct laboratory testing to identify health hazards and risks | 99.4 | 94.9 | −4.5 | 97.2 | 89.8 | −7.6 |
| Analyze data on community health status and health determinants | 96.9 | 51.8 | −46.5 | 95.5 | 41.9 | −56.1 |
| Analyze data on preventive services use | 61.7 | 16.3 | −73.6 | 56.8 | 12.6 | −77.9 |
| Policy and Planning | ||||||
| Routinely provide community health information to elected officials | 95.8 | 75.9 | −20.7 | 91.5 | 63.9 | −30.2 |
| Routinely provide community health information to the public | 96.1 | 71.8 | −25.3 | 93.1 | 66.7 | −28.4 |
| Routinely provide community health information to the media | 94.8 | 73.8 | −22.1 | 93.1 | 70.5 | −24.3 |
| Prioritize community health needs | 97.5 | 63.9 | −34.4 | 97.7 | 54.7 | −44.1 |
| Engage community stakeholders in health improvement planning | 90.1 | 39.2 | −56.5 | 92.6 | 33.9 | −63.3 |
| Develop a communitywide health improvement plan | 98.2 | 70.6 | −28.1 | 99.4 | 57.5 | −42.2 |
| Allocate resources based on community health plan | 76.4 | 16.4 | −78.5 | 80.2 | 15.7 | −80.4 |
| Develop policies to address priorities in community health plan | 89.0 | 38.2 | −57.1 | 92.0 | 30.7 | −66.6 |
| Maintain a communication network among health‐related organizations | 93.6 | 77.2 | −17.5 | 97.7 | 72.1 | −26.3 |
| Assurance and Evaluation | ||||||
| Link people to needed health and social services | 79.1 | 44.8 | −43.3 | 75.1 | 36.1 | −52.0 |
| Implement legally mandated public health activities | 93.5 | 92.2 | −1.4 | 93.7 | 92.8 | −0.9 |
| Evaluate health programs and services in the community | 65.7 | 20.9 | −68.2 | 63.4 | 17.2 | −72.8 |
| Evaluate public health agency capacity and performance | 72.7 | 38.9 | −46.5 | 71.8 | 34.5 | −52.0 |
| Monitor and improve implementation of health programs and policies | 73.9 | 31.2 | −57.8 | 66.1 | 21.3 | −67.7 |
Abbreviation: Comp., comprehensive.
Analysis
We generated descriptive statistics to examine variations in public health system strength and the delivery of individual population health capabilities. We measured longitudinal and geographic trends in system strength across years and by urban or rural location. We identified communities as rural using Rural‐Urban Commuting Area codes. 29
We then used a linear probability model to estimate which of the 20 population capabilities included in NALSYS had the largest association with the probability that a community had a comprehensive population health system. As opposed to a logistic regression, we selected a linear probability model for this analysis, because of problems with both perfect predictions and near perfect predictions regarding our explanatory and outcome variables. Having both binary dependent and independent variables may end up showing that the presence of an explanatory characteristic perfectly predicts having the outcome of interest. For example, in this analysis, 100% of those sample communities with comprehensive system capital implemented the investigation of adverse health events (see Table 1). Similarly, there were a number of capabilities for which very few comprehensive communities did not implement the associated activity, creating near perfect predictions. When this happens, a logistic regression model cannot be estimated. Using the linear probability model also provided regression coefficients that are easier to interpret and allowed us to estimate the absolute difference in the probability of being comprehensive when each activity is offered.
We first estimated a pooled model including all communities in the sample (n = 1,490). We then ran a second set of models stratifying communities by urban (n = 1,026) and rural (n = 464) location to determine whether differences in the relationship existed between the implementation of activities and comprehensive system capital based on geography. All the models included community and year fixed effects to control for the temporal correlation of the results from examining the same communities over time. We then estimated those models with robust standard errors to adjust for heteroscedasticity. To control for the state's engagement, we also included a variable measuring the number of capabilities in which the state health agency participated in implementation. To control for factors that might have influenced the provision of population health capabilities and system capital, we linked the NALSYS data from each year with contemporaneous data on community socioeconomic, demographic, and health care systems characteristics from the Area Health Resource File.
Results
The number of communities with comprehensive system strength has remained relatively consistent across time in urban communities but has fluctuated slightly in rural communities (Figure 1). In both the urban and rural communities in our sample, the majority did not have a comprehensive classification. In 2014 and 2016, 40% of urban communities were identified as having comprehensive system structure. This proportion dipped slightly in 2018 to 39%. Compared with their urban counterparts, rural communities had fewer communities classified as comprehensive, with the proportion ranging from 28% to 26% between 2014 and 2018. Although the average number of communities in our sample with comprehensive capital remained relatively stable, changes in the local public health system showed that only 58% of communities maintained their system capital classification over time. The remaining 42% moved through capital categories in the years observed.
Figure 1.
Proportion of Communities With Comprehensive Public Health System Structure by Urban and Rural Geographic Location, 2014‐2018
Our results suggest that comprehensive communities implement the 20 population health capabilities at a higher rate, regardless of their geographic location (Table 1). Those systems with a comprehensive classification implemented the investigation of adverse health events 100% of the time in urban and rural communities. In regard to capabilities, the difference in provision between comprehensive and noncomprehensive public health systems ranged from little to a great deal. Both urban and rural comprehensive communities allocated resources based on their community health plan at a high rate, compared with 16.4% and 15.7% of their noncomprehensive counterparts. We found similar differences in our analysis of data on the use of preventive services, the evaluation of health programs and services in the community, the development of policies to address community health priorities, and the linkage of individuals to health and social services.
Although on average, both urban and rural comprehensive communities implemented their capabilities at a similar rate, we should note that rural communities provided nine capabilities at a slightly higher rate than urban communities did. Comprehensive rural communities allocated resources based on their community health plan 80.2% of the time, compared with 76.4% of urban communities. Conversely, urban communities monitored and improved their implementation of health programs and policies at a rate almost 11% higher than rural communities did.
Results from the linear probability model indicate that certain population health capabilities are associated with the probability of a community having comprehensive system strength. Table 2 shows the results for the pooled model and the stratified urban/rural models. In the pooled model, a total of nine capabilities are significantly associated with the probability of a community having a comprehensive classification. The magnitude at which they distinguished between being comprehensive or not varied greatly across the nine capabilities. The allocation of resources based on a community's health plan had the largest impact, with the implementation of that capability being associated with a 21 percentage point increase in the probability of a community being comprehensive (p < 0.01). Surveying the community for behavioral risk factors, analyzing data on the use of preventive services, and engaging community stakeholders in health improvement planning were also found to distinguish communities that had comprehensive system strength and those that did not (p < 0.01).
Table 2.
Results From Fixed Effects Linear Probability Models Examining Which Population Health Capabilities Discriminate Between a Community's Being Comprehensive or Not, 2014‐2018
| Pooled Urban & Rural (n = 1,490) | Urban (n = 1,026) | Rural (n = 464) | |
|---|---|---|---|
| Assessment | |||
| Conduct periodic assessment of community health status and needs | −0.023 | −0.010 | −0.058 |
| Survey community for behavioral risk factors | 0.178 a | 0.173 a | 0.196 a |
| Investigate adverse health events, outbreaks, and hazards | −0.064 | 0.017 | −0.130 |
| Conduct laboratory testing to identify health hazards and risks | 0.050 | 0.123 | −0.048 |
| Analyze data on community health status and health determinants | 0.102 a | 0.096 b | 0.120 b |
| Analyze data on preventive services use | 0.157 a | 0.142 a | 0.207 a |
| Policy and Planning | |||
| Routinely provide community health information to elected officials | 0.063 b | 0.071 | 0.083 |
| Routinely provide community health information to the public | 0.017 | 0.023 | −0.010 |
| Routinely provide community health information to the media | −0.000 | 0.008 | −0.011 |
| Prioritize community health needs | 0.021 | 0.033 | 0.034 |
| Engage community stakeholders in health improvement planning | 0.155 a | 0.155 a | 0.126 b |
| Develop a communitywide health improvement plan | −0.048 | −0.058 | −0.046 |
| Allocate resources based on community health plan | 0.213 a | 0.246 a | 0.116 b |
| Develop policies to address priorities in community health plan | 0.139 a | 0.115 a | 0.238 a |
| Maintain a communication network among health‐related organizations | 0.012 | 0.019 | −0.037 |
| Assurance and Evaluation | |||
| Link people to needed health and social services | 0.113 a | 0.142 a | 0.045 |
| Implement legally mandated public health activities | 0.064 | 0.000 | 0.178 b |
| Evaluate health programs and services in the community | 0.102 | 0.107 a | 0.123 b |
| Evaluate public health agency capacity and performance | 0.034 | 0.037 | 0.021 |
| Monitor and improve implementation of health programs and policies | 0.081 a | 0.090 b | 0.067 |
p < 0.01.
p < 0.05.
All models control for community socioeconomic, demographic, and health care supply characteristics. See online technical appendix Tables A1, A2, and A3 for full models.
Stratifying by urban and rural geographic location produced variations in the linear probability model results. In the urban model, we found nine capabilities associated with the probability of a community being comprehensive. Effect sizes were similar to the pooled model, with the allocation of resources based on a community's health plan having the largest impact (p < 0.01). The rural model identified eight capabilities associated with comprehensive system capital, with the variation in effect size compared with that of the pooled and urban models. The development of policies to address priorities in a community's health plan had the greatest association with a rural community being comprehensive (p < 0.01), with implementation being associated with an almost 24 percentage point increase in likelihood compared with 11 percentage points in urban communities. In all three models, the distribution of capabilities that distinguish between being comprehensive or not was almost even among assessment, assurance, and policy development.
Discussion
Our results identified those population health capabilities most strongly associated with a community's likelihood of reaching the comprehensive level of public health system strength. These capabilities appear to be of particularly high value in helping communities achieve meaningful improvements in their public health system. Given the relatively small proportion of communities that are currently designated as comprehensive systems, it is important to identify feasible pathways for many more communities to achieve this level of capability. Public health systems seeking to move toward comprehensive classification as a mechanism to strengthen their infrastructure and improve their health outcomes would benefit from being able to prioritize the implementation of activities that distinguish system types. For example, a noncomprehensive urban community allocates resources based on its health plan 16.4% of the time. This capability was shown to have the greatest distinguishing effect on the probability of an urban community being comprehensive. Working to implement this activity may be the most valuable factor in moving the system along a pathway that will ultimately strengthen the public health system.
Results for the rural communities in our sample also identified a set of value‐added capabilities, although the findings indicate that a set different from those for urban communities had the greatest association with rural communities’ comprehensive system strength. This finding is consistent with previous research suggesting that rural population health systems are different from their urban counterparts. 23 Our findings also indicate, however, that a rural community's having comprehensive system strength helps equalize the delivery of population health capabilities when compared with their urban counterparts. On average, comprehensive rural and urban communities deliver capabilities at a similar rate, with some activities being carried out more often in rural communities. Greater breadth in the diversity of community partners that share in the delivery of population health capabilities may be one explanation for this finding. Rural communities that work to build cross‐sector partnerships may find that the implementation of population health capabilities increases as more resources are brought to the table and the duplication of efforts is reduced.
Although the value‐added capabilities we found were evenly distributed among the assessment, assurance, and policy development functions of public health, our results suggest capabilities addressing community‐level needs and risk factors may be particularly important to both urban and rural communities—specifically, undertaking activities that not only use data to examine community health status and determinants but also deploy resources in alignment with priorities, focus on policy development, and ensure that individuals are linked with needed health and social services. Comprehensive systems are more likely to undertake activities that both assess and address community needs, suggesting that value is added and systems are strengthened when implementing a set of capabilities that cut across the public health functions. Additional studies using mixed methods to examine more fully how a community uses these capabilities would provide further insight into the reasons these capabilities may be more important than others. Qualitative interviews with local and state health officials would be particularly helpful in uncovering more detailed information about how a community operationalizes its capabilities, how they evolve over time, and their relationship to the system's strength.
While our results suggest that the overall portion of urban and rural communities with comprehensive status has remained relatively stable over time, we also found heterogeneity at the local public health system level in how communities move through system capital classifications. Public health systems can change their status during the two‐year time period between panels, suggesting that a system's strength can be changed in a relatively short time. This in turn leads to several questions about how public health infrastructure changes over time in relation to the capabilities. For example, should communities take particular pathways when moving toward or away from comprehensive capital? Do the most valuable capabilities act as independent drivers of changes in the infrastructure, or are they interdependent? Although these questions are outside the scope of our study, future research examining these questions with additional waves of NALSYS data would lead to a better understanding of how public health systems evolve and whether more stable patterns of system capital classification would result.
Using the results of our study to identify priority capabilities for implementation in noncomprehensive urban and rural communities is only a first step toward strengthening public health systems. Both financial and human resources are key to the successful delivery of population health capabilities. 30 , 31 The narrow scope of funding available to governmental public health agencies and the constraints placed on practice based on categorical funding requirements often limit where funds can be targeted. Although we did not account for public health funding in our analysis because of limitations with the available expenditure data, better‐resourced systems may find implementing capabilities easier. Specifically, those systems that receive a greater portion of their revenue from local taxes may have more discretion in where those funds are spent, thereby implementing capabilities linked to comprehensive structure. 31 , 32 Policy efforts to diversify funding for public health would help increase flexibility and expand the scope of capabilities that can be provided. Communities might also consider deploying hospital community benefit dollars or insurers’ targeted investments to support additional capabilities for population health.
Making sure the burden of implementing capabilities does not fall solely on local public health and social services agencies, which are often already working with limited resources, will be important, particularly in rural communities that typically have fewer partners available for collaboration. We did not examine patterns of engagement to determine whether capabilities are delivered in a concentrated set of organizations or are more diffused across the network. Additional research examining patterns of cross‐sector delivery of the capabilities in comprehensive communities may provide important insights for communities wanting to improve their system's infrastructure. Information on how community organizations participate in capabilities would provide insights into the way resources flow across partners. The extent to which cross‐sector engagement complements or substitutes for efforts by local public health agencies regarding capabilities, particularly in under‐resourced public health systems, is helpful to know, as is knowing which organizations to target and what capabilities can help communities target engagement more effectively.
Further consideration of additional mechanisms that help bolster funding and encourage the cross‐sector delivery of health and social services is essential to carrying out the value‐added population health capabilities we identified in this article. Public health department accreditation may be one important mechanism for increasing system strength. Prior research using descriptive methods linked accreditation status to improved system capital over time, although more research is needed to fully understand the relationship between accreditation and a system's strength. 33 Nonetheless, the alignment between NALSYS capabilities and PHAB accreditation standards may enable capability implementation. Our results also found a positive and significant association (p < 0.05) between a state health agency's engagement in the delivery of capabilities and the probability of a comprehensive local public health system in the pooled and urban models. This suggests a relationship between a state's support of the capabilities and the strength of a local system. Examining the degree to which state health departments fund and support NALSYS capabilities and how this might impact local effectiveness would also be an important line of future inquiry.
Our study has several limitations. Data on the implementation of the 20 NALSYS activities are reported by local public health officials and so may not represent the perspective of all partners in the network. It is likely, however, that this results in an underreporting of activities being provided in the community. Further studies that integrate a whole network perspective would help produce a more complete picture of health and social services delivery. Information about the capabilities is a dichotomous measure of delivery in our analysis, and variability in how activities are delivered in a community likely exists. Developing additional survey instruments that capture more granular information on the delivery of population health capabilities would aid in the assessment of their relative value to communities. While our models control for a number of community characteristics that influence the delivery of population health activities, unobserved factors may confound our results.
COVID‐19 has underscored the need to improve public health infrastructure. Public health systems are the cornerstones of the effort to protect communities against emergent and persistent threats to population health. Our findings indicate that the structure of public health systems can be strengthened through the targeted implementation of certain population health capabilities and can be tailored to geographic location. These capabilities were first described in the Institute of Medicine's landmark 1988 Future of Public Health report as critical to the protection of population health. Thirty‐three years later, fewer than half of communities have achieved comprehensive public health system strength. 8 A strong and effective public health system is essential, and a substantial number of US communities could improve their systems using the capabilities identified in this article. Targeted initiatives that increase the provision of core population capabilities through policy development and the deployment of resources may improve health outcomes and ultimately strengthen the public health system as a whole.
Funding/Support: This research was supported by funding from the Robert Wood Johnson Foundation through the Systems for Action National Program Office, ID 75708, and funding from the US Centers for Disease Control and Prevention (Contract # 75D30118C03568 00001).
Conflict of Interest Disclosures: All authors completed the ICMJE Form for Disclosure of Potential Conflicts of Interest. No conflicts were reported.
Supporting information
Table A1. Results from fixed effects linear probability models examining which population health capabilities discriminate between a community being comprehensive or not, pooled sample, 2014‐2018
Table A2. Results from fixed effects linear probability model examining which population health capabilities discriminate between a community being comprehensive or not in urban areas, 2014‐2018
Table A3. Results from fixed effects linear probability model examining which population health capabilities discriminate between a community being comprehensive or not in rural areas, 2014‐2018
References
- 1. Maani N, Galea S. COVID‐19 and underinvestment in the public health infrastructure of the United States. Milbank Q. 2020;98(2):250‐259. 10.1111/1468-0009.12463. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Trust for America's Health . The Impact of Chronic Underfunding on America’s Public Health System: Trends, Risks, and Recommendations, 2020. Washington, DC: Trust for America's Health; 2020. [Google Scholar]
- 3. Krisberg K. Already‐strained US public health workforce grapples with COVID‐19: despite resource gaps, workers fight on. Nation's Health. 2020;50(4):1‐12. [Google Scholar]
- 4. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45‐52. 10.1016/S0140-6736(14)60648-6. [DOI] [PubMed] [Google Scholar]
- 5. Song S, Trisolini MG, LaBresh KA, Smith SC Jr, Jin Y, Zheng Z‐J. Factors associated with county‐level variation in premature mortality due to noncommunicable chronic disease in the United States, 1999–2017. JAMA Netw Open. 2020;3(2):e200241. 10.1001/jamanetworkopen.2020.0241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Woolf SH, Schoomaker H. Life expectancy and mortality rates in the United States, 1959–2017. JAMA. 2019;322(20):1996‐2016. 10.1001/jama.2019.16932. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Diez Roux AV. Population health in the time of COVID‐19: confirmations and revelations. Milbank Q. 2020;98(3):629‐640. 10.1111/1468-0009.12474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Institute of Medicine Committee for the Study of the Future of Public Health . The Future of Public Health. Washington, DC: National Academies Press; 1988. [Google Scholar]
- 9. Institute of Medicine Committee on Assuring the Health of the Public in the 21st Century . The Future of the Public's Health in the 21st Century. Washington, DC: National Academies Press; 2002. [Google Scholar]
- 10. Institute of Medicine . For the Public's Health: Investing in a Healthier Future. Washington, DC: National Academies Press; 2012. [PubMed] [Google Scholar]
- 11. Public Health Accreditation Board . Public health accreditation board standards and measures, version 1.5. https://phaboard.org/wp‐content/uploads/PHABSM_WEB_LR1‐1.pdf. Accessed September 8, 2020.
- 12. DeSalvo KB, O'Carroll PW, Koo D, Auerbach JM, Monroe JA. Public health 3.0: time for an upgrade. Am J Public Health. 2016;106(4):621‐622. 10.2105/AJPH.2016.303063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Public Health Leadership Forum . Defining and Constituting Foundational “Capabilities” and “Areas,” Version 1. Washington, DC: RESOLVE; 2014. [Google Scholar]
- 14. Mays GP, Scutchfield FD, Bhandari MW, Smith SA. Understanding the organization of public health delivery systems: an empirical typology. Milbank Q. 2010;88(1):81‐111. 10.1111/j.1468-0009.2010.00590.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Mays GP, Mamaril CB, Timsina LR. Preventable death rates fell where communities expanded population health activities through multisector networks. Health Aff (Millwood). 2016;35(11):2005‐2013. 10.1377/hlthaff.2016.0848. [DOI] [PubMed] [Google Scholar]
- 16. Brewster AL, Wilson TL, Frehn J, Berish D, Kunkel SR. Linking health and social services through area agencies on aging is associated with lower health care use and spending. Health Aff (Millwood). 2020;39(4):587‐594. 10.1377/hlthaff.2019.01515. [DOI] [PubMed] [Google Scholar]
- 17. Howell EA, Balbierz A, Beane S, et al. Timely postpartum visits for low‐income women: a health system and Medicaid payer partnership. Am J Public Health. 2020;110(S2):S215‐S218. 10.2105/AJPH.2020.305689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Bovell‐Ammon A, Mansilla C, Poblacion A, et al. Housing intervention for medically complex families associated with improved family health: pilot randomized trial. Health Aff (Millwood). 2020;39(4):613‐621. 10.1377/hlthaff.2019.01569. [DOI] [PubMed] [Google Scholar]
- 19. McConnell KJ, Kaufman MR, Grunditz JI, et al. Project Nurture integrates care and services to improve outcomes for opioid‐dependent mothers and their children. Health Aff (Millwood). 2020;39(4):595‐602. 10.1377/hlthaff.2019.01574. [DOI] [PubMed]
- 20. Hamer MK, Mays GP. Public health systems and social services: breadth and depth of cross‐sector collaboration. Am J Public Health. 2020;110(S2):S232‐S234. 10.2105/AJPH.2020.305694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Hogg RA, Mays GP, Mamaril CB. Hospital contributions to the delivery of public health activities in US metropolitan areas: national and longitudinal trends. Am J Public Health. 2015;105(8):1646‐1652. 10.2105/AJPH.2015.302563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Mays GP, Hogg RA. Economic shocks and public health protections in US metropolitan areas. Am J Public Health. 2015;105(Suppl.2):S280‐S287. 10.2105/AJPH.2014.302456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Owsley KM, Hamer MK, Mays GP. The growing divide in the composition of public health delivery systems in US rural and urban communities, 2014–2018. Am J Public Health. 2020;110(S2):S204‐S210. 10.2105/AJPH.2020.305801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Miller CA, Moore KS, Richards TB, Monk JD. A proposed method for assessing the performance of local public health functions and practices. Am J Public Health. 1994;84(11):1743‐1749. 10.2105/ajph.84.11.1743. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Miller CA, Richards TB, Davis SM, et al. Validation of a screening survey to assess local public health performance. J Public Health Manag Pract. 1995;1(1):63‐71. 10.1097/00124784-199500110-00012. [DOI] [PubMed] [Google Scholar]
- 26. Turnock BJ, Handler A. Evaluating the performance of local health agencies. 2. The 10 public health practices vs the 10 public health services: a clarification. Am J Public Health. 1995;85(9):1295‐1296. Author's reply 1296–1297. 10.2105/ajph.85.9.1295-a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Turnock BJ, Handler A, Hall W, Potsic S, Nalluri R, Vaughn EH. Local health department effectiveness in addressing the core functions of public health. Public Health Rep. 1994;109(5):653‐658. [PMC free article] [PubMed] [Google Scholar]
- 28. Turnock BJ, Handler AS. From measuring to improving public health practice. Annu Rev Public Health. 1997;18:261‐282. 10.1146/annurev.publhealth.18.1.261. [DOI] [PubMed] [Google Scholar]
- 29. US Department of Agriculture (USDA), Economic Research Service . 2010 rural‐urban commuting area (RUCA) codes. https://www.ers.usda.gov/data‐products/rural‐urban‐commuting‐area‐codes/documentation/. Accessed September 8, 2020. [Google Scholar]
- 30. Mamaril CBC, Mays GP, Branham DK, Bekemeier B, Marlowe J, Timsina L. Estimating the cost of providing foundational public health services. Health Serv Res. 2018;53(Suppl.1):2803‐2820. 10.1111/1475-6773.12816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Mays GP, McHugh MC, Shim K, et al. Institutional and economic determinants of public health system performance. Am J Public Health. 2006;96(3):523‐531. 10.2105/AJPH.2005.064253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Mays GP, Smith SA. Geographic variation in public health spending: correlates and consequences. Health Serv Res. 2009;44(5 P2):1796‐1817. 10.1111/j.1475-6773.2009.01014.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Ingram RC, Mays GP, Kussainov N. Changes in local public health system performance before and after attainment of national accreditation standards. J Public Health Manag Pract. 2018;24(Suppl.3):S25‐S34. 10.1097/PHH.0000000000000766. [DOI] [PubMed] [Google Scholar]
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Supplementary Materials
Table A1. Results from fixed effects linear probability models examining which population health capabilities discriminate between a community being comprehensive or not, pooled sample, 2014‐2018
Table A2. Results from fixed effects linear probability model examining which population health capabilities discriminate between a community being comprehensive or not in urban areas, 2014‐2018
Table A3. Results from fixed effects linear probability model examining which population health capabilities discriminate between a community being comprehensive or not in rural areas, 2014‐2018