Relationships Among Providing Maternal, Child, and Adolescent Health Services; Implementing Various Financial Strategy Responses; and Performance of Local Health Departments

Abstract

Objectives. We explored the relationships between local health department (LHD) structure, capacity, and macro-context variables and performance of essential public health services (EPHS).

Methods. In 2012, we assessed a stratified, random sample of 195 LHDs that provided data via an online survey regarding performance of EPHS, the services provided or contracted out, the financial strategies used in response to budgetary pressures, and the extent of collaborations. We performed weighted analyses that included analysis of variance, pairwise correlations by jurisdiction population size, and linear regressions.

Results. On average, LHDs provided approximately 13 (36%) of 35 possible services either directly or by contract. Rather than cut services or externally consolidating, LHDs took steps to generate more revenue and maximize capacity. Higher LHD performance of EPHS was significantly associated with delivering more services, initiating more financial strategies, and engaging in collaboration, after adjusting for the effects of the Affordable Care Act and jurisdiction size.

Conclusions. During changing economic and health care environments, we found that strong structural capacity enhanced local health department EPHS performance for maternal, child, and adolescent health.

The recent recession and changing health care environment both present local health departments (LHDs) with opportunities and challenges. The challenges stem from reduced state payments for services, the concurrent diminished tax base for county and state general revenue, and a potential change in the LHD role because of system realignments in a health care reform environment. At the same time, a variety of opportunities exist for LHDs to address these challenges, including changing their delivery of services (e.g., eliminating or contracting), enacting a variety of financial responses to cutbacks, and collaborating with partners to ensure delivery of services or program activity. All of these changes might affect performance of the 10 essential public health services (EPHS). Little is known about the relationships among the delivery of maternal, child, and adolescent health (MCAH) programs and services, the financial strategies used by LHDs to respond to external forces and LHD collaborations, and the performance of the EPHS. We sought to understand the correlates of LHD performance in a climate of economic instability and a changing health care environment, with a particular focus on service delivery, financial strategies, and collaborations to provide information important for organizational strategic decision-making by LHDs.

BACKGROUND

Data from the National Association of City and County Health Officials’ (NACCHO) 2010 National Profile of Local Health Departments¹ revealed that most LHDs provide multiple MCAH services. Of the LHDs that responded to the 2010 survey, 64% provided Women’s, Infant and Children’s Nutritional Supplement (WIC) services, 61% provided MCAH home visits, 55% provided family planning services, 40% provided early periodic screening, diagnosis, and treatment, and 36% hosted a well-child clinic.¹ Each of these services was viewed as part of a well-established, evidence-based approach to eliminating disparities in reproductive, perinatal, and child health outcomes.^2,3 However, in that same year, the NACCHO Job Loss Survey of 2010 found that 18% of LHDs had actually made cuts in MCAH services, making MCAH programs or services 1 of the most frequently cut programs; this trend continued into 2013.⁴

Although previous research looked at factors that drove LHD decisions to cut services and examined how budget cuts were managed,⁵ it was unclear whether eliminating services was the only strategy used by LHDs in response to the economic downturn. Other possible responses, all of which had financial implications, included generating revenue, increasing efficiencies, or changing the configuration of services. To date, financial research conducted with respect to LHDs focused on the monetary aspects, such as expenditures per population or revenues generated by the LHD.^6,7 The exclusive attention on costs and expenditures per population provided an incomplete picture of the LHD experience because it neglected the consideration of organizational strategic choices made by LHD administrations. In addition, it was also unclear whether any of the service and financial decisions made by LHDs, particularly in response to external environmental stressors, affected LHD performance of the EPHS.

Generally, LHDs are known to collaborate with their traditional public health partners (e.g., the state health department), but also with various multisector partners (e.g., schools) to improve programming and population health outcomes. Such collaborations are assumed to be important in the overall LHD performance, although that relationship remains understudied.

Over the last 2 decades, the National Public Health Performance Standards Program has supported LHDs in measuring their performance based on the 10 EPHS.⁸ As such, the EPHS have increasingly been used by LHDs and researchers to study the performance of LHDs.⁹ However, few studies have looked at the performance of the EPHS relative to a specific population, such as the MCAH population, or in relationship to providing specific services and possible correlates of EPHS performance.¹⁰

Conceptual frameworks developed to guide public health systems research^11–13 view performance of EPHS as a set of processes that can be influenced indirectly by factors in the macro-context, including both the changing health care environment (i.e., implementation of the Affordable Care Act [ACA]) and the changing economic environment (i.e., the economic downturn). These frameworks consider performance that is also influenced by structural or organizational capacity factors, such as financial management strategies, interagency collaboration, and provision of services. In this study, we explored the relationship of a small set of structure and capacity variables (provision of MCAH programs or services, extent of LHD adoption of a variety of financial management strategies, and the extent of collaborations with other organizations) and 1 macro-context variable (perceived effect of the ACA), and the performance of EPHS for the MCAH population during the changing economic environment. By looking at those discrete relationships, we contributed to the potential refinement of conceptual models of public health systems, and we might be able to inform strategic decision-making by LHDs with respect to current service delivery and future opportunities.

METHODS

To obtain detailed information about LHD experiences with respect to MCAH in a changing economic and health care environment, we selected a random sample of all LHDs stratified by jurisdiction size within the strata. Of the 546 LHDs we invited to participate, there was a response rate of 49% (n = 269) for part 1 of the survey and 48% (n = 295) for part 2. For the part 1 survey, the respondents were the LHD staff members who were most knowledgeable about MCAH activities, typically the MCAH director, or the LHD’s top agency executive. For the part 2 survey, the respondents were the LHD administrators. We merged the part 1 and part 2 surveys, for a combined 195 respondents (35% response rate). Compared with the 2013 NACCHO Profile data,¹⁴ our sample had a slightly higher percentage of LHDs in large jurisdictions (11.9% vs 6.4%) and a lower percentage of LHDs in small jurisdictions (44.4% vs 57.5%), although the distribution across the 3 types of governance structures (state, local, and mixed) was very similar. The oversampling of larger LHDs followed the sampling approach used by NACCHO when conducting national surveys. To correct for the oversampling and a higher portion of large LHDs responding, we used sample estimation weights for all descriptive analyses.

Data Collection Procedure

Questionnaire development included feedback from LHD focus groups regarding readability and item relevance, particularly for the financial strategies variable, and a small online pilot to assess flow in Qualtrics API version 2.2 (Qualtrics, Provo, UT). We sent the final questionnaire to the potential respondents in spring 2012. NACCHO staff contacted each LHD to verify name and e-mail address of the potential respondent.

We made several attempts to encourage completion of the questionnaire: reminder emails, follow-up telephone calls, and entry into a contest to receive a free book on MCAH disparities.

Study Variables

Dependent variable.

For this study, we developed 41 items were to assess MCAH performance across the 10 EPHS. The items draw upon the Public Health Accreditation Board (PHAB) standards as well as the Capacity Assessment for State Title V (CAST-5). CAST-5, which was developed to specifically measure MCAH services, is intended to be used by state MCAH programs, and functions as a parallel version to the standards developed by the PHAB for measuring the 10 EPHS.^15,16 Each essential service domain includes 3 to 6 items, and references the MCAH population respondents, as indicated on a Likert scale, the extent to which the EPHS activity was carried out by the LHD (1 = do none at all, 2 = a little, 3 = some, 4 = do a great deal). We calculated a scale score for the performance of each EPHS domain, and standardized it to maintain the 4-point Likert scale. The Cronbach’s α reliability for the overall performance scale was 0.98, and from 0.85 to 0.95 for each domain.

Independent variable: services.

We generated a list of 35 services from those in the NACCHO Profile survey, the Association of State and Territorial Health Officials Profile of State Public Health survey, and in consultation with practitioners, encompassing all possible public health MCAH services. For each service, respondents indicated whether the LHD provided that service either directly or through contract. We generated a total sum number of services provided or contracted out by the LHD. We created a 6-level categorical variable to address the non-normal distribution of the sum of services, which was used for the analysis; in regression analyses, the category reflecting the fewest number of services was used as the reference for the 5 subsequent dummy variables created.

Independent variable: financial strategies.

We considered 7 financial strategies that might be used to address budgetary pressures: increase revenue, maximize capacity, decrease expenses, make innovative change, internally consolidate, externally consolidate, and eliminate services. For each of the 7 possible financial strategies, respondents indicated the extent to which that strategy was used between January 1, 2010, and December 31, 2011, on a Likert scale (1 = no steps, 2 = few, 3 = moderate, 4 = numerous steps). The sum of each of the 7 strategy items included examples of the strategy to increase face validity. The sum across the 7 strategies yielded a higher score if more steps were taken across more strategies. The α reliability for the 7-item scale, the financial strategies activity score, was 0.83.

Independent variable: collaboration.

We defined collaboration with other organizations by the LHD as shared labor, a shared purpose or goal, and varying levels of joint ownership of the work, risks, results, and rewards. Respondents indicated whether each of 21 agencies and providers existed in the jurisdiction and noted those with which they or their MCAH staff collaborated. A sum of the number of community agencies and providers with whom staff currently collaborated was generated. In addition, a collaboration percentage was created by dividing the number of collaborations by the number of existing entities. The use of a collaboration percentage, rather than a simple count, took into account the availability of existing entities in the jurisdiction. Finally, we created a 6-level categorical variable that detailed percent collaboration rather than create a log-transformed variable to account for highly skewed data; this allowed us to analyze levels of collaboration as 5 dummy variables in the regression models.

Macro-context variable: effects of the Affordable Care Act.

The ACA as a macro-context factor likely influenced the LHDs' choice of provision of services, financial strategies used, or extent of collaborations within the community. We therefore asked respondents to indicate the extent to which each of 13 possible effects of the ACA was experienced by the LHD. The ACA Scale captured the perceived effects of the ACA as of spring 2012, and we used a Likert scale (1 = none, 2 = slight, 3 = somewhat, 4 = good deal, 5 = greatly) for the assessment. Six items that specifically mentioned an effect on MCAH programs or services had an α reliability of 0.78. Another 7 items referenced the effects of the ACA on LHDs overall, with an α reliability of 0.85.

Analysis

Data cleaning and analyses were conducted using Stata (version 12.1; StataCorp, College Station, TX). We created sample weights to account for oversampling of LHDs in larger jurisdictions; these were calculated based on a single-stage stratified sampling method without replacement that used the distribution of LHDs across the 7 jurisdiction sizes. Analyses used the merged dataset consisting of the part 1 and 2 surveys and the corresponding merged sample weight.

Initial analyses examined each of the variables by jurisdiction size using analysis of variance (Table 1). The next analyses investigated differences in EPHS scores (analysis of variance or pairwise correlations) for each category of the independent variables (Table 2). The mean overall EPHS scale score for each level of each variable is presented. In addition, we presented the name (e.g., inform) of the highest and lowest EPHS subscale associated with each level of all the examined variables.

TABLE 1—

Relationship of Local Health Department Jurisdiction Size to Dependent Variables, Independent Variables, and Covariates: United States, 2012

	LHD Jurisdiction Size
Variable	< 50 000 (n = 93), Mean (SD)	50 000–499 999 (n = 76), Mean (SD)	≥ 500 000 (n = 23), Mean (SD)	Full Sample (n = 192), Mean (SD)	α
Total no. MCAH services	12.4 (7.9)	14.1 (7.8)	12.3 (9.8)	13.0 (7.8)	NA
Categorical services
0–3	1.5 (1.1)	2.0 (0.79)	1.0 (1.15)	1.7 (1.05)	NA
4–9	6.1 (1.91)	6.3 (1.48)	5.4 (1.51)	6.0 (1.71)	NA
10–12	11.4 (0.50)	11.6 (0.53)	11.7 (0.58)	11.5 (0.51)	NA
13–16	14.5 (1.31)	14.9 (1.10)	16.0 (0.0)	14.8 (1.20)	NA
17–21	18.6 (1.56)	17.9 (0.99)	18.0 (1.41)	18.3 (1.40)	NA
22–31	25.4 (2.40)	25.1 (2.09)	26.4 (3.44)	25.4 (2.38)	NA
Financial strategiesa,***	1.77 (0.62)	2.12 (0.63)	2.31 (0.73)	1.91 (0.65)	0.83
Revenue increase***	2.04 (1.09)	2.69 (1.04)	2.76 (0.87)	2.28 (1.11)	NA
Maximize capacity*	2.11 (1.11)	2.61 (1.08)	2.61 (1.18)	2.29 (1.13)	NA
Decrease expense**	2.03 (1.11)	2.55 (1.22)	2.78 (1.28)	2.24 (1.18)	NA
Innovative change***	1.30 (0.75)	1.79 (1.09)	2.17 (1.31)	1.50 (0.94)	NA
External consolidation	1.41 (0.80)	1.40 (0.75)	1.32 (0.88)	1.40 (0.79)	NA
Internal consolidation*	1.44 (0.87)	1.79 (1.08)	2.06 (1.21)	1.58 (0.97)	NA
Eliminate services	1.32 (0.74)	1.62 (0.86)	1.57 (1.04)	1.43 (0.81)	NA
Collaboration variables
Mean no. of collaborationsa,***	11.06 (5.04)	13.30 (4.97)	16.22 (3.71)	12.05 (5.14)	NA
Collaboration %	0.63 (0.28)	0.68 (0.25)	0.80 (0.19)	0.66 (0.27)	NA
Collaboration % categorya,*	3.33 (1.51)	3.55 (1.60)	4.58 (1.39)	3.46 (1.55)	NA
0–40	2.52 (3.36)	3.2 (2.83)	0.0 (0.0)	2.67 (3.13)	NA
41–60	11.17 (0.81)	10.87 (0.92)	11.0 (0.0)	11.07 (0.84)	NA
61–74	13.35 (0.49)	13.67 (0.49)	13.60 (0.55)	14.48 (0.51)	NA
75–84	15.61 (0.50)	15.47 (0.51)	15.75 (0.50)	15.56 (0.50)	NA
85–89	18.00 (0.0)	17.31 (0.48)	17.43 (0.53)	17.42 (0.50)	NA
90–100	20.00 (0.0)	19.50 (0.55)	19.80 (0.45)	19.67 (0.49)
ACA scalea,**	1.47 (0.82)	1.75 (0.87)	2.10 (0.58)	1.66 (0.84)	0.88
Perceived effects of ACA on LHD***	1.62 (0.89)	2.08 (1.03)	2.48 (0.66)	1.82 (0.96)	0.85
Perceived effects of ACA on MCAH	1.28 (0.87)	1.32 (0.86)	1.76 (0.64)	1.33 (0.86)	0.75
MCAH EPHSa,***	2.15 (0.70)	2.57 (0.69)	3.0 (0.51)	2.34 (0.74)	0.98
Inform***	2.50 (0.89)	2.82 (0.78)	3.27 (0.66)	2.64 (0.87)	0.88
Workforce***	2.37 (0.91)	2.89 (0.87)	3.38 (0.67)	2.59 (0.94)	0.89
Assure access***	2.39 (0.99)	2.75 (0.88)	3.28 (0.74)	2.55 (0.98)	0.92
Diagnose***	2.19 (0.87)	2.63 (0.92)	3.16 (0.50)	2.38 (0.91)	0.94
Mobilize***	2.18 (0.85)	2.54 (0.92)	3.35 (0.61)	2.36 (0.90)	0.95
Monitor***	2.09 (0.73)	2.49 (0.73)	3.00 (0.56)	2.26 (0.76)	0.85
Evidence or research***	2.03 (0.89)	2.36 (0.94)	3.02 (0.89)	2.19 (0.94)	0.92
Develop policy***	1.94 (0.80)	2.42 (0.85)	3.04 (0.69)	2.16 (0.87)	0.93
Enforce***	1.62 (0.76)	2.10 (0.88)	2.36 (1.14)	1.82 (0.86)	0.90
Evaluate***	2.18 (0.93)	2.73 (0.96)	2.95 (0.60)	2.40 (0.96)	0.93

Note. ACA = Affordable Care Act; EPHS = essential public health services; LHD = local health departments; MCAH = mother, child, and adolescent health; NA = not applicable.

^aAnalysis of variance.

*P < .05; **P < .01; ***P < .001.

TABLE 2—

Description of Independent Variables and Covariates by the Essential Public Health Services Total Scale and Subscales: United States, 2012

		EPHS Scale Score		Highest EPHS Subscale			Lowest EPHS Subscale
Variable	No.	Mean (SD)	Correlation	Subscale	Mean	Correlation	Subscale	Mean	Correlation
No. of MCAH servicesa,*	191
0–3	31	1.91 (0.73)		Inform	2.22		Evidence	1.75
4–9	32	2.40 (0.65)		Inform	2.70		Enforce	1.69
10–12	30	2.36 (0.76)		Inform	2.68		Enforce	1.86
13–16	28	2.22 (0.73)		Workforce	2.63		Enforce	1.73
17–21	39	2.52 (0.68)		Workforce	2.84		Enforce	1.87
22–31	31	2.61 (0.73)		Inform	3.14		Enforce	2.12
Financial strategiesb
Revenue increase	190		0.24**	Workforce		0.24***	Enforce		0.08
Maximize capacity	188		0.23**	Monitor		0.23***	Evidence		0.14
Decrease expense	190		0.30***	Evaluation		0.34***	Enforce		0.17
Innovative change	190		0.27***	Evaluation		0.26***	Develop policy		0.18
External consolidation	190		0.19**	Evidence		0.22**	Mobilize		0.10
Internal consolidation	190		0.18*	Diagnose		0.21**	Inform		0.08
Cut services	189		0.24***	Evaluate		0.27**	Mobilize		0.15*
Collaboration %a,***	192
0–40	23	1.38 (0.57)		Inform	1.68		Enforce	1.17
41–60	28	1.99 (0.63)		Inform	2.19		Enforce	1.64
61–74	46	2.43 (0.58)		Workforce	2.96		Enforce	1.76
75–84	38	2.54 (0.65)		Inform	3.05		Enforce	1.98
85–89	29	2.75 (0.45)		Evaluate	3.00		Enforce	2.11
90–100	28	2.76 (0.68)		Access	3.11		Enforce	2.16
ACA effectsb
Scale score	192		0.32	Monitor***		0.37	Access**		0.21
Perceived effects of ACA on LHD	192		0.33***	Monitor***		0.39	Access		0.19
Perceived effects of ACA on MCAH	191		0.21**	Monitor***		0.24	Workforce		0.11

Note. ACA = Affordable Care Act; EPHS = essential public health services; LHD = local health departments; MCAH = mother, child, and adolescent health.

^aAnalysis of variance.

^bPairwise correlations.

*P < .05; **P < .01; ***P < .001.

In the final analysis, we used linear regression to examine the effects of the independent variables on EPHS performance (Table 3). The main model (model 1) included the 3 independent structure and capacity variables: MCAH services (categorical dummy variables), financial strategies activity score, and collaboration (categorical dummy variables). We then added the effects of the ACA on LHDs variables into model 2. The influence of jurisdiction size was assessed in model 3, using dummy variables for jurisdiction size, because the LHD jurisdiction size is well documented as having an effect on LHD processes and structural capacity.

TABLE 3—

Linear Regression With the Essential Public Health Services Total Scale as Dependent Variable, Using the Weights: United States, 2012

	Model 1		Model 2		Model 3
Variable	b	SE (95% CI)	b	SE (95% CI)	b	SE (95% CI)
Sum of MCAH services categorya
4–9	0.234	0.140 (−0.0.043, 0.510)	0.187	0.150 (−0.110, 0.483)	0.226	0.144 (−0.058, 0.509)
10–12	0.232	0.144 (−0.052, 0.526)	0.167	0.155 (−0.139, 0.474)	0.236	0.148 (−0.057, 0.528)
13–16	0.199	0.146 (−0.090, 0.487)	0.166	0.158 (−0.146, 0.478)	0.192	0.150 (−0.105, 0.489)
17–21	0.340*	0.134 (0.076, 0.604)	0.242	0.143 (−0.041, 0.525)	0.332	0.137 (0.061, 0.603)
22–31	0.362*	0.151 (0.064, 0.659)	0.347*	0.164 (0.022, 0.671)	0.351*	0.156 (0.043, 0.659)
Financial strategies activities score	0.141*	0.067 (0.009, 0.272)	0.223**	0.079 (0.068, 0.379)	0.151*	0.077 (−0.000, 0.302)
Collaboration % categoryb
41–60	0.548***	0.155 (0.242, 0.855)	0.559***	0.170 (0.222, 0.894)	0.547***	0.170 (0.222, 0.867)
61–74	0.968***	0.140 (0.692,1.244)	1.010***	0.153 (0.709, 1.312)	0.974***	0.153 (0.688, 1.261)
75–84	1.080***	0.145 (0.793, 1.367)	1.047***	0.160 (0.731, 1.363)	1.082***	0.160 (0.782, 1.382)
85–89	1.098***	0.163 (0.777, 1.419)	1.213***	0.178 (0.862, 1.564)	1.108***	0.178 (0.772, 1.445)
90–100	1.124***	0.164 (0.801, 1.447)	1.169***	0.179 (0.815, 1.524)	1.128***	0.179 (0.790, 1.466)
ACA scale score: LHD			0.076	0.066 (−0.053, 0.205)	0.016	0.064 (−0.110, 0.142)
ACA scale score: MCAH			−0.087	0.071 (−0.228, 0.053)	−0.035	0.069 (−0.172, 0.102)
Jurisdiction sizec
Mediumd					0.354***	0.095 (0.167, 0.542)
Largee					0.660***	0.188 (0.289, 1.032)
Model statistics
Constant	0.838***	0.174 (0.495, 1.182)	0.833***	0.193 (0.449, 1.212)	0.840***	0.193 (0.449, 1.212)
No.	189		188		188
F	(13, 175) = 13.06***		(13, 174) = 9.54***		(15, 172) = 10.60***
R2	0.492		0.416		0.480
Adjusted R2	0.455		0.373		0.435

Note. ACA = Affordable Care Act; CI = confidence interval; EPHS = essential public health services; LHD = local health departments; MCAH = mother, child, and adolescent health.

^aReference variable is 0–3 services.

^bReference variable is 0%–40%.

^cReference is small (< 50 000 population).

^d50 000–499 999 population.

^e≥ 500 000 population.

*P < .05; **P < .01; ***P < .001.

RESULTS

On average, LHDs delivered, either directly or by contract, a mean of 13.0 (SD = 0.56; 95% confidence interval [CI] = 11.8, 14.1) services, or 36.8% of the 35 services listed in the survey (Table 1). More than 60% of LHDs provided WIC services and breastfeeding education, and more than 50% provided childhood immunizations, pregnancy testing, family planning services, lead poisoning prevention, sexually transmitted infection testing and treatment, and home visiting of infants. Less than 10% of the LHDs provided behavioral health, foster care, and intimate partner violence prevention services (data not shown). The provision of MCAH services varied by LHD size. On average, small LHDs provided 12.4 services or 35.5% (95% CI = 31.8%, 39.2%) of the services listed, whereas medium size LHDs provided 14.1 services or 38.8% (95% CI = 35.6%, 44.1%) of the services. LHDs in large jurisdictions provided 12.3 services, or 37.3% (95% CI = 25.7%, 48.9%) of the 35 services. Nonetheless, the mean number of MCAH services and the categorical services variables did not differ significantly by LHD jurisdiction size (Table 1).

LHDs reported being more likely to take steps to generate revenue and maximize their capacity to provide services compared with engaging in efforts to adopt strategies, such as cutting services or externally consolidating services (Table 1). Regardless of size, LHDs were significantly (P < .001) more likely to focus on revenue generation strategies than to internally consolidate, eliminate services, externally consolidate, or innovate. However, there was some variation in financial strategies used to cope with the economic downturn by LHD size. Small LHDs were more likely to take steps to maximize capacity than to utilize other strategies. Large LHDs were least likely to externally consolidate and most likely to decrease expenditures.

MCAH staff tended to collaborate with health agencies (n = 162; 84%), nonhealth agencies (n = 158; 82%), and government agencies (n = 153; 80%), but were far less likely to collaborate with colleges or universities (n = 100; 52%; data not shown). LHDs in larger jurisdictions were significantly more likely to have a greater number of collaborative relationships (based on the collaboration sum variable), although this relationship was not significant when based on the collaboration percentage variable (Table 1).

Macro-Context Variable: Effects of the Affordable Care Act

Overall, in 2012, LHDs perceived very few effects of the ACA, as reflected in the low scores on the ACA scale. Nonetheless, the perceived effects of the ACA on LHDs did vary significantly by LHD jurisdiction size (Table 1).

In addition, the perceived effects seemed to be greater for LHDs overall than for MCAH focused services.

Essential Public Health Services Performance

Overall, LHDs in larger jurisdictions had higher scores on each of the 10 EPHS subscales than either the small- or medium-sized LHDs (Table 1). Across the sample, LHDs were most likely to report their involvement in informing and educating the public. The highest performance score (mean = 3.38; SD = 0.67) was for assuring a competent workforce among large LHDs, and the lowest performance score (mean = 1.62; SD = 0.76) was for enforcing policies among small LHDs.

The overall EPHS score varied significantly by the number of MCAH services. For a deeper understanding of EPHS performance, we identified the EPHS subscale associated with the highest and lowest scores or correlations for each independent variable or category of variables or covariates (Table 2). The highest subscale score was for assuring a competent workforce among LHDs that provided 17 to 21 MCAH services or programs, whereas the lowest subscale score was for enforcing regulations among LHDs that provided 4 to 9 MCAH services.

On a more nuanced level, we also sought to understand the relationship of financial strategies to performance of each EPHS. Investigating the association of the performance of each financial strategy with each EPHS subscale revealed a complex relationship (Table 2). Each financial strategy had a significant and high correlation with a different EPHS subscale, which suggested that different financial decisions might affect EPHS performance in varying ways. Interestingly, the highest correlation between a financial strategy and the performance of any EPHS was between decreasing expenses and applying evidence to MCAH services (R² = 0.34).

The relationship of collaboration and EPHS seemed less complicated. As the percentage of collaborations increased, so did the overall EPHS score (Table 2). Across each level of collaboration, the EPHS subscale of enforcing polices had the lowest mean scores (Table 2). Interestingly, assuring access to services had the highest mean score; this was found among the LHDs with the highest level of collaboration.

In the regression model with EPHS as the dependent variable, providing a greater number of MCAH services, the financial strategies activity score, and all levels of collaboration were significantly and positively related to EPHS; the overall model was significant (P < .001). Based on model 2, the EPHS score was expected to increase by 0.223 for each unit of increase in the financial strategies score and by 0.347 if the number of services being provided was greater than 17 (Table 3). When these analyses were conducted, adding jurisdiction size, the results remained similar, but only providing 17 to 21 MCAH services remained significant.

DISCUSSION

The findings from our study were among the first to elucidate the relationships among delivery of MCAH services, utilization of financial strategies, engagement in community collaborations, and perceived macro-context influence, with the performance of EPHS by LHDs. First, the study provided new insights about the structure and capacity variables studied. For example, the number of services varied slightly, but they did not vary significantly, by jurisdiction size. The relevance of jurisdiction size to financial strategies and collaboration, however, deserved attention from both researchers and practitioners, particularly in light of the across jurisdictional sharing that was discussed and occurred. Overall, collaboration by MCAH staff and financial strategies used by the LHD varied significantly by LHD jurisdiction size. These results seemed to support that economies of scale, as likely occurs in larger jurisdictions, might be beneficial. We found attention to revenues, capacity, expenses, innovation, and internal consolidation to be stronger among larger LHDs, which suggested a possible cost–benefit threshold for pursuit of marginal gains. In terms of collaboration, our measure appeared to be sensitive to LHD variations, and therefore, it might potentially be useful in future research. As of 2012, the macro-context variable, the perceived effects of the ACA, varied by jurisdiction size, but it was not related to other structure and capacity variables.

The major emphasis of our study was the investigation of the effects of these structure and capacity variables on the performance of EPHS for the MCAH population. To accomplish this, we developed a new measure that blended the CAST-5 and PHAB standards, and thus extended a venerable body of scholarship.^16–18 Because of the scale’s reliability, face validity, and ease of administration, we believe LHDs might be able to use the EPHS scale used in this study for self-assessment; it might also be beneficial to researchers with an interest in the MCAH population and LHD performance. Not surprisingly, scores for each of the 10 individual EPHS varied by the number of MCAH services, financial strategies, and collaboration. The lack of a discernable pattern hinted at the complexity of the relationship of EPHS performance to structure and capacity.

With respect to the relationship of structure or capacity to performance, the higher rates of EPHS performance overall, as shown in the results in Table 3, appeared to reflect a more robust and engaged LHD, not only with respect to the number of services and collaborations, but also to more active engagement in a response to the economic downturn. Although not significant after adjusting for jurisdiction size, it appeared that engaging in more financial strategies and collaborating with a higher percentage of entities were both strongly related to higher performance of EPHS. The importance of collaboration to LHD EPHS performance, although intuitively logical, was a novel research finding. Our findings were consistent with recent research that showed an association between collaborative capacity and population health.¹⁸ Our findings were also consistent with those of Handler and Turnock,¹⁹ who found that LHDs that were more engaged appeared to have higher levels of EPHS. However, we did not know from our data whether more collaboration as an independent activity led to a higher EPHS or whether it was a consequence of EPHS activities. A synergistic effect might also be at work, but further and more nuanced research into the directionality of the relationship is indicated. This relationship also should to be considered in refinement of existing conceptual public health systems research frameworks.

Limitations

The strengths of our study included that the survey was administered to a large, national representative sample of LHDs, and the questionnaire’s face validity was strengthened by having been reviewed and pilot tested by MCAH practitioners. Nonetheless, the study had several weaknesses that might have affected the findings. The survey was conducted at only 1 point in time, which limited our ability to comment on directionality or causality. In particular, it was not clear if LHDs with higher levels of performance of the EPHS were more likely to be engaged with the delivery of services and with myriad partners rather than vice versa. The self-reported data were not verified through secondary sources. Errors because of limited knowledge of the respondent were therefore a possibility. The response rate of 35%, while typical of NACCHO surveys of this type, was less than ideal, and might have introduced respondent bias. Reasonable efforts were made to increase the response rate. The subsequent slightly different response rates by jurisdiction size could have introduced some spurious effects, despite our use of sampling weights. Our sample, nonetheless, appeared similar to the national population of LHDs in terms of distribution by jurisdiction size and governance structure. To address the potential external validity threat from studying only 7% of all LHDs in the nation, we recommend replication of the survey with a larger sample. We worded the questionnaire items to reference the MCAH services and population. Although LHDs were likely to have similar services, strategies, and performance, across key populations, our findings applied only to the MCAH services. Lastly, data collected referenced January 2010 to December 2011, making the study only applicable to that timeframe.

Conclusions

The LHDs’ responses to the challenges stemming from the economic downturn and a changing health care environment provided insights into possible sources of resilience. LHDs continued to provide a high portion of the possible MCAH services, collaborated extensively with available entities in their jurisdiction, and took steps to implement responsive financial strategies. These factors cumulatively or synergistically contributed to the LHDs' ability to perform the 10 EPHS.