Factors Associated with Improved MCH Epidemiology Functioning in State Health Agencies

Factors Associated with Improved MCH EpidemiologyFunctioning in State Health Agencies

Deborah Rosenberg • Amy Herman-Roloff •

Joan Kennelly • Arden Handler

Published online: 17 September 2010

� Springer Science+Business Media, LLC 2010

Abstract This paper discusses characteristics that are

associated with enhanced maternal and child health (MCH)

epidemiology functioning in state health agencies. The

concept of the ‘‘MCH Epidemiology Effort’’ is introduced

as ‘‘the epidemiologic work carried out by multiple units

and agencies aimed at informing program planning and

policy development on behalf of women, children and

families.’’ This concept focuses attention on MCH epide-

miology functioning at the organizational level rather than

on individual MCH epidemiologists. The analysis used

data from all 50 states and the District of Columbia. Each

state participated in a telephone interview and submitted

material that demonstrated the breadth, depth, and capacity

of its MCH Epidemiology Effort. Several organizations,

including the Council for State and Territorial Epidemiol-

ogists, the Health Resources and Services Administration/

Maternal and Child Health Bureau, and the Centers for

Disease Control and Prevention provided additional sec-

ondary data. The outcome for analysis was a three-category

measure of MCH epidemiology functioning. The findings

are consistent with, and add specificity to, those from prior

assessments. In a multivariable model, agenda-setting by

consensus, involvement of external stakeholders, the total

of doctorally trained staff, and accessing CDC assignees or

other staff were all significantly related to higher level

MCH epidemiology functioning (ORs of 6.1, 6.6, 2.5, and

6.4, respectively; P \ 0.05). Organizational visibility of

the MCH Epidemiology Effort and a data environment

marked by routine data-sharing and data integration were

marginally related. We provide recommendations for

action at the state and federal level for advancing evidence-

based decision-making in maternal and child health.

Keywords MCH epidemiology � Public health agency

capacity � Analytic capacity � Capacity assessment

Background

Maternal and child health (MCH) epidemiology emerged as

a distinct field over the past 20 years as state and local

health agencies transitioned from the delivery of personal

health services to carrying out the core functions of public

health. The transition requires agencies to commit them-

selves to data-based decision-making for program planning,

evaluation, policy development, and advocacy, to hire

individuals with different types of skills and abilities, and to

provide additional training to current staff. To support this

transition, the Health Resources and Services Administra-

tion/Maternal and Child Health Bureau (HRSA/MCHB)

and the Division of Reproductive Health/Centers for Dis-

ease Control and Prevention (DRH/CDC) and its partners

have implemented a variety of initiatives to enhance the

analytic capacity of state and local health agencies.

Pivotal among these initiatives was the creation of

the CDC/HRSA MCH Epidemiology Program (MCHEP),

which has assigned MCH epidemiologists to public

health agencies since 1986 [1]. These individuals serve as

senior scientists to provide state MCH agencies with the

analytic leadership necessary to engage in data-based

D. Rosenberg (&) � A. Herman-Roloff

Epidemiology and Biostatistics, School of Public Health,

University of Illinois at Chicago, 1603 W. Taylor, Chicago,

IL 60612, USA

e-mail: [email protected]

J. Kennelly � A. Handler

Community Health Sciences, School of Public Health,

University of Illinois at Chicago, 1603 W. Taylor, Chicago,

IL 60612, USA

123

Matern Child Health J (2011) 15:1143–1152

DOI 10.1007/s10995-010-0680-x

decision-making [1, 2]. Other federal initiatives include

the provision of trainings and workshops, publication of

analytic methods workbooks, support of pre-doctoral and

doctoral training as well as student internships, and the

establishment of the CDC/Council of State and Territorial

Epidemiologists (CSTE) MCH Epidemiology Fellows

Program [3]. In parallel to these workforce development

activities, the Title V Block Grant has provided supple-

mental funding to states through the State Systems

Development Initiative (SSDI) to facilitate improvement in

the data infrastructure.

Over the past decade, there have been several efforts to

assess the capacity of the state MCH epidemiology enter-

prise [2, 4–9]. While the tools developed for these

assessments acknowledged the multi-dimensional nature of

MCH epidemiology functioning, they focused on describ-

ing existing capacity and not on identifying factors related

to the variation in capacity across the states. The analysis

presented here takes the next step by examining factors—

singly and in combination—that are associated with

improved MCH epidemiology functioning.

Methods

Conceptual Framework

This analysis used a version of the conceptual framework

described by Handler et al. [10] to examine public health

performance. Figure 1 illustrates the adaptation of this

framework to MCH epidemiology, including examples of

indicators which correspond to the structure-process-output

components of the framework. Note that structure is divided

into: (1) Health Agency structure, and (2) MCH Epidemi-

ology Effort structure. We defined the state MCH Epide-

miology Effort as the epidemiologic work carried out by

multiple units and agencies aimed at informing program

planning and policy development on behalf of women,

children and families.

Data Collection

We conducted telephone interviews lasting 1–1.5 hours with

all 50 states and Washington, DC between March and July,

2006. (The word ‘‘state’’ is used to refer to all 51 jurisdic-

tions.) The interview questions focused on organizational as

opposed to individual competencies. Typically, 2 study

personnel and 3–5 state staff participated in the interview,

including an MCH data spokesperson and the Title V

Director, or designee. Additionally, each state submitted a

portfolio of data sharing agreements, reports, presentations,

policy briefs, a listing of routine data linkages, databases

used in the Title V Needs Assessment, and other documents

illustrating the breadth, depth, and capacity of its MCH

Epidemiology Effort.

We also accessed other capacity-related data sources

[11] to inform the development of our interview questions.

A few data elements from these other sources were directly

incorporated into analysis, but the findings presented here

are predominantly from our primary data collection effort.

MA

CR

O C

ON

TE

XT

Population Context

Public Health

Context

MCH-Epi Effort Process

1.Provides analytic direction2.Provides expertise for data integration*3.Conducts high-level data analysis

Output

1.Disseminates reports*2.Generates policy brief s

Health Status

Intermediate Outcome

1.Program improvement*2.Policy development

Health Agency Structure

1.Sufficient funding for continuing education and staff development2.Updated database and web servers, statistical analysis software,

GIS software, etc.

MCH-Epi Effort Structure

1.Sufficient staff for data collection, analysis, dissemination, etc.2.Participation in the leadership of the agency

Examples: Organizational Feedback Loop

Integrating / linking data is a process ; when the process is ongoing, it is also a structural feature.Dissemination is an output; when diissemination is expected and routine, it is also a structural feature.

Program improvement is an intermediate outcome ; when improvement is an agency imperative, it is also a structural feature.

*

*

*

Fig. 1 Conceptual framework

1144 Matern Child Health J (2011) 15:1143–1152

123

Analytic Approach

We created a summary measure to characterize each state’s

level of MCH epidemiology functioning based on the

review of: (1) the interview responses, and (2) the materials

in the state portfolios. Four members of the research team

independently assigned two scores to each state–one based

on the interview, and the other based on the portfolio; these

8 scores (2 assessments by 4 reviewers) were averaged. We

then coded the state averages into three categories, classi-

fying each state as ‘‘below average’’, ‘‘average’’, or ‘‘above

average’’, reflecting relative levels of MCH epidemiology

functioning. This is the outcome variable in all analyses.

During the interview, we asked about several factors

hypothesized to be related to the level of MCH Epidemi-

ology functioning. These independent variables included

the following structural (#1–5), process (#6–7), and output

(#8–9) factors:

1. An identifiable MCH Epidemiology Unit

2. An identifiable leader of the MCH Epidemiology

Effort

3. A collaborative approach to setting the MCH Epide-

miology Effort agenda

4. High-level staff with access to continued training

5. Incorporation of CDC assignees, fellows, and interns

6. Internal/external data sharing and routine data integra-

tion (linkage)

7. Sophisticated epidemiologic analysis

8. Well-developed approaches for dissemination

9. High-level interpretation and translation of data

We assessed the crude relationship between each of

these factors and the outcome measure of MCH epidemi-

ology functioning, and in addition used crude cumulative

logistic and crude generalized logistic regression models to

further assess whether the magnitude of any association

was consistent (or close to consistent) across the range of

the outcome measure. In the generalized logistic models,

we separately examined the dichotomous contrasts of

above average versus average and average versus below

average functioning. Finally, we built multivariable mod-

els, again using cumulative and generalized logistic

regression to consider level of functioning as either an

ordinal or nominal outcome, with attention to the test of

proportional odds. We first specified models with subsets

of related variables and then combined the important

variables from each into a single overall model. Population

size and resources of a state (defined by median household

income, percent college graduates, percent foreign born,

and percent of children under 5 years who are uninsured)

were also considered.

We present tests of statistical significance, but they

should be considered with caution. With a total of 51

observations, statistical power to detect differences is

constrained, particularly for multivariable modeling. In

fact, statistical power dictated that only results of cumu-

lative logit models be presented, with findings from sepa-

rate comparisons of above average versus average, and

average versus below average only described when rele-

vant. Given the inherent limitations in sample size, we

considered associations with odds ratios of 1.5 or greater,

and p-values of less than 0.20 of interest to report.

Results

Table 1 shows the distribution of states on key indicators.

Thirty-nine reported having an identifiable MCH Epide-

miology Effort, with or without a named organizational

home; twelve reported a weak or nonexistent MCH Epi-

demiology Effort. In 36 states the MCH epidemiology

agenda was established through consensus; in twenty of

these, partners included external stakeholders. Fifteen

states reported agenda-setting driven solely by a few key

staff or no unified agenda-setting process. In 36 states there

was an identified leader of the MCH Epidemiology Effort;

more than half had hosted CDC assignees, fellows, or

interns. Only eighteen states were classified as having a

well-developed data infrastructure.

Sixteen states reported routinely conducting multivari-

able analysis, and thirty reported that MCH epidemiolo-

gists provide interpretation and translation of data. Of the

21 states reporting limited data translation, ten answered

that translation is not the job of MCH epidemiologists.

Approximately half of the states reported that evidence

produced by the MCH Epidemiology Effort guided pro-

gram development and modification.

On the summary outcome measure of MCH epidemi-

ology functioning, approximately one-third of the states

were classified as above average, average, or below aver-

age. (Note: the authors were under no constraints regarding

the categorization of states on the range of functioning.)

We identified eight states as small and with fewer resour-

ces, and we hypothesized that they may be meaningfully

different from the other states. Four of these states were

classified in the average category and the other four in the

below average category—none were classified in the above

average group (data not shown).

Table 2 shows results for four cumulative logit models.

Having an increasingly formalized MCH Epidemiology

Effort in the state agency is associated with a higher level

of functioning, as is having an agenda setting process based

on consensus. Having a lead MCH epidemiologist was not

associated with increased functioning (Table 2a). The total

number of doctorally trained key staff, and involving fel-

lows and interns were both statistically significant aspects

Matern Child Health J (2011) 15:1143–1152 1145

123

Table 1 Percent distribution on

select characteristics of MCH

epidemiology capacity 50 states

and the District of Columbia

# %

Organizational position of the MCH Epidemiology Effort

Named unit in Title V or in epidemiology section 21 41.2

No named unit, but recognized presence in Title V

and/or agency-wide

18 35.3

Diffuse/weak or no MCH Epidemiology Effort 12 23.5

Total 51 100.0

Process for setting the agenda of the MCH Epidemiology Effort

Consensus: MCH EPI staff, Title V director,

and external partners

20 39.2

Consensus: MCH EPI staff and Title V director 12 23.5

Consensus: MCH EPI staff 4 7.8

Lead MCH EPI and Title V director 7 13.7

Diffuse, program specific 8 15.7

Total 51 100.0

Identified leader of the MCH Epidemiology Effort

Yes 36 70.6

No 15 29.4

Total 51 100.0

CDC assignee some time during 1987–2005?

Yes 19 37.3

No 32 62.8

Total 51 100.0

Ever have assignees, fellows, and interns

Yes 29 56.9

No 22 43.1

Total 51 100.0

Permissive data sharing environment and routine data integrationa

Yes 18 35.3

No 33 64.7

Total 51 100.0

Data analysis: incorporate multivariable methods

Very frequently or frequently 16 31.4

Sometimes, rarely, or never 35 68.6

Total 51 100.0

Involvement in interpretation and translation of data

Provide interpretation and recommendations 30 58.8

Provide little interpretation or recommendations 21 41.2

Total 51 100.0

Translation is part of the job of MCH epidemiologists

Yes 41 80.4

No 10 19.6

Total 51 100.0

Data used for program development


Sometimes, rarely, or almost never 26 51.0

Total 51 100.0

Data used for program modification



Total 51 100.0


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of the MCH Epidemiology Effort. Having CDC assignees

and[40% of MCH Epidemiology Effort staff identified as

epidemiologists also appeared to be associated with higher

functioning, although odds ratios were unstable (Table 2b).

An environment that promotes and permits data sharing

both internally and externally, and routine data integration,

were both markers of higher level functioning (Table 2c.).

While the use of multivariable analysis by the MCH Epi-

demiology Effort was not found to be significantly asso-

ciated with higher level functioning, 55.6% of the 18 states

with a well-developed data environment frequently used

multivariable methods, while only 18.1% with a less con-

ducive data environment frequently did this work (data not

shown). Finally, publishing at least sometimes in the peer

reviewed literature and submitting abstracts to (and pre-

sumably presenting at) the annual MCH Epidemiology

Conference were both associated with higher levels of

functioning (Table 2d).

Table 3 synthesizes the results described in Table 2. The

variables in this final model are structural indicators—no

process, output, or intermediate outcome indicators showed

a strong enough association with level of functioning to be

included. An increasingly formal organizational presence,

having a consensus approach to agenda-setting, specifically

involving external stakeholders in agenda-setting, having

staff with advanced training, supplementing staff with

external assignees, fellows, and interns, and having a more-

developed data infrastructure were all associated with

improved MCH epidemiology functioning after mutual

adjustment. It is noteworthy that involvement of external

partners contributed to higher functioning, both in terms of

participation in agenda-setting (OR 6.6, 95% CI 1.3–33.2)

and possibly as a marker of an advanced data infrastructure

(OR 4.0, 95% CI 0.9–18.3).

Table 4 summarizes generalized logistic regression

models (data not shown) that examined dichotomous

Table 2 Cumulative logit models considering: (a) organizational structure, (b) human resources, (c) data infrastructure, and (d) data dissem-

ination and their associations with higher levels of MCH epidemiology functioning

Odds ratio 95% CI P

(2a) Organizational structure and higher levels of MCH epidemiology functioning

Lead MCH epidemiologist 0.9 0.3–3.3 0.92

Organizational position of the MCH Epidemiology Effort 2.6 1.2–5.6 0.02

Agenda-setting by consensus process 8.2 2.1–31.9 \0.01

(2b) Human resources and higher levels of MCH epidemiology functioning

Total key staff at the doctoral level 2.4 1.3–4.4 0.01

[40% of MCH Epidemiology Effort staff identified

as epidemiologists

3.0 0.8–10.8 0.09

CDC assignee 2.5 0.7–8.4 0.16

Number of fellows and interns 1.5 1.03–2.2 0.03

(2c) Markers of a strong data infrastructure and higher levels of MCH epidemiology functioning

# of Datasets available to MCH epidemiology staff 1.3 1.0–1.7 0.03

# of Datasets available to external partners 1.3 1.1–1.5 0.01

Data integration routine 2.1 0.7–6.3 0.19

(2d) Markers of a strong data dissemination effort and higher levels of MCH epidemiology functioning

Increased frequency of peer reviewed publication 1.8 1.0–3.3 0.05

Abstract submission (per registrant) 7.5 0.7–80.0 0.10

Table 1 continued

a Data sharing is expected and

routine with policies in place,

and data linkage is expected and

a priority

# %

Data used for program termination

Frequently or very frequently 4 7.8


Total 51 100.0

Overall assessment of MCH epidemiology functioning

Above average 16 31.4

Average 20 39.2

Below average 15 29.4

Total 51 100.0


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contrasts of above average versus average and average

versus below average functioning. Many indicators were

important across the continuum of functioning, but

reporting a single, lead MCH epidemiologist appeared to

differentiate only average from below average functioning,

while several other indicators, most particularly those

related to engaging in higher-level dissemination and

translation appeared mostly to differentiate above average

from average functioning states.

A closer look at the sixteen states (data not shown)

classified as functioning at an above average level

provides additional insight. The structural features

observed in these high functioning states were not iden-

tical. In fact, the only factor present in all sixteen states

was ‘‘having at least one member of the MCH Epidemi-

ology Effort with doctoral training’’ (either MD or PhD).

Agenda setting by consensus was reported by fifteen of

these sixteen states, but only ten reported having a lead

MCH epidemiologist or having [40% of the MCH Epi-

demiology Effort staff identified as epidemiologists.

Finally, just nine of the sixteen highest performing states

had a named MCH epidemiology unit.

Table 3 Cumulative logit model for the associations between key features across domains and higher levels of MCH epidemiology functioning

Odds ratio 95% CI P

Organizational positiona 2.0 0.8–4.8 0.14

Agenda-setting by consensus 6.1 1.1–34.3 0.04

Agenda-setting by consensus including external partners 6.6 1.3–33.2 0.02

Total key staff with doctoral training 2.5 1.3–5.0 0.01

Additional staff: assignees, fellows, or interns 6.4 1.3–32.1 0.03

Routine data sharing (internal and external) & data integration occurring 4.0 0.9–18.3 0.07

a Organization position is a 3 level ordinal variable: named MCH epidemiology unit, no named unit, but recognized presence, and no or diffuse

effort

Table 4 Summary of how selected factors were associated with level of functioning

Markers associated with enhanced

functioning overall

Markers associated primarily with

average vs. below average functioning

Markers associated primarily with above

average vs. average functioning

Organizational visibility: a named MCH

epidemiology unit (or a recognized

presence)

Reports of having an identified lead MCH

epidemiologist

Consensus process for setting the MCH

epidemiology agenda

Consensus process specifically includingexternal stakeholders

Key staff at the doctoral level

High proportion of staff identified as

epidemiologists (as opposed to other

data-related titles)

CDC or other assignees

Involving fellows and interns

Attendance at the MCH epidemiology

conference

Ready access to data for MCH

epidemiology staff

External users can access data

Increasingly regular data integration

(data linkage)

Publishing in the peer-reviewed literature

Submitting abstracts to the MCH

epidemiology conference

Advocates use the work of the MCH

Epidemiology Effort


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Discussion

The goal of this analysis was to identify characteristics

associated with enhanced MCH epidemiology functioning

in state health agencies. Organizational rather than indi-

vidual competencies were of interest, including defining

the ‘‘MCH Epidemiology Effort’’ as a cross-agency, mul-

tiple unit endeavor. Our conceptual framework accommo-

dated the complexity of the environment within which

states move toward higher level of functioning, by recog-

nizing the circular (nonlinear) relationships between

structure, process, outputs, and outcomes. Given the broad

focus and small sample of 51, it is remarkable that many

associations between capacity indicators and the measure

of MCH epidemiology functioning were not only strong,

but reached statistical significance. This underscores the

considerable variability in the way in which states descri-

bed their MCH Epidemiology Efforts.

The analysis provides evidence that the MCH Epide-

miology Effort is supported by having organizational vis-

ibility. While having a named unit is optimal, even having

a less formal, but recognized presence within the state

appears important to above average functioning. Moreover,

the findings also support the importance of a collaborative

process for setting the MCH epidemiology agenda.

Examples of such collaborative activities included: work-

ing with universities, specifically Schools of Public Health,

jointly hiring staff, promoting professional relationships

with local health departments, and establishing cross-pro-

gram internal working groups within the state agency(ies)

to carry out MCH planning cycle activities.

Intuitively, it makes sense that promoting organizational

visibility and successfully managing a collaborative pro-

cess requires strong, effective leadership; therefore, the

finding that having a lead MCH epidemiologist was not

associated with the highest level of functioning was

unexpected. One explanation is that the leadership model

in well-developed states is not focused on a single indi-

vidual, but rather on shared leadership. States operating

this way might have answered ‘‘no’’ to the question of

having a ‘‘lead’’ MCH epidemiologist even when they have

an individual who according to job title and administrative

category fits this description. This distinction between

organizational designation and operational approach is

important. The value of having experienced, senior indi-

viduals in advancing the work of the MCH Epidemiology

Effort needs to be more fully understood and acknowl-

edged, regardless of whether these individuals are formally

called the ‘‘lead’’ of the MCH Epidemiology Effort.

In fact, the findings indicate that increasing numbers of

key staff with advanced training is a critical element of

enhanced MCH epidemiology functioning. One way to

obtain highly trained staff and augment the MCH

Epidemiology Effort is to apply for CDC assignees and

other fellows and interns who can contribute epidemiologic

expertise and a new perspective on state issues. Outside

expertise may provide the impetus for breaking through

barriers to change imposed by an entrenched data culture.

Likewise, individuals from an outside agency often bring

the imprimatur of their home organization, providing in-

state personnel with the additional influence necessary to

make change.

These cross-sectional data preclude a full understanding

of whether hosting assignees, fellows, and interns con-

tributes to enhanced functioning or whether states already

functioning at a higher level are more likely to apply for

and obtain these resources. Historically, CDC assignments

were not tied directly to a state’s level of functioning, but

were based on a combination of factors, including need and

state MCH leadership presence. Other fellowship-type

programs, however, may use criteria that relate to host

capacity. Regardless, the data suggest that infusion of

external resources can at the very least support the func-

tioning of the Effort—especially as states move further

along the range of functioning.

Not surprisingly, it appears that states with a permis-

sive data sharing environment are more likely to have a

high functioning MCH Epidemiology Effort; if MCH

epidemiology staff have access to a variety of datasets

and if data sharing extends to external partners, the

likelihood that data will be analyzed and translated to

answer program and policy questions is increased. Like-

wise, states in which data integration is becoming routine

are more likely to have a higher functioning MCH Epi-

demiology Effort. These findings suggest that a more

permissive data sharing environment can increase a state’s

ability to accurately assess and forecast problems, deter-

mine the best interventions to address these problems, and

appropriately evaluate whether interventions are making a

difference.

Those states able to submit scientific abstracts and

publish in peer reviewed journals appear to have more

highly functioning MCH Epidemiology Efforts; again,

these cross-sectional data do not allow us to determine

whether publications and abstract submissions lead to

enhanced MCH epidemiology functioning or vice versa.

We hypothesize that the relationships are synergistic—

publishing and presenting increases the visibility and utility

of a state’s MCH Epidemiology Effort which in turn

energizes and enhances future activities. Similarly, partic-

ipation at the annual MCH epidemiology conference is a

venue for disseminating work while accessing analytic

trainings, collegial support, and exchanging ideas.

Although not directly measured here, it is also likely that

states whose work has gained scientific recognition through

publication and/or participation in national networks


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become attractive as potential employers and as sites for

fellows and interns.

The limitations imposed by having cross-sectional data

cannot be over-emphasized. Without complete knowledge of

temporal sequence, states’ level of functioning may have been

misclassified. For example, a state might have recently lost

key staff or gone though a reorganization which weakened its

MCH Epidemiology Effort, but sent materials in its portfolio

that were created before these losses. Conversely, a state

might have recently upgraded data sharing policies, added

senior staff/assignees, or otherwise strengthened its capacity,

but sent materials created before these gains. In either case, the

responses to the interview and the contents of the state port-

folio probably reflected different levels of functioning, mak-

ing accurate classification of the state difficult. One state also

spoke of the ‘‘burnout syndrome’’, in which a state lacks

structural features necessary for sustainability, but has highly

motivated and qualified staff who are able to produce quality

outputs for a short time. More generally, the differences in

states’ organizational circumstances are difficult to fully

measure. Only longitudinal data would permit more accurate

classification of states into levels of functioning, capturing the

impact of lags between decreased capacity and output, or

increased capacity and output.

Misclassification may also have occurred due to reporting

bias. For some factors it is likely that states reported differ-

entially according to their level of expectation and exper-

tise—higher functioning states may have under-reported

capacity and lower functioning states may have over-

reported capacity. For example, reporting the use of specific

analytic approaches may have been biased because the

states’ threshold for placing themselves in the ‘‘frequently’’

category differed according to level of functioning. While

reporting bias may also have occurred for intermediate out-

comes such as program and policy development, it is more

likely that variability across states was minimal. Regardless

of the level of functioning of the MCH Epidemiology Effort,

having an impact at the program and policy level is difficult,

being highly dependent on state budgets, program funders

and the political process in general.

Assuming uneven development of MCH epidemiology

capacity, variability across states might increase on some

indicators and decrease on others over time. As a result,

factors not found to be important in this analysis might be

significantly associated with the level of functioning of the

MCH Epidemiology Effort in future studies and vice versa.

When an indicator lacked variability in this analysis, it might

have reflected the reality that states were indeed functioning

at the same level on that indicator, or that a true association

was obscured due to small sample size and/or measurement

error.

The sensitivity and specificity of indicators, even when

reasonably high, likely differed across indicators, and

within and across domains, so that the relative strength of

the reported associations may be imprecise. Similarly, the

magnitude of any reporting biases probably differed across

indicators. In addition, there were unequal numbers and

types of indicators in each domain, implicitly giving more

importance to domains with a greater number of indicators.

Some of the differences in the sensitivity and specificity

of indicators was related to the use of terms also lacking

these traits. This became clear as some states asked for

clarification of certain terms such as: qualitative analysis,

multivariable analysis, access to data, and translation. For

example, is translation a synonym for dissemination, or is it

making program and policy recommendations, or are data

not truly translated until change occurs? It will be impor-

tant for the field to clarify these and other concepts, and to

refine expectations of the MCH Epidemiology Effort with

respect to ‘‘best’’ practices.

After two decades of capacity-building initiatives, a

renewed effort to advance data-based decision-making for

maternal and child health is necessary. The ‘‘Appendix’’

provides recommendations for action at both the state and

federal levels to further the field of MCH epidemiology.

Importantly, many states explicitly stated that federal ini-

tiatives play a critical role in strengthening their ability

to do high level epidemiologic work. Mandating more

sophisticated reporting is one such initiative, but states also

cite the opportunities to involve assignees, fellows, and

interns, along with SSDI as instrumental in helping them

increase the capacity of their MCH Epidemiology Effort.

In addition to the expansion of reporting requirements

and provision of workforce trainings, which remain

essential activities, the federal government should offer

financial incentives to states that use the tools of epide-

miology to plan, implement, monitor, and evaluate pro-

grams and policies. This linkage of funding to data-based

decision-making should not penalize states still working to

build their capacity, as long as they are able to document

concrete steps toward best practices.

In particular, it will be important to assist states in

building their ability to translate data into program and

policy change, since such change will be the standard

against which MCH epidemiology will be measured. In this

analysis, the level of MCH epidemiology functioning was

not related to states’ reports of the use of data to guide

programmatic and policy change, suggesting that transla-

tion is a challenging area for all states. New federal funding

focusing specifically on data translation activities could

further enhance the use of data to inform program and

policy. States might use new resources to add or train staff

in the art and science of policy analysis and development;

might develop more formalized collaborations between the

MCH epidemiologists and policy staff, or might purchase

technical assistance to support their ability to do either of


123

these. The opportunity to apply for funding through this

proposed new data translation initiative as well as through

the SSDI mechanism would provide the flexibility to

engage in a range of capacity-building activities.

Finally, it must be recognized that all states, regardless

of their current level of functioning can only engage in

evidence-based decision-making with ongoing federal and

state support for the data infrastructure and databases,

including vital records, that are the lifeblood of the MCH

Epidemiology Effort.

Acknowledgments This work was funded by ASPH/CDC Coop-

erative Agreement #S3241. The authors wish to thank Dr. Roger

Rochat and Dr. William Sappenfield along with our other maternal

and child health colleagues who served as members of the project’s

Advisory and Reviewer groups.

Appendix

See Table 5.

Table 5 Recommendations for action at the state and federal level to support enhanced MCH epidemiology functioning

State health agencies and their associated Title V programs

should

Federal agencies that support the field of MCH epidemiology should

Organizational visibility, leadership, and collaboration

S1. Establish a named unit to anchor the MCH Epidemiology

Effort, or at a minimum, ensure a specified, visible focus

within the Title V Program, the Division of Epidemiology,

Center for Health Statistics, or other appropriate location

within the state agency

S2. Ensure that the MCH Epidemiology Effort has leadership

with organizational recognition and authority, typically a

single individual with a job title and administrative

responsibilities characteristic of upper level management.

This is of particular importance as the MCH Epidemiology

Effort is developing

S3. Acknowledge the broad scope of the MCH Epidemiology

Effort by breaking down administrative barriers to shared

leadership and promoting a broad, collaborative approach to

setting the MCH epidemiology agenda that engages both

internal and external partners (for example, while WIC,

PRAMS and Title V may be housed in separate administrative

structures they are all part of the MCH Epidemiology Effort

and should be viewed as such by senior leadership)

F1. Provide funds for training and technical assistance focusing on

strengthening the organizational position of the MCH Epidemiology

Effort, on developing collaborative leadership and building

consensus, and on effectively working with internal and external

partners

Human resources

S4. Invest in hiring increasing numbers of individuals with

doctoral degrees who can contribute high level expertise and

analytic leadership to the MCH Epidemiology Effort

S5. Invest in building an MCH epidemiology staff that is

comprised of a critical mass of individuals considered to be

epidemiologists

S6. Actively pursue opportunities to obtain supplementary

external support and resources for the MCH Epidemiology

Effort, such as CDC/HRSA assignees, CSTE fellows,

university interns, etc. and support these individuals for a

sufficient length of time so that the state fully benefits from

their skills and expertise

S7. Provide MCH Epidemiology staff with time and funding to

access national and local epidemiology training opportunities

F2. Continue and strengthen financial support for graduate training in

MCH epidemiology at the doctoral and master’s level. To maximize

the benefits of these resources, require individuals who receive

federal MCH epidemiology targeted funds during their training to

conduct their master’s internships or doctoral dissertations in

collaboration with state (or local) health agencies

F3. Develop an articulated pipeline for graduating MPH and doctoral

level trainees in MCH epidemiology to obtain appropriate positions

in MCH epidemiology in state (or local) health agencies

F4. Continue and expand financial support for the placement of a

variety of skilled external staff in state health or related agencies,

paid for solely or in part by the federal government (e.g., CDC/

HRSA assignee) and through partnerships with organizations and

agencies such as CSTE

Data infrastructure

In order to promote sophisticated and informative data

utilization:

S8. Ensure that MCH epidemiology staff and also external

partners have direct access to a wide variety of datasets

relevant to MCH—those residing within and outside the state

agency

S9. Establish protocols for routine data integration (linkage)

beyond birth–death data

F5. Expand the use of the Title V block grant annual report,

application, and needs assessment as a mechanism for promoting

reporting that requires accessing multiple, integrated data systems to

carry out sophisticated analysis

F6. Provide funds for the infrastructure development necessary to

fulfill any new block grant mandates


123

References

1. Rochat, R., Atrash, H., & Handler, A. (1999). Developing

maternal and child health epidemiology capacity in state health

departments. Journal of Women’s Health and Gender-BasedMedicine, 8(9), 1135–1139.

2. Handler, A., Geller, S., & Kennelly, J. (1999). Effective MCH

epidemiology in state health agencies: Lessons from an evalua-

tion of the maternal and child health epidemiology program.

Maternal and Child Health Journal, 3(4), 217–224.

3. Alexander, G., & Kogan, M. (2005). The expanding role of MCH

epidemiologists: Evolving job description, tasks and skill areas,

and sources of training support. Maternal and Child HealthJournal, 9(2), 121–123.

4. Alexander, G., Slay, M., Petersen, D., Pass, M., & Chadwick, C.

(2001). Graduate and continuing education needs in maternal and

child health: Regional summary series from the National NeedsAssessment, 2000–2001: Region V Summary. Maternal and Child

Health Leadership Skills Training Institute Technical Report,

Department of Maternal and Child Health, School of Public

Health, University of Alabama at Birmingham.

5. Association of Maternal and Child Health Programs (AMCHP).

(2001). Guidelines for state MCH data capacity, Data Committee.

6. Assessment and Monitoring Tool for MCH Epidemiology and

Data Capacity. (2004). Maternal and Child Health Epidemiology

Program, Division of Reproductive Health, Centers for Disease

Control and Prevention.

7. Council of State and Territorial Epidemiologists. (2002,

December). National assessment of epidemiologic capacity inmaternal and child health: Findings and recommendations.

Atlanta, Georgia: CSTE.

8. Council of State and Territorial Epidemiologists. (2004).

National assessment of epidemiologic capacity: Findings andrecommendations. Atlanta, GA: CSTE.

9. Ruderman, M, & Grason, H. (2001). Capacity assessment forstate title V programs (Preliminary Ed.). Baltimore, MD.

Women’s and Children’s Health Policy Center, Johns Hopkins

School of Public Health; and Washington DC: Association of

Maternal and Child Health Programs.

10. Handler, A., Issel, M., & Turnock, B. (2001). A conceptual

framework for measuring public health system performance.

American Journal of Public Health, 91, 1235–1239.

11. Secondary data sources: Council of State and Territorial Epidem-

iologists: National Assessment of Epidemiologic Capacity in

Maternal and Child Health (2002 and 2004); Health Resources and

Services Administration/Maternal and Child Health Bureau Title V

Information System Capacity Indicators (1996–2005); Florida

State University, College of Medicine Center for Medicine and

Public Health, State Public Health Survey (2001) (provided by

Leslie M. Beitsch, MD, JD); Centers for Disease Control and

Prevention/Division of Reproductive Health/Maternal and Child

Health Epidemiology Program and Health Resources and Services

Administration/Maternal and Child Health Bureau annual maternal

and child health epidemiology conference registration data

(2004–2005); Centers for Disease Control and Prevention assignee

history (compiled by Roger Rochat with assistance from Bill

Sappenfield and Hani Atrash) (1996–2005); training participant

data for the Training Course in Maternal and Child Health Epide-

miology funded by Health Resources and Services Administration/

Maternal and Child Health Bureau and the Association of Maternal

and Child Health Programs pre-conference Skills-building work-

shops (2002–2005).

Table 5 continued

State health agencies and their associated Title V programs

should

Federal agencies that support the field of MCH epidemiology should

Dissemination and translation

S10. Disseminate the work of the MCH Epidemiology Effort

using multiple approaches and venues such as the peer

reviewed literature and presentations at scientific and

professional meetings

S11. Establish mechanisms for MCH Epidemiology Effort

staff and other program and policy staff to jointly translate

epidemiologic findings into information for executive and/or

legislative action

S12. Encourage and support the ability of external partners

to turn data into information

F7. Provide support for joint training of the MCH Epidemiology

Effort staff and their internal and external partners on the most

effective ways to disseminate and translate their reports and findings

into information for action

F8. Expand the use of the Title V block grant annual report,

application, and needs assessment as a mechanism for promoting

reporting that requires inclusion of policy-relevant interpretation and

recommendations for action


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Factors Associated with Improved MCH Epidemiology Functioning in State Health Agencies

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