1. Inaugural Keynote Lecture International Indian Statistical Association, Opportunities for Biostatistics in implementation science
Donna Spiegelman, ScD
Professor of Epidemiologic Methods
Departments of Epidemiology, Biostatistics, Nutrition and Global Health
Harvard TH Chan School of Public Health

2. Plan for Today
Introduction to Implementation and Dissemination Science, definitions and motivation
3 Examples of how implementation science and related disciplines can be used to generate knowledge and improve public health at scale
Statistical approaches
3 Myths and methods that resolve them
Causal inference for network-randomized studies
Cost-effectiveness analysis
Two-stage designs
Ongoing research and conclusion i

3. Introduction to Implementation Science

4. Next slide gives the punchline

5. It takes 17 years to turn 14 percent of original research to the benefit of patient care

6. The “Know-Do” Gap Preventable Under 5 mortality
11 million children under-5 years old die every year – 90% of them in the developing world
2/3rd of these deaths (7 million) can be prevented by available, effective and cheap interventions
Only 2/3rd are preventable by AVAILABLE, EFFECTIVE, and CHEAP intervention
Lancet 2005; 365:1147

7. India and the MDGs
                                                                                                                                                                                                                                                                              
The fourth Millennium Development Goal aims to reduce mortality among children under five by two-thirds. India’s Under Five Mortality (U5MR) declined from 125 per 1,000 live births in 1990 to 49 per 1,000 live births in The MDG target is of 42 per 1000, which suggests that India is moderately on track, largely due to the sharp decline in recent years.

8. Implementation Science -- Definitions
“A systematic, scientific approach to ask and answer questions about how to get ‘what works’ to people who need it with greater speed, fidelity, efficiency, quality and relevant coverage”
“The scientific study of programs and interventions which promote the systematic uptake of clinical research findings and other evidence-based approaches into routine clinical practice and public health policy, hence improving the quality (effectiveness, reliability, safety, appropriateness, equity, efficiency) of health care.”
“Implementation science is about determining what works, in real-life full-scale settings.”

10. The implementation pipeline
efficacy 
effectiveness and cost-effectiveness 
implementation 
dissemination

11. Features of Implementation and Dissemination Science Research
3 types of research with unique features, designs, and objectives
Theory-driven
Mixed methods
Adaptation vs. Fidelity
Contextual (?when? ?where?) vs. External validity
Economic evaluation/cost-effectiveness
Sustainability
Diffusion (network science)

12. Hybrid effectiveness/Implementation designs

13. Models and Frameworks

14. Mixed Methods Qualitative research to understand barriers to uptake
Quantitative research to assess alternative implementation modes to improve process outcomes and health impacts
Qualitative research while ongoing to understand fidelity, acceptability, feasibility
Qualitative research at the end to understand sustainability

15. Dissemination Research

16. Dissemination Research – myth vs. reality

17. examples of How will knowledge generated by IS & Related be used
To develop the evidence base for cost-effective preventive strategies to achieve domestic and global health, and for the elimination of both domestic and global health disparities
Photo Credit: Sandra Cohen-Rose and Colin Rose,
E.g., mitigate the global obesity epidemic, which is leading to skyrocketing rates of diabetes, CVD, cancer in the US and worldwide
Diabetes accounted for a full 12% of health expenditures in 2010 (Zhang P Diabetes Res Clin Pract; 2010).
60% of diabetes can be prevented by eliminating obesity (Hu et al., N Engl J Med, 2001)
Lifestyle intervention trials have reduced diabetes incidence by 58% (US ), 29% (India), 42% (China).
The diabetes epidemic is an example of one of the many major domestic and global health challenges where the technical solutions are well known, and the barriers to prevention are those of practical implementation and dissemination
Contribute to the achievement of the Millennium Development Goals 4-6:
#4: Reduce under-5 child mortality by 50%;
#5: Reduce maternal mortality by 75% by 2015; and
#6: End HIV/AIDS epidemic, providing universal access to AIDS treatment.

18. How will knowledge generated by these disciplines be used?
2. Ensure that our resources are effectively deployed to reduce health care costs. In 2011, the United States spent more on health care per capita ($8,608), and more on health care as percentage of its GDP (17.2%), than any other nation.
Photo Credit: Sandra Cohen-Rose and Colin Rose,
In the global HIV/AIDS arena,
U.S. gov’t expending vast resources to combat the AIDS epidemic, having provided 54% of all global funding in 2010, for example
Contrast the 2014 NIH budget request of $30.2 billion to the President’s request of $29.7 billion for AIDS relief;
Clearly we want to spend our AIDS prevention and relief resources wisely!
the U.S. scored at or near the bottom in nine key indicators of health:
Chronic lung disease; Low birth weight;
Drug-related deaths; Teen pregnancy and sexually transmitted infections;
General disability; Obesity and diabetes
Heart disease; infant mortality;
Injuries and homicides;
the U.S. had the highest diabetes rate and the second-highest death rate from a common form of heart disease and lung disease
National Research Council and Institute of Medicine. U.S. Health in International Perspective: Shorter Lives, Poorer Health. Washington, DC: The National Academies Press, 2013 – US compared to the 16 other highest income countries
U.S. men had the shortest average life expectancy, at 75.6 years, nearly four years shorter than Switzerland, the best-performing country among men
U.S. women had the second-shortest life expectancy, at 80.8 years, which is five years shorter than Japan, which had the highest average life expectancy for women
US compared to the other 16 highest income countries (National Research Council and Institute of Medicine. U.S. Health in International Perspective: Shorter Lives, Poorer Health; 2013)
U.S. men had the shortest average life expectancy
U.S. women had the second-shortest life expectancy
Americans face the lowest probability of living up to age 50

19. Example 3: Maternal Mortality – the world’s biggest health disparity (WHO)
Maternal Mortality is the world’s most extreme health disparity.
The bad news is that rates in sub-saharan africa and south asia are 100-fold or more greater than those in much of the rest of the world.
The rates in these high maternal mortality regions are similar to those found in the US and Europe in the 17th and 18th centuries.
The good news is, as exemplified by its extremely low rates in Europe and North America, MM is virtually completely preventable.
From a Maternal Mortality Rate (MMR) of 437 per 100,000 live births in , India is required to reduce MMR to 109 per 100,000 live births by Between 1990 and 2006, there has been some improvement in the Maternal Mortality Rate (MMR), which has declined to 167 per 100,000 live births in However, despite this, India’s progress on this goal has been slow and off track.

20. Maternal mortality is nearly entirely preventable
Nearly 3/4s of maternal deaths are due to hemorrhage, infections, eclampsia, obstructed labor and unsafe abortion. Again, the clinical and preventive solutions to each of these causes is well known.

21. Proposed: Multi-factorial CRT (cluster-randomized trial) to prevent maternal mortality
2x2x2 factorial design will be sufficiently powered for each intervention
Unit of randomization could be village, district, or individual
Interventions: RR
I propose a 2x2x2 randomized factorial trial to prevent maternal mortality. The unit of randomization could be the village, district or individual. The factorial design makes it possible to study all 3 interventions with the sample size needed to study one, and could be conducted in any of the countries on the previous slide with the sample sizes given – ranging from 40,000 in Tanzania, or 120,000 in Pakistan. 96% of maternal deaths are expected to be prevented by the combined effect of these 3 interventions, which target the top 4 causes of maternal mortality, one by one.
ed Wanda Barfield today, Director of the Division of Reproductive Health of the National Center for Chronic Disease Prevention and Health Promotion at the Centers for Disease Control in Atlanta, invited for talk!
TBA training & supplies (infections, obstructed labor)
0.74
Calcium supplement (eclampsia)
0.17
Oral misoprostol (bleeding)
0.33
Combined effect
0.04

22. Statistical APPROACHES
Image source:

23. Design strategies in D&I Research
Adaptive intervention designs
Mixed or multi-­method designs for qualitative and quantitative studies
Adaptive research designs
Multiple baseline designs
Clinical trial design
Natural experiments
Cohort­-sequential (accelerated longitudinal) designs
Quasi-experimental designs
Cross-over designs
Regression discontinuity designs
Cross-sectional designs
Sequential multiple-­assignment randomized trials (SMARTs)
Effectiveness and implementation studies
Stepped wedge design
Factorial designs
Time series designs
Fractional factorial designs
Two-stage designs
Group­- or cluster-­randomized trial

24. Quantitative analysis methods In D&I research
Mixture models (including growth and regression mixture models)
Agent based modeling
Analysis of high-­dimensional data
Moderation/Effect Modification/Interaction analysis
Analysis of small sample data
Multi-­level/hierarchical/mixed-model regression
Bayesian methods
N of 1 experiments
Complier Average Causal Effect (CACE) analysis
Network analysis
Causal inference
Propensity score methods (matching, weighting, etc.)
Cost-­effectiveness methods
Psychometric methods
Data mining
Simulation methods
Decision analysis
Statistical power analysis
Econometric methods
Structural equation models
General linear modeling (including regression, multivariate analysis)
Subgroup analysis
Survey data analysis
Gene-environment interactions
Survival analysis
Generalized linear modeling (logistic, Poisson, Gamma, etc.)
System dynamics
Systems engineering methods
Genome-­wide statistical analysis
Geospatial analysis
Growth modeling
Individual person-level meta-analysis
Integrative data analysis
Item response theory
Latent class and latent variable modeling
Measurement theory and methods (EFA, CFA, etc.)
Mediation analysis
Meta-­analysis (of summary statistics)
Methods for analysis of intensive or long longitudinal data
Microsimulation methods
Missing data methods (imputation, full information maximum likelihood, etc.)

25. Myths
Three widely held myths limit more widespread promulgation of implementation research and its translation into policy and programs:
Evaluations must be randomized
Valid results require individual level data
Evaluation of population-level health interventions is time- and cost- prohibitive.
I’ll briefly discuss each of the 3 now and show how the work proposed will serve to overcome them.
Female health workers perform routine check-ups for children, mothers, and pregnant women of the remote village Kishorimohanpur, located in the Sunderbans delta region of India. © 2012 Tushar Sharma, Courtesy of Photoshare

26. Myth 1: Evaluations must be randomized
New developments in causal inference for observational research provide a wealth of methods for reducing or eliminating confounding, selection bias and information bias
Computationally feasible causal methods are needed for the evaluation of program impacts in the presence of time-varying confounding, using marginal structural models and g-causal models as the point of departure (Robins, 1986+)
Causal methods need to be extended to handle data with dissemination for estimating individual, disseminated and total effects, obviating the no interference assumption)
Loss to follow-up can restrict the validity of results from longitudinal studies
Contextually-specific instrumental variables for participant selection need to be identified and used to adjust for bias due to loss to follow-up, using Heckman’s selection model and two stage designs
Non-adherence is a form of exposure measurement error and may be differential
Maternal mortality example:
Analysis – Dodoma study of CBK, rolled out to some villages in the Dodoma regions; not randomized,
LTF – high, some trackers are better than others, may therefore detect higher maternal mortality rates; conditional on intervention assignment, tracker identity should have no association with the outcomes; tracker identity correlated with intervention assignment
Application: Assess the cost-effectiveness of second line anti-retroviral initiation in large U.S.-funded programs in Sub-Saharan Africa (SSA)
Access to antiretroviral therapy in low-income and middle-income countries has been scaled-up effectively in the past decade; however, failure of the first-line regimen is increasing.1 
The reason for this complexity is simple: we do not know the best strategy for management of fi rst-line ART failure, in any country.10 Incredibly, the research that could have already answered this question has not been done. Therefore the question is: how do we provide access to simple and tolerable second-line cART that will reliably rescue those in whom fi rst-line therapy has failed and can be prescribed according to a simplifi ed approach?

27. Title: Assessing Individual and Disseminated Effects in a Network Intervention for HIV Prevention
Authors: Ashley Buchanan, Sten Vermund, Samuel Friedman, and Donna Spiegelman
With former Postdoctoral Research Fellow, now Assistant Professor at URI, Ashley Buchanan, we are developing methods to estimate the causal effects of ratio and difference measures in network randomized studies in which there is dissemination of the intervention in social/risk networks through directly-exposed index participants.
Many implementation trials must address clustering via social networks; only some randomized to a given intervention are exposed directly.
The individual effect reflects the direct impact on participants receiving the intervention beyond being an intervention network member
the disseminated effect reflects the indirect impact on persons sharing a network with the directly-treated individual.
Estimation of crude effects (i.e., average treatment effect among all study participants) and composite effects (i.e., combined individual and spillover disseminated effects) is also of interest.

28. Indirect, Externality, Diffusion,
Recommended Term
Alternative Terms
Definition
Parameter
Individual
Direct
Effect on those directly receiving intervention beyond being in an intervention network
Disseminated
Indirect, Externality, Diffusion,
Contamination, Spillover
Effect on those are in an intervention network but not an index participant
Composite
Total
Combined individual and disseminated effects
Crude
Overall
Effect among the treated contrasted with effect among the control
Y(a,b) a=index or network member; b=intervention or control network

29. MV- Adjusted Individual effect: aRR=0.53, 95% CI = 0.22, 1.28
Results:
HPTN037 included 515 participants, of whom 48% were in treated networks and 52% were in control networks.
Crude effect: 44% reduction in risk (95% CI= 0.37, 0.84) for “sharing works”
MV- Adjusted Individual effect: aRR=0.53, 95% CI = 0.22, 1.28
MV-Adjusted Disseminated effect: aRR= 0.65, 95% CI = 0.40, 1.05
MV-Adjusted Composite effect: aRR = 0.35, 95% CI = 0.16, 0.75
Conclusions: This analysis highlights the utility of these novel methods to quantify the complex information available in network-based interventions. These new methods can improve best preventive practices among people who inject drugs and their risk networks by leveraging network-based effects.

30. With Biostatistics & Epidemiology Assistant Professor Molin Wang and Research Scientist Polyna Khudyakov, we are developing empirical methods for estimation and inference for the ICER using modern semi-parametric survival data methods

31. Incremental cost-effectiveness ratio (𝑰𝑪𝑬𝑹)
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

32. Restricted mean life yearS
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

33. Restricted mean life years

34. Restricted mean life yearS

35. MOTIVATING Example
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

36. List of covariates
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

38. Cost effectiveness analysis
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

39. There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

40. Myth 2: Valid results require individual-level data
Two stage designs are well suited for translational science settings, where a mixture of individual-level and group-level data can be integrated to maximize learning at minimum cost and speed. Routinely available group-level health data can be augmented by smaller sub-studies with individual-level data or with more detailed confounder, exposure and outcome data, to produce valid and efficient estimates of intervention.
Application: Familia Salama is a 2x2 factorial cluster-randomized study of maternal health through an enhanced community health worker intervention to increase uptake of the WHO recommended ≥ 4 ANC visits and prevention of mother-to-child transmission of HIV among more than 150,000 pregnancies in DSM, Tanzania (
Routinely collected administrative registry data conducted by the Tanzanian government and intermittent government population surveys can be linked to a smaller but higher quality population survey in a two-stage design and analysis, to assess the impact of the intervention utilizing all available information

41. With Post-doctoral Research Fellow Claudia L
With Post-doctoral Research Fellow Claudia L. Rivera-Rodriguez and Biostatistics faculty member Sebastien Haneuse, we are developing methods to use the information in large administratively collected data such as captured among the over 150,000 pregnancies observed in the Familia Salama. We are also using a much smaller but higher data quality survey of around 2,400 women in the same 2 districts and during the same time as FS.
Methods will account for the cluster randomized nature of the study design.

42. Stepped Wedge Designs

44. Early Access to ART in Swaziland, CHAI

45. SWD: Power for binary endpoints
We first assume there are no time effects, considering outcomes that occur quickly after the intervention is applied
The risk difference is the parameter of interest
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

46. SWD: Power for binary endpoints
Hence, the log-likelihood :
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

47. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)
where ℎ 𝑟𝑠 is the 𝑟th row and 𝑠th column of the expectation matrix above

48. SWD vs. CRD: role of ICC
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

49. SWD: Power for binary endpoints
When time effects are to be assumed,
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

50. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

51. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

52. Comparison with different random effect distributions
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

53. Methodologic Projects Currently Underway
Estimation and inference for the mediation proportion (Statistics in Medicine, 2017 ) (Nevo)
Estimation and inference for the incremental cost-effectiveness ratio (𝑰𝑪𝑬𝑹)) and other absolute measures of effect (Khudyakov, Wang)
The impact of measurement error on estimates of the health burden estimates attributable to air pollution (Allman, Hong, and others)
The design of stepped wedge studies (Zhou, Liao, Kuntz, Wang), submitted
Methods for the analysis of clustered two-stage designs (Rivera- Rodriguez, Haneuse), submitted
Methods for analysis of direct and indirect effects in network- randomized studies (Buchanan, Vermund, Friedman, Lok), submitted

54. Methodologic Projects Currently Underway
“Learn-as-you-go” Designs: In learn-as-you-go designs, the intervention is changed over time and adapted based on past outcomes (Lok, Nevo)
Reducing the dimension of the service readiness index (Kruk, Leslie, Zhou) In press, Bulletin of the WHO, 2017
The Effect of Risk Factor Misclassification on the Partial Population Attributable Risk (Wong) Published, SIM, 2017
Intra-class correlation coefficients (ICCs) for cluster-randomized designs in HIV care and treatment (Barnhart) Published
Evaluation of the effectiveness of PEPFAR expeditures for PMTCT on PMTCT-related outcomes (Barnhart)
Optimizing Colorectal Cancer Screening Regimens (Zhou, Cai, Robins)
HIV prevalence estimation in public health screening programs (Thomas) In press, SIM, 2017
Impact of exposure measurement error on latency estimation (Peskoe, Wang) Submitted
Impact of Affordable Care Act on colorectal cancer incidence and mortality (Xu, Koh)
Suggestions welcome!

55. Conclusion
Implementation science and its related disciplines are new and growing areas of research, ideally situated in departments of biostatistics in schools of public health and medicine.
Biostatistics has much to offerin of the methods we’ve developed and methods that grow out of them. The greater the complexity and messiness of the data, the greater the methodologic challenges for obtaining valid causal inference.
There is much to learn
There are many new opportunities for finding support for translational public health research, both methodologic and substantive

56. THANk YOU!
Add picture of perle

57. With former Biostatistics Ph. D
With former Biostatistics Ph.D. student Lauren Kunz, now a staff statistician at NHLBI, former Epidemiology & Biostatistics Research Scientist Xiaomei Liao, and current post-doc Xin Zhou, we are developing methods and software tools for stepped wedge designs.
Manuscript in revision, Biostatistics

58. Enrollment and randomization plans for the a hypothetical cluster randomized trial and stepped wedged design with the same number of clusters and same proportion randomized to the intervention
Cluster
CRT
Stepped Wedge Design
Time 1 2 3 4 5 x o 6 7 8

59. Myth 3: It is too time-consuming, difficult, expensive, even unethical to collect and process the data needed for evidence-based health care decisions in real time
The stepped wedge design, in which an intervention is phased in over randomly selected administrative units over time, is another useful approach in IS
Methods for binary, and survival endpoints need to be developed
Software is needed for these methods

60. Letter to the Editor – In press, Contemporary clinical trials, 2015; Liao, Zhou & Spiegelman
A note on “Design and analysis of stepped wedge cluster randomized trials”
Editor:
Hussey and Hughes1 published the design and analysis of stepped wedge cluster randomized trials, which is a great addition to the literature. They derived the variance formula for the design with time effect, and also compared the relative efficiency for the different estimates without time effect. However, we found an error in Eq. (9) of Section 3.5 in the paper, in which a factor of was missing on the denominator. …
 Thus, and Eq. (9) of Section 3.5 in Hussey and Hughes 1 should be
, which has an extra factor on the denominator compared to the original Eq. (9) in Hussey and Hughes 1.

61. SWD: Power for binary endpoints
We assume there are no time effects, considering outcomes that occur quickly after the intervention is applied
The risk difference is the parameter of interest
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

62. SWD: Power for binary endpoints
Hence, the log-likelihood :
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

63. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)
where ℎ 𝑟𝑠 is the 𝑟th row and 𝑠th column of the expectation matrix above

64. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

65. SWD: Power for binary endpoints
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

66. SWD vs. CRD: role of ICC
SWD may be more feasible & ethical in effectiveness studies at scale
SWD seems to be less sensitive to the ICC value across realistic ranges
CRT may be able to be completed faster for the same number of subjects (in a time to event setting)

67. The impact of measurement error on burden estimates
Breanna L Alman1, Biling Hong2, Neal Fann1, Francine Laden2,3, Kirk Baker1, Karen Wesson1, Donna Spiegelman2,4
Office of Air Quality Planning and Standards, US EPA;
Harvard T.H Chan School of public health: (2) Department of Epidemiology (3) Department of Environmental Health (4) Department of Biostatistics
Presented at EHSFest 2016, manuscript in preparation

68. How does exposure measurement error in epidemiologic studies impact the quantification of long-term, PM2.5 -attributable all-cause mortality?
Outcome: Percent and total number of deaths attributable to a reduction in PM2.5 over one year
Exposure: Annual PM2.5 concentrations as estimated from a Community Multi-Scale Air Quality (CMAQ) model and source apportioned model (Comprehensive Air Quality Models with Extensions), and annual concentrations as reported by the EPA
Health Impact Function: The BenMAP-CE tool quantified the number of excess deaths attributable to PM2.5 in a single year using a health impact function. The function combines information on air quality levels, baseline outcome incidence rates, population sizes in each year and a concentration- response relationship. The health impact function to estimate the burden of PM2.5-attributable mortality takes the following form:
∆𝒚= 𝒚 𝒐 𝒆 𝜷∆𝒙 −𝟏 𝑷𝒐𝒑
where 𝑦 𝑜 the baseline incidence of mortality, 𝛽 is the effect estimate from the epidemiologic study, ∆𝑥 is the change in PM2.5 concentrations, and 𝑃𝑜𝑝 is the population size impacted by the change in air quality

69. Percent of baseline mortality attributable to PM2.5 Exposure *
Comparison of measurement-error adjusted and
unadjusted burden estimates
Percent of baseline mortality attributable to PM2.5 Exposure *
Standard
Error-Adjusted
% Difference²
Point estimate (95% CI)
Point estimate (95% CI)3
Sector (2016)
Industrial point
1.8 (1.2, 2.4)
2.4 (1.3, 3.5) 35
Area
2.5 (1.3, 3.7)
3.4 (1.2, 6.0)
Electrical Generating Units
1.4 (0.5, 2.3)
1.9 (0.3, 3.5)
Mobile
1.3 (0.8, 1.9)
1.8 (0.9, 2.8) 36
Fires and windblown particles
0.8 (0.5, 1.2)
1.1 (0.4, 1.8) 34
International
1.5 (0.8, 2.3)
2.0(0.7, 3.4)
Secondary Organics & Biogenics
1.4 (0.5, 2.2)
1.9 (0.3, 3.4)
Monitor rollback (2005)
Percentage
7.1 (2.4, 12)
12 (1.7, 21) 67
Incremental
1.8 (0.6, 3.1)
3.2 (0.4, 5.8) 73
Peak Shave
3.1 (1.0, 5.2)
5.4 (0.7, 9.7) 71
Total PM2.5 (2005)¹
11 (4.4, 18)
15 (2.3, 26) 31
* Numbers rounded to two significant digits
¹ “Total PM” was calculated using a CMAQ model independent of the other models, and does not reflect adding together each sector
² Differences is expressed in % increase
3 Using NHS-measurement Error corrected estimate (Hart et Al, EH, 2015)
Hart JE, Liao X, Hong B, Puett RC, Yanosky JD, Suh H, Kioumourtzoglou MA, Spiegelman D, Laden F. The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction. Environmental health : a global access science source 2015;14(1):38.

70. Incremental cost-effectiveness ratio (𝑰𝑪𝑬𝑹)
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

71. Restricted mean life year
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

72. An Example
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

73. List of covariates
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

75. Cost effectiveness analysis
There’s no time now to go over the detailed derivation of the proposed new method and I’ll move right on to a preliminary example of its application.

76. Optimal treatment regimes for colorectal cancer (CRC) screening
CRC is one of the few cancers for which screening has been found to prevent incidence and mortality
Recommended guidelines been given for the age to start screening and recommended intervals between screens, based on the outcome of the screening
We propose to assess the effectiveness and cost-effectiveness of the existing guidelines in NHS, HPFS and PLCO, as well as to empirically identify the optimally effective and cost- effective guidelines in the same studies
Methods will be applicable to screening for other cancers (e.g. cervical cancer) and screening for other chronic diseases

77. Methods development in this area are being led by Epidemiology and Biostatistics post-doctoral research fellow Xin Zhou. Epidemiologist Yin Cao and biostatistician Jamie Robins
His dissertation focused on using machine learning techniques to develop optimal treatment regimes for precision medicine. His research interests include machine learning, optimal treatment regimes, dynamic treatment regimes and high dimensional data analysis.

78. Optimizing CRC Screening Regimens
Colorectal cancer is the third most commonly diagnosed cancer and the second leading cause of cancer deaths in the United States.
Screening for colorectal cancer (CRC) in asymptomatic people can reduce the incidence and mortality of CRC by as much as 50%.
Current guidelines for colonoscopy surveillance recommend repeated screening at an interval of ten years for the general population, and at an interval of 3-5 years for those at high risk, starting at age 50. I
In this project, we aim to empirically identify the most cost-effective screening interval from observational data.
Over 5,000 different screening patterns appeared in NHS, which makes the problem of high dimension quite challenging
Can we empirically identify patterns that are associated with lower mortality than the current APHS guidelines?
Can we empirically identify patterns that are associated with lower mortality for subgroups of individuals, e.g. smokers, aspirin users, or those with co-morbidity?

79. Estimation and inference for the mediation proportion (in revision, Statistics in Medicine)
With Biostatistics and Epidemiology post-doctoral research fellow Daniel Nevo and former Research Scientist Xiaomei Liao, we are developing methods to improve estimation and inference for the mediation proportion in generalized linear models and the Cox model. The mediation proportion is the proportion of the effect of the exposure explained by the mediator.
Mediation analysis is can increase our confidence that an exposure-outcome relationship is causal: if the mediator is found to explain a substantial proportion of the effect according to an a priori theory of the mechanism, the interpretation of the causality of the exposure/intervention is strengthened. It is assumed that a mediator (M) sits on the causal pathway between the /intervention (X)
and the outcome of interest (Y).

80. The difference method for mediation analysis consists fitting of two exposure-outcome models, marginally and conditionally with respect to the mediator.
For generalized linear models, we implement a GEE method and use a data duplication algorithm that allows consistent estimation of the variance.
Theoretical issues concerning the validity of the simultaneous models assumed are considered. Publicly available, user-friendly software is available on my website (SAS macro %mediate)

81. Illustrative example: mediation analysis for pre-menopausal breast cancer incidence with mammographic density as the mediator in the NHS and NHSII studies (559 cases and 1727 controls). When the mediation proportion estimate was not in [0,1], inference for the mediation proportion was not carried out.
Risk Factor
RR-Total
(log RR total)
RR-direct
(log RR direct)
Mediation proportion
95% Confidence Interval
Mediation test
p-value
Personal history of benign breast disease
1.42** (0.35)
1.28 (0.25)
0.30
< 10 −6
Family history of breast cancer
1.52* (0.42)
1.52 (0.42)
0.004
0.47
Adolescent somatotype
(per 3 unit increase)
0.72*
(-0.34)
0.88
(-0.12)
0.63
< 10 −7
BMI at age 18
(per 5 unit increase)
0.79*
(-0.23)
0.95 (-0.05)
0.78
< 10 −8
Age at first birth (per 5 years increase)
1.17* (0.15)
1.16 (0.15)
0.03
Age at menarche (per 2 years increase
0.86*
(-0.16)
0.84 (-0.18)
N/A
Height (per 3 units increase)
1.13* (1.14)
1.14 (0.14)

82. The Nepal Pioneer project for CVD and Diabetes Prevention
Goal: Development of methods for the reduction of cardiovascular and diabetes risk through worksite dietary and physical activity interventions
With post-doctoral fellow, Dr. Archana Shrestha, we are developing methodology for implementation science research for the prevention of cardiovascular disease and diabetes through environmental-level interventions.
We have conducted formative research to understand the feasibility of and barriers to change in the cafeterias of Dhulikhel Hospital (DH) and Hulas Wire Industry in Nepal, to inform the development of a culturally accepted and appropriate environmental and individual level intervention, which we then plan to test the effectiveness of at DH.
We completed 7 focus group discussions (FGDs), and 13 interviews as planned. We have prepared a manuscript and it is currently under review in a peer-reviewed journal.
For the intervention study, we have received the IRB approval from the Harvard IRB and Nepal Health Research Council (NHRC). We have screened over 750 participants and enrolled over pre-diabetic and pre-hypertensive participants in the study, from whom baseline measurements have been obtained.
In addition, we have submitted a meta-analysis of worksite dietary interventions for diabetes prevention. We have completed the systematic literature search, selected 17 studies, conducted the data analysis and prepared the manuscript.
Furthermore, we are conducting a systematic review of effectiveness of cafeteria interventions within the workplace. The protocol has been finalized, the initial search resulted in 5016 articles. We are selecting the articles to be included in the review and two reviewers are independently extracting data from them now.
Finally, we have developed a mathematical model to identify the price point for identifying a budget-neutral shift in the pricing of healthy and unhealthy foods in workplace cafeterias and have gathered data to apply the model to the DH canteen with the assistance of HSPH. We have obtained the data from the DH canteen and are currently analyzing the data..

83. New proposals: Substantive
Optimizing Integrated Cervical Cancer Prevention at Tanzanian HIV Clinics
Goals: Evaluate effectiveness and cost-effectiveness of “see and treat” versus “screen and refer” for screening for and preventing cervical cancer through early stage treatment
Design: Cluster randomized controlled trial to evaluate “see and treat” versus “screen and refer”, include 20 HIV treatment and care facilities across Dar es Salaam, Tanzania, with 670 patients/facility
Aim 1: To evaluate clinic-based screening combined with referral to higher-level cryotherapy clinics facilitated by CHWs to maximize retention/adherence, vs. “see and treat” site-based screening with immediate cryotherapy, when indicated. We will compare the effectiveness of the two strategies with respect to screening coverage and appropriate treatment rates.
Aim 2: To conduct cost-effectiveness analyses (CEA) to evaluate clinic site-based screening combined with referral to cryotherapy clinics facilitated by CHWs vs. “see and treat” site-based screening with immediate cryotherapy.

84. New proposals: Substantive
Expanded access to HPV vaccination in SSA and in US high cervical cancer incidence pockets
Evaluation of the impact of the Affordable Care Act in improving health through the uptake of preventive interventions such as CRC and cervical cancer screening, contraceptive access, and screening for diabetes and hypertension (Howard Koh, Francesca Dominici, Kirk Vanda)
BIG DATA methods will be needed to access the vast data resources available for these evaluations, for the defining of phenotypes and interventions, etc.
Evaluation of the health effects of clean cooking methods and methods to increase uptake of them

85. Intra-Cluster Correlation Estimates for HIV-related Outcomes from Care and Treatment Clinics in Dar es Salaam, Tanzania
With Epi Sc.D. student Dale Barnhart and other colleagues in GHP and in Dar es Salaam, we have compiled ICCs across a wide range of HIV-related outcomes in a large, urban treatment and care program (n=>100,000 patients) in Dar es Salaam, Tanzania,
Outcomes include death, lost to follow-up, non-adherence, obesity, wasting, anemia, immunologic failure, liver dysfunction
These ICCs will make it possible for new cluster-randomized interventions to be designed for settings similar to this with more accurate power

87. Implementation Science Theories
© The New Yorker Collection 1998 Roz Chast from cartoonbank.com. All Rights Reserved.

88. Theories, Frameworks and Models
Theory: A plausible or scientifically acceptable general principle or body of principles offered to explain a phenomena (Merrian-Webster, 2013)
Conceptual Framework: A type of intermediate theory that attempts to connect to all aspects of inquiry; can act like maps that give coherence to empirical inquiry (Wikipedia, 2013)
Model: A description of analogy used to help visualize something that cannot be directly observed (Merriam-Webs, 2013)

89. Why use Theories?
Links aims, research designs, measures and analytic strategies
Generalize knowledge about how to implement and sustain interventions
Replicate successful implementation

90. Implementation Science Theories: Tabak et al. Review
Identified 109 models
Exclusions
26 focus on practitioners
12 not applicable to local dissemination
8 end of grant knowledge translation
2 duplicates
Included 61 models
Tabak, Khoong, Chambers, Brownson, AJPM, 2012

92. Roger’s Theory of Diffusion of Innovation

93. Definition (Proportions)
RE-AIM Model
Dimension
Definition (Proportions)
Level
Reach
Target population participating
Individual
Effectiveness
Positive minus negative outcomes
Adoption
Settings planning to implement
Organization
Implementation
In place as intended in “real world”
Maintenance
Program sustained over time
Individual & Organization
Impact = R x E x A x I x M
Source: Glasgow et al. Am J Pub Hlth 1999; 99:

94. Damschroder’s Consolidated Framework for Implementation Research (CFIR)

95. Ottawa Model of Research Use
Graham ID, Logan J. Innovations in knowledge transfer and continuity of care. Can. J. Nurs. Res. 2004;36(2):

96. Framework of Dissemination in Health Services Intervention Research
Crosses into impact theory – not a neat categorization.
Mendel et al, 2008

97. Selecting a Model Context Level Individuals Teams Organization System
Study characteristics
Professional discipline/perspective
Intervention characteristics
Inner and outer setting
Individuals involved
Implementation process
Level
Individuals
Teams
Organization
System

98. Selecting a Model Multiple theories often needed Process theories
How implementation should be planned, organized and scheduled
Impact theories
Hypotheses and assumptions about how implementation activities will facilitate a desired change, as well as the facilitators and barriers for success
Adapted from: Grol RP, Bosch MC, Hulscher ME, Eccles MP, Wensing M. Planning and studying improvement in patient care: the use of theoretical perspectives. Milbank Q. 2007;85(1):

99. Case Study