This Data for Impact webinar was held in December 2020. Access the recording and learn more at https://www.data4impactproject.org/resources/webinars/managing-missing-values-in-routinely-reported-data-one-approach-from-the-democratic-republic-of-the-congo/
Data for Impact hosted a one-hour webinar sharing guidance for using routine data in evaluations. More: https://www.data4impactproject.org/resources/webinars/routine-data-use-in-evaluation-practical-guidance/
This Data for Impact webinar took place October 29, 2020. Learn more at https://www.data4impactproject.org/resources/webinars/use-of-routine-data-for-economic-evaluations/
Monitoring is the continuous collection of data and information on specified indicators to assess the implementation of a development intervention in relation to activity schedules and expenditure of allocated funds, and progress and achievements in relation to its intended outcome.
Evaluation is the periodic assessment of the design implementation, outcome, and impact of a development intervention. It should assess the relevance and achievement of the intended outcome, and implementation performance in terms of effectiveness and efficiency, and the nature, distribution, and sustainability of impact.
Data for Impact hosted a one-hour webinar sharing guidance for using routine data in evaluations. More: https://www.data4impactproject.org/resources/webinars/routine-data-use-in-evaluation-practical-guidance/
This Data for Impact webinar took place October 29, 2020. Learn more at https://www.data4impactproject.org/resources/webinars/use-of-routine-data-for-economic-evaluations/
Monitoring is the continuous collection of data and information on specified indicators to assess the implementation of a development intervention in relation to activity schedules and expenditure of allocated funds, and progress and achievements in relation to its intended outcome.
Evaluation is the periodic assessment of the design implementation, outcome, and impact of a development intervention. It should assess the relevance and achievement of the intended outcome, and implementation performance in terms of effectiveness and efficiency, and the nature, distribution, and sustainability of impact.
Self-Assessment of Organizational Capacity in Monitoring & EvaluationMEASURE Evaluation
Presentation that captures self-assessments of two teams of Ethiopian health officers (most of whom have M&E responsibilities): those from SNNP Regional Health Bureau and those from the Sidama Zonal Health Department.
6 M&E - Monitoring and Evaluation of Aid ProjectsTony
A series of course modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
This is part 6 of 11, beginning with 2 modules on leadership and conflict resolution, then 9 modules on project cycle management.
This module has 3 handouts and presenter notes as separate documents.
Sample Proposal: http://www.slideshare.net/Makewa/6-watsan-training-sample-proposal-09
Slides as a handout: http://www.slideshare.net/Makewa/6-me-handout
Presenter notes: http://www.slideshare.net/Makewa/6-module-6-presenter-notes
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
Part 7 of 11.
There are two handouts to go with this module, Population Indicators, and a Logframe with blanks. http://www.slideshare.net/Makewa/population-indicators-handout and http://www.slideshare.net/Makewa/exercise-watsan-logframe-with-blanks
Step 9: Monitoring, Evaluation and LearningPMSD Roadmap
In Monitoring, Evaluation and Learning you will learn to prepare, implement and manage a monitoring plan that not only uncovers the logic behind the intervention, but also uses this logic to identify key indicators to measure its progress.
The step provides comprehensive guidance on mapping out results chains so that the intervention logic can be clearly visualised.
Self-Assessment of Organizational Capacity in Monitoring & EvaluationMEASURE Evaluation
Presentation that captures self-assessments of two teams of Ethiopian health officers (most of whom have M&E responsibilities): those from SNNP Regional Health Bureau and those from the Sidama Zonal Health Department.
6 M&E - Monitoring and Evaluation of Aid ProjectsTony
A series of course modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
This is part 6 of 11, beginning with 2 modules on leadership and conflict resolution, then 9 modules on project cycle management.
This module has 3 handouts and presenter notes as separate documents.
Sample Proposal: http://www.slideshare.net/Makewa/6-watsan-training-sample-proposal-09
Slides as a handout: http://www.slideshare.net/Makewa/6-me-handout
Presenter notes: http://www.slideshare.net/Makewa/6-module-6-presenter-notes
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
Part 7 of 11.
There are two handouts to go with this module, Population Indicators, and a Logframe with blanks. http://www.slideshare.net/Makewa/population-indicators-handout and http://www.slideshare.net/Makewa/exercise-watsan-logframe-with-blanks
Step 9: Monitoring, Evaluation and LearningPMSD Roadmap
In Monitoring, Evaluation and Learning you will learn to prepare, implement and manage a monitoring plan that not only uncovers the logic behind the intervention, but also uses this logic to identify key indicators to measure its progress.
The step provides comprehensive guidance on mapping out results chains so that the intervention logic can be clearly visualised.
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
Presentation on the examination of microbiological data for assessment and trending.
Includes: normalizing data, graphs, and assessment of alert and action levels.
This presentation deals with the formal presentation of anomaly detection and outlier analysis and types of anomalies and outliers. Different approaches to tackel anomaly detection problems.
Face Recognition is one of the revolution technology, which is based on various machine learning and deep learning algorithms, which is mostly used for bio metrics, but it is also used to detect criminals in traffic signals, identify the and track the lost persons, etc., This project is written in python, where a GUI appears with a button, which let us to open the face recognition program, that captures the video frames from the webcam of a laptop or a pc, and then analyses it
Lincoln Lau, PhD, Director of Research for International Care Ministries discusses the results of a study regarding the relationship between the level of trust in faith leaders and the efficacy of health interventions.
Lessons learned in using process tracing for evaluationMEASURE Evaluation
Access the recording for this Data for Impact (D4I) webinar at https://www.data4impactproject.org/lessons-learned-in-using-process-tracing-for-evaluation/
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Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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2 Case Reports of Gastric Ultrasound
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Managing missing values in routinely reported data: One approach from the Democratic Republic of the Congo
1. Managing missing values in routinely reported data:
One approach from the DRC
Matt Worges
Data for Impact Webinar Series
December 2, 2020
2. • Framing the Webinar through the D4I lens
• DHIS2 data: advantages and issues
• Exploring a DHIS2 data set
• What to do with blanks?
• Interpolation
• Recreate the “Truth”
• Interpolation diagnostics
Overview
3. • The D4I team was tasked with conducting an impact evaluation of
the USAID Integrated Health Project (IHP) implemented in 9
provinces of the DRC
• IHP goal: Reduce maternal, newborn, and child deaths through delivery of
integrated health services
• IHP objectives: Increase access to and use of quality health services in
the targeted health zones
IHP Impact Evaluation
4. • D4I research question: What was the impact of IHP on the
utilization of health services (e.g., treatment for childhood illnesses)
over the course of the study period?
• Measuring impact: D4I is assessing impact through a difference-
in-differences (DID) with propensity score matching (PSM) model
• Data source: We are using DHIS2 data for this impact evaluation
IHP Impact Evaluation – Approach
5. • PSM is widely used to mitigate confounding in observational
studies
• Complications arise when the covariates used to estimate the propensity
scores are only partially observed
• Interpolation/imputation approaches provide a potential solution for
handling missing data in the estimation of the propensity scores
• Recommended to derive the propensity score after applying interpolation or
imputation
IHP Impact Evaluation – Propensity Score Matching
6. • Addition/removal of health facilities at different time points
• Long runs of missing values
• Zero counts are typically not entered – they are left blank
• Cannot distinguish between truly missing and zero
• Data entry errors manifesting as outliers/anomalous points
• Reporting has improved over time making older time points less
complete
Some DHIS2 Issues
7. • Missing data can result in:
• Reduced statistical power
• Biased estimators
• Reduced representativeness of the sample
• Generally incorrect inference and conclusions
Why do we care about missingness?
Overview of Approaches for Missing Data – Susan Buchman
8. • Time Series Characteristics
• Restricted to Haut-Katanga Province, DRC
• Uncomplicated + severe malaria cases (all ages)
• 24-month period from October 2018 to September 2020
• Health facility count = 1,362
• The monthly-aggregated time series appears to include both a seasonal
and positive trend component
Data Set
9. Unprocessed Data – Missingness Visualized
HF Oct-18 Nov-18 Dec-18 Jan-19 Feb-19 Mar-19 Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20
hk Panda Hôpital Général de Référence 514 637 637 910 563 1375 678 483 839 773 929 792 694 1355 1219
hk Serge Amie Centre de Santé 300 306 274 300 320 440 522 582
hk AENAF Centre de Santé de Référence 91 60 212 154 65 279 114 59 213 55 131 38 399 227 222
hk Asvie Centre Médical 439 556 475 379 370 335 279 280 256 381 627 639
hk Mupanda Centre de Santé 610 479 363 610 641 408 573 248 237 279 455 319 203
hk Boma Publique Centre de Santé 294 293 304 293 308 318 178 225 326 325 240
hk Kawama Centre de Santé 174 176 2 283 280 304 286 288 4 275 379 319 264 313
hk Kabambakuku Centre de Santé 317 396 372 434 368 298 255 314 303 251 287 283
hk Kaboka Centre de Santé 419 314 201 240 350 199 151 197 274 257
hk Kasomeno Centre de Santé de Référence 282 307 306 265
hk Kikula Centre de Santé de Référence 221 241 246 275 167 318 393
hk Belle Vue Centre de Santé 135 157 555 350 124 102 92
10. Unprocessed Data – Missingness Visualized
Missing (28.6%) Present (71.4%)‘visdat’ package
Malaria Cases – Haut-Katanga Province
11. Unprocessed Data – Histogram of Missingness
No missing values
(complete case analysis)
Completely blank records
(remove from data set) One missing value
Two missing values
‘ggplot2’ package
284
193
137
27
12. Unprocessed Data – Outliers?
What are these doing here?
Are they malaria outbreaks?
Are they data entry errors?
13. Unprocessed Data – Outliers.
‘anomalize’ package
Something looks off here This point didn’t show up as anomalous
14. • One method to remove outliers is to delete those values that are
± X standard deviations from the median
• The median is insensitive to extreme values in your time series
• Experiment with different thresholds (i.e., ± 4 SDs from the median
or ± 6 SDs from the median) to examine what happens to your data
Removing Egregious Outliers – One Approach
17. Removing Egregious Outliers - Effects
Average Malaria Cases – Haut-Katanga Province
+4.5 SDs from the median
Removed 8 values or 0.025%
Unprocessed data set
19. Link between Missingness & Median Case Counts
1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 >150
Median Health Facility Malaria Cases (binned)
Generalization: the lower the median case counts the
higher the number of average missing values
20. • Assume no item nonresponse?
• Examine this notion with two extreme examples
• One HF time series with large monthly values and 1 missing
• One HF time series with low monthly values and 1 missing
• Replace missing with zero and run anomaly detection
Assumption: Missing Values are Zeros
21. Initial missing value was replaced with 0
Initial missing value was replaced with 0
‘anomalize’ package
23. • A univariate time series is a sequence of single observations at
regular and successive points in time
• Possible to decompose the time series into its trend, seasonal, and
irregular components
• We can use these time series characteristics in the interpolation process
Univariate Time Series
27. • Values in a series do not have violent, unexplained fluctuations
• The rate of change (increases/decreases) between points occurs at
a uniform rate
Assumptions of Interpolation
28. • Easy to code (one line in R for long form data frame)
• df$int_cases <- na_interpolation(df$cases, option = "linear", maxgap = 2)
• Intuitive understanding of linearly interpolating across very short
gaps of missing values
• Probably a good approach for high case load facilities
• May not grossly deviate from the ‘truth’ when applied to low case load
facilities
A Role for Linear Interpolation?
‘imputeTS’ package
33. • Take seasonality into account
• na.interp from the ‘forecast’ package in R
• By default, uses linear interpolation for non-seasonal series. For seasonal series, a
robust STL decomposition is first computed. Then a linear interpolation is applied to
the seasonally adjusted data, and the seasonal component is added back.
• na.StructTS from the ‘zoo’ package in R
• Interpolate with seasonal Kalman filter
• These two functions use similar mechanisms to interpolate missing
data in that they both can ‘handle’ seasonality in the time series
Univariate Time Series Interpolation
41. • Use a data set containing only complete time series records
• 2.5% of data are zero values (primarily limited to smaller facilities)
• Introduce random missingness
• Randomly delete15% of data points
• Delete 90% of remaining zero values
• Include runs of more than 2 missing values
• Apply various imputation methods and compare against the “truth”
• Replace all blanks with zeros
• Linear interpolation on gaps ≤ 2
• Use the two identified interpolation strategies that consider seasonality
A Quick Example
47. The RMSE difference is positive for 1,847
HFs indicating that the ‘na.StructTS’
approach had a lower RMSE for 68% of HFs
‘na.StructTS’ approach has lower RMSE
‘na.interp’ approach has lower RMSE
48. • Assess missingness
• Address egregious outliers
• Manage new/defunct facility records
• Decompose the time series
• Try a few different interpolation techniques and plot results
• Isolate a subset of records with no missing data
• Introduce missing data and then recreate the “truth”
Recap
49.
50. This presentation was produced with the support of the United States Agency for International
Development (USAID) under the terms of the Data for Impact (D4I) associate award
7200AA18LA00008, which is implemented by the Carolina Population Center at the University of
North Carolina at Chapel Hill, in partnership with Palladium International, LLC; ICF Macro, Inc.;
John Snow, Inc.; and Tulane University. The views expressed in this publication do not
necessarily reflect the views of USAID or the United States government.
www.data4impactproject.org
51. • DHIS 2 time series do not always lend themselves well to multiple
imputation
• Multiple imputation is a preferable choice when there are variables
predictive of missingness that could be included in the imputation model
• With DHIS 2 data, it can be difficult to locate other time dependent variables to aid in
the imputation process
• DHIS 2 time series may exhibit MNAR missingness structure
• Earlier time points have more missing data
• Zero values are more likely to be missing
Imputation
52. • Advantages of using DHIS2 data
• Access to a wide breadth of data elements/services
• Analyze at various levels of the health system
• National, regional, district, health facility
• Data are generally collected via standardized reporting tools
• Data tend to be reported at regular intervals allowing for frequent updates
to analyses
• However, not all data elements are well-reported, and it is typically
necessary to process/clean DHIS2 data
Why Use DHIS2 Data?