x̄ - > ATTACHMENT REPORT AT AMPATH AS FROM MAY 2ND TO JULY 31ST 2012

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AMPATH Attachment Report – 2012

📘 Cover Page

Chepkoilel University College

Flame of Knowledge and Innovation

Title

ATTACHMENT REPORT AT AMPATH AS FROM MAY 2ND TO JULY 31ST 2012

Department Attached at AMPATH

RESEARCH

Name

ZACHARIA NYAMBU

Reg No

SC/153/09

School

SCIENCE

Department

MATHEMATICS AND COMPUTER SCIENCE

Year of Study

THIRD YEAR

Date of Submission

31/7/2012

📑 Table of Contents

TABLE OF CONTENTS

1. ACKNOWLEDGEMENT — 3
2. DECLARATION — 4
3. ACRONYMS — 5
4. ABSTRACT — 7
5. CHAPTER 1
   • 1.1 INTRODUCTION — 9
   • 1.2 OBJECTIVES OF AMPATH — 9
   • 1.2 HISTORY OF AMPATH — 10
   • 1.3 MISSION AND VISION — 11
   • 1.4 VALUES AND PRINCIPLES — 12
   • 1.5 AMPATH DEPARTMENTS AND BRIEF DESCRIPTION — 13
6. CHAPTER 2
   • 3.0 EXPERIENCES AT AMPATH — 15
   • 3.1 DUTIES AND RESPONSIBILITIES — 15
   • 3.2 NEW KNOWLEDGE AND SKILLS LEARNT — 16
   • 3.3 THINGS ENJOYED — 17
   • 3.4 CHALLENGES — 17
   • 3.5 BENEFITS OF ATTACHMENT — 18
7. CONCLUSION — 18
8. RECOMMENDATIONS — 19
9. REFERENCES — 19
10. APPENDICE — 21

🙏 Acknowledgement

ACKNOWLEDGEMENT

Thanks to AMPATH for giving me this opportunity to be in this organization and its entire staff for making my attachment a remarkable experience. Special appreciation to:

• Mrs. Jepchirchir Kiplagat: Research Manager
• Ms. Eunice Gift: Assistant Research Manager
• Mr. Alfred Koskel: Research Assistant
• Dr. Ann Mwangi head of biostatistics department
• Mr. Koech and Mr. Keter: Biostatisticians
• Mr. Gilbert Simiyu, Ms. Monica Mwaniki and Mr. James Osanya: Data Managers
• My fellow interns.

✍️ Declaration

DECLARATION

I hereby declare that this attachment report is my work and has not been published or written elsewhere

NAME: ZACHARIA NYAMBU

SIGN:

[Handwritten signature]

DATE: 31/7/2012

SUPERVISOR: ALFRED KOSKEY

SIGN:

[Handwritten signature]

DATE: 31/7/12

Stamp: AIRPORT CENTRE RESEARCH, P.O. Box 46006 - 00100, dated 31 JUL 2012

🎯 CHAPTER 1

CHAPTER 1

1.1 INTRODUCTION

1.2 OBJECTIVES OF ATTACHMENT

• To acquire knowledge on data collection, data entry, data cleaning, data coding, data exporting, data quality, data analyzing and data presentation.
• To learn how to use statistical packages like STATA, SPSS, R and SAS.
• To learn how to create databases using Microsoft Access and Excel.
• To learn how to create questionnaires.
• To acquire knowledge on field and functional activities of a participating organization.
• To build a curriculum vitae.
• To acquire knowledge on health data collection.

AMPATH

P.O. BOX 4606, ELDORET, KENYA 30100

TEL: +254532203471/2

FAX: 25453206072

WEB: WWW.AMPATHKENYA.ORG

📚 Abstract

ABSTRACT

This is an attachment report in AMPATH from 2nd May to 31st July 2012. It outlines the scope of the report, including how AMPATH works, its brief history, the duties and responsibilities undertaken, and the new skills and knowledge gained. The abstract highlights the collaboration among different departments to create an academic model aimed at improving the health of the Kenyan population.

🔠 Acronyms

ACRONYMS

• AMPATH - Academic Model Providing Access to Healthcare
• IU - INDIANA UNIVERSITY
• SPSS - Statistical Package for the Social Science
• SAS - Statistical Analysis System
• STATA - Data Analysis and Statistical Software
• R - Open Source Programming Language for Statistical Computing
• HAART - Highly Active Antiretroviral Therapy
• PMTCT - Prevention of Mother to Child Transmission
• PHI - Haart and Harvest Initiative
• HCT - HIV Counseling and Testing
• FPI - Family Preservation Initiative
• LACE - Legal Aid Centre
• IFS - Immuno Suppressed
• AMRS - Academic Medical Recording System
• DTC - Diagnostic Testing and Counselling
• PCP - Pneumonia
• PTB - Pulmonary Tuberculosis
• CD4 - Cluster Domain
• NFDA - No Known Food and Drug Allergies
• HNAN - HIV Associated Nephropathy
• PHC - Primary Health Care

🏥 COMPANY BRIEF HISTORY

1.2 HISTORY OF AMPATH

AMPATH (Academic Model Providing Access to Healthcare) is an academic model for the prevention and treatment of HIV/AIDS. It is a collaboration between Indiana University School of Medicine and Kenyan institutions, offering food, income, and other support to enhance the existing health infrastructure. It began in November 2001 with two sites and has expanded to 55 sites, treating approximately 130,000 patients.

🎯 AMPATH MISSION, VISION & PRINCIPLES

1.3 MISSION AND VISION

• To provide and expand sustainable access to high quality care through excellent healthcare for individuals and population
• Develop passionate leaders in pharmacy
• Perform research focused on local needs and global solutions
• Establish critical healthcare infrastructure and systems

VISION

Innovative, provide and enhance quality care for all people through teamwork and dedication

1.4 VALUES AND PRINCIPLES

• Service with humility
• Spirit of collaboration and partnership
• Mutual respect and mutual benefit in organization partnership
• Focus on vulnerable populations
• Efforts to eliminate health disparities

🧪 1.5 RESEARCH DEPARTMENT

RESEARCH DEPARTMENT

Conducts clinical and operational research. It entails data collection, data entry, data management, data cleaning, data exporting, data presenting so that health care services can be of high quality.

🧭 AMPATH DEPARTMENTS

AMPATH DEPARTMENTS AND DESCRIPTION

AMPATH has different departments that offer different services but with an objective of establishing critical healthcare infrastructure and systems. The departments are:

CLINICAL SERVICES

Services offered by the clinic or modules whereby clients are tested again to confirm HIV status. If positive, the client is registered and given an AMPATH ID number and lab investigations are done.

HAART PHARMACY

Provides reliable and consistent supply of medications along with appropriate counseling

OUTREACH DEPARTMENT

Clients fill locator forms that help the outreach department locate them if they miss appointments for more than two months.

NUTRITION SERVICES

Provides nutritional support to clients to help them maintain health and improve immune system through education and food supplements.

🧑‍💻 CHAPTER 2

CHAPTER 2 – EXPERIENCES AT AMPATH

3.1 DUTIES AND RESPONSIBILITIES

DATA COLLECTION

Data collection took place in the MTRH ward, divided into four wards. Data was collected only if the patient was an S or S-exposed child from inpatient registration forms.

DATA ENTRY

Information collected was entered into the AMPATH database, which checks for duplicates and provides feedback on readmissions.

DATA EDITING AND RETRIEVAL

Missing data is retrieved from the ward and re-entered into the AMRS using the nurse's unit station.

3.2 NEW KNOWLEDGE AND SKILLS LEARNT

• Data collection methods
• Data entry
• Creation of database
• Collecting health data
• Creation of questionnaire
• Data management
• Data analysis
• Statistical packages like SAS, STATA and R
• EpiInfo and InfoPath
• Administrative skills
• Work ethics
• Medical field terminologies

3.3 THINGS ENJOYED

• Learning statistical packages
• Access to internet for research
• Interacting with biostatisticians and data managers
• Collecting and entering data

3.4 CHALLENGES

• Seeing patients pass away while collecting data
• Computer viruses causing data loss
• Staff availability issues affecting data collection

3.5 BENEFITS OF ATTACHMENT

Learned new statistical packages, work ethics, career choices, strengthened CV, and gained interview preparation experience.

✅ CONCLUSION

CONCLUSION

This attachment enabled practical application of theoretical knowledge in statistics. I gained skills in data collection, management, and analysis using STATA, SAS, and R, while developing professional interpersonal skills.

💡 RECOMMENDATIONS

RECOMMENDATIONS

• Test patients on admission
• Install antivirus software
• Increase computer access for attaches
• Implement strict activity schedules

📚 REFERENCES

REFERENCES

• AMPATH Training Institute (ATI)
• AMPATH inpatient registration form
• AMPATH website

📎 APPENDIX

APPENDICE

Supporting materials and documentation.

Author: Zacharia Nyambu • Chepkoilel University College • Attachment at AMPATH (May 2 – July 31, 2012)

x̄ - > SAS Learning Roadmap — Stage 1 to 6 (Responsive)

A forward-thinking, traditional guide — learn what matters, then touch SAS.

Contents

• Stage 1 — Foundations
• Stage 2 — SAS Basics
• Stage 3 — Core Skills
• Stage 4 — Analytics
• Stage 5 — Advanced SAS
• Stage 6 — Best Practices
• Recommended Resources
• Formatting & Style Examples

Stage 1: Foundations

Before touching SAS, ground yourself in these core pillars so the language feels like an intuitive tool rather than complex syntax:

• Basic statistics — Mean, median, variance, regression, distributions. Know what summary numbers tell you about data.
• Data structures — Tables, rows, columns. Think relationally: each row represents an observation; each column holds an attribute.
• Programming logic — Variables, loops, conditions. Flow control is theQuiet muscle beneath every analysis.

💡 Pro Tip: Always ask where the data came from and who collected it before starting your analytical pipeline.

Stage 2: Learning SAS Basics

Begin with the essentials. Install or open SAS Studio / SAS OnDemand for Academics and learn to write small, structured programs.

Environment & Setup

SAS OnDemand for Academics is the free cloud platform for learning SAS Studio.

DATA Step (Data Manipulation)

Used to read, clean, and transform datasets. The DATA step is where rows are created, filtered, and altered.

DATA mydata;
    SET sashelp.class; /* Copies built-in dataset */
    WHERE age > 12;     /* Filters rows */
RUN;

PROC Step (Procedures)

Procedures analyze, summarize, or report on data with concise statements.

PROC MEANS DATA=sashelp.class;
    VAR height weight;
RUN;

Input & Output

Use PROC IMPORT for Excel/CSV files, INFILE for raw text, and export results using PROC EXPORT.

Stage 3: Core Skills

• Data Cleaning: IF, WHERE, KEEP, DROP, RENAME
• Merging & Appending: SET and MERGE statements
• Formatting: PROC FORMAT for custom, readable values
• Sorting & Summarizing: PROC SORT, PROC FREQ, PROC SUMMARY

Stage 4: Analytics

Apply statistical procedures to answer complex questions:

• Linear Regression: PROC REG, PROC GLM
• Time Series: PROC ARIMA
• Logistic Regression: PROC LOGISTIC
• Survival Analysis: PROC LIFETEST

Stage 5: Advanced SAS

• Macros: Automate repetitive workflow execution using %MACRO and %MEND.
• PROC SQL: Query SAS datasets using familiar SQL syntax and relational joins.
• SAS Functions: Manipulate dates, character strings, and arrays efficiently.
• Performance Tuning: Indexing datasets and optimizing memory usage.

Stage 6: Best Practices

• Comment code: Explain why an operation is done, not just what it does.
• Readability over terseness: Indent statements and keep code clean for maintenance.
• Debugging: Master PUTLOG statements and monitor the SAS Log window carefully.

Recommended Resources

• The Little SAS Book — Lora D. Delwiche & Susan J. Slaughter (classic beginner guide)
• Learning SAS by Example — Ron Cody
• Practice Datasets: Explore built-in SAS libraries like sashelp.class and sashelp.cars

Formatting & Style Examples

Clarity beats compactness. Compare poorly formatted code with clean, readable standards:

1. Statement Spacing & Line Breaks

Bad (Multiple statements crammed on one line):

Data Urate_ny; Set Urate_US; if state='NY'; Run;

Good (One statement per line with proper casing):

DATA Urate_ny;
    SET Urate_US;
    IF State = 'NY';
RUN;

2. Formatting PROC SQL Queries

Bad (Unformatted SQL string):

Proc sql; CREATE table health_plan_choices as SELECT Company, Job, Health_plan FROM library.occ_source WHERE quarter_begin <= &Mquarter and quarter_end >= &Mquarter; quit;

Good (Indented clauses for easy debugging):

PROC SQL;
    CREATE TABLE health_plan_choices AS 
    SELECT Company, 
           Job, 
           Health_plan,
           Worker_id /* Inline documentation */
    FROM library.occ_source 
    WHERE quarter_begin <= &Mquarter 
      AND quarter_end >= &Mquarter;
QUIT;

Quick Exercises

1. Open SAS Studio and execute a PROC MEANS step on sashelp.class.
2. Write a DATA step that filters records from a CSV dataset and exports the clean output.
3. Take a single-line PROC SQL query and format it into clean multi-line SAS syntax.