pophealth-observatory 0.3.0

Open-source population health & nutrition analytics toolkit (NHANES focus)

PopHealth Observatory

Actionable population health & nutrition analytics: acquisition → harmonization → stratified insights → visualization.

PopHealth Observatory is an open-source toolkit for exploring population health and nutrition metrics using publicly available survey microdata (current focus: NHANES). It streamlines secure data acquisition, cleaning, demographic stratification, trend analysis, and visualization—designed for reproducible epidemiologic and health disparities research.

Overview

The project provides a Python-based framework for ingesting, harmonizing, and analyzing public health survey data (initially NHANES). NHANES (National Health and Nutrition Examination Survey) is a nationally representative program assessing the health and nutritional status of the U.S. population. PopHealth Observatory abstracts common data wrangling and analytic patterns so you can focus on questions, not boilerplate.

Features

• Automated Acquisition: Pull SAS transport (.XPT) files directly from CDC endpoints
• Caching Layer: Avoid redundant downloads within a session
• Schema Harmonization: Standardized variable selection & human-readable labels
• Derived Metrics: BMI categories, blood pressure categories, summary anthropometrics
• Demographic Stratification: Rapid group-wise descriptive statistics
• Cycle Comparison: Simple cross-cycle trend scaffolding
• Visualization Suite: Boxplots, distributions, stratified means, interactive widgets
• Extensible Architecture: Plug in additional NHANES components or other survey sources
• Reproducible Reporting: Programmatic summary report generation
• Rich Metadata Manifest: Enumerate all component table rows with standardized schema & filtering

Installation

Repository Structure

├─ apps/                       # End-user applications (Streamlit, future CLI wrappers)
│  └─ streamlit_app.py         # Interactive NHANES exploration UI
├─ examples/                   # Simple executable usage examples
│  └─ demo.py                  # Former main.py demonstration script
├─ manifests/                  # Generated manifest JSON artifacts (not source code)
│  └─ component_files_manifest_...json
├─ notebooks/                  # Exploratory & development Jupyter notebooks
│  ├─ nhanes_demographics_link_finder.ipynb
│  ├─ nhanes_explorer_demo.ipynb
│  ├─ nhanes_url_testing.ipynb
│  ├─ observatory_exploration.ipynb
│  └─ README.md
├─ pophealth_observatory/      # Library source (core observatory & explorer classes)
├─ tests/                      # Automated tests (unit / integration)
├─ requirements.txt            # Python dependencies
├─ pyproject.toml / setup.py   # Packaging configuration
├─ CHANGELOG.md                # Versioned change log
└─ README.md                   # Project documentation (this file)

Apps Directory

apps/streamlit_app.py provides an interactive interface to:

• Select NHANES cycle and view merged demographics + clinical metrics
• Slice metrics by demographic categories with summary statistics
• Inspect laboratory and questionnaire file inventory via manifest sampling
• Preview raw merged data (first N rows) for QA

Future additions may include:

• CLI data export tool (e.g., apps/nhanes_export.py)
• Dashboard variants (e.g., multi-page Streamlit or FastAPI backend)

From Source (Development)

1. Clone:

   git clone https://github.com/paulboys/PopHealth-Observatory.git
   cd PopHealth-Observatory
   

2. (Recommended) Create & activate a virtual environment.
3. Install in editable mode with dev extras:

   pip install -e .[dev]
   

(Future) From PyPI

Once published:

pip install pophealth-observatory

Until then, use the source install above.

Quick Start

from pophealth_observatory import NHANESExplorer

# Initialize the explorer (NHANES-focused implementation)
explorer = NHANESExplorer()

# Validate data quality before analysis (recommended)
validation_report = explorer.validate('2017-2018', ['demographics', 'body_measures'])
print(f"Data Validation: {validation_report['status']}")  # PASS/WARN/FAIL

# Download and merge demographics, body measures, and blood pressure data
data = explorer.create_merged_dataset('2017-2018')

# Generate a summary report
print(explorer.generate_summary_report(data))

# Analyze BMI by race/ethnicity
bmi_by_race = explorer.analyze_by_demographics(data, 'bmi', 'race_ethnicity_label')
print(bmi_by_race)

# Create visualization
explorer.create_demographic_visualization(data, 'bmi', 'race_ethnicity_label')

Interactive App (Streamlit)

An interactive exploration UI is provided via streamlit_app.py.

Run locally:

streamlit run streamlit_app.py

Features:

• Select NHANES cycle
• Choose metric & demographic for aggregation (mean / median / count)
• View summary table & bar chart
• Inspect laboratory & questionnaire manifest sample (schema-aligned)
• Optional raw data preview (first 500 rows)

Requirements: streamlit (installed via requirements.txt).

Metadata Manifest (NHANES Component Tables)

The explorer can build a structured manifest of NHANES component listing tables (Demographics, Examination, Laboratory, Dietary, Questionnaire) including:

Fields per row:

• year_raw, year_normalized (e.g. 2005_2006)
• data_file_name
• doc_file_url, doc_file_label
• data_file_url, data_file_label
• data_file_type (XPT | ZIP | FTP | OTHER)
• data_file_size (e.g. 3.4 MB if present)
• date_published
• original_filename, derived_local_filename (cycle-year appended for XPT when possible)

Schema control:

• Top-level manifest includes schema_version (semantic version; current: 1.0.0) and generated_at (UTC ISO timestamp).
• Future structural changes will increment the manifest schema version (MAJOR = breaking, MINOR = additive, PATCH = non-breaking fixes).

Generate a Manifest

from pophealth_observatory.observatory import NHANESExplorer
e = NHANESExplorer()
manifest = e.get_detailed_component_manifest(
   components=['Demographics','Laboratory'],
   file_types=['XPT'],            # optional filter
   year_range=('2005','2014'),    # inclusive overlap on normalized spans
   as_dataframe=True              # attach pandas DataFrame
)
print(manifest['schema_version'], manifest['total_file_rows'])
print(manifest['summary_counts'])
df = manifest['dataframe']
print(df.head())

Persist to JSON

e.save_detailed_component_manifest(
   'nhanes_manifest.json',
   file_types=['XPT','ZIP'],
   year_range=('1999','2022')
)

Overriding Schema Version (Advanced)

You can pass a custom schema_version if producing a forked or experimental layout:

e.get_detailed_component_manifest(schema_version='1.1.0-exp')

Caching & Refresh

• Component listing HTML pages are cached in-memory per session.
• Use force_refresh=True to re-fetch a component page.

Filtering Logic

• year_range=('2005','2010') keeps any row whose normalized span overlaps that interval.
• file_types=['XPT'] restricts to XPT transport files.

Summary Structure

summary_counts is a nested dict: { component: { data_file_type: count } } for quick inventory.

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Example Analyses

BMI by Race/Ethnicity

Analyze how Body Mass Index (BMI) varies across different racial and ethnic groups.

Blood Pressure by Gender

Compare systolic and diastolic blood pressure measurements between males and females.

Health Metrics by Education Level

Explore how health indicators vary by educational attainment.

Data Components

Implemented ingestion helpers (download + basic harmonization) currently cover:

• Demographics (DEMO)
• Body Measurements (BMX)
• Blood Pressure (BPX)

Additional component codes are mapped internally (see PopHealthObservatory.components) but do not yet have dedicated loader convenience methods:

• Cholesterol (TCHOL)
• Diabetes (GLU)
• Dietary Intake (DR1TOT)
• Physical Activity (PAQ)
• Smoking (SMQ)
• Alcohol Use (ALQ)

Planned expansion will add per-component loaders patterned after get_body_measures() with column selection, semantic renaming, and derived metrics where appropriate.

Roadmap (Planned Enhancements)

• Additional NHANES components (lab panels, dietary day 2, activity monitors)
• Weighted analyses helper utilities (survey design integration)
• Cross-cycle variable harmonization registry
• BRFSS / other public health dataset adapters
• Optional DuckDB / Parquet caching for large multi-cycle assemblies
• CLI interface for scripted batch exports

Retrieval-Augmented Generation (RAG) Scaffolding (Experimental)

An LLM-agnostic RAG layer is scaffolded to let users experiment with question answering over curated pesticide narrative snippets without requiring a local GPU or committing to a specific model provider.

Key pieces:

• pesticide_ingestion.py – builds JSONL snippet files from raw narrative text.
• pophealth_observatory.rag package – lightweight embedding + retrieval utilities.
  • RAGConfig – paths & settings.
  • DummyEmbedder – deterministic CPU-only test embedder (no external downloads).
  • SentenceTransformerEmbedder – optional (install with pip install pophealth-observatory[rag]).
  • RAGPipeline – orchestrates loading snippets, embedding (with caching), retrieval, and prompt assembly.

Usage example (after generating a snippets JSONL using the ingestion scaffold):

from pathlib import Path
from pophealth_observatory.rag import RAGConfig, RAGPipeline, DummyEmbedder

cfg = RAGConfig(
      snippets_path=Path('data/processed/pesticides/snippets_pdp_sample.jsonl'),
      embeddings_path=Path('data/processed/pesticides/emb_cache'),
)
pipeline = RAGPipeline(cfg, DummyEmbedder())
pipeline.prepare()  # loads snippets & builds or loads cached embeddings

def echo_generator(question, snippets, prompt):
      # In real usage, call your LLM API or local model here.
      return f"(stub) {len(snippets)} snippets considered"

result = pipeline.generate("What are DMP trends?", echo_generator, top_k=3)
print(result['answer'])

To use real embeddings:

pip install "pophealth-observatory[rag]"

Then substitute DummyEmbedder() with:

from pophealth_observatory.rag import SentenceTransformerEmbedder
pipeline = RAGPipeline(cfg, SentenceTransformerEmbedder())

Provide any LLM by passing a generator function: (question, snippets, prompt) -> answer.

Future directions: FAISS-based index (already partially supported via optional dependency), hybrid lexical + vector retrieval, snippet ranking refinement, streaming answer helpers.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome. Open issues for: feature requests, new NHANES components, performance improvements, documentation gaps. Use conventional commits where possible.

Dev Workflow

# Install pre-commit hooks (one-time setup)
pre-commit install

# Hooks run automatically on commit, or manually:
pre-commit run --all-files

# Lint
ruff check .

# Format (check) / apply
black --check .
black .

# Run tests with coverage
pytest -q
coverage run -m pytest && coverage report -m

Pull Requests

• Keep changes focused
• Add/extend tests for new logic
• Update CHANGELOG.md if user-facing changes
• Ensure CI passes (lint, tests, build)

Acknowledgments & Disclaimer

• Data provided by the National Health and Nutrition Examination Survey
• Centers for Disease Control and Prevention (CDC) / National Center for Health Statistics (NCHS)

PopHealth Observatory is an independent open-source project and is not affiliated with, endorsed by, or sponsored by CDC or NCHS. Always review official NHANES documentation for variable definitions and analytic guidance, especially regarding complex survey design and weighting.

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Tagline: Population health analytics from acquisition to insight.

Suggested GitHub Topics: population-health, epidemiology, public-health, nutrition, analytics, data-science, health-disparities, python, nhanes, visualization

© 2025 Paul Boys and PopHealth Observatory contributors