# 🍼 KPrematurity: Analyzing Prematurity in Brazil

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## Overview

Welcome to the KPrematurity repository! This project presents the findings from a retrospective observational study conducted in 2020. We explored socioeconomic profiles and the adequacy of prenatal care in relation to the occurrence of prematurity within various health units across Brazil. Our goal is to contribute to a deeper understanding of the factors influencing preterm births in the region.

## Table of Contents

- [Introduction](#introduction)
- [Background](#background)
- [Study Objectives](#study-objectives)
- [Methodology](#methodology)
- [Data Sources](#data-sources)
- [Findings](#findings)
- [Technologies Used](#technologies-used)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Introduction

Prematurity remains a significant public health concern worldwide. Understanding the socio-economic factors and adequacy of prenatal care can help address this issue effectively. KPrematurity focuses on analyzing the conditions that contribute to preterm births in Brazil, using health data from 2020.

## Background

In Brazil, the rate of prematurity has raised alarms in the public health sector. Prematurity, defined as births occurring before 37 weeks of gestation, can lead to serious health complications for infants. Factors such as access to quality prenatal care, maternal health, and socioeconomic status play crucial roles in influencing these outcomes.

## Study Objectives

This study aims to:

- Analyze socioeconomic profiles of mothers who experienced prematurity.
- Evaluate the adequacy of prenatal care provided.
- Identify trends and correlations between socioeconomic factors and rates of prematurity.
- Offer actionable insights for policymakers and health professionals.

## Methodology

We employed a retrospective observational design to collect and analyze data from health units. Our approach included:

- Gathering data from the Brazilian Institute of Geography and Statistics (IBGE) and the Sistema de Informações sobre Nascidos Vivos (SINASC).
- Using k-means clustering to categorize socioeconomic profiles.
- Conducting statistical analyses to find significant correlations.

## Data Sources

The primary data sources include:

- **IBGE**: For socioeconomic indicators and demographic data.
- **SINASC**: For information on live births and prenatal care adequacy.
- Health unit records across multiple states in Brazil for a comprehensive dataset.

## Findings

The findings of our study reveal critical insights into the socio-economic factors influencing prematurity rates:

- Mothers from lower socioeconomic backgrounds had higher rates of preterm births.
- Inadequate prenatal care correlated with increased occurrences of prematurity.
- Regional disparities exist, indicating a need for targeted health interventions.

## Technologies Used

This project utilizes several technologies and programming languages, including:

- **R**: For data analysis and statistical modeling.
- **K-means Clustering**: For identifying patterns in socioeconomic profiles.
- **GitHub**: For version control and collaboration.

## Getting Started

To explore the data and results from our study, follow these steps:

1. Clone this repository to your local machine: