{
  "format": "dataset-manifest/1",
  "slug": "public-health-data",
  "version": 1,
  "seed": "public-health-data:1",
  "spec": {
    "format": "dataset/1",
    "slug": "public-health-data",
    "name": "Public Health Insights",
    "tagline": "Health surveys, vaccinations, prescriptions, and chronic conditions data from diverse patient populations.",
    "description": "Healthcare providers analyze vaccination rates and chronic conditions to inform public health initiatives. Clinics and hospitals collect health survey data to understand community needs and track health trends. Each health survey captures patient responses regarding their medical history, lifestyle choices, and current health status, providing a rich source of insights for healthcare professionals. Vaccination events are documented to monitor immunization rates, which are crucial for preventing disease outbreaks. Prescriptions detail medications prescribed to patients, giving insight into common health issues faced by the population. Chronic conditions, recorded for each patient, help in identifying prevalent health challenges and planning targeted interventions.\n\nThe tables in this dataset relate closely, forming a comprehensive picture of public health. Each row in the health_surveys table corresponds to an individual patient's responses, which can be linked to specific vaccination_events through patient identifiers. Prescriptions are tied to patients, allowing users to analyze medication trends in relation to chronic_conditions. By correlating vaccination events with health survey data, one can assess the effectiveness of vaccination campaigns on the health of various demographics.\n\nThis dataset answers critical questions about public health, such as: What vaccination rates are being achieved in different regions? How do chronic conditions influence prescription patterns? Are there any correlations between vaccination events and reported adverse health outcomes? The data is excellent for practicing data analysis techniques, such as joins and aggregations, and for generating insights that can lead to improved public health strategies. Keep in mind that the records are synthetic, designed for educational purposes, ensuring that no real patient information is included.",
    "domain": "pharmacy",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "public health",
      "vaccination",
      "health surveys",
      "prescriptions",
      "chronic conditions",
      "data analytics",
      "synthetic data"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "health_surveys": {
        "description": "Each row represents a health survey response from a patient, linking to their demographic and health data.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each health survey response."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "ID of the patient who completed the survey."
          },
          "survey_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the survey was completed.",
            "trend": "seasonal"
          },
          "health_status": {
            "kind": "enum",
            "values": [
              "good",
              "fair",
              "poor"
            ],
            "description": "Self-reported health status of the patient."
          },
          "chronic_conditions": {
            "kind": "words",
            "max": 50,
            "description": "List of chronic conditions reported by the patient.",
            "nullable": true,
            "when": {
              "column": "health_status",
              "in": [
                "fair",
                "poor"
              ]
            }
          }
        }
      },
      "vaccination_events": {
        "description": "This table records vaccination events, detailing which patients received vaccinations and when.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each vaccination event."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "ID of the patient who received the vaccination."
          },
          "vaccine_type": {
            "kind": "enum",
            "values": [
              "flu",
              "MMR",
              "COVID-19",
              "HPV"
            ],
            "description": "Type of vaccine administered."
          },
          "vaccination_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the vaccination was administered.",
            "trend": "seasonal"
          },
          "adverse_event": {
            "kind": "bool",
            "trueRate": 0.1,
            "description": "Indicates if any adverse event was reported post-vaccination.",
            "nullable": true,
            "when": {
              "column": "vaccine_type",
              "in": [
                "flu",
                "MMR",
                "COVID-19"
              ]
            }
          }
        }
      },
      "prescriptions": {
        "description": "Each row details a prescription issued to patients, linking to the corresponding health survey and vaccination records.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each prescription."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "ID of the patient who received the prescription."
          },
          "medication_name": {
            "kind": "words",
            "max": 50,
            "description": "Name of the medication prescribed."
          },
          "prescription_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the prescription was issued.",
            "trend": "growing"
          },
          "dosage": {
            "kind": "words",
            "max": 20,
            "description": "Dosage instructions for the prescribed medication."
          }
        }
      },
      "chronic_conditions": {
        "description": "This table catalogs chronic conditions reported by patients, linking to their health surveys.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each chronic condition entry."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "ID of the patient with the chronic condition."
          },
          "condition_name": {
            "kind": "words",
            "max": 50,
            "description": "Name of the chronic condition."
          },
          "diagnosis_date": {
            "kind": "date",
            "from": "2010-01-01",
            "to": "2023-12-31",
            "description": "Date when the chronic condition was diagnosed.",
            "trend": "growing"
          }
        }
      },
      "patients": {
        "description": "The patients table holds demographic information of the patients involved in health surveys and vaccinations.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each patient."
          },
          "age": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Age of the patient."
          },
          "gender": {
            "kind": "enum",
            "values": [
              "male",
              "female",
              "other"
            ],
            "description": "Gender of the patient."
          },
          "region": {
            "kind": "words",
            "max": 50,
            "description": "Region where the patient resides.",
            "nullable": true,
            "when": {
              "column": "gender",
              "in": [
                "other"
              ]
            }
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "high-cardinality",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average age of patients receiving vaccinations?",
      "How do vaccination rates vary by region and gender?",
      "Are there any correlations between chronic conditions and the types of medications prescribed?",
      "What percentage of patients reported adverse events after vaccination?",
      "How has the prevalence of chronic conditions changed over the years?"
    ],
    "suggestedQueries": [
      {
        "title": "List all patients with their vaccination events",
        "sql": "SELECT p.id, p.age, v.vaccine_type, v.vaccination_date FROM patients AS p JOIN vaccination_events AS v ON p.id = v.patient_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count of vaccinations by vaccine type",
        "sql": "SELECT vaccine_type, COUNT(*) FROM vaccination_events GROUP BY vaccine_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average age of patients with chronic conditions",
        "sql": "SELECT AVG(p.age) FROM patients AS p JOIN chronic_conditions AS c ON p.id = c.patient_id;",
        "dialect": "postgres"
      },
      {
        "title": "Patients with adverse events post-vaccination",
        "sql": "SELECT p.id, p.gender FROM patients AS p JOIN vaccination_events AS v ON p.id = v.patient_id WHERE v.adverse_event = TRUE;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Public Health Analysts",
        "body": "Public health analysts can utilize this dataset to examine health trends across different demographics and regions. By analyzing vaccination rates and chronic conditions, they can identify health disparities and recommend targeted health interventions to improve community health outcomes."
      },
      {
        "title": "Pharmaceutical Researchers",
        "body": "Pharmaceutical researchers can leverage the detailed prescription data to study the effectiveness of medications for chronic conditions. This dataset allows them to correlate health survey responses with medication outcomes, helping to inform clinical trials and drug development."
      },
      {
        "title": "Healthcare Policy Makers",
        "body": "Healthcare policymakers can use this dataset to understand the impact of vaccination programs and public health campaigns. Insights derived from the data can guide policy decisions aimed at increasing vaccination uptake and managing chronic diseases in the population."
      },
      {
        "title": "Data Scientists",
        "body": "Data scientists can practice their data analysis and ETL skills using this dataset. The variety of tables and relationships offers a rich ground for building predictive models, exploring data visualization techniques, and developing data-driven strategies for health improvements."
      }
    ]
  },
  "counts": {
    "patients": 500,
    "chronic_conditions": 250,
    "health_surveys": 500,
    "prescriptions": 400,
    "vaccination_events": 300
  },
  "stats": {
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