# data[].attributes.headline
I would divide them into to 2 major groups (exposure to PM2.5) and (non-exposure TO PM2.5) 

# data[].attributes.subject
I would divide them into to 2 major groups (exposure to PM2.5) and (non-exposure TO PM2.5) 

# data[].attributes.body
Cohort is looking into the concept of following a particular exposure and see if it will be causing the disease

# data[].attributes.body
Individuals living in areas with high levels of PM2.5 pollution. This could be determined using air quality monitoring data to identify regions with consistently high PM2.5 levels. And individuals living in areas with low levels of PM2.5 pollution. These areas should have PM2.5 levels well below the thresholds considered harmful by health standards

# data[].attributes.headline
there are many confounding variable in this case including (smoking, lack of physical activity, unhealthy diet, alcohol consumption, genetics)

# data[].attributes.subject
there are many confounding variable in this case including (smoking, lack of physical activity, unhealthy diet, alcohol consumption, genetics)

# data[].attributes.body
We should define Two groups of people who live in are with high exposure, and the other with less exposure.

# data[].attributes.headline
take a sample that accurately represents the population, and divide them equally to reduce bias each group should have the same characteristics compared to the other group. (one group will not change their place and still be exposed to air pollution) other group will be displaced into another region) regular checkups will be made to check their cardiovascular health. (surveys, medical record, interviews)

# data[].attributes.subject
take a sample that accurately represents the population, and divide them equally to reduce bias each group should have the same characteristics compared to the other group. (one group will not change their place and still be exposed to air pollution) other group will be displaced into another region) regular checkups will be made to check their cardiovascular health. (surveys, medical record, interviews)

# data[].attributes.body
Participants will be grouped into three cohorts based on long-term PM2.5 exposure: high, moderate, and low. Each group will be further divided into healthy individuals and those with CVD risk factors. Over 10–15 years, the incidence of cardiovascular disease will be tracked to assess the impact of PM2.5 exposure on CVD risk

# data[].attributes.body
Confounders include age, sex, socioeconomic status, smoking, physical activity, diet, comorbidities, occupation, and genetics. These should be controlled for to accurately assess the link between PM2.5 and CVD.

# data[].attributes.body
Confounding factors to consider are pollution, lifestyle, age, and gender.

# data[].attributes.body
Data will be collected from air quality monitors, health assessments, lifestyle questionnaires, and follow-up check-ups to track CVD development.

# data[].attributes.body
Through surveys and air quality monitoring/tracking.

# data[].attributes.body
to define the cohorts in this study the exposed group will include individuals in high PM2.5 areas and the control group in low PM2.5 areas and both will be free of CVD at the start and tracked over time

# data[].attributes.body
the confounding factors to consider include age smoking diet physical activity genetics socioeconomic status and pre existing health conditions as these factors can influence cardiovascular disease risk independently of PM2.5 exposure

# data[].attributes.body
to collect data for this study air quality monitoring stations and satellite data will be used to measure PM2.5 exposure while medical records health surveys and periodic check ups will be conducted to track the development of cardiovascular disease in participants

# data[].attributes.body
Cohorts would be defined by individuals with varying long-term exposure levels to PM2.5, tracked for cardiovascular disease development.

# data[].attributes.body
Confounding factors to consider include age, gender, socioeconomic status, smoking habits, physical activity, diet, pre-existing health conditions, and access to healthcare.

# data[].attributes.headline
surveys, medical records, air quality monitoring, health screenings

# data[].attributes.subject
surveys, medical records, air quality monitoring, health screenings