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Summary of Work:

To calculate the crude death rate and age-standardised death rate for chronic obstructive pulmonary disease (COPD) in the United States and Uganda for 2019, I followed these steps:

  1. Gathered age-specific death rates for COPD in both countries for 2019. Source: Notion
  2. Gathered populations for both countries for 2019. Source: UN World Population Prospects (2022) — Population Estimates 1950-2021 data tool
  3. Calculated the crude death rate by summing up all age-specific death rates and dividing by the total population.
  4. Calculated the age-standardised death rate using the WHO standard population to account for differences in age distributions between the countries.
  5. Used the WHO World Standard population distribution percentages for age groups for age-standardisation. Source:WHO Standard Population — Table 1 in 'Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M (2001). Age standardization of rates: a new WHO standard.'
  6. Rounded the results to one decimal place to represent deaths per 100,000 people.

Overall, while the crude death rate indicates a higher burden of COPD mortality in Uganda compared to the United States, the age-standardised death rates suggest that this difference may be partially attributed to variations in age distributions between the two populations. Other factors such as healthcare access, quality of care, lifestyle choices

# Importing necessary libraries
import pandas as pd

# Age-specific death rates for COPD in the United States and Uganda for 2019 (deaths per 100,000 people)
age_groups = ['0-4', '5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49',
              '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80-84', '85+']

us_death_rates = [0.04, 0.02, 0.02, 0.02, 0.06, 0.11, 0.29, 0.56, 1.42, 4.00, 14.13, 37.22, 66.48, 108.66, 213.10, 333.06, 491.10, 894.45]
uganda_death_rates = [0.40, 0.17, 0.07, 0.23, 0.38, 0.40, 0.75, 1.11, 2.04, 5.51, 13.26, 33.25, 69.62, 120.78, 229.88, 341.06, 529.31, 710.40]

# Create DataFrames from the lists
df_us_death_rates = pd.DataFrame({'Age_Group': age_groups, 'Death_Rates_US': us_death_rates})
df_uganda_death_rates = pd.DataFrame({'Age_Group': age_groups, 'Death_Rates_Uganda': uganda_death_rates})

# Total population of each country in 2019 

us_total_population = 19848556
uganda_total_population = 7328968

# WHO standard population distribution percentages for age groups
who_world_standard = {
    'Age_Group': age_groups,
    'Proportion': [8.86, 8.69, 8.60, 8.47, 8.22, 7.93, 7.61, 7.15, 6.59, 6.04, 5.37, 4.55, 3.72, 2.96, 2.21, 1.52, 0.91, 0.63]
}

# Create a DataFrame for WHO standard population
df_who_world_standard = pd.DataFrame(who_world_standard)

# Calculate crude death rates for the United States and Uganda
us_crude_death_rate = (sum(df_us_death_rates['Death_Rates_US']) / us_total_population) * 100000
uganda_crude_death_rate = (sum(df_uganda_death_rates['Death_Rates_Uganda']) / uganda_total_population) * 100000

print("Crude Death Rate for COPD in the United States (per 100,000 people):", round(us_crude_death_rate, 1))
print("Crude Death Rate for COPD in Uganda (per 100,000 people):", round(uganda_crude_death_rate, 1))

# Calculate age-standardised death rates for the United States and Uganda
us_age_standardised_death_rate = sum(df_us_death_rates['Death_Rates_US'] * df_who_world_standard['Proportion'])
uganda_age_standardised_death_rate = sum(df_uganda_death_rates['Death_Rates_Uganda'] * df_who_world_standard['Proportion'])

print("Age-Standardised Death Rate for COPD in the United States (per 100,000 people):", round(us_age_standardised_death_rate, 1))
print("Age-Standardised Death Rate for COPD in Uganda (per 100,000 people):", round(uganda_age_standardised_death_rate, 1))