__STYLES__
In the mid-19th century, Stockholm experienced a devastating cholera outbreak. This analysis dives into detailed church records to understand the impact of the epidemic on the city's population, focusing on mortality rates, affected demographics, and strategies for future prevention.
Date of death``Name``Profession``Sex``Age``Assembly``Archive Ref``Page``0``1853-08-24``Lars Ersson Lindström``Carpenter``MAN``50``Catherine``FI:11``424``1``1853-08-23``Adolf Krook``Former Pharmacist``MAN``52``Catherine``FI:11``424``2``1853-08-24``J Florin``Young man``MAN``21``Catherine``FI:11``424``3``1853-08-27``John Weilback``Master shipbuilder``MAN``42``Catherine``FI:11``424``4``1853-08-26``Theodor Blomgren``Accountant``MAN``33``Catherine``FI:11``425``...``...``...``...``...``...``...``...``...``243``1853-11-06``Carl August``Son of shoemaker``MAN``1``Catherine``FI:11``451``244``1853-11-08``CH Gillberg``The master silk weaver``MAN``75``Catherine``FI:11``451``245``11/07/1853``Cajsa Wickbom``widow of customs officer``Woman``58``Catherine``FI:11``452``246``1853-11-21``Ulrika Sofia``Daughter of a working man``Woman``3``Catherine``FI:11``453``247``11/12/1853``Carolina Carlsson b. Rangberg``widow of master baker``Woman``54``Catherine``FI:11``453
248 rows × 8 columns
The dataset used for this analysis is derived from hand-written church records, which were meticulously maintained in Stockholm during the 19th century. The church was responsible for all population registration until around 1870, allowing us to gather detailed information about deaths during the cholera outbreak. The dataset includes the following columns:
total_deaths = Cholera.shape[0]
total_deaths
The dataset reveals that 248 individuals succumbed to cholera during this outbreak.
profession_distribution = Cholera['Profession'].value_counts()
profession_distribution
Profession
Mamsell 10
Boy child 9
? 7
Baby girl 7
Worker 7
..
Chamber sink 1
Lieutenant's wife 1
Tractor 1
Torparenka 1
widow of master baker 1
Name: count, Length: 151, dtype: int64
Certain professions seemed more susceptible to cholera:
Sex
Woman 130
MAN 118
Name: count, dtype: int64
Cholera affected both genders, with a slightly higher number of women than men:
Cholera['Date of death'] = pd.to_datetime(Cholera['Date of death'], errors='coerce')
Cholera['Month'] = Cholera['Date of death'].dt.month
month_distribution = Cholera['Month'].value_counts().sort_index()
print(month_distribution)
Month
8.0 12
9.0 161
10.0 26
11.0 6
Name: count, dtype: int64
The month of September saw the highest number of deaths, indicating a peak in the outbreak during this time.
age_bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
age_labels = ['0-10', '11-20', '21-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81-90', '91-100']
Cholera['Age Group'] = pd.cut(Cholera['Age'], bins=age_bins, labels=age_labels, right=False)
age_group_distribution = Cholera['Age Group'].value_counts().sort_index()
print(age_group_distribution)
Age Group
0-10 53
11-20 12
21-30 26
31-40 54
41-50 41
51-60 32
61-70 17
71-80 9
81-90 4
91-100 0
Name: count, dtype: int64
The age group 31-40 had the highest number of deaths, suggesting that adults in their prime were significantly affected.
Based on the analysis, several strategies can be proposed to prevent future cholera outbreaks:
The cholera outbreak in 19th-century Stockholm highlights the importance of a multifaceted approach to disease prevention. By improving sanitation, educating the public, and enhancing healthcare infrastructure, we can mitigate the risk of future outbreaks. Implementing these strategies requires coordinated efforts from governments, healthcare providers, and communities.
Addressing the root causes of cholera and empowering individuals with knowledge and resources are key steps toward a healthier, cholera-free future.