## Introduction

This R markdown document describes a portion of the data analysis for a reporting project examining the effects of climate-change driven temperature increases on the health of people who live in cities. The project was done in partnership with the University of Maryland Philip Merrill College of Journalism, Capital News Service, the Howard Center for Investigative Journalism, NPR, Wide Angle Youth Media and WMAR. It also moved on the Associated Press wire.

For each sentence in the stories “As Rising Heat Bakes U.S. Cities, The Poor Often Feel It Most” and “How High Heat Can Impact Mental Health” based on Howard Center data analysis, this document provides the original fact, the code and code output that support that fact, and an explanation where necessary.

CNSMaryland

NPR

WMAR

Associated Press

## Line-by-Line Fact Check [Rising Heat]

### Fact: Franklin Square heat [cq]

• Audio Story: “Her neighborhood – Franklin Square, no relation to her name – is hotter than about two thirds of the neighborhoods in Baltimore.”
• Web Story: “Her neighborhood, Franklin Square, is hotter than about two thirds of the neighborhoods in Baltimore – about six degrees hotter than the city’s coolest neighborhood.”

#### Explanation [cq]

Using the urban heat island measurements taken in August 2018 showing block-by-block temperature variations, we calculated median afternoon temperatures for each of the city’s 278 neighborhoods. At 95.4 degrees, Franklin Square was warmer than 67 percent (about two-thirds) of Baltimore’s neighborhoods. The city’s coolest neighborhood – “Gwynns Falls/Leakin Park” – was 89.3 degrees F, 6.1 degrees less than Franklin Square.

#### Supporting code and output [cq]

# Rank, neighborhoods cooler
nsa_tree_temp %>%
select(nsa_name, temp_median_aft) %>%
mutate(rank = dense_rank(temp_median_aft), pct_nhoods_cooler = (rank/max(rank))*100) %>%
filter(nsa_name == "franklin square")
# Difference between Franklin Square and coolest
nsa_tree_temp %>%
select(nsa_name, temp_median_aft) %>%
filter((nsa_name == "franklin square") | (temp_median_aft == min(temp_median_aft))) %>%
mutate(difference = franklin square - gwynns falls/leakin park)

### Fact: Franklin Square poverty [cq]

• Audio Story: “It’s also in one of the city’s poorer areas.”
• Web Story: “It’s also in one of the city’s poorest communities, with more than one third of residents living in poverty.”

#### Explanation [cq]

Franklin Square is entirely contained by a “community statistical area” known as “Southwest Baltimore”. The poverty rate here is 36 percent, or more than one-third. There are 55 CSAs in Baltimore. Southwest Baltimore has the 51st highest poverty rate, making it poorer than more than 90 percent of CSAs.

#### Supporting code and output [cq]

csa_tree_temp_demographics %>%
select(csa2010, percent_of_family_households_living_below_the_poverty_line) %>%
mutate(rank = dense_rank(percent_of_family_households_living_below_the_poverty_line), pct_nhoods_wealthier = (rank/max(rank))) %>%
filter(csa2010 == "southwest baltimore")

### Fact: Heat and poverty [cq]

• Audio Story: “And the hottest parts of the city also have higher rates of poverty.”
• Web Story: “Across Baltimore, the hottest areas tend to be the poorest and that pattern is not unusual.”

#### Explanation [cq]

In Baltimore’s “community statistical areas”, we examined the relationship between heat (median afternoon temperature in our urban heat island data) and poverty. An r of 1 would indicate a perfect positive linear relationship, an r of -1 would indicate a perfect negative linear relationship, and 0 would indicate no relationship. There were moderate positive correlations between heat poverty (r =.38). In general, the hotter the neighborhood, the higher the poverty rate, and vice versa.

#### Supporting code and output [cq]

csa_tree_temp_demographics %>%
select_if(is.numeric) %>%
as.matrix() %>%
correlate() %>%
focus(matches("temp_")) %>%
mutate(variable=rowname) %>%
select(variable, temp_median_aft) %>%
filter(variable=="percent_of_family_households_living_below_the_poverty_line")

### Fact: Neighborhood heat difference [cq]

• Audio Story: “Citywide in Baltimore, the hottest neighborhoods can differ by as much as 10 degrees from the coolest.”
• Web Story: “According to a Howard Center analysis of U.S. Census data and air temperature data obtained from Portland State University and the Science Museum of Virginia, the hottest neighborhoods in Baltimore can differ by as much as 10 degrees from the coolest.”

#### Explanation [cq]

Using median afternoon temperature in a 2018 study by researchers analyzing the city’s urban heat island, there was a 9.98 degree difference between the city’s hottest neighborhood (McElderry Park) and coolest neighborhood (Gwynns Falls/Leakin Park).

#### Supporting code and output [cq]

# Hottest and coolest neighborhoods
nsa_tree_temp %>%
select(nsa_name, temp_median_aft) %>%
filter((temp_median_aft == min(temp_median_aft)) | (temp_median_aft == max(temp_median_aft)))
# Difference between hottest and coolest
nsa_tree_temp %>%
select(nsa_name, temp_median_aft) %>%
filter((temp_median_aft == min(temp_median_aft)) | (temp_median_aft == max(temp_median_aft))) %>%
mutate(difference = mcelderry park - gwynns falls/leakin park)

### Fact: EMS calls for heat stroke [cq]

• Audio Story: “When the heat index reached dangerous levels last summer, EMS calls increased citywide for heat stroke.”
• Web Story: “In the summer of 2018 in Baltimore, when the heat index reached 103 degrees — the threshold deemed dangerous by the National Weather Service — EMS calls increased dramatically citywide for potentially fatal heat stroke.”

#### Explanation [cq]

By merging a dataset of EMS calls with a dataset of hourly Baltimore heat index values, we were able to determine the heat index at the time of each EMS call during summer 2018. We looked at select conditions affected by heat, and compared how call rates changed when it was very hot – over 103 heat index, a level defined by NWS as "dangerous – and when the heat index was under 80. The second and third columns below reflect the number of hours that passed between calls (on average) when the heat index was below 80 degrees or above 103 degrees.

As expected, calls for heat stroke increased dramatically. When it was under 80 degrees, there was a call for heat stroke every 480 hours, or about once every three weeks. When it was 103 degrees or higher, there was a call for heat stroke every 1.4 hours.

#### Supporting Code [cq]

# Select conditions
conditions <- c("Heat Exhaustion/Heat Stroke")

# Calculate the total number of hours over the course of Summer 2018 that the heat index fell into each heat index level, as defined by the national weather service: not unsafe (under 80), caution (80-89), extreme caution (90-102), danger (103-124).

heat_index_count_per_nws_five_scale_bucket <- dmh_ems %>%
select(heat_index_nws_five_scale_bucket) %>%
group_by(heat_index_nws_five_scale_bucket) %>%
summarise(heat_index_count_per_nws_five_scale_bucket=n()) %>%
arrange(heat_index_nws_five_scale_bucket)

# For each target condition, calculate the number of hours between calls at each temperature level.  This metric allows us to account for the fact that simply counting calls in each bucket would be flawed, because it wouldn't adjust for the rarity of very hot temperatures.

EMS_all %>%
filter(primary_impression_group %in% conditions) %>%
summarise(condition_calls_count_per_bucket=n()) %>%
inner_join(heat_index_count_per_nws_five_scale_bucket, by = c("adjusted_heat_index_nws_five_scale_bucket" = "heat_index_nws_five_scale_bucket")) %>%
mutate(hours_per_call = heat_index_count_per_nws_five_scale_bucket/condition_calls_count_per_bucket) %>%
select(primary_impression_group, not_unsafe_under_80,danger_103_124)

### Fact: EMS calls for chronic conditions [cq]

• Audio Story: “But calls also increased for chronic conditions, including several cardiovascular and respiratory conditions.”
• Web Story: “But calls increased for chronic conditions too: EMS calls for chronic obstructive pulmonary disorder (COPD) increased by nearly 70 percent. Calls for respiratory distress increased by 20 percent. Calls for cardiac arrest rose by 80 percent and those for high blood pressure more than doubled. Other conditions also spiked: Psychiatric disorders, substance abuse and dehydration, among others.”
• Web Story: “And living day after day in an environment that’s literally hotter isn’t just uncomfortable, it can have dire and sometimes deadly health consequences – a fact we found reflected in Baltimore’s soaring rates of emergency calls when the heat index spiked to dangerous levels.”

#### Explanation [cq]

By merging a dataset of EMS calls with a dataset of hourly Baltimore heat index values, we were able to determine the heat index at the time of each EMS call during summer 2018. We looked at select conditions affected by heat, and compared how call rates changed when it was very hot – over 103 heat index, a level defined by NWS as “dangerous” – and when the heat index was under 80. The third and fourth columns below reflect the number of hours that passed between calls (on average) when the heat index was below 80 degrees or above 103 degrees. The table below reflects the percentage difference (second column) in call rates between the two temperature groupings.

#### Supporting code and output [cq]

# Select conditions
conditions <- c("Dehydration","Respiratory Distress", "COPD (Emphysema/Chronic Bronchitis)", "Cardiac Arrest", "CHF (Congestive Heart Failure)", "Behavioral/Psychiatric Disorder", "Dehydration", "Chest Pain - STEMI","Hypertension","Substance/Drug Abuse","Withdrawal/Overdose Drugs","Withdrawal/Overdose ETOH")

# Calculate the total number of hours over the course of Summer 2018 that the heat index fell into each heat index level, as defined by the national weather service: not unsafe (under 80), caution (80-89), extreme caution (90-102), danger (103-124).

heat_index_count_per_nws_five_scale_bucket <- dmh_ems %>%
select(heat_index_nws_five_scale_bucket) %>%
group_by(heat_index_nws_five_scale_bucket) %>%
summarise(heat_index_count_per_nws_five_scale_bucket=n()) %>%
arrange(heat_index_nws_five_scale_bucket)

# For each target condition, calculate the number of hours between calls at each temperature level.  This metric allows us to account for the fact that simply counting calls in each bucket would be flawed, because it wouldn't adjust for the rarity of very hot temperatures.

EMS_all %>%
filter(primary_impression_group %in% conditions) %>%
summarise(condition_calls_count_per_bucket=n()) %>%
inner_join(heat_index_count_per_nws_five_scale_bucket, by = c("adjusted_heat_index_nws_five_scale_bucket" = "heat_index_nws_five_scale_bucket")) %>%
mutate(hours_per_call = heat_index_count_per_nws_five_scale_bucket/condition_calls_count_per_bucket) %>%
select(primary_impression_group, not_unsafe_under_80,danger_103_124) %>%
mutate(calls_per_day_under_80 = 24/not_unsafe_under_80) %>%
mutate(calls_per_day_over_103 = 24/danger_103_124) %>%
mutate(difference_day_percent = ((calls_per_day_over_103-calls_per_day_under_80)/calls_per_day_under_80)) %>%
select(primary_impression_group, difference_day_percent, everything())

### Fact: Medicaid and chronic conditions [cq]

• Audio Story: “And even when controlling for income, there were differences across the city. From 2013 to 2018, Medicaid patients in Baltimore’s hottest areas visited the hospital with those conditions at higher rates than Medicaid patients in the cooler areas, according to the Howard Center analysis.”
• Web Story: “The heat affected residents citywide, but even when controlling for income by only looking at the patterns of Medicaid patients, there were differences across the city. From 2013 to 2018, Medicaid patients in Baltimore’s hottest areas visited the hospital at higher rates than Medicaid patients in the city’s coolest areas. The low-income patients in the city’s hot spots visited more often with several conditions, including asthma, COPD and heart disease, according to hospital inpatient and emergency room admissions data from the state’s Health Services Cost Review Commission.”

#### Explanation [cq]

We examined rates of chronic condition prevalence for low-income people in different parts of Baltimore by examining in-patient hospital admissions for people with Medicaid. We discovered that low-income people in different parts of the city had different prevalence rates for chronic conditions affected by heat – asthma, COPD, heart disease and diabetes. And, we found, those differences varied in line with temperature differences in the area in which they lived.

There were moderate to strong positive relationships (copd, r=.75; asthma, r=.52; heart_disease, r=.71; diabetes, r=.5) between a ZIP code’s prevalence rate for chronic medical conditions as diagnosed in inpatient hospital visits and a ZIP code’s median afternoon temperature as measured by urban heat island researchers in August 2018. There were moderate to strong positive relationships (copd, r=.51; asthma, r=.38; heart_disease, r=.44; diabetes, r=.44) between a ZIP code’s prevalence rate for chronic medical conditions as diagnosed in emergency room visits and a ZIP code’s median afternoon temperature as measured by urban heat island researchers in August 2018. That is to say: the higher the neighborhood temperature, the higher the disease rate among the poorest inhabitants, and vice versa. This is not a causal relationship we are describing.

#### Supporting Code [cq]

# Inpatient medicaid admissions
ip_full_zip_medicaid_correlation_matrix %>%
filter(str_detect(rowname, "asthma|copd|heart_disease|diabetes")) %>%
select(rowname, temp_median_aft)
# ER medicaid visits
op_er_full_zip_medicaid_correlation_matrix %>%
filter(str_detect(rowname, "asthma|copd|heart_disease|diabetes")) %>%
select(rowname, temp_median_aft)

### Fact: Franklin Square tree canopy [cq]

• Audio Story: "The neighborhood where Shakira Franklin lives has increased its tree canopy over time. But in recent years, it’s still been among the city’s lowest.
• Web Story: “The neighborhood where Shakira Franklin lives has increased its tree canopy over time. But by 2015, it still was among the city’s lowest.”

#### Explanation [cq]

In 2007, 14.2 percent of the neighborhood was covered by tree canopy in the summer. By 2015, that had increased to 16.1 percent, a 1.9 percentage point increase. In 2015, two-thirds of Baltimore’s 278 neighborhoods had more tree canopy than Franklin Square’s 16 percent coverage.

#### Supporting Code [cq]

# change
nsa_tree_temp %>%
select(nsa_name, avg_canopy_%_07 = 07_lid_mean, avg_canopy_15 = 15_lid_mean, %_point_change = lid_change_percent_point) %>%
filter(nsa_name == "franklin square")
# rank
nsa_tree_temp %>%
select(nsa_name, avg_canopy_15 = 15_lid_mean) %>%
arrange(nsa_name) %>%
mutate(rank = dense_rank(desc(avg_canopy_15)), pct_nhoods_w_more_tree_canopy = (rank/max(rank))) %>%
filter(nsa_name == "franklin square")

## Line-by-Line Fact Check [Mental Health]

### Fact: Rate of increase of psychiatric calls [cq]

• Audio story: “The Howard Center analyzed data from emergency response calls in Baltimore. They found that in the summer of 2018, calls for psychiatric conditions increased by nearly 40 percent when the heat index spiked above 103.”
• Web story: An analysis by the University of Maryland’s Howard Center for Investigative Journalism found emergency response calls relating to psychiatric conditions increased nearly 40% in Baltimore in the summer of 2018, when the heat index spiked above 103.

#### Explanation [cq]

By merging a dataset of EMS calls with a dataset of hourly Baltimore heat index values, we were able to determine the heat index at the time of each EMS call during summer 2018. We looked at select conditions affected by heat, and compared how call rates changed when it was very hot – over 103 heat index, a level defined by NWS as “dangerous” – and when the heat index was under 80. The third and fourth columns below reflect the number of hours that passed between calls (on average) when the heat index was below 80 degrees or above 103 degrees. The table below reflects the percentage difference (second column) in call rates between the two temperature groupings.

In Summer 2018, when the heat index was under 80 degrees, there was a medical call for a behavioral and psychiatric disorder every 1.77 hours (1 hour, 46 minutes). When the heat index hit 103 degrees, the rate of calls increased dramatically – to one call every 1.29 hours (1 hour, 17 minutes). That’s an increase in the rate of 29 minutes, or an increase of 37 percent.

#### Supporting code and output [cq]

# Select conditions
conditions <- c("Behavioral/Psychiatric Disorder")

# Calculate the total number of hours over the course of Summer 2018 that the heat index fell into each heat index level, as defined by the national weather service: not unsafe (under 80), caution (80-89), extreme caution (90-102), danger (103-124).

heat_index_count_per_nws_five_scale_bucket <- dmh_ems %>%
select(heat_index_nws_five_scale_bucket) %>%
group_by(heat_index_nws_five_scale_bucket) %>%
summarise(heat_index_count_per_nws_five_scale_bucket=n()) %>%
arrange(heat_index_nws_five_scale_bucket)

# For each target condition, calculate the number of hours between calls at each temperature level.  This metric allows us to account for the fact that simply counting calls in each bucket would be flawed, because it wouldn't adjust for the rarity of very hot temperatures.

EMS_all %>%
filter(primary_impression_group %in% conditions) %>%
summarise(condition_calls_count_per_bucket=n()) %>%
inner_join(heat_index_count_per_nws_five_scale_bucket, by = c("adjusted_heat_index_nws_five_scale_bucket" = "heat_index_nws_five_scale_bucket")) %>%
mutate(hours_per_call = heat_index_count_per_nws_five_scale_bucket/condition_calls_count_per_bucket) %>%
select(primary_impression_group, not_unsafe_under_80,danger_103_124) %>%
mutate(percent_change = (not_unsafe_under_80-danger_103_124)/danger_103_124) %>%
mutate(calls_per_day_under_80 = 24/not_unsafe_under_80) %>%
mutate(calls_per_day_over_103 = 24/danger_103_124) %>%
mutate(difference_day_percent = ((calls_per_day_over_103-calls_per_day_under_80)/calls_per_day_under_80))

### Fact: Substance abuse calls rate [cq]

• Audio story: “Calls relating to substance abuse more than doubled in extreme heat, according to data analyzed by the Howard Center.”
• Web story: “Often, mental health patients are struggling with more than a mental illness. Studies have linked heat and increased drug overdoses, as well as heightened effects of alcohol poisoning - and the Howard Center found calls relating to substance abuse more than doubled during dangerous heat in the summer of 2018.”

#### Explanation [cq]

By merging a dataset of EMS calls with a dataset of hourly Baltimore heat index values, we were able to determine the heat index at the time of each EMS call during summer 2018. We looked at select conditions affected by heat, and compared how call rates changed when it was very hot – over 103 heat index, a level defined by NWS as “dangerous” – and when the heat index was under 80. The third and fourth columns below reflect the number of hours that passed between calls (on average) when the heat index was below 80 degrees or above 103 degrees. The table below reflects the percentage difference in call rates between the two temperature groupings.

In Summer 2018, when the heat index was under 80 degrees, there was a medical call for substance abuse every 2.96 hours (nearly 3 hours). When the heat index hit 103 degrees, the rate of calls increased dramatically – to one call every 1.36 hours. That’s an increase in the rate of about an hour and a half, or an increase of 118 percent. A 100 percent increase is a doubling, so it’s fair to say more than doubled.

#### Supporting code and output [cq]

# Select conditions
conditions <- c("Substance/Drug Abuse")

# Calculate the total number of hours over the course of Summer 2018 that the heat index fell into each heat index level, as defined by the national weather service: not unsafe (under 80), caution (80-89), extreme caution (90-102), danger (103-124).

heat_index_count_per_nws_five_scale_bucket <- dmh_ems %>%
select(heat_index_nws_five_scale_bucket) %>%
group_by(heat_index_nws_five_scale_bucket) %>%
summarise(heat_index_count_per_nws_five_scale_bucket=n()) %>%
arrange(heat_index_nws_five_scale_bucket)

# For each target condition, calculate the number of hours between calls at each temperature level.  This metric allows us to account for the fact that simply counting calls in each bucket would be flawed, because it wouldn't adjust for the rarity of very hot temperatures.

EMS_all %>%
filter(primary_impression_group %in% conditions) %>%
select(primary_impression_group, not_unsafe_under_80,danger_103_124) %>%
mutate(percent_change = (not_unsafe_under_80-danger_103_124)/danger_103_124) %>%
mutate(calls_per_day_under_80 = 24/not_unsafe_under_80) %>%
mutate(calls_per_day_over_103 = 24/danger_103_124) %>%
mutate(difference_day_percent = ((calls_per_day_over_103-calls_per_day_under_80)/calls_per_day_under_80))