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20 Years of National Life Jacket Wear Observation Study

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20 Years of National Life Jacket Wear Observation Study

  1. 1. Presenting 20 Years of Data: National Life Jacket Wear Rate Observational Study Natalie Spitzer Research Associate Thomas W. Mangione Senior Research Scientist JSI Research & Training Institute, Inc. Life Jacket Association 2019 Annual Conference St. Pete Beach, Florida May 14-15, 2019
  2. 2. National Life Jacket Wear Rate Observation • 124 Sites in 30 states; • 675,000 adult recreational boaters and nearly 140,000 boats observed • Comparison of life jacket wearing behaviors among 8 different boat types
  3. 3. Dynamic Risk Factors: Water temperature Air temperature Wind speed Wave height Current Weather Visibility Boat movement Boater position Boater activity Boater Impairment Static Risk Factors: Boat size Life jacket regulations Fishing tournament rules Boater experience Boater training Perceived Risk of Capsizing or Falling Overboard Perceived Negative Consequences of Entry into the Water Decision to Wear Life Jacket Beliefs in Benefits/Drawbacks of wearing Life Jackets # of passengers Presence of children Boater swimming ability Boat type
  4. 4. Condition “RISKY” vs. “NON-RISKY” Justification Visibility POOR vs. GOOD/FAIR Difficulty navigating and operating boats, responding to obstacles or other boats, finding boaters who have fallen overboard General Weather RAINING/STORMY vs. SUNNY/CLOUDY Risks related to visibility and wave height. Strength of Current STRONG vs. WEAK/MODERATE Increased chances of loss of control, capsizing, falling overboard, boater/swimming fatigue Water and Air Temperature* COLD WATER (<65°F) and/or COLD AIR (<70°F) vs. WARM WATER (≥65°F) and WARM AIR (≥70°F) Increased chances of hyperventilation, swimming fatigue, loss of function, hypothermia Wave Height and Wind Speed†* CHOPPY/ROUGH WAVES and/or HIGH WIND vs. CALM WAVES and LOW WIND Increased chances of capsizing, falling overboard, swimming fatigue Children on Board CHILD PRESENT vs. NO CHILD Increased chances of entering water (to rescue child), boater distraction, unpredictable movements that contribute to capsizes or falls overboard Size of Boat† SMALL vs. LARGE Reduced stability, increased chances of capsizing and falling overboard Boater Activity† FISHING/RACING/WHITE WATER vs. OTHER (pleasure) Increased chances of standing, loss of balance, entering water, capsizing, falling overboard Boat Movement† MOTORING/PADDLING/SAILING vs. OTHER (drifting/anchored) Increased chances of loss of control, capsizing, falling overboard Number of Boaters† SINGLE vs. 2+ BOATERS Less likely to be rescued if falling overboard (no one to throw flotation, search and rescue, report accident) Boater Position PASSENGER vs. OPERATOR Passengers less aware of boating hazards *Combined variables for analysis due to high correlation; †Risk classification varies by boat type Categorizing Binary Risks
  5. 5. For each boat type: which risky conditions are significantly related to higher rates of adult life jacket use? – Chi-square test for equality of proportion Risky condition present? Life Jacket Use? No Yes No a% A% Yes b% B%
  6. 6. Runabout Cabin Cruiser Pontoon Skiff/ Utility Canoe Kayak Day Sailor Cabin Sailboat Cold Water and/or Cold Air         Poor Visibility   o o   o o Choppy Waves and/or High Wind o o  o     Raining       o o Strong Current   o     Bivariate analysis: Environmental conditions associated with increased adult life jacket wear rates : chi square test p<0.05 where presence of risk -> higher wear rate o: risk not recognized - chi square test p≥0.05 or presence of risk -> lower wear rate
  7. 7. Runabout Cabin Cruiser Pontoon Skiff/ Utility Canoe Kayak Day Sailor Cabin Sailboat Small Boat Size      *   Dangerous Activity   o      Dangerous Movement o o o      Children on Board       o o Single Boater     o *  o Passenger Position    o o  o o Bivariate analysis: Boat/Boater conditions associated with increased adult life jacket wear rates * Opposite condition considered risky based on literature : chi square test p<0.05 where presence of risk -> higher wear rate o: risk not recognized - chi square test p≥0.05 or presence of risk -> lower wear rate
  8. 8. For each boat type: As the number of risky conditions present increases, what happens to the adult life jacket wear rate? – Cochran-Armitage linear trend test (one-sided)
  9. 9. 0 20 40 60 80 100 0 risks 1 risk 2 risks 3+ risks LifeJacketWear(%) Number of Boating Risks Skiff / Utility* Runabout* Cabin Cruiser* Pontoon* Canoes* Kayaks* Day Sailors* Cabin Sailboats* Adult life jacket wear rate, by cumulative risk count * One-sided Cochran-Armitage linear trend test significant at p<0.0001
  10. 10. For each boat type: What are the top 4 risky conditions that (in combination with one another) are most significantly related to higher rates of adult life jacket use? – Automated stepwise logistic regression model selection
  11. 11. Boat type First OR P-value Second OR p-value Third OR P-value Fourth OR P-value Runabout Cold temperatures (water and/or air) 1.9 **** Dangerous activity 1.9 **** Child present 1.7 **** Small boat 2.0 **** Cabin Cruiser Cold temperatures (water and/or air) 3.7 **** Small boat 1.5 **** Child present 1.7 **** Poor visibility 2.5 **** Pontoon Child present 2.1 **** Small boat 1.5 **** Cold temperatures (water and/or air) 1.5 **** Raining or stormy weather 1.8 **** Skiff / Utility Cold temperatures (water and/or air) 2.1 **** Boating alone 1.6 **** Dangerous activity 1.3 **** Child present 1.4 **** Canoe Choppy waves and/or high wind 2.7 **** Child present 3.2 **** Small boat 1.9 **** Dangerous activity 2.8 **** Kayak Cold temperatures (water and/or air) 2.3 **** Strong current 3.1 **** More than one boater 1.7 **** Choppy waves and/or high wind 1.4 **** Day Sailor Small boat 2.9 **** Boating alone 2.2 **** Dangerous activity 38.8 **** Choppy waves and/or high wind 1.4 **** Cabin Sailboat Cold temperatures (water and/or air) 3.0 **** Small boat 1.9 **** Dangerous activity 2.5 **** Sailing or motoring 1.6 *** Multivariate Analysis: Automated stepwise model selection “Top 4 priority” risky conditions significantly associated with adult life jacket use Wald Chi-Square for MLE estimate: ****p<0.0001; *** p<0.001
  12. 12. First Second Third Fourth Runabout Cold temperatures (water and/or air) Dangerous activity Child present Small boat Cabin Cruiser Cold temperatures (water and/or air) Small boat Child present Poor visibility Pontoon Child present Small boat Cold temperatures (water and/or air) Raining or stormy weather Skiff / Utility Cold temperatures (water and/or air) Boating alone Dangerous activity Child present Canoe Choppy waves and/or high wind Child present Small boat Dangerous activity Kayak Cold temperatures (water and/or air) Strong current More than one boater Choppy waves and/or high wind Day Sailor Small boat Boating alone Dangerous activity Choppy waves and/or high wind Cabin Sailboat Cold temperatures (water and/or air) Small boat Dangerous activity Sailing or motoring Multivariate Analysis: Automated stepwise model selection “Top 4 priority” risky conditions significantly associated with adult life jacket use Wald Chi-Square for MLE estimate: ****p<0.0001; *** p<0.001
  13. 13. Boat type First Second Third Fourth Runabout Cold temperatures (water and/or air) Dangerous activity Child present Small boat Cabin Cruiser Cold temperatures (water and/or air) Small boat Child present Poor visibility Pontoon Child present Small boat Cold temperatures (water and/or air) Raining or stormy weather Skiff / Utility Cold temperatures (water and/or air) Boating alone Dangerous activity Child present Canoe Choppy waves and/or high wind Child present Small boat Dangerous activity Kayak Cold temperatures (water and/or air) Strong current More than one boater Choppy waves and/or high wind Day Sailor Small boat Boating alone Dangerous activity Choppy waves and/or high wind Cabin Sailboat Cold temperatures (water and/or air) Small boat Dangerous activity Sailing or motoring Multivariate Analysis: Automated stepwise model selection “Top 4 priority” risky conditions significantly associated with adult life jacket use Wald Chi-Square for MLE estimate: ****p<0.0001; *** p<0.001
  14. 14. Boat type First Second Third Fourth Runabout Cold temperatures (water and/or air) Dangerous activity Child present Small boat Cabin Cruiser Cold temperatures (water and/or air) Small boat Child present Poor visibility Pontoon Child present Small boat Cold temperatures (water and/or air) Raining or stormy weather Skiff / Utility Cold temperatures (water and/or air) Boating alone Dangerous activity Child present Canoe Choppy waves and/or high wind Child present Small boat Dangerous activity Kayak Cold temperatures (water and/or air) Strong current More than one boater Choppy waves and/or high wind Day Sailor Small boat Boating alone Dangerous activity Choppy waves and/or high wind Cabin Sailboat Cold temperatures (water and/or air) Small boat Dangerous activity Sailing or motoring Multivariate Analysis: Automated stepwise model selection “Top 4 priority” risky conditions significantly associated with adult life jacket use Wald Chi-Square for MLE estimate: ****p<0.0001; *** p<0.001
  15. 15. Boat type First Second Third Fourth Runabout Cold temperatures (water and/or air) Dangerous activity Child present Small boat Cabin Cruiser Cold temperatures (water and/or air) Small boat Child present Poor visibility Pontoon Child present Small boat Cold temperatures (water and/or air) Raining or stormy weather Skiff / Utility Cold temperatures (water and/or air) Boating alone Dangerous activity Child present Canoe Choppy waves and/or high wind Child present Small boat Dangerous activity Kayak Cold temperatures (water and/or air) Strong current More than one boater Choppy waves and/or high wind Day Sailor Small boat Boating alone Dangerous activity Choppy waves and/or high wind Cabin Sailboat Cold temperatures (water and/or air) Small boat Dangerous activity Sailing or motoring Multivariate Analysis: Automated stepwise model selection “Top 4 priority” risky conditions significantly associated with adult life jacket use Wald Chi-Square for MLE estimate: ****p<0.0001; *** p<0.001
  16. 16. Situational Awareness Discussion • Risky conditions associated with higher rates of adult life jacket use varies by boat type – First step of Situational Awareness: boaters evaluate/respond to risks based on the type of boat they are on • The bivariate and cumulative risk analysis shows that despite differences by boat type, boaters are more likely to wear their life jacket when risky conditions are present • These associations strongly suggest that boaters understand the connection between: – Awareness of perceived risk  Unexpected water entry – Unexpected water entry  drowning – Wearing a life jacket  drowning prevention
  17. 17. • Multivariate analysis: Lower priority risks are still important! – Many also associated with higher rates of life jacket use – Some risks not recognized: not significant; related to other “higher priority” risks; opposite association – Worth trying to understand why these risks are not in the top 4 in order to tailor educational campaigns / interventions that prevent drowning • Framing is important: drownings occur in all situations. – How to teach adult boaters how to become more situationally aware and to respond to risk by wearing a life jacket? While ALSO understanding that: – No matter what the perceived risks are, boaters should ALWAYS wear a life jacket while boating because risks can change? Situational Awareness Discussion, continued

Editor's Notes

  • Natalie Spitzer
    Research associate at JSI
    Will be presenting findings from 20 years of data collection that will demonstrate with real data key components of the theoretical model you’ve just learned about
  • We’ve been working with the US Coast Guard and Coast Guard Auxiliary to conduct boating observations in the summer across the US for the past 20 years
    This map shows in blue the states where we have been collecting data
    Overall we are at 124 different sites in 30 states (2 in California)
    The findings I’m presenting today draw from a sample of about 675,000 adult recreational boaters observed in nearly 140,00 boats
  • This is the framework Tom presented earlier
    The stars indicate which risk factors JSI is able to capture during data collection

    Even if we can’t measure boaters’ perception of risk or negative consequences of unexpected water entry, or beliefs in the benefits of wearing life jackets, we can get a sense of how different boat types and risk conditions influence adult life jacket use
  • The first thing we did was categorize each indicator into a binary form based on risk of unexpected entry into the water and/or drowning
    We conducted a literature review to guide this categorization process
    You can see in this table how each variable was categorized into either risky or non-risky conditions, and our justification
    There are a few cases where the risky condition is different by boat type:
    Dangerous wind varies by boat type: sail and power = 6+ knots; paddle = 4+ knots
    Size of boat: large kayaks riskier than small; exact boat size dependent on distribution of boats most likely in that specific boat category
    Number of boaters: more than one boater in kayak riskier than single boating
    Dangerous boat movement varies by boat type: sail = sailing or motoring only; power=motoring only; paddle = paddling or motoring only
    Boater activity varies by boat type: sail = fishing or racing only; power=fishing, racing or white water if any; paddling = fishing, racing or white water if any
  • The first thing we did was figure out, for each boat type which risky conditions were significantly related to higher rates of adult life jacket use
    We did this using chi-square tests for equal proportions
    For each boat type and each binary risky condition, we compared the proportion of adults wearing life jackets when the risky condition was present and not present
  • Here’s what we found.
    In this table you’ll see that 8 boats types of interest are along the columns
    The binary risky conditions are along the rows.
    Wherever you see a checkmark is where we found that adult life jacket wear rates were significantly higher in the riskier condition than in the non-risky condition
    Wherever you see a red circle is where this was not the case – we’ll call that an unrecognized risk
    As you can see for most of our environmental binary risks, there are very few non-significant findings
  • Here is the same thing for boat and boater risk factors.
    Again we are seeing very few red circles, and lots of checkmarks that indicate that in riskier situations adult boaters are wearing their life jackets at higher rates than in lower risk situations

    * Opposite condition considered risky based on literature. Therefore opposite risk tested for significance. Ex. larger kayaks are more likely to tip than smaller, therefore larger kayaks were classified as risky. Checkmark indicates that larger kayaks are associated with larger proportion of boaters wearing life jackets than smaller.
  • The next step in our analysis was to figure out how adult life jacket wearing behaviors change as the number of risky conditions changed.
    Based off of our situational awareness model, we would expect to see higher rates of life jacket use in riskier situations, and also expect to see slight differences by boat type
  • And that is what we see.
    Here are the results for all boats, all of which had statistically significant linear trends based off of the Cochran-Armitage linear trend test that show positive increases
    Things to notice here are the starting and end life jacket wear rates for each boat as well as the slope to get a sense of how life jacket use varies by boat type as the number of risks changes
  • Now the last thing we did was try to sort through all of these significant risky conditions to determine which were the most important for predicting adult life jacket use.
    To make things simpler I’m just going to show you the results from a stratified automated stepwise logistic regression model selection
    We’ll be comparing the first 4 risky conditions selected by this procedure by boat type
  • Here are the results of the stepwise selection
    Along the rows we have each boat type and in the columns we have the top 4 priority risks and odds ratios chosen in the automated model building procedure
    You can see by the odds ration that all are greater than one, so adults are more likely to be observed wearing life jackets when the listed risky condition is present compared to the non-risky alternative
    To make this more transparent I’m going to switch between slides to pull out some key patterns


    Example interpretation: On runabouts, the odds of wearing life jacket when a child is present is 1.7 times that of the odds of wearing a life jacket without a child on board, controlling for boater activity, water/air temperature, and boat size. This association is statistically significant at p<0.0001
  • Here in this first slide I’ve highlighted how cold air and water temperatures appears as a priority risk in all but 2 boat types
    In 5 cases we see that it was the first variable selected out of all 11 risk factors
  • In this slide you can see that small boat size is a top 4 significant predictor for all boats except skiffs and kayaks (for kayaks small boats isn’t a risk factor)
  • Here we can see that child presence is a significant predictor for all powerboats and canoes
  • Boats known to tip: choppy/rough water and/or high winds significant predictor

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