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CASE STUDY PROMPTS

CASE 1

Case Study: Assessment of Burden of Disease AssignmentRead/Review:Chapter 3, Case Study 3.1Respond to the following question sets. Include a description of how you derived the response.

1. What are the top three cancer for DALYs? Are these differ from one race/ethnic group to another? What is the value of this information to the healthcare manager?

2. In which race/ethnic group are lung cancer DALYs the lowest? Describe the impact of these results.

3. Which race/ethnic group has the highest DALY’s from prostate and liver cancer? How can this information be used by a healthcare manager?

4. How much does stomach cancer contribute to DALYs in each race/ethnic group? Why is this information important to the healthcare manageR

CASE 2

case Study: California Measles Outbreak 2014 AssignmentRead/Review:Chapter 7, Case Study 7.1Respond to the following question sets. Include a description of how you derived the response.

1. What was the approximate length of the incubation period? Explain your answer with the help of data from the outbreak and Figure 7.10.

2. Was this a point source, common source, or mixed propagated outbreak? Explain your answer with help from the data from the outbreak.

3. When did the peak of the outbreak occur? Explain your answer with the help of data from the outbreak and Figure 7.10.

4. Did the outbreak end spontaneously, or did it end because of control measures implemented by the local health authorities? Explain your answer with the help of data from the outbreak.

CASE 3

Case Study: Planning for Mental Health Services AssignmentRead/Review:Chapter 11, Case Study 11.2Respond to the following question sets. Include a description of how you derived the response.

1. What proportion of treated patients with severe mental disorders was treated by general practitioners only? Explain your response.

2. What proportion of patients with severe mental disorders was not treated by health services? Explain your answer with help from the data provided/calculated?

3. Based on the data presented in Table 11.5, what are the resource implications of recommended targets in Queensland for inpatient acute and non-acute beds, ambulatory care clinical staffing, and financial implications of NGO-managed community support services in 2005-2017?

4. In addition to planning agencies, describe additional direct and indirect methods of healthcare planning.Case Study: Prospective Cohort Study

cASE 4

AssignmentRead/Review:

Case Study 14.2Respond to the following question sets. Include a description of how you derived the response.

1. What research question was addressed in this study, or what hypothesis was tested

2. Why are incidence rates of DM in this study reported per 1,000 person-months rather than per 100 per 1,000 women, and what does person-months mean?

3. What do hazard rations in Table 14.14 indicate? Are these results statistically significant? Explain your answer

4.Why in this study were the estimates of the health outcome (DM) statistically adjusted for variables such as age, maternal risk factors, neonatal outcomes, and postpartum maternal lifestyle?

case one

In a study published in the American Journal of Preventive Medicine in 2016, Lortet-Tieulent et al. estimated the burden of cancer in different racial and ethnic groups in the United States by estimating the total number of DALYs in 2011. The authors used 2013 data on the incidence of 24 most common malignant tumors and “all other cancers” from population-based cancer registries in the United States. To estimate the number of DALYs by sex and race, they first derived the two components of DALYs: (1) the years of life lost (YLL) to premature death and (2) years lived with disability (YLD). YLL was estimated by multiplying the number of deaths in each age group by the life expectancy at the midpoint of that age group. DALYs were calculated by adding estimates for YLL and YLD. Mortality data for 2011 were extracted from the NCHS files. To estimate YLD, data were obtained from the National American Association of Central Cancer Registries. Disability weights established by the 2013 GBD study for the level of disability resulting from various conditions, including cancer, were used to estimate differential YLDs for different cancers. The burden of cancer was also estimated through age-standardized DALY rates per 100,000 population using the U.S. standard population in 2000. To get the race/ethnicity-specific DALYs and age-standardized rates per 100,000 population, the authors employed the following race/ethnic categories in data analysis: non-Hispanic white, non-Hispanic black, Hispanic, and non-Hispanic Asian.

The results indicated that the burden of cancer combined for men and women in the United States in 2011 exceeded 9.8 million DALYs, with 4.9 million DALYs in each sex group. Lung cancer, with 24% of all DALYs, was the biggest contributor to cancer-related loss of health (FIGURE 3.6). Lung (24%), breast (10%), colorectal (9%), and pancreatic cancer (6%) collectively were responsible for nearly half of all DALYs. Approximately 91% of all DALYs resulted from the YLLs to premature deaths. The age-standardized DALY rate for men was 3,046 per 100,000 and 2,694 per 100,000 for women. The age-standardized DALY rates for breast, gallbladder, and thyroid cancer were higher in women than in men. All other cancer rates were higher in men.

case 2

A suspected case of measles—an 11-year-old unvaccinated hospitalized child with a rash that started on December 28, 2014—was reported to the California Department of Public Health (CDPH) on January 5, 2015. The only notable part of the history was a visit to one of the two adjacent Disney theme parks in Orange County, California. On the same date, CDPH was notified of six other suspected cases of measles; four were California residents and two were Utah residents. All six had visited one or both Disney theme parks during the period December 17–20, 2014. By January 7, 2015, there were seven confirmed cases in California. By February 11, a total of 125 confirmed U.S. resident cases (110 California residents and 15 in seven other states) linked to this outbreak with rash onset between December 28, 2014 and February 8, 2015 had been reported. Additionally, 11 linked cases were reported from Mexico (1) and Canada (10). Out of the 110 California resident cases, 39 (35%) had visited one or both of the theme parks during December 17–20, 2014. Of the remaining 71 California cases, 34 were secondary cases (mostly household or close contacts), while source of exposure for 37 was unknown. Among the 110 California cases, 49 (45%) were unvaccinated, 47 (43%) had unknown vaccination status, and 14 were partially (12) or completely (1) vaccinated or had immunoglobuline G seropositivity (1). Among the 49 unvaccinated cases, 12 were too young to be vaccinated and 28 were intentionally unvaccinated because of personal beliefs. The age range for cases was 6 weeks to 70 years. The two Disney theme parks in California have approximately 24 million visits every year, with many international visitors from measles-endemic countries.

case 3

According to the guidelines developed by the World Health Organization, mental health service planning involves specification of strategies, time frames, indicators, and resources to achieve the objectives articulated in the health policy of a country or region. The following case study describes the methods adopted by a team of Australian researchers to develop resource target recommendations to guide the development of Queensland Plan for Mental Health Services 2007–2017. The purpose of the plan is to deliver the following core public mental health services: (1) inpatient services, (2) adult residential rehabilitation services, (3) supported accommodation, (4) ambulatory care mental health services, and (5) community support services. The methods adopted by the researchers were based on a combination of empirical evidence and planning models reported in the literature on health services planning. The work to develop recommendations for Queensland Plan for Mental Health Services 2007–2017 was done in the five steps described next. In step 1, the researchers assessed the need for mental health services in the population by estimating the prevalence of mental disorders in Queensland by age group and severity. This was done by using a model that employs data from the Australian National Survey of Mental Health and Well Being, supplemented by data from other local and international surveys. National-level prevalence estimates of mental disorders thus derived were then applied to the population of Queensland to get the number of mental health patients in each age and severity group. Employing health services utilization data from various sources, the researchers then estimated the proportion of people with a specific mental disorder and level of severity who would have received treatment in various parts of the healthcare system. In step 2, the mix and level of specialized mental health services availability in Queensland were compared with reference standards against which the performance of Queensland could be assessed. The indicators used for benchmarking included the number of designated psychiatric beds, full-time equivalent direct-care staff in ambulatory care settings, and community accommodation (housing) support services. In step 3, the impact of standards set in 1996 under Queensland Ten-Year Mental Health Strategy was assessed by examining 5-year trends in the utilization of inpatient and ambulatory care public mental health services. In step 4, benchmark information on resource targets from government-endorsed mental health plans and other sources was reviewed to develop age-specific standardized mental health resource targets per 100,000 population for the five core mental health service components in Queensland. In the fifth and final step, information obtained in steps 1–4 was combined to set resource targets for the five core service components in Queensland. The researchers estimated that, overall, 16.6% of the population suffered from mental disorders, with 2.5% having severe and 4.5% having moderate-intensity mental health problems. Among children and adolescents (0 to 17 years), 15.6% suffered from mental disorders, with 2.1% having severe and 5.5% having moderate-intensity mental disorders. Among individuals 65 years or older, the prevalence of mental disorders was 12.9%. TABLES 11.4 and 11.5 show some of the results of this study, including the number of people at each level of severity treated by health services in 2004 and resource implications of the recommended targets for service delivery in Queensland. Based on the data shown in Tables 11.4 and 11.5, answer the following questions.

case 4

Gestational diabetes mellitus (GDM) is a disorder of glucose tolerance that affects 5%–9% of all U.S. pregnancies (approximately 250,000 pregnant women). Women who experience GDM have a 7 times greater risk of subsequent diabetes mellitus (DM) than women who do not. Breastfeeding or lactation is a modifiable postpartum behavior that improves glucose and lipid metabolism and has favorable metabolic effects that persist after weaning. The purpose of this study was to examine whether breastfeeding had any effect on or a relationship with the occurrence of DM in the 2-year period following delivery among women who had GDM during pregnancy. A total of 1,035 pregnant women who had been diagnosed with GDM and who delivered a baby after 35 weeks or more of pregnancy were enrolled and followed from August 2008 to December 2011. Three in-person examinations of these women from 6 to 9 weeks after delivery were conducted to collect baseline data. Thereafter, annual follow-ups included anthropometric measurements, personal interviews, and glucose tolerance testing 2 hours after oral administration of 75 grams of glucose. Of the 1,035 women initially enrolled, 25 were excluded from the study because they either had DM 6–9 weeks after delivery or delivered a baby before 35 weeks of pregnancy. Out of the remaining 1,010 women who delivered a baby after 35 or more weeks of pregnancy and did not have DM 6–9 weeks after delivery, the researchers were able to follow 959 (95%) for up to 2 years, and 113 (11.8%) of them were noted to have developed DM during the course of this time.

Data were analyzed using advanced statistical methods, including regression analysis, to examine the independent association of different levels and durations of breastfeeding with the incidence of DM after adjusting for potential confounding factors such as age, race, and weight.

Crude incidence rate of Type 2 DM within 2 years of follow-up of women with GDM by lactation intensity groups at 6 to 9 weeks after delivery showed that women in the “exclusively formula milk” group had an incidence rate of 8.79 per 1,000 person-months of follow-up, those in the “mostly formula milk” group had an incidence rate of 6.47, those in “mostly lactation” group had an incidence rate of 4.88, and those in the “exclusively lactation” group had an incidence rate of 3.95 per 1,000 person-months of follow-up. TABLE 14.14 shows lactation intensity groups 6–9 weeks after delivery and adjusted hazard ratios (representing the risk of DM) of the incidence of DM within the 2-year follow-up period among women who had GDM during pregnancy.

TABLE 14.14 Lactation Intensity Groups 6–9 Weeks After Delivery and Adjusted Hazard Ratios of the Incidence of Diabetes Mellitus Within the 2-Year Follow-Up Period Among Women Who Had Gestational Diabetes During Pregnancy

In the table, the column header reads: Types of the regression model and Adjusted hazard ratio of Incidence of diabetes Mellitus Within 2 years of follow-up by lactation Intensity. The second column header is divided into “Exclusively Formula n equals 153, (95 percent CI asterisk), Mostly Formula and Inconsistent Lactation n equals 214, (95 percent CI asterisk), Mostly Lactation n equals 387, (95 percent CI asterisk), and Exclusively Lactation n equals 205, (95% CI asterisk). The rows read as follows. Age-adjusted, 1.00 (reference group), 0.72 (0.43 to 1.23), 0.54 (0.33 to 0.89) 0.43 (0.23 to 0.82); Maternal risk factors (A), 1.00 (reference group) 0.64 (0.37 to 1.12), 0.54 (0.32 to 0.92) 0.46 (0.24 to 0.88). A + newborn outcomes (B), 1.00 (reference group), 0.65 (0.37 to 1.13) 0.53 (0.31 to 0.91), 0.47 (0.25 to 0.91). A + B + postpartum lifestyle, 1.00 (reference group), 0.66 (0.38 to 1.14), 0.56 (0.32 to 0.95), 0.48 (0.25 to 0.92). Asterisk represents 95% confidence interval.

n the table, the column header reads: Types of the regression model and Adjusted hazard ratio of Incidence of diabetes Mellitus Within 2 years of follow-up by lactation Intensity. The second column header is divided into “Exclusively Formula n equals 153, (95 percent CI asterisk), Mostly Formula and Inconsistent Lactation n equals 214, (95 percent CI asterisk), Mostly Lactation n equals 387, (95 percent CI asterisk), and Exclusively Lactation n equals 205, (95% CI asterisk). The rows read as follows. Age-adjusted, 1.00 (reference group), 0.72 (0.43 to 1.23), 0.54 (0.33 to 0.89) 0.43 (0.23 to 0.82); Maternal risk factors (A), 1.00 (reference group) 0.64 (0.37 to 1.12), 0.54 (0.32 to 0.92) 0.46 (0.24 to 0.88). A + newborn outcomes (B), 1.00 (reference group), 0.65 (0.37 to 1.13) 0.53 (0.31 to 0.91), 0.47 (0.25 to 0.91). A + B + postpartum lifestyle, 1.00 (reference group), 0.66 (0.38 to 1.14), 0.56 (0.32 to 0.95), 0.48 (0.25 to 0.92). Asterisk represents 95% confidence interval.