An easy introduction to Twitter (part 1)

Edwin Kruys is a Sunshine Coast GP who blogs about healthcare, social media and eHealth. If you work in healthcare and have a blog topic you would like to write for doctorportal, please get in touch.

“It’s like being delivered a newspaper whose headlines you’ll always find interesting.” ~ Twitter

I was recently at a conference in Brisbane, organised by the Australasian Medical Writers Association. I met some interesting people and learned a lot about writing from speakers like Dr Justin Coleman and Ben Harris-Roxas.

Interestingly, many speakers mentioned Twitter. Social media are essential if you want to bring a health message across. Twitter is also a great tool to connect and collaborate with others and learn new things. It’s my favourite social media platform.

Twitter seems a bit daunting in the beginning, but it’s really easy to use. After reading both parts of this post, which should take you no more than five minutes, you will be ready to take the plunge.

Getting started

Because of the limited character count of 140, Twitter is called a microblogging platform. The social media giant describes itself as an information network made up of 140-character messages called tweets. A tweet is the expression of a thought or idea. It can contain text, links, photos and videos. Millions of tweets are shared in real-time, every day, all over the world.

You can read the tweets of people or organisations you follow in your timeline, and your followers can read your tweets, click on any links or hashtags you have included in your messages, or they can retweet your tweets, which means that they share your messages with their followers. I’ll explain it in more detail below. You can use twitter from your phone, computer or tablet.

To get started, first sign up at twitter.com or directly from the app on your phone or tablet, and choose a public Twitter username (also called a Twitter ‘handle’). The user name is always preceded by the @ symbol. I recommend to use your own name or business/practice name, but any available name is fine.

I picked @EdwinKruys, and Twitter has assigned this Twitter URL (or web address) to me: https://twitter.com/EdwinKruys. Twitter users will see your preferred name next to your Twitter username. This is how my names appear: ‘Dr Edwin Kruys (@EdwinKruys)’. It doesn’t matter if you use capitals or not.

You may want to register a few variants of your name or business name. I have also registered @DrKruys and @DrEdwinKruys.

Here are a few examples of Twitter user names:

Medical Journal of Australia: @the MJA
Royal Australian College of General Practitioners: @RACGP
RACGP President: @RACGPPresident
Australian Medical Association: @ama_media
AMA President: @amapresident
Australian College of Rural and Remote Medicine: @ACRRM

Next, you will have to set up your profile. Make sure you add a profile photo or Twitter will give you an egg-head (see above). For professional accounts I recommend a 400×400 pixels close-up photo of your face – not the dog, cat, flowers or a stethoscope. Fill out a short description of yourself and a link to your website or blog.

If you like you can add a background header photo (recommended dimensions are 1500×500 pixels). Once you’ve done all this, start following people. See who others follow and follow the interesting people, organisations and businesses.

Click here for my list of Australian GPs on Twitter.

Twitter lingo

There is a bit of Twitter lingo you need to learn, but it’s easy. Let’s start with hashtags. A hashtag is any word or phrase preceded by the # symbol. Conferences and television shows often use a hashtag, e.g. #GP15Melb. Hashtags are also used for advocacy campaigns, like #AHPRAaction, #ScrapTheCap and #CopayNoWay.

A hashtag is like a label added to your tweets to better file and retrieve messages with a certain topic or theme. It doesn’t matter where you place it. And you can add a few hashtags if you like, although two is probably ideal. When you click on a hashtag in someone’s tweet, you will see all other tweets containing the same word or topic.

Here are some other Twitter buzzwords:

Tweet: A Twitter message
Tweeting: the act of sending tweets
Tweeps: Twitter users
Favouriting a tweet: this indicates that you liked a specific tweet
A follow: someone following your Twitter account. You can see how many follows (or followers) you have from your Twitter profile
Home: your real-time stream of tweets from those you follow, also called a timeline.

Want to learn more? Click here for part 2 of Edwin Kruys’ An easy introduction to Twitter.

This blog was previously published on doctorsbag and has been republished with permission.

Other doctorportal blogs

Main image: PiXXart / Shutterstock.com

An easy introduction to Twitter (part 2)

This is the second part of Edwin Kruys’ Easy Introduction to Twitter. Click here to read part 1.

Your first tweet

When you compose your first tweet, you could write something like:

“Hi there, I’m new on Twitter. Still figuring out how this works.”

But if you haven’t got many followers, few people will read it. So you could tell someone that you have joined Twitter by adding their username to your tweet. I’ll use my username as an example, but of course anyone’s username can be inserted instead:

“Hi there, I’m new on Twitter. Still figuring out how this works. @edwinkruys”

Now I will receive a notification that you have mentioned me, and I may respond, retweet your message or suggest a few people to follow.

If you would put my username at the beginning of your tweet, your message is still public but only those who follow you and me will see the message:

“@edwinkruys. Hi there, I’m new on Twitter. Still figuring out how this works.”

If you put something in front of my name, all your followers will see your message (instead of only those who follow you and me):

“Hi @edwinkruys, I’m new on Twitter. Still figuring out how this works.”

Try adding a hashtag and a link:

“Hi @edwinkruys, I’m new on Twitter. Still figuring out how this works. #newontwitter. Read my profile here http://www.mywebsite.com”

You can link to websites, pdf-files, videos etc. The hashtag increases the chance that others with similar interests will read your tweet.

Retweets and replies

A great way to get started is to retweet someone’s message. Ask questions or make some friendly comments to get a conversation going.

A tweet from someone else, forwarded by you to your followers, is known as a retweet or RT. Often used to pass along interesting messages on Twitter, retweets always retain original attribution. Respect the original message and make sure you don’t change the original tweet when you retweet. If you do change it, for example when you delete a few words to save characters, it will become a modified tweet or MT instead of a retweet.

Here is one example of a retweet. Imagine I have just tweeted this message:

“Have a look at this great resource to get started on #Twitter: http://www.linktoresource.com”

You could retweet this – assuming you wanted to share it with your followers:

“RT: @edwinkruys: Have a look at this great resource to get started on #Twitter:http://www.linktoresource.com”

You could also add a brief comment to tell your followers what you think of it or to start a conversation:

“Excellent resource, thanks for sharing! RT: @edwinkruys: Have a look at this great resource to get started on #Twitter: http://www.linktoresource.com”

There are other ways to retweet, for example by retweeting the complete original message without adding your own text, or by retweeting the original message in a box and adding your own 140 character message. Press the retweet button under a message (the two arrows going up and down) to discover the various options.

You can send the same message by replying. Note that, by putting my username at the beginning of your tweet, your message is still public but only those who follow you and me will see the message:

“@edwinkruys Excellent resource, thanks for sharing!”

Again, if you want others to see your reply so they can follow our conversation, you need to add something in front of my name, even a full stop will do:

“.@edwinkruys Excellent resource, thanks for sharing!”

Or:

“Excellent resource @edwinkruys, thanks for sharing!”

When you share a resource you have found via someone else, it’s always nice to mention that person:

“Here’s and excellent resource to get started on Twitter: http://www.linktoresource.com – via@edwinkruys”

Direct messages, lists and login verification

Use Twitter direct messages to start a private or group conversation with your followers. It is possible to enable a setting to receive direct messages from anyone, not just followers, which may be useful for businesses. Direct messages have no character-limit so you can type as much as you want.

You can add images to your Tweets and even a link plus an image. Although you’re limited to 140 characters, it is easy to get around this by taking a screenshot from a large amount of text and attaching it as an image to your tweet.

Twitter lists are often used to create a group of other Twitter users by topic or interest. Lists contain a timeline of tweets from the users that were added, offering a way to follow individual accounts as a group on Twitter.

There are many third-party apps available to manage your Twitter account(s). I often use buffer to schedule tweets. To avoid getting hacked I recommend using two-step login verification as explained in this video. Have fun!

This blog was previously published on doctorsbag and has been republished with permission. If you work in healthcare and have a blog topic you would like to write for doctorportal, please get in touch.

Other doctorportal blogs

Main image: Denys Prykhodov / Shutterstock.com

Success in Closing the Gap: favourable neonatal outcomes in a metropolitan Aboriginal Maternity Group Practice Program

Australian Aboriginal women are at greater risk of complications during pregnancy and labour than non-Indigenous Australian women. There are for many reasons for this, including a higher prevalence of medical, lifestyle and socioeconomic risk factors, and lower antenatal care participation rates. Providing culturally competent services improves antenatal care uptake, but historically there has been a lack of such services in Western Australia.1 Element Two of the National Partnership Agreement on Indigenous Early Childhood Development (IECD2), part of the Closing the Gap suite of health care reforms initiated in late 2008, aimed to improve the access of Aboriginal women (particularly teenagers) to antenatal care and other women’s health care services.2

The Aboriginal Maternity Group Practice Program (AMGPP) was funded under this element, and commenced operating at various locations in the area of Perth served by the South Metropolitan Health Service (SMHS) in early to mid 2011. The SMHS spans the entire metropolitan area south of the Swan River (estimated population in 2012: 893 379, of whom 1.8% are Aboriginal residents)3; the remainder of metropolitan Perth is served by the North Metropolitan Health Service (NMHS). There are five health districts in the SMHS, each with its own hospital (four hospitals are public and one is private). The district hospitals provide antenatal care to local women, except for those at the greatest risk, who are referred to the sole public tertiary maternity hospital in Perth (King Edward Memorial Hospital [KEMH]; located in the NMHS). The criteria for referral differ between hospitals, but generally include type 1 diabetes, illicit substance use, and being younger than 16 years of age. During 2011, 369 children were born to local Aboriginal women in this area, equating to 3.1% of all births to SMHS residents and 21.4% of all births to Aboriginal women in WA.4

Before the AMGPP was introduced, local Aboriginal community members were concerned that some women were presenting late in pregnancy or giving birth at KEMH irrespective of their risk status. The AMGPP aimed to improve timely access to existing antenatal and maternity services in south metropolitan Perth, and to thereby increase the number of women giving birth safely in a local hospital. The program employed Aboriginal Health Officers (AHOs), Aboriginal grandmothers and midwives in each district to work with the existing services. The program model was culturally secure, with a focus on early access to antenatal care, employment of Aboriginal staff, and holistic care, including awareness of the social determinants of health (Box 1). Clients with low-risk pregnancies gave birth at the local district hospital, and higher-risk pregnancies were referred to KEMH, as per the standard SMHS policy.

Our study aimed to explore any differences in neonatal health outcomes that were associated with AMGPP participation.

Methods

Study design

The study was a non-randomised intervention, with the intervention defined as participation in the AMGPP. The intervention group consisted of all Aboriginal women who gave birth while participating in the AMGPP between 1 July 2011 and 31 December 2012. These women received standard antenatal care and the additional services provided by the AMGPP (Box 1). The intervention group was compared with two control groups that were frequency matched on the basis of maternal age at the time of delivery (younger than 20 years or at least 20 years old) and gravidity (primigravida or multigravida). The historical control group consisted of Aboriginal women who resided in the SMHS and had given birth between 1 January 2009 and 30 June 2011; the contemporary control group consisted of Aboriginal women who resided in the NMHS and had given birth between 1 June 2011 and 31 December 2012. Women in the control groups were eligible to receive standard antenatal care. The outcome measures of the study were preterm delivery, low birthweight, neonatal resuscitation at birth, and the baby’s hospital length of stay (LOS).

Data sources

Data from the WA Midwives Notification System (MNS) was analysed. The MNS is a statutory database that records all births in WA occurring at a gestational age of at least 20 weeks, or where the birthweight is at least 400 g. The available data included maternal demographics, pre-existing medical conditions, smoking status, pregnancy complications and neonatal characteristics. Pregnancy complications included threatened miscarriage before 20 weeks, threatened preterm labour, urinary tract infection, pre-eclampsia, antepartum haemorrhage (placenta praevia, placental abruption, and other), pre-labour rupture of membranes, gestational diabetes, and “other”. Pre-existing medical conditions included asthma, diabetes, genital herpes, chronic hypertension, and “other”. Gestational age at the first antenatal visit was the only antenatal care variable recorded by the MNS, and this information was recorded only from January 2010. As the MNS does not identify AMGPP clients, midwives from each of the districts provided client lists directly to the Data Linkage Branch of the WA Department of Health for linkage to the relevant MNS record; in this manner, all but one AMGPP client could be identified.

Index of Relative Socioeconomic Disadvantage (IRSD) scores, one of the Australian Bureau of Statistics’ Socio-Economic Indexes for Areas (SEIFA), are routinely linked with MNS records using geocodes based on the latitude and longitude of the client’s address. In our study, population-level socioeconomic status was determined by the IRSD reported in the 2006 census at the collection district level (about 225 households), the smallest geographic unit of analysis available for the 2006 census.5 The IRSD was reported in quintiles that compared the raw score with other IRSD scores in WA, with the first quintile including the most disadvantaged 20% of collection districts in WA.

Services provided by AMGPP staff were reported biannually as part of Closing the Gap IECD2 funding requirements. However, reporting practices varied across the five program districts and during the course of the study, so that program service data must be interpreted with caution.

Data analysis

Baseline demographic, pre-existing medical and pregnancy characteristics for the intervention group were compared with those for each of the control groups. Health outcomes for the intervention group were compared with each control group, and reported as proportions and adjusted odds ratios (aORs). aORs with 95% confidence intervals were calculated using binomial logistic regression for the four dependent binary variables: birth before 37 weeks (preterm delivery, yes/no), birthweight under 2500 g (yes/no), neonatal resuscitation (yes/no), and baby LOS (>5 days or ≥5 days). Covariates included in the regression models were: the continuous variable, maternal age; the two categorical variables, IRSD quintile and parity (nulliparous, 1–4, or more than 4 previous pregnancies of at least 20 weeks’ gestation); and the five binary variables, previous caesarean delivery, caesarean delivery this pregnancy, one or more pregnancy complications, one or more pre-existing medical conditions and smoking during pregnancy. Covariates were retained in the final models only if they were independently associated with the neonatal outcome of interest.

Comparisons were made using Pearson or linear-by-linear χ² analyses (categorical variables) or Mann–Whitney U tests (continuous variables), with P < 0.05 defined as statistically significant.

Ethics approvals

Ethics approvals were obtained from the WA Aboriginal Health Ethics Committee (reference 493) and the SMHS Human Research Ethics Committee (reference 13/53). The WA Department of Health Human Research Ethics Committee provided approval for linkage to and analysis of statutory data (reference 2013/76).

Results

During the study period, there were 350 pregnancies and 353 babies born to 343 women in the AMGPP participant group, representing 58.2% of all pregnancies (350 of 601) and 66.0% of teenage pregnancies (99 of 150) in locally resident Aboriginal women. There were 350 pregnancies and 353 babies born in each of the two control groups.

Program participants

The mean age of AMGPP participants was 23.8 years, and 52.5% of the women resided in areas included in the most disadvantaged IRSD quintile (Box 2). Almost half of the women (44.6%) smoked during pregnancy. The most commonly recorded pre-existing medical conditions were “other” and asthma, occurring in 51.4% (180 of 350) and 13.1% (46 of 350) of pregnancies, respectively. The most common pregnancy complications were “other” and urinary tract infection, occurring in 14.9% (52 of 350) and 8.3% (29 of 350) of pregnancies, respectively.

Baseline characteristics

There were no significant differences between the AMGPP participant group and the control groups with respect to age, smoking status, parity or gravidity, body mass index (where data available), or multiple pregnancy (Box 2; multiple pregnancy data are not reported here because of MNS data-sharing agreement restrictions on the disclosure of data related to small numbers of individuals). Women in the contemporary control group were significantly less likely to reside in areas in the most disadvantaged IRSD quintile (χ² = 6.31, P = 0.01). Women in the historical control group were significantly less likely to have a pre-existing medical condition (χ² = 10.57, P = 0.001), although no significant differences were evident if the “other” diagnosis category was excluded from the analysis (AMGPP group, 50 of 350 (14.3%) v historical controls, 50 of 350 (14.3%): χ² = 0, P = 1.00; v contemporary controls, 58 of 350 (16.6%): χ² = 0.70, P = 0.40). The AMGPP participants were significantly less likely to have had a previous caesarean delivery (v historical controls, χ² = 6.29, P = 0.01; v contemporary controls, χ² = 9.76, P = 0.002).

Antenatal care and other services

Without adjusting for missing data, there were no significant differences in the proportions of women for whom an antenatal visit in the first trimester was recorded (AMGPP group, 102 of 337 (30.3%) v historical controls, 50 of 161 (31.1%): χ² = 0.03, P = 0.86; v contemporary controls, 84 of 341 (24.6%): χ² = 2.71, P = 0.10). For the AMGPP group, in addition to clinic-based antenatal visits, there were 294 outreach services by the AHO or an Aboriginal grandmother, with or without the midwife, during the study period. Individual brief smoking and alcohol interventions were delivered on 484 and 463 occasions, respectively. Program staff delivered a total of 62 antenatal education workshops, 1191 individual antenatal education services and 1155 individual sexual health education services.

Neonatal outcomes

The proportion of preterm births to AMGPP participants was significantly lower than in the two control groups (Box 3), and the program was associated with a significantly lower aOR for preterm birth (Box 4). Birthweight was correlated with gestational age (r_s = 0.53, P < 0.001), but significant differences between the groups in the proportions of low-birthweight babies were not found. The likelihood of neonatal resuscitation at birth or of having a hospital LOS of more than 5 days were significantly lower for babies of AMGPP participants (Box 4). There were significant differences between groups in the distribution of baby LOS (for the AMGPP, historical control and contemporary control groups, the respective means were 2.37 days, 3.01 days and 4.17 days; AMGPP v historical controls P = 0.002; v contemporary controls P < 0.001). The majority of AMGPP babies requiring a LOS of more than 5 days were born preterm (11 of 14 = 79%).

Discussion

Our study identified more favourable health outcomes for the babies of AMGPP participants than for babies of mothers in matched control groups, including significant reductions in the likelihood of preterm birth, neonatal resuscitation and a hospital LOS of more than 5 days. Notably, the proportion of preterm births to women in the program (9.1%) was similar to that reported for all births in WA during 2011 (8.6%, 2755 preterm births),4 and lower than that for all births to Aboriginal women in the SMHS area (15.6%, 56 preterm births)6 and in all of WA (14.4%, 251 preterm births).4

During 2008–2010, spontaneous preterm delivery was the most frequent contributor to Aboriginal neonatal mortality in WA (14 deaths in the first 28 days of life, 37.8% of neonatal deaths) and the second most frequent contributor to Aboriginal infant mortality (17 deaths during the first year of life, 27.9% of infant deaths).7 Premature birth, regardless of birthweight, has been associated with hypertension and insulin resistance in Aboriginal children.8 Reducing the likelihood of preterm birth is therefore likely to have long-term health benefits. Antenatal programs similar to the AMGPP in other states have found statistically significant reductions in the proportions of preterm births, but not of low-birthweight babies.9,10 In our study, having one or more pregnancy complications (both control groups) and smoking during pregnancy (comparison with contemporary control group only) were also independent predictors of a preterm birth.

Extended LOS can reflect complications for the mother, the baby or for both.11 WA data show that gestational age is a better predictor of neonatal LOS than birthweight.4 The LOS for AMGPP participants was significantly lower than in either control group, with potential impacts on hospital costs. The majority of AMGPP participants with a LOS greater than 5 days had delivered preterm babies (79%).

A significant proportion (58.2%) of locally resident Aboriginal women and an even greater proportion of Aboriginal teenagers (66.0%) who gave birth during the study period participated in the AMGPP. In 2008, 53.1% of locally residing Aboriginal women (179 women) gave birth at KEMH, compared with 36.8% (148 women) in 2013, with a commensurate increase in the proportion of pregnant Aboriginal women giving birth locally.6 Moreover, the proportion of local women participating in the AMGPP continued to grow in 2014–2015 (data not shown).

In 2011, birth rates were six times higher for WA Aboriginal teenagers than for non-Aboriginal teenagers.4 Compared with adult women, teenagers are more likely to experience complications during pregnancy, such as urinary tract infections and hypertension, and their babies are more likely to be of low birthweight or stillborn.12 Improving antenatal care uptake in this demographic was a major objective of the IECD2 program, and the AMGPP appeared to reach this risk group.

There were limited data in the MNS on the provision of antenatal care during the study period.13 However, separate qualitative data collected as part of an evaluation of the program have shown the positive impact of the Aboriginal staff on ensuring early and continued engagement of pregnant women with the AMGPP.13 Further, the 6-monthly district reports provided data about the outreach services, brief interventions and antenatal education delivered by the program staff.

Selection bias was potentially a limitation of the study design,14 as women presenting for care possibly had different risk profiles to those who did not. In this study, the risk of selection bias was reduced (although not eliminated) by the involvement of the Aboriginal grandmothers, who brought women into the program through their community networks.13 Almost two-thirds of teenage pregnancies were managed by the AMGPP, suggesting that high-risk females were making use of antenatal health care. In addition, no significant differences between AMGPP participants and controls were detected with respect to maternal age, body mass index (when data were available), smoking status, parity or multiple pregnancy. In fact, some baseline characteristics of the contemporary control group suggested that it was a lower-risk group than the AMGPP participants; a greater proportion of the contemporary control group lived in socioeconomically less disadvantaged areas and this group included a lower proportion of grand multiparas. However, it is possible that the groups differed in ways that could not be quantified with the MNS data, such as the frequency of substance misuse. Further, the nature of the program, with AMGPP staff working alongside various hospital- and community-based antenatal services, meant that complete data on antenatal care provision were not always available, and this limits the conclusions that can be made about the direct effect of AMGPP participation on neonatal outcomes.

The AMGPP endeavoured to deliver culturally competent and holistic antenatal care services for Aboriginal women in the south metropolitan region of Perth, and babies born to participants were at lower risk for several adverse health outcomes, including preterm birth. Given the association between preterm birth and infant mortality, as well as the impact of prematurity on chronic disease throughout life, programs providing access to culturally secure antenatal care for Aboriginal women may have long-term benefits for their children. The AMGPP enhanced existing maternal health services and enabled more Aboriginal women to give birth locally and safely. This model of care could be adapted for use in similar settings with the support of local Aboriginal communities.

1
Features of the Aboriginal Maternity Group Practice Program (AMGPP) in the South Metropolitan Health Service (SMHS), Perth, Western Australia

All aspects of program planning, implementation and progression were guided by Aboriginal community members through district steering group meetings. These meetings were held quarterly, and were also attended by AMGPP staff, South Metropolitan Population Health Unit (SMPHU) contract management staff, maternity ward staff from each local district hospital and antenatal care providers.
The Aboriginal Health Officer (AHO) was required to have the Certificate IV in Primary Health Care (or equivalent) as a condition of employment, and provided care coordination, including referrals to other health and social services providers.
The Aboriginal grandmothers were respected women in the local community with good community networks. They identified pregnant women, assisted with access to services (including transport), provided support (including being present at appointments, if requested), and advised on cultural and health promotion matters.
The AMGPP midwife delivered antenatal care in partnership with local antenatal care providers. Clinical staff provided clinical governance, working within existing hospital guidelines.
Women were referred to the program by AMGPP staff, community members, general practitioners, hospital antenatal clinics, Medicare Locals and social services providers.
A home-visiting service was available. Outreach clinics were provided in various locations, including women’s refuges, Aboriginal community centres and mobile GP services.
Aboriginal staff were trained to deliver culturally appropriate, brief interventions to assist with stopping smoking and alcohol use. Training was provided by the Drug and Alcohol Office (Strong Spirit, Strong Future), the Cancer Council WA (Fresh Start) and the SMPHU (Yarning It Up).
The AMGPP staff delivered antenatal and sexual health education on an individual basis. Antenatal education included information about the stages of pregnancy, managing problems occurring during pregnancy, healthy lifestyle behaviours (nutrition; stopping smoking and alcohol use), mental health, available services, birth registration, breastfeeding, baby care, and the prevention of sudden infant death syndrome. Sexual health education included information about the symptoms of sexually transmitted infections, the importance of Pap smears, and contraception. Aboriginal staff received training in health promotion from the Aboriginal Maternal Services Support Unit (WA Department of Health).

2
Characteristics of Aboriginal Maternity Group Practice Program (AMGPP) participants and mothers in the two control groups

Characteristic

AMGPP participant group (350 pregnancies)

Historical control group (350 pregnancies)

Contemporary control group (350 pregnancies)

Maternal age, years (mean, range)

23.8 (15–44)

23.5 (14–42)

24.2 (13–44)

Gravidity (primigravida)

99 (28.3%)

Parity

0 (nulliparous)

132 (37.7%)

127 (36.3%)

125 (35.7%)

1–4 births (multiparous)

188 (53.7%)

192 (54.9%)

209 (59.7%)

5 or more births (grand multiparous)

30 (8.6%)

31 (8.9%)

16 (4.6%)

Index of Relative Socioeconomic Disadvantage (IRSD) quintile

1st (most disadvantaged 20%)

179/341 (52.5%)

174/330 (52.7%)

150/339 (44.2%)^*

2nd

85/341 (24.9%)

94/330 (28.5%)

87/339 (25.7%)^*

3rd

43/341 (12.6%)

33/330 (10.0%)

49/339 (14.5%)^*

4th

21/341 (6.2%)

18/330 (5.5%)

37/339 (10.9%)^*

5th (least disadvantaged 20%)

13/341 (3.8%)

11/330 (3.3%)

16/339 (4.7%)^*

Body mass index

Underweight (< 18.5 kg/m²)

15/298 (5.0%)

4/136 (2.9%)

Normal weight (18.5–24.9 kg/m²)

122/298 (40.9%)

64/136 (47.1%)

Overweight (25–29.9 kg/m²)

72/298 (24.2%)

26/136 (19.1%)

Obese (= 30 kg/m²)

89/298 (29.9%)

42/136 (30.9%)

Smoking status

156 (44.6%)

163/349 (46.7%)

160 (45.7%)

One or more pre-existing medical conditions

201 (57.4%)

158 (45.1%)^*

185 (52.9%)

One or more complications during pregnancy

105 (30.0%)

127 (36.3%)

119 (34.0%)

Labour onset

Spontaneous

248 (70.9%)

246 (70.3%)

212 (60.6%)^†

Induced

77 (22.0%)

69 (19.7%)

90 (25.7%)^†

No labour

25 (7.1%)

35 (10.0%)

48 (13.7%)^†

Previous caesarean delivery

29 (8.3%)

50 (14.3%)^*

56 (16.0%)^*

Caesarean delivery this pregnancy

Elective caesarean delivery

20 (5.7%)

27 (7.7%)

40 (11.4%)^*

Non-elective caesarean delivery

46 (13.1%)

61 (17.4%)

na = not available. The denominator for the calculations is included where data for a variable were incomplete. *P < 0.05, †P < 0.001, each compared with AMGPP group.

3
Health outcomes for the babies of Aboriginal Maternity Group Practice Program (AMGPP) participants and of mothers in the two control groups

Health outcome

AMGPP participants (353 babies)

Historical control group (353 babies)

Contemporary control group (353 babies)

Preterm birth (< 37 weeks)

32 (9.1%)

56 (15.9%)^*

54 (15.3%)^*

Low birthweight (< 2500 g)

38 (10.8%)

51 (14.4%)

56 (15.9%)

Requiring resuscitation at birth

63 (17.8%)

86 (24.4%)^*

110 (31.2%)^†

Baby length of stay > 5 days

14 (4.0%)

40 (11.3%)^†

41 (11.6%)^†

*P < 0.05, †P < 0.001, each compared with the AMGPP group.

4
Multivariate models of neonatal health outcomes for Aboriginal Maternity Group Practice Program (AMGPP) participants compared with mothers in the two control groups

Health outcome

Historical control group

Contemporary control group

Predictive factor

aOR (95% CI)

Preterm birth

AMGPP

0.56 (0.35–0.92)

0.02

0.75 (0.58–0.95)

0.02

Pregnancy complications

6.24 (3.79–10.25)

< 0.001

3.69 (2.29–5.93)

< 0.001

Smoking

2.95 (1.79–4.84)

< 0.001

Low birthweight

AMGPP

0.79 (0.49–1.30)

0.36

0.83 (0.66–1.07)

0.14

Pregnancy complications

8.41 (4.95–14.27)

< 0.001

5.70 (3.52–9.23)

< 0.001

Smoking

2.94 (1.77–4.87)

< 0.001

3.33 (2.03–5.47)

< 0.001

Previous caesarean delivery

2.05 (1.10–3.81)

0.02

Requiring resuscitation at birth

AMGPP

0.68 (0.47–0.98)

0.04

0.71 (0.60-0.85)

< 0.001

Caesarean delivery this pregnancy

2.06 (1.36–3.12)

< 0.001

2.12 (1.45-3.10)

< 0.001

Baby length of stay > 5 days

AMGPP

0.34 (0.18–0.64)

0.001

0.56 (0.41–0.77)

< 0.001

Pregnancy complications

2.53 (1.44–4.47)

0.001

2.79 (1.58–4.93)

< 0.001

Smoking

2.38 (1.32–4.30)

0.004

aOR = adjusted odds ratio. *Not significant, and therefore not included in the final models for the comparison with the historical control group.

Sudden sensorineural hearing loss secondary to metronidazole ototoxicity

A 30-year-old Indian gentleman presented to the emergency department of the Royal Victorian Eye and Ear Hospital, Melbourne, with a history of bilateral profound deafness, tinnitus and headache associated with upper- and lower-limb paraesthesia and myalgia. He had been taking metronidazole (400 mg tds) and amoxycillin (500 mg tds) over the preceding 4 days to treat gingivitis. Further questioning revealed that his maternal uncle had experienced identical symptoms while taking metronidazole. Consequently, his metronidazole was immediately discontinued.

After 2 days, he was able to hear faint sounds, and audiography revealed a symmetrical moderate-to-profound sensorineural hearing loss (SNHL) (Box, A). As per our hospital protocol for SNHL management, oral prednisolone was administered (50 mg daily), followed by a slow wean over 3 weeks.

After 8 days, subjective hearing and paraesthesia had improved, and his headache had abated. Audiometry indicated moderate SNHL up to 2000 Hz, with persisting severe high-frequency SNHL (Box, B).

At 6 weeks, repeat audiogram indicated that hearing was normal up to 2000 Hz, but severe-to-profound high-frequency SNHL persisted (Box, C).

Metronidazole is a nitroimidazole antibiotic widely used in various medical specialties. Common side effects include nausea, diarrhoea and abdominal discomfort. Patients receiving high or intravenous doses may experience neurotoxicity, but it is uncommon to suffer ototoxicity.

The first documented case of metronidazole-induced ototoxicity was reported in 1984.1 Since then there have been infrequent but typical reports of SNHL associated with metronidazole. In 1999, two cases of SNHL following about 2 days of treatment with metronidazole for dental sepsis were described.2 More recently, sudden bilateral SNHL after 4 days of metronidazole treatment for diarrhoea has been reported.3

We can only speculate about the mechanism of metronidazole-induced ototoxicity. The clear familial link in our case suggests a potential genetic susceptibility, similar with other ototoxic agents, such as susceptibility for the ototoxic effects of aminoglycosides associated with the mitochondrial A1555G deletion.

Several neurotoxic effects of metronidazole have been hypothesised, including toxic excitation of NMDA receptors leading to production of free radicals and cell death, as well as effects on GABAergic transmission and direct RNA-binding effects.4 Whether or not there is a genetic susceptibility for its effects has not yet been investigated, but it is reported that the onset of neurotoxicity is more rapid and occurs at lower doses in patients of Indian descent than in those of European origin.5

In conclusion, metronidazole is a known neurotoxic antibiotic that can be ototoxic, if only rarely. These adverse effects are reversible after withdrawing the drug. Given its widespread use, it is important that prescribers are aware of these severe adverse reactions.

A: Initial audiogram, showing profound bilateral sensorineural deafness. B: Audiogram, 8 days after cessation of metronidazole, showing improvement in pure tone audiometry at frequencies up to 2000 Hz. C: Audiogram, 6 weeks after cessation of metronidazole, showing near-normal hearing at frequencies up to 2000 Hz, with severe high-frequency hearing loss.

Medibank saga remains unpreventable

The full page ads last week in some capital city papers may have heralded ‘peace in our time’ in the dispute between Medibank Private and Calvary Health, but the big insurer’s approach to safety and quality in our hospitals is still in question by hospitals, doctors, and patients.

While Medibank and Calvary may have finally signed a contract, the detail of the belated agreement remains top secret.

While the AMA agrees that any commercial details should remain private, it is in the public interest that any agreement over Medibank’s draconian list of 165 preventable events should be disclosed.

Calvary CEO, Mark Doran, told Adelaide radio that Medibank Private had agreed to engage with the Australian Commission on Safety and Quality in Health Care on what they believe are preventable events, and that they will act on the call for an independent clinical review process. But that’s about all we get to know at this stage.

AMA Vice President Dr Stephen Parnis said that Medibank’s ‘trust us, we’ll do the right thing by you’ response is not good enough.

“I’m a doctor and I don’t say that sort of thing to patients anymore,” Dr Parnis said.

“I’ve got to give them the specifics. And I think Calvary and Medibank Private need to do the same here.

“We’d like to understand exactly what the arrangements are with regard to that long list of 165 complications, which Medibank was erroneously calling mistakes, to understand what is going on with those as a result of this new agreement.

“The concern, of course, is that if you’re insured it’s the detail that tells you what you’re covered for and what you’re not covered for.

“The treating doctors need to understand what their patients will be covered for so that they can treat them in the appropriate setting.

“Up to now it’s been hardball by Medibank.

“The AMA rarely intervenes in these sorts of disputes but, because it has such wide-reaching implications for the health system, both private and public, we have regarded this as essential that, one, it gets sorted out, and, two, that it is done in a transparent way.

“It is positive that the Commission for Safety and Quality in Health Care is now involved.

“The Commission does things the right way when these complications are being assessed to try and reduce risk, rather than what was happening with Medibank saying these are not complications, they’re mistakes, and if they occur we’re not funding them or we’re dramatically reducing our funding.

“So we need more detail here because it doesn’t just affect Calvary and it doesn’t just affect Medibank Private. Every other player in the health system is watching on here.

“If this sets a good precedent, wonderful. If it doesn’t, then it’s going to have repercussions for everyone.”

John Flannery

Latest news:

Suboptimal medication-related quality of care preceding hospitalisation of older patients

Chronic diseases are the leading cause of death and disability worldwide, and their prevalence is increasing, particularly in the older population.1 In Australia, chronic diseases account for 70% of total health expenditure, costing $91.2 billion in the 2010–11 financial year.2 Optimal management of chronic disease therefore has significant potential to reduce health care expenditure, as well as to improve health outcomes for individuals.

In Australia, it is estimated that between 2% and 3% of all hospital admissions are medication related.3 There were 9.3 million hospital separations in Australia during 2011–2012 at an average cost of $5204 per separation; this suggests that there are about 232 500 medication-related admissions per year at an annual cost of $1.2 billion.4 Many of these hospitalisations could potentially be prevented by delivery of appropriate primary care.3

To facilitate the reduction of medication-related morbidity, clinical indicators have been developed that assess processes of care associated with medication use and ensuing adverse outcomes of hospitalisation.5,6 These medication-related clinical indicator sets were originally developed more than 10 years ago by expert panels in the United States, United Kingdom and Canada, based on the principles that medication-related problems are recognisable, that the adverse outcomes are foreseeable, and that their causes and outcomes are identifiable and controllable. On the basis of these clinical indicators, it has been reported that between 3% and 20% of hospitalised patients had suboptimal care before admission, depending on the country and population studied.7–9

Clinical indicators have been widely adopted as a measure of health system performance and quality of care provided to patients, ranging from the acute care to primary care settings, across a number of disease states.10 Use of clinical indicators to determine the appropriateness and timeliness of care for patients with chronic disease and associated medication use is a potentially underused measure for assessing health system performance. Such indicators may facilitate the identification of areas with potential for improving health care and health outcomes, as well as reducing the frequency of adverse events.

We have developed evidence-based medication-related indicators of suboptimal processes of care before hospitalisation that are specific to the Australian health care setting.11 The indicators are based on Level III or greater evidence, and were validated by an expert panel as aspects of medication use that clinicians should be able to identify and resolve in primary care.12 The aim of this study was to apply these medication-related clinical indicators to investigate the prevalence of suboptimal medication-related processes of care preceding hospitalisation of older patients.

Methods

Ethics approval for this study was obtained from the Human Research Ethics Committees of the University of South Australia (protocol number 0000025588) and the Department of Veterans’ Affairs (DVA) (protocol number E012/003).

Data source

We analysed DVA administrative health claims data to determine the prevalence of clinical indicators of suboptimal medication-related processes of care before hospitalisation in a treatment population of about 300 000 veterans during the study period (1 July 2007 to 30 June 2012). The DVA claims database contains patient-specific demographic data, including date of birth, date of death, sex, level of entitlement and residential status, as well as details of all prescription medicines, medical and allied health services, and hospitalisations provided to veterans for which the DVA pays a subsidy. Medicines are coded in the dataset according to the World Health Organization anatomical and therapeutic chemical (ATC) classification13 and the Pharmaceutical Benefits Schedule (PBS) item codes.14 Services are coded according to the Medicare Benefits Schedule (MBS),15 and hospitalisations are coded according to the World Health Organization International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM).16

Prevalence of clinical indicators in the DVA database

Details of the development of the clinical indicators of suboptimal medication-related processes of care before hospitalisation have been published elsewhere.11 As an example of an indicator where the outcome of interest is hospitalisation for acute coronary syndrome, the associated process of care is defined as the combination of “patient has coronary artery stent (in 1 year before admission)” and “no use of aspirin or clopidogrel (in 12 months before admission)”.11

We reviewed the clinical indicators to identify those that were suitable for testing with the DVA administrative health claims data. As the DVA database is an administrative claims dataset, it contains records only for medicines and health services that attract a subsidy. Health care activities that do not have an individual funding item number, such as blood pressure measurement, are not recorded in the administrative claims database. While the use of health services (such as testing for glycated haemoglobin [HbA_1c] levels) can be determined from the claims data, the test results are not available. The criteria for appropriate use of health services as part of the process of care adopted by the indicators were based on practice recommendations in Australian evidence-based guidelines.11 Some of the validated indicators included processes of care that could not be identified in the administrative claims database, and therefore had to be excluded from this analysis. A total of 21 of the 29 validated indicators included medication-related processes of care that could be identified in the claims database and were therefore included in this analysis. They were drawn from six disease groupings: cardiovascular disease, respiratory disease, gastrointestinal disease, osteoporosis or fracture, renal disease, and diabetes. Of these 21 indicators, 13 are based on Level I evidence.11 Indicators that could not be included related to conditions that could not be accurately identified in the data: moderate to severe chronic obstructive pulmonary disease with frequent exacerbations, dyspepsia, and positive test results for Helicobacter pylori; influenza and pneumococcal vaccinations are not recorded in the database, nor are the doses of medicines used (corticosteroids) or the measurement of vitamin D or calcium levels.11

Data rules were developed for identifying each pattern of care and hospitalisation outcome for each indicator in the administrative claims dataset. These data rules included ICD-10-AM codes that identified each hospitalisation outcome, ATC or PBS item codes that identified medications, and MBS codes that identified testing procedures or claims related to the process of care. DVA administrative health claims between 1 July 2007 and 30 June 2012 were analysed to identify all hospitalisations with a primary diagnosis for the outcomes, and all MBS and PBS claims were analysed for patterns of care for the clinical indicator set.

We calculated the prevalence of hospitalisations with suboptimal medication-related processes of care before hospitalisation, as defined by the clinical indicator set. The prevalence was defined as the proportion of individuals with both the pattern of care and the associated hospitalisation divided by the total number of hospitalisations for that indicator. Demographic data were obtained for patients at study entry. All analyses were undertaken with SAS for Windows, v9.4 (SAS Institute).

Results

There were 164 813 hospitalisations for the conditions included in the clinical indicator set over the 5-year study period, encompassing 83 430 patients. The median age of the study population was 81 years (interquartile range, 78–84 years); 54.5% were men, and 6.9% resided in an aged care facility at the time of admission (Box 1).

Box 2 contains the final list of clinical indicators included in the study and the prevalence of suboptimal medication-related processes of care preceding hospitalisation. More than one-third (34.5%) of the study population had at least one hospitalisation and 10.4% had two or more hospitalisations where there had been suboptimal medication-related processes of care before admission (Box 1). The overall proportion of hospitalisations that were preceded by suboptimal medication-related processes of care was 25.2% (41 546 hospitalisations). The most common hospitalisations were for cardiovascular disease (including acute coronary syndromes and heart failure), fracture and gastrointestinal conditions. Fracture and congestive heart failure (CHF) caused the highest numbers of hospitalisations that were preceded by suboptimal medication-related processes of care (Box 2). Of the fracture hospitalisations, 85.4% were for patients aged 65 years or older who had been dispensed a falls-risk medicine before admission; 19.7% and 17.2% of fracture hospitalisations were for men and women, respectively, who had a history of fracture or osteoporosis but had not received a medicine for osteoporosis. There were 4744 CHF admissions (17.1%) of patients with a history of CHF who had not been dispensed an angiotensin-converting enzyme inhibitor (ACEI) or an angiotensin receptor blocker (ARB) in the 3 months before admission. More than one in 10 admissions for gastrointestinal bleeding or ulcer were associated with long-term use of non-steroidal anti-inflammatory drugs (NSAIDs). About one in 10 admissions for renal failure occurred in patients with a history of diabetes who had not received a renal function test in the year before admission and were not dispensed an ACEI or ARB (Box 2).

Although there were more than 33 363 hospitalisations for acute coronary syndromes during the study period, less than 2% involved individuals with a history of myocardial infarction or who had received cardiac stents and had not been dispensed acute coronary syndrome medicines recommended by the guidelines. Similarly, although there were more than 17 149 hospitalisations for gastrointestinal bleeding, ulcer or gastritis during the study period, less than 1% involved patients with a prior history of gastrointestinal bleeding or ulcer who had been dispensed an NSAID without a concurrent gastroprotective agent (Box 2). There were 1751 admissions for hyperglycaemia or hypoglycaemia; only 209 of these patients (11.9%) were prescribed insulin and had not received an HbA_1c test in the 6 months before admission.

Discussion

This is the first study to examine suboptimal medication-related processes of care before hospitalisation. We applied newly developed evidence-based clinical indicators specific to the Australian health care setting and found that 25.2% of hospitalisations for conditions identified in the clinical indicator set were preceded by suboptimal medication-related processes of care. Of the 28 807 patients in the study who had hospitalisations preceded by suboptimal medication-related processes of care, 30% (8640 patients) had multiple such hospital admissions. At least one in 10 hospitalisations for CHF, ischaemic stroke, asthma, gastrointestinal ulcer or bleeding, fracture, renal failure or nephropathy, hyperglycaemia or hypoglycaemia were preceded by suboptimal medication-related processes of care that clinicians should be able to identify and avoid. The frequency of falls-risk medicine use before hospitalisation for a fracture was particularly high (85.4%), highlighting the need to review appropriate prescribing of these medications for older people, who may be particularly vulnerable to their adverse effects.

A recent Australian study (CareTrack) examined the provision of appropriate health care. The investigation was based on medical records from health care practices (primary and secondary care) and hospitals, and it found that 43% of Australian patients had not received appropriate care.17 The CareTrack study examined process indicators only, and these were not linked to outcome measures, such as hospitalisation. The indicators included in the study were either consensus or evidence-based in nature, and were related to individual patient data. Gaps in the provision of appropriate care for specific conditions were identified (including for diabetes, osteoporosis, asthma and stroke), consistent with the results of our study.

Many of the conditions for which suboptimal processes of care were identified by our study fall within National Health Priority Areas for Australia or are associated with a high disease burden in Australia.18 This highlights the potential suitability of the medication-related indicators for monitoring appropriate provision of health care in Australia.

Other studies have highlighted the suitability of clinical indicators as quality indicators for monitoring health system performance and assessing the quality of patient care.5,7,10 Our study showed that administrative health databases can be used to investigate suboptimal medication-related processes of care before hospitalisation through the application of clinical indicators, and to assess the appropriateness of health care in current clinical practice. Routine prospective monitoring of trends in suboptimal processes of care associated with medicine use, and using the indicators in administrative health datasets or as data-mining tools in primary care, could provide a valuable tool for both monitoring and improving health system performance. Primary care interventions, such as patient-specific feedback to medical practitioners, could be focused on improving processes of care that have known and significant risks for patient outcomes and health care expenditure.

The suboptimal processes of care associated with the medication-related indicators applied in our study were validated by an expert panel as problems that clinicians should be able to recognise as suboptimal, with adverse outcomes that are foreseeable, and which could be both identified and controlled. Collaborative home medicines reviews that involve the patient, the pharmacist and the general practitioner have been shown to increase the identification and resolution of medication-related problems,19 and to reduce hospitalisation of patients with heart failure20 and those taking warfarin.21 The suboptimal care processes leading to hospitalisation outcomes in our study are the types of problems that could be identified and potentially resolved with a medication review (eg, reviewing the use of laxatives by chronic users of opioids or of falls-risk medications). Future research could be conducted to confirm whether such reviews are effective in reducing the incidence of suboptimal medication-related processes of care.

A limitation of our study is that we did not assess whether implementation of appropriate care processes would have avoided hospitalisation. It may be that hospitalisations would still have occurred even if the appropriate pattern of care had been implemented. Of interest for future studies would be an examination of the occurrence and effect on hospital admissions of the care processes defined by the indicator set. In addition, there could have been a subset of patients in the study population for whom certain medications were contraindicated, possibly related to comorbid conditions that we were not able to identify. An additional limitation was the inability to distinguish between individuals with diastolic and systolic heart failure on the basis of the available data; we acknowledge that the evidence base for the efficacy of ACEIs and ARBs in reducing long-term morbidity and mortality in those with diastolic heart failure is currently lacking.22 Furthermore, we were unable to assess the use of over-the-counter medicines.

Our study analysed DVA administrative data, which cover an older population of patients with a median age of 81 years. However, our results are probably applicable to other older Australians. Age-specific comparisons of DVA Gold Card holders (those eligible for all health services subsidised by the DVA) without service-related disability with the wider Australian population have found similar rates of GP visits, filling of prescriptions, and hospitalisations per year.23

Although differences in the definitions of clinical indicators may limit their applicability to other population groups, the indicators we employed are based on high-level evidence for common chronic conditions and are linked to patient outcomes. More than 60% of the indicators examined were based on Level I evidence, which, where applicable, included clinical studies of those aged 75 years or older (eg, the use of anti-osteoporosis medicines to reduce the incidence of fractures).11

In summary, this study highlights conditions associated with suboptimal medication-related processes of care in the primary care setting. The patterns of care on which the indicators are based incorporate high-level evidence and are therefore likely to be applicable internationally. Failure to implement appropriate patterns of care suggests that an opportunity to improve health care outcomes is being missed. Routine prospective monitoring of the prevalence of suboptimal processes of care and adverse outcomes in the Australian health care system using clinical indicators may provide a means for assessing the appropriateness of care for common chronic conditions, and for identifying evidence–practice gaps in primary care. The results could be used to inform and focus the development of interventions and efforts to improve the quality of health care delivery, potentially reducing morbidity and health care costs.

Box 1
Demographics of the study population: hospitalisation for diagnoses in the medication-related clinical indicator set (n = 83 430)

Age, median (interquartile range)	81 years (78–84 years)
Sex, n (%)
Male	45 456 (54.5%)
Female	37 974 (45.5%)
Location of residence, n (%)
Residential aged care facility	5725 (6.9%)
Community	77 705 (93.1%)
Hospitalisations with suboptimal processes of care before admission, n (%)
0	54 623 (65.5%)
1	20 167 (24.2%)
≥2	8640 (10.4%)

Box 2
Prevalence of hospitalisations after suboptimal processes of care as defined by the medication-related clinical indicator set

No.

Hospitalisation outcome

Process of care (preceding hospitalisation)

Total hospitalisations (TH)

Hospitalisations after suboptimal care [% TH, 95% CI]

Cardiovascular disease indicators

Acute coronary syndrome

History of myocardial infarction (in 2 years before admission)
Not on aspirin, β-blocker, ACEI or ARB and statin (in 3 months before admission)

33 363

567 [1.69%, 1.56%–1.84%]

Acute coronary syndrome

Patient has coronary artery stent (in 1 year before admission)
No use of aspirin or clopidogrel (in 12 months before admission)

33 363

640 [1.91%, 1.75%–2.05%]

CHF

History of CHF (in 2 years before admission)
Not on an ACEI or ARB (in 3 months before admission)

27 828

4744 [17.05%, 16.66%–17.54%]

CHF or heart block

History of CHF and heart block or advanced bradycardia (in 2 years before admission)
Use of digoxin (in 6 months before admission)

31 039

195 [0.63%, 0.54%–0.72%]

Ischaemic stroke

History of chronic atrial fibrillation or ischaemic stroke (in 2 years before admission)
No use of warfarin or aspirin (in 3 months before admission)

6637

677 [10.20%, 9.47%–10.93%]

Respiratory disease indicators

Asthma

History of asthma
Use of short-acting β-agonist more than three times per week
No use of inhaled corticosteroids

1335

214 [16.03%, 14.13%–18.07%]

Asthma

History of asthma
Use of long-acting β-agonist
No use of inhaled corticosteroids

1335

10 [0.75%, 0.32%–1.28%]

Gastrointestinal disease indicators

Gastrointestinal bleed, perforation or ulcer or gastritis

History of gastrointestinal ulcer or bleeding
NSAID use for at least 1 month
No use of gastroprotective agent (eg, proton pump inhibitor)

17 149

107 [0.62%, 0.48%–0.72%]

Chronic constipation or impaction

Regular use of a strong opioid analgesic (fentanyl, oxycodone, morphine)
No concurrent use of a laxative

6780

604 [8.91%, 8.22%–9.58%]

Gastrointestinal ulcer or bleed

Patient with osteoarthritis
Dispensed long-term NSAID therapy (including cyclooxygenase-2 inhibitors)

17 125

2166 [12.65%, 12.20%–13.20%]

Osteoporosis or fracture indicators

Fracture

Female patient
History of osteoporosis or fracture
No use of hormone replacement therapy, bisphosphonate, teriparatide, selective oestrogen receptor modulators or strontium

20 213

3467 [17.15%, 16.68%–17.72%]

Fracture

Male patient
History of osteoporosis or fracture
No use of bisphosphonate or teriparatide

12 231

2406 [19.67%, 18.98%–20.38%]

Fracture

Patient aged 65 years or older
Use of a falls-risk medicine6,7,24 (eg, long-acting hypnotic or anxiolytic, tricyclic antidepressant)

31 486

26 892 [85.41%, 85.01%–85.79%]

Renal disease indicators

Renal failure or nephropathy

History of diabetes
Microalbuminuria and plasma creatinine not monitored in previous 12 months
Patient not on ACEI or ARB

7335

665 [9.07%, 8.44%–9.76%]

Renal failure

NSAID use for >&nbsp3 months
Serum creatinine not monitored in the previous 12 months

7113

102 [1.43%, 1.13%–1.67%]

Diabetes indicators

Hyperglycaemia

Use of an oral hypoglycaemic agent
HbA_1c level not monitored in previous 6 months

223

42 [18.83%, 13.67%–23.93%]

Hypoglycaemia

Use of a long-acting oral hypoglycaemic agent (glibenclamide or glimepiride)
HbA_1c level not monitored in the previous 6 months

1528

67 [4.38%, 3.37%–5.43%]

Hyperglycaemia or hypoglycaemia

Use of insulin
HbA_1c level not monitored in the previous 6 months

1751

209 [11.94%, 10.38%–13.42%]

Hyperglycaemia or hypoglycaemia

Use of insulin or oral hypoglycaemic medicines
Use of medicines that may alter blood glucose concentration
HbA_1c level not monitored in the previous 6 months

1751

103 [5.88%, 4.80%–7.01%]

Hypoglycaemia

Use of glibenclamide or glimepiride
Renal function not monitored in the previous year

1528

42 [2.75%, 1.97%–3.63%]

Cardiovascular disease

History of diabetes
Not on lipid-lowering drug

67 177

2541 [3.78%, 3.66%–3.94%]

ACEI = angiotensin-converting enzyme inhibitor; ARB = angiotensin receptor blocker; CHF = congestive heart failure; HbA1c = glycated haemoglobin; NSAID = non-steroidal anti-inflammatory drug.

AMA ensures doctors heard on Medicare review

The AMA this week hosted more than 60 leaders from across all medical specialties in Canberra to discuss the medical profession’s participation in the Governments’ Medicare Benefits Schedule (MBS) review, and the recent behaviour of Medibank Private in negotiations with hospitals. Welcoming delegates to the meeting, AMA President, Professor Brian Owler, said it was important that the medical profession was informed and united in its approach to the MBS review.

The AMA has cautiously welcomed the review, led by Sydney University Medical School Dean Professor Bruce Robinson, and supports having a schedule that allows patients access to modern medical procedures, and reflects the modern-day treatments that are provided in our health system.

But Professor Owler has voiced concern that the Government might use the review mostly to remove items from the MBS as part of a cost-cutting exercise, rather than ensuring the schedule is up-to-date and reflects advances in care and medical practice.

“We need to make sure that the Government continues to work with clinicians, that this is a clinician-led process that is based on evidence, and that the process is held in conjunction with the colleges and specialist societies where the knowledge base and expertise lies,” Professor Owler said.

He added that the AMA will continue to work with the Government in the review, and will work with the specialists and colleges in ensuring the best outcome from the review – provided it is not just about cutting costs and provided that doctors maintain access to health care for their patients.

AMA transcript – MBS Review and Medibank Private

This post was first published on GP Network News

Are vaccines making viruses more dangerous?

Despite the near-universal acceptance of the benefits of vaccination, some people still worry about risks associated with their use. Luckily, scientists are vigilant about identifying possible risks, so they can be addressed before problems emerge.

Still, people sometimes forget that science is the process by which we arrive at solutions. And they worry about incremental scientific steps that often expose weakness in these solutions.

A recent study published in the journal PLOS Biology, for instance, was presented by some media as claiming that certain vaccines make viruses more dangerous. The research showed chickens treated with its vaccine are more likely to spread a highly virulent strain of Marek’s disease virus, a condition that affects poultry.

The reason was simple: the vaccine used in the study targets Marek’s disease, not the virus that causes it. These types of vaccines are known as “leaky vaccines” because they don’t affect the ability of the virus to reproduce and spread to others; they simply prevent the virus from causing disease.

Marek’s disease vaccines use a non-disease-causing virus to infect cells. This preventive infection stops tumour formation and death when those cells are infected by the Marek’s disease virus.

But the virus can replicate and still produce more virus particle, which can infect other chickens. All Marek’s disease vaccines, since their introduction in the 1970s, have been leaky; they allow chickens to carry and spread the virus without getting the disease.

‘Imperfect-vaccine hypothesis’

The effect of leaky vaccines on how disease spreads is explained by the “imperfect-vaccine hypothesis”. It holds that, without vaccination, a very virulent virus can get into a population and kill infected hosts (people or animals) very quickly – before they have a chance to spread it. This means that the virus will die out very quickly too, as all potential hosts will be dead or immune to it.

A leaky vaccine can prevent the very virulent virus from killing the host, but doesn’t stop that host from spreading the virus to others. This means that a very virulent virus can survive for long periods in the vaccinated hosts. And it can continue to spread in this time, making it less likely to die out.

The PLOS Biology study showed chickens vaccinated against Marek’s disease were more likely to spread the disease to other chickens, than unvaccinated chickens were. The unvaccinated chickens all died in less than ten days – before they could spread the virus.

The vaccinated chickens, on the other hand, were protected from the disease so were able to spread the virus to other (unvaccinated) chickens for weeks and weeks. And they made those chickens immune to the virus in the process.

Are vaccines making viruses more dangerous? - Featured Image

Marek’s disease, which affects poultry, has a ‘leaky’ vaccine’.
David Goehring/Flickr, CC BY-SA

One of the reasons the researchers looked at Marek’s disease in chickens is because it has been getting progressively deadlier. Originally, the disease occurred mainly in older chickens and caused paralysis. But an acute form of the disease emerged in the 1950s and has since become the dominant form. This rather virulent version can kill up to 100% of unvaccinated birds.

Leaky but not sinking

But what does all this mean for the future of vaccination?

Well, the first thing to note is that in this study the vaccinated chickens always had the best outcome. In one experiment, only three out of 50 unvaccinated chickens survived the disease, while vaccination protected the majority of chickens (46 out of 50 survived).

The authors also noted that vaccination has been very effective in preventing deaths in chickens due to Marek’s disease. They said their study didn’t indicate whether vaccination played any role in the development of the serious form of Marek’s disease.

Vaccines prevent disease, even if they’re leaky. But it’s important to note there are currently no vaccines against viruses that infect humans that are leaky. Current human vaccines mimic the body’s own response to viruses.

But yet-to-be-developed vaccines for diseases such as HIV, Ebola or malaria, where humans have been unable to mount an effective natural defence, are likely to be leaky. And even imperfect vaccines for these illnesses would be an enormous step forward.

The possible effect of “leaky vaccines” on how viruses spread is an interesting new observation. But there are a number of other ways by which viruses can change in response to vaccination.

An arms race

One response of viruses to vaccines involves the evolution of viral proteins that allow them to escape the vaccine. This is known as “epitope evolution” and it’s the reason flu vaccines change each year.

Even if a vaccine is effective in preventing a particular strain of virus, other strains may take its place. This was a concern when the human papillomavirus (HPV) vaccine was introduced nearly ten years ago. But researchers have investigated whether any HPV types not in the vaccine have become more common since the vaccine was introduced and there’s no evidence this is happening.

The interaction between viruses and their targets can change over time. In the case of Marek’s disease, the infection has become progressively deadlier. Increased use of broiler chickens, lack of genetic diversity in flocks and high-density rearing may all have played a role in the changes seen in the disease.

The benefits of vaccination far outweigh its risks. And it is research like this that helps medical researchers actively identify possible dangers so we can safely continue to avoid the diseases that terrified our parents’ generation.

Dave Hawkes is Honorary Fellow at Department of Pharmacology and Therapeutics at University of Melbourne

This article was originally published on The Conversation. Read the original article.

Other doctorportal blogs

Fourth University of Queensland measles case confirmed

A vaccination clinic will be set up at the University of Queensland (UQ) after a fourth measles case was confirmed.

The student attended lectures at the St Lucia campus last week with another student who had contracted the illness.

They also had visited a pub and a shopping centre when they were unaware they were infectious.

The person visited:

University of Queensland, St Lucia, Tuesday 11/8 & Wednesday 19/8
Indooroopilly Shopping Centre Thursday 13/8
The Royal Exchange Hotel Saturday 15/8
Taringa Day & Night Medical Centre evening of Sunday 16/8

Metro North Public Health Unit will set up a vaccination clinic at UQ’s St Lucia Campus this week and students who live in the university’s colleges are urged to get vaccinated if they’re unsure of their status.

Queensland Health has put out an alert telling students and others who were in the above premises to be alert for measles symptoms.

This is the fourth Queensland measles alert in the last month. The first was from a UQ student who contracted the disease overseas in July.

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‘Teaching by humiliation’ experienced by two thirds of medical students

A study has found that 74% of university medical students had been humiliated by their teachers during clinical rotations. 83% of students witnessed their peers being humiliated.

The research, published in the Medical Journal of Australia, featured an anonymous survey of 146 University of Sydney and University of Melbourne students.

According to the report, students considered humiliation to be teachers being nasty, rude or hostile, and when they belittled students. Less common behaviours were teachers yelling, shouting, cursing and swearing at students.

Some students were disgusted by the practice of teaching by humiliation, one writing: “The culture of bastardisation in the medical profession has to stop. Had I known it was like this, I never would have given up a good job that I loved to do medicine.”

Others, however, felt it was necessary for learning in the medical profession, with up to half of the survey responses saying teaching by humiliation was ‘useful to learning’.

“It is ‘humiliating’ to be put on the spot and have your knowledge and understanding tested publicly, but I find it to be a fantastic way to learn and consolidate. Rudeness and insults, however, should have no place in this method,” one student wrote.

Dr Karen Scott, Senior Lecturer at the University of Sydney, and her coauthors reported that it wasn’t just medical or surgery teachers being rude to students.

“The specific professional group most frequently named was nursing and midwifery, reported by 59% of University of Melbourne students and 35% of University of Sydney students. Administrative staff were also named”, the authors wrote.

The report concludes that there needs to be long term research and action to understand the complexity of the situation and identify ways to shift the culture.

“At the same time, current and future teachers deserve meaningful, ongoing support and professional development, and students deserve support to be assertive and resilient.”

Read the full report online at the Medical Journal of Australia.