El papel de los medios de comunicación y el alcance de la información

The mHealth Tanzania-PPP submitted Wazazi Nipendeni registration data from the Rasello platform to the authors. The query was performed on 27 March 2019. A ‘working’ dataset covering a two-year period (11 November 2016 to 3 November 2018) addresses a number of data issues:

• Creating an additional category that represents women who registered when pregnant but have since given birth and have continued using the service as a mother with a child.

• Spikes in registrations. On 10 November 2016, the date when API registrations were adopted by multiple partners, and a spike in USSD registrations over a three week period in November 2018.

• Anomalies with opt-out dates, e.g. over 9,000 records were marked as having opted out on 3 April 2017.

The analysis in this section is supported by more detail given in Annex D.

6.2 Growth in user numbers

Both new registrations and opt-outs fluctuate monthly. In the period May 2017 – October 2018 new registrations were running at an average of around 20,000/month, whereas people were leaving the service at a much lower rate of less than 1,400/month, i.e. 7% of new users. Combining new users with people leaving the service gives the cumulative profile of active users presented in Figure 9.

Figure 9: Growth in active users

Source: Authors

In the absence of reliable data, it is not possible to track patterns of growth in users from the introduction of the service. The total number of users may be up to 130,000 higher than suggested by Figure 9, because many of the original users that were migrated onto the new system have not

Mobile phones, nutrition, and health in Tanzania: Business modelling endline report

been included in these numbers. Nevertheless, the patterns of growth and opt-outs remain accurate.

In November 2017, the mHealth Tanzania-PPP estimated the number of active users to be 350,000. This is consistent with the active user numbers in Figure 9, as adding 130,000 to the November 2017 estimate of 200,000 from the chart gives a total of 330,000, which is close to the mHealth Tanzania-PPP estimate. Making an assumption of straight-line growth in active user numbers, and assuming a starting base of 100,000 active users (rounded down), the number of active users after a two-year period (December 2016 to November 2018) is estimated at 550,000.

This figure was validated by the mHealth Tanzania-PPP.

6.3 Understanding users and partners

The majority of users (75%) were signed up as pregnant women (sum of ‘PW’ and ‘PW=>MC’

categories in Figure 3). 12% were general information-seekers, and 3% were supporters of one type or another.

The NHIF has brought the largest number of users onto the system (Figure 10). Among the 11 partners that registered more than 1,000 users, all apart from UNICEF used either API or USSD exclusively (almost). None used the short code. When the USSD system was introduced, SMS remained the channel the general public could use to register. Among users registered with no partner, 62% registered using an API or USSD (Table 2) suggesting that many could well have been registered with the assistance of a field partner. It is not, therefore, possible to determine precisely how many users truly self-registered, i.e. without any assistance. Up to 83% of all users could have been registered with the assistance of some kind of partner if API and USSD

registrations are included.

Figure 10: Registrations by partner

Source: Authors

Registration data provide some insights into the ways in which different programmes work. For example, the rate of registrations by NHIF was roughly constant, whereas self-registrations fluctuate widely as people respond to local and national campaigns.

0 50000 100000 150000 200000 250000

Mobile phones, nutrition, and health in Tanzania: Business modelling endline report

There is a peak in registrations of pregnant women at the three-month mark, which coincides with the first trimester of pregnancy. Users who were supporting pregnant women also tended to sign up at the end of the first trimester. Users who registered as mothers (and supporters of mothers) mostly signed up within the first month after giving birth.

Most users leaving the system were registered as pregnant women and left before giving birth. The relatively low number of women in the ‘PW=>MC’ category leaving suggests that women who used the system throughout their pregnancy liked it, and then continued to use it in motherhood. The sub-set of data that represents users who have registered and then left the service is relatively small (n = 25,300).

TFNC hypothesised that women who were assisted with registration might have a stronger commitment to the service, stay on the service for longer, be more likely to read the messages, and be more likely to adopt improved behaviours. Table 2 sheds some light on the users leaving the service. While 12% of those registered by partners using USSD subsequently dropped out, only 0.3% of API registrations opted out. Only the general public continued to register using the short code, so only 38% of those with no partner can be classified as self-registered with some confidence. 42% of these users opted out, which provides some evidence to support the TFNC hypothesis.

Table 2: Registrations and opt-outs; assisted and self-registrations Source (for registration)

API Excel import Short code USSD Total No partner

Number registered (all) 6,508 973 84,257 132,431 224,169

% of total registrations 2.9% 0.4% 37.6% 59.1% 100.0%

Number opted out 1232 105 35132 42688 79157

% of total who have opted out 1.6% 0.1% 44.4% 53.9% 100.0%

% of source who have opted out

18.9% 10.8% 41.7% 32.2% 35.3%

Registered by partner

Number registered (all) 233,364 4,898 0 53,062 291,324

% of total registrations 80.1% 1.7% 0.0% 18.2% 100.0%

Number opted out 796 27 0 6204 7027

% of total who have opted out 11.3% 0.4% 0.0% 88.3% 100.0%

% of source who have opted out

0.3% 0.6% 0.0% 11.7% 2.4%

Notes: Number opted out is based on records with a valid opt-out date (not necessarily from May 2017 onwards).

Source: Authors’ own

Mobile phones, nutrition, and health in Tanzania: Business modelling endline report

7 Financial viability of business models